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1732 articles for “Predicting”
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Comparative Analysis of Supervised Learning Algorithms
Abstract: Supervised learning is a fundamental and widely used branch of machine learning in which models are trained on labeled datasets, meaning that each input is associated with a known output. Supervised learning algorithms develop predictive capability by understanding the mapping between input variables and corresponding output labels, enabling them to accurately forecast outcomes for previously unseen data. Due to this capability, supervised learning has found extensive applications across diverse domains …
Published in Journal of Computer Technology & Applications · Vol. 17, Issue 1, 2026 · pp. 25–30 Read article
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Multi-Layered AI-Driven Security in Wireless Ecosystems
Abstract: The proliferation of next-generation wireless technologies, from 5G/6G networks to the pervasive Internet of Things (IoT), has birthed a hyperconnected digital ecosystem of unprecedented scale and dynamism. This interconnectedness, however, introduces a vast and volatile attack surface, rendering conventional, signature-based security paradigms fundamentally obsolete. This paper posits that the only viable defense is an offensive, self-adaptive one, predicated on the integration of artificial intelligence (AI) directly into the wireless security …
Published in International Journal of Wireless Security and Networks · Vol. 4, Issue 1, 2026 · pp. 21–28 Read article
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The Influence of Self-Efficacy on Anxiety and Perfectionism among Dancers
Abstract: The world of dance, though celebrated for its creativity and artistic freedom, often carries considerable psychological demands on performers. Beyond the physical rigor, dancers navigate pressures of performance standards, audience expectations, and internalized goals of perfection. This study examined how self-efficacy—an individual’s belief in their own capability—shapes the experiences of anxiety and perfectionism among dancers, with a particular focus on gender differences. A balanced sample of 120 dancers (60 males …
Published in International Journal of Behavioral Sciences · Vol. 3, Issue 1, 2026 · pp. 37–44 Read article
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Development of a Generative AI Model for Early Detection and Prevention of Electrical Faults in Thermal Power Plants
Abstract: Electrical faults in thermal power plants can lead to severe equipment damage, production downtime, and safety hazards if not detected in advance. This study presents the development of a Generative Artificial Intelligence (GenAI) model for the early detection and prevention of electrical faults using predictive analytics. The proposed framework integrates Generative Adversarial Networks (GANs) with deep learning (CNN) and machine learning algorithms (Random Forest, Logistic Regression) to enhance data diversity, …
Published in International Journal of Energy and Thermal Applications · Vol. 4, Issue 1, 2026 · pp. 45–54 Read article
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Particle Swarm Optimization Framework for Accurate Battery State-of-Charge and Remaining Useful Life Estimation
Abstract: Accurate estimation of the State of Charge (SOC) and State of Health (SOH) of a battery is key to safe and efficient management of batteries in electric vehicles and energy-storage systems. However, it is challenging due to high nonlinearity, varying operating conditions, measurement noise, and limited access to comprehensive electrochemical parameters. Traditional data-driven models often generalize poorly and require heavy tuning, which can produce unstable predictions. To address these problems, …
Published in Journal of Automobile Engineering and Applications · Vol. 13, Issue 1, 2026 · pp. 53–64 Read article
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AI-Driven Topology Optimization of Woven Fiber-Reinforced Composite Chassis Structures for Electric Vehicles Under Crash Loading
Abstract: The structural design of an electric vehicle (EV) chassis represents a unique engineering challenge to achieve minimal weight while meeting occupants' safety requirements during high-energy crash conditions without compromise to the battery housing's integrity or the geometrical constraints of the electric powertrain package. In this paper, a single framework is proposed to integrate physics-based artificial intelligence (AI) surrogate models using PINNs, CNN-accelerated topology optimization, and FEA to design woven fiber-reinforced …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 72–89 Read article
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Diabetes Risk & Al Nutrition Assistant
Abstract: The rising prevalence of diabetes mellitus has emerged as a major global health challenge. Early identification of individuals at risk, combined with personalized lifestyle-based interventions, can significantly reduce future complications. This study presents an AI-driven Nutrition Assistant integrated with a Diabetes Risk Prediction model. The system uses a machine learning classification approach to estimate the likelihood of diabetes based on clinical and nutritional factors, including body mass index, glucose levels, …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 13, Issue 1, 2026 · pp. 31–38 Read article
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Damage Evolution and Delamination Resistance in Polymer Matrix Functionally Graded Laminates
Abstract: Functionally graded laminates (FGLs) in polymer-matrix systems represent a promising pathway to enhance damage tolerance and delay delamination in advanced structural composites. In this study, we explore the mechanisms of damage initiation, propagation, and delamination resistance in polymer matrix functionally graded laminates (PM-FGLs) through a combined experimental–computational approach. Laminates with linear, exponential, and bio-inspired gradation profiles were fabricated using vacuum-assisted resin transfer molding (VARTM) and additive manufacturing techniques. Comprehensive mechanical …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 321–337 Read article
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Forecasting Climate-Driven Healthcare Demand in Agricultural Regions: A Multi-Modal AI Approach
Abstract: The rapidly increasing instability of world climatic regimes has made past meteorological thresholds irrelevant, especially in the agricultural areas where monetary stability and well-being of humans are closely intertwined with an environmental situation. The more the frequency of 1 in every 1000-year events, i.e., heatwaves and catastrophic flooding increase, the greater the rural healthcare systems are in crisis, i.e., unable to predict a surge in demand because of data scarcity, …
Published in International Journal of Climate Conditions · Vol. 2, Issue 2, 2025 · pp. 28–38 Read article
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AI-Driven Framework for Accelerating Polymer Nanocomposite Commercialization in Computational Materials Engineering
Abstract: The remarkable mechanical strength increased functional qualities, lightweight structure, and thermal stability of polymer nanocomposites have prompted modern materials research to prioritize their rapid commercialization. Advanced materials can be created by adding nanoscale fillers such as carbon nanotubes, graphene, silica, and metal oxides to polymer matrices. These materials have applications in biomedical engineering, aerospace, electronics, packaging, and automobile manufacture. Research and development of polymer nanocomposites has traditionally relied on costly …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1–19 Read article
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Machine Learning in Nuclear Medical Applications: A Review of Research Frontiers
Abstract: Nuclear medicine, encompassing PET, SPECT, and targeted radionuclide therapy, generates high-dimensional, quantitative data uniquely suited for machine learning (ML) analysis. This review synthesizes current research applications of ML across six key domains. Positron emission tomography (PET), single-photon emission computed tomography (SPECT), and targeted radionuclide therapy are examples of nuclear medicine modalities that generate high- dimensional, quantitative datasets that are particularly well-suited for machine learning (ML)-driven analysis. These imaging methods provide …
Published in Journal of Nuclear Engineering & Technology · Vol. 16, Issue 1, 2026 · pp. 19–24 Read article
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Machine Learning Assisted Design and Analysis of Polymer Composite Materials for Sustainable Renewable Energy Systems
Abstract: Accurate prediction and optimization of polymer composite properties is of paramount importance in the design of these lightweight, durable, and sustainable materials within renewable energy technologies. This work will provide a holistic machine learning-assisted framework that unites materials informatics with domain-specific features and state-of-the-art ML methodologies in the prediction of the mechanical properties of polymer composites, such as tensile strength. This includes embedding several ensemble models, including Random Forest and …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 391–402 Read article
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Structure Property Correlation of Polymer Dielectrics Using Electrical Response Data
Abstract: Polymer dielectrics are foundational to insulation, capacitors, embedded passives, and flexible electronics, where performance is governed by the frequency-dependent electrical response rather than a single dielectric constant. This study presents a spectroscopy-aware structure–property correlation framework that transforms dielectric response data into physically interpretable spectral fingerprints and learns mappings from polymer descriptors to these fingerprints for prediction and interpretation. Broadband spectra are standardized on a log-frequency grid and parameterized using relaxation-informed …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 315–324 Read article
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Computational Modeling of Polymer Semiconductors for Electronic Applications
Abstract: Polymer semiconductors have become important materials in modern electronic applications because they combine semiconducting behavior with mechanical flexibility, low-cost processing, and tunable molecular structure. Their growing use in organic field-effect transistors, organic photovoltaics, organic light-emitting diodes, and flexible sensing devices has increased the need for accurate computational approaches that can predict material properties and device performance before experimental fabrication. This paper reviews the major computational modeling techniques used for polymer …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 132–146 Read article
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Fracture Analysis of Functionally Graded Material (FGM) Plates Using Extended Finite Element Method: A Review.
Abstract: Functionally Graded Materials (FGMs), have drawn a lot of interest in various engineering applications due to their superior mechanical properties and ability to withstand extreme conditions. Fracture analysis in FGMs focuses on understanding how cracks initiate and propagate within these complex materials. The stress distribution becomes irregular due to spatial property variations which produces different crack paths than what occurs in homogeneous materials. The examination of FGM plates under fracture …
Published in International Journal of Fracture Mechanics and Damage Science · Vol. 4, Issue 1, 2026 · pp. 9–15 Read article
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Explainable Artificial Intelligence in Personalized Medicine: Emerging Clinical Perspectives
Abstract: The convergence of artificial intelligence (AI) and precision medicine has transformed contemporary healthcare by enabling data-driven clinical decision-making, individualized therapeutic interventions, and predictive diagnostics. However, despite remarkable advances in machine learning (ML) and deep learning (DL), the widespread adoption of AI in healthcare remains constrained by the “black-box” nature of many computational systems. Clinicians, regulatory agencies, and patients increasingly demand transparency, interpretability, and trustworthiness in AI-guided medical recommendations. Explainable Artificial …
Published in Emerging Trends in Personalized Medicines · Vol. 3, Issue 2, 2026 · pp. 13–29 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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Molecular Docking, QSAR Modeling, and ADMET Evaluation of Novel Pyrazolo-Pyrimidine Derivatives as Potential CDK-2 Inhibitors for Cancer Therapy
Abstract: Cyclin-dependent kinase-2 (CDK-2) is an essential regulator in cell cycle progression and is an important therapeutic target in cancer drug development. In the present study, an integrated computational approach involving molecular docking studies, QSAR modeling, ADMET prediction, and artificial intelligence-based analysis was used to identify pyrazolo-pyrimidine derivatives as potential CDK-2 inhibitors. Based on the molecular docking results, it was found that selected compounds exhibited high binding affinity towards the ATP …
Published in Research and Reviews: A Journal of Pharmacology · Vol. 16, Issue 2, 2026 Read article
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Multi-Scale Analysis of Polymer Based Energy Storage Systems for High Performance Battery Applications
Abstract: The energy storage systems based on polymers are becoming promising materials for the next generation of high performance batteries because of their excellent mechanical flexibility, improved safety, and favorable electrochemical properties. Even with computational tools in Python, polymer-based energy storage systems remain plagued by poor ionic conductivity, complicated electrochemical reactions and potential thermal runaway. Therefore, a multi-scale model is proposed to improve battery performance, thermal stability, reliability, and large-scale deployment …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 1035–1048 Read article
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Enhance Thermal and Conductive Properties through Graph Neural Network-Based Machine Learning-Driven Advanced Polymer Material Design
Abstract: Advanced polymer materials are widely used in modern engineering and manufacturing because of their lightweight nature, flexibility, durability, and adaptability to different applications. However, designing polymer materials with enhanced thermal and electrical properties remains a challenging task. The performance of polymers is influenced by a complex combination of molecular structures, filler materials, processing parameters, and nanoscale interactions. Conventional optimization methods often require extensive experimental trials and computational resources, making it …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 · pp. 386–407 Read article