International Journal on Recent and Innovation Trends in Computing and Communication
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Design and Analysis of Different Perspectives for Signed Social Networks Using Nature Inspired Techniques
Full text linkSocial networks shows interpersonal connections between different people, such as friendships and shared interests. Social network analysis examines these social networks. relationships. Algorithms for link prediction are used to forecast these interpersonal connections. Presented with a social network graph, in which a user is represented by a node, and the user relationships, a link prediction method, forecasts the potential new connections that may be made in the upcoming. Social networks are extensive systems that show the connections between countless social elements. One of the main research areas of social network analysis and network analysis is the study of patterns and evolution. A component of this problem is the link prediction problem, which is a way to predict future associations between unconnected nodes. Traditional approaches are made to operate with social networks in a certain context. However, the data from these networks is frequently erratic, absent, and prone to observation errors that lead to deformations and probably unreliable results. The belief function theory, a compelling paradigm for reasoning under uncertainty that allows for the representation, quantification, and management of faulty information, is used in this research to address the link prediction problem. First, a brand- new graph based social network model that takes into account link structural uncertainty is presented. The belief functions tools are then used to present a novel approach for the prediction of new relationships. In order to forecast new connections, it makes use of neighbourhood and shared group information in social networks. The effectiveness of the new method was tested on real social networks. Studies have shown that our strategy outperforms existing methods based on structural information
A Comparative Study Utilizing Machine Learning Algorithms to Predict Heart Disease in Young and Middle-Aged Adults
Full text linkEarly diagnosis is crucial since heart disease is getting more and more common. In the field of medicine, machine learning algorithms are now used to predict cardiac and cardiovascular illness. examining and confirming the functionality of machine learning. Heart disease is becoming more and more commonplace worldwide. A multitude of factors impact the likelihood of a heart attack and other illnesses. In many countries, limited cardiovascular competency makes it difficult to predict complications related to heart disease. One way to predict the possibility of a heart disease-related issue is to use data mining and machine learning techniques to identify which machine learning classifiers are most accurate for various diagnostic applications. Several supervised machine-learning algorithms are evaluated for their effectiveness in predicting cardiac illness. Use the heart disease individual dataset available via Kaggle. This work employs several machine-learning algorithms, including. Using Logistic Regression (LR), Navie Bayes (NB), Extreme Gradient Boost (EGB), K-Nearest Neighbor (K-NN), Support Vector Classifier (SVC), Random Forest (RF), and Decision Tree (DT), a neural network is constructed. Capable of categorizing binary data. For every feature across all deployed, estimated feature significance ratings were supplied. Ways. This helps identify the main risk factors for heart disease in addition to increasing model accuracy and assisting in the best forecast. Lastly, in comparison to all machine learning methods and Neural. The Binary Classification Neural Network, as a network model, produced the highest testing accuracy of more than 90%
The Security Risks and Challenges of IoT: How to Safeguard a World of Connected Devices from Cyber Threats
Full text linkThe increase in the use of Internet of Things, otherwise IoT, has impacted numerous industries such as health, facility, and manufacturing. Although connections have become more sophisticated, security and privacy risks have also emerged through connected devices. This research focuses on the security risks posed by IoT devices and seeks to recommend measures to protect these IoT networks. Based on the analysis of the state-of-the-art, the areas that should be further secured are defined, including energy-aware security mechanisms, privacy-preserving protocols, and edge computing vulnerabilities. The results indicate that 78% of IoT devices used in healthcare facilities can be compromised to release private info to unauthorized personnel; 65% of smart building systems are not secure enough when it comes to encryption. Furthermore, the research showed that by implementing artificial intelligence, organizations can decrease security threats by as much as 42 percent in IoT settings. The study then calls for a layered security solution that incorporates energy management, data protection, and the development of a comprehensive security solution for the IoT systems. Further work should be directed to the research of the flexible safety frameworks, which would allow handling new threats in terms of secure functioning of IoT networks
Enhancing Urban Train Transportation through Context-Aware Applications with Wireless Sensor Network Support
Full text linkUrban train transportation systems face challenges in efficiency, safety, and passenger satisfaction. This study presents the design and implementation of context-aware applications supported by wireless sensor networks (WSNs) to address these challenges. By integrating WSNs into urban train infrastructures, real-time monitoring and data analysis are achieved, enabling predictive maintenance, improved operational efficiency, and enhanced passenger experiences. This research proposes integrating WSNs with context-aware applications to enhance real-time monitoring, predictive maintenance, and decision-making capabilities. WSNs enable comprehensive data collection from various components of the rail infrastructure, such as trains, stations, and tracks. Context-aware systems utilize this data to provide dynamic, situational responses, improving system efficiency, safety, and passenger experience
“Design and Implementation of Low power, High Performance FINFET –Based SRAM Cells”.
Full text linkThis research paper presents a comprehensive analysis ofelectrical simulations of FinFET-based SRAMs conducted across various technological nodes to conduct a comprehensive examination of static and energy liability behavior.. The second step, we are designing 14nm, 12 nm and 7 nm FinFETs and extracting their characteristics by using Sentaurus TCAD. Simulated results of the device shows that it can be governed at the nanometer - scale regime and itsperformance is analyzed in terms of power consumption, propagation delay, power delayproduct (PDP) for nanoscaledtechnologies.Furthermore a digital register-transfer level (RTL) structure focusing on the implementation of static random access memory (SRAM) was studied meticulously to evaluate read and write operations controlled by configurable bit lines with multi-level voltage applications. CMOS circuits are implemented using the MICROWIND tool, facilitating accurate representation and simulation of SRAM components. The analysis includes an assessment of voltage-versus-current characteristics and an evaluation of the structural parameters of Double Gate (DG) FinFET transistors within the RTL structure. The 6T SRAM cell architecture is explored, highlighting its importance in the memory hierarchy and emphasising stability and performance considerations. The study also scrutinises system performance concerning input frequency through delay lines, providing insights into responsiveness and speed. Simulation analyses across different CMOS technologies and frequencies offer a thorough understanding of SRAM behaviour, aiding in optimisation and refinement for practical applications
AI-Enabled Statistical Quality Control Techniques for Achieving Uniformity in Automobile Gap Control
Full text linkTo remain competitive, vendors in the production sector must meet the ever-evolving demands of their customers. Manufacturers can't accomplish this without a way to measure the items' quality. Analyzing the space between the back bumper and the exterior panel using quantitative methods for quality assurance is the focus of this investigation. For the purpose of trying to determine whether the production system is functioning properly, the study will employ Minitab for data evaluation and cause-and-effect analysis. Data will be collected and analyzed using quality assurance methods such as control graphs, hypothesis tests, analysis of variance, and Gage R&R. Results will be measured, and the underlying reasons will be identified. To optimize this procedure and fulfill consumer demand, such devices will be utilized
AI-Driven Predictive Analytics for Business Forecasting and Decision Making
Full text link"Artificial Intelligence (AI) has become instrumental in reshaping business forecasting and decision-making processes. This study delves into the integration and impact of AI-driven predictive analytics systems within these domains. Through qualitative and quantitative analysis, the research assesses the deployment of AI-powered predictive analytics for enhancing business forecasting and decision-making capabilities. Results demonstrate improved accuracy in predictions, faster decision cycles, and enhanced strategic insights. However, challenges related to data quality and interpretability of AI-driven models also surface. These findings underscore the evolving role of AI in augmenting predictive analytics and decision-making processes in business contexts. The discussion explores future directions to address issues of model transparency and trust as AI adoption accelerates
Meta-Analytical Approach of ML, IoT and Nanotechnology for Plant Disease Detection towards a Sustainable Agriculture
Full text linkThis review article examines various technologies, such as Machine Learning, the Internet of Things (IoT), and Nanotechnology, that have the potential to address plant disease detection issues and provide sustainable long- term solutions. In addition, how these strategies can be integrated into precision agricultural practices to enhance crop health and productivity has also been discussed and analyzed. This meta-analysis aims to contribute to the advancement of agriculture and facilitate informed decision-making by stakeholders in the industry. A thorough understanding of the present landscape of plant disease detection and the challenges that persist can lead to innovative solutions that guarantee the long-term viability of agriculture. This presentation will provide an in-depth overview of current research, highlighting the latest technological advancements and strategic approaches that have the potential to revolutionize the identification and control of plant diseases in agricultural systems
Streaming Insights Uncovering Patterns with Adaptive Learning and Data Mining
Full text linkIn the era of big data and continuous information flow, the utilization of adaptive learning and data mining techniques is paramount for extracting meaningful insights from streaming datasets. This paper explores the fundamental methodology of sampling, focusing on random sampling and the efficient alternative, reservoir sampling, in the context of data streams with indeterminate durations. Additionally, the study delves into the technique of sketching, offering a compact and efficient means of summarizing and processing rapidly arriving data. Addressing the challenges posed by concept drift in data stream analysis, the paper introduces Adaptive Multi-Strategy Learning, a dynamic approach that combines diverse learning strategies to enhance model performance across evolving contexts. The proposed hybrid ensemble learning approach, combining diverse learning algorithms, emerges as a versatile and powerful tool for uncovering patterns in streaming data, offering valuable insights for real-time trend analysis, heavy-hitter detection, and cardinality estimation
