Telematique

The Use of Electrotechnological Methods in the Grain Infestation Phase of Grain Moth (Cytotroga Cerealella Olive) Reproduction in the Context of Biofabrics

2025-01-16T06:22:18+00:00

The article presents the results of an experiment to reduce harmful microorganisms that appear in the production process based on the normalization of grain moisture by spraying water (rn=10.5-11) with ultraviolet light on the grain moth egg, which is used to damage the grain, and to improve the technology for producing trichograms using an environmentally friendly method.

Aritificial Intelligence in Test Automation Platform – A Comparative Review

2025-01-27T09:54:16+00:00

Software testing is considered as the critical aspect in terms of developing the software. As software systems become more intricate, old-style testing procedures are increasingly impractical. Recently, Artificial Intelligence (AI) has appeared as a promising alternative testing the software. This review study investigates the latest trends and current practices in Artificial Intelligence -driven software testing, evaluating various approaches, techniques, and tools to assess their effectiveness. Testing the software is considered as the fundamental section of the Software Development Life Cycle, and with the tech sectors focus on delivering superior quality applications, many testers are transitioning from manual to test automation to save time and expenses. There is a vast selection of software testing tools available, both open-source and commercial, making it challenging to choose the best tool due to the growing number of options. As the variety of tools increases, so does the range of features and cost differences. Web application testing tools, which are widely used today, provide developers with more convenient testing options, and their integration with web browsers has made testing more modular. Selecting the right testing tool involves assessing various factors to ensure it meets the specific needs of the software being tested. This work aims to recognize and contrast popular testing tools, offering a contrast review of open-source and commercial tools in a tabular format based on diverse parameters. It also designates the topographies of several testing tools to aid end users and programmers in selecting the most suitable ones for their needs.

Power Edge Domination Number of Chemical Structure of Acids in Daily Life

2025-01-30T11:29:42+00:00

For a graph with size , and for any edge , a set is said to be an power edge dominating set of graph if each edge is dominated by the following rules: (i) an edgein is in power edge dominating set (in short PEDS), then it dominates itself and dominates all the adjacent edges of (ii) an observed edge in has > 1 adjacent edges and if – 1 of these edges are observed earlier, then the remaining non- observed edge is also observed by . The minimum cardinality of a power edge domination number of is denoted by (). In this paper we investigate the power edge domination number for certain acids in our daily life.

Enhancing Vehicle Inspection: A Comparative Study of VGG16, VGG19, Densenet and Mobile Net for Real-Time Car Damage Detection

2025-02-03T05:43:46+00:00

Due to the rising number of car incidents, the insurance industry is being overstretched right now by the fact that it has a lot of claims to attend to, and this poses claim leakage as a fast-growing occurrence. On the other hand, AI-driven processes that use machine learning and deep learning techniques have turned out highly effective in solving these challenges. The subject of this research is exactly how to redesign existing damage assessment systems and making them better with the help of machine learning models. In this paper, an evaluation of four deep learning-based algorithms' performances in classifying vehicle damage, consisting of VGG16, VGG19, DenseNet121 and MobileNet. Each of these models are trained independently and the assessment of their accuracy is carried out to decide the most effective one. To enhance user accessibility, the adoption of an interface running under Streamlit where users have an opportunity to upload any image for assessment of the sustainability of the vehicle. This interface will offer accurate predictions of existing car damages together with probability values of their occurrence. In addition, the study has implemented a camera live feed feature with the main interface of the system such that users can observe damage detection by turning their smartphone camera on. This novel method reduces the possibility of the claim reprocessing cycle and significantly reduces the possibility of misreports by determining the damaged vehicles and non-vehicles accurately.

The Main Geographical and Artistic Features of the Illustrated Version of Romance of the Three Kingdoms in the Ming Dynasty

2025-02-05T06:22:42+00:00

The Ming Dynasty was a peak period for the publication and dissemination of "Romance of the Three Kingdoms," with many illustrated books emerging, forming various styles of schools. This article takes the illustrated version of "Romance of the Three Kingdoms" from the Ming Dynasty as the research object. It comprehensively uses methods such as version studies and image studies to analyze its central publishing regions and versions and summarize the style characteristics presented by its primary regional schools. Firstly, this article conducts a version review of the illustrated version of "Romance of the Three Kingdoms" from the Ming Dynasty. Based on the standards of publication time, region, and bookstore, it is divided into regional schools such as Huizhou, Jianyang, Jinling, Suzhou, and Hangzhou, focusing on composition, scene depiction, and line application and summarizing its artistic characteristics. For example, the illustrations in Jianyang are known for their rustic and rugged style and strong folk atmosphere. The illustrations in Jinling present the characteristics of exquisite elegance and rich literary interest. The illustrations in Suzhou blend the north and south styles, forming a unique "Su style" style. In addition, this article also explores the development and evolution process of each regional school at different stages. Finally, this article summarizes the artistic achievements and historical value of the illustrated version of "Romance of the Three Kingdoms" in the Ming Dynasty, pointing out that it provides rich visual materials for future generations and essential references for studying ancient Chinese printmaking history.

Investigating the Effect of Creatine Monohydrate Supplement on Fatigue Caused by Overtraining Syndrome in Male Athletes

2025-02-13T10:31:25+00:00

Purpose: The present study aimed to investigate the effect of creatine monohydrate supplement compared to placebo on fatigue caused by overtraining syndrome in male athletes of Tehran province of Enghelab Sports Complex. Methods: This double-blind study was conducted on 38 male amateur bodybuilders of Tehran Province, Enghelab Sports Complex. The subjects were placed in an experimental group (n=19) and a placebo group (n=19). The subjects were assessed by fatigue severity scale (FSS) in pre-test, post-test, and follow-up stages. The obtained data were analyzed in SPSS26 software. Results: The results revealed no significant difference between the creatine monohydrate supplement group and the placebo group regarding the scores of fatigue caused by overtraining syndrome in the pre-test (P>0.05) and the post-test and follow-up stages. Using a creatine monohydrate supplement compared to a placebo could reduce the severity of fatigue caused by overtraining syndrome (P<0.05). Conclusion: Using creatine monohydrate supplements can reduce fatigue severity scores caused by overtraining syndrome.

Sentence Level Feature Selection for Production Prescriptive Opinion Mining

2025-02-25T05:48:17+00:00

The rise of online reviews and social media has fueled the need for advanced opinion mining techniques that can extract and analyze subjective information from text data. This research introduces a novel framework for Sentence Level Feature Selection for Production Prescriptive Opinion Mining (SLFSPPOM), aimed at enhancing sentiment analysis accuracy in production and service domains. The framework includes three key modules to extract actionable insights from online reviews and social media data. The Context Graspable Aspect-based Sentiment Analysis centered Sentence-level Opinion Extractor (CGASA-SOE) performs fine-grained, context-aware aspect-based sentiment analysis, capturing specific sentiments toward product or service features. The Part-of-Speech Tagging Feature Representation Module (POST-FRM) refines feature extraction using linguistic patterns to improve sentiment and aspect recognition. The Fuzzy Rule-based Prescription Generator (FRPG) converts extracted data into tailored production-centric recommendations. By combining these modules, SLFSPPOM provides a comprehensive solution that translates detailed opinions into actionable insights for decision-makers. This framework offers industries a robust tool for gaining nuanced insights and optimizing customer satisfaction and operational strategies. One of the well-known benchmark datasets from Amazon reviews is used to evaluate the performance in terms of Accuracy, Precision, Sensitivity, Specificity, and F-Score of the proposed work. A vivid improvement in performance is observed through the experiments carried out.

AI-Powered Evaluation Tool for Addiction Rehabilitation: A Comprehensive Review

2025-02-28T06:31:06+00:00

Addiction rehabilitation is a critical global health issue, often hindered by stigma, accessibility challenges, and a lack of personalized care. Artificial intelligence (AI) offers transformative potential through innovations such as conversational AI, predictive modeling, digital therapeutics. This review synthesizes findings from over 17 peer-reviewed studies published between 2015 and 2024, focusing on AI-driven conversational assistants, adaptive treatment plans, behavioral notifications, and community-driven platforms. This AI-driven evaluation tool is designed to personalize and streamline the rehabilitation intake process for individuals battling alcohol and cigarette addiction. Using advanced large language models (LLMs), the system engages users through a conversational virtual assistant to gather crucial information about their addiction history, mental health, triggers, and support systems. The tool then generates detailed medical reports with personalized treatment plans, fitness guidance, dietary recommendations, and addiction-specific precautions. It aims to reduce intimidation often associated with traditional rehabilitation methods, enhance accessibility to treatment, and improve outcomes through real-time, personalized support. By incorporating natural language processing (NLP) and machine learning techniques, the system ensures a comprehensive and empathetic assessment, contributing to more effective and individualized recovery plans. Key contributions include multilingual conversation systems, real-time intervention, and care models for personalized care. However, challenges such as data privacy, ethics, and scalability remain. Through the examination of interlinks and gaps in current research, the contribution of AI in enhancing addiction care is emphasized, and future research priorities are set.

Power Aware and Latency Optimized Dma Connected Ddr5 with Clock Gating Using Axi

2025-03-03T07:28:42+00:00

The project aims to design a softcore processor system with Advanced eXtensible Interface (AXI) processor bus which deals with different data capacities with 32-, 64-, 128-, and 256-bits data width. The system deals with Direct Memory Access (DMA) unit to transfer data between the system memory and external peripheral. Memory Controller Block – Dual Data Rate (MCB-DDR5) external memory is introduced to act as main memory system. Registers in DMA controller are designed using general ring counter which consumes more power. As an extension of this concept, ring counter is modified using clock gating technique to reduce power consumption.

Utilizing the Fourier and Cosine Transforms To Detect Brain Activity on Mri Images

2025-03-05T05:49:11+00:00

Rapid and uncontrolled growth of cells is the root cause of brain malignancies. It can be dangerous if not addressed in its earliest phases. The objective of this study is to provide researchers with an exhaustive review on discovering MRI's are used for cancers in the brain. In order to provide specific recommendations, this research suggests a technique for identifying cancers in brain from a variety of brain scans and compares it with the brain's images that are normal. The suggestion made in this work is efficiently given by the Rudin Osher Fatemi filter. Following the use of Fourier characteristics and discrete cosine transforms, SVM classification is employed to derive inferences.

Enhancing Image Sensor Performance with Novel Thin Film Coatings: A Comprehensive Review

2025-03-05T05:59:25+00:00

Recent developments in thin film coating technologies for enhancing image sensor performance are examined in this paper. With an emphasis on their effects on quantum efficiency, spectrum response, and overall sensor sensitivity, the paper examines a number of cutting-edge coating materials and deposition methods that have surfaced in the last ten years. We determine important trends in coating development and their useful applications in contemporary imaging systems by conducting a thorough analysis of recent research and experimental results. The study demonstrates the great potential of sophisticated thin-film coatings in overcoming the conventional drawbacks of image sensors and creating new avenues for imaging applications in the future.

Medical Imaging of Brain Hemorrhage CT Scan Using PDE-Based Filtering with Feature Extraction and Classification

2025-03-05T06:13:13+00:00

Objectives: To propose a suitable technique for employing Computed Tomography (CT) scans to identify brain hemorrhage.

Methods: Images of CT scans of the brain were collected from the open-source Kaggle website. Two hundred photos total—one hundred normal and one hundred affected—are included in that dataset. In image filtering, Ω, f, ƞ, u, and λ were the parameters that indicated the test image function, noise-containing picture, extra noise, image solution, and smoothing function. An analysis was conducted to compare the accuracy of various feature extraction methods. Features were extracted from the head CT scans using the NGTDM, LGP, and GLSZM methods.

Findings: The study found that the GLSZM feature extraction method achieved the highest precision in detecting brain hemorrhage using a Logistic Regression classifier, outperforming NGTDM and LGP. Noise reduction and evaluation metrics validated the method's effectiveness across various datasets.

Novelty: The dataset on brain hemorrhage is processed using the ROF filter, a PDE-based filter. The accuracy of the results produced by this novel method is 95.01%.

Analysis of Diseased One Prey Two Predator Model with Holling type –II Functional Response

2025-03-05T06:25:31+00:00

Two potential illness types that could affect the predator population in an ecological model aredevelopedandstudiedinthisarticle. Whenasuspectablepredatorcameintocontactwith an infected predator, the first infectious disease (SIS) propagated horizontally. Furthermore, because to an external source, the second (SI disease) is transmitted vertically from envi- ronmental effect. There is no route of disease transmission from predator to prey based on contact or predation. In addition to employing linear incidence to illustrate the progression of diseases, Holing type-II and linear functional response are used to explain how healthy and suspectable predators prey on one another. For this model, every conceivable equilib- rium location was looked at. The local and global dynamics of the model are investigated by numerical simulation and also analysing the sensitivity of the parameters.

Intelligent Phishing Detection and Mitigation Framework Using Advanced AI Techniques

2025-03-08T07:26:44+00:00

The Ai Based Phishing Detection and Prevention System is a complete artificial intelligence-based solution (AI) techniques to combat the growing threat of phishing attacks. The system aims to identify and prevent fraudulent activities by analysing various aspects of phishing, such as social engineering techniques, deceptive URLs, and malicious content. By leveraging machine learning algorithms and deep neural networks, the system able to examine vast amounts of data and find patterns that indicate potential phishing attacks. It utilizes natural language processing to analyse the content of email messages or website pages, as well as computer vision techniques to identify visual cues commonly used in phishing attacks. The AI system continuously learns and adapts to new phishing techniques by training on real-time data, ensuring its effectiveness in detecting previously unseen threats. To prevent phishing attacks, the system employs a combination of methods, including email filtering, URL analysis, and user awareness training. By analyzing email headers and content, the system can identify suspicious emails and quarantine or block them before they reach the user's inbox. It also conducts real-time analysis of URL links, examining their reputation and comparing them to known phishing URLs. Additionally, the system provides education and awareness training to users, helping them recognize and report potential phishing attacks. Through the integration of AI technologies, this phishing detection and prevention system offers a proactive and powerful defense against the ever- evolving landscape of phishing threats.

Visualizing the Efficiency of Implementing the Agile Practices

2025-03-10T11:54:40+00:00

Agile practices are rapidly being used in a variety of sectors as organizations strive to enhance their project management and product development processes. While the benefits of Agile techniques are generally recognized, organizations must nevertheless measure and visualize their effectiveness in order to successfully monitor and optimize their deployment. It was primarily concerned with creating visualizations to measure and visualize the effectiveness of applying Agile practices within organizations. The goal is to give stakeholders practical information regarding the efficacy of Agile approaches and how they affect project results. To develop relevant visual representations, the study uses data from project management systems, team collaboration platforms, and performance measures. The visualizations are intended to cover a number of essential areas of Agile deployment, such as sprint planning, work distribution, team participation, progress monitoring, and iterative improvements. Stakeholders may acquire a comprehensive picture of the efficiency of Agile practices, identify bottlenecks, and make educated decisions to improve project performance by presenting the data in a visual style.

Emerging Global Crime Trends: A Comprehensive Comparative Analysis of Criminal Development in Vietnam Vs. Global Perspectives

2025-03-11T06:01:07+00:00

Globalization and technological advancements have led to the expansion of both traditional and new forms of crime. Cybercrime, environmental crime, transnational organized crime, and financial crimes in the digital era represent some of the most pressing challenges humanity faces today. Each of these crimes has the potential to cause significant harm, with their scale and impact growing daily. As technology continues to evolve, these criminal activities are becoming increasingly sophisticated and challenging to control. Addressing these issues requires a unified global response, as no single nation can tackle them alone. Vietnam, like many other countries, has been actively combating these crimes and their devastating consequences. This article explores the emerging crime trends in the digital age, focusing on the ways they are reshaping criminal activity and examining potential strategies to counteract them. The fight against these new-age crimes requires international cooperation, effective policy, and advanced technological solutions.

Advanced Digital Image Enhancement Techniques for Medical and Environmental Imaging

2025-03-12T05:30:59+00:00

Medical image enhancement is important for the improvement of the quality of the image for the accurate diagnosis and treatment. Typical enhancement techniques such as histogram equalization, filtering, and frequency based such as these often suffer from noise, low contrast and degradation in resolution. In the recent years, there have been substantial improvements of medical image enhancement due to the recent progress in the field on Machine Learning (ML), Deep Learning (DL) and Artificial Intelligence (AI). Noise reduction, contrast enhancement and feature extraction are enabled by ML based approach, such as supervised and unsupervised learning methods. Computing process has been denoised, super resolution, structural detail preservation with the help of deep learning models: Convolutional Neural Networks (CNNs), Autoencoders, and Generative Adversarial Networks (GANs). AI based enhancement has been used on MRI, CT, X-ray, ultrasound, and retinal imaging. The research on image processing faced issues including data scarcity, generalizability problems, computational complexity, and interpretable model. Future work that can be developed includes supervised learning, federated learning, hybrid AI models, and AI aided radiology workflows. This work surveyed what state of the art uses of ML, DL, and AI medical image enhancement and analyses the solutions, datasets, challenges, and future directions of medical image enhancement, particularly those that are AI driven.

Study Of Nonlinear Ordinary Differential- Partial Differential Equations By Fourier Decomposition Method

2025-03-12T05:46:01+00:00

In this study, the Fourier Decomposition Method (FDM) is implemented to solve two important nonlinear differential equations, namely the Benney-Luke equation and the Emden-Fowler equation. The solution obtained by FDM is compared with exact solution. The results demonstrated the effectiveness of FDM in providing accurate solutions for both equations.

Enhancing Transmission Efficiency and Data Encryption Evaluation Using Fuzzy Logic: A Comprehensive Review and Novel Approach

2025-03-15T11:03:24+00:00

Fuzzy logic, inspired by human reasoning and intuition, extends traditional Boolean logic by incorporating degrees of truth between" completely true" and "completely false." It enables handling vague or imprecise concepts, such as "large" or "small," using partialtruths to arrive at conclusions. Versatile in nature, fuzzy logic can be implemented in hardware, software, or a combination of both, making it suitabl for applications ranging from small-scale devices to large industrial systems. It has been widely adopted in industries like automotive manufacturing, where it enhances quality, reduces development time, and lowers costs. Originally developed for data classification and handling, fuzzy logic has become a preferred approach for various control systems. In wireless sensor networks (WSNs), for example, fuzzy logic is utilized to optimize node clustering and xtend network lifetime. By selecting cluster heads through fuzzy logic, systems achieve improved efficiency, reduced energy consumption, and enhanced performance. Compared to existing methods, this approach offers superior results in metrics like First Node Dead (FND) and overall network functionality. Fuzzy logic also contributes to data security in network transmission. A novel method integrates fuzzy set theory with cryptography to enhance text data protection. Using the AES Rijndael algorithm, text is encrypted with a key and transformed into numeric values through fuzzy logic, with values ranging between 0 and 1. Decryption requires the original key, ensuring secure data retrieval. To bolster security further, a matrix transformation employing fuzzy membership functions is applied.

Trust-Based Intrusion Detection System (IDS) With Energy Efficient Secure Communication in Wireless Sensor Network Using Modified Chicken Swarm Optimization and Modified Elman Neural Network (MENN)

2025-03-19T09:43:54+00:00

Wireless Sensor Networks (WSNs) consists of tiny sensor nodes deployed in various geographic conditions to gather the information about the environment. The Intrusion Detection system (IDS) in Wireless Sensor Network is used to detect various attacks occurring on sensor nodes of WSNs that are placed in various hostile environments. In the last few years, many innovative and efficient approaches have emerged in this area, and we mainly focus on Trust based approaches of Intrusion Detection system. In this work initially all the nodes in the wireless sensor network will form clusters based on the communication range to reduce the energy consumption and to increase the network lifetime. And then cluster heads will be selected using Improved Chicken Swarm Optimization (ICSO). Once the nodes sensed the information it will send the gathered data to its cluster heads and cluster heads will transfer it to the base station. And then to detect attacks accurately significantfeatures will be selected using Improved Whale Optimization (IWOA). Finally trust based intrusion detection will be performed based on Modified Elman Neural Network (MENN) to establish secure communication in wireless sensor network. Also a Trust-Based Intrusion Detection System (IDS) in WSNs using the NSL-KDD'99 dataset combines trust management mechanisms and IDS techniques to detect and mitigate security threats in WSNs. This approach leverages the notion of trust to monitor and evaluate the behavior of sensor nodes and identify potential malicious activities. The system leverages the NSL-KDD'99 dataset for robust detection of security threats, extending network lifetime and reducing energy consumption.

Power Aware Ofdm Lte System Architecture for Mu-Mimo

2025-03-27T06:34:25+00:00

This project proposes the performance of two suboptimal channel estimation algorithms, denoted as RB and RBG, to be employed in a downlink MUMIMO communication between a BS and multiple MSs. Communications are compliant with the LTE standard in FDD mode and are based on OFDM modulation. Since the LTE standard defines a fundamental block, denoted as PRB, consisting of a given number of consecutive subcarriers in a time slot, the key idea is to approximate the channel as constant over multiples of this unit. This leads to a significant saving in terms of size of the feedback to be transmitted to the BS for precoding purposes, with minimal (often negligible) BER performance loss in all the considered scenarios. The LTE standard uses three different modulation schemes to adapt to various channel conditions in order to improve achievable data rates. These modulation schemes are the QPSK, 16-QAM and 64-QAM. This paper presents an overview of an LTE digital communication system. A Simulation model, designed in order to study the effects of the different modulation schemes on the basis of BER performance with an AWGN channel model. Different subsystems within the transmitter and receiver blocks are implemented in MATLAB. It is noted that the LTE system uses different coding techniques to offer reliable and secure services to the users. Depending on the assumed channel condition (clear, medium clear or noisy), the 64- QAM, 16-QAM or QPSK modulation scheme, on the transmitter side as well as the corresponding demodulation scheme, on the receiver side is used. Based on the recovered data bits, the obtained bit error rates are analyzed, compared and discussed.

A Novel Catboost-Categorical Cross Square Entropy Method for Binary and Multiclass Classification

2025-03-29T09:06:35+00:00

The Organization of data based on the types of information it contains is challenging, and it is critical to manage unstructured textual data from varied domain. In addition to many classification algorithms like Support Vector Machines, Random Forests, Decision Trees, and Logistic Regression, a large number of them employ boosting techniques like Adaboost, XGboost, and LightGBM. Despite classifying the data using the catboost method, we are aiming to obtain 100% accuracy by further refining the model. So here we propose a novel algorithm called Catboost Categorical Cross-Square Entropy (CCCSE) to improve performance. Using the catboost categorical cross square entropy, the loss function, which is ultimately for accurate data classification, is constructed. A categorization procedure is employed to arrange the dataset’s 12, 94, 772 records. The experiment is carried out for both classifications using a variety of datasets obtained from Kaggle, Github, Google Scholar, and the IEEE data. It is preferable to use the proposed CCCSE method for the loss function rather than the categorical cross-entropy for multiclassification, and sigmoid cross entropy for binary classification respectively.The proposed CCCSE formula for binary and multiclass classification classifies document with 100% accuracy than the existing state of the art methods.

An Automatic Approach for Identification of Anthracnose Disease from Mango Leaves Using Soft Computing Techniques

2025-04-01T08:59:58+00:00

Nowadays the concept of healthy environment has gained a lot of importance. The distractions and abnormalities that affect the environment will also affect the entire living organisms. Plants play a significant role in human life and the environment. Healthy plants produce a healthy environment. Also, the plants contribute a lot of benefits to living organisms. Leaves are the major source of food, medicine, water regulations, air purification’s and photosynthesis. It is observed that large number of plant diseases that affects the productivity and growth of a plant. It is not possible to diagnosis plant diseases by individual effort. In recent days, many researchers have developed and discussed various automatic techniques that are available for detecting the plant diseases. Among the available techniques, Soft computing techniques seem to be more relevant compared to other approaches because of its adaptive nature. In this paper, a method called Bacterial Foraging Optimization based Learning Vector Quantization for segmentation and identification of Anthracnose disease on mango leaves has been discussed. This approach also uses Scale Invariant Feature Transform algorithm for feature extraction.

"Analyzing Editorial Strategies and Reader Engagement across Regional and National Newspapers"

2025-04-01T11:38:51+00:00

The rise of social media, particularly Twitter, has revolutionized how news is consumed, shared, and engaged with, transforming traditional patterns of readership. This study explores the influence of Twitter on news consumption, focusing on European and American readerships. It examines how tweets that share news articles reflect engagement patterns across various newspaper sections, offering insights into user preferences, content trends, and the editorial decisions that shape digital news landscapes. The research investigates which types of news—political, cultural, or socio-economic—generate the most interaction and how these align with the editorial profiles of newspapers in these regions. By analyzing the interplay between social media dynamics and traditional journalism, this study contributes to understanding how digital platforms reshape news dissemination and audience behavior. Additionally, this research juxtaposes these findings with an analysis of Indian newspapers, including The Hindu, Times of India, Daily Thanthi, Dinakaran, and Dinamalar, to provide a global perspective on how news consumption evolves across distinct cultural and regional contexts. The Indian case study dissects news categories such as straight reporting, editorials, and opinion pieces while evaluating tones, narrative strategies, and thematic priorities, including political coverage, crime, and socio-economic issues. By integrating cross-regional insights, the study reveals how editorial intent, visual storytelling, and digital engagement mechanisms influence the construction of public discourse. This work underscores the shifting role of newspapers in an era dominated by social media, contributing to ongoing discussions about journalism’s adaptation in the 21st century.

Cloud Computing and Machine Learning: A Symbiotic Relationship

2025-04-04T05:38:59+00:00

A symbiotic relationship between cloud computing and machine learning has been a driving force in the development of new and powerful solutions to solve complex real-world problems. Cloud computing offers the processing capacity, scalability, and infrastructure required to support the intensive computational demands of machine learning algorithms. Conversely, machine learning (ML) algorithms transformed the way things are done we process, analyze also, extract insights from the vast amounts of data generated and stored in the cloud. This collaborative effort combining cloud computing and machine learning has led to the emergence of a diverse array of possible applications that are transforming sectors, ranging from smart homes and devices to self-driving technologies, industrial robotics, and intelligent detection systems.

The Word Occurrence Using Zipf’s Law in the Climate Change During 2019-2023: A Scientometric Study

2025-04-04T05:46:13+00:00

Climate Change is nowadays a challenge in the present scenario at the world level. A lot of changes in society and the environment. The eradication of the forest and natural resources in the effects of global warming, greenhouse effects and natural disasters. Now the above reasons are threatening human life and a major challenge in the current situation. The present study focused on the word occurrence using zipf’s law in climate change. The data was collected from the Scopus database during 2019-2023. 39,165 documents were found in the study. The study analysis has the top ten authors’ productivity, subject areas, document type, source title, language-wise publications, source type, country-wise contributions, funding sponsorship of climate change research and affiliation-based research.

Mediblock: Adopting Decentralized Ledger Technology for the Management of Patient Data

2025-04-09T05:56:28+00:00

Patient data management is one of the most urgent issues facing in the healthcare sector. These issues are mostly related to data security and privacy issues that arise from flaws in centralized systems. These flaws put patient data at risk of impending cyberattacks and breaches, which could have dire repercussions for patients and healthcare providers alike. Patient’s lack of control over their data also poses serious privacy and consent difficulties, which undermines their confidence in the healthcare system. MediBlock is at the vanguard of healthcare innovation by utilizing decentralized ledger technology more particularly, the Ethereum blockchain—to change patient data management. MediBlock creates a transparent and safe platform that gives people complete ownership over their health data by utilizing the Ethereum blockchain. Users can feel secure knowing that their medical records are private because Ethereum is decentralized and guarantees data accessibility, integrity, and privacy. With the assurance that only authorized individuals can access MediBlock, patients can safely keep a multitude of health data, such as test results, treatment details, and medical histories. By facilitating smooth data interchange between patients and healthcare providers, the platform lowers the chance of medical errors and eliminates needless data collecting. The decentralized design of MediBlock fosters trust and transparency between patients and medical providers by promoting efficiency and interoperability throughout the healthcare ecosystem.

Fuzzy Logic-Based Bayesian Trust Routing (Flbt-R) For Trust-Based Secure Routing In Vehicular Ad Hoc Networks (Vanets)

2025-04-11T05:34:10+00:00

Vehicular Ad Hoc Networks (VANETs) play a crucial role in intelligent transportation systems, enabling efficient and secure vehicle-to-vehicle (V2V) communication. However, ensuring trust and security in routing remains a significant challenge due to the dynamic nature of VANETs and the presence of malicious nodes. This paper proposes Fuzzy Logic-Based Bayesian Trust Routing (FLBT-R), an innovative trust-based routing model that integrates fuzzy logic and Bayesian inference to enhance security and reliability in VANET communications. The proposed model evaluates node trustworthiness based on multiple parameters such as past behavior, packet forwarding rate, and neighbor recommendations. Fuzzy logic enables adaptive trust assessment, while Bayesian inference refines trust values over time, effectively reducing false positives and negatives. Simulation results demonstrate that FLBT-R achieves higher malicious node detection rates (MDR), lower false positive rates (FPR), reduced computational overhead, and optimized average hop count compared to traditional trust-based routing schemes. These improvements establish FLBT-R as a robust approach for mitigating security threats and ensuring reliable data transmission in VANET environments.

Ai-Based Smart Health Monitoring Using Machine Learning & Iot

2025-04-12T07:13:22+00:00

Growing requirements for continuous health monitoring have spurred the adoption of Artificial Intelligence (AI), the Internet of Things (IoT), and cloud computing in modern healthcare. This paper demonstrates an end-to-end smart health monitoring system that leverages wearable IoT sensors for real-time physiological monitoring, cloud infrastructure for scalable processing, and high-performance AI models for anomaly detection and predictive diagnosis. The architecture in question leverages edge computing for low-latency preprocessing and uses strong machine learning algorithms such as KNN, Autoencoders, Isolation Forest, and LSTM for the detection of abnormal health patterns and the prediction of medical conditions. Multi-layered encryption, OAuth2.0 authorization, and GDPR-compliant processing guarantee security. Experimental studies with real-world and synthetic data validate high accuracy, quick response, and scalability. The system enhances active care delivery, improves patient health, and simplifies healthcare costs through telemedicine diagnosis and patient-specific recommendations. Future advancements with federated learning, explainable AI, and blockchain deployment will further enhance the transparency, trustworthiness, and flexibility of the system.

Aquanutri: A Mobile Platform for Skin-Based Deficiency Detection and Nutrient-Rich Fish Recommendation

2025-04-14T07:01:06+00:00

AQUANUTRI functions as a modern AI-powered system which examines skin images for nutritional deficiencies while promoting sustainable fish farming on terraces for dietary improvements. This system distinguishes visible skin symptoms related to vitamin and mineral deficiencies using a CNN named ResNet50 that operates on a DermNet dataset which received specialized training. The system references identified deficiencies to its fish database and suggests personalized recommendations based on user location together with environmental conditions ranging from freshwater to brackish to marine conditions. AQUANUTRI functions by referring suitable fish species which maintain nutritional richness for small aquaponic farming ac- tivities whereas the information point toward eco-friendly self- sufficiency options. AQUANUTRI emerges as a valuable solution for nutritional health management because it implements deep learning connectivity between expert-data and inexpensive agri- cultural recommendations.

Inheritance of The Trait of Early Maturity in Special and Interspecial F1 Plants

2025-04-15T08:41:46+00:00

Early maturity is an important morpho-biological characteristic of the plant. In this regard, great attention is paid to improving this trait in cotton plants. In particular, the period from sowing to budding, from sowing to flowering, and from sowing to boll opening determines the early maturity of the plant. The duration of the periods depends on the activity of polygenes, and the inheritance of this trait was studied in F1 plants.

Adaptive Federated Reinforcement Learning Framework for Secure and Efficient Traffic Management in VANET-Enabled Smart Cities

2025-04-15T08:52:45+00:00

To ensure smooth accessibility, security, and efficiency, the increasing complexity of traffic management in Vehicular Ad Hoc Networks (VANETs) within smart cities necessitates flexible and secure systems. Existing centralized methods face challenges related to flexibility, communication delays, and information security. To address these issues, this study proposes an Adaptive Federated Reinforcement Learning (AFRL) framework that leverages deep reinforcement learning (DRL) for dynamic traffic optimization and federated training to enable privacy-preserving information processing through distributed VANET nodes. By utilizing vehicle data to train local models and extracting insights without compromising information security, the proposed framework dynamically adapts to real-time traffic conditions. An enhanced secure aggregation technique and a flexible incentive strategy optimize traffic signal control and congestion management, addressing key challenges such as communication overhead, model convergence, and adversarial information attacks. The primary objectives of VANETs in intelligent urban environments include improving traffic flow, reducing delays, and minimizing collision risks. Studies demonstrate that the AFRL framework is an adaptable and reliable solution for smart transportation planning in modern cities, achieving a 20% reduction in average vehicle wait times, a 15% increase in traffic efficiency, and enhanced resilience against adversarial attacks.

Multi-Lingual Chat Application for Group Communication

2025-04-16T05:27:08+00:00

The capacity to effectively communicate across linguistic barriers is more crucial than ever in today’s increasingly globalized society. This paper outlines the development of a groundbreaking new multilingual chat application that has been created specifically to facilitate seamless group communication. By utilizing the versatile Flutter frame- work and Firebase backend services, the Project aims to transcend traditional linguistic limitations by integrating real-time translation capabilities directly into the application. This means that users can join groups and engage in conversations with other members in their preferred language, The messages can be translated within the app. This research represents an important step forward in the development of inclusive communication platforms that can accommodate diverse linguistic backgrounds. By harnessing the power of cutting- edge technology, This ap- plication seeks to enhance global connectivity and foster collaboration among individuals, regardless of language differences. The user-friendly interface, which has been carefully crafted using the Flutter framework, users can navigate the app with ease and enjoy a smooth user experience. Meanwhile, Firebase ensures seamless operation and reliability by managing data storage and message synchronization. At the heart of this project is the primary objective of overcoming language barriers and promoting effective communication in group settings. By integrating real- time translation capabilities directly into the chat application, users can engage in conversations without being held back by language constraints, thereby facilitating cross-cultural interaction and collaboration.

Machine Learning-Based Strategy to Detect Sybil Attacks on Mobile Ad Hoc Networks

2025-04-18T05:41:35+00:00

Mobile ad hoc networks pose significant security challenges due to their decentralized nature and limited resources. These networks allow nodes to join or leave freely, without any central authority controlling their entry or exit. Dynamic multi-hop networks can be categorized as either stationary or mobile, and provide rapid and effortless access to information. However, the unpredictable and constantly changing topology of MANETs, along with the dispersion and self-organization of nodes, can make it difficult to predict how the network will evolve. Mobile ad hoc networks are inherently less secure than wired networks due to vulnerabilities in security and constraints in energy resources. In contrast to fixed networks, mobile ad hoc networks use wireless transmission, which exposes them to higher loss rates, delays, and jitter. Moreover, the nodes in these networks rely on finite energy sources, such as batteries. To gain unauthorized access to a large portion of the system, a Sybil attack refers to the ability of a small group of actors to mimic several peers. This study proposes a machine learning-based strategy to identify a Sybil attacks in MANETs by collecting network metrics such as traffic characteristics, communication patterns, and node behaviours.

Innovative Approach in Environmental Education of Preschoolers

2025-04-18T10:48:09+00:00

This article examines the strategic direction of developing the education system in modern society—the use of innovative technologies. It is based on an individual's purposeful and independent activity in various fields, contributing to their intellectual and moral development. One of the strategically important issues in education today is the ecological upbringing of the younger generation. Ecological education for preschoolers can be implemented in all educational areas. In the process of ecological education, preschool children's interaction with nature promotes their psychological and physical development, making them strong and healthy. The methodology of ecological education includes methods that allow children to interact with nature both directly—through engagement with natural objects—and indirectly, using visual materials such as pictures and slides. Indirect methods of familiarization with nature include visual-illustrative materials, films, storytelling by educators, children's reading of nature-related books, discussions, and fostering a conscious and correct attitude toward natural phenomena.

Development and Validation of Analytical Methods for Simultaneous Estimation of Voglibose, Sitagliptin and Metformin Hydrochloride in Bulk and Tablet Dosage Form by Rp-Hplc.

2025-04-19T07:33:56+00:00

The RP-HPLC method was developed to determine the levels of metformin HCl (MET), sitagliptin (SIT), and voglibose (VGB) in an inexpensive, accurate, precise, and repeatable manner. With a fine ODS C18 column (250mm) and a mobile phase mixture including a mixed acetonitrile: phosphate buffer ratio of 85:15 (PH 4), the RP–HPLC method was developed on LC2030C plus HPLC equipment. The effluent was measured at 242 nm, and the flow rate was 1 ml/min. Voglibose, Sitagliptin, and Metformin HCL had respective retention times of 4.9, 3.7, and 3.0 minutes. Linearity, precision, accuracy, specificity, and system appropriateness characteristics were used to validate the approach. For MET, VOG, and SIT, the calibration curves were linear with regression coefficients of 1, 0.9999, and 0.9998, respectively, at concentration ranges of 10–50 μg/ml, 1–5 μg/ml, and 10–50 μg/ml. The results of the suggested approach were deemed satisfactory and appropriate for the simultaneous determination of voglibose, sitagliptin, and metformin HCl for routine quality control of medicines in bulk and formulation. The results were exact with (%) relative standard deviation.

Studying the Drought-Resistance of Berry Plants

2025-04-23T05:50:24+00:00

The article presents the data about drought-resistance of berry fruits cultivars, such as strawberry and golden currant, and also studies the water amount and water deficiency on the leaves of strawberry and golden currant cultivars which belong to different ecological groups. Having taken the samples of the leaves of strawberry and golden currant before irrigation and after irrigation in experimental fields of investigation, the air temperature, its relative humidity, the influence of water amount and soil moisture on water deficiency have been studied too.

Advanced Customizable Suction Mechanism for Enhanced Hospital Hygiene

2025-04-24T06:08:58+00:00

Manpower requirements in healthcare settings are receiving increased attention due to the rising number of patients and the prevalence of various infections. The healthcare burden continues to grow because of a shortage of skilled health workers, and medical errors may occur due to work overload and excessive pressure in the workplace. Hospitals require more staff beyond just medical doctors and nurses, but the lack of skilled professionals remains a significant concern. Alongside qualified staff, maintaining a high standard of cleanliness is essential, as hospitals receive many visitors for various health concerns. The fear of communicable diseases is increasing due to the presence of mutated viruses, bacteria, and cells that can lead to new diseases and easily affect the hospital environment. While sterilized areas and operation theaters are generally well-maintained, common wards often do not meet the same hygiene standards. Preventing the transmission of infections and minimizing disease occurrence is vital. Effective cleaning practices not only ensure better hygiene but also save time and improve convenience. To address these issues, we propose a solution to enhance hospital hygiene even in routine wards and laboratories through the use of a flexible suction device. This device can automatically detect and remove fluids such as blood, urine, chemicals, and other biofluids that may cause the spread of infection. It can be either fixed or portable and customized to meet specific needs. With a built-in Bluetooth module, the device can be operated remotely via a mobile phone, offering convenience and accuracy. It also includes a sanitizing scrubber that effectively removes blood and other pathogens, contributing to improved hospital sanitation. This innovative system automates the cleaning process, reduces the need for manual labor, and ensures a consistently high standard of cleanliness. By minimizing healthcare-associated infections, it improves patient safety and allows medical staff to focus more on patient care. This, in turn, increases operational efficiency, optimizes resource allocation, and enhances the overall quality of healthcare services.

Intelligent Anesthesia Management System for Optimized Anesthesiologist Support and Complication Prevention

2025-04-24T06:16:53+00:00

The Personalized Anesthesia Management project focuses on predicting postoperative complications in patients undergoing surgery, through the use of advanced machine learning algorithms such as Support Vector Machine (SVM), Random Forest, and XG Boost. We can create complex models that inform predictive insights and data-driven decision-making. The dataset provides rich and detailed insights, delivering detailed information about patients, including demographic data, surgery type, anesthesia used, surgical duration, and postoperative conditions. The primary aim of the project is to develop and design a predictive model that will be capable of making true decisions about the possibility of postoperative complications like nausea, mild hemorrhage, respiratory issues, and extended recovery utilizing appropriate preoperative and intraoperative predictors. The result of the operation is classified into two types: 0 (no complications) and 1 (complications). The model is developed to help physicians better manage patient anesthesia by spotting dangerous patients and refining perioperative care, leading to enhanced surgical outcomes and improved patient safety. The system is constructed with a JavaScript, HTML, and CSS frontend and Python-based backend to have an interactive interface and accurate prediction power.

Digital Phonocardiography for Predicting Heart Diseases: An Embedded System Approach

2025-04-24T06:35:26+00:00

Cardiovascular disease is a major cause of death throughout the world, particularly in resource-poor countries and developing countries, where windows of early detection are narrow. The Digital Phonocardiography (PCG) of this work is a low-cost, portable biomedical system for immediate identification and ongoing monitoring of cardiac pathology. Equipped with a digital stethoscope, an OLED display, and Bluetooth, the system uses embedded C programming techniques to analyze cardiac sounds and provide immediate diagnostic feedback. Due to its low cost and improved accessibility, the device is designed to enable early diagnosis and timely intervention, especially in healthcare environments that are resource-poor.

Voice Assistive CPR for Enhancing Rescuer Performance with Real-Time Feedback

2025-04-24T06:43:14+00:00

The Voice Assistive CPR Device is a simple-to-operate system that helps bystanders perform proper CPR in cardiac arrest scenarios. It gives voice instructions for step-by-step chest compressions and rescue breathing, making it possible for anyone even non-medical personnel to respond properly. Compression depth and rate are monitored internally, providing immediate feedback for accurate and effective CPR. Encased in a small, lightweight package, the unit is simple to transport to homes, schools, or public areas. Multilingual capabilities and optional visual output enhance accessibility to a wide variety of users. Models can be equipped with child and adult modes, as well as session data storage for subsequent professional review. Through its ability to offer instantaneous instructions, the unit conserves precious time and enhances user confidence during emergencies. It meets international CPR standards and can be replaced when necessary. With longer battery life and an integrated alert feature, it always provides readiness. Where there is no immediate medical attention, it becomes a critical first-response device. More advanced devices can interface with emergency apps or alert local responders. By minimizing hesitation and maximizing CPR quality, this device significantly enhances survival prospects before professional help can arrive. Its ease of use promotes broader public uptake of CPR skills. AI-based adaptation to tailor directions in real time could also be part of future developments.

Development of a Non-Invasive Glucose Monitoring System Using Breath Acetone Analysis with AI-Based Estimation Model

2025-04-24T06:54:59+00:00

Diabetes is a growing worldwide health problem, with over 530 million people affected and expected to grow substantially in the coming decades. One of the main challenges in the treatment of diabetes is the painful and invasive nature of traditional glucose monitoring methods, which require repeated finger-prick blood tests during the day. This need often translates into poor compliance and irregular monitoring. To address this problem, we have created a non-invasive and cost-effective glucose monitoring system that utilizes breath acetone analysis combined with a smart microcontroller setup. Our system utilizes an MQ-138 gas sensor to detect acetone levels in the breath, which reflects a correlation with blood glucose levels. A PIR sensor detects the presence of the user and starts a countdown process before taking the breath sample. The information gathered from the sensors is processed using a light-weight AI-based algorithm to predict glucose levels, which are then displayed on an LCD screen together with health categorization (High, Normal, and Low). This approach eliminates the need for blood sampling, encourages frequent monitoring, and improves the comfort of diabetic patients. Due to the growing prevalence of diabetes, our approach provides a simple, painless, and portable method of monitoring glucose concentration, especially suitable for resource-constrained and home-care environments.

Real-Time Gas Leak Detection and Prevention System Using Pressure Sensors and IoT-Connected Devices for Hospital Safety

2025-04-24T07:02:47+00:00

For safe hospitals, there must be gas leak protection to shield patients and maintain uninterrupted treatment. This project is a suggestion for an automated system to identify pressure drops and activate precautionary safety protocols in real time. The system uses pressure sensors, Arduino Uno, buzzers, and solenoid valves to check for a leak of gas and respond immediately. Upon detection of leakage, the system activates the alarm, sends warning, and automatically closes gas valves to prevent continuing leakage and potential dangers. Monitoring and automated feedback minimize the need to constantly monitor manually, enabling health environment operational effectiveness and safety. Proper sensing is made possible through sensors and real-time safety is achieved by solenoid valves.

mechanism to prevent leaks. It is an economic and scalable solution that can be applied to various medical gas distribution systems. Compared to traditional leak detection methods, the system here has faster response times, improved reliability, and enhanced safety. Future developments will be centered on improving sensor precision, wireless notification support, and increasing its flexibility to various hospital facilities. Through prevention of leaks of gas initially, this system minimizes patient risks, optimizes gas utilization, and increases hospital operating efficiency, which renders it a valuable solution for medical institutions.

Enhancing Emotional and Social Skills in Children with ASD through a YOLO- Driven Gamified Music Therapy Approach

2025-04-24T07:13:23+00:00

Children with Autism Spectrum Disorder (ASD) frequently war with emotional recognition, self-regulation, and social interactions, impacting their common improvement. This observe proposes a progressive play-based mental fitness intervention that integrates gamification, real-time face emotion reputation the usage of the YOLO (You Only Look Once) algorithm, and customized auditory comments to enhance emotional intelligence in youngsters with ASD. YOLO appropriately detects and classifies facial expressions inclusive of happiness, unhappiness, anger, and marvel in real time. Upon detecting an emotion, the machine dynamically selects and plays suitable audio songs to alter mood, enhance high-quality behaviors, and encourage emotional expression. Gamified obligations with rewards and innovative trouble stages are integrated to preserve engagement and motivation, ensuring consistent participation. The machine adapts to each baby’s character wishes, dynamically adjusting task complexity, remarks, and audio picks based totally on the kid’s progress. Through this customized technique, the system creates an enticing learning environment that promotes emotional reputation, self-regulation, and social interplay. Additionally, unique progress monitoring gives caregivers and therapists with treasured insights for customized intervention making plans, optimizing therapeutic results. The intervention’s multisensory method, combining gamification and auditory feedback, reinforces emotional learning in a supportive, dynamic surroundings. This AI-powered machine addresses the emotional and social demanding situations confronted by way of children with ASD via mixing modern technology with evidence-primarily based healing strategies, supplying a scalable and adaptable answer for improving emotional well-being.

Eco-Smart Vaccine Storage: A Sustainable Carrier with Hemp Fiber Housing

2025-04-24T07:22:09+00:00

Eco-smart vaccine storage is a modern innovation which primarily is directed to ensuring safe and reliable transport of vaccines at an optimum temperature range of 2°C to 8°C. The system caters to the developed insulated system, energy-efficient cooling modules, and real-time monitoring for maintaining cold chain conditions during transport. Likewise, a combination of Peltier cooling modules, phase change materials (PCMs), and solar-powered energy systems are used in the box to manage and stabilize temperature fluctuations. Within the box, there is always a DHT11 sensor monitoring the internal temperature and AI-based microcontroller optimizes and predicts changes in the cooling system accordingly. The addition of GPS tracking and communication modules comprising GSM allows real-time location monitoring of temperature fluctuations or system failures. Eco-smart vaccine storage is scalable, cost-effective, and energy-efficient; making it an ideal solution for use in remote healthcare, by offering a continuous cold chain management of vaccines at the same time providing a reduced environmental footprint. It helps solve global cold chain issues in healthcare logistics through a blend of AI and renewable energy to offer a green and economical solution for transportation of vaccines- a solution that will suit millions of users across continents. Decades of immunization programs promise healthy communities shrouded with the promise of immunization against infectious diseases, but that promise is still reduced by one unending challenge: last-mile vaccine delivery. It is long distances traveled by vaccines under harsh weather, sometimes without power supply; hence can hardly be kept under the all-important temperature range of 2°C to 8°C in areas with fewer resources and extreme remoteness. Even small deviations can cause vaccines to become ineffective, thereby wasting resources, causing outbreaks of preventable diseases, and slowing the improvement of public health.

Now upgraded fringes are expected to come up with vaccines up to 2030. WHO estimates wastage of vaccines of up to 50% worldwide, much attributable to failures in temperature control and inefficiencies in logistics during the cold chain. Due to irregular cooling, limited period of cooling in ice packs, and exposure to very high or low temperature, vaccines have become ineffective. These are ice-lined refrigerators, passive cooling boxes, and diesel-powered refrigeration units: no solution has been proposed to them. Ice packs may also freeze vaccines accidentally simply by being overcooled, while standard refrigerators are operated in constant electricity supply, which makes them impractical at places that often experience outages. They include also manual checking of temperature as it raises chances of unnoticed deviations and puts potency of vaccines at stake further. These emblems necessitate an immediate intelligent, self-regulating, and sustainable solution that spans the last mile between home and vaccine deliverability.

For the purpose of re-engineering cold chain logistics as far as vaccine storage is concerned, Eco-Smart Vaccine Storage integrates a form of AI thermal management, real-time monitoring, and renewable energy solutions. A Peltier cooling module, phase change materials (PCMs), and solar-powered energy systems allow the carrier to preserve an optimal in-storage environment without tie to grids of electricity normally used in cooling. It is expected to work with a DHT11 temperature sensor for continual monitoring while AI-enabled micros do the dynamic prediction and adjustment of devices regarding cooling techniques. GPS tracking and GSM communication modules also provide real-time location data and instant alerts whenever there is a temperature fluctuation or failure in the system.

This innovation will, therefore, offer scalable, affordable, and environmentally friendly options to health care conglomerates worldwide by attacking the critical areas that fail conventional approaches to vaccine storage. Eco-Smart Vaccine Storage guarantees even the remotest communities access to the life-saving vaccine, provided it is accessible in potency. In a sense, by ensuring AI-powered automation fused with renewable energy, this amounts to a new beginning where no vaccine is wasted and where communities get equal access to essential healthcare.

Next-Gen Advanced Smart Wheelchair Customizable RF Control and IoT-Enabled Adaptive Seat Mechanism

2025-04-24T07:31:00+00:00

A new smart wheelchair system to enable mobility, independence, and improve the safety of individuals with physical disabilities is proposed in this paper for IoT. For a holistic assistive solution, the system has electropneumatic (mechanical) actuation, RF control, and real-time remote monitoring over wireless radio frequency. Central to the design is the ESP32 microcontroller module talks to different modules, such as sensors, communication systems, and controllers for adjusting the seat. Vertical seat elevation: A motorized screw jack mechanism, that is likely to be a significant Mechanical innovation in the system. The ability to control seat height is more user-friendly and caregiver independent because the user has to adjust their seat elevation on their own. It also features bed-type tilting for posture correction, through a worm gear which makes sitting for long periods more comfortable. We employ an accelerometer sensor that continuously supervises tilt orientation to maintain the safety of the user in the Wheelchair. This maintains stability in balance and movement since the system begins to correct itself automatically at certain critical detected angles. Wi-Fi-based short-range Wireless control (RF transmitter-receiver module: 433 MHz) is also applied alongside a GPS Module for real-time tracking of the location even in Offline Scenarios. Among these, it's a great connection to the Blynk IoT platform (SIM800L module, GSM-based communication channel) It is an essential feature of this smart wheelchair. The Blynk app is friendly to users and serves as a live interface for manipulating live control and monitoring the system, operating on the smartphone connected to any user or caregiver. ESP32, through GSM communication, can send the tilt angle, seat position, GPS coordinates, and battery life along with motion activity to the Blynk cloud. People can access the parameters of this cloud-to-app interaction anywhere globally, and even take charge of the wheelchair remotely. Real-time alerts are pushed to the Blynk Dashboard in caregiver and emergency categories, Events include fall, instability, or no movement for a long time. Furthermore, the platform has access to manual control via virtual buttons, sliders, and graphs, which also aids in interaction with the wheelchair a bit more and for improved adaptability. This is because, with GSM not requiring local WiFi, it allows for the system to function.

Smart BMI Weighing Scale with Personalized Nutrition-Based Food Charts

2025-04-24T07:40:22+00:00

With the increasing prevalence of obesity and malnutrition, maintaining a healthy diet and monitoring body weight have become essential aspects of preventive healthcare. Poor weight management and the lack of proper dietary habits contribute to various lifestyle dis-eases, including hypertension, diabetes, and obesity-related complications, particularly among adults. Traditional weight monitoring methods rely on manual BMI calculations, which consider only limited health parameters, requiring individuals to depend on human dietitians for nutritional guidance. This approach is not only time-consuming and costly but also lacks personalized follow-ups and continuous health recommendations, making it diffi-cult for individuals to maintain a structured weight management plan. This project introduc-es Smart BMI Weighing Scale, a solution intended to address the problems mentioned. It also computes BMI and provides personalized diet recommendations for users based on their health conditions. Further comparing with the existing types, which only measure weight, this system evaluates an individual, using an analysis of the body health condition, to assess additional health parameters and allow for proper guidance that can be further personalized. The system thus manages to collect health inputs such as age, sex, and problems with body organs, specific, and prepares the user with a personalized diet plan as a guide to healthy eating habits. With some periodic follow-ups after each consultation, the system also aids users in tracking progress and changing diet plans. One of the best features of this system is that it can deliver instant insights into what to eat and still ensure the security of data and remote access. An individual can access his or her data via individual logins secured by their passwords. Thus, allowing health information to be continually monitored from any place or at any time.

This automated approach eliminates the need for human dietitians, making nutritional coun-selling more cost-effective and accessible to a larger population. In addition, this helps con-trol their weight, therefore minimizing the risk of diseases induced by obesity, and inculcat-ing proper lifestyle practices. The Intelligent BMI Weighing Scale operates efficiently in providing immediate and accurate results, all the while keeping up with conventional weigh-ing methods. The system not only tracks BMI calculations but also accesses comprehensive health reports, making it a better alternative to manual monitoring systems. Given its less-costly implementation, it can affordably give advanced health monitoring solutions to a wider audience, bridging the gap between technology and preventive healthcare. A combi-nation of BMI analysis and personalized diet planning means that this project is an innova-tive and scalable solution to addressing the global challenges of obesity, malnutrition, and weight management. Intelligent BMI Weighing Scale can change the approach we follow for health monitoring, allowing people to take charge of their health with a technology-driven solution.

Design and Implementation of Smart Iot Framework For Medication and Meal Schedule Monitoring

2025-04-24T07:49:34+00:00

Monitoring medication schedule and diet is essential for patients with chronic diseases, older adults, and spatients with rigorous health monitoring requirements. This IoT-based real-time health reminder system is meant to monitor timely medication and healthy living through automatic reminders and real-time monitoring of adherence. The reminders are both audio and visual in nature to remind users to take medication or consume food. The execution of the action is tracked, designating the result as "Taken" or "Missed." Adherence data are transmitted to a cloud platform, allowing caregivers or family members to track remotely. The system allows the patient to manage his/her own health independently of caregivers and empowers caregivers with information to act immediately. Reminding and monitoring compliance automatically, the system prevents the risk of missed doses, enhances drug compliance, and streamlines long-term health care. Integrating IoT technology and real-time monitoring in a non-invasive process closes the drug timing and patient compliance loop and leads to enhanced health outcomes.

A Modular ECG-Controlled Myoelectric Prosthetic Arm With Detachable Electrode Band

2025-04-24T07:56:58+00:00

A person who lost their hand in accidents or surgical removal of arm due to accidents and limb disabilities severely impact the quality of life, especially in economically challenged populations where access to advanced prosthetic limbs is limited and traditional prosthetic arms are more expensive, mechanically complex, and not easily adaptable for personalized use. To address these limitations, we developed a low-cost, Arduino-based myoelectric prosthetic arm controlled using bio-signals from the AD8232 ECG sensor. This system detects muscle electrical activity through three surface electrodes with the help of ECG sensor it translates the electrical impulse into a signal to control servo motor actuation and mimicking the movement of natural fingers. The prosthetic arm model designed by using cardboard and softwood, combined and moulded with Flex Quick, ensuring affordability, durability, and ease of replication. Sensor data is analysed in real time and compared against modified threshold values to determine which finger movement is intended, enabling accurate and responsive actuation of the finger movements. This non-invasive and wearable solution promotes accessibility and independence for individuals who lost their arm in accident or disable from birth, especially in low-resource settings. Our modular design and open-source platform (Arduino uno) make it ideal for educational, rehabilitation, and rural healthcare applications.

Alzwatch: Iot-Based Wearable System for Monitoring Vital Signs and Tracking Alzheimer’s Patients

2025-04-24T08:07:08+00:00

Over 55 Millions around the world are living with dementia, and the count is likely to grow 78 million by the year of 2030. One of the biggest issue Alzheimer’s patients face is wandering which means they leave home without remembering their way back, which is dangerous and stressful for themselves and their families. Traditional methods like manual supervision and basic GPS trackers aren’t having enough facilities which lacks of smart monitoring because they don’t provide real-time alerts or monitor the patient’s health. For this, we developed an efficient solution using a smart wearable device integrated with IoT, real time GPS and health tracking. It tracks the patient’s location in real time using GPS, and if they leave a predefined safe zone, an automatic alert message is sent to the caregiver’s mobile app. It also monitors vital signs like heart rate (SpO₂), body temperature, and fall detection using a gyroscope sensor. If there’s any abnormal fluctuation, the system automatically notifies the caregiver. Plus, caregivers can even send a reminder message with just one click telling the patient to return home. With cases of dementia increasing every year, a smart, automated system like this is necessary to ensure safety and quick emergency response. Our system helps reduce caregiver stress, keeps patients independent for longer, and provides a real-time safety solution for Alzheimer's care.

Design and Implementation of a Smart Oxygen Delivery System Using Spo₂ Sensing and Iot Connectivity

2025-04-24T08:12:49+00:00

The Smart Oxygen Delivery System with SpO₂-Based Automation and IoT-Enabled Remote Monitoring is a cutting-edge and smart solution for respiratory care aimed to support patients with chronic and acute respiratory diseases like Chronic Obstructive Pulmonary Disease (COPD), asthma, and COVID-19-induced hypoxemia. The system combines sophisticated sensing technology, embedded hardware, and Internet of Things (IoT) connectivity to provide an intelligent and adaptive oxygen therapy solution. The core of the system is the MAX30102 photo plethysmography (PPG) sensor, which precisely measures blood oxygen saturation (SpO₂) and heart rate through dual-wavelength light absorption. The information is passed on to an ESP8266 microcontroller with Wi-Fi capabilities for wireless connectivity with cloud services. The system constantly monitors SpO₂ levels and triggers an automatic oxygen delivery procedure when the level goes below a predetermined value (e.g., 90%). This is done by actuating a solenoid valve that delivers oxygen from a concentrator or cylinder without the need for human intervention. At the same time, real-time data is streamed to an IoT platform like Thing Speak, allowing healthcare professionals to monitor patient vitals remotely through a secure web-based dashboard. Notifications and alerts can also be set up for life-threatening conditions, thus facilitating quicker medical interventions and minimizing reliance on continuous bedside monitoring. The whole system has affordability, mobility, and scalability in its mind. It consists of low-cost components such as the ESP8266 microcontroller, so it can be implemented in hospitals, rural clinics, home care units, and telemedicine environments. The closed-loop aspect of the system not only improves patient safety and therapeutic outcome but also maximizes the utilization of healthcare staff in high-need or limited-resource environments. With automation merged with cloud-based access to data, this intelligent oxygen supply system is a giant leap in the direction of computerizing respiratory therapy and progressing forward-thinking, technology-based healthcare solutions.

Shared HR Services and Improved Employee Performance: A Study in IT Industry

2025-04-25T09:27:06+00:00

The primary aim of the study was to investigate different aspects of shared HR services and their influence on employee performance This research explores the impact of organizational culture, adoption of innovation, HR practices, workforce agility, HR service quality, and shared HR services on improved employee performance in the IT industry. As the IT sector faces rapid technological advancements and constant market changes, understanding the interplay of these factors becomes crucial for sustaining high employee performance. The study highlights how a supportive organizational culture fosters innovation and agility, leading to increased employee engagement and productivity. It also examines the role of effective HR practices, such as performance management and employee development, in enhancing workforce capabilities. Furthermore, the research investigates how the quality of HR services and the use of shared HR services contribute to employee satisfaction, reducing administrative burdens and enabling employees to focus on core tasks. The findings suggest that organizations that integrate these factors into their HR strategies can create a work environment that supports innovation, agility, and high performance. The paper provides valuable insights for IT firms aiming to improve employee outcomes through a holistic approach to organizational culture and HR management.

A Review on Development of Real-Time Driving Cycle of Vehicles

2025-04-28T05:25:35+00:00

Vehicle emissions are a significant contributor to air pollution in urban areas across India. Typically, vehicles are responsible for emitting around 20–30% of particulate matter (PM2.5) into the atmosphere. Along with substantial carbon dioxide (CO₂) emissions, they also release high levels of carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOₓ), suspended particulate matter (SPM), and other harmful air pollutants, leading to severe environmental degradation and adverse health effects. Estimating PM2.5 emissions from vehicles is crucial, as this pollutant has a direct impact on lung function.

To accurately assess vehicle emissions, it is essential to develop real-time driving cycles, as these vary depending on city-specific traffic patterns and driving behaviors. This article reviews existing research and highlights areas that require further investigation. Understanding traffic flow characteristics through data collection and analysis is key to informing policy decisions and implementing effective measures to mitigate pollution.

Identification of Colon Cancer Using CNN in Deep Learning

2025-04-29T07:58:47+00:00

Deep learning (DL) has made substantial progress in healthcare during the previous two decades [1]. During this time, researchers discovered the true causes of a number of ailments, established new diagnostic tools, and produced novel remedies. Colon and lung cancer are two of the most serious and fatal diseases that individuals worldwide confront, and they have become a major medical concern. On the other hand, early identification of the disease considerably increases the odds of survival[3].This study employs the EfficientNetB7 Transfer Learning (TL) technique to develop a cataloging model that incorporates histopathology images to distinguish between five types of lung and colon tissues (two benign and three malignant). Furthermore, using histogram photos from the Kaggle collection, a mechanism for predicting lung and colon cancer has been developed [8]. The EfficientNetB7 model obtained a significant accuracy of 98 percent, according to the results. This model will aid medical experts in developing an effective and appropriate approach for detecting various types of lung and colon cancers [5].

Finding Lung Cancer on a Chaotic Map using a Modified Crow-Search and a Probabilistic Deep Neural Network

2025-04-30T07:32:31+00:00

Objective: Lung cancer is a type of malignancy that occurs most commonly among men and the third most common type of malignancy among women. The timely recognition of lung cancer is necessary for decreasing the effect of death rate worldwide. Since the symptoms of lung cancer are identified only at an advanced stage, it is essential to predict the disease at its earlier stage using any medical imaging techniques. This work aims to propose a classification methodology for lung cancer automatically at the initial stage. Methods: The work adopts computed tomography (CT) imaging modality of lungs for the examination and probabilistic neural network (PNN) for the classification task. After pre-processing of the input lung images, feature extraction for the work is carried out based on the Gray-Level Co-Occurrence Matrix (GLCM) and chaotic crow search algorithm (CCSA) based feature selection is proposed. Results: Specificity, Sensitivity, Positive and Negative Predictive Values, Accuracy are the computation metrics used. The results indicate that the CCSA based feature selection effectively provides an accuracy of 90%. Conclusion: The strategy for the selection of appropriate extracted features is employed to improve the efficiency of classification and the work shows that the PNN with CCSA based feature selection gives an improved classification than without using CCSA for feature selection.

Deep Learning Based Energy Intensive Computation Method for Anomaly Detection in Wireless Sensor Networks

2025-05-05T06:22:07+00:00

Abstract Wireless Sensor Networks (WSN) can be part of a tremendous number of applications. There are countless uses for Wireless Sensor Networks (WSN). A lot of WSN applications need real-time communication, meaning that the sensed data must reach the sink node by a specific date that the application has set. Real-time applications in WSNs are severely hampered by the limited resources of the sensor nodes (such as memory and power) and the lossy wireless communications. Furthermore, a lot of WSN routing algorithms place a heavy emphasis on energy efficiency, with delay not being the main issue. As a result, WSNs urgently require new routing protocols that are suitable for real-time applications, dependable, and energy-efficient. It accomplishes this by selecting potential neighbors who can transport the packet ahead of schedule and are qualified to take part in the routing process. It also calculates the relaying speed for each qualified candidate to reduce the latency of the selected paths. Additionally, it considers the available buffer size, hop count, and link quality of the chosen relays, which reduces end-to-end latency and uses the least amount of energy. By tackling these energy issues, we hope to achieve a compromise between anomaly detection models' sustainability and performance, guaranteeing that these technologies can be effectively used in actual healthcare settings. In order to preserve the efficacy of machine learning methods for identifying network anomalies, the paper's conclusion highlights the significance of optimizing computing resources. The suggested approach, EIC, uses anomaly detection for deep learning algorithms. Now that you are an expert, picture developing a system to identify cardiac arrhythmias. You may detect abnormal heartbeats early and notify doctors of possible dangers, such as heart attacks, by using thousands of EKG readings to train a machine learning model. Early alerts from this type of detection can save lives.

Smart Biomedical System with Rapid Screening for Older People in Small Scale Indoor Environment

2025-05-06T06:58:47+00:00

This project proposes a smart bedsheet— i-Sheet—for remotely monitoring the health of cardiovascular, chronic respiratory, cancer, Parkinson's, and COVID-19, patients. Typically, real-time health monitoring is very crucial for diseased patients to prevent their health from deteriorating. Conventional healthcare monitoring systems are manual and require patient input to start monitoring health. However, it is difficult for the patients to give input in critical conditions as well as at night. For instance, if the oxygen saturation level decreases during sleep, then it is difficult to monitor. Furthermore, there is a need for a system that monitors post-disease effects as various vitals get affected, and there are chances of their failure even after the recovery. i-Sheet exploits these features and provides the health monitoring of diseased patients based on their pressure on the bedsheet. It works in three phases: 1) sensing the pressure exerted by the patient on the bedsheet; 2) categorizing the data into groups (comfortable and uncomfortable) based on the fluctuations in the data; and 3) alerting the caregiver about the condition of the patient. Experimental results demonstrate the effectiveness of i-Sheet in monitoring the health of the patient.

Critical Approaches for Recidivism Prevention

2025-05-08T05:20:02+00:00

This study provides insights regarding the critical approaches for juvenile recidivism prevention. Data were collected from three juvenile centres in Tanzania through in-depth interviews and observation. 27 respondents participated in the study. The findings revealed that successful recidivism prevention require intensive investigation in rehabilitation methods and in post release services. Examining the trajectories of juvenile offenders in rehabilitation centres, the general reintegration programs does not support recidivism prevention. The government needs to intensify reintegration process, invest heavily on rehabilitation methods which should empower youth with relevant skills. The study concluded that the government should increase the budget allocation in rehabilitation centres, and the fund should reach the centres on time, improve the quality of the training staff and the adoption of environmental friendly methods in empowering the children. Furthermore reintegration officers should be creative in designing environmental friendly programs for the youth.

Voice 2 Sign: An Intelligent Gateway to Indian Sign Language

2025-05-08T10:59:55+00:00

Communication plays an integral role in the lives of the deaf and hard-of-hearing, mainly because sign language is not broadly understood. This paper aims at filling that gap in communication by creating a system for interpreting speech into ISL, or Indian Sign Language, by using images or GIFs from NLP. This system converts the audio inputs to text using Python and Google's Speech Recognizer API and applies NLP functionalities to process this text for generating sign language tokens, which are then rendered as ISL gestures. Innovations in technique combined with tailoring itself specifically to operate along with the deaf community of India provide a scalable accessible solution toward inclusivity and reducing the communication gap. Central to its functionality are features like speech-to-text conversion, processing with NLP techniques, and generation of ISL altogether within a user-friendly application. It leaves the limitations present in the existing solutions about getting the right translation along with processing speed and privacy concerns by using modern algorithms and architecture. Its modular design will provide great opportunities in its further extensions and enhancements. A paper that would empower the hearing impaired and bring along with it better access to education, employment, and social participation will help in overcoming barriers in communication as a contribution toward making society more inclusive while promoting technological innovation in assistive communication systems.

Next-Gen Solar Desalination: AI-Driven PV-T-PCM System with Blockchain Monitoring for Sustainable Water Production

2025-05-09T05:42:06+00:00

This study introduces an advanced solar desalination system integrating forced circulation, hybrid photovoltaic-thermal (PV-T) technology, phase change materials (PCMs), and AI-driven optimization to maximize freshwater production. A multi-objective optimization framework combining CFD simulations, deep reinforcement learning (DRL), and swarm intelligence algorithms enhances system efficiency. Experimental results demonstrate a 60–90% increase in freshwater yield compared to conventional solar stills. The system employs adaptive graphene-enhanced nanofluids, predictive digital twin modeling, and blockchain-enabled IoT monitoring for real-time control. A techno-economic analysis confirms viability for off-grid deployment, aligning with SDG 6 (Clean Water and Sanitation).

Ai-Optimized Hybrid Pv-T-Pcm Solar Desalination System: A Breakthrough in Efficiency and Yield

2025-05-09T05:57:00+00:00

This study explores advanced methodologies to enhance solar still efficiency by integrating forced circulation, hybrid photovoltaic-thermal (PV-T) systems, and phase change materials (PCMs). A novel multi-objective optimization framework is developed using computational fluid dynamics (CFD) and machine learning (ML) to maximize distillate yield while minimizing energy consumption. Experimental validation confirms a 50–85% increase in freshwater production compared to conventional solar stills. The study also introduces adaptive nanofluid cooling and AI-driven predictive control for real-time performance optimization. A techno-economic analysis demonstrates the feasibility of deploying these systems in off-grid communities, aligning with Sustainable Development Goal (SDG) 6 for clean water access.

Cracked Heel Rescue Revitalizing Polyherbal Scrub & Gel

2025-05-12T05:33:58+00:00

The most crucial still neglected part of human body is Feet and caring for skin on feet is also important to avoid pain which may thwart off balance and help to reduce the risk of further damage. Consistently, foot is one of the most neglected parts in aesthetic considerations. This study discovers the formulation and efficacy of polyherbal foot scrub and gel, demonstrates significant improvements in skin texture, moisture retention, hydrate, soothe, rejuvenate tired feet and overall foot health. This formulation not only addresses common foot issues but also highlights the synergistic effects of its herbal ingredients. The combination of natural ingredients aligns with a growing preference for holistic and eco-friendly skincare solutions. Regular application resulted in enhanced hydration, healing of cracked skin, and soothing relief from discomfort.

Analyzing the Efficacy of Optimization Algorithms in Traffic Simulations

2025-05-12T06:06:01+00:00

As cities continue to grow, especially in developing countries the roads are getting congested due to vehicular traﬃc, giving rise to various problems including the undetermined mobility and sustainability eﬃciencies. Congestion and breakdown phenomenon are issues that are pervasive in the transportation system, decreasing throughput, and efficacy. Systems with limited resources require modelling to ensure systems performance, which may be in terms of cost, data transmitted or vehicles discharged. Stochastic traffic is predominant in empirical traffic systems and has reached the consensus. A number of simulation tools based on mathematical models and intelligent algorithms are in practice with respect to transportation systems. The primary objectives of these simulation tools are traffic modelling, planning, and operations of transportation systems. Irrespective of enormous data, high computing power and advanced techniques of intelligent transportation, several simulators are used to solve the traffic congestion problems. This paper reviews different traffic simulation tools and presents a comparative table based on Intelligent Transportation System (ITS) functionalities. The paper also presents an implementation of traffic simulators based on Reinforcement Learning, Genetic Algorithm and Queuing theory.

CFD Analysis of Heat Transfer in Venture Tube Using Different Models with Ceo2 Nano Addition to Water

2025-05-12T06:37:50+00:00

A venturitube is an instrument for determining the flow rate of fluids in pipes. Several industries, including those dealing with aircraft, automobiles, chemicals, and petroleum, among others, make use of this technology. A venturi tube is a device used in the automobile industry for measuring the distribution of fuel and air in carburetors. an extensive CFD study of a venturimeter operating with a nanofluid of cerium oxide (CeO2) made of this material. The primary objective is to investigate the influence of nanofluid concentration and flow rate on the venturimeter's performance parameters, including pressure drop, discharge coefficient, and flow rate measurement accuracy. A 3D model of the venturimeter is developed using a commercial CFD software, and the governing equations are solved numerically.

A Survey on Wireless Sensor Network with Advance Networking Recent Trends

2025-05-13T05:08:33+00:00

Wireless Sensory Associations are an indispensable constituent of contemporary data-driven systems, allowing for effective monitoring and management in a variety of settings, including smart cities, industry, healthcare, and agriculture. Energy economy, dependable communication, scalability, and integration with progressive expertise like IoT and edge computing are the main concerns and current developments in WSN architecture that are examined in this study. In order to solve the trade-offs between security, power consumption, and performance, it examines important protocols and algorithms created for optimal routing, data aggregation, and node management. Additionally, the report emphasizes how machine learning and artificial intelligence are becoming more and more important in improving network decision-making and flexibility. This paper highlights important research gaps and suggests future possibilities for creating intelligent, autonomous, and sustainable WSNs that are appropriate for large-scale, real-world applications through a thorough examination.

Methodology of Studying English at a Non-Philological University

2025-05-13T11:18:16+00:00

This article examines the methodology of studying English at a non-philological university, focusing on approaches, techniques, and strategies employed to facilitate language acquisition and proficiency among students from diverse disciplinary backgrounds. Drawing upon a mixed-methods research approach, including surveys, interviews, and classroom observations, the study investigates student demographics, teaching methodologies, integration of language and content, and the role of technology in English language instruction. Results highlight the importance of adopting communicative language teaching, task-based learning, and content and language integrated learning to meet the diverse needs of students and promote effective language learning outcomes.

Modeling the Effect of Nanopolymer Preparations on the Contents of Micro and Macroelements in Soybean Seeds and Рlants

2025-05-13T11:30:32+00:00

Conducted research to study the influence of nanomaterials on seeds, plants and resulting products, on metabolic processes in plants is one of the pressing problems. The purpose of the study was to study the effect of nanopolymer preparations on the concentrations of micro and macroelements in seeds and plants. In all experimental variants studied, in thirty-day-old soybean plants, the copper concentration decreased significantly compared to seeds. Thus, in the seeds of the soybean variety Baraka treated with the preparation PMC Cu2 +:Ag 8:2, the concentration of copper decreased in thirty-day-old plants by almost 1.5 times compared to the seeds. The concentration of iron in thirty-day-old plants also decreased compared to seeds treated with nano and polymer preparations. Our studies revealed that nanopolymer preparations Nanoascorbate chitosan 0.5% (4:1), PMC Cu 2+ :Ag 7:3, PMC Cu 2+ :Ag 8:2 have high biological activity, the content of micro and macroelements in plants whose seeds treated with nanopolymer preparations contributed to a more uniform distribution of the concentration of these elements in all phases of soybean plant development than polymer ones. The nanopreparation PMC Cu 2 + :Ag 7:3 acts on the substrate-enzyme complex of plants and enhances biochemical reactions due to copper, which has a small ionic radius and many orbitals of different symmetries (S, P, d), allowing one to get very close to the enzyme co-factor. The simulation results show that the closer the two parabolas (I and II) are, the greater the Franco-Condon Factor will be, which increases the probabilities of biochemical reactions.

Semantic Retrieval and Fine-Tuned T5-Small for Investor Query Answering from Annual Reports

2025-05-15T05:26:37+00:00

The complexity and volume of corporate annual reports often make it challenging for investors to extract key financial insights. This research presents a semantic question-answering system designed to simplify this process. The contribution of this paper is an end-to-end system that combines a fine-tuned T5-small model with Facebook AI Similarity Search (FAISS)-based semantic retrieval to deliver context-aware answers to investor queries from annual reports. It processes corporate annual reports, retrieves relevant sections, and generates real-time structured responses. The system is deployed through a user-friendly Streamlit interface, ensuring seamless user interaction. This paper introduces an end-to-end financial question-answering pipeline that combines semantic retrieval and transformer-based answer generation, which reduces the manual effort in investment analysis.

Customer Relationship Management: The Key to Success in Modern Business

2025-05-15T07:16:21+00:00

In today’s dynamic business environment, marked by intense competition, rapid technological advancements, and increasing complexity in customer needs, the importance of Customer Relationship Management (CRM) as a strategic approach has become more critical than ever. This research aims to identify and explain the key success factors of CRM in modern businesses and present a conceptual framework for its effective implementation. The study follows a mixed-method (sequential exploratory) approach. Initially, a systematic review of previous studies and a content analysis of CRM-related concepts were conducted to extract primary factors. In the second phase, a questionnaire was distributed among managers and professionals in sales, marketing, and IT departments across service and manufacturing organizations. Confirmatory factor analysis was used to validate the proposed framework. The findings led to the identification of 14 key factors, categorized into two conceptual clusters: customer-centric factors (such as customer orientation, service quality, data analysis, etc.) and organization-centric factors (such as leadership, training, organizational structure, etc.). Ultimately, an integrated framework for effective CRM implementation in modern businesses is proposed. The results can significantly assist organizational managers in developing marketing strategies, improving customer loyalty, and increasing profitability.

Classification of Multimodal Biometric Recognition System Using Fuzzy Logic

2025-05-16T05:42:01+00:00

Nowadays, biometric technology plays a major role in secure identity verification yet, they still face challenges like as spoofing attacks, intra-class variations, and noisy input data. In order to solve these issues, this paper proposes a multimodal biometric classification system that improves precision and resilience using fuzzy logic. The system extracts fingerprint patterns using Local Binary Patterns and extracts textural information from iris images using Gabor filters to merge fingerprint and iris identification. Fuzzy logic is essential for this model because it manages biometric data's variability and uncertainty well, something that conventional approaches frequently find difficult. The system is better able to manage noisy data and possible spoofing attempts by implementing a rule-based decision-making framework that is specific to the characteristics of biometric traits.

Our experimental assessment shows that this method works well, with a classification accuracy of 99.7%, outperforming both traditional unimodal and multimodal methods. The ability to combine a variety of biometric features with intelligent decision-making is a higher performance accuracy. Overall, the proposed method is a reliable and adaptable option for identity verification, especially in situations where security and accuracy are crucial. This work provides a practical and creative method for enhancing multimodal biometric systems by fusing the flexibility of fuzzy logic with the benefits of fingerprint and iris biometrics.

An Integrated Topic Modeling with Classification for Semantic Information Retrieval in Large Scale Text Documents

2025-05-16T08:08:15+00:00

Big data has attracted considerable attention across scientific and engineering domains due to its vast potential and wide-ranging applications. Despite its advantages, several challenges must be addressed to improve the quality of service, particularly in information retrieval (IR)—a key area of computer science focused on efficiently retrieving relevant information from large datasets based on user queries. As the need for precise, expressive, and contextually relevant results grows, semantic IR from big data has become increasingly important for decision-making and analysis.This work proposes a deep learning approach for semantic information retrieval using a hybrid BERT-LDA model on large-scale text datasets. Following a pre-processing phase, the model integrates LDA for generating probabilistic topic distributions and BERT for capturing sentence-level semantic embeddings. These outputs are combined and served into a deep learning framework that incorporates a CNN module to extract inter-feature relationships, along with an Attention Mechanism (AM) module to emphasize significant features. Experimental evaluation on the DBpedia dataset demonstrates that this approach improves retrieval performance in terms of Accuracy, Precision, Recall, and F1-measure.

Enhancing DDoS Attack Detection: Leveraging Decision Tree Machine Learning Model for Real-Time Monitoring and Adaptive Threat Identification

2025-05-19T05:39:13+00:00

The traditional detection methods are insufficient to address Distributed Denial of Service (DDoS) threats accurately and promptly because of their increased occurrence frequency and complexity. The implementation of Decision Tree models succeeded in developing attack detection strategies against DDoS attacks at higher accuracy levels than SVM and Random Forest models. The system operates through continuous monitoring which allows adaptive gearing and scaling multiple times to perform real-time network traffic analysis for emerging threat detection. The Decision Tree model helps the system to detect attacks better while lowering false alerts while enabling an efficient DDoS security system than traditional methods. The defensive capabilities of network security dramatically improve because of attack and dynamical proactive measures applied to face evolving DDoS threats.

Secure Vision: Integrated Anti-Spoofing and Deepfake Detection System Using Mobilenet and Resnext

2025-05-19T05:59:51+00:00

In recent times, new phenomena such as deepfake videos and facial spoofing attacks imply new problems for security systems based on biometric authentication and digital content checks. The Secure Vision project provides a single solution for both spoofing attack and deepfake detection by using a deep learning model. Real-time anti-spoofing is accomplished using MobileNet that is trained for detecting liveness clues from facial images whereas ResNeXt is used for deepfake detection due to its capability to detect artefacts and discrepancies in the manipulated videos. With these two approaches integrated, Secure Vision can offer a single efficient yet optimised solution of securing visual media content and authentication processes in real-time. The experimental results prove that both the model’s overall accuracy is considerable, whereas MobileNet reached 96.2% identification rate for spoofing and ResNeXt realised 94.5% rate of identification for deep fakes. This system has potential in privacy and access, physical and cyber security, social media, identity theft and fake news.

Enhancing Deepfake Detection through Hybrid Mobilenet-LSTM Model with Real- Time Image and Video Analysis

2025-05-19T06:07:18+00:00

The rapid rise of deepfakes, which involve the use of artificial intelligence to create hyper-realistic manipulated media, has introduced significant challenges in maintaining information integrity and societal trust. As deepfake generation techniques become more sophisticated, they pose substantial risks across various sectors, including media, politics, and law enforcement. Existing detection methods, often reliant on analyzing visual artifacts or inconsistencies in facial expressions, are increasingly vulnerable to circumvention by advanced deepfake algorithms. Furthermore, many current solutions are limited in scope, focusing exclusively on either images or videos, thereby restricting their applicability in real-world scenarios where deepfakes appear in diverse formats. This research proposes a novel hybrid deepfake detection model that leverages the strengths of MobileNet, a lightweight convolutional neural network (CNN), and Long Short-Term Memory (LSTM) networks to address the limitations of existing systems. MobileNet efficiently extracts spatial features from individual frames, identifying subtle visual cues such as texture anomalies and facial inconsistencies. These spatial features are subsequently processed by an LSTM network, which analyzes temporal patterns across frames to detect temporal artifacts in video sequences, making it well- suited for video-based deepfake detection. The hybrid MobileNet-LSTM model is trained on an extensive dataset of real and deepfake media, encompassing a wide range of deepfake generation techniques. This diverse training ensures that the model is robust and adaptable to emerging deepfake methods. The proposed system supports real-time analysis of both images and videos through a user-friendly interface, allowing users to upload media files or provide URLs for deepfake detection. The system outputs a detection score, along with visual explanations of identified artifacts, enhancing transparency and interpretability. The novelty of this approach lies in its hybrid architecture, combining the complementary strengths of MobileNet for spatial analysis and LSTM for temporal modeling. Additionally, the system's support for both image and video inputs expands its practical applicability, while its lightweight nature enables near real-time analysis. This system has the potential to advance deepfake detection in various domains, such as social media platforms, news organizations, and law enforcement agencies, protecting the integrity of digital media and mitigating the harmful effects of deepfakes.

Beyond Knowledge: Development of Metacognitive Methods

2025-05-20T07:37:20+00:00

The article considers the development of metacognitive abilities, which play a key role in the process of learning and self-development. Metacognitive abilities include the ability to recognize, control and regulate one's own thinking, which contributes to more effective problem solving and goal achievement. Particular attention is paid to the theoretical aspects of the concept of metacognition, as well as practical approaches to their development in the educational and professional environment. Methods for diagnosing and training metacognitive skills are given, and factors influencing their formation are analyzed. The results of the study emphasize the importance of targeted development of metacognitive abilities to improve cognitive flexibility, independence and critical thinking.

Digestive Performance Changes in Birds in Social and Solitary Living

2025-05-21T09:38:54+00:00

This review article examines the impact of social structures on the digestive performance of birds. We analyze how collective versus solitary living affects birds’ feeding behavior and digestive health. Case studies indicate that social interactions, whether competitive or cooperative, significantly influence the digestive efficiency of birds. This study also explores the ecological and biological implications of these findings, highlighting their importance in environmental conservation and understanding bird social behavior. Finally, we identify current research gaps and suggest future directions to address them.

Enhancing Multimodal Biometric Recognition Using Whale Optimization Algorithm and Feedforward Neural Networks

2025-05-22T06:09:34+00:00

This paper presents a robust multimodal biometric recognition system that integrates fingerprint and iris recognition, utilizing the Whale Optimization Algorithm to enhance performance. The Feedforward Neural Networks ensure high accuracy for effective feature classification from both biometric modalities. The WOA system optimizes neural network parameters, concentrating on the most significant characteristics of the fingerprint and iris data. Experimental results indicate that the proposed system achieves a 99.6% accuracy, with minimal false acceptance and rejection rates. This makes it highly suitable for real-world applications such as secure access control, identity verification, and fraud detection. The integration of multiple biometric traits is not only security. But also improves the performance accuracy. This research highlights the potential of combining multimodal biometric systems with advanced neural network architectures and optimization techniques to create more secure and efficient biometric authentication solutions across various fields.

The Usage of International Terms in English

2025-05-26T09:57:37+00:00

The article examines the usage of international terms in English and their impact on the language. It discusses the various reasons for the incorporation of foreign words and phrases in English, including globalization and cultural exchange. The article also explores the challenges posed by the use of international terms, such as difficulty in pronunciation and understanding. Additionally, the article analyzes the changing patterns in the adoption of foreign vocabulary in different fields, such as technology and business. The authors conclude that while the incorporation of international terms has enriched the English language, it is essential to find a balance between preserving the language's original identity and embracing foreign vocabulary. The article offers insights into the complex and dynamic nature of language evolution and the role of globalization in shaping it. While this may bring some linguistic challenges, it also reflects the evolution of language and the growing influence of technology, business, and other global forces. The article explores the impact of the usage of international terms on English language identity and vocabulary.

A Method and Results for Determining the Length of Time to Soaksaxaul Seeds in Electroactivated Water

2025-05-28T06:42:19+00:00

The article presents the results of the research on determining the optimal mode of environmentally pure electrotechnological effect parameters for the purpose of increasing the level of fertility, cleaning from harmful microorganisms that cause diseases, and treating it in anolyte, catholyte, i.e. acidic, alkaline environments and ultraviolet light before planting saxaul seeds in the greening of the bottom of the Aral sea.

AI Based Novel Approach for Early Flood Warning Using Android and Iot

2025-06-02T07:33:29+00:00

India has a sub-tropical monsoonal climate characterized by heavy rainfall which in turn causes massive flooding. To avert such situations, it is very important to monitor and receive timely emergency alerts about the flow of water and water level situation based of the riverbed. The main objective of this concept is to design an efficient flood pre-alerting system. This project predicts Floods in advance with the help of emerging technologies, such as MATLAN, Embedded and Internet of Things (IoT) this work develops an IoT-based prototype to collect hydrological data and meteorological data of river water. Hydrological data like water flow, water level, and water discharge along with meteorological data like temperature, humidity, wind speed, and wind direction are used to classify the flood type. In matlab software long short-term memory (LSTM) model is introduced based on calibrated data to yield alerts. Classifications like “no alert,” “yellow alert,” “orange alert,” or “red alert.”

Virtual Nest: A Gamified 2D Spatial Communication Platform for Enhanced Remote Interaction

2025-06-03T08:49:50+00:00

Immersive communication platform Virtual Nest provides a 2D gamified environment aimed at improving remote collaboration with avatar-based navigation, spatial interactions, and real-time communication. Developed to overcome growing problems of user fatigue, emotional disconnection, and drop-off in engagement from traditional video conferencing platforms, Virtual Nest provides proximity-activated audio and video features, combined with personalized avatars and dynamic digital spaces. The platform allows participants to join and leave discussions naturally, based on spatial closeness, in accordance with social behavior in the physical world. Built on top of technologies such as Phaser.js, WebRTC, and PostgreSQL, the platform offers low-latency performance despite varied hardware capability and network environments. Pilot-user testing with participants like students and remote workers resulted in high user satisfaction, increased user engagement, and reduced cognitive fatigue. Innovative features such as gamified work, badge reward systems, and a responsive UI result in sustainable user interest and a richer web presence. Early feedback from pilots verified usability, technical responsiveness, and simplicity of the platform. Virtual Nest went one step further than redesigning online communication to provide an inclusive, browser-hosted, interactive experience introducing function, fun, and a community feeling to the virtual world.

Enhanced Multi-View Fuzzy Clustering Algorithm Using Machine Learning

2025-06-05T05:46:52+00:00

The algorithm, Fuzzy C-Means (FCM) stands as a cornerstone of advanced data clustering techniques. Its ability to handle uncertainty and partial membership makes it a potent tool for image segmentation, aiding in the extraction of meaningful patterns from complex visual data. This article embarks on a technical journey to explore the intricacies of the algorithm in the context of imageprocessing, delving into its foundations, methodologies, challenges, and applications. The single-view-clustering algorithm utilizing Multiview Data (MVD) processing has certain limitations. It becomes problematic when clustering of results in a particular view exhibits considerable divergence or when disparities exist among clustering outcomes across different views. This part experiment provides a comparative examination of the effects of each method on picture segmentation in order to verify the superiority of the IMV-FCM algorithm. Evaluating the algorithm's noise-handling capability, various levels of Gaussian noise are introduced to the images.

Ecological and Biological Analyses of the Dendroflora of Western Tanirtau

2025-06-05T05:54:36+00:00

Ecological analysis of flora is one of the most important areas of typological analysis of flora by environmental factors. Usually, when determining the ecological structure of any flora, the distribution in habitats with different moisture patterns is taken as a basis. According to the classical division of such ecological groups [1-5] in the dendroflora of Western Tanirtau, we distinguish the following ecological groups: Hygrophytes are plants that live on abundantly moist soils. In plants of this category, leaf blades are often thin [4]. Mesophytes are plants that live with sufficient (not excessive and not too limited) moisture. Typical mesophytes are usually associated with both moderate thermal conditions and good mineral nutrition conditions [4]. Mesoxerophytes are plants of habitats with periodically insufficient moisture [1; 5;], more closely approaching xerophytes [4]. Xerophytes are plants of rainy places with severe moisture deficiencies [1;4;5]. Xeromesophytes are plants of habitats with less severe moisture deficiencies, more closely approaching mesophytes [4]. The ecological structure of the dendroflora of Western Tanirtau on this basis is given in Table 1.

Enhanced Secured Data Transmission Methodology for Wireless Sensor Network

2025-06-09T09:42:25+00:00

Applications for wireless sensory associations are several and include industrialized mechanization, healthcare, military surveillance, and environmental monitoring. However, WSNs are extremely susceptible to a variety of security risks because to their intrinsic constraints in processing capacity, energy, and memory. An improved secure data transmission technique that is adapted to the particular features and limitations of WSN designs is presented in this research. To guarantee the confidentiality, integrity, and validity of the data being communicated, the suggested method combines data aggregation algorithms, safe routing protocols, and lightweight cryptographic approaches. Additionally, a trust-based approach is included to dynamically detect and isolate hostile nodes, enhancing the network's resilience. In comparison to traditional systems, simulation findings show that the suggested technique greatly improves security while preserving energy economy and lowering packet loss. The goal of this effort is to aid in the creation of energy-conscious, scalable, and reliable security frameworks for next-generation WSN installations.

A Robust Intrusion Detection System with Randomized Search and Balanced Ensemble Models

2025-06-09T11:20:59+00:00

Intrusion detection systems are crucial for protecting the network infrastructure from attacking activities. The effectiveness of these systems may be hampered by class imbalance, feature redundancy, and high-dimensional datasets. In the study, we introduce a robust anomaly detection system based on the Synthetic Minority Over-sampling Technique (SMOTE), feature engineering, and ensemble learning. The study uses Support Vector Machines, Logistic Regression, Decision Trees, and Random Forest for the Voting Classifier framework powered by a Randomized Search Cross-Validation. This approach underwent feature engineering that resulted in a reduction in the dimensionality and limitation to multicollinearity, while SMOTE was concerned with the balance of classes. The model provided achieved an impressive accuracy of 99.72%, along with macro average scores of 0.95 for precision, 0.85 for recall, and 0.88 for the F1 score. The classification report showed that the classifier worked perfectly for the majority classes and also maintained relatively good performance for the minority classes, raising the issue of its superior performance. The proposed work shows that advanced resampling methods and ensemble learning stand as strong tools for intrusion detection in complicated network settings.

Analysis of Comparative Studies of Cattle-Breeding Terminology in World Linguistics

2025-06-11T05:37:49+00:00

The study of specialized terminologies, such as cattle-breeding lexicons, reveals significant insights into cultural, economic, and linguistic evolutions across societies. This paper presents an analysis of comparative studies on cattle-breeding terminology, examining the linguistic diversity, historical roots, and semantic structures underlying these terminologies globally. It underscores the importance of comparative approaches in understanding shared agricultural practices, regional variations, and the cultural significance of cattle-breeding. Furthermore, this research contributes to bridging linguistic studies with anthropological and economic perspectives.

AI-Powered Risk Assessment Models in Financial Management: Enhancing Accuracy and Efficiency in Corporate Finance – A Case Study-Based Approach

2025-06-12T12:17:37+00:00

The integration of Artificial Intelligence (AI) into financial risk assessment has marked a significant shift in the way corporations evaluate, predict, and manage risk. Traditional models, though systematic, often fall short in addressing the dynamic and complex nature of financial markets. This paper explores how AI technologies particularly machine learning, predictive analytics, and natural language processing are revolutionizing risk management in corporate finance. By adopting a case study-based approach, this research provides in-depth insights into how four leading organizations JPMorgan Chase, BlackRock, HDFC Bank, and Tesla Inc. have implemented AI to transform their risk assessment frameworks. Each case reveals a unique AI application: from JPMorgan’s machine learning models for credit evaluation, to BlackRock’s Aladdin platform enabling real-time market risk predictions, to HDFC’s fraud detection systems, and Tesla’s AI-driven forecasting tools. These cases collectively demonstrate how AI enhances accuracy, speeds up decision-making, and minimizes operational inefficiencies. The findings highlight not only the technical aspects of implementation but also the strategic benefits achieved by early adopters. Moreover, this study sheds light on key enablers such as data infrastructure, regulatory readiness, and leadership vision. Through these real-world applications, the paper underscores the transformative potential of AI in corporate financial management, making a strong case for its broader adoption in the evolving financial ecosystem.

Classifying and Examining Deforestation Patterns and its Environmental Implications

2025-06-14T11:27:42+00:00

Deforestation has surged significantly in recent years, driven by factors such as agricultural expansion, livestock grazing, mining, industrial activities, construction, transportation infrastructure, and forest fires. Forests cover approximately 31% of the Earth’s surface, providing essential ecosystem services including oxygen production and carbon dioxide (CO2) sequestration, while also housing nearly 80% of terrestrial biodiversity. Maharashtra, the second most industrialized state in India, faces severe environmental and socioeconomic impacts from deforestation. This research aims to classify the 36 districts of Maharashtra into deforested and non-deforested areas. The study is bifurcated into two parts:

In the first part, satellite imagery datasets are processed using the Siamese Algorithm, a deep learning model optimized for analyzing paired images to detect changes and patterns. In the second part, multiple geospatial factors—such as distance from roads, construction activities, forest fire occurrences, elevation, and river erosion—are considered. The count of green pixels within the imagery is converted into numerical values and, alongside the geospatial data, input into the AdaBoost algorithm. AdaBoost, a robust machine learning classifier, optimizes these inputs to enhance classification accuracy.

By integrating both image-based and geospatial data, this approach offers a comprehensive assessment of deforestation patterns. The results are displayed through an interactive web-based application featuring maps, charts, and graphs for effective visualization. The dataset comprises Sentinel-2 satellite images spanning six years (2017-2022), capturing critical geographical features. The Siamese Neural Network exhibits high validation accuracy (96.15%), and the AdaBoost algorithm demonstrates exceptional classification performance with an accuracy of 97.82%. This study not only advances the methodology for deforestation detection but also provides valuable insights for sustainable forest management in Maharashtra, aligning with the United Nations’ Sustainable Development Goals.

Harnessing Artificial Intelligence for Personalized Global Marketing Campaigns: Impacts on Consumer Behavior and Competitive Advantage

2025-06-18T06:21:41+00:00

Artificial Intelligence (AI) has transformed global marketing by enabling hyper-personalized campaigns that leverage vast consumer data to deliver tailored experiences. This scholarly article investigates the transformative function of artificial intelligence in reshaping marketing methodologies, with particular emphasis on its influence on consumer conduct and the competitive benefits it confers upon enterprises of varying scales. Significant advancements in artificial intelligence, featuring machine learning, natural language processing, and predictive analytics, are assessed alongside their practical uses in individualization, market differentiation, and multi-channel coherence. The manuscript further investigates the manner in which artificial intelligence impacts consumer engagement, decision-making processes, and brand loyalty, while emphasizing competitive advantages such as enhanced operational efficiency, reduction in costs, and scalability potential. Alongside this, the scrutiny of ethical conflicts, which involves apprehensions over data privacy and the bias inherent in algorithms, takes place, in parallel with prospective movements including hyper-personalization, conversational artificial intelligence, and nascent technologies like augmented reality and blockchain. Through case studies of companies like Netflix, Amazon, and Starbucks, the article illustrates AI’s transformative potential. The findings underscore the need for brands to balance innovation with ethical practices to maintain consumer trust and achieve sustainable success in a competitive global market.

A Modern Approach to Route and Itinerary Management Using Angular 18 and Google Maps Services

2025-06-20T05:54:56+00:00

This research aims to develop a Trip Planner web application using Angular 18 and the Google Maps API to help users plan their trip easily and efficiently. The application will allow users to search for destinations, create itineraries, and optimize routes with real-time navigation and distances. The user can also make a place as favourites for the further planning. It will also provide recommendations for nearby hotels, restaurants, resorts and transportations. The system will include user authentication, allowing travellers to save and manage their trip plans. Using Google Maps API features such as Places API, Directions API, and Geocoding API, the application will offer an interactive map experience. With a user-friendly interface and smart route suggestions, this trip planner will make travel planning simple and convenient.

Analysing Performance Metrics In 5G-Based Mobile Ad-Hoc Networks Using AODV And RLI Algorithm

2025-06-20T07:19:46+00:00

The current direction of technology is towards 5G. As we move towards 5G, networks will continue to connect people and objects through other individuals. These objects may remain stationary or may be mobile. MANET, or Mobile Ad Hoc Networks, will function for these mobile entities or objects. Therefore, MANET will serve as the test network to determine how environmental factors such as density, mobility, data rate, etc., will affect the transition to 5G technology. This document presents the results of multiple tests conducted with different densities, mobilities, and data rates in 5G. The results of these tests are accompanied by recommendations for the related routing protocols. The study examines AODV, a reactive routing system. In this paper Using AODV and RLI-AODV algorithm the Packet Delivery Ratio,End to End Delay and Speed is achieved better than AODV in 5G mobile adhoc network.

Cross-Linguistic Insights into Cattle-Breeding Terminology: A Comparative Approach

2025-06-24T05:51:48+00:00

The terminology surrounding cattle breeding provides a window into the agricultural practices, cultural values, and socio-economic conditions of different linguistic communities. This paper explores the cross-linguistic differences and similarities in cattle-breeding terminology by comparing languages from diverse regions and linguistic families, including Indo-European, Niger-Congo, Sino-Tibetan, and Uralic. By analyzing terms related to livestock management, breeding practices, and cultural significance, this study uncovers how language reflects the relationship between people and their environment, particularly through agricultural practices. The findings highlight not only linguistic diversity but also the profound ways in which language shapes and is shaped by the socio-cultural contexts in which cattle breeding is central.

Exploring Effective Strategies for Improving Oral Communication Skills among Engineering Students

2025-06-28T05:46:31+00:00

To have academic excellence, and personal and professional development, competent communication is the need of the hour. Excellent communicators are more successful in bringing forth their ideas and opinions logically. Further, being able to communicate competently can enhance relationships with peers, parents, and teachers (Hunt, Wright, & Simonds, 2014; Morreale & Pearson, 2008).

Enhanced Arrhythmia Diagnosis via Three-Heartbeat Multi-Lead Ecg And Deep Learning Fusion Models

2025-06-30T10:45:03+00:00

Accurate classification of cardiac arrhythmias is essential for effective clinical diagnosis and treatment. In this study, we propose a novel methodology that, for the first time, utilizes Three-Heartbeat Multi-Lead (THML) ECG data, where each data segment includes three complete heartbeat cycles across multiple ECG leads. This enriched temporal and spatial representation provides a more comprehensive view of cardiac activity, enabling enhanced pattern recognition. Two classification models are developed and compared: a Weight Convolutional Neural Network (WCNN) and a hybrid Light Gradient Boosting Machine-Convolutional Neural Network (LGBM-CNN). The performance of the models was rigorously evaluated using accuracy (Acc), sensitivity (Sen), and positive predictive value (PPV). Experimental results demonstrate that the LGBM-CNN significantly outperforms the WCNN across all metrics, indicating its superior capability in capturing the complex morphological features of arrhythmic ECG signals. These findings underscore the effectiveness of THML ECG representation combined with advanced hybrid architectures for automated arrhythmia classification.

Formulation & Evaluation of Wood Apple Candy for Good Antitussive Action

2025-07-03T06:52:26+00:00

The creation and assessment of wood apple (Limonia acidissima) candies as a natural antitussive agent is the main objective of this study. Rich in bioactive substances like tannins, flavonoids, and phenolic acids, wood apples have long been used to treat respiratory conditions. This study's main goals were to create a tasty, shelf-stable candy recipe using wood apple pulp and evaluate any possible cough-suppressive effects. Using normal methods, the candy was made by concentrating fruit pulp with sugar and gelling chemicals, then molding and drying it. Microbial stability, texture, pH, and moisture content were among the physicochemical characteristics that were assessed. The presence of bioactive components was verified by in vitro screening for phytochemicals and antioxidants. A citric acid-induced cough paradigm in albino rats was used to investigate the antitussive efficacy, and the outcomes were contrasted with those of dextromethorphan, a common antitussive medication. The wood apple candy shown encouraging antitussive activity by significantly reducing the frequency of coughing. according to the research, wood apple candy may be a useful sweet that has therapeutic advantages for treating coughs.

Mineral Profile, Proximate Composition, Anti-Nutritive Activity and Chemical Characterization of Rare and Endemic Medicinal Plant Extracts from Henckelia Fischeri (Gamble) A.Weber & B. L. Burtt

2025-07-04T05:42:50+00:00

Henckelia fischeri (Gamble) A.Weber & B. L. Burtt is rare endemic medicinal plant of Gesneriaceae family. The plant parts are used as medicine by the local peoples for various ailments. The leaves of selected medicinal plant is powdered and analysed its proximate analysis and anti-nutritional content. And it is also subjected to mineral profile (Micro and macro- nutrient) activity was held to see 13 components. The plants were then treated with six dissolving agent such as Petroleum spirit, trichloromethane, Acetone, ethyl ethanoate, ethyl alcohol and aqueous medium. And these samples were analyze for the presence of primary compounds like Alkaloids, Flavonoids, Sterols, Terpenoids, Anthraquinones, anthocyanin, proteins, phenolic compounds, quinones, carbohydrates, tannin, saponins, cardiac glycosides, glycoside’s test, Lignin, coumarins and volatile oils. Among the results of primary metabolites only two samples were undergone the secondary metabolites like (Phenol, tannin and flavonoids) estimated in it. The display study results highly suggest the potentiality of both primary and secondary compounds in various samples and these compounds enhances the treatment of various disorders.

Evaluation of Secondary Metabolites Antioxidant and Anti-Inflammatory Activity of Capparis Grandis L.F. Leaf Extracts

2025-07-04T06:09:00+00:00

The leaves of Capparis grandis L.f are an essential, wild, and traditionally edible plant ingested by the tribal populations of the Western Ghats. Despite their use, its curative properties remain underexplored. This study aimed to assess its secondary metabolites, including phenols, tannins, and flavonoids, and to evaluate its antioxidant and anti-inflammatory effects through biochemical assays. The ethanolic extract in leaves were found to be affluent in bioactive compounds, having 119.88 mg GAE/100 g of total phenols, 106.69 mg GAE/100 g of tannins, and 331.41 mg RE/100 g of flavonoids in ethyl acetate fraction. Among these extracts analyzed, the ethanolic leaf extract showed the strongest antioxidant activity across multiple assays, including DPPH (IC50: 21.12 μg/mL), ABTS (64027.78 μM TE/g extract), FRAP (382.46 mM Fe(II)/mg extract), superoxide radical scavenging (45.32%), nitric oxide radical scavenging (83.19%), and phosphor molybdenum assay (89.36 mg AAE/g).Moreover, the ethanol extract exhibited potent anti-inflammatory effects, with 66.68% inhibition in hypotonic solution-induced hemolysis check & an IC50 value of 39.3 μg/mL in the heat-induced hemolysis assay. These results suggest that C. grandis L.f leaf extracts possess significant antioxidant and anti-inflammatory properties, making it a potential native supplement to lessen oxidative stress-related disorders, as well as inflammation.

Comparative Perspectives on Elementary School in Educational Change: Insights into National and Regional Development Strategies

2025-07-04T09:53:55+00:00

Educational reforms just at primary school level is indeed an essential motorist like global and subnational growth, but nevertheless schemes vary substantially throughout situations. The said understanding of the interactions roughly similar observations to either early education transformation, going to analyze what multiple nations as well as areas enact policies to reinforce connectivity, shares, but also value. Attempting to draw forward forms of evidence and by developing countries, but also North America, these same reports show prevalent challenges—such even though commodity differences, teacher education spaces, as well as free enterprise debates—and creative solutions, which include ICT integration, civic engagement, but instead coursework revamp. Main insights posit a certain effective welfare reform take priority educator formalization, institutionalized fairness, as well as situationally alterations, whereas the best standard setting invariably reaps unforeseen consequences. Its survey both examines conflicts with both worldwide baselines, but instead confined does need, trying to highlight where and nation peoples weigh global oversight as for collective action technology. Through biosynthesis teachings because after varied system is a system, one such study offers lawmakers someone framework for developing financially viable, inclusionary primary school schemes associated of overall development objectives.

Secure and Energy-Efficient Communication in Wsns Using Ntru-Based Post-Quantum Cryptography

2025-07-07T05:30:31+00:00

Quantum computing poses a threat to existing cryptographic algorithms used in Wireless Sensor Networks (WSNs). This paper presents PQ-LEWSN, a lightweight encryption framework based on NTRUEncrypt, a lattice-based algorithm known for quantum resistance. Designed for energy-constrained environments, PQ-LEWSN integrates techniques like key indexing, delta compression, and modular packetization. Simulation results from the Contiki-NG environment demonstrate a 21.3% reduction in encryption time and an 18.7% decrease in energy consumption when compared to traditional ECC-AES implementations. These improvements suggest that PQ-LEWSN is a practical solution for securing WSNs in the post-quantum era.

Method Validation and Anticorrosion Property of Extant and Expired Tramadol Hydrochloride and Paracetamol by UV Spectrophotometry

2025-07-10T09:52:54+00:00

Tramadol hydrochloride is an opioid analgesic which has a noradrenergic and serotonergic property that contributes to its analgesic activity and is used for the management of moderate to severe pain. In combination with the opioid analgesic, Paracetamol can also use in the management of post-surgical pain and palliative care in cancer patients. Disposing of expired medicines particularly those used in larger quantities results to financial loss and causes environmental damage. Hence it is necessary to investigate the effectiveness of expired drugs and access its reuse as a corrosion inhibitor which could reduce both financial and environment loss. In this study, a precise, accurate, and simple UV Visible spectrophotometry has been developed for the determination of expired and extant tramadol hydrochloride and paracetamol by UV-visible spectrophotometry.

1139 Implementation of Preprocessing In Face Detection and Crime Avoidance Using Deep Learning Techniques

2025-07-12T11:07:47+00:00

Identification and recognition of criminals is a tough and time-consuming process at any crime scene. Criminals can be recognized through fingerprints, DNA evidence, captured images from CCTV or other surveillance equipment, or eyewitness testimonies. However, methods like fingerprint matching, DNA analysis, and face recognition from images require an already established and reliable database for effective identification. It is not just criminal investigations that rely on such technologies; various human recognition systems for access control, attendance tracking, and security verifications also require a strong database and image capturing systems. In this article, a method for recognizing human faces along with estimating age and gender using facial features is presented. Since images are multidimensional and may be influenced by several external factors such as lighting, angle, and noise, developing an accurate and reliable recognition model remains a challenging task. To enhance the system’s accuracy, various preprocessing techniques and feature extraction methods are employed to convert the input image into pixel-level representations suitable for machine learning models. The processed data is then fed into a Convolutional Neural Network (CNN) for recognition and classification based on age and gender. The primary goal of preprocessing is to eliminate redundancy, reduce distortions, and improve the quality of the images. Additionally, this work analyzes the impact of three different preprocessing techniques on the overall image classification performance, providing insights into their effectiveness in enhancing recognition accuracy.

Dream Emotion Prediction Using Artificial Intelligence: A Comparative Analysis with Eeg Signals

2025-07-12T11:17:29+00:00

This research paper seeks to deliver an in-depth exploration of the practical uses of artificial intelligence in forecasting emotional states during dreams through the analysis of EEG signals. It specifically evaluates the effectiveness of four machine learning models—Random Forest (RF), Support Vector Machines (SVM), Neural Networks (NN), and Gradient Boosting (GB)—in categorizing emotions derived from EEG data collected during REM sleep, a phase most closely linked to vivid dreaming (1). The dataset comprises EEG signals from various participants, with emotional states classified as Happy, Sad, Neutral, Angry, Calm, and Fearful. The models are assessed based on metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Findings indicate the promise of AI in interpreting EEG signals to anticipate emotions during dreams, with Random Forest attaining the highest accuracy and F1-score. To strengthen the validity of the results, cross-validation was conducted, and the models were optimized using grid search methods. Techniques for feature extraction, including power spectral density and wavelet transform, were utilized to enhance model efficacy by isolating frequency-domain features associated with emotional states. This study underscores the importance of utilizing EEG biomarkers for emotion recognition, offering insights into subconscious emotional processing (2). The application of AI in this field could pave the way for innovative diagnostic tools in mental health, such as early identification of emotional disorders, tailored therapy, and a deeper understanding of the emotional processing related to dreams.

Automated Fetal Brain Growth Assessment Using AI For Early Diagnosis of Neurological Disorders

2025-07-14T11:38:33+00:00

Fetal brain development is a sensitive indicator of neurological well-being, and early detection of malformation critically impacts perinatal outcome. Conventional fetal neuroimaging modalities like ultrasound and MRI are inhomogeneous, interpreter-dependent, and time-consuming. A new Automated Fetal Brain Growth Assessment (AFBGA) system using Artificial Intelligence (AI) and deep machine learning algorithms is introduced to facilitate rapid and homogeneous fetal brain growth assessment. Spatiotemporal Neural Growth Network is thought to be a new architecture of whole-fetal brain growth pattern modeling and whole-fetal brain segmentation. The model receives serial ultrasound and MRI scans through morphological and structural brain development characteristics of fetal brain development. To extract features accurately, a Neuro-Optimized Feature Extraction Algorithm (NOFEA) consists of an adaptive selection mechanism, which automatically selects informative features from multi-scale neuroimaging data. It eliminates redundant information and enhances interpretability, which helps in enhancing brain development measurement accuracy. Another Growth Trajectory Prediction Network (GTP-Net) is created that integrates Long Short-Term Memory (LSTM) and a Neuro-Adaptive Variational Model (NAVM) to predict fetal brain growth variation profiles with high temporal resolution. Blockchain-secured federated learning environment maintains data privacy and supports collaborative training among multi-institutional fetal image data sets to achieve more generalizability across heterogeneous populations. Experimental validation on large-scale fetal neuroimaging datasets confirms that the proposed AFBGA framework provides greater accuracy, sensitivity, and specificity compared to conventional evaluation protocols. The system can perform real-time risk stratification of microcephaly, ventriculomegaly, and neural tube defects, allowing timely intervention. This research contributes to AI-driven fetal medicine by providing an automated, scalable, and privacy-preserving solution for early neurological disorder diagnosis. Future work will focus on integrating multi-modal imaging fusion and deploying AI-assisted fetal brain assessment tools in clinical practice.

Decentralized Blockchain-Enhanced Security Framework for Secure Data Storage and Access Control in Cloud Computing

2025-07-14T11:55:56+00:00

As cloud computing is being adopted more and more, data integrity and security are of utmost concern. Centralized security paradigms are susceptible to unintended access, data exposure, and failure points. All such vulnerabilities in this research will be addressed by proposing a new Blockchain-Enhanced Secure Cloud Storage (BESC) Framework through synergistic combination of blockchain technology and distributed cloud storage to enable Enhanced Data Protection. The proposed architecture uses a multi-layer security architecture in the mode of Zero-Knowledge Proofs (ZKP) as the authentication, Attribute-Based Access Control (ABAC) as smart contract-based access control, and hybrid encryption algorithm (AES-256 + ECC) for encryption. It further proposes the usage of Hash-Based Splitting Algorithm (HBSA) to split data into storage across multiple cloud nodes in order to render it redundant and fault-tolerant. A novel algorithm Blockchain-Based Secure Data Storage and Access Control (B-SDSAC) is proposed wherein Proof-of-Storage Consensus (PoSC) is applied to check for integrity and homomorphic hashing to support Real-Time Consistency Checks. Immutable blockchain ledger metadata provides open transparency of access control without tampering by any unwanted activity. The new paradigm has enhanced data security by preventing central points of failure, minimizing attack surfaces, and quantum-resistant encryption. Experimental evaluation confirms that the BESC model provides enhanced data recovery efficiency, secure access control, and storage efficiency over legacy cloud security models. The study contributes scholarly to secure cloud computing in that it introduces extremely scalable, decentralized, and tamper-evident security architecture for secure storage, access, and recovery of data. Further research will be carried out in order to reduce the computational expense of blockchain transactions as well as integrating lightweight cryptography schemes in low-resource cloud computing.

Deep Learning Based Tool (Dblt) to Assist Movement Inside Closed Spaces for People Affected with Blindness

2025-07-16T05:39:37+00:00

Given the large population of blind and visually impaired individuals (VIIs), there is an increasing need for intelligent assistive tools that can provide real-time warnings about potential collisions. Navigation for VIIs using conventional aids like white canes and guide dogs has limitations implying the need for them to overcome movements in physical environments. VIIs have issues even in own their homes. They may find it difficult to navigate interior parts of homes including corridors or confront potential hazards. Hence, this work suggests an aid that can help VIIs walk based on their surroundings. The schema called Deep Learning Based Tool (DBLT) to help VIIs can detect obstacles that block their paths and help in assisting their movements from one place to another including homes. The schema uses generated images of obstacles and notifies the user of actions like moving forward or left or right. This work’s evaluation results show promise in implementations and if attached to smart devices that capture images of front sides, DBLT warns VIIs about obstacles, allows them to walk straight with referral actions.

Experimental Studies and Analysis of Turning Parameters of Aluminium Hybrid Composite (Al-Al2o3 5% -Sic 5%-Csa 6%) Using Rsm

2025-07-16T06:20:54+00:00

The demand for lightweight and high-performance materials led to the creation of aluminium hybrid composites (AHCs) that are well known for their remarkable mechanical properties. However, because of their abrasive and heterogeneous nature, AHCs are still difficult to machine. This study examines the impact of CNC turning settings on stir-cast aluminium hybrid composites (Al-SiC 5%-Al2O3-5%-CSA 6%). Three machining parameters, including depth of cut, feed rate (0.103–0.294 mm/rev), and cutting speed (80–120 m/min), were used to machine the AHC using CNC. Three input factors were taken into consideration when designing the trials utilizing a Box-Behnken Design in Response Surface Methodology (RSM) with process parameters of depth of cut (0.3–0.9 mm), feed rate (0.103–0.294 mm/rev), and cutting speed (80–120 m/min). Additionally, surface roughness (Ra) is performed to analyze smoothness of the samples using a Mitutoyo SJ-210 tester. Analysis of Variance was used to determine the machining parameters. Three out- of-bounds machining parameters were considered, such as surface roughness, material removal rate and machining time. The mathematical equation for three outputs was formulated by using design expert software. Results indicated that feed rate and cutting speed were the most significant parameters affecting surface roughness. Interaction effects were also significant, especially between feed rate and cutting speed. The optimized parameters reduced surface roughness from 8.617 μm to 2.145 μm. Validation experiments confirmed the accuracy of the RSM model. This study offers insights for industrial applications in industries including sports equipment, automotive, and aerospace and shows how well RSM works to optimize machining parameters for AHCs.

An Ensemble Based Clustering and Classification Framework for Prediction of Agricultural Crop Yield

2025-07-17T05:22:59+00:00

One of the most important sectors of India's economy, affected by a variety of physical and climatic factors including soil quality, temperature, rainfall, and irrigation methods is agriculture. If farmers and other players in the agricultural supply chain are to make decisions based on accurate knowledge, they must be able to exactly project crop yields. On the other hand, the methods now in use find it difficult to manage the variability of agricultural fields, which leads to less than perfect accuracy and generalizability. By means of the integration of physical and climatic elements, this work presents a unique ensemble-based clustering and classification framework, so improving the accuracy of crop yield prediction. The proposed framework consists in three basic components: preprocessing agricultural datasets in order to handle missing and noisy data; ensemble clustering using Chaotic Cuckoo Search Optimization (CCSO) and Simulated Annealing (SA); and classification of clustered datasets using Support Vector Machine (SVM). This approach solves farmers' problems by pointing out significant physical and climatic signals that guide their selection of crops suitable for particular regions. Using paddy crop yield datasets, the framework was evaluated in line with Java environment implementation. For validation's purposes, performance criteria including accuracy, precision, recall, F-measure, and percentage error were used. The results show that the proposed CCSO-SA clustering ensemble with SVM classification performs rather better than the methods currently applied. Its 95.3% high accuracy, 94.8% precision, 94.1% recall, and 3.9% reduced percentage error point to one other. This progress indicates that including dependable classification algorithms and sophisticated optimization methods helps one to reach the expected results.

Optimized EEG-BCI Techniques to determine the level of concentration using CSP with KNN

2025-07-17T05:47:25+00:00

The increasing prominence of Digital Learning Methodologies, particularly self-learning through video tutorials, highlights a critical need to assess listener engagement and instructional efficacy without direct feedback. This paper presents a novel approach to determine the concentration level of a listener while watching video tutorials using Electroencephalography (EEG) brain wave signals, and consequently, to estimate the performance of the trainer. The methodology involves a multi-stage process: initially, EEG brain wave datasets undergo rigorous data preprocessing to ensure signal quality. Subsequently, relevant features are extracted using the Common Spatial Pattern (CSP) algorithm, optimizing discriminative information. Finally, the K-Nearest Neighbors (KNN) algorithm is employed to accurately classify the listener's concentration level and infer the efficiency of the tutorial video. Experimental results demonstrate a robust performance evaluation, achieving an accuracy of approximately 93%. This research primarily aims to provide an objective method for predicting learning concentration from EEG brain waves, offering valuable insights for enhancing digital educational content.

Improved Mitigation of Security Challenges in Ai Powered Mobile Cloud Application Using Deep Belief Attention Network with Support Linear Regression

2025-07-17T05:51:33+00:00

AI-powered mobile cloud apps raise serious security issues with more sophisticated and flexible attacks targeting at these systems. Since conventional security solutions are mostly reactive and only address issues once they have shown up, they could not be enough in the face of fast shifting attack routes. Driven by artificial intelligence, this effort addresses the desire for a more proactive, predictive approach to find and minimise any security breaches in mobile cloud apps. We propose a new hybrid model integrating a Deep Belief Attention Network (DBAN) for enhanced feature extraction with Support Linear Regression (SLR) for efficient classification of security threats. The DBAN component uses its deep learning architecture to identify complex patterns and dependencies inside the data, therefore enhancing the real-time extraction of relevant information. Concurrently, the SLR model identifies these traits to project potential security breaches before they materialise. Our approach was tested on a large dataset comprising more than 100,000 records containing both benign and malicious activity common of mobile cloud systems. Experimental results demonstrate that the proposed model is rather effective with an accuracy of 96.7%, a precision of 94.8%, a recall of 95.5%, and an F1 score of 95.1%. Furthermore, the model proved to be resilient in lowering false alarms by getting a False Positive Rate (FPR) of 3.2% and a False Negative Rate (FNR) of 2.8%, thereby explicitly identifying actual threats. Our hybrid approach offers a whole solution for enhancing security in mobile cloud apps driven by artificial intelligence since it demonstrates superior performance in detection speed and accuracy than current approaches. This predictive method is particularly suited for dynamic environments where quick threat detection and mitigating are quite important.

Denoising Voip Calls Using Discrete Wavelet Transform

2025-07-17T06:01:29+00:00

Voice over Internet Protocol (VoIP) communication has become ubiquitous due to its cost-effectiveness and flexibility. However, the quality of VoIP calls can be significantly degraded by various types of noise present in the acoustic environment and introduced during transmission. This paper presents a novel approach to noise reduction in VoIP calls utilizing the Discrete Wavelet Transform (DWT). The proposed method effectively addresses both stationary and non-stationary noise sources, which are common challenges in real-world VoIP scenarios. By decomposing the noisy speech signal into different frequency sub-bands, DWT allows for targeted noise attenuation. We investigate various wavelet families and decomposition levels to identify the optimal parameters for noise suppression while preserving speech quality. The core of the algorithm involves applying a thresholding technique to the DWT coefficients, effectively distinguishing between speech and noise components. Coefficients below a defined threshold are either shrunk or set to zero, thereby reducing noise energy. Experimental results, evaluated using objective metrics such as Signal-to-Noise Ratio (SNR) improvement, demonstrate that the DWT-based noise reduction technique significantly enhances the clarity and intelligibility of VoIP calls, outperforming traditional noise reduction methods in diverse noisy environments. This research contributes to improving the overall user experience and reliability of VoIP communication.

Application of Game Theory in Analyzing Urban and Rural Demographic Stability

2025-07-17T06:08:38+00:00

This paper examines the age-specific variations in marriage trends across urban and rural populations in India for males and females, utilizing data from the years 2005-2006, 2015-2016, and 2019-2021. Through a comparative analysis, the study explores the socio-economic and cultural factors influencing age at marriage, focusing on gender disparities and regional differences. Leveraging large-scale datasets, the research highlights trends in child marriage, delayed marriages, and shifts in societal norms over time. Furthermore, these variations are analyzed using game theory principles, applying the saddle point method to identify optimal strategies for addressing disparities in marriage age across rural and urban settings. By integrating mathematical frameworks with socio-demographic data, this paper provides a comprehensive understanding of how urbanization and policy interventions have impacted marriage age across genders and regions. The findings contribute to policy discourse, emphasizing the need for targeted interventions to address rural-urban disparities and ensure equitable development.This study analyzes demographic data of urban and rural populations for males and females across various age groups from 2005, 2015, and 2019 using game theory principles, specifically focusing on the concept of the saddle point. The saddle point represents an equilibrium in the data, where the row minimum equals the column maximum, indicating optimal stability in the dataset. Key observations reveal that urban male data for the age group 0–4 exhibits a saddle point of 9 in 2015, while rural male data demonstrates a saddle point of 10.2 in 2005 for the 5–9 age group. For females, urban areas show a saddle point of 9.1 in 2015, and rural areas exhibit a saddle point of 9.1 in 2019 for the 15–19 age group. The results highlight demographic shifts over time, including reductions the interplay of stability points in demographic data, emphasizing the role of equilibrium concepts in understanding temporal changes. The findings contribute to strategic planning in population percentages across various age groups. This game-theoretical analysis demonstrates in resource allocation and policy-making for urban and rural development.

IoT-Based Smart Battery Management System for Electric Vehicles: Real-Time Monitoring, AI-Driven Predictive Analytics, and Cloud Integration

2025-07-23T05:33:21+00:00

The global shift towards electric vehicles (EVs) necessitates the advancement of intelligent battery management technologies. This paper presents an IoT-based Smart Battery Management System (SBMS) that combines real-time monitoring, AI-driven predictive analytics, and seamless cloud integration to improve EV battery performance, safety, and lifecycle. Traditional BMS architectures are limited by static monitoring and lack predictive capabilities. The proposed SBMS overcomes these limitations through a three-layered architecture: IoT-based sensing, AI-powered analytics, and cloud infrastructure. Real-time data such as voltage, current, temperature, and state of charge (SoC) are collected via embedded sensors and processed using machine learning models to predict state of health (SoH) and detect anomalies. Cloud platforms provide remote diagnostics, centralized storage, OTA updates, and fleet management tools. Experimental results from a prototype validate the system’s accuracy, responsiveness, and predictive maintenance capabilities, offering a scalable and future-ready solution for the EV industry.

Predictive Modeling of COVID-19 Outbreaks Using Machine Learning Algorithms

2025-07-23T05:42:43+00:00

The COVID-19 pandemic revealed the necessity of effective forecasting and management methods for controlling the transmission and effects of infectious diseases. Machine learning (ML) techniques have emerged as a promising tool to predict the outbreak, spread, and severity of coronavirus infections. This paper the use of ML techniques to predict the course of the COVID-19 pandemic, focusing on epidemic spread, case severity, and mortality rates. Time series analysis, regression models, and compartmental models like SIR (Susceptible-Infected-Recovered) are employed to forecast the number of infections and hospitalizations. Additionally, classification algorithms are utilized to predict patient outcomes based on demographic and medical factors. The study also examines the potential of NLP (Natural Language Processing) for analyzing public sentiment and social behaviors that may impact the virus's transmission. Lastly, optimization and reinforcement learning models are considered for efficient resource allocation and vaccine distribution strategies. Despite the challenges posed by incomplete or inconsistent data and the evolving nature of the virus, ML models offer valuable insights to inform public health policies, enhance early warning system and optimize health care responses.

User’s Preference of Android Phone over I Phone

2025-07-23T06:01:36+00:00

This study identifies key reasons contributing to the favoritism towards Android devices.This paper aims to investigate and compare the customer preference of android phone over i-phone.This research paper gathered information from both primary and secondary data sources. The number of respondents taken into consideration for collecting data for this research is 150.The questionnaire method asa survey is used as a tool for collecting the primary data, which was designed based on the study's objectives. Analytical tools used for this study, Percentage analysis&weighted average method.

Investigating and Optimizing Resistance Welding Conditions for Superior Weld Quality in HSLA Material

2025-07-23T06:09:16+00:00

This study explores the optimization of resistance welding parameters to achieve superior weld quality in high-strength steel sheets used in automotive structures. By utilizing a Taguchi L9 orthogonal array for design of experiments (DoE), key process parameters—welding current, electrode force, and welding time—were varied and their effects on weld quality indicators such as nugget diameter, tensile shear strength, and weld indentation were evaluated. Analysis of variance (ANOVA) was conducted to identify the most influential parameters. Scanning Electron Microscopy (SEM) and metallographic analysis were used to evaluate the microstructural characteristics of the weld zone. The optimized parameters resulted in a significant improvement in both mechanical performance and weld integrity, confirming the effectiveness of statistical optimization in resistance spot welding (RSW) applications.

Lung Cancer Prediction and Optimization Using Improved Vgg16 With Grey Wolf Optimizer (Gwo)

2025-07-24T07:32:13+00:00

This paper suggests a strong deep learning model for predicting lung cancer utilizing an Improved VGG16 structure and the Grey Wolf Optimizer (GWO). We think that this method will improve the accuracy of classifications and lower the number of wrong forecasts. The chest CT scan photographs taken by the system show a lot of different kinds of cancer, including adenocarcinoma, squamous cell carcinoma, large-cell carcinoma, and normal cells. Feature extraction is carried out with VGG16 convolutional layers and model parameter optimization is attained with grey wolf social behavior simulation in GWO. The model stabilizes and avoids overfitting with batch normalization and dropout layers. The model yields enhanced prediction performance with 92.31% sensitivity, 98.29% specificity, and 94.23% accuracy. Comparison with other models such as DNN, RBF SVM, Adaboost, and DCNN also indicates better performance in all the metrics. Experimental analysis performed using MATLAB verifies the efficacy of the hybrid system through performance curves and convergence patterns. The model also exhibits fast convergence during training and low error rates when utilizing a couple of iterations. Plots indicating model accuracy, loss patterns, and relative metrics further demonstrate its superiority. Grey Wolf Optimizer helps in avoiding the local optima and increasing the capacity for generalization. The contribution reflects the clinical importance of the proposed model for early detection, allowing timely treatment. Its capacity to distinguish between cancer and non-cancer images guarantees proper diagnosis. The system overall offers a scalable and understandable AI algorithm for real-time lung cancer detection. The proposed hybrid model sets a promising benchmark for future medical image analysis software.

Conglomeration of Endodontology and Periodontology - A Case Report

2025-07-25T09:31:38+00:00

Introduction: A condition known as an endo-perio lesion occurs when periodontal and pulpal diseases coexist in the same dental component. The periodontal complex comprising of alveolar bone, periodontal ligament, root cementum and the overlying linked with the periodontal tissues is the dental pulp, which may communicate with the periodontium through: the apical foramen, dentin, tubules, lateral root canals, furcation root canals, fractures lines inside the root. Case Report: The patient in this case report was 28year old female with pain and abscess with vertical bone loss. It was treated with drainage and curettage followed by periodontal regenerative surgery. Conclusion: A collaborative and interdisciplinary approach can aid in the enhancement and preservation of the natural dentition in order to attain health, comfort, aesthetics, and function even in teeth that are thought hopeless using conservative methods.

Role of Medical Students Pertaining to Rules and Regulations of Road Safety - an Original Research

2025-07-25T09:36:56+00:00

Background: Road traffic accidents (RTAs) are a major public health concern, particularly among young adults. This study assessed road safety awareness, attitudes, and behaviors among medical students in Chennai, India. Methods: A cross-sectional study was conducted with 320 undergraduate medical students and their interns. Data were collected using a semi-structured questionnaire and analyzed using SPSS version 22.0. Logistic regression was used to identify the associations between demographic variables and outcomes. Results: Overall, 66.9% of the participants demonstrated good knowledge of road safety, 77.2% exhibited good behavior, and 52.2% held positive attitudes. Sex significantly influenced both knowledge (females: adjusted OR = 0.42, 95% CI: 0.23–0.77, p = 0.005) and attitudes (females: OR = 1.84, 95% CI: 1.06–3.19, p = 0.030). Formal road safety education was associated with better behavior (OR = 0.54, 95% CI: 0.32–0.92, p = 0.023). Conclusion: Although knowledge and self-reported behavior were relatively high, positive attitudes were less prevalent. Gender and formal education significantly impacted road safety awareness and adherence. These findings highlight the need for targeted interventions to improve road safety practices among healthcare professionals.

Interrelation between Agricultural Workers and Morbidity – A Cross Sectional Study

2025-07-26T09:04:14+00:00

Background: Agricultural workers form a significant part of India’s workforce, yet they face multiple occupational health hazards. Despite their contributions to food production, their health concerns remain underreported and inadequately addressed. Aims/Objectives: To assess the morbidity pattern and determine the factors associated with it among agricultural workers in Chengalpattu district. Methodology: A cross sectional study was conducted among 400 agricultural workers in Chengalpattu district from July 2024 to February 2025.Multistage random sampling method was used to choose study participants.Data collection was carried out using a semi structured questionnaire.Statistical analysis was performed using SPSS version 27. Results: Mean age of the participants was 51.85 years. 49.5% were male. 43.5% were illiterate, and 41% earned less than ₹3000 per month. Musculoskeletal disorders (71.5%), was the most prevalent followed by Skin diseases (44%), Respiratory illnesses (42%), Heat-related illnesses (40.3%). Significant associations were found between morbidity and various socio-demographic/work-related factors. Conclusions: The study highlights a high burden of occupational health issues among agricultural workers.There is an urgent need for including awareness programs, Personal protective equipment promotion, regular health screenings, and improved working conditions to safeguard the well-being of agricultural workers.

Beyond Grammar and Syntactic Meaning: Language as the Formation of Children's Social Identity in Elementary School

2025-07-28T05:49:28+00:00

Language research studied a notion like maneuver as either an important factor there in structure yeah young kids’ interpersonal authenticity in under elementary setup, traveling further than conventional grammar-focused effects on language educational. Mod, described as even the complex and dynamic use communication in particular situations, seems to be explored as just a weapon thru which young kids try negotiating about their personas, partnerships, as well as gang associates. Attempting to draw to either socially constructed but also conversation analysis architectures, the said study examines what elementary students hire pronunciation also for correspondence but for attempting to build hierarchical structures, normative pertaining, as well as soul. Data collected through the class observations, peer relationships, but also instructor interview session in such a heavily urbanized primary level, going to reveal what children’s utterances indicate but instead structure about their cultural placement. Findings show a certain mod practice—such since password, narration, but also humor—serve just like frameworks such as incorporation but rather isolation, allowing young kids to claim authority inside friendship groups. A paper advances its role of the teachers but instead systemic social rules either in going to reinforce and demanding syntactic power structures a certain impact identity development. Besides diverting attention and by correct punctuation appropriateness towards the interpersonal continues to function after all pronunciation, the said data analysis reaffirms need for schools and teachers complete embrace inclusionary instructional practices the said affirm students' vast and varied maneuver processes. Finally, its thoroughly reviewing and it 4 ways is a crucial, albeit often ignored, space after all youngsters’ socioeconomic advancement throughout school systems.

The Future of Video on Demand Regulation: Legal Implications and Effectiveness

2025-07-28T07:47:00+00:00

The digitalization of entertainment has brought about a seismic shift in consumer behaviour worldwide. This paper meticulously traces the evolution of the Indian entertainment industry, with a particular focus on the central role played by OTT players in this digital transformation (Lehdonvirta, 2013, pp.18-35). It provides a local context to traditional entertainment media and their gradual shift with the advent of digital technologies, followed by the steep rise of OTT services. It delves into the notion of 'Technological Determinism' and its use in understanding this transformation. The study comprehensively represents the definition, operation, and dynamics of the nascent market of OTT. It thoroughly examines the influence of OTT on traditional broadcasters, consumer behaviour, and content monetization (Drew, R., 2016, pp.165-183). Leading platforms such as YouTube, Netflix, and Amazon Prime Video vividly illustrate the competitive strategies, growth of subscribers, and, most importantly, the content diversification that defines the present entertainment paradigm. The approach for this study is to define how OTT platforms have not just redefined the concept of entertainment, but have also made it more accessible, personal, and engaging for a diverse range of audiences, thereby underlining the central role of these platforms in the transformation of the Indian entertainment industry (Mehta and Cunningham,2023, pp.276-290).

Comparative Effectiveness of Combination Therapy of Fluticasone/Formoterol Versus Budesonide/Formoterol in Mild to Moderate Asthma

2025-07-28T11:25:57+00:00

Introduction: The chronic inflammatory respiratory condition known as bronchial asthma affects millions of people all over the world and has an important impact on the quality of life of those affected. The most effective method for managing asthma is the combination of inhaled corticosteroids (ICSs) and long-acting beta-agonists (LABAs). However, there has been a relatively limited amount of research conducted on the safety and effectiveness of fluticasone and formoterol (FFF) and budesonide and formoterol (BFF) in patients who have mild to moderate asthma.

Aims and Objectives: The primary objective is to compare the improvement in “FEV1/FVC ratio” and health status among both groups. FEV1/FVC ratio and FEV1 values will be assessed using spirometry at baseline, weeks 2, 4 and 8 weeks. Secondary objective is to compare the adverse effects among both the groups

Methodology: This institution-based, prospective, randomized, observational, open-labeled study was conducted at the Pulmonary Medicine Outpatients Department of “Sree Balaji Medical College and Hospital, Chennai”, over one year. Eighty adult patients were randomized into two groups: Group 1 (FFF) and Group 2 (BFF), receiving respective treatments twice daily. Spirometry were recorded at baseline, Weeks 2, 4, and 8. Adverse effects were also monitored.

Results: Both treatment groups showed significant improvement in lung function and symptom control. However, FFF demonstrated greater improvement in spirometry values at Weeks 2, 4, and 8 (p < 0.05). Additionally, the FFF group exhibited better tolerability, with fewer adverse effects reported at Weeks 2 and 4.

Conclusion: While both treatment regimens effectively managed asthma symptoms, the FFF combination exhibited superior efficacy and safety. It proved to be a more effective option for asthma management, ensuring better pulmonary function and symptom relief with fewer adverse effects.

Application of Preprocessing Techniques in Facial Recognition

2025-08-01T05:51:40+00:00

Identifying and recognizing criminals at a crime scene can be a complex and time-consuming process. Criminals may be identified through various methods such as fingerprints, DNA analysis, CCTV footage, or eyewitness testimony. The use of images captured by security cameras, along with fingerprint and DNA matching, requires access to a pre-existing database for effective recognition. Similarly, systems designed for human identification, such as those used for access control or attendance tracking, also rely on image capture and a database for accurate identification. This article discuss the methods for recognizing noised human faces by analyzing their features. Since images are multidimensional and can be affected by external factors that impact their clarity, creating an effective recognition model is a challenging task. To improve the system's accuracy, preprocessing techniques and feature extraction methods are applied to convert images into pixel-based data. The processed data is then fed into a Convolutional Neural Network (CNN) for classification and recognition. The study also examines the impact of three different preprocessing techniques and a comparative study of their effectiveness.

Advanced Investment Analysis Using Butcher’s Seventh Order RK Methods and ITFS in MAGDM for Indian Banks

2025-08-01T06:01:30+00:00

MAGDM (Multi-Attribute Group Decision Making) proves to be the most effective solution when compared to other decision-making methods. The data set used in this analysis is extracted from Intuitionistic Triangular Fuzzy Number (ITFNs) matrices, where the values are calculated according to specific orders of Runge-Kutta methods, a widely recognized numerical technique for solving differential equations. In this context, decision-making involves applying the Intuitionistic Triangular Fuzzy Weighted Geometric Operator (ITFWG) and the Intuitionistic Triangular Hybrid Geometric Operator (ITFHG). These operators help handle the uncertainty and imprecision inherent in the decision-making process, providing a more robust evaluation of alternatives. To rank the alternatives, an extended version of the Normalized Hamming Distance formula is employed. This formula measures the dissimilarity between different alternatives, providing a more accurate and comprehensive ranking based on the fuzzy data. The methodology ensures that each alternative is evaluated not only for its individual attributes but also for the interactions and trade-offs among those attributes in the decision-making process. The paper further illustrates the application of this approach through a numerical example, highlighting the elasticity and effectiveness of the proposed methodology. The numerical results demonstrate how the use of Intuitionistic Triangular Fuzzy Numbers, coupled with Runge-Kutta methods, enhances the precision and reliability of the decision-making process, making it an effective tool for complex real-world problems. MAGDM is the best solution that comes into play, compared to other solutions. From Intuitionistic triangular fuzzy number matrices, the data set is taken. The values shall be determined in accordance with some order of Runge Kutta methods. By using decision making, we have used Intuitionistic Triangular Fuzzy Weighted Geometric Operator and Intuitionistic Triangular Hybrid Geometric operator solutions. The values shall be determined in accordance with some order of Runge Kutta methods. By using decision making, we have used Intuitionistic Triangular Fuzzy Weighted Geometric Operator and Intuitionistic Triangular Hybrid Geometric operator solutions. For ranking alternatives, a new extended Normalized Hamming Distance formula is used. This paper presents the numerical illustration of elasticity and effectiveness.

A Comprehensive Review of Artificial Intelligence Applications in Precision Agriculture: Trends, Challenges, and Future Directions

2025-08-01T06:12:11+00:00

Artificial Intelligence (AI) has emerged as a transformative force in modern agriculture, revolutionizing the way crops are monitored, pests are controlled, irrigation is managed, and diseases are diagnosed. This research synthesizes and critically evaluates ten recent scholarly works (2020–2025) focusing on AI applications in precision agriculture. These studies span across disciplines and technologies, including deep learning, Internet of Things (IoT), Unmanned Aerial Vehicles (UAVs), sensor networks, and edge computing. Collected research articles are assessed for its strengths, weaknesses, and identified research gaps. The analysis reveals a growing trend toward integrating AI with practical field tools and hardware systems, leading to increased precision and sustainability in agricultural operations. While some works provide conceptual frameworks and systematic reviews, others offer concrete deployments, such as AI-IoT pivot systems and ANN-based pest detection through wireless sensor networks. Common strengths include multimodal integration, sustainability orientation, and a strong push toward automation. However, significant gaps remain, including limited large-scale deployment, lack of economic feasibility studies, challenges in model interpretability, and insufficient adoption models for smallholder farmers. This review highlights the need for future research that bridges conceptual promise with field-level implementation, enabling AI to address complex, real-world agricultural challenges more effectively.

A Novel Framework for Glaucoma Classification and Detection

2025-08-01T06:26:18+00:00

Glaucoma is a progressive optic neuropathy and one of the leading causes of irreversible blindness worldwide. Early detection and accurate classification are crucial for effective treatment and preventing vision loss. This study proposes a robust and automated approach for glaucoma detection and classification using advanced machine learning techniques applied to retinal fundus images. The methodology involves preprocessing, feature extraction, and classification stages to distinguish between normal, early-stage, and advanced glaucoma cases. Various classifiers, including support vector machines, random forests, and deep convolutional neural networks, were evaluated for their performance. Experimental results demonstrate high accuracy, sensitivity, and specificity, confirming the effectiveness of the proposed system. This automated framework can assist clinicians in timely diagnosis and improve patient management, ultimately reducing the risk of glaucoma-related blindness.

A Commerce-Driven Green Credit Score Wallet for Enhancing Green Banking Adoption and Customer Engagement in Retail Banking

2025-08-01T10:54:49+00:00

Due to the increasing environmental issues and pressure on financial institutions to ensure they finance sustainable development, green banking practices have become a necessity. Nonetheless, the gap between awareness and actual practice of green banking among customers is observable in the form of retail banking in the real world, and mostly in urban areas such as Chennai. Such a lag is based on the lack of individual incentives and concrete commercial advantages that are consistent with the daily financial experience of the customers. To overcome this problem, the proposed study introduces an innovative, commerce-oriented solution, namely, the Green Credit Score Wallet (GCSW), a digital solution that can be built into the existing retail banking apps and which measures the financial activities (continued online transactions, paperless statements, off-line purchases of environmentally friendly goods, payment of electric vehicle) adopted by customers to calculate their Green Credit Score in real-time. This is an algorithmically generated score based on a weighted formula that considers conduct and is directly linked to commercial motivation. This includes dynamic cashback, favorable loan interest rates, and partnerships with green merchants at the point of sale, offering a discount incentive based on the point of purchase. Using the transaction transparency provided by a blockchain and the incentive offered by reward-driven gamification, the GCSW would generate an objective, quantifiable system that induces self-interest among customers to engage with the GCSW and the network and be more responsible for the environment. The potential of the model is confirmed by information provided by a survey of retail banking customers based in Chennai, where there was a high degree of correlation between the score assigned to them through the Green Credit Score and their raised satisfaction, engagement, and the desire to use sustainable banking activities. It is concluded by the study that integrating commerce-based sustainability incentives into digital banking platforms can be a crucial step in furthering the adoption of green banking in general, not to mention the improved instantiation of the will to act on climate-friendly intentions, which is the primary gap to be filled by injecting digital banking systems with global sustainability incentives. This model provides a scalable pattern for banking organizations to blend green intentions with economic benefits in emerging markets.

The Influence of Influencer Attractiveness and Relatability on Consumer Purchase Decisions

2025-08-01T11:19:46+00:00

In the digital era, influencers have emerged as crucial agents in determining consumer purchase behaviours, and most importantly, through publicity platforms like Instagram, YouTube, and Facebook. The research focuses on the two vital determinants of consumer behavior, namely the attractiveness and relatability of an influencer. By using the concept of quantitative research, the researcher employed a survey research approach, engaging 330 participants to determine the correlation between the attributes of the influencer and the probability of purchase. The research results indicate that attractiveness significantly influences a consumer's purchase decision, and the same applies to relatability. It is interesting to note that attractiveness increases consumer trust and emotional appeal, whereby 68.2 percent of the respondents reported it to be one of the critical factors in their choice. Conversely, relatability, generating the feeling of connection and authenticity, proved to be a vital force, especially with young audiences (1824 years), who considered influencers as being more relatable. Based on statistical tests, including Chi-Square tests and ANOVA, the research indicates that there are considerable variations in the demographics, where gender and age are both relevant factors influencing the understanding of the trait of influencers. The drawbacks of the research are its self-report character and the fact that it takes into consideration a relatively homogenous group of social media users. These results are crucial for helping marketers improve their practices by selecting stimulating influencers whose characteristics resonate with the target population, thereby providing greater details on the value of the connection and physical beauty to the success of influencer marketing.

Bioactive Secondary Metabolites and Free Radical Scavenging Potential of Endophytic Fungi Associated With Elaeocarpus Recurvatus Corner

2025-08-06T06:42:34+00:00

Endophytic fungi have recently gained attention due to their possible ability to produce bioactive compounds in nature. Recent studies have found several antioxidant compounds in the many secondary metabolites produced by endophytic fungus. Genetic engineering has the potential to increase the production of these beneficial antioxidant chemicals by these organisms, whether they are unicellular or micro-multicellular. These substances can enhance energy, health, and a variety of biotechnological applications. From Elaeocarpus recurvatus, endophytic fungi (Daldinia grandis, Daldinia loculata, and Aspergillus fumigatus) were isolated. The study evaluates Elaeocarpus recurvatus ethyl acetate secondary metabolites, in vitro antioxidant properties. The findings showed that the Daldinia loculata ethyl acetate extract contained substantial amounts of flavonoids (172.01 mg RE/g), tannin (50.35 mg GAE/g), and total phenols (82.63 mg GAE/g). Good resistance to DPPH (61.83 μg/L), ABTS (101527.8 μM TE/g extracts), superoxide (28.66%), and FRAP (205.18 mM Fe(II)E /mg Extract) was demonstrated by Daldinia loculata ethyl acetate extract. These results indicate that Elaeocarpus recurvatus leaves of the endophytic fungus Daldinia loculata, in particular the ethyl acetate extract, constitute a substantial antioxidant source with great potential.

The Relevance and Pedagogical Solutions of Forming Tolerance among Adolescent Students - Youth

2025-08-06T11:55:07+00:00

This article discusses the author's methodological developments on global social relations between nations, the lack of tolerance among nations for a dangerous process, its causal consequences, and socio-pedagogical solutions to eliminate intolerance among young people, especially among adolescents.

Developing Historical Concepts and Skills in Primary School Students through Digital Technologies While Studying Artistic Works: The Role of Uzbek Folklore and Fairy Tales

2025-08-07T05:49:10+00:00

This article explores the integration of digital technologies into primary education to enhance students' understanding of historical concepts through the study of artistic works, particularly Uzbek folklore and fairy tales. Folklore serves as a valuable resource for teaching cultural heritage and historical awareness, while digital tools such as interactive e-books, animations, and virtual reality provide innovative ways to engage young learners. By combining traditional storytelling with modern technology, educators can create an immersive learning environment that fosters critical thinking, creativity, and empathy. The article highlights the benefits of this approach, addresses potential challenges, and emphasizes the importance of preserving cultural heritage in the digital age. Practical strategies for implementing these methods in the classroom are also discussed, along with recommendations for future research.

Fuzzy Logic Based Smart Cluster Head Selection and Enhanced Particle Swarm Optimization for Energy Optimization in WSNs

2025-08-08T11:33:33+00:00

Wireless Sensor Networks (WSNs) are the fundamental part of the majority of applications like environmental monitoring, industrial automation and healthcare. These networks are highly power-constrained in operation and hence, resource-efficient protocols are designed in a manner that the network remains on for a prolonged duration period. Maximum Optimal Cluster Head (CH) selection is the easiest WSN problem of achieving even power consumption and enhanced network performance. Existing cluster protocols cause energy distribution imbalance. Thus, network stability is decreased. In this paper, Fuzzy Logic (FL)-based Cluster Head selection strategy and Enhanced Particle Swarm Optimization (EPSO) algorithm are utilized with the objective of reducing WSNs power consumption. Fuzzy Logic (FL) controller approximates the parameters like residual energy, base station distance and node population with the objective of making optimum CH decisions. Concurrently, the EPSO algorithm adaptively updates inertia weight and hybrid mutation strategies to rectify the optimization process for evading premature convergence and local optimum issues. Long-term simulation is performed to compare the newly introduced FL-EPSO scheme with other existing network lifetime, energy and Packet Delivery Ratio (PDR)-based clustering schemes. Results show that the proposed FL-EPSO model reduces energy consumption distribution, network life and communication efficiency to a remarkable degree. Adaptive CH selection mechanism provides real-time adjustment ability, thus making the model highly suitable for practical WSN applications. Experimental results show that FL and EPSO combined is a highly effective and scalable method of energy-aware clustering in WSNs. The hybrid models are suitable candidates to design and optimize for the future in order to have optimal performance.

Dynamic Autoencoder Framework for Advanced Anomaly Detection in Sdn Security

2025-08-08T11:37:19+00:00

Dynamic AutoEncoder-based Anomaly Detection (DAEAD) is an edge machine learning algorithm, with extensive application to real-time SDN network anomaly detection. DAEAD, which is dynamically based on autoencoders, self-adapts and learns based on adaptive network traffic patterns in order to construct efficient detection with very low false positives. The model exploits error in reconstruction and entropy analysis for an anomaly score computation and thereby identifies cyber attacks like DDoS attacks, probing, and zero-day attacks. In contrast to conventional approaches, the DAEAD dynamically adjusts itself to detection threshold based on current network conditions in real time and tries to provide high accuracy according to changing network conditions. DAEAD performs better than Graph Neural Networks (GNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), and Principal Component Analysis (PCA) when compared on all the performance metrics such as Mean Squared Error (MSE), Anomaly Score (AS), False Positive Rate (FPR), and True Positive Rate (TPR). SDN telemetry integration with the hybrid deep learning model can improve real-time threat inspection and system performance. Its capacity to learn ensures it the capacity to learn to detect cancerous network anomalies that are far too severe for best-of-the-best cyber defense. Its flexibility ensures functioning even with ginormous SDN networks without accumulation of anomalies over time. DAEAD over other traditional models by its performance deterioration through accumulation overtime, increases detection considerably by way of its ability to self-improve. Application of entropy-based analysis and decision-making focus on network topology and data heterogeneity improves decision-making. Intrusive attack security in breaking the security policy is implemented by the algorithm. Adaptive tuning of DAEAD in accordance with the network environment ensures appropriate security policy at the cost of resistance. Adaptive thresholding, deep learning, and application of SDN telemetry support scalability as well as adaptability in security architecture. The result confirms that DAEAD is the highest performing and most accurate anomaly detector. DAEAD is the ideal solution for emerging SDN infrastructures, ensuring real-time intelligent security.

The Process of Dusting the Air during Laboratory Studies of the Effectiveness of Cleaning Devices

2025-08-09T05:30:16+00:00

The article discusses the problem of environmental pollution with dust and the negative impact of particulate matter on human health. As a technical solution to reduce dust emissions, a set of measures is shown to ensure sanitary cleaning of the air flow from dust in laboratory conditions: determining the effectiveness of the cleaning apparatus, the method of dusting the air flow with a standard dust concentrate to ensure a uniform field of dust concentration across the cross-section of the air duct. The results of a study of the process of air dusting by using a flat diaphragm with a round hole of a smaller diameter and ambient air through an annular slot between the plane of the diaphragm and the end of the air duct installed at the inlet of the air duct perpendicular to its axis are presented. This process makes it possible to obtain a uniform concentration field across the cross-section of the air duct and reduce the distance of mixing of the dust concentrate with the air flow in the air duct. A well-founded technical solution to ensure uniform distribution of dust concentrate during laboratory research allows one to increase the accuracy of dust concentration measurements and reliably determine the effectiveness of the cleaning apparatus recommended for production for cleaning process dust emissions.

Reinforcement Learning in AI: Transforming Real-Time Optimization through Deep Neural Networks

2025-08-11T06:11:08+00:00

Reinforcement Learning (RL) has emerged as a powerful approach for real-time optimization, enabling autonomous decision-making in dynamic environments. By leveraging deep neural networks (DNNs), RL models can efficiently approximate complex value functions and policies, facilitating improved learning and generalization across various domains. This paper examines the transformative impact of RL in real-time optimization, emphasising the integration of deep learning techniques, including Deep Q Networks (DQN), Policy Gradient Methods, and Actor-Critic frameworks. Key applications of deep reinforcement learning (DRL) are examined in various fields, including robotics, autonomous systems, finance, and healthcare, highlighting its potential in real-world scenarios. The inherent challenges, including computational demands, sample inefficiency, and ethical considerations, as well as future directions aimed at enhancing the efficiency, interpretability, and scalability of RL, are also discussed. The findings underscore the significance of deep Reinforcement Learning in driving innovation and efficiency in AI-driven decision-making systems.

Peripheral Ossifying Fibroma Mimicking Traumatic Fibroma- A Case Report

2025-08-12T05:20:23+00:00

Peripheral ossifying fibroma is a reactive non-neoplastic lesion usually located in the maxillary anterior region. Young females are more likely to develop peripheral ossifying fibroma as a single, slow-growing, exophytic nodule mass of the gingiva that is no larger than 2 cm in diameter. Here we present a case of a 40-year-old male patient reported to the Department of Oral Medicine and Radiology with the chief complaint of a growth in the upper left posterior region 1 months earlier.

A Novel Approach Using the Enhanced OLSR Protocol for Node Exit and Entry in a Moving Cluster with a Reserved Cluster Head in an Ad-Hoc VANET Architecture

2025-08-13T06:14:14+00:00

Vehicular Ad-hoc Networks (VANETs) are crucial for enabling intelligent transportation systems, but their high node mobility poses significant challenges to maintaining stable communication. This paper presents a novel methodology for managing the entry and exit of nodes within moving clusters using a Reserved Cluster Head (RCH) mechanism combined with an Enhanced Optimized Link State Routing (E-OLSR) protocol. VANETs are highly dynamic due to the frequent movement of vehicles, leading to frequent topology changes. Traditional routing protocols like OLSR are not optimized for handling frequent node mobility, leading to unstable routing and reduced Quality of Service (QoS). To address these issues, we propose a cluster-based routing framework incorporating a Reserved Cluster Head (RCH) and an enhanced version of OLSR to manage node entry and exit efficiently. The proposed system introduces proactive cluster maintenance by designating backup cluster heads and utilizing predictive mobility models to ensure uninterrupted communication. The RCH acts as a seamless replacement when the primary Cluster Head (CH) leaves the cluster, thereby reducing control overhead, maintaining topology consistency, and improving route reliability. The E-OLSR further enhances the protocol’s adaptability by incorporating mobility-aware metrics into route selection. Simulation results validate that this approach significantly improves packet delivery ratio, reduces latency, and enhances network resilience compared to the traditional OLSR protocol, especially under high mobility conditions typical of urban VANET scenarios.

Context-Aware Enhanced Weight-Based Clustering Algorithm for Mission-Critical Military Vanet Communication

2025-08-13T06:29:44+00:00

In mission-critical military operations, Vehicular Ad Hoc Networks (VANETs) play a fundamental responsibility in enabling real-time communication among mobile military units. Ensuring seamless, protected, along with efficient statement in such exceedingly energetic environments presents a major challenge. This research article propositions a Context-Aware Enhanced Weight-Based Clustering Algorithm (CA-EWBCA) tailored for military VANET scenarios. The proposed algorithm incorporates contextual parameters such as vehicle velocity, direction, transmission range, hop count, and priority level of military units to dynamically elect and maintain robust cluster heads (CHs). Unlike conventional weight-based clustering algorithms that solely rely on static or semi-dynamic factors like node degree and residual energy, CA-EWBCA introduces a contextual awareness model that adapts in real time to the battlefield conditions. The algorithm also implements a secure cluster maintenance mechanism that prioritizes communication continuity during high-mobility transitions such as convoy splits, re-joining, or battlefield maneuvers. Simulation results using NS demonstrate a substantial improvement in concert metrics such as End-to-End Delay, Packet Delivery Ratio, Cluster Stability, furthermore Control Overhead compared to existing clustering protocols like WCA, EWC, and HEED. The enhanced context-aware mechanism ensures reliable communication while maintaining security through lightweight encryption and authentication models integrated with the clustering process. The proposed CA-EWBCA not only improves QoS in high-mobility VANETs but also supports real-time decision-making and situational awareness in military contexts. This work highlights the significance of context adaptation, dynamic weight balancing, and secures communication strategies in military VANET clustering, marking a step toward intelligent and resilient battlefield communication systems.

Hierarchical Fuzzy Inference System for Edge Detection Using Adaptive Multi-Scale Gradient Analysis and Evolutionary Threshold Selection

2025-08-13T06:47:09+00:00

Edge detection remains a fundamental challenge in computer vision and image processing, requiring robust methods that can handle varying illumination conditions, noise levels, and image complexities. This paper presents a novel hierarchical fuzzy inference system (HFIS) that integrates adaptive multi-scale gradient analysis with evolutionary threshold selection for enhanced edge detection performance. The proposed system employs a three-tier hierarchical architecture where each level processes gradient information at different scales, utilizing fuzzy logic to handle uncertainty and imprecision inherent in edge detection tasks. An evolutionary algorithm optimizes threshold parameters across multiple scales, ensuring adaptive performance across diverse image types. Experimental validation on standard benchmark datasets demonstrates superior performance compared to conventional edge detection methods, achieving an average F-measure of 0.847 and significantly reduced false positive rates. The system exhibits robust performance across various noise conditions and image complexities, making it suitable for real-time applications in autonomous systems and medical imaging.

A Novel Blockchain Based Efficient Searchable Security Record Model in Electronics Health Records Sharing Over the Clouds

2025-08-13T12:10:47+00:00

Blockchain technology offers transformative solutions for the healthcare sector, from securing Electronic Health Records (EHRs) to enhancing transparency in the organ trade and pharmaceutical supply chains. Leveraging blockchain, numerous EHR models have been proposed, with many in the healthcare industry moving to cloud-based, encrypted record storage. Searchable encryption has proven effective for processing such data. Balancing security and equitable access to records is critical, especially when interests of record owners and users may diverge. This study introduces an Efficient Searchable Secure Record Model (ESSRM) utilizing blockchain and smart contracts to optimize this balance. The ESSRM employs an optimal computing module for processing ciphertext and keywords, and utilizes smart contracts for increased efficiency. Comparative performance evaluations show the ESSRM model excels in computation and storage efficiency over existing cloud-based searchable EHR models.

Harnessing Artificial Intelligence to Bridge the Rural Financial Divide in India

2025-08-14T05:43:43+00:00

This study explores how artificial intelligence (AI) can bridge the rural financial divide in India through interdisciplinary collaboration and inclusion of marginalized voices. Conducted across four Tamil Nadu districts, the mixed-method research involved 200 rural respondents and key stakeholders. SPSS and AMOS analyses revealed that digital literacy (β = .45) and trust in AI tools (β = .30) significantly influence adoption, while barriers like poor connectivity and age limit usage. Structural modeling confirmed strong links between collaboration, inclusion, and social change. The study concludes that inclusive design, community engagement, and policy support are essential for AI to drive equitable rural development.

Evaluating the Effectiveness of Influencer Marketing Versus Traditional Advertising in Fostering Brand Loyalty

2025-08-14T05:49:45+00:00

This study examines the effectiveness of influencer marketing versus traditional advertising in building brand loyalty. With the rise of digital media, understanding what drives consumer trust and engagement is crucial. The research focuses on how demographic factors like age, income, and occupation affect consumer response to different marketing strategies. Key areas explored include trust in brand messaging, repeat purchase behaviour, and the perceived value of influencer-driven versus traditional campaigns. Using statistical tools like correlation analysis, chi-square tests, and ranking methods, the study finds that influencer marketing significantly boosts engagement and trust, while traditional advertising remains effective among certain groups. The findings highlight the need for adaptive, data-driven marketing strategies to strengthen long-term brand loyalty.

An Intelligent Framework for Automated Social Media Content Creation Using Textual and Visual Cues

2025-08-14T06:07:11+00:00

In the age of digital communication, social media has evolved into a powerful platform for branding, information dissemination, and public engagement. However, consistently generating engaging, personalized, and context-aware content remains a significant challenge. This study presents a deep learning-based, AI-driven multimodal approach for automated social media content generation that leverages textual, visual, and semantic signals to produce high-quality, platform-optimized posts. The objective is to enable scalable, human-like content creation that adapts to different audiences, topics, and engagement goals. The system is trained on a diverse dataset comprising over 1.2 million social media posts from platforms like Twitter, Instagram, and LinkedIn, including associated images, hashtags, captions, and engagement metrics. Posts are categorized across domains such as e-commerce, public health, education, and entertainment. Each data point is enriched with metadata such as post timing, sentiment, and audience interaction level. Technologically, the model employs a multimodal architecture that combines Transformer-based NLP models (such as BERT and GPT), Convolutional Neural Networks (CNNs) for visual analysis, and attention-based fusion mechanisms to align textual and visual inputs. A content planner module ensures contextual relevance, while a reinforcement learning layer optimizes for engagement metrics like likes, shares, and comments. Experimental results demonstrate that the AI-generated content not only maintains linguistic fluency and visual relevance but also outperforms baseline models in user engagement by up to 27%. This research offers a scalable solution for digital marketers, content strategists, and public agencies aiming to automate high-quality content creation while retaining personalization and contextual awareness.

Evaluation on Anti-Obesity Effects of Leucophyllum Frutescens (Berland) I. M. Johnst Flower – A Future Trend Forecasting

2025-08-14T06:39:22+00:00

Medicinal plants have long served as sources of bioactive compounds for managing metabolic disorders. The alarming rise in obesity worldwide has intensified the search for safe and effective natural therapeutics. This study investigates the phytochemical composition and anti-obesity potential of the aqueous flower extract of Leucophyllum frutescens through GC-MS analysis, in vitro enzyme inhibition and molecular docking. GC-MS analysis revealed that the main ingredients were phenolic and flavonoid-rich chemicals. Docking research revealed that beta-mangostin, glabridin and stigmasterol have strong binding affinities toward the active regions of targets linked to obesity, suggesting possible regulatory impacts on energy balance and lipid metabolism. Supporting the computational results, enzyme inhibition experiments showed significant activity against pancreatic cholesterol esterase (IC₅₀: 8.5 μg/mL) and pancreatic lipase (IC₅₀: 20.5 μg/mL). The aqueous flower extract of Leucophyllum frutescens shows promise anti-obesity efficacy due to its high phytoconstituent content. This work stimulates additional isolation and clinical research of the discovered compounds and provides preliminary evidence for the plant's therapeutic use in managing obesity.

Economic and Commercial Impact of Ethanol Production - Opportunities, Challenges, and Rural Development for Farmers in India

2025-08-20T05:25:53+00:00

Ethanol production has emerged as a significant contributor to India's sustainable energy goals, offering an alternative to conventional fossil fuels. With India being an agrarian economy, the integration of ethanol production into the agricultural sector has created new opportunities for farmers while addressing the country’s energy needs. This research focuses on the economic and commercial impacts of ethanol production in India, emphasizing its influence on rural development and infrastructure. The study examines the potential benefits for Indian farmers, including increased demand for crops like sugarcane and maize, income diversification, and market stabilization. It also explores challenges such as the food versus fuel debate, environmental sustainability, and policy uncertainties. A critical analysis of India’s National Bio-Energy Policy and blending mandates highlights the role of government support in advancing the ethanol industry. Furthermore, this research delves into the environmental implications of ethanol production in India, emphasizing the need for sustainable practices to minimize carbon emissions and water usage. The findings underscore the importance of adopting second-generation biofuels and promoting technological innovations to ensure long-term economic and environmental sustainability. This study aims to provide insights into how ethanol production can shape the future of energy, agriculture, and rural livelihoods in India.

Fabrication of Solar Water Heater Using Nano Fluids

2025-08-25T05:44:15+00:00

The investigation outlines the development, production, and practical assessment of an evacuated tube solar water heater (ETCSWH) incorporating Al₂O₃ nanofluids. Utilizing a fundamental blend of ethylene glycol and water in a 60:40 ratio, the study examines how different mass fractions of Al₂O₃ nanoparticles affect the system's thermal behavior and heat transfer capabilities relative to traditional working fluids. The outcomes of the experiments show notable boosts in thermal conductivity and the system's overall effectiveness, resulting in better practical application of solar energy. These results highlight the capability of nanofluid-based heat transfer media as a hopeful and sustainable option for effective water heating, particularly in areas rich in solar resources.

The Influence of Social Commerce on Impulse Buying Behaviour

2025-08-27T06:08:12+00:00

The integration of social media with e-commerce has engendered social commerce (s-commerce), a movement that utilizes interactive elements such as live streaming, user reviews, and influencer endorsements to transform consumer purchase behavior. Although impulsive purchasing in conventional e-commerce is extensively studied, the distinctive influence of social commerce on spontaneous purchases is little examined. The objective of this research is to analyze the impact of certain s-commerce aspects on impulsive purchase behavior, emphasizing the mediating roles of psychological elements, including Fear of Missing out (FOMO) and hedonic motivation. The methodology included a quantitative survey of 300 active social commerce users, aged 18 to 35, who had recently engaged in impulsive purchases on platforms such as Instagram Shopping, or Facebook Marketplace. The analysis used Structural Equation Modeling (SEM) to examine the associations among s-commerce attributes (live streaming, social proof), psychological mediators (FOMO, hedonic enjoyment), and the propensity for impulsive purchase. These results provide actionable insights for marketers to enhance s-commerce strategies by emphasizing high-engagement elements (e.g., live sales events). The research enhances theoretical comprehension by including social commerce dynamics into the literature on impulsive purchasing.

Effect of Greenwashing and Consumer Perception towards Skepticism

2025-08-27T06:19:18+00:00

In a comprehensive assessment of over 1,000 self-labeled "green" or eco-friendly products, it was discovered that nearly all but one product exhibited some degree of greenwashing. Greenwashing, characterized by the deceptive use of public relations or marketing to convey an environmentally safe or "friendly" image, is a prevalent phenomenon in consumer markets. This study delved into the concept of perceived consumer skepticism as an extended consequence of greenwashing, thereby expanding upon previous research that primarily focused on the relationship between greenwashing and green trust, with a broader perspective on the ultimate outcomes. The authors of this study formulated ten hypotheses and constructed a structural model incorporating six variables. They examined these relationships using a purposive sampling technique, which involved conducting both online and offline surveys among a sample of green consumers in Bangalore, Karnataka. The findings revealed a positive correlation between greenwashing and green consumer skepticism (GCC), perceived consumer skepticism (PCS), and green perceived risk (GPR). Surprisingly, the study also unveiled an intricate linkage between GCC-PCS-GPR and green trust (GT). The study's implications are profound, shedding light on the intricate dynamics between green-washing, consumer skepticism, perceived risk, and trust. These findings have practical implications for companies aiming to cultivate genuine environmental credibility and consumer trust. Moreover, the study provides valuable insights for future research directions in understanding and addressing the complexities of green marketing and consumer behavior in environmentally conscious markets.

Demographic-Aware Deep Portfolio Selection for Sip Management Using a Hybrid Temporal Transformer–Policy Network

2025-08-27T06:26:10+00:00

Systematic Investment Plans (SIPs) need personalization that goes beyond generic risk scores. Conventional robo-advisors underuse demographic signals (age, income, occupation, location, goals) that strongly shape risk capacity and liquidity needs. How can we fuse investor demographics with market dynamics to select and rebalance mutual-fund SIP portfolios that better align with life-stage and risk appetite while remaining robust to regime shifts? We propose DATS-PS (Demographic-Aware Temporal Selector for Portfolio Selection), a new deep learning framework. A Demographic Encoder (tabular transformer) produces a latent “risk-capacity vector.” A Market Temporal Transformer ingests multi-factor fund time series (returns, drawdowns, macro proxies). A Risk-Aware Policy Head (distributional actor-critic) outputs SIP weights under budget/solvency constraints. Training is multi-objective: maximize risk-adjusted return (Sortino), minimize downside CVaR, penalize turnover, and enforce goal-attainment via target-tracking loss. A life-event simulator perturbs demographics (e.g., income shocks, relocation) to learn adaptive allocations. On historical mutual-fund data with synthetic demographic cohorts, DATS-PS improves 12-month goal-tracking accuracy and reduces 95% CVaR versus baselines, while lowering turnover. Portfolios stay closer to investor objectives across life-stage changes, yielding higher satisfaction proxies and improved resilience.

Impact of a New Winding Scheme - Single Layer on the Performance of Induction Motor Using Experimental and Simulation Techniques

2025-08-27T07:30:47+00:00

The objective of this work is to find a new winding configurations with low harmonics, efficient and economical use of copper for the winding induction motors (IM). For this reason, a laboratory study was carried out with an investigation using ANSYS - Motor Cad tools for simulating the results. A 1.1kW, 2-pole, 24-slot, 380V, 50Hz three-phase, single-layer squirrel cage induction motor was used in the work. Various coil pitches were tested and simulated; the coil spans were 165° (fractional pitch 11/12), 180° (full pitch), 90° (half pitch 6/12) and the approach of a new winding scheme 105° (7/12). Both experiments and simulation results are presented and compared. The overall testing had shown that the winding scheme in the design with a coil span of 105° has a significant reduction in 3rd, 7th and total harmonic distortions (THD), and also providing efficient output torque compared to other single layer winding configurations. Additionally, this type of configuration requires fewer copper windings.

History of Agricultural Machinery in Uzbekistan

2025-09-02T05:52:27+00:00

The article contains information about the introduction of new agricultural tractors into the economy of the republic in the 1920-30s and the socio-economic life of the local population.

To Perform Formulation Development and Evaluation of Pantoprazole Floating Tablet by Using 23 Factorial Design

2025-09-02T06:24:40+00:00

Pantoprazole is a protein pump inhibitor (PPI) used to treat acute duodenal ulcers, acute benign gastric ulcers, gastroesophageal reflux disease (GERD), and as a preventative measure for duodenal ulcer. It has a local effect on the stomach and works by competitively inhibiting the enzyme H+/K+ ATP, which is found in the gastric parietal cells. For acute duodenal ulcers, acute benign gastric ulcers, and gastroesophageal reflux disease (GERD), the usual oral dosage recommendation is 40 mg, and it is taken for 8–12 weeks. The preparations of Pantoprazole floating tablets was attempted in the current investigation and optimize the formulation using different excipients like HPMC K4M, cyclodextrin, sodium bicarbonate, citric acid and microcrystalline cellulose were used in the direct compression method to create Pantoprazole floating tablets (250 mg).

Formulation Development and Evaluations of Quetiapine fumarate Extended Release Tablet by using 23 Factorial design

2025-09-02T07:11:55+00:00

The primary goal of the current study was to create and assess extended- release matrix tablets containing quetiapine fumarate, which binds to serotonin 2A and dopamine type 2 receptors, using a 23 factorial design. Formulations with extended release supply a specified rate for a set amount of time. The matrix tablet was made using a wet granulation technique with ethyl cellulose, HPMC K100, and HPMC K15 polymer concentrations. The drug's compatibility with polymers and other excipients was assessed using DSC and FT-IR spectroscopy. The medication was shown to be compatible with polymers and other excipients.

Automatic Human Detection System for Air Conditioner Using Integration of Sensors

2025-09-04T11:25:56+00:00

Modern buildings rely on Heating, Ventilation, and Air Conditioning (HVAC) systems to control indoor temperature and air conditions. Traditional HVAC systems tend to run without interruption for heating or cooling operations, even when the rooms remain unoccupied. The strict fixed operation of these systems causes substantial energy loss because they keep running while occupying spaces such as empty offices, conference rooms, and residential areas. This study presents an automated AC control system that operates based on real-time human occupancy to enhance energy efficiency. The proposed platform employs sensor tools coupled with artificial intelligence methods to boost energy performance together with maintaining satisfactory indoor comfort. PIR sensors detect body heat for motion detection and IR sensors scan thermal signatures to identify stationary people. These two sensor types work harmoniously to watch room occupancy. The system uses microcontrollers as its central processing unit to receive data from sensors in real-time. A microcontroller evaluates occupancy patterns through its specialized algorithm to automatically control AC operations. When there is no detected activity the automated system either activates its low-energy state or disables air conditioner operation. People entering the room initiate the system to restore heating or cooling functions to the set comfort parameters. The system design eliminates unneeded energy consumption during empty time because it provides comfort control at strategic times. Scientists performed field experiments with the proposed model in both office areas and private living spaces to demonstrate its value relative to standard HVAC equipment. The practical evaluation showed a 40% decrease in power usage while users experienced identical comfort levels.

Simulation of Tribological Behavior of Abs Composites Reinforced with Graphene under Dry Sliding Condition

2025-09-05T09:19:55+00:00

Acrylonitrile Butadiene Styrene (ABS), an amorphous thermoplastic polymer with a glass transition temperature near 105 °C, consists of styrene (40–60%), acrylonitrile (15–35%), and polybutadiene (5–30%). In this work, graphene-reinforced ABS composites were fabricated by twin-screw extrusion and compression moulding. Microstructure was evaluated via optical microscopy; elemental composition was confirmed using Energy Dispersive Spectroscopy (EDS). Tribological performance was tested on a pin-on-disc tribometer under ASTM G99 standards, systematically varying load and sliding velocity. Grey Relational Analysis (GRA) identified optimal parameters for wear resistance. A finite element simulation of the pin-on-disc setup was performed in ANSYS, with outcomes validated against experimental findings. Results demonstrate that 2 wt% graphene reduced the wear rate of ABS by over 50% at 1.5 m/s, indicating strong potential for industrial applications requiring improved wear resistance.

Formulation and Evaluation Immediate Release Antidiabetic Tablet

2025-09-06T05:55:26+00:00

Alogliptin are used for involve the development of an oral dosage form aimed at managing Type 2 Diabetes Mellitus by inhibiting the enzyme Dipeptidyl Peptidase-4 (DPP-4). The objective of this study to design the formulate and evaluate film coated immediate release containing alogliptin antidiabetic drug. The prepared tablets were tested for their pharmacopoeial requirements. The UV spectroscopy method was developed based on quantitative estimation of alogliptin absorption at maximum wavelength 222nm by using methanol and water as a solvent. Different binders are used to help maintain gel structure by using HPC. Dry granulation and wet granulation are chosen for preparation of tablet were evaluated for their impact on tablet characteristics, including hardness, friability, disintegration time. Another objective of this study to provide pharmaceutical composition with high bioavailability, high physical and chemical stability and long shelf life. The tablets were subjected to stability testing, content uniformity to ensure they meet pharmacopoeials standards. Based on preliminary study and different formulation batches (F1-F6) were carried out. From the various formulation it was concluded that F6 batch showed satisfactory results.

In Silico Evaluation of Phytoconstituents from Actinodaphne Bourdillonii Gamble (Laureaceae) As a Potential Multi-Target Therapeutic Agents against Breast Cancer Receptors

2025-09-10T05:59:46+00:00

Breast cancer remains a leading cause of cancer-related mortality among women worldwide, necessitating innovative therapeutic strategies. This study employs molecular docking and pharmacokinetic analysis to examine the anticancer potential of Actinodaphne bourdillonii phytoconstituents against breast cancer receptor— Progesterone receptor (PGR), Estrogen receptor (ER) and human epidermal growth factor receptor 2 (HER2). Strong receptor-ligand binding affinities were found by molecular docking and 2-(4-nitrophenoxy)-N-(pyridine- 3-yl) acetamide stood out as the most promising molecule since it consistently performed well across all three receptors. Key interactions, such as hydrogen bonds and hydrophobic contacts with key receptor residues, were found, which improved ligand stability and specificity. SwissADME research revealed good pharmacokinetic features, including high gastrointestinal absorption (HIA), blood-brain barrier (BBB) permeability and drug- likeness for most substances. The results highlight the potential of A. bourdillonii phytoconstituents as multi- target treatments for breast cancer, especially 2-(4-nitrophenoxy)-N-(pyridine-3-yl) acetamide. In order to confirm the effectiveness of these medications and their therapeutic use, more investigation is required, including molecular dynamics simulations, in-vitro and in-vivo validation and structural optimization.

Fourier Finite Mellin Transform Based Data Hiding Methods for Data Security and Privacy in Cloud

2025-09-11T06:01:03+00:00

In recent years, cloud computing has seen significant growth due to its cost-effective and convenient services. Cloud data services have become a popular choice for storing enterprise data, as an increasing number of companies and users are migrating their data to the cloud. However, ensuring the privacy and security of this data remains a critical challenge that must be addressed to fully leverage cloud services. Data security plays a vital role in both the storage and transmission of cloud data. Using data hiding techniques can be an effective approach to enhance the security of data storage and transmission in the cloud. This paper presents a review of various data encryption methods, steganography techniques, and hybrid approaches that have been widely used in this field. However, many of these methods face challenges related to the overheads associated with encryption and the considerable computational time required. There is a growing need for a new approach that ensures cloud security without relying on encryption processes. Such an approach would reduce the burdens of encryption, leading to improved overall performance. At this point, data hiding methods emerge as a promising and effective alternative to encryption-based security measures for cloud data storage. In this paper, finite Mellin transform based hybrid encryption model is developed for securing the sensitive data.

Democratic Deficit and Charismatic Dominance in Tamil Nadu’s Political Landscape

2025-09-11T06:07:09+00:00

The political career of Tamil Nadu has long been characterised by a long period of dominating charisma, where the personal popularity of established leaders like M.G. Ramachandran, Karunanidhi, and Jayalalithaa produced a polarised political culture of personality-based power as opposed to the party-based institutionalised democratic politics. The death of these giants has created a significant leadership gap, which has caused sectarianism, a lack of transparency in decisions made and the undermining of party democracy within. Recent trends, such as the flourishing of Tamilaga Vettri Kazhagam (TVK) led by a movie star, Vijay, illuminate how cinema can still be considered a tool of political mobilisation, attractive to younger generations disappointed with older parties. Yet, on the one hand, such emerging organisations hold out promise of change; on the other hand, the risk is that they renew top-down elitist structures they critique, thereby contributing to the entrenchment of the democratic deficit. To overcome this limitation, the present study combines content analysis of party manifestos, media discourse, and leadership practices with empirical data from 650 respondents across Tamil Nadu, stratified by age from 18 to 67 years. Both survey and interview evidence point to the most urgent use of transparent methods of election of leaders, policy debate, and the involvement of members, on one hand, and proof of the strength of codification of rules, decentralisation, and transparency in leadership changes, on the other. The paper presents a model for the structural reform of Tamil Nadu's parties, based on democratisation through institutional arrangements such as in-house elections, youth representation, and participatory policy-making, while acknowledging the cultural significance of charisma as a mobilising force. Responding to the lesson of the past, through incorporating both real-time citizen perceptions and theoretical formulations, the research findings show how a middle path of political organisation, with charisma serving as an adjunct rather than a replacement to democratic processes, can restore strong political organisation focused on the future and that embodies a great deal of transparency. These outcomes not only resolve the imminent political crisis in Tamil Nadu's political system but also serve as a model for enhancing party democracy in other regions of India.

Intermittency and Turbulent Spectra of Solar Activity: A Five-Year Analysis during Solar Cycle 25

2025-09-11T08:22:49+00:00

This paper investigates the intermittent nature and turbulent spectra of solar wind fluctuations during the critical five-year period of Solar Cycle 25 (2019–2024). Using data from ACE, WIND, Parker Solar Probe, and Solar Orbiter, this study identifies patterns of turbulence and intermittency through structure function analysis, scaling exponents, and multifractal modeling. Key events, such as the X-class flare on October 24, 2024, are analyzed to illustrate real-time impacts of turbulent phenomena. Findings highlight the dynamic interplay between solar magnetic activity, proton density fluctuations, and magnetic cloud passage, offering implications for modeling heliospheric conditions and improving space weather resilience.

Formulation and Characterization of Amorphous Coumarin Solid Dispersions by Spray Drying For Improved Aqueous Solubility

2025-09-17T06:45:23+00:00

Coumarin, a natural heterocyclic compound, exhibits significant pharmacotherapeutic potential including antibiotic, fungicidal, anti-inflammatory, anticancer, and anti-HIV activities. However, its poor aqueous solubility (10 µg/mL) and lipophilic character (Log P = 1.28) severely limit its oral bioavailability and therapeutic efficacy. This study aimed to overcome this challenge by enhancing coumarin's solubility and dissolution rate through the formulation of solid dispersions using physical mixing and spray drying techniques. Polyvinylpyrrolidone K30 (PVP K30) and beta-cyclodextrin were employed as hydrophilic carriers. Pure coumarin was identified and characterized by melting point (700c), UV spectroscopy (lambda max=317nm), FTIR (characteristic peaks at 1715cm−1, 1625cm−1), DSC (endothermic peak at 73.03 and PXRD (crystalline nature). Drug-excipient compatibility studies via FTIR and DSC indicated no significant interactions. Physical mixtures (PMs) with drug-to-carrier ratios of 1:1, 1:2, and 1:3 were prepared and showed marginal improvements in saturation solubility (up to 178 µg/mL for PM6) and dissolution. In contrast, solid dispersions (SDs) prepared by spray drying demonstrated markedly superior results. All spray-dried batches exhibited significantly enhanced saturation solubility, with SD3 (coumarin: PVP K30, 1:3) reaching 246 µg/mL in distilled water and 0.17µg/mL in pH 6.8 phosphate buffer. Dissolution studies revealed a dramatic increase in release rates, with SD3 achieving 25% drug release within 5 minutes, significantly outperforming PMs (5-8% in 5 min) and pure coumarin (75% > 120 min). The amorphous state of coumarin in SDs was confirmed by the absence of its characteristic melting peak in DSC thermo grams. Flow properties for both PMs and SDs were found to be acceptable. Drug release kinetics of spray-dried dispersions best fitted the Korsmeyer-Peppas model, indicating a complex release mechanism involving diffusion and/or polymer relaxation. These findings strongly suggest that spray drying with hydrophilic carriers, particularly PVP K30, is an effective strategy for overcoming the solubility limitations of coumarin, thereby paving the way for improved oral bioavailability and wider therapeutic applications.

Using Hybrid Generative Adversarial Network with Cognitive Routing Cryptography to Improve Network Security and Quality of Service

2025-09-20T11:35:00+00:00

Data communications can assist multimedia applications with complicated QoS (Quality of Service) specifications. Different varieties of networks, such as wired and wireless are to be had to ensure the quality of multimedia applications. These networks demonstrate the exceptional QoS characteristics and heterogeneity, in addition to varying QoS parameters including bandwidth, delay, and jitter. Several network design properties can result in congestion in networks with unregulated bandwidth. This paper primarily aims to explore network scalability and security concerning precision, end-to-end delay, scalability, accuracy, and throughput. To achieve this, a novel machine learning method called Hybrid General Adversarial Network-Cybersecurity Risk Profile (HGAN-CRP) has been implemented along routing protocols and included with authentication structures. In this method, various packet sizes were utilized to assess the performance of network scalability and safety. The results demonstrate improved network scalability and protection when compared to existing algorithms such as Self-Organizing Maps (SOM) and Double P-value of Transductive Confidence Machines for K-Nearest Neighbors (DPTCM-KNN). Besides, to enhance security, an authenticated cryptographic intrusion detection mechanism has been implemented.

Healing Beyond Borders: India’s Rise as a Global Medical Tourism Hub

2025-09-22T06:39:52+00:00

India has rapidly emerged as a leading global hub for medical tourism, driven by its unique blend of affordability, high-quality healthcare, advanced technology, and traditional wellness practices. Offering world-class treatments at a fraction of Western costs, India attracts patients from across the globe for complex procedures such as organ transplants, cardiac surgeries, and oncology treatments, as well as for holistic recovery through Ayurveda, yoga, and naturopathy. Government initiatives, private sector investments, and internationally accredited hospitals have further strengthened India’s position. However, challenges like infrastructure gaps, limited insurance integration, and policy fragmentation remain. With the market projected to reach USD 13 billion by 2026, India is poised to not only sustain its cost advantage but also redefine itself as a global leader in holistic, patient-centric healthcare.

ONCOVATE: A Framework for AI-Powered Motivational Assistance in Cancer Care

2025-09-22T06:52:48+00:00

Cancer patients frequently face not only physical but also emotional and psychological challenges throughout their treatment journey. The integration of artificial intelligence (AI) into healthcare provides new opportunities for enhancing patient motivation and adherence to treatment. This paper introduces Oncovate, a framework designed to develop an AI-powered motivational support system for oncology patients. The proposed framework leverages secondary datasets for design, model training, and simulation testing in its initial stages. It consists of four core modules: data acquisition, personalization, motivational support, and evaluation. Using advanced models such as Conditional Random Fields (CRF) and Bidirectional LSTM (Bi-LSTM), the system generates personalized motivational interventions. In addition to emotional modeling, wearable data integration enables real-time tracking of physical and behavioral indicators. The framework was validated on secondary datasets and simulated patient journeys, demonstrating its potential to improve emotional resilience, treatment adherence, and patient engagement. Oncovate is designed for future deployment as a web and mobile application. This paper highlights the design, technical implementation, evaluation outcomes, and ethical implications of deploying such AI-powered tools in oncology care.

Edge-AI Driven NeuroGaitNet: A Predictive Smart Insole Framework for Early Neuromuscular Disorder Detection and Adaptive Gait Profiling

2025-09-26T06:03:23+00:00

Neuromuscular disorders (NMDs) such as Parkinson, Amyotrophic Lateral Sclerosis (ALS) and Peripheral Neuropathy are the progressive disorders that severely affect motor interaction and living standards. Early detection is vital for early intervention but conventional means and methods of diagnosis miss the finer points of gait-related anomalies in the prodromal periods. This article proposes an intelligent gait-based framework (NeuroGaitNet) to predict NMD within a population at an early stage. This system uses MultiZone Adaptive Pressure Mapping (MZAPM) for the measurement of local plantar pressures and SpatioTemporal Harmonic Encoding (STHE) for joint spatial temporal gait feature extraction. Individualized baselines are made and the deviations are qualified with Neuro-Signature Deviation Index (NSDI), so that the nuances of abnormality are identified in relation to body-specific patterns. Privacy-preserving Edge Adaptive Federated Learning (EA-FL) is the concept of distributed training of a model without sharing raw data. Hence, the adaptation is ensured at the patient-specific level and it is not violated. A Predictive Risk Stratification Layer (PRSL) integrates smoothed NSDI scores, calibrated probabilities and stability measures towards the capability of creating the clinically meaningful risk levels. Experimental analysis contrasted NeuroGaitNet from the existing models in terms of accuracy, precision, recall, F-measure as well as specificity. Experimental results show that the proposed MZAPM model performed better, exhibiting better performance in all the metrics. In particular, it has achieved maximum accuracy (92.4%), precision, recall and specificity which confirm its strength in the differentiation of pathological gait and natural variability. The fact that the precision and recall rose indicated that the algorithm is capable of reducing false-positives (37%) and identifying the genuine cases of anomalies. Local personalization and global knowledge hybrid treatment scheme of learning increased the sensitivity of detection. In general, NeuroGaitNet offers a safe, privacy-preserving and scalable architecture that is further used in the early detection of NMD based on the intelligent gait analysis.

Chronic Obstructive Pulmonary Disease Detection Using Electronic Nose Breath Sensor Data and a Machine Learning Framework

2025-09-27T09:44:05+00:00

Chronic Obstructive Pulmonary Disease (COPD) is a progressive respiratory disorder that significantly affects health, leading to reduced quality of life and increased mortality. Conventional diagnostic techniques for COPD are often invasive, expensive, and time-consuming. In this study, we propose a non-invasive diagnostic approach using breath-based Volatile Organic Compounds (VOCs), which reflect underlying metabolic processes associated with disease. To enhance detection accuracy, optimal feature selection is performed using a hybrid Scatter Search–Aquila Optimization (SS-AO) algorithm. The optimal features obtained by the hybrid Scatter Search–Aquila Optimization (SS-AO) algorithm are classified using the proposed Adaptive Neuro-Fuzzy Inference System with Caputo Fractional Gradient Descent (ANFIS-CFGD). In this framework, ANFIS effectively models nonlinear relationships, while CFCD enhances learning stability and convergence, thereby reducing misclassification rates and improving diagnostic accuracy. Experimental results demonstrate that ANFIS-CFGD achieves an accuracy of 94% and outperforms existing methods across multiple performance metrics, thus confirming its potential as an efficient tool for the early diagnosis of COPD from breath analysis.

High-Cohesion Multi-Objective Optimized Deep Neural Network for Energy Efficient and Load Balanced Environmental Data Transmission in WSN

2025-10-03T10:29:54+00:00

A Wireless Sensor Network (WSN) is a network comprising a large number of independent sensors deployed across various locations to sense and collect environmental data. These sensors communicate wirelessly and are used in a wide range of applications, including environmental monitoring, agriculture, healthcare, smart cities, industrial automation, and military systems. Environmental analytics applications include monitoring air and water quality, tracking climate change, analyzing human activities, and more. In such contexts, WSNs play a crucial role by enabling efficient data collection, processing, and transmission to provide valuable information from remote or inaccessible areas. During data transmission, energy optimization in WSNs is essential to increase the network’s lifetime and enhance the operational time of sensor nodes. The conventional methods have been developed for energy-efficient data transmission, especially regarding delay-aware and load-balanced transmission remain challenges. To address these challenges, a novel method called High-Cohesion Multi-objective Optimized Deep Neural Network (HiMO-DNN) is proposed. This method ensures energy-efficient environmental data communication with minimal latency and high data delivery rates. The HiMO-DNN model incorporates optimization and regression techniques to achieve energy-efficient load balancing in WSNs. The main aim of the HiMO-DNN is a Deep Neural Network, which consists of four layers namely an input layer, two hidden layers, and an output layer. The input layer comprises a varying number of sensor nodes. First, the HiMO-DNN model evaluates the residual energy of the sensor nodes. Based on this energy measurement, the nodes are grouped using a high-cohesion correlation clustering technique. A cluster head is then selected for each cluster to enhance the efficiency of data transmission. Next, a Multi-objective Stochastic Sampled Crow Search Optimization algorithm is employed to identify the nearest neighbor cluster head with the less loaded, thereby improving data transmission throughput while minimizing latency. Finally, the output layer observes the results of resource-efficient and load-balanced environmental data transmission. Experimental analysis is conducted using metrics such as including energy efficiency, Transmission Success Ratio, transmission latency, throughput, and Data drop rate, evaluated across varying amounts of environmental data and different numbers of sensor nodes.

IOT-Enabled Real-Time Driver Drowsiness Detection System

2025-10-25T05:47:32+00:00

Road safety has remained a world issue, and driver fatigue has been one of the major road accident causative factors when engaged in long-distance journeys and driving late at night. Conventional safety measures, such as lane departure warnings and seatbelt alarms, cannot address the problem of drowsiness before accidents occur. The proposed paper introduces an IoT-based real-time driver drowsiness detection system utilising computer vision technology, integrated with the Internet of Things (IoT). It features a low-cost integrated camera that tracks the driver's eye movements and estimates the Eye Aspect Ratio (EAR) to assess their level of alertness. When this eye closure happens over an extended period, a local alarm is sent immediately to alert the driver, and a warning is also transmitted to remote monitoring equipment or cloud platforms through an IoT module. Experimental trials conducted on experimental subjects demonstrate that the system can reliably and early detect drowsiness with low false alarm rates and low latency of communication. The provided model suggests that road safety can be enhanced by preventing fatigue-related accidents, which is a cost-effective measure; both individual drivers and the organisations of commercial fleets can be improved. The models can be enhanced by the addition of future enhancements through machine learning to categorise better and utilise multimodal sensors, which will make the models more reliable.

Comparative Analysis of Digital Payment Adoption during Demonetization and the Covid-19 Pandemic: A Study of Chennai.

2025-10-25T05:59:14+00:00

This study highlights the transformation of digital payment systems from the period of demonetization to the time of the COVID-19 pandemic. The initial shift was prominently observed during demonetization, when people moved away from cash transactions and adopted digital payment methods. During the pandemic, the need for maintaining social distance and adopting contactless payment options further encouraged individuals to rely more heavily on digital modes of payment rather than using physical cash. The increasing frequency of digital transactions reflects the steady growth of a digital India. Residents of Chennai found digital payments to be highly convenient during the pandemic period. Based on the results of the significance test, it is concluded that the COVID-19 pandemic accelerated the adoption of digital payment systems. The findings reveal that nearly half of Chennai’s population began using digital payments during the pandemic. Thus, achieving a vision of a fully digital India characterized by reduced corruption, greater accountability, and enhanced transparency is no longer a distant goal.

Video Marketing: The Power of Storytelling in the Digital Age

2025-10-25T06:07:12+00:00

Video marketing has become a powerful means of consumer outreach, storytelling in regard to brands, and a stimulus to buying patterns in the era of digital transformation. At the heart of any successful video marketing lies the art of storytelling - a strategy that makes a brand more human and helps to build an emotional connection. This paper dwells upon the impacts of video storytelling on consumer perception, brand loyalty, and engagement within the digital ecosystem. The research shows the strategic importance of storytelling approaches to content production through the analysis of case studies, market research, and consumer psychology, focusing on such platforms as YouTube, Instagram, and TikTok. It also analyses the technological and algorithmic factors defining the video reach and performance. The study was descriptive and exploratory in design. It is a sample of 100. The sampling location Chennai city. This study descriptive analysis, ANOVA, is carried out using the tools. Based on the study, the paper will give insights into best practice, and implications will be discussed about marketers in both B2B and B2C arenas. Finally, it stresses the idea of storytelling as a major distinction factor in a sea of content.