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143 articles for “hybrid frameworks”
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Multimodal Data Fusion with Hybrid Machine Learning for Enhanced Prediction of Li-Ion Battery Remaining Useful Life and State of Charge
Abstract: Lithium-ion battery materials used in modern energy storage systems are required to exhibit high reliability, safety, and long lifecycle performance under varying operational and environmental conditions. Accurate prediction of Remaining Useful Life (RUL) and State of Charge (SoC) is therefore essential for understanding material degradation behavior, improving manufacturing quality, and enabling effective lifecycle management. However, nonlinear electrochemical aging, load variability, and thermal uncertainty significantly complicate accurate estimation of these parameters. …
Published in International Journal of Electro-Mechanics and Material Behaviour · Vol. 4, Issue 1, 2026 · pp. 1–5 Read article
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Hybrid Intelligence in Cyber Security: A Study
Abstract: The digital landscape is a battlefield of escalating complexity, where the volume, velocity, and sophistication of cyber threats have exponentially outpaced human-centric defense models. Traditional rule-based security systems and siloed artificial intelligence (AI) solutions, while valuable, are increasingly brittle, overwhelmed by zero-day exploits, polymorphic malware, and coordinated, state-sponsored campaigns that operate in the shadows of big data. This paper posits that the paradigm of cybersecurity must fundamentally shift from one …
Published in International Journal of Wireless Security and Networks · Vol. 4, Issue 1, 2026 · pp. 01–09 Read article
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Synthesis of Silver Nanoparticles of Cissus Quandrangularis and It’s Virtual Screening Against Gastric Cancer
Abstract: Gastric cancer, also known as stomach cancer, is the third leading cause of cancer-related deaths globally and remains a major health challenge due to its poor prognosis and high mortality, largely attributed to late-stage diagnosis. In this context, plant-based nanomaterials are gaining momentum for targeted therapeutic applications. Cissus quadrangularis, a medicinal plant native to India from the Vitaceae family, is traditionally used for its diverse healing properties. Its stem, in …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 1551–1569 Read article
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Triple-Threat Analysis: Measuring Mythril, Slither and Oyente Against Real-World Smart Contract Vulnerabilities
Abstract: Smart contracts have become fundamental building blocks of blockchain ecosystems, yet their immutable nature makes security vulnerabilities particularly devastating. This pa- per presents a comprehensive evaluation of three prominent static analysis tools—Mythril, Slither, and Oyente—for detecting vulnerabilities in Ethereum smart contracts. Through systematic experimentation with real-world contract categories (voting sys- tems, land registries, and crowdfunding platforms), we quantify the effectiveness of each tool across eight critical vulnerability types, including reentrancy, …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 1, 2025 Read article
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Microvita: A New Hybrid Particle Bridging the Fermionic and Bosonic Domains in Particle Physics
Abstract: In particle physics, the established classification of particles into fermions and bosons—obeying Fermi-Dirac and Bose-Einstein statistics, respectively—has shaped theoretical and experimental frameworks for nearly a century. This study introduces 'Microvita', a novel theoretical particle that exhibits hybrid characteristics modulated by a continuous parameter α ∈ [0,1]. Through this parameter, Microvita interpolates between bosonic and fermionic behavior in terms of spin, statistical distributions, and operator algebra. We explore the foundational algebra …
Published in Research & Reviews : Journal of Physics · Vol. 15, Issue 1, 2026 · pp. 14–27 Read article
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A Review on Artificial Intelligence Techniques for Analyzing Deforestation and Illegal Logging Using Satellite Imagery
Abstract: Deforestation and illegal logging remain critical environmental threats, driving biodiversity loss, climate change, and socio-economic disruption. Conventional monitoring techniques frequently do not yield real-time, large-scale insights. Recent developments in Artificial Intelligence (AI), especially in deep learning and computer vision, have revolutionized the ability to analyze high-resolution satellite images for detecting deforestation and monitoring illegal logging. This review synthesizes recent developments in AI-driven approaches, highlighting convolutional neural networks (CNNs), anomaly detection …
Published in International Journal of Satellite Remote Sensing · Vol. 4, Issue 1, 2026 · pp. 1–9 Read article
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Enhancing IoT Network Security with Hybrid Deep Learning Classifiers for DDoS Attack Detection
Abstract: The security and operational dependability of Internet of Things (IoT) networks are seriously threatened by the growing susceptibility to Distributed Denial of Service (DDoS) assaults brought about by their rapid expansion. The intricacy and dynamic character of these advanced attacks can provide a challenge to conventional intrusion detection systems. This study presents a novel method for strengthening IoT network security by combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory …
Published in Journal of Web Engineering & Technology · Vol. 13, Issue 1, 2026 · pp. 23–33 Read article
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An Intelligent Neural Networks Approach for Monitoring of Soilless Urban Farms
Abstract: Urban agriculture is increasingly recognized as a sustainable approach to addressing food security challenges in rapidly growing and densely populated cities. Conventional soil-based farming often faces limitations such as space scarcity, excessive water consumption, and environmental degradation. To overcome these challenges, soilless farming techniques such as hydroponics and aeroponics have gained significant attention due to their efficient utilization of space, reduced water requirements, and potential for year-round crop production. However, …
Published in Journal of Water Resource Engineering and Management · Vol. 12, Issue 3, 2025 · pp. 31–37 Read article
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A Threshold-Weighted Mathematical Fusion Model for Epidemic Outbreak Prediction Using SEIR Residual Dynamics and Cloud-Based Machine Learning
Abstract: Accurate prediction of epidemic outbreaks is critical for effective public health management, resource planning, early warning generation, and timely intervention by municipal authorities. Traditional compartmental models such as Susceptible–Exposed–Infectious–Recovered (SEIR) offer valuable epidemiological insights and mathematical interpretability; however, they may not adequately capture the complex nonlinear relationships present in real-world urban health systems. Conversely, data-driven machine learning techniques can identify hidden patterns in large datasets but often lack epidemiological structure …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 12–19 Read article
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Predictive Learning Powered by AI and Sophisticated Student Engagement Techniques
Abstract: The contemporary landscape of education has witnessed a paradigm shift in integrating advanced technologies that have revolutionized the learning experience. Innovative methodologies have emerged to address longstanding challenges, such as enhancing student engagement, accurately predicting academic performance, and personalizing the learning journey. However, despite the numerous benefits that technology brings to education, there remains a crucial hurdle - sustaining student motivation and engagement. Traditional teaching methodologies often struggle to generate …
Published in International Journal of Behavioral Sciences · Vol. 3, Issue 1, 2026 · pp. 127–140 Read article
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Machining-Induced Surface Integrity Optimization of High-Carbon Alloy Steel for Enhanced Polymer–Metal Composite Interface Performance
Abstract: The functional performance and structural reliability of polymer–metal hybrid composites are strongly influenced by the surface integrity of metallic substrates used for interfacial bonding and load transfer. In this context, machining-induced surface characteristics play a critical role in determining adhesion behavior, dimensional stability, and mechanical compatibility within composite architectures. The present study investigates the hard turning performance of a newly developed high-carbon alloy steel intended for composite-integrated structural applications, with …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1531–1546 Read article
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ML-Enhanced Self-Healing Fiber-Reinforced Polymer Composites with Embedded IoT Sensors for Damage Prediction
Abstract: Fiber-reinforced polymer (FRP) composites are widely used in aerospace and structural systems; nevertheless, the potential for microcracking and fatigue-induced performance degradation remains an obstacle with respect to improved service life. Traditional self-healing methods, while performing well on a chemical level, often lack real-time diagnostic awareness and adaptive control. To circumvent this, we developed a machine-learning augmented self-healing FRP composite, in which a DCPD–Grubbs catalytic matrix was combined with IoT sensor …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 188–208 Read article
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A Systematic Literature Review on Security Challenges in Cloud–Edge Hybrid Systems
Abstract: Cloud–edge hybrid systems have become a key framework in today’s distributed computing landscape, combining fast, near-source data processing at the edge with the flexible scalability and resource richness of centralized cloud infrastructures. However, this in- tegration introduces a complex security landscape where tradi- tional perimeter- based cloud security measures are insufficient for resource- constrained and physically exposed edge nodes. This literature review synthesizes findings from established research publications (2020–2025), focusing …
Published in International Journal of Wireless Security and Networks · Vol. 4, Issue 1, 2026 Read article
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Multiphysics Optimization of Polymer–Metal Hybrid Electrode Geometry in Electrostatic Precipitators for Enhanced Particle Collection Efficiency
Abstract: Electrostatic precipitators (ESPs) remain one of the most effective technologies for controlling fine particulate emissions in industrial exhaust systems. However, their performance is strongly influenced by electrode geometry and material characteristics, which govern electric field distribution, corona stability, and particle migration behaviour. In this study, a comprehensive numerical investigation is carried out to optimize electrode geometry using a multiphysics modelling framework, while introducing a novel polymer–metal hybrid design to enhance …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 701–716 Read article
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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
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Modelling and Control of Grid-Connected PV–Battery Hybrid System Using Dynamic Voltage Restorer for Enhanced Power Quality
Abstract: The integration of large-scale photovoltaic (PV) systems into modern power grids introduces significant challenges in maintaining power quality, including voltage sags, swells, and harmonics. To address these issues, this study presents a comprehensive modelling and control framework for a grid-connected PV–Battery hybrid system equipped with a Dynamic Voltage Restorer (DVR). The PV array is modelled using detailed mathematical equations, while the DC–DC converter incorporates advanced Maximum Power Point Tracking (MPPT) …
Published in International Journal of Electrical Power and Machine Systems · Vol. 3, Issue 2, 2025 · pp. 43–58 Read article
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A Theoretical Model for a Fermi–Boson Hybrid Particle in Nuclear and Particle Physics
Abstract: We present a theoretical model for a Fermi–Boson Hybrid Particle (FBHP) that unifies fermionic half-integer spin matter fields with bosonic integer-spin force fields within a single quantum framework. By extending conventional quantum field theory, a hybrid creation operator is formulated that combines fermionic and bosonic operators through a continuous mixing parameter, allowing smooth interpolation between Fermi–Dirac and Bose–Einstein statistical behaviour. A generalized statistical mechanics formalism is developed, leading to quantitative …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 10–18 Read article
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Wear and Tribological Characteristics of Novel Metal Matrix Composites
Abstract: The development of advanced metal matrix composites (MMCs) with enhanced tribological performance has become increasingly important due to the premature failure of critical engineering components operating under severe wear conditions in automotive, aerospace, marine, defense, and power generation systems. Conventional composites such as Copper–Alumina and Aluminium–Silicon Carbide have demonstrated improved mechanical and wear characteristics; however, their widespread application is often limited by issues including particle agglomeration, non-uniform reinforcement distribution, porosity …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 1326–1346 Read article
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Spintronic Logic Circuits for Ultrafast Processing
Abstract: Spintronic logic has emerged as one of the most promising post-CMOS paradigms capable of addressing the speed, density, and energy challenges of deeply scaled silicon technologies. By relying on the intrinsic properties of electron spin and magnetization dynamics, spintronic devices—particularly Magnetic Tunnel Junctions (MTJs), Spin-Transfer Torque (STT), and Spin–Orbit Torque (SOT) structures—enable ultrafast, non-volatile data processing with significantly reduced energy consumption. Despite remarkable device-level advancements, circuit- level realization of high-speed, …
Published in International Journal of VLSI Circuit Design & Technology · Vol. 3, Issue 2, 2025 · pp. 35–43 Read article
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Deep Reinforcement Learning-Based Intelligent Energy Management Strategy for Battery–Supercapacitor Hybrid Energy Storage Systems in Electric Vehicles
Abstract: As the number of EVs increases, smart solutions for energy management are needed that will optimize energy use, prolong battery life and boost vehicle performance. The application of conventional rule based and optimization-based Energy Management Strategies (EMS) for Battery–Supercapacitor Hybrid Energy Storage Systems (HESS) often leads to sub-optimal power management, supercapacitor mismatch and battery degradation when subjected to varying driving conditions. This study aims to design an intelligent energy management …
Published in International Journal of Advanced Control and System Engineering · Vol. 4, Issue 2, 2026 Read article