Search
43 articles for “convergence adaptation”
-
A Comprehensive Review on Impact of Natural Selection Leading to Convergence
Abstract: This tale explores the complex relationship between genetic creativity, adaptation and common ecological problems that is arranged by the master sculptor, natural selection. Convergence reveals the adaptive genius that runs throughout the structure of evolution and is an acknowledgment to the continued power of natural selection. This journey aims to solve the enigma of convergent evolution. Natural selection has been influencing every link in the complex web of life on …
Published in Research and Reviews : A Journal of Biotechnology · Vol. 14, Issue 1, 2024 · pp. 1–7 Read article
-
Adaptive E-Learning Algorithms and Heutagogy: A Systematic Analysis
Abstract: The proliferation of artificial intelligence (AI) and machine learning (ML) technologies has transformed the digital education landscape by enabling adaptive e-learning systems capable of personalizing content and optimizing learning paths. This study provides a systematic analysis of adaptive e-learning algorithms within the framework of heutagogy, an educational paradigm that emphasizes learner autonomy, self-direction, and capability development. The convergence of adaptive technologies with heutagogical principles offers new avenues for creating more …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 33–38 Read article
-
Radiation-Resilient AI: Next-Generation Robotic Systems with Adaptive Machine Learning for Nuclear Facility Management
Abstract: The increasing complexity of nuclear facility operations, decommissioning activities, and emergency response scenarios necessitates the development of advanced autonomous systems capable of functioning in highly radioactive environments. This paper presents a comprehensive review of radiation-resilient artificial intelligence systems integrated with next-generation robotic platforms, specifically designed for nuclear facility management applications. We examine the convergence of adaptive machine learning algorithms, radiation-hardened hardware architectures, and intelligent robotic systems that can operate autonomously …
Published in Journal of Nuclear Engineering & Technology · Vol. 15, Issue 2, 2025 · pp. 12–21 Read article
-
Radiation-Resilient AI: Next-Generation Robotic Systems with Adaptive Machine Learning for Nuclear Facility Management
Abstract: The increasing complexity of nuclear facility operations, decommissioning activities, and emergency response scenarios necessitate the development of advanced autonomous systems capable of functioning in highly radioactive environments. This paper presents a comprehensive review of radiation-resilient artificial intelligence systems integrated with next-generation robotic platforms, specifically designed for nuclear facility management applications. We examine the convergence of adaptive machine learning algorithms, radiation-hardened hardware architectures, and intelligent robotic systems that can operate autonomously …
Published in Journal of Thermal Engineering and Applications · Vol. 15, Issue 2, 2025 · pp. 12–21 Read article
-
Routing Protocols in FANETs with Future Enhancements
Abstract: Flying Ad Hoc Networks (FANETs), which are swarms of Unmanned Aerial Vehicles (UAVs), are an emerging solution which revolutionized the area of mission-critical and infrastructure-less communication systems. These networks provide real-time data transfer for use cases such as disaster relief, battlefield observation, environmental monitoring, and 6G-based smart cities. However, the dynamic profile of FANETs, which is defined by high 3D mobility, limited energy resources, unstable wireless links, and constant topology …
Published in Journal of Aerospace Engineering & Technology · Vol. 15, Issue 3, 2025 · pp. 8–13 Read article
-
AI-Assisted Gain Scheduling for Real-Time Temperature Control in Chemical Reactors
Abstract: Temperature control in continuous stirred-tank reactors (CSTR) represents a critical challenge in chemical process industries due to inherent nonlinearities, time-varying dynamics, and parametric uncertainties. Conventional proportional-integral-derivative (PID) controllers with fixed gains often fail to maintain optimal performance across varying operating conditions, leading to temperature excursions that compromise product quality and safety. This paper presents a novel AI-assisted gain scheduling framework that integrates artificial neural networks (ANN) with adaptive PID control …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 1, 2026 · pp. 24–33 Read article
-
Systematic Review of Application of Nature-Inspired Algorithms for Resource Optimization in Multi-Programmed Operating Systems
Abstract: Multi-programmed operating systems are increasingly confronted with complex challenges in efficiently managing system resources, primarily due to the need to handle numerous concurrent processes with diverse and often conflicting resource demands. As these systems evolve, ensuring optimal performance across various dimensions, such as CPU scheduling, memory allocation, and load balancing, has become crucial. In this context, nature-inspired algorithms have emerged as promising solutions for enhancing resource optimization. These algorithms, which …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 2, 2025 · pp. 08–14 Read article
-
Integrated Policy Approaches to Disaster Risk and Climate Change Governance: A Global Review of Institutional Strategies, Techniques, and Community Resilience
Abstract: Climate change and disaster risks have emerged as interconnected threats that demand integrated and adaptive governance systems. While both domains, disaster risk management (DRM) and climate change adaptation (CCA), have independently evolved over time, their convergence remains a complex challenge for policymakers. This review explores the multidimensional integration of policies addressing disaster and climate risks, focusing on institutional coordination, community participation, and the use of innovative governance mechanisms. Drawing insights …
Published in International Journal of Sustainability · Vol. 2, Issue 2, 2025 · pp. 27–36 Read article
-
Enhancing Power Conversion Efficiency in Tandem Solar Cells with Temporal Dynamic Graph Neural Network
Abstract: In modern homes, people want good comfort and also less electricity bill, so managing heating load and cooling load become very important. Heating Load (HL) and Cooling Load (CL) depend on many things like wall material, window size, sunlight, ventilation, and weather. Because of this many factors, calculation and optimization of HL and CL is little difficult and many time normal formulas give wrong or not perfect results. So in …
Published in Journal of Semiconductor Devices and Circuits · Vol. 13, Issue 2, 2026 Read article
-
Swarm Intelligence: Nature-Inspired Problem Solving
Abstract: Swarm Intelligence (SI) is a computational paradigm inspired by the collective behavior of natural systems, such as flocks of birds, schools of fish, and colonies of ants. It involves decentralized, self- organized systems where simple agents follow simple rules, yet their interactions lead to complex global behaviors. SI has gained significant attention in recent years due to its potential applications in solving optimization problems, routing, scheduling, and artificial intelligence tasks. …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 12, Issue 3, 2025 Read article
-
Lyapunov-Stable Adaptive Fractional-Order Interval Type-2 Fuzzy Control for Robust Anti-Lock Braking Under Uncertain Road Adhesion Conditions
Abstract: This paper proposes a Lyapunov-stable Adaptive Fractional-Order Interval Type-2 Fuzzy Logic Controller (FO-IT2FLC) for robust anti-lock braking system (ABS) control under nonlinear vehicle dynamics and uncertain road adhesion conditions. The proposed framework integrates fractional-order error dynamics to capture memory-dependent tire–road interaction, interval Type-2 fuzzy inference to model uncertainty via footprint-of-uncertainty representation, and a Lyapunov-based adaptive learning mechanism for real-time parameter tuning. A rigorous stability proof guarantees boundedness of all closed-loop …
Published in Journal of Control & Instrumentation · Vol. 17, Issue 2, 2026 Read article
-
E-Commerce Study Using AR/VR and Ethical Convergence of Commerce
Abstract: The landscape of E-commerce is undergoing a fundamental transformation, shifting from a platform-centric model of transactional exchange to an immersive, ecosystem-driven experience. This analysis examines the critical trends and disruptive forces that define the immediate and long-term trajectory of digital commerce. The study identifies three foundational pillars driving future growth: Hyper-Personalization via Generative AI, Spatial Commerce (AR/VR Integration), and Sustainable Supply Chain Resilience. Future E-commerce will be characterized by the …
Published in E-Commerce for Future & Trends · Vol. 12, Issue 3, 2025 · pp. 20–26 Read article
-
Learning of Maximum Power Point Tracking Architecture with Various Algorithms for Photovoltaic Systems: A Review
Abstract: Currently, as the requirement on the Earth for ever more electricity grows, so too accordingly must demands upon renewable energy. These days, with the growth of renewable energy on all fronts, countries everywhere watch its development. Since demand for power generation goes up again, fossil fuels become less and less available, and expense is not coming down. When there is a rapidly changing irradiance, temperature, or partial shading, the output …
Published in Journal of Power Electronics and Power Systems · Vol. 16, Issue 2, 2026 Read article
-
AI EdTech Synergy: From Chalkboards to Smartboards
Abstract: Beyond textbooks and classrooms, AI paints a future from adaptive tutors to immersive realities. AI is not just a tool sculpted by algorithms but an architect of a learning revolution where knowledge becomes truly boundless. The convergence of AI marks an era of revolution in learning, promising individualized learning pathways, optimized evaluative metrics, interactive virtual pedagogies, and enhanced accessibility. This convergence examines the emergent field of AI-powered educational innovation, shedding …
Published in Journal of Open Source Developments · Vol. 12, Issue 3, 2025 · pp. 18–25 Read article
-
An Adaptive and Privacy-Aware Federated Learning Framework for Efficient and Secure Model Training Across Heterogeneous Datasets
Abstract: The problem of efficiency and privacy regarding heterogeneous data in modern distributed machine learning systems is a vital point that should be taken into account. The absence of IID data distribution, client heterogeneity, and privacy invasion during the aggregation model are the bane of conventional federated learning (FL) approaches to learning like FedAvg and FedProx. The paper proposes that the adaptive and privacy-aware FL framework (AFL-P) can be used to …
Published in Journal of Mobile Computing, Communications & Mobile Networks · Vol. 13, Issue 1, 2026 · pp. 16–25 Read article
-
Parallel Privacy-Preserving Adaptive Federated Learning on GPU-Enabled Multi-Core Architectures
Abstract: The increasing deployment of parallel and distributed intelligent systems has intensified the need for privacy-preserving learning frameworks that can exploit multi-core and GPU-based architectures without centralizing sensitive data. This work proposes a parallel Adaptive Federated Learning (AFL) framework that integrates Differential Privacy and Secure Aggregation over heterogeneous multi-core and GPU platforms to enhance both data confidentiality and convergence efficiency. The framework dynamically adjusts client participation, learning rates, and aggregation weights …
Published in Recent Trends in Parallel Computing · Vol. 13, Issue 1, 2026 Read article
-
Dynamic Routing and Performance Assessment in IPv6 Networks
Abstract: In the fast-paced world of networking technologies and the growing demands of contemporary communication, it is crucial to thoroughly examine adaptive routing in IPv6 networks. This project undertakes a thorough analysis and evaluation of adaptive routing protocols, particularly OSPFv3 and Border Gateway Protocol (BGP)+, within the context of IPv6. Through meticulous scrutiny of network dynamics, traffic behavior, and routing protocol decisions, this study aims to elucidate the efficacy and adaptability …
Published in Journal Of Network security · Vol. 12, Issue 3, 2024 · pp. 18–28 Read article
-
Hybrid Quantum-Classical Reinforcement Learning Enabled Thermal-Aware Electronic Design Automation Framework for Energy-Efficient Next-Generation VLSI Systems Applications
Abstract: Modern Very Large-Scale Integration (VLSI) systems are becoming more complicated, which has increased need for sophisticated Electronic Design Automation (EDA) frameworks that can concurrently optimise thermal behaviour, power consumption, and performance. This study proposes a Hybrid Quantum-Classical Reinforcement Learning (HQCRL) Enabled Thermal-Aware EDA Framework for next-generation energy- efficient VLSI systems. The proposed framework integrates quantum-inspired optimization techniques with classical reinforcement learning algorithms to address the challenges of placement, routing, and …
Published in Journal of Electronic Design Technology · Vol. 17, Issue 2, 2026 Read article
-
Fractal-Entropy Guided Adaptive Signal Reconstruction for Non-Stationary Biomedical and Communication Systems
Abstract: This paper presents a novel Fractal-Entropy Guided Adaptive Signal Reconstruction (FEG- ASR) framework designed for accurate processing of non-stationary signals in biomedical and communication systems. The proposed approach integrates fractal dimension analysis with entropy- based feature evaluation to capture the intrinsic complexity and irregularity of time-varying signals. By dynamically adapting reconstruction parameters based on fractal-entropy measures, the method effectively separates noise from meaningful signal components while preserving critical information. The …
Published in Current Trends in Signal Processing · Vol. 16, Issue 1, 2026 Read article
-
Automating Compiler Optimization: A Machine Learning Approach
Abstract: This study reports on an ML-based approach to compiler optimization, complementing traditional optimization methods that rely strongly on hand-tuned settings. Compiler optimization plays a key role in performance-speedup and energy optimization of complex contemporary software systems. However, the traditional approach to optimizer settings involves laborious, error-prone, and scale-insensitive human-in-the-loop intervention, especially in the complex and high-demand environments in which today's computing application thrives. By integrating RL and GA, we can …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 12–16 Read article