Global Journal of Advanced Engineering Technologies and Sciences
The "Global Journal of Advanced Engineering Technologies and Sciences" (GJAETS) is a peer-reviewed, monthly, online international research journal, which publishes original articles, research articles, review articles with top-level work from all areas of Engineering Research and their application including Mechanical, Civil, Electrical, Chemical, Electronics, Mathematics and Geological etc. Researchers in all technology and engineering fields are encouraged to contribute articles based on recent research. Journal publishes research articles and reviews within the whole field of Engineering Research, and it will continue to provide information on the latest trends and developments in this ever-expanding subject. Online Engineering is the future trend in engineering and science. This journal covers almost all disciplines of engineering and applied sciences. Researchers and students of M.S., M.Phil and PhD are requested to send their original research articles to GJAETS. All articles after approval from reviewers shall be published on the Internet. Call for Research and Review articles: Aeronautical Engineering, Aerospace Engineering, Automobile Engineering, Agricultural Engineering, Bio Medical Engineering, Ceramic Technology, Chemical Engineering, Civil Engineering, Computer Science & Engineering, Earthquake Engineering, Electrical Engineering, Engineering Chemistry, Engineering Management, Engineering Mathematics, Engineering Physics, Engineering Sciences, Environmental Engineering, Fire Engineering, Genetic Engineering, Industrial Engineering, Information Technology, Instrumentation Engineering, Manufacturing Engineering, Marine Engineering, Material Engineering, Mechanical Engineering, Metallurgical Engineering, Mining Engineering, Natural Sciences, Humanities Network Engineering, Nuclear Engineering, Ocean Engineering, Petroleum Engineering, Polymer Engineering, Production Engineering, Safety Engineering, Software Engineering, Structural Engineering, Systems Engineering, Transportation Engineering, Telecommunication Engineering, Textile Engineering, Electronics & Communications Engineering, Cloud Computing, Big Data, Big Data Analytics, Data Science, IOT, Artificial Intelligence, Six Sigma, Embedded Systems, Robotics, Compiler Designs, High Performance Computing, Machine Learning, Neuron Networks, Blockchain, Design and Manufacturing, Fluid Mechanics, Cyber Security, Data Analytics, Bio Mechanics, 3D Object Modelling Security, Privacy and Automation, Applied Electromagnetics & RF Circuits, Computer Vision Control Systems, Embedded Systems, ECE Education Research, Integrated Circuits and VLSI, MEMS and Microsystems, Network, Communication and Information Systems, Optics & Photonics, Plasma Science & Engineering, Power & Energy, Quantum Science & Technology, Robotics & Autonomous Systems, Signal & Image Processing and Machine Learning, Solid-State Devices & Nanotechnology, Bluetooth Fibre Optic Communication, Embedded Systems, Nanoelectronics, VLSI, OLED(Organic Light Emitting Diode), Zigbee Technology, Human Area Network, GPRS, HSPA Types of Articles invited: Research Papers Survey Papers Experimental Result Papers Analysis Study Research Papers Informative Article Comparison Papers Case Studies Papers Review Papers Comparative Studies Dissertation Chapters Research Proposals or Synopsis Working Projects New Innovation & Idea Prototypes and Models Book Review Project Report Abstract and Proceeding Paper Research article Review article (Literature review,Systematic review, Meta-analysis)
CLOSING THE LOOP IN SEMICONDUCTOR MANUFACTURING: REAL-TIME RESOURCE EFFICIENCY MONITORING, SCRAP VALORIZATION, AND END-OF-LIFE MATERIAL RECOVERY IN THE MICROELECTRONICS SUPPLY CHAIN
The global semiconductor industry confronts an intensifying intersection of supply chain risk, regulatory pressure, and environmental accountability. Dependence on critical raw materials concentrated in geopolitically sensitive supply chains, combined with the toxic chemical intensity of advanced node fabrication and the mounting volume of electronic waste, has made resource circularity a strategic rather than optional priority for semiconductor manufacturers. The U.S. CHIPS and Science Act, the EU Critical Raw Materials Act, and ESG investor disclosure requirements are creating simultaneous fiscal and regulatory incentives for fabs to invest in real-time resource efficiency monitoring, in-line scrap valorization, and end-of-life material recovery infrastructure. This paper proposes a translational engineering pathway for integrating circular economy principles into semiconductor fab operations, centered on a digital twin-enabled monitoring layer that characterizes waste streams in real time, routes scrap to valorization pathways, tracks critical material flows throughout the supply chain, and generates the ESG reporting data that regulatory frameworks require. Evidence from digital twin implementations in electronics manufacturing demonstrates that real-time monitoring can reduce defect rates by 57% and increase recycled content by 34%, establishing the technical feasibility of this approach at production scale. We characterize the critical raw material dependency profile of advanced semiconductor manufacturing, describe six recovery pathways with estimated value creation, and propose a governance framework aligned with the CHIPS Act, EU CRM Act, and ISO 14040 lifecycle assessment requirements.
Semiconductor Manufacturing; Circular Economy; Critical Raw Materials; Digital Twin; Scrap Valorization; End-Of-Life Recovery; Chips Act; Esg Reporting; Resource Efficiency; Rare Metals.
CLIMATE-DRIVEN ZOONOTIC SPILLOVER SURVEILLANCE: INTEGRATING ENVIRONMENTAL MONITORING, WILDLIFE HEALTH DATA, AND FEDERATED AI FOR PRE-PANDEMIC EARLY WARNING AT HUMAN-ANIMAL INTERFACES
Approximately 60 percent of known infectious diseases are zoonotic in origin, and the accelerating drivers of zoonotic spillover risk, including deforestation, climate-induced species range shifts, habitat fragmentation, and expanding human encroachment into wildlife habitat, are measurable weeks to months before the first human cases of a novel pathogen are detected by conventional clinical surveillance systems. This paper proposes ZooFed, a federated AI framework for pre-pandemic early warning that moves the surveillance window upstream of human case emergence by integrating five heterogeneous One Health data streams: climate and environmental change signals, wildlife health surveillance, animal reservoir pathogen genomics, interface community syndromic data, and land-use change monitoring. The framework employs a federated learning architecture that enables cross-border integration of these sensitive ecological and health datasets without requiring raw data to leave national or institutional boundaries, addressing the sovereignty and privacy constraints that prevent centralized One Health surveillance at the global scale the problem demands. A cross-domain risk model combining ecological, genomic, and epidemiological inputs generates spatiotemporal spillover risk hotspot maps, zoonotic variant tracking alerts, and intervention guidance targeted at identified high-risk interface zones. Empirical evidence from deployed multimodal federated surveillance systems demonstrates that integrating environmental data streams alongside clinical, genomic, mobility, and social signals achieves substantial detection lead times over conventional surveillance, motivating the extension of this integration approach to the pre-spillover ecological domain. The framework is evaluated against six existing surveillance systems across seven dimensions, demonstrating that ZooFed is the first system to simultaneously achieve full One Health multistream integration, federated privacy-preserving architecture, and pre-spillover detection capability.
Zoonotic Spillover; One Health Surveillance; Federated Learning; Climate Change; Pandemic Preparedness; Wildlife Surveillance; Reservoir Genomics; Pre-Pandemic Intelligence; Deforestation; Human-Animal Interface.
A REVIEW OF ASSOCIATION RULE MINING TECHNIQUES TOWARD EFFICIENT FREQUENT PATTERN DISCOVERY
The rapid growth of digital technologies and transactional systems has generated massive volumes of structured and unstructured data, creating a strong demand for efficient data mining techniques capable of extracting meaningful knowledge from large datasets. Association Rule Mining (ARM) is one of the most widely used data mining approaches for discovering hidden relationships, frequent patterns, and correlations among items in transactional databases. Traditional ARM algorithms such as Apriori, FP-Growth, and ECLAT have been extensively used for frequent itemset generation and rule discovery. However, these techniques still suffer from several limitations, including repeated database scans, excessive candidate generation, memory overhead, scalability challenges, and increased computational complexity when handling large-scale and sparse datasets. This paper presents a comprehensive review of association rule mining techniques and critically examines the working principles, advantages, and limitations of major ARM algorithms. A comparative analysis of widely used approaches, including Apriori, FP-Growth, ECLAT, weighted association rule mining, and hybrid mining strategies, is discussed based on performance parameters such as execution time, database scanning requirements, memory consumption, and scalability. Furthermore, the paper highlights major research challenges associated with large transactional environments and discusses emerging research trends toward efficient and adaptive mining frameworks. The findings indicate that future developments in association rule mining may focus on improving mining efficiency through optimized and scalable hybrid strategies capable of handling complex real-world datasets.
Association Rule Mining, Data Mining, Apriori Algorithm, FP-Growth, Frequent Pattern Mining, Hybrid Mining, Transactional Database.
A MACHINE LEARNING-BASED HYBRID FRAMEWORK FOR REAL ESTATE PRICE PREDICTION
Accurate prediction of real estate prices plays a significant role in financial planning, investment decision-making, urban development, and smart city management. Traditional property valuation approaches mainly rely on statistical regression techniques and expert judgment, which often fail to model nonlinear market behavior and heterogeneous housing datasets. To overcome these limitations, this research proposes a novel Hybrid Adaptive Real Estate Prediction Algorithm (HAREPA) within a machine learning–based hybrid framework. The proposed system integrates Linear Regression, K-Nearest Neighbor, and Support Vector Regression models through adaptive ensemble learning. Feature engineering techniques are employed to extract meaningful attributes and improve learning efficiency. Adaptive weights are automatically computed using inverse error analysis, enabling dynamic contribution of each model during prediction. Experimental evaluation is conducted using a real-world real estate dataset obtained from Kaggle. Performance comparison using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and prediction accuracy demonstrates that the proposed HAREPA model significantly outperforms individual machine learning algorithms. The results confirm improved prediction accuracy, reduced error variance, and enhanced generalization capability.
Real Estate Prediction, Machine Learning, Hybrid Model
A DISTRIBUTED OPTIMIZATION FRAMEWORK FOR SCALABLE BIG DATA ANALYTICS USING STOCHASTIC GRADIENT DESCENT
The explosive data growth in volume, velocity, and variety of data in modern applications has made the old data processing paradigms obsolete, thus making mathematical frameworks for scalable analytics necessary. This paper proposes a distributed optimization algorithm that is based on an adaptive stochastic gradient descent (ASGD) algorithm designed for large-scale machine learning tasks. We provide mathematical formulation incorporating a communication-efficient consensus mechanism for solving the objective function using distributed workers nodes. The proposed method solves the most important problem of convergence speed and fault tolerance, which are the specific problems of heterogeneous cluster environments. We validate our approach using the TPC-DS benchmark dataset at the scale of 1TB, and provide a 23% reduction in training time and 15% increase in the convergence of the objective function in comparison to the standard synchronous SGD implementations. Our results therefore suggest that it is possible to optimise overall system throughput by (significantly) adaptive parameter tuning at node level without reducing model accuracy.
Big Data; Distributed Optimization; Stochastic Gradient Descent; TPC-DS; Scalable Machine Learning
ENERGY MANAGEMENT SYSTEMS FOR MULTI-SOURCE MARINE POWER (HYDROGEN + SOLAR + WIND)
The growing demand for sustainable maritime operations has accelerated the adoption of hybrid power systems integrating renewable and alternative energy sources. This paper investigates the design and optimization of an Energy Management System (EMS) for a multi-source marine power system combining hydrogen fuel cells, solar photovoltaic (PV) arrays, and wind energy conversion systems. The proposed EMS aims to ensure efficient power distribution, minimize fuel consumption, and enhance system reliability under dynamic marine operating conditions. Advanced control strategies, including rule-based and optimization-based approaches, are analyzed to manage energy flow among sources, storage units, and onboard loads. Simulation results demonstrate improved energy efficiency, reduced emissions, and enhanced operational flexibility compared to conventional diesel-based systems. The study highlights the potential of integrated renewable-hydrogen systems as a viable solution for decarbonizing the maritime sector.
Energy Management System (EMS), Hybrid Marine Power Systems, Hydrogen Fuel Cells, Solar Photovoltaic (PV), Wind Energy Conversion System, Renewable Energy Integration, Marine Electrification, Power Optimization, Smart Grid Control, Sustainable Shipping.
THE ROLE OF COGNITIVE BIASES IN SHAPING CONSUMER PERCEPTIONS OF LUXURY BRANDS
Luxury brands occupy a unique space in consumer markets, where products are purchased not merely for functional benefits but for symbolic value, social status, and emotional gratification. Unlike everyday consumption, luxury consumption is highly influenced by cognitive biases—systematic patterns of deviation from rational judgment that shape how consumers perceive value, prestige, and exclusivity. This literature review synthesizes insights from behavioral economics, consumer psychology, and luxury marketing research to examine how biases such as anchoring, scarcity, halo effect, social proof, endowment effect, confirmation bias, price-quality heuristic, and decoy effect influence consumer perceptions of luxury brands. By systematically reviewing the literature, the paper highlights how luxury marketers exploit these psychological tendencies through pricing strategies, heritage storytelling, limited editions, and influencer-driven campaigns. The review further identifies emerging perspectives, including digital luxury consumption, generational differences, and cross-cultural variations in bias activation. A conceptual framework is proposed linking cognitive biases to consumer perceptions and outcomes, including willingness to pay, brand loyalty, and advocacy. Finally, managerial implications are drawn for brand strategy, alongside research gaps emphasizing the ethical concerns of manipulating consumer biases in the luxury sector.
Cognitive biases, luxury marketing, consumer perception, behavioral economics, anchoring bias, scarcity, halo effect, social proof, endowment effect, price-quality heuristic, consumer behavior, brand loyalty
NATURAL BIOACTIVE COMPOUNDS AS MULTI-TARGET MODULATORS OF NEUROINFLAMMATION AND NEURODEGENERATION: A SHORT REVIEW
Neurodegenerative disorders are multifactorial conditions characterized by progressive neuronal loss, cognitive decline and functional impairment, driven by interconnected processes such as neuroinflammation, oxidative stress, protein aggregation, impaired autophagy and DNA damage. Owing to the limited success of single-target therapies, increasing attention has been directed toward natural bioactive compounds with multi-target therapeutic potential. This short review highlights the neuroprotective significance of Bacopa monnieri, curcumin, quercetin and asiatic acid, with emphasis on their modulatory effects on key molecular pathways involved in neuroinflammation and neurodegeneration. Available evidence indicates that these compounds influence important targets including NLRP3, Nrf2, GSK-3β, HDAC6, PARP1 and mTOR through both direct and indirect mechanisms. Their actions are associated with suppression of inflammatory signaling, attenuation of oxidative stress, reduction of protein aggregation, regulation of autophagy and enhancement of neuronal survival. Among these compounds, curcumin and quercetin appear to have relatively broader mechanistic evidence, whereas Bacopa monnieri and asiatic acid show promising neuroprotective effects mainly through upstream signaling modulation and antioxidant activity. Despite these encouraging findings, challenges such as poor bioavailability, limited structural validation and insufficient clinical evidence continue to hinder their translational application. Overall, these natural compounds represent promising multi-target agents for the management of neurodegenerative disorders, although further mechanistic and clinical studies are required to establish their therapeutic utility.
Neurodegeneration; Neuroinflammation; Natural bioactive compounds; Bacopa monnieri; Curcumin; Quercetin; Asiatic acid; NLRP3; Nrf2; GSK-3β; HDAC6; PARP1; mTOR
Hybrid AI–ML Architecture for Precision Healthcare Analytics Using Multimodal Clinical Data
The rapid evolution of artificial intelligence and machine learning has significantly transformed precision healthcare by enabling data-driven clinical decision-making. However, traditional single-modality approaches often fail to capture the complexity and heterogeneity inherent in healthcare data. This paper proposes a Hybrid AI–ML Architecture for Precision Healthcare Analytics that integrates multimodal clinical data, including electronic health records, medical imaging, genomic sequences, and real-time sensor data. The proposed framework leverages hybrid learning paradigms combining deep learning, ensemble machine learning, and feature fusion techniques to enhance predictive accuracy, robustness, and interpretability. By employing multimodal data fusion strategies and adaptive learning mechanisms, the architecture supports early disease detection, personalized treatment planning, and real-time monitoring. Furthermore, the system addresses critical challenges such as data heterogeneity, scalability, and model explainability. The study highlights the potential of hybrid AI–ML systems in improving diagnostic precision, optimizing clinical workflows, and advancing patient-centric healthcare delivery.
Hybrid AI, Machine Learning, Precision Healthcare, Multimodal Data, Clinical Analytics, Deep Learning.
Machine Learning-Based Early Disease Prediction Using Electronic Health Records
The increasing digitization of healthcare systems has led to the widespread adoption of Electronic Health Records (EHRs), offering unprecedented opportunities for early disease prediction and preventive healthcare. Machine learning (ML) techniques have emerged as powerful tools for analyzing complex and high-dimensional EHR data to identify patterns associated with disease onset. This study explores the application of ML algorithms for early disease prediction using structured and unstructured EHR data. It emphasizes predictive modeling approaches, feature engineering techniques, and model interpretability in clinical decision-making. The research highlights the effectiveness of supervised and deep learning models, including Random Forest, Gradient Boosting, and recurrent neural networks, in predicting chronic diseases such as diabetes, cardiovascular disorders, and respiratory illnesses. Furthermore, the study discusses challenges related to data quality, privacy, and model transparency. The findings suggest that ML-driven EHR analysis significantly enhances early detection capabilities, enabling proactive interventions and improving patient outcomes. The paper concludes by identifying future research directions aimed at integrating explainable AI, real-time data analytics, and multi-modal healthcare data for robust predictive healthcare systems.
DENSITY FUNCTIONAL THEORY AND MOLECULAR DOCKING STUDIES OF 2-(3, 4-DIHYDROXYPHENYL)-3,5,7-TRIHYDROXY-4-OXO-4H-CHROMEN-3-YL Β-D-GLUCOPYRANOSIDE (5H1P1C4O1G)
Flavonoid glycosides are significant pharmacologically relevant natural products that have not been studied sufficiently, and whose molecular interactions with cancer-related proteins are scarcely known. The bioactive flavonoid glycoside known to have antioxidant, anti-inflammatory and anticancer effects is 2-(3,4-dihydroxyphenyl)-3,5,7-trihydroxy-4-oxo-4H-chromen-3-yl 5H1P1C4O1G, which was not thoroughly studied in terms of its specific binding mechanisms The article employed molecular docking to determine important amino acid residues that stabilize the ligand and protein in this study that was conducted between 5H1P1C4O1G and cancer-associated proteins 3ERT, 6J90, and 7RPU. Of the targets analyzed, 3ERT had the highest binding affinity, which means it could be the most desirable anticancer target of the compound. “Moreover, the electronic structure of the compound, its stability, and reactivity were assessed by means of the density functional theory (DFT) calculations at the B3LYP/6-311++G(d,p) level. Analysis of frontier molecular orbital, molecular electrostatic potential mapping and global reactivity descriptors gave a clue on the charge distribution and the reactive site that was involved in strong binding interactions. Altogether, the joint computational findings indicate that 5H1P1C4O1G has the best anticancer potential in 3ERT target, and can be used as a useful scaffold to develop multi-target anticancer agents.”
5H1P1C4O1G, 3ERT, 6J90, 7RPU anticancer activity, flavonoid glycoside.
AN ANALYSIS OF THE EFFECTS OF AGILE AND HYBRID METHODOLOGIES ON SOFTWARE PROJECT SUCCESS
In the rapidly evolving field of software development, project success is increasingly influenced by the choice of development methodology. Agile and Hybrid methodologies have emerged as dominant approaches, offering flexibility, adaptability, and improved stakeholder engagement compared to traditional models. This research paper analyzes the impact of Agile and Hybrid methodologies on software project success by examining factors such as delivery time, cost efficiency, product quality, and customer satisfaction. The study employs both qualitative and quantitative approaches, including case studies and survey analysis, to identify trends and outcomes. The findings suggest that Agile methodologies significantly improve responsiveness to change, while Hybrid models provide a balanced approach suitable for complex and large-scale projects.
Agile Methodology, Hybrid Methodology, Software Development, Project Success, Scrum, Waterfall, Project Management.
A STUDY ON ETHICAL AI IN PRACTICE: ORGANIZATIONAL POLICIES AND COMPLIANCE CHALLENGES
Artificial Intelligence (AI) is rapidly transforming organizational operations, decision-making processes, and customer engagement. However, the deployment of AI raises significant ethical, legal, and compliance concerns. This paper examines the practical application of ethical AI within organizations, focusing on policies, governance frameworks, and compliance challenges. The research explores best practices, organizational strategies, and regulatory landscapes to ensure AI technologies are responsibly implemented while minimizing risks of bias, discrimination, and misuse. Through a mixed-method approach combining literature review, case studies, and data analysis, this paper highlights key challenges and offers actionable insights for organizations to develop sustainable and ethical AI practices.
Ethical AI, AI governance, compliance, organizational policies, AI bias, AI ethics, AI regulation, responsible AI
REGULATING GROWTH: BUSINESS LAW IN THE AGE OF STARTUPS
Startups have become the defining organizational form of innovation in the twenty-first century, reshaping industries ranging from healthcare and finance to transportation and artificial intelligence. Minnesota—home to a vibrant ecosystem of medical technology, biotechnology, software, and clean-energy startups—sits at the intersection of this transformation. Yet the legal architecture governing business in Minnesota remains largely rooted in frameworks designed for traditional, capital-intensive corporations rather than high-growth, innovation-driven enterprises. This Article argues that Minnesota’s existing business law regime—while stable and predictable—often fails to accommodate the distinctive characteristics of startups: rapid scalability, reliance on intellectual capital, heavy dependence on external financing, and iterative experimentation. Through an examination of corporate governance under Minnesota Statutes Chapter 302A, securities regulation, intellectual property law, employment classification, and emerging technology governance, this Article demonstrates that current legal structures can unintentionally impede entrepreneurial growth. Drawing on comparative models such as regulatory sandboxes, tiered compliance systems, and innovation-friendly corporate governance reforms, the Article proposes a reimagined legal framework that better aligns with the realities of startup ecosystems while preserving investor, worker, and consumer protections. Ultimately, it contends that Minnesota must modernize its business law infrastructure to sustain its competitive advantage in the national and global innovation economy.
MARKOV-BASED AVAILABILITY MODELLING AND OPTIMIZATION USING SPIDER MONKEY OPTIMIZATION
This paper presents the availability optimization of a screw manufacturing plant using the Spider Monkey Optimization (SMO) algorithm. The plant consists of four major subsystems, two of which operate with cold standby redundancy. Assuming exponential failure and repair distributions, the system is modelled using a probabilistic approach and differential equations are developed through the Markov birth–death process. Steady-state availability is determined using normalization conditions. To enhance system performance, SMO, a swarm-based metaheuristic inspired by the social foraging behaviour of spider monkeys, is employed to maximize availability by optimizing subsystem failure and repair rates within specified bounds. The algorithm simultaneously coordinates twelve decision variables to obtain the optimal parameter combination. The results indicate that SMO significantly improves system availability and provides an efficient and robust alternative to traditional evolutionary optimization techniques for complex industrial systems.
Markov birth–death process; Spider Monkey Optimization; Steady-state availability; Reliability optimization; Swarm intelligence.
PERFORMANCE-GUARANTEED DATA REPLICATION FOR DATA-INTENSIVE SCIENTIFIC APPLICATIONS
A Data Grid is composed of multiple interconnected sites organized in a hierarchical structure with one top-level site and several institutional sites. The top-level site functions as the central management unit and is responsible for maintaining the Replica Catalogue (RC), which stores metadata about data files and their replicas across different sites. Each site possesses both computational and storage capabilities, enabling job execution and data storage within the Grid environment. Communication within a site is assumed to have negligible delay due to high internal bandwidth. In the data replication framework, multiple data files are generated by designated source sites, and a single site may act as the source for more than one file. Since each Grid node has limited storage capacity, it can replicate and store only a limited number of data files. Efficient replication strategies are therefore essential to optimize storage utilization, data availability, and overall system performance. This study focuses on addressing the data file replication problem in such a distributed Grid environment while considering storage constraints and system architecture.
AI-POWERED DIGITAL WELL-BEING APPLICATION FOR MANAGING ADOLESCENT SCREEN TIME AND PROMOTING MENTAL HEALTH
The proliferation of smartphone usage among adolescents has raised significant concerns regarding excessive screen time and its impact on physical and mental health. This paper presents a concept paper of an innovative AI-powered mobile application designed to address the growing problem of smartphone addiction among the adolescents. This mobile application employs intelligent monitoring, automated application locking, activity tracking, and AI-based reminders to help parents guide their children toward healthier digital habits. The application features dual-parent control dashboards, customizable time limitations, and comprehensive website monitoring capabilities. By integrating parental oversight with AI-driven behavioural nudges, this mobile application aims to reduce screen time, prevent procrastination, improve academic performance, and support the mental and physical well-being of adolescents. This paper discusses the problem statement, core features, target market, competitive advantages, and alignment with United Nations Sustainable Development Goal 3 (Good Health and Well-Being). This mobile application represents a significant contribution to digital well-being solutions, offering a comprehensive approach to managing adolescent online activities in an increasingly connected world.
Digital well-being, smartphone addiction, parental control, screen time management, adolescent mental health, AI-powered intervention, mobile application.
A STUDY ON KANBAN METHODOLOGY: INFLUENCE ON WORKFLOW MANAGEMENT, AND IMPACT ON TEAM DYNAMICS
This study investigates the Kanban approach, specifically its impact on workflow management and team relations. Kanban, which was originally intended for manufacturing, is now widely used in software development, project management, and the service industry. This study seeks to provide a comprehensive understanding of Kanban by assessing the methodology's impact on workflow management, team relations, and overall project success. Key findings include the methodology's improved workflow visibility and task management, as well as increased team productivity and collaboration, challenges in implementing Kanban in traditional or complex environments, issues with defining and maintaining effective work in progress limits, and a comparison of Kanban to SCRUM and Lean.
Kanban, Lean Management, Project Management, Team Dynamics, Workflow Management.
Nawaz Ali Hamdulay, Dr. Mukesk Kumar Verma
CLOUD COST EFFICIENCY STRATEGIES AND IMPLEMENTATION WITH A FOCUS ON AWS
Scalability, agility and global accessibility are the drivers of modern digital transformation which make use of cloud computing to provide benefits on industries. Nevertheless, the increased adoption of cloud has additionally created a huge challenge in terms of cost especially when it comes to enterprises using extensive multi-cloud and AWS infrastructure. In this paper, the cost-efficiency strategies in clouds are explored, the services that are considered the best in the industry are analyzed, and the best-practice approaches to implementing the cost-reduction strategies are identified. This work is an in-depth, evidence-based examination of the cost optimization based on analyzing current literature, cloud migration models and templates, resource allocation systems, and server less architecture. Results indicate that cloud governance, rightsizing, reserved instances, server less compute, AI-based optimization, workload visibility, and best practices could help enterprises to save 25-60 percent in cloud costs. This paper is relevant to the practical knowledge and proven practices of organizations with the aim of enhancing cloud effectiveness, as well as through performance and ROI maximization.
AN EFFICIENT SCHEDULING ALGORITHM FOR CLOUD SERVICE REQUESTS
Cloud computing provides scalable and cost-effective computing services, but efficient scheduling of service requests remains a major challenge due to dynamic and heterogeneous workloads. Inefficient scheduling leads to increased execution time, poor resource utilization, and higher service costs. This thesis proposes a Priority-based Batch Scheduling algorithm to reduce overall service utilization time in cloud computing environments. Service requests are classified into three priority levels based on Service Level Agreement (SLA) parameters. High-priority requests are executed immediately using FCFS scheduling, while medium- and low-priority requests are grouped into batches based on similar resource requirements to minimize communication and initialization delays. Experimental results demonstrate that the proposed approach outperforms existing priority-based scheduling algorithms in terms of execution time, with performance improvements becoming more significant as the number of service requests increases. The proposed algorithm effectively enhances scheduling efficiency, reduces execution cost, and improves overall cloud system performance.
Cloud Computing, SLA, Service Request, Scheduling
A NOVEL ALGORITHM FOR EFFICIENT FREQUENT PATTERN MINING
The rapid expansion of digital data across various sectors—including retail, healthcare, education, and finance—has increased the need for effective techniques that can extract meaningful knowledge from large databases. Among these techniques, association rule mining plays a crucial role in identifying frequent itemsets and uncovering hidden relationships within transactional data. Although traditional algorithms such as Apriori, FP-Growth, and Eclat are widely used for this purpose, their performance depends heavily on a user-defined minimum support threshold. Selecting this value without proper domain understanding often leads to inappropriate outputs, either by generating a large number of insignificant patterns or by overlooking valuable associations.To overcome these limitations, this research introduces a new association rule–based mining algorithm that determines the minimum support threshold mathematically instead of relying on user input. This automated approach simplifies the mining process, enhances accuracy, and reduces the need for expert intervention. The algorithm is implemented using Python and tested on standard datasets obtained from the UCI Repository. Its effectiveness is evaluated by comparing the number of frequent itemsets generated and the execution time with those of the conventional Apriori algorithm. The experimental analysis reveals that the proposed method consistently outperforms Apriori. It produces fewer redundant frequent itemsets, resulting in lower memory usage and improved clarity in the discovered patterns. Additionally, the algorithm demonstrates faster execution times across datasets of varying sizes, highlighting its efficiency and scalability. By eliminating manual threshold selection and improving computational performance, the proposed approach offers a more reliable, practical, and user-friendly solution for frequent pattern mining. This contributes to more intelligent and automated decision-support systems capable of handling large and complex datasets.
Data Mining, frequent itemsets, Apriori algorithm.
A NOVEL TECHNIQUE FOR RESEARCH PROPOSAL CLASSIFICATION
Research paper selection is a crucial decision-making activity for government funding agencies and research institutes. Ontology is a knowledge repository that defines ideas, terminology, and the relationships between them. In this study, Ontology is a collection of previous research papers including keywords and their frequency in funding agency research papers. Ontology improves the effectiveness, efficiency, and interactivity of activities involving searching for comparable text patterns. The present work grouping algorithm for research paper selection is based on keyword similarities and ontology research paper frequencies. Text mining is the process of extracting relevant, and sometimes previously undiscovered, information from huge documents. The Research Papers in each domain are grouped using text mining techniques. Grouped research papers are systematically assigned to the relevant reviewer or subject matter experts for peer assessment. Reviewer results are gathered, and publications are evaluated according to the findings of expert reviews.
A NOVEL FACE RECOGNITION TECHNIQUE BASED ON COLOR AND EDGE FEATURES
In current age of the internet, numerous services and apps have gone online, and each service is provided based on their identity management methods. This technique requires a user id and a strong password that cannot be broken by a standard brute force assault. However, maintaining a large number of passwords secret is a difficult effort, hence bio-matric authentication systems are used in a variety of critical environments to prohibit suspected or malevolent individuals. As a result, the various bio-matric authentication methods are evaluated first, and it is chosen to work with a face-based authentication system. In such systems, accuracy is the key criterion for system development, while resource preservation is the secondary concern of the face recognition system. Therefore, the goal of the proposed work to identify a face picture identification method that was both accurate and efficient. Accuracy, error rate, time complexity, and space complexity are utilised as performance metrics. Because of its high degree of accuracy in identifying user faces, the suggested model can be adopted. Additionally, the performance yields an image categorisation accuracy of 90–100%. As a result, the suggested face recognition method is effective and suitable for both object and face identification applications.
Baviskar Laxminarayan Suresh , Rameshwar Singh Sikarwar
SINE COSINE OPTIMIZATION-BASED APPROACH FOR OPTIMIZING THERMOELECTRIC MODULES IN WASTE HEAT RECOVERY SYSTEMS
In this paper, the topic of thermoelectric modules (TEMs) optimization in waste heat recovery systems is discussed, and it is addressed on the optimization of power to the maximum and efficiency to the optimum. The new strategy of employing the Sine Cosine Optimization (SCO) algorithm is proposed to optimize the most important parameters of TEMs, including length and cross-sectional area of thermoelectric legs. The objective of the study is to overcome the drawback of the conventional optimization methods, which include the Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), as they are time-consuming and inconvenient with respect to making real-time changes. Using dynamic adjustment of the dimension of thermoelectric legs according to certain temperature gradients, SCO algorithm will help to improve the functionality of TEMs in reality. The simulation results indicate that the SCO-optimized TEM is superior to the base TEM, giving it power generation enhancing results, where the output becomes 33% more with optimal current amounts. The study is a suggestive solution to the enhancement of thermoelectric waste heat recovery systems effectiveness and fastidiousness.
Cross-Sectional Area, Leg length, Seebeck Effect, Sine Cosine Optimization (SCO), Temperature Gradients, Thermoelectric Generator (TEG), Thermoelectric Module (TEM), Waste Heat Recovery.
Yashwardhan Singh Chandrawat, Khemraj Beragi
ENHANCING THE PERFORMANCE OF VAPOR COMPRESSION REFRIGERATION SYSTEMS THROUGH MACHINE LEARNING AND BAYESIAN OPTIMIZATION
The use of vapor compression refrigeration (VCR) systems in industrial cooling is essential, but the efficiency of their operation is usually less fortunate because of the rigidity and fixity of the operation that such control systems are utilized in, as it should. The paper addresses how machine learning (ML) and Bayesian optimization (BO) may be combined to improve VCR systems real-time optimization. Optimized floating-point parameterized models of Bayesian Optimized Neural Network (BONN) aims to alter major parameters in the system including refrigerant flow rate and compressor speed depending on the fluctuating environmental and operating conditions. Current study overcomes the limitations of traditional optimization techniques in offering an open-ended, adaptive solution that is found to predict and optimize system performance in different conditions. By so doing, better energy efficiency, cheaper costs of operations, and enhanced reliability of the system are expected to be attained through the research. The experimentation indicates that Bayesian-optimized model with greater accuracy in estimating energy usage and fridge refrigerants staff is substantially better than the traditional models. This paper will help in advancing the cooling technologies which are sustainable because it allows VCR systems to be adaptively controlled in real time.
Artificial Neural Networks, Bayesian Optimization, Machine Learning, Refrigerant Flow, Refrigerant Leaks, Vapour Compression Refrigeration (VCR)
MODELLING AND OPTIMIZING SHELL-AND-TUBE HEAT EXCHANGERS WITH AIR INJECTION
This research paper is devoted to optimization of the shell-and-tube heat exchangers (STHEs) with air injection using machine learning tools to estimate thermohydraulic performance. Comparative study of four machine learning models, Random Vector Functional Link (RVFL), Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Random Forest (RF), to assess their usefulness in predicting key performance indicators of temperature of outlets and pressure drops were analysed. As put forth in the study, data preprocess, feature selection, and hyperparameter tuning are evident in enhancing the precision of such models. The obtained results reveal that RVFL is preferable with respect to predictive capabilities as it produced high correlation coefficients in predicting outlet temperature and pressure drops. The work proves that machine learning can have significant capabilities to optimize the design of heat exchangers, with the advantage of using data to guide the pursuit of energy efficiency and thermal performance of an industrial process.
Heat Exchangers, Machine Learning, Shell-And-Tube, Random Vector Functional Link, Support Vector Machine, K-Nearest Neighbors, Random Forest.
OPTIMIZING BATTERY THERMAL MANAGEMENT SYSTEMS FOR ELECTRIC VEHICLES USING FIREFLY OPTIMIZATION AND SUPPORT VECTOR MACHINES
Battery Electric Vehicles (BEVs) have a high dependency on effective Battery Thermal Management Systems (BTMS) to guarantee better performance, safety and long-life cycle of lithium-ion battery. Such issues associated with temperatures variations demand efficient thermal control systems. The research paper is carried out under the scope of exploring the applicability of machine learning (especially, Support Vector Machines (SVM) and Firefly Optimization Algorithm (FOA)) in optimising BTMS behaviour in real-time. This combination of techniques enables efficiency and flexibility in manipulating cooling systems in the changing conditions of operation, greatly benefiting efficiency and minimizing the requirements of the costly testing facilities. Findings demonstrate that the proposed model manages to predict and optimize cooling power and Coefficient of Performance (COP) with a relatively low cost and scalable solution of thermal management of BEVs.
Battery Electric Vehicles, Battery Thermal Management System, Firefly Optimization Algorithm, Machine Learning, Support Vector Machines.
ENHANCED NOMA OPTIMIZATION USING BLUE WHALE ALGORITHM FOR BER AND SPECTRAL EFFICIENCY ENHANCEMENT
Non-Orthogonal Multiple Access (NOMA) is referred to as a disruptive technology in the 5G and future technology wireless networks on traffic capabilities that require massive connectivity and better spectral efficiency. Power allocation among users plays an important role in significantly determining the performance of NOMA, especially, in high spectral efficiency. This paper suggests an Enhanced NOMA scheme that is optimized by using the Blue Whale Optimization (BWO) scheme. The proposed methodology seeks to optimize the Bit Error Rate (BER) and Spectral Efficiency (SE) by dynamic power coefficients of the users. Under both Rayleigh and Rician fading conditions, simulations indicate that the proposed BWO-optimized NOMA system has a large gain compared to conventional power allocation schemes and other all heuristic-based system, in terms of both BER and SE performances.
5G, Bit Error Rate, Blue Whale Optimization, NOMA, Spectral Efficiency.
AUTHENTICATION SECURELY THROUGH A 3D IMAGE PASSWORD
Currently, computers and the internet have become a crucial aspect of everyone's lives. Various applications can send and store vast amounts of critical data. User-friendly programs are given for easy use. Various approaches are developed to secure such apps. Authentication methods include textual passwords, graphical passwords, and colour passwords. But these authentication techniques are vulnerable to a variety of assaults. Many times, the username is simple, allowing the attacker to quickly remember it and guess the password. So in the suggested system, we created three authentication systems that offer a secret username, a graphical password, and a colour password. After verifying the graphical password, the user can enter the secret username and the colour password. After successfully verifying three schemes, the user is able to access the application. So the new approach will give strong security by combining graphical passwords with secret usernames and colour passwords.
Authentication, graphical password, colour password, security, secret username, PCCP.
Mecwan Umang Rajkumar, Mr. Arpit Solanki
Numerous shoulder surfing techniques have been developed, however predictable secret word patterns are now vulnerable to dictionary assaults, eve dropping, and shoulder surfing. At the same time, text passwords are the most often utilised authentication publicising strategy. In previous years, there were some graphical password schemes that were intended. The majority of We have suggested word-based graphical password schemes since users prefer word-based passwords over pure graphical ones. Unfortunately, no hybrid digital graphical password system is created by any of the current approaches. In this study, we propose an enhanced graphical password that is primarily text-based, numerical-based, shoulder surfing resistant, and also employs additional techniques such as social engineering resistance, eves dropping, and dictionary attack resistance through the use of colours. The operator can observe the proposed system's security, usability, and resilience to different threats by logging in to the system in a reliable, easy, and efficient manner.