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277 articles for “model interpretability”
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Advanced Lithium-Ion Battery Prognostics: A Comprehensive Review of Machine Learning Approaches for Remaining Useful Life Prediction
Abstract: The lithium-ion battery (LIB), as one of the main sources for portable power systems, has been increasingly popular owing to its widespread applications in electric vehicles, consumer electronics, aerospace and renewable energy. Despite their advantages in high energy density and long cycle life, LIBs suffer from degradation over time of aging and cycling, resulting in loss of performance, safety issues, and economic bottlenecks. Predicting their Remaining Useful Life (RUL) is …
Published in International Journal of Mechanical Dynamics and Systems Analysis · Vol. 3, Issue 2, 2025 · pp. 12–27 Read article
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Fuzzy Mathematics in Decision-Making: A Quantitative Perspective
Abstract: Fuzzy mathematics plays an increasingly generalized role in decision-making, and thus, this paper details different types of fuzzy mathematics and highlights other possible alternatives alongside fuzzy methodologies. Fuzzy models offer a versatile and precise approach to assessing complex and uncertain situations using fuzzy sets, membership functions, linguistic variables, and aggregation methods. Through the lenses of time, cost, and quality, the project management case study illustrates how fuzzy logic effectively evaluates …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 12, Issue 1, 2025 · pp. 6–12 Read article
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Physics-Informed Machine Learning and Multiscale Modeling for Structure–Property Quantification of Polymer Composites
Abstract: The growing need for light-weight, high strength, and sustainable polymer composites has led to the development of smart methods that enable accurate structural-property quantification and material design. However, conventional methods have been predominantly data-based, thus ignoring physical constraints as well as multi-scale interactions involving fiber, matrix, interface, and process parameters, leading to lower accuracy and poor robustness and interpretability of the models. In this study, a Cat Swarm Optimization-Tuned Physics-Informed …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Mathematical Modelling of Semiconductor Device Physics: An Analytical Approach
Abstract: Semiconductor device physics forms the foundation of modern electronic and optoelectronic technologies. Mathematical modelling provides a rigorous framework for understanding, predicting, and optimizing the behavior of semiconductor devices by linking physical principles with device-level performance. This work presents an analytical approach to the mathematical modelling of semiconductor devices, emphasizing the derivation and interpretation of governing equations that describe charge transport and electrostatic behavior. The model is based on fundamental physical …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 1, 2026 · pp. 36–42 Read article
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Topology and Geometry in Data Science: Persistent Homology and Beyond
Abstract: In recent years, the interplay between topology, geometry, and data science has gained substantial momentum, offering powerful frameworks to analyze and interpret complex datasets. Traditional statistical and machine learning methods often rely on linear or metric- based assumptions, which may fail to capture the intrinsic structure of high-dimensional or nonlinear data. In contrast, topological and geometric methods provide shape-oriented, scale- invariant tools that focus on the continuity, connectivity, and global …
Published in Recent Trends in Mathematics · Vol. 1, Issue 2, 2024 · pp. 21–27 Read article
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QSAR Modeling Techniques A Comprehensive Review of Tools and Best Practices
Abstract: Quantitative Structure–Activity Relationship (QSAR) modeling has become an essential tool in drug discovery, toxicity assessment, and environmental chemistry. By correlating chemical structure with biological activity or toxicity, QSAR enables the prediction of compound behavior without extensive experimental testing. This approach not only saves time and resources but also supports ethical practices by reducing reliance on animal studies. The evolution of QSAR from basic linear models to advanced machine learning and …
Published in International Journal of Cheminformatics · Vol. 3, Issue 1, 2025 · pp. 56–63 Read article
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Challenges in the Field of Soft Robotics.
Abstract: One of the interesting topics in robotics research of recent years has been Soft Robotics. Theuse of unconventional materials in robotic systems is an idea that is revolutionizing roboticsand automation. Robotic systems are being designed so that they can mimic biologicalsystems. The detailed study of interactions between a physical system and the environment iscrucial in developing this technology. Novel robotic components are being designed using softmaterials but the complexity of …
Published in Trends in Electrical Engineering · Vol. 10, Issue 3, 2020 · pp. 46–52 Read article
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The Weibull Distribution: A Revisit
Abstract: AbstractWeibull distribution is the most popular model in modeling data from various fields. From its introduction, comprehensive work has been done on this model providing distinct interpretations, constructions, generalizations, inferential methods, discrete cases and extensions to bivariate and multivariate. More than 4000 papers have appeared describing all these aspects and developments. This paper provides a review on the historical background and parallel developments of the Weibull distribution. Further, various generalizations …
Published in Research & Reviews : Journal of Statistics · Vol. 9, Issue 1, 2020 · pp. 16–26 Read article
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Support Vector Machine Inspired Load Forecasting of a State University in Haryana
Abstract: Estimating the possible environmental impact and determining probable capital requirements are made easier with a solid grasp of electricity demand. Beginning in the middle of the 20th century, demand forecasting for electric power networks was studied theoretically. Prior to that, the study of demand forecasting had not developed because of the small scale of power networks. With the use of statistical prediction techniques, plans for the electric power industry have …
Published in Trends in Electrical Engineering · Vol. 15, Issue 2, 2025 · pp. 33–40 Read article
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From Differential Equations to Data Science: A Survey on Analytical Methods in Contemporary Problems
Abstract: The integration of differential equations and data science methods represents a dynamic and evolving approach to solving contemporary challenges across a wide range of disciplines, including engineering, physics, biology, economics, and finance. Differential equations have long served as fundamental tools for modeling continuous systems and processes, offering powerful insights into the behavior of natural and man-made phenomena. For example, they describe how heat diffuses through materials, how populations grow in …
Published in Recent Trends in Mathematics · Vol. 2, Issue 2, 2025 · pp. 1–6 Read article
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Artificial Intelligence and Constitutive Modeling Equations for Predictive Design of High-Performance Polymer Composites
Abstract: Growing polymer composite applications demand accurate mechanical prediction, yet complex interactions and conventional constitutive models limit predictive capability and require extensive calibration. To report these challenges, this research recommends a combined Artificial Intelligence (AI) and constitutive modeling approach based on an Enhanced Tasmanian Devil Optimizer-tuned Residual Neural Network with Multilayer Perceptron (ETDO-ResNet-MLP) for the predictive design of high-performance polymer composites. The study uses a publicly available Polymer Composite Property Dataset …
Published in Journal of Polymer & Composites · Vol. 14, Issue 4, 2026 Read article
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Adversarial Attacks on Machine Learning Models in Cybersecurity: A Systematic Literature Review
Abstract: Adversarial machine learning (AML) is a field that is growing swiftly, especially as machine learning models are employed more and more in places where security is critical. This review goes into great depth over 746 publications from the Scopus database, with an emphasis on the connection between AML and network security. Using Biblioshiny and Scopus tools, we looked at trends in publications, study fields, productive authors, collaboration networks, and theme …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 1, 2025 · pp. 23–38 Read article
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Machine Learning-Based Approach for Heart Disease Prediction
Abstract: Heart disease is a significant global health challenge, with early diagnosis and prediction being essential for reducing mortality rates. Machine Learning (ML), an efficiently developing field within Artificial Intelligence, provides innovative methods for analyzing complex clinical data to predict heart disease. This review examines the basic machine learning techniques, data, and metrics used in cardiovascular disease prediction. It explores the role of supervised learning, such as decision trees and logistic …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 2, 2025 · pp. 64–73 Read article
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Artificial Intelligence in Entomology: Global Advances, Applications, and Future Directions in Insect Research and Pest Management
Abstract: Artificial Intelligence (AI) is transforming entomology by enabling scalable, data-driven approaches to insect identification, ecological monitoring, and sustainable pest management. This review synthesizes recent global advances in AI applications across taxonomy, behavioral ecology, predictive modeling, and precision agriculture. Machine learning and deep learning techniques—including convolutional neural networks, acoustic classification models, and ensemble predictive algorithms—have demonstrated high classification accuracies (often exceeding 90% under controlled conditions) and improved early detection of pest …
Published in International Journal of Insects · Vol. 3, Issue 1, 2026 · pp. 29–40 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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Deep Learning Approaches for Nanoplasmonics: Expanding Horizons
Abstract: Deep learning, a subfield of machine learning, has revolutionized various domains by providing powerful tools to process and analyze complex data. In the realm of nanoplasmonics, deep learning techniques have emerged as promising approaches to tackle the challenges associated with large-scaledata analysis, modeling, and optimization of plasmonic systems. This article presents an in-depth exploration of deep learning approaches applied to nanoplasmonics research, focusing on the advances, opportunities, and future directions …
Published in Journal of Nanoscience, NanoEngineering & Applications · Vol. 13, Issue 1, 2023 · pp. 30–38 Read article
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AI-Enhanced Interpretation of Cardiac Troponins: Toward Predictive Precision in Myocardial Injury
Abstract: Background: Cardiac troponins (cTn) represent the gold standard biomarkers for myocardial injury detection, yet their interpretation remains challenging due to various confounding factors and clinical contexts. Artificial intelligence (AI) technologies provide remarkable possibilities to improve the interpretation of troponin levels by utilizing pattern recognition, predictive modeling, and clinical decision-making support. Objective: This review examines the current state and future potential of AI-enhanced cardiac troponin interpretation, focusing on machine learning applications, …
Published in Research and Reviews: A Journal of Medicine · Vol. 15, Issue 3, 2025 · pp. 1–9 Read article
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A SEIR-Informed Stacked Fusion of Prophet, XGBoost, and LSTM for Ward-Level Epidemic Forecasting in Amravati Municipal Corporation
Abstract: Municipal epidemic preparedness depends on accurate short-horizon forecasts at fine spatial granularity. Ward-level incidence series are typically nonstationary due to changing contact patterns, interventions, reporting delays, and heterogeneous demographic and environmental factors. This paper presents a mathematically formulated hybrid forecasting architecture designed for Amravati Municipal Corporation (AMC). The method decomposes observed incidence into (i) a mechanistic SEIR baseline that enforces epidemiological structure and (ii) a data-driven residual learned using Prophet …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 13, Issue 1, 2026 · pp. 17–23 Read article
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Static and Dynamic Analysis of Foundation considering SSI under different Soils and Vertical Load Conditions using FEM
Abstract: The behavior of foundations when rested upon various types of soil such as loose sand, medium dense sand, dense sand, silty sand and sand and gravel is studied in this project. The effect of soil-structure interaction is also considered during the modelling. The foundation was considered a square type isolated footing with varying vertical loads. Ground motion data for El Centro Earthquake was utilized for the dynamic analysis. Results for …
Published in Journal of Geotechnical Engineering · Vol. 5, Issue 2, 2018 · pp. 8–14 Read article
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Analytical influence of soil-structure-interaction on structural performance by using ETABS
Abstract: Soil conditions have a great deal to do with damage to structures during earthquakes. Hence the study on the energy transfer mechanism from soils to buildings during earthquakes is critical for the seismic design of multi-story buildings and for upgrading existing structures. Thus, the need for research into soil–structure interaction (SSI) problems is greater than ever. Moreover, recent studies show that the effects of SSI may be detrimental to the …
Published in Journal of Structural Engineering and Management · Vol. 5, Issue 3, 2018 · pp. 1–13 Read article