Search
309 articles for “adaptive optimization”
-
Application of B-trees for Design of Optimal Page Replacement Technique in Modern Operating Systems
Abstract: Algorithms related to replacing the memory pages in operating systems are critical components of modern operating systems that manage virtual memory efficiently. Current algorithms such as LRU (Least Recently Used), Clock algorithms as well as FIFO (First-In-First-Out), often struggle with the increasing demands of contemporary applications and larger memory hierarchies. This research work proposes a novel approach utilizing B-tree data structures to design an optimal page replacement technique. The proposed …
Published in International Journal of Data Structure Studies · Vol. 3, Issue 2, 2025 · pp. 23–30 Read article
-
Optimized Machine Learning Framework for Battery State Prediction in Smart Charging Systems
Abstract: Good estimation of battery states, including State-of-Charge (SoC), State-of-Health (SoH), and Remaining Useful Life (RUL), are important in managing energy wisely and controlling the adaptive charging. This work introduces a streamlined machine learning model based on the ability to use multi-dimensional sensor measurements in terms of voltage, current, temperature, and cycle number to forecast battery conditions with high accuracy. Decent preprocessing, such as noise elimination, feature scaling, and calculated features, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 11–23 Read article
-
A Trends in Dietary Supplement Related to Exercise Nutrition
Abstract: A product made to address physiological or nutritional demands that might occur during sports activity is called a dietary supplement. It could offer a convenient method for fulfilling specific nutritional needs during exercise, or it could be utilized to prevent or address common nutritional deficiencies experienced by athletes. It is crucial to include the supplement in a thorough plan for the best possible sports nutrition or clinical care of nutritional …
Published in International Journal of Nutritions · Vol. 1, Issue 2, 2024 · pp. 1–6 Read article
-
AIducate: Your Smart Tutor–A Survey
Abstract: The ongoing “datafication” of our social reality has led directly to the emergence of new data-driven business models across various sectors, including education. As a result, the market for educational services is rapidly expanding. Educational technology (EdTech) companies are at the forefront, offering data-driven pedagogical solutions that are reshaping the landscape of learning. Despite the market's growth, few companies are harnessing the full potential of artificial intelligence (AI) and machine …
Published in Journal of Multimedia Technology & Recent Advancements · Vol. 11, Issue 2, 2024 · pp. 25–30 Read article
-
A Comprehensive Review of Deep Compressive Sensing for Efficient IoT Data Management
Abstract: The Internet of Things has revolutionized data-driven ecosystems and offers advanced services, such as live monitoring and automation in various domains: smart cities, healthcare, and industrial automation. However, with the exponential growth of IoT devices, comes a large amount of data generation, which poses considerable problems like network congestion, latency, and energy inefficiency. Compressive sensing (CS), one of the newest signal processing methodologies, has emerged as an enabler to meet …
Published in Trends in Electrical Engineering · Vol. 14, Issue 3, 2024 Read article
-
Nanomedicine in Combination with Artificial Intelligence (AI): Transforming Cancer Treatment
Abstract: The convergence of nanomedicine and artificial intelligence (AI) holds transformative potential for advancing cancer treatment, particularly in liver cancer. Nanomedicine enables the development of targeted drug delivery systems, enhanced imaging modalities, and precise therapeutic interventions, while AI facilitates data-driven decision-making, personalized treatment plans, and predictive analytics. This synergistic approach can significantly improve the diagnosis, treatment, and monitoring of liver cancer by optimizing the use of nanoparticle-based therapies. AI-powered algorithms can …
Published in Trends in Drug Delivery · Vol. 12, Issue 1, 2025 · pp. 27–30 Read article
-
Emerging Trends in Membrane-Based Gas Separation Technologies
Abstract: Membrane technology has emerged as a groundbreaking solution in various fields, revolutionizing industries such as water treatment, energy production, biomedicine, and environmental protection. Over the past few decades, significant advancements have been made in membrane materials, fabrication techniques, and performance optimization. With the growing global demand for efficient and sustainable separation processes, research has increasingly focused on enhancing membrane permeability, selectivity, and durability to improve performance across various industries, including …
Published in International Journal of Membranes · Vol. 2, Issue 1, 2025 · pp. 16–22 Read article
-
Review JoEDT N58 Open CPU-Based Milk Collection System
Abstract: The N58 Open CPU-based milk collection system is designed to enhance the efficiency and transparency of milk collection processes in rural and urban dairy operations. By integrating a computerized platform with open-source technology (Open CPU), the system enables real-time monitoring, automatic data logging, and streamlined milk collection from multiple sources. The system allows farmers to register and track their milk deliveries, monitor milk parameters such as fat content and volume, …
Published in Journal of Electronic Design Technology · Vol. 16, Issue 2, 2025 · pp. 17–23 Read article
-
AI-Driven Multi-Objective Optimization of Conductive Polymer Composites for High-Performance Flexible Electronics
Abstract: The development of conductive polymer composites (CPCs) is critical for advancing flexible and wearable electronic technologies. However, the conventional trial-and-error approach to material formulation is time-consuming and often inefficient due to the high-dimensional nature of the design space. This study introduces a novel AI-driven framework that integrates machine learning (ML) with multi-objective optimization to accelerate the discovery of high-performance CPCs. A dataset of 1,000 experimentally reported formulations was compiled, capturing …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 734–745 Read article
-
Neuroimaging in Clinical Trials for Huntington’s Disease: Emerging Research Findings: The Advancement Directions and Implications
Abstract: Neuroimaging is very important in coordinating and conducting Huntington’s disease clinical trials as a tool in selecting patients, managing safety concerns, and assessing the benefits of interventions. This review presents the current uses and potential future uses of structural and functional magnetic resonance imaging (MRI), diffusion imaging, positron emission tomography (PET), proton magnetic resonance spectroscopy (MRS), perfusion imaging, and magneto encephalography (MEG) in HD trials. We describe how these modalities …
Published in International Journal of Brain Sciences · Vol. 2, Issue 2, 2025 · pp. 32–44 Read article
-
Artificial Intelligence Techniques for Smart Polymer Nanocomposite Materials and Industrial Applications
Abstract: Protecting sensitive material data, manufacturing processes, and intelligent monitoring platforms is essential for the fast development of innovative polymer nanocomposite systems in fields such as aerospace, medicine, electronics, automobiles, and energy. In order to safeguard, consistently enhance, and optimize distributed industrial systems that consist of polymer nanocomposite materials, this study presents an AI-driven cybersecurity and cloud computing architecture. The suggested solution employs artificial intelligence (AI), machine learning (ML), cloud computing, …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 Read article
-
Neuromorphic Self-Learning Polymer–MXene Photovoltaic Composites with Embedded Memristive Energy Routing for Adaptive Solar Energy Harvesting
Abstract: This dynamic and fast-growing intelligent renewable energy system requires photovoltaic materials that can autonomously adapt to fast-changing environmental conditions. In this study, a novel system is proposed for adaptive harvesting of solar energy based on Neuromorphic Self-Learning Polymer–MXene Photovoltaic Composites (NSPMPCs) with embedded memristive energy routing networks. To boost the charge generation and charge transport in the polymer–MXene heterostructure, the flexibility and processability of conductive polymers are integrated with the …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
-
Performance Analysis And Battery Management System Optimization In Electric Vehicles
Abstract: The rapid electrification of the automotive industry has led to an increased need for battery management systems that are not only efficient and safe but also intelligent. BMS is the device that guarantees the best use of the battery, prolongs its life, and allows its safe operation even under different environmental and load conditions. Through the synthesis of literature and industry practices, this paper acts as a tribute to the …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 1, 2026 Read article
-
An Investigative Study on Cache-Oblivious Data Structures
Abstract: Cache-oblivious data structures and data management systems have emerged as critical components in modern computing environments, aiming to optimize memory access patterns across different levels of the memory hierarchy without explicit knowledge of cache sizes or configurations. This study presents an overview of cache-oblivious techniques, including adaptive data structures, compression, parallel processing, and security considerations. The workexplores future directions in cache-oblivious systems, such as non-volatile memory support, graph processing, edge …
Published in International Journal of Data Structure Studies · Vol. 1, Issue 2, 2023 · pp. 33–37 Read article
-
Advancements in Polymer Chemistry for Biomedical Applications: A Focus on Injection Guides
Abstract: Polymers have revolutionized biomedical applications, offering versatility, biocompatibility, and cost-effective manufacturing. This paper explores the role of polymer chemistry in the development of an advanced injection guide for precise drug delivery, aiming to enhance both safety and efficiency in clinical settings. Emphasis is placed on polymer selection, structural design, and functional modifications to improve injection procedures. By leveraging biodegradable and biocompatible polymers, the guide minimizes adverse tissue reactions while ensuring …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 129–136 Read article
-
DR. REVIVE: An AI-Powered Medical Recommendation System for Optimised Resources and Improved Patient Care
Abstract: Dr. Revive is an AI-powered medical recommendation system designed to enhance virtual healthcare interactions by connecting patients, doctors, and healthcare stakeholders. Leveraging advanced machine learning algorithms, it analyses user-reported symptoms to provide initial medical recommendations, serving as a reliable first point of guidance. With access to a comprehensive medical database, the platform delivers accurate and timely advice, empowering patients while supporting healthcare professionals with data-driven decision-making. By offering a complete …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 3, 2024 · pp. 1–10 Read article
-
Green AI-Enabled Opto-Electronic Communication Systems for Carbon-Neutral Digital Networks
Abstract: The rapid expansion of digital communication infrastructure, driven by cloud computing, Internet of Things (IoT), 6G networks, and artificial intelligence applications, has significantly increased the energy consumption and carbon footprint of modern communication systems. Conventional optical communication networks often rely on static resource allocation and energy-intensive signal processing mechanisms, resulting in inefficient utilization of network resources and elevated operational costs. This study proposes a Green Artificial Intelligence (Green AI)-Enabled Opto-Electronic …
Published in Trends in Opto-electro & Optical Communication · Vol. 16, Issue 2, 2026 Read article
-
Develop the Design of Sustainable Polymer Materials: Applying Reinforcement Learning, IoT-Enabled Monitoring, and Data-Driven Manufacturing Approaches
Abstract: Sustainable polymer materials development is a must due to resource constraints, environmental concerns, and the demand for designed materials with high performance. When it comes to material optimization, energy utilization, process unpredictability, and lifecycle sustainability, traditional polymer production methods have their challenges. Reinforcement Learning (RL), Internet of Things (IoT) monitoring, and data-driven production are utilized in the design and manufacturing of sustainable polymer materials. It is recommended to use Internet …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 Read article
-
Intelligent Electromagnetic Synthesis: An AI-Driven IoT Framework for Adaptive Antenna Design in Missile Navigation
Abstract: The rapid evolution of hypersonic and long-range tactical missile systems necessitates antenna architecture capable of maintaining robust communication links under extreme thermal, mechanical, and signal-jamming environments. Traditional antenna design methodologies often relying on iterative simulation cycles and static optimization are increasingly insufficient for the real-time requirements of modern aerospace navigation. This paper proposes an AI-driven, IoT- integrated framework that facilitates autonomous antenna design and performance optimization. By deploying a distributed …
Published in International Journal of Radio Frequency Innovations · Vol. 4, Issue 1, 2026 Read article
-
Designing Self-Optimizing Operating Systems: Information-Theoretic Approaches to Thread Scheduler Implementation
Abstract: Thread Level Scheduling (TLS) in multi-core and many-core processor environments represents a critical frontier in next-generation operating system design. As computing systems grow increasingly heterogeneous and concurrent, traditional scheduling strategies often rely on heuristics or localized resource metrics, frequently overlooking the deeper, quantifiable relationships and uncertainties inherent in complex concurrent workloads. This study explores the application of information-theoretic approaches, specifically entropy-based task allocation, mutual information-driven dependency analysis, and channel capacity-inspired …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 3, 2025 · pp. 31–39 Read article