Volume 13 | Issue 6 |

 Abstract

Accurate and rapid location of fault in distribution network is of great significance to improve the reliability of power supply in distribution network. At present, the fault location of distribution management system main station requires high data quality of line terminals, and there are problems such as poor fault tolerance and low accuracy of fault location, and it is not suitable for fault location of multi-point simultaneous fault.

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 Keywords

Fault location, Distribution network, Power supply reliability, Distribution Management System (DMS), Data quality, Line terminals, Fault tolerance, Location accuracy.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

: Heart disease remains one of the leading causes of mortality globally, necessitating the development of early and accurate diagnostic tools. This project focuses on predicting the likelihood of heart attacks using various machine learning (ML) algorithms. A publicly available clinical dataset, including features such as age, gender, chest pain type, blood pressure, cholesterol, and ECG results, is used for training and evaluation. The dataset undergoes preprocessing steps including data cleaning, normalization, and feature encoding. Supervised learning algorithms including Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Decision Tree, and Random Forest are implemented and compared based on performance metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. The Random Forest algorithm outperformed others in terms of accuracy and generalization ability. The system is integrated into an Android application using Firebase as a backend service, enabling real-time user interaction and prediction delivery. The study demonstrates that ensemble learning methods offer robust and interpretable solutions for heart disease prediction, which can support clinical decision-making and preventive care. Future enhancements may include integration with wearable devices and deployment in real-time hospital environments.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Heart Disease Prediction, Machine Learning, Random Forest, Support Vector Machine, K-Nearest Neighbors, Decision Tree, Clinical Data, Android Application, Firebase Integration, Healthcare Analytics

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Migrant garment workers in Tirupur, Tamil Nadu, constitute the backbone of the region's thriving knitwear industry, which significantly contributes to India's export economy. However, these workers often face numerous challenges, including low wages, poor working conditions, long working hours, and substandard living arrangements. Despite the economic contributions of these workers, there is limited access to a comprehensive social support system that can address their needs. This paper examines the existing social support mechanisms available to migrant garment workers in Tirupur, focusing on labor rights, healthcare, housing, and welfare schemes. Through a combination of qualitative and quantitative research, the study identifies the gaps in the current support system and highlights the urgent need for robust policy interventions. The paper aims to explore the extent of support from government, employers, and non-governmental organizations (NGOs) and suggests strategies to enhance social protection and improve the overall welfare of migrant workers in the garment sector.

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SOCIAL SUPPORT SYSTEM FOR MIGRANT GARMENTS WORKERS IN TIRUPUR

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The increasing resistance of mosquitoes to synthetic repellents has led to the growing interest in herbal alternatives. This study aims to formulate and evaluate a herbal mosquito repellent cone using natural ingredients with mosquito-repelling properties. A combination of essential oils from plants such as citronella, eucalyptus, and lemongrass, known for their insect-repellent activity, was used to create the formulation. The repellent cones were developed by incorporating herbal extracts into a base material, and their physical characteristics, such as texture, burning rate, and repellent efficiency, were evaluated. The repellent efficacy was tested in controlled environments, measuring the reduction in mosquito landing and biting rates. Additionally, the stability of the repellent over time and under varying environmental conditions was assessed. The results suggest that the herbal mosquito repellent cone is an effective, eco-friendly alternative to conventional synthetic repellents, with significant mosquito deterrent properties. The formulation proved to be safe, biodegradable, and cost-effective, making it a viable option for widespread use.

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 Keywords

Herbal mosquito repellent, formulation, evaluation, essential oils, citronella, eucalyptus, lemongrass, cone, insect-repellent, eco-friendly, mosquito deterrent, biodegradable, natural alternative.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The surge in lifestyle-related disorders has driven interest in natural alternatives for health maintenance. Herbal nutraceuticals, derived from plant sources, offer potential benefits without the adverse effects associated with synthetic medications. This study formulates and evaluates a herbal tablet combining bioactive extracts known for antioxidant, anti-inflammatory, and immune- enhancing properties. The formulation process involved selecting synergistic herbs and excipients to produce a stable dosage form. Tablets were evaluated for physical integrity, uniformity, and dissolution, along with in vitro antioxidant and antimicrobial assays. Results indicated promising therapeutic potential and quality, supporting further clinical trials for safety and efficacy validation.

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"FORMULATION AND EVALUATION:- HERBAL NUTRACEUTICAL TABLET"

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 Abstract

There are various scientific theories and folkloric theories , theistic explanations about the origin and creation of everything in the Universe . From those theories , Fixed matter, Energy of nature, matter with various characteristic shapes produced from Moving matter, Particles of light , Sun , Stars, main elements of Nine -layered nature , Planets, Atomic level , Satellites, Dimensions of nature of main elements of Nine -layered nature , Living world, Human beings, Eternal nature , this theory is different and evidence based . These discussed by the special question, point, heading and paragraph arrow. Namely --> 1. First of all , what was the before expanded of the matter particle of light in the vacuum from darkness ? (Figure 1/2) 2. Secondly, where did the state of light particles come from in vacuum ? 3. Mathematical proofs and calculations of infinite terms (Figure 3) 4. Thirdly, why is the explanation of tiny particles of light originating from particles of light measured in the vacuum of Eternal sky ? (Figure 4,5) 5. Origin of favorable and unfavorable substances 6. Origin of animals on the planet called on Earth of Nature (Figure 6,7) 7. How did the senses organ originates 8. How did species originate 9. Explanation of the time and atoms of all shaped visible light particles.

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? Elements of Nature /- Only two elements exist in Nature --- 1. Shapeless, Boundless fixed Vacuum form darkness Eternal sky element . 2. Shaped , bounded forms of energy in nature are particles of light in motion . Shaped light particles later evolved into different shaped material with different properties , behaviors . ? The Nine -layered pricipal elements of Nature /- Nature refers to all the main elements of Nature . Viz ---> Eternal darkness form vacuum sky, Soil, Water, Fire, Air

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

3DPC, Energy efficient structure, U-value, FEM

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Accurate forecasting of air pollution, especially fine particulate matter (PM?.?), is crucial for protecting public health and guiding environmental policies. Traditional statistical models often struggle to capture the complex nonlinear and temporal patterns inherent in air quality data. This study introduces a hybrid model that integrates Long Short-Term Memory (LSTM) deep learning networks with the Particle Swarm Optimization (PSO) algorithm to enhance the prediction accuracy of PM?.? concentrations. LSTM networks are well-suited for modeling sequential time-series data due to their ability to retain long-term dependencies, while PSO efficiently optimizes hyperparameters to improve model performance. The proposed LSTM-PSO model was evaluated using extensive real-world air quality datasets collected from major urban centers over multiple years. Results demonstrate that the hybrid model significantly outperforms standalone LSTM and traditional machine learning approaches, achieving lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Moreover, the integration of PSO not only improved prediction accuracy but also accelerated the convergence speed of the LSTM training process. These findings highlight the effectiveness of combining deep learning with metaheuristic optimization algorithms for robust and efficient air quality forecasting, offering valuable insights for environmental monitoring and public health management.

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AIR POLLUTION PREDICTION USING LSTM DEEP LEARNING AND PARTICLE SWARM OPTIMIZATION ALGORITHM

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This study explores the risks and effectiveness of using micro-influencers as a cost-effective marketing strategy in the electronics sector. Micro-influencers, with smaller yet highly engaged audiences, offer brands targeted outreach and perceived authenticity. However, promoting technical electronic products through them presents challenges such as trust issues, fake engagement, and inconsistent brand alignment. Using a descriptive research design and data from 155 respondents, the study identifies authentic product reviews and social media posts as the most impactful promotional tools. Chi-square analysis reveals income as a significant demographic factor influencing marketing perception. The study recommends transparency, improved engagement evaluation, and stricter regulatory measures to mitigate risks and enhance the credibility and ROI of micro-influencer campaigns.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Micro-influencer, Social media personality, Creator economy, Reach & impressions, Brand collaboration.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

AI-powered animated product guides with interactive chatbots are transforming customer engagement by delivering immersive, personalized, and scalable experiences. Integrating realistic talking-head animations, text-to-speech (TTS) technologies, voice cloning, and large language model (LLM)driven chatbots powered by natural language processing (NLP), these systems enhance user interaction across industries. This survey examines the enabling technologies, reviews historical and recent advancements, and evaluates applications in e-commerce, education, healthcare, and customer support. A comparative analysis highlights trade-offs in animation quality, speech naturalness, and computational efficiency. Challenges, including ethical concerns, scalability, and user trust, are analyzed, alongside future directions for improving accessibility and interactivity. This paper provides a comprehensive foundation for researchers and practitioners in this interdisciplinary field.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Animated Product Guide, Interactive Chatbot, Talking-Head Animation, Text-to-Speech, Natural Language Processing.

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Creative Commons Attribution 4.0 and The Open Definition