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313 articles for “data-driven learning”
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Smart Monitoring and Controlling of the Battery and Motor
Abstract: The demand for effective battery and motor monitoring and management to guarantee dependability, safety, and peak performance has increased due to the quick development of electric vehicles and smart industrial systems. The smart monitoring and control methods used in battery management systems and motor control systems are thoroughly reviewed in this paper. In addition to speed, torque, efficiency, and fault situations in motors, it emphasizes important characteristics like temperature, voltage, …
Published in International Journal of Electrical Machine Analysis and Design · Vol. 4, Issue 1, 2026 · pp. 11–15 Read article
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Mechanical Strength Prediction of Nano-Silica Concrete Composites Using Machine Learning Techniques
Abstract: Nano-silica, or nanosilica, refers to silicon dioxide nanoparticles, which are a kind of silica (SiO₂) with diameters that often fall below 100 nanometers. This nanomaterial has attracted considerable attention because of its distinctive characteristics and diverse array of uses, notably in augmenting the performance of materials such as concrete. The integration of nanoparticles with cementitious matrix in nano-silica concrete offers a viable approach to improving the mechanical characteristics and longevity …
Published in Journal of Polymer & Composites · Vol. 13, Issue 1, 2025 · pp. 963–973 Read article
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A Hybrid Machine Learning Approach for Cardiovascular Disease Prediction
Abstract: Heart disease ranks among the top causes of death globally. Accurately predicting cardiovascular conditions has become a key challenge in the realm of clinical data analysis. It has been shown that machine learning is an effective means of assisting with predicting and decision-making based on the large volume of data produced by the medical industry. In this study, we describe a unique approach that increases the prediction accuracy of heart-related …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 12, Issue 1, 2025 · pp. 69–75 Read article
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Machine Learning-Assisted Design and Optimization of Lightweight Polymer Composites for IoT-Enabled Automotive Applications
Abstract: This study aims to develop an integrated machine learning and optimization framework for the intelligent design of lightweight polymer composites suited for IoT-enabled automotive applications. The goal is to enhance material performance while satisfying multiple design constraints such as mechanical strength, thermal stability, and process compatibility. A curated dataset of polymer composite formulations was used to train a Random Forest Regression (RFR) model capable of predicting tensile strength, thermal conductivity, …
Published in Journal of Polymer & Composites · Vol. 13, Issue 5, 2025 · pp. 12–27 Read article
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Depiction-inspired Recipe Generator Using Deep Learning
Abstract: Machine learning has become a crucial part of modern life, influencing various domains. Its applications range from enhancing data-driven business decisions to enabling autonomous vehicles. Advances in machine learning have brought about notable changes in how we interact with technology. In the culinary world, the idea of creating food recipes from images has gained increasing interest. This entails the development of innovative systems that seamlessly convert visual input, such as …
Published in Journal of Open Source Developments · Vol. 11, Issue 2, 2024 · pp. 47–55 Read article
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Enhancing ABET Summative Direct Assessment with AI and Blockchain: A Framework for Personalized Learning and Secure Evaluation
Abstract: Accreditation Board for Engineering and Technology (ABET) emphasizes the achievement of specific measurable learning outcomes. However, conventional assessment methods often find it challenging to accurately capture the complexities of student learning and program effectiveness within the ABET framework. This study proposes a novel framework that enhances ABET summative direct assessment by integrating a carefully structured, weighted assessment system with the transformative potential of artificial intelligence (AI) and blockchain technologies. The …
Published in International Journal of Algorithms Design and Analysis Review · Vol. 2, Issue 1, 2024 · pp. 28–42 Read article
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A Factorial Investigation of Hyperparameter Tuning Strategies for Lasso- Based Genomic Prediction
Abstract: In an earlier comparative study of machine-learning methods for genomic prediction of wheat grain yield, we reported a counter-intuitive result: automated nested-cross-validation tuning of the Lasso regularization penalty reduced mean predictive ability relative to a fixed, arbitrarily chosen penalty (mean Pearson r falling from 0.408 to 0.349 across four environments), the opposite of the expected effect of hyperparameter tuning. We hypothesized two possible explanations at the time — high-variance penalty …
Published in Research and Reviews : Journal of Computational Biology · Vol. 15, Issue 2, 2026 Read article
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Smart Weather Monitoring and Forecasting System Using Machine Learning (ML)
Abstract: The Smart Weather Monitoring System & Forecasting using Machine Learning (ML) represents an innovative approach to modern weather prediction and monitoring. This system combines the capabilities of machine learning algorithms with vast sets of weather data to provide accurate and timely weather forecasts. By collecting and analyzing data points like temperature, humidity, light intensity, rainfall, and atmospheric pressure, the system can generate precise predictions for a wide range of applications. …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 13, Issue 1, 2024 · pp. 12–21 Read article
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Data-Driven Digital Twin Model for Real-Time Strength Estimation in Polymeric Materials
Abstract: The real-time prediction of mechanical properties in polymeric materials is essential for ensuring quality, consistency, and operational efficiency in modern manufacturing systems. As industrial processes become increasingly complex, traditional trial-and-error approaches to material characterization are no longer sufficient to meet the demands of high-throughput production environments. This study introduces a digital twin-integrated machine learning approach for the real-time estimation of tensile strength in polymeric materials by combining simulation-driven insights with …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 246–257 Read article
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Improving Supply Chain Resilience through Predictive Analytics and Real-Time Data Integration
Abstract: Demand forecasting has under gone major changes because to the incorporation of automated analytics into supply chain management (SCM), which has improved company productivity, accuracy, and responsiveness. Central to this transformation is the application of machine learning (ML), which enables the analysis of large and complex datasets to identify patterns, detect trends, and generate precise forecasts. Conventional methods for predicting frequently rely on linear models and historical sales data, which …
Published in International Journal of Industrial and Product Design Engineering · Vol. 3, Issue 2, 2025 · pp. 8–17 Read article
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Intelligent Design Approaches in Microwave Engineering Using Machine Learning Techniques
Abstract: In microwave engineering, machine learning (ML) has become a potent technology allowing quicker design cycles, improved modelling accuracy, and automatic optimisation of complicated systems. Recent developments in the use of ML methods to microwave components and systems, including antennas, filters, and high-frequency circuits, are summarised in this study. In the framework of electromagnetic simulation, surrogate modelling, and parameter extraction, supervised and unsupervised learning algorithms are addressed. Moreover, the study looked …
Published in Journal of Microwave Engineering and Technologies · Vol. 12, Issue 2, 2025 · pp. 31–38 Read article
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Disease Prediction Using Ensemble Learning Models: A Comprehensive Approach
Abstract: In recent years, ensemble learning techniques have become pivotal in advancing predictive analytics within healthcare, particularly for early disease detection. The inherent variability and complexity of medical data, often characterized by high dimensionality, class imbalance, and noise, make it challenging for standalone classifiers to maintain high predictive accuracy. Ensemble learning, by integrating multiple models through bagging, boosting, or stacking, offers a more robust and generalizable approach. This study explores the …
Published in Journal of Communication Engineering & Systems · Vol. 15, Issue 3, 2025 · pp. 26–33 Read article
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The Integration of AI Technologies in Automating Cyber Defense Mechanisms for Cloud Services
Abstract: The swift growth of cloud computing has transformed how organizations handle and store data, providing greater scalability and adaptability. However, the transition to cloud-based environments has heightened the complexity of cybersecurity challenges, especially in detecting and responding to security incidents. Conventional methods of incident response, which heavily depend on manual efforts, are no longer adequate to address the rapidly evolving and complex nature of modern cyber threats. This study explores …
Published in Journal of Operating Systems Development & Trends · Vol. 12, Issue 1, 2025 · pp. 1–14 Read article
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Deep Learning Meets IoT: Hybrid Approaches for Botnet Detection
Abstract: Rapid advancement in the Internet of Things (IoT) changed everything, making it possible for seamless interconnectivity of devices and altering data-driven decision processes. This study delves into the intersection of IoT with deep learning approaches and hybrid approaches for managing botnet in IoT systems, especially security, efficiency, and performance optimization. Leveraging deep learning models, for example, CNNs and RNNs, will help the network achieve more intrusion detection and data analysis. …
Published in Recent Trends in Electronics Communication Systems · Vol. 12, Issue 1, 2025 · pp. 18–27 Read article
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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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Mathematical Modeling of Epidemics Using Stochastic Differential Equations: A Review
Abstract: The accurate modeling of infectious disease dynamics is crucial for predicting outbreaks and informing public health interventions. While deterministic models such as the SIR (Susceptible-Infected-Recovered) framework have traditionally been used to understand disease transmission, they often fail to account for the randomness inherent in real-world scenarios. Disease spread is influenced by numerous uncertain factors, including individual behavioral changes, environmental fluctuations, and imperfect data reporting. These uncertainties can significantly impact model …
Published in Recent Trends in Mathematics · Vol. 1, Issue 2, 2024 · pp. 1–6 Read article
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An Overview on AI-Driven IoT Based Decision Making in Climate change Study: KSK approach in Climate Change Study
Abstract: As the Earth’s climate enters a state of unprecedented volatility, the traditional methods of ecological observation—characterized by delayed reporting and fragmented data—are no longer sufficient. This study investigates the paradigm shift toward AI-driven IoT (KSK Approach)-based decision-making frameworks as the primary frontier in climate science. By deploying a "planetary nervous system" of interconnected sensors—measuring everything from soil moisture in the Sahel to glacial melt rates in the Arctic—we generate a …
Published in International Journal of Climate Conditions · Vol. 3, Issue 1, 2026 · pp. 1–10 Read article
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A Gamified Digital Platform for Sustainable Farming Practices: Simulation, Statistical Analysis, and Water Resource Management Implications
Abstract: Sustainable farming practices play a critical role in enhancing agricultural water use efficiency, conserving limited water resources, and ensuring long-term food security under increasing environmental and climatic pressures. Despite their importance, farmer participation in conventional agricultural extension and training programs remains limited due to low engagement and a lack of sustained motivation. To address this challenge, this study proposes a gamified digital decision-support platform aimed at promoting sustainable agricultural and …
Published in Journal of Water Resource Engineering and Management · Vol. 13, Issue 1, 2026 · pp. 13–24 Read article
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Entropy, Symmetry, and Data Fusion: Emerging Methods in Multi-Objective Decision- Making and Smart Systems
Abstract: In the era of intelligent technologies and data-driven systems, multi-objective decision-making (MODM) has become an essential aspect of managing complex environments such as smart cities, autonomous systems, and cyber-physical networks. As decision-making scenarios become increasingly dynamic and uncertain, there is a growing need for advanced methodologies that can handle diverse objectives, conflicting constraints, and incomplete information. This review highlights the emerging role of entropy, symmetry, and data fusion as foundational …
Published in Emerging Trends in Symmetry · Vol. 1, Issue 2, 2025 · pp. 44–49 Read article
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Harvestify: ML Based Tool for Home Gardening and Farming
Abstract: This study presents a cutting-edge application that will transform home gardening and agriculture practices using machine learning (ML) approaches. The main goal is to provide data-driven insights to home gardeners and farmers, enabling them to implement efficient and sustainable farming practices. Crop disease detection, fertiliser recommendation, and a community section for user engagement comprise the three main elements that make up the system's architecture. The Crop Disease Detection module analyses …
Published in International Journal of Electrical and Communication Engineering Technology · Vol. 2, Issue 2, 2024 · pp. 18–28 Read article