As the global population continues to age, it is more important to ensure the safety and well-being of senior citizens. Falls are a frequent and dangerous concern for elders, frequently resulting in fatalities or very serious injuries. Modern technology, such as the Elderly Fall Detection System, have been created to solve this issue. This technology attempts to detect falls quickly and react to them, offering aid right away and maybe saving lives. Early Intervention and Medical Aid: When a fall is detected, fall detection systems immediately notify carers or emergency services, enabling early intervention. Improving Safety: Elderly falls can result in serious injuries, limited mobility, and a reduction in general health.Early Intervention and Medical Aid: Fall detection systems promote early intervention by quickly notifying carers or emergency services when a fall is discovered. Independence and Quality of Life: Being independent and able to live in one's own house are two things that many elderly people enjoy. Support for carers and peace of mind: detection systems not only aid the elderly but also provide their carers peace of mind. Cost-cutting: Falls among the elderly frequently lead to hospital stays, rehab, and higher healthcare expenses. The Weighted Product Model (WPM) is a technique for ranking and evaluating alternatives based on a variety of factors. It is an easy and obvious method that enables decision-makers to weigh the relative weight of many variables and make wise decisions.. Smart surveillance system, Smart cane, Smart Carpet, Smart phone/watch and app Unobtrusiveness, reliability, privacy, Cost Smart phone is the highest and smart carpet is the lowest As the global population continues to age, it is more important to ensure the safety and well-being of senior citizens. Falls are a frequent and dangerous concern for elders, frequently resulting in fatalities or very serious injuries The Weighted Product Model (WPM) is a technique for ranking and evaluating alternatives based on a variety of factors Unobtrusiveness, reliability, privacy, Cost, : smart phone is the highest and smart carpet is the lowest

Biodiesel production from waste cooking oil (WCO) presents a compelling opportunity to transform discarded oil into a renewable energy resource.Through the conversion of WCO into biodiesel, not only is waste effectively reduced, but a greener, more sustainable alternative to conventional fossil fuels is provided-furthering the shift towards environmentally conscious energy solutions.The importance of this research cannot be overstated.It plays a crucial role in advancing sustainable energy practices, especially by tapping into WCO as a viable and underutilised feedstock for biodiesel production.Consider the scale of global WCO generation: in Canada alone, 135,000 tons are produced annually, while in Asia, the figures soar to a staggering 5.5 million tons.The vast potential for converting this surplus waste into high-value biofuel not only promises substantial environmental benefits but also unlocks significant economic opportunities.The methodology leveraged three distinct machine learning models: Linear Regression (LR), Random Forest Regression (RFR), and Support Vector Regression (SVR).These models were rigorously trained and tested on experimental data derived from biodiesel production processes.The study delved into four critical parameters: Free Fatty Acid (FFA) content, fluctuating between 1.7% and 3.5%, moisture percentage ranging from 0.05% to 0.3%, viscosity measured at 35 to 43 cSt, and reaction time spanning 2 to 3.3 hours.The results were striking, underscoring the robust predictive power of all three models.SVR stood out, achieving the highest training accuracy (R = 0.998), while RFR exhibited a remarkable ability to generalise well on unseen test data (R = 0.989).The analysis uncovered compelling correlations: notably, a robust negative relationship between FFA content and biodiesel yield (-0.91), alongside a positive correlation between viscosity and yield (0.85).These findings underline the capacity of machine learning models to accurately predict biodiesel yields from waste cooking oil (WCO).Each model revealed unique strengths, yet even the simpler Linear Regression model, with an impressive R of 0.979 on test data, pointed to a predominantly linear link between the process parameters and the final yield.Such insights provide invaluable guidance for refining industrial biodiesel production processes, championing the shift towards sustainable energy alternatives and addressing the pressing issues of waste management.

The emergence of micro frontend architectures has revolutionized the way organizations approach frontend application development, enabling distributed teams to work independently while maintaining system consistency. However, performance optimization in these distributed systems presents unique challenges that differ significantly from traditional monolithic approaches. This study examines performance strategies for micro frontend-based applications through a comprehensive analysis of 30 applications across six key performance metrics. The research reveals significant performance variations across micro frontend implementations, with bundle sizes ranging from 345KB to 550KB and API response times ranging from 155ms to 300ms. Our analysis demonstrates strong correlations between optimization strategies and application performance, particularly highlighting the critical role of lazy loading implementations. Applications achieving lazy loading rates above 50% consistently outperformed those below 40%, with performance score improvements of up to 37 points. The study uses XGBoost regression models to predict key performance metrics, identifying challenges in CPU usage prediction due to overfitting concerns, while achieving exceptional accuracy for bundle size prediction (R² = 0.9647).The performance patterns indicate that successful micro frontend applications require integrated optimization across multiple dimensions, including composition strategies, dependency management, and inter-service communication protocols. The research identifies threshold values for optimal performance, including maintaining bundle sizes below 400KB and implementing aggressive lazy loading strategies. These findings provide actionable insights for development teams working with micro frontend architectures, providing data-driven guidance for architectural decisions and performance strategies in distributed frontend systems.Objective: This study examines performance optimization in micro-frontend-based applications. It analyzes 30 settings across six key metrics, focusing on bundle size, lazy loading, CPU utilization, and response times. The research highlights optimization constraints and a predictive model to guide architectural decisions for scalable, efficient distributed frontends using XGBoost regression.

The need for semantic interoperability in healthcare has never been more critical as institutions strive to unify disparate data sources while maintaining regulatory compliance and operational efficiency. This paper introduces a multi-layered cloud-based framework designed to enhance semantic interoperability, integrating artificial intelligence (AI), ontology mapping, and federated learning. Utilizing advanced technologies such as AWS Neptune for knowledge graphs, Amazon Comprehend Medical for entity extraction, and Amazon Sage Maker for predictive analytics, our approach streamlines healthcare data exchange while preserving security and compliance standards like HIPAA and GDPR. Through real-world implementation across multiple healthcare institutions, our results demonstrate a 91% accuracy in semantic data mapping and an 84% reduction in cross-institutional data retrieval time. This research establishes a scalable, intelligent, and adaptive interoperability architecture that paves the way for AI-driven diagnostics, real-time health analytics, and precision medicine.

The rapid growth of big data has sparked considerable interest in its use to improve decision-making through predictive analytics. This method, which uses historical and real-time data to predict outcomes, is increasingly being used across industries. However, one important question remains: does the amount of data directly improve forecasting accuracy? This study answers that question by analyzing a variety of datasets and scenarios, demonstrating how artificial intelligence can strengthen forecasting performance even in sparse data environments.Research significance: This research is significant because it empirically examines the relationship between data size and predictive performance, especially on fine-grained, sparse datasets. It highlights how AI can improve scalability, resource efficiency, and decision-making accuracy by integrating cloud computing and predictive analytics. The findings contribute to a deeper understanding of how data characteristics, not just size, affect the success of predictive models in real-world applications across industries.Alternative taken asYahoo Movies, Book Crossing, Dating, Flickr, Ta-Feng.Evaluation Preferencetaken as Active elements, Instances, Features, Sparseness.The results indicate that Flickrachieved the highest rank, while Book Crossingreceived the lowest rank being attained.The value of the dataset for Predictive Modeling at Scale Powered by AI in Big Dataaccording to the Weighted Sum Method (WSM), demonstrates that Flickr achieves the highest ranking.Key words: Predictive analytics, artificial intelligence, sparse data, cloud computing, decision making, data volume.

This research presents extensive advances in audio processing technologies and computer performance optimization methods. The study includes several innovative approaches to audio signal management, including a novel dual-function monitoring device that simultaneously records separate secondary audio program (SAP) signals through advanced switching algorithms that combine tuner modulator IF and multi-sound integrated circuits. The work extends to the design of an object-oriented database for audio data types that includes dedicated interfaces for storage, retrieval, and manipulation functions along with custom transport protocols that enable continuous streaming capabilities. In addition, the research explores personalized audio delivery systems that can simultaneously provide personalized audio experiences to multiple users through electromagnetic wave encoding and directional audio emission technologies. Performance evaluation metrics, including process parameter signal strength, response time measurements, and resolution accuracy ratings, are integrated with customer satisfaction analysis to establish comprehensive system quality frameworks. The implementation uses advanced machine learning techniques, specifically Ada Boost regression algorithms, for predictive modelling and error correction through iterative weak learning additive modelling. The results show significant improvements in audio signal processing flexibility, spatial audio quality preservation, and real-time data processing capabilities, providing the basic architectures for next-generation multimedia database systems and personalized media experiences