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277 articles for “model interpretability”
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Interpretive Structural Modelling Apply to Green Supply Chain Management
Abstract: This study aims to investigate the green supply chain management (GSCM) practices likely to be adopted by the electronic industry of electronics products in India. The approach of the present research includes depth literature review about the current scenario of electronic industry in India and the relationship between GSCM practices and environmental performance. The electronics products industries in India were sampled for empirical study. The parameters were analyzed using “Interpretive …
Published in Journal of Industrial Safety Engineering · Vol. 2, Issue 1, 2015 · pp. 13–27 Read article
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Interpretive Structural Modeling for Water Supply Service in India
Abstract: This paper develops a systematic assessment of the sustainability of water services provided to the consumers in rural, urban and municipality area in India and finds the interrelation between different dimensions by interpretive structural modeling. Given the conflicting preferences among the stakeholders and the incomplete, uncertain and contradictory understanding about water service by the Indian consumers, it is recognized that managing water resources sustainably is a wicked problem. In India …
Published in Journal of Offshore Structure and Technology · Vol. 2, Issue 1, 2015 · pp. 17–33 Read article
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Interpretive Structural Modelling (ISM) for Prioritization of Green Supply Chain Indicators
Abstract: AbstractPurpose: Today the innovative tool used for enhancing the efficiency of Supply Chain Management is Green Supply Chain Management (GSCM). This paper aims to identify and rank the major indicators and sub-indicators that help achieve successful implementation of Green Supply Chain Management (GSCM) in Indian Manufacturing Industries. Design/Methodology/Approach: Interpretive Structural Modelling (ISM) is used to identify indicators, which have been identified through literature review and experts’ opinions from industry person …
Published in Journal of Advanced Database Management & Systems · Vol. 6, Issue 1, 2019 · pp. 39–54 Read article
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Enhancing Explainability in Machine Learning Based Spam Email Detection Using LIME and SHAP
Abstract: Spam emails represent a continual and irritating cybersecurity issue due to the fast expansion of cyberspace communication. These messages are unwanted and, in some cases, dangerous, such as phishing schemes, identity theft, and the propagation of malware. They cause severe financial and data security threats to individuals and organizations. Conventional spam detection techniques, such as rule-based spamming, blacklisting, and keyword classification, are techniques that find it difficult to match adaptive …
Published in Journal Of Network security · Vol. 14, Issue 2, 2026 · pp. 35–48 Read article
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AI-Based House Price Prediction
Abstract: The housing market is one of the most dynamic and significant sectors of any economy, influencing both individual wealth and broader economic stability. Buyers, sellers, investors, and policymakers all rely on accurate housing price predictions. With the advent of artificial intelligence (AI) technologies, particularly machine learning algorithms, the task of house price prediction has seen remarkable advancements. This study provides a detailed overview of AI-based techniques for house price prediction. …
Published in Current Trends in Signal Processing · Vol. 13, Issue 3, 2023 · pp. 1–7 Read article
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R-M and M-R Model for Interpreting Logic State of Memristor Aided NOR with Enhanced Noise Margin
Abstract: Memristive crossbar arrays are one of the fascinating subjects in the field of nanoelectronics and memory devices. The arrangement of arrays consists of grid-like structure with rows and columns of memristors which are resistive devices that can preserve their resistance state even after power is turned OFF. They have now emerged as a promising alternative to traditional memory technologies due to their high density, non-volatility and low power consumption. This …
Published in Recent Trends in Mathematics · Vol. 1, Issue 1, 2024 · pp. 35–41 Read article
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Modelling and Interpretation of A Novel Battery-Motor amalgamated Thermal Management System using rGO/CO3O4 based Hybrid Nano-composite Coolant for Electric Vehicle Applications
Abstract: Battery and motor have to be given equivalent importance to maintain the lifetime, thermal characteristics, efficiency and safety of Electric Vehicles (EVs). Thermal management of EVs need to be considered for battery and motor because of dynamic loading conditions. This research proposes a novel Battery-Motor Integrated Thermal Management System (BMITMS) for EV applications. An EV assembled with LiFePO4 battery pack and a three-phase induction motor has been considered on this …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 225–243 Read article
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Stock Market Prediction Using Machine Learning: Techniques, Challenges, and Future Directions
Abstract: The continuous advancement of machine learning (ML) technologies has significantly transformed the field of financial forecasting, particularly in the area of stock market prediction. The ability to accurately forecast stock price movements and market trends plays a crucial role in supporting informed investment strategies and effective risk management. This paper provides a comprehensive review of recent developments in the application of ML techniques for predicting stock market behavior. It classifies …
Published in E-Commerce for Future & Trends · Vol. 13, Issue 1, 2026 · pp. 10–16 Read article
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Evaluation of Industry 4.0 Adoption Obstacles Through the Use of SMEs
Abstract: Industry 4.0 offers significant technology advancements, but businesses must overcome several obstacles before implementing it. Although a lot of work has gone into identifying the hurdles that most businesses face, the literature currently in publication has not taken the time to examine how these barriers relate to one another or what that means for practitioners. Within the framework of Portugal's manufacturing sector, we employ the interpretative structural modelling (ISM) technique …
Published in Journal of Production Research & Management · Vol. 13, Issue 3, 2023 · pp. 24–38 Read article
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Interpretable Skin Cancer Detection via Optimized CNN Models for Smart Healthcare Solutions
Abstract: Skin cancer is a common and potentially life-threatening condition, highlighting the importance of reliable and efficient diagnostic techniques. Recently, convolutional neural networks (CNNs) have demonstrated significant potential in automating the classification of skin cancer using thermoscopic images. Despite these advancements, the lack of interpretability in these models poses a barrier to their widespread use in clinical settings. In this study, we propose an interpretable CNN architecture optimized for skin cancer …
Published in Research & Reviews: A Journal of Bioinformatics · Vol. 12, Issue 1, 2025 · pp. 41–45 Read article
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Prediction of Customer Churn Using Machine Learning Classification Models
Abstract: Customer churn prediction is a critical task in both the telecommunication and medical industries, where retaining customers or patients is essential for ensuring long-term profitability and maintaining high-quality service. To address this, a range of machine learning models—including logistic regression, decision trees, random forests, gradient boosting machines, and support vector machines—were employed to accurately forecast churn behavior. Prior to model training, the dataset underwent thorough preprocessing, which included handling missing …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 2, 2025 · pp. 86–92 Read article
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Adaptive and Explainable ML Framework for Personalized E-Commerce User Experience and Satisfaction
Abstract: The user experience and user satisfaction have taken the center of the stage as a determinant of platform success in the fast-changing e-commerce ecosystem. Conventional recommendation systems are usually run in black-box models that give minimal transparency and dynamicity to altered user preferences. The study will introduce a personalization framework of explainable machine learning (XML) based on explainable artificial intelligence (XAI) and hybrid recommendation strategies. The model uses Collaborative Filtering …
Published in Current Trends in Information Technology · Vol. 16, Issue 2, 2025 · pp. 23–32 Read article
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Implement Explainable Machine Learning to Improve Conductivity in Polymer-CNT Nanocomposites: Supporting Adaptive, Flexible, and Long-Lasting IoT Wrap-Around Electronics Applications
Abstract: The rapid growth of Internet of Things (IoT) technologies requires electronic components that are adaptable, lightweight, and durable, and that can continue to function well in diverse contexts and circumstances. Polymer–carbon nanotube (CNT) nanocomposites have become interesting choices for these kinds of uses because they are more flexible, conduct electricity better, and can be made to fit specific needs. However, improving conductivity in these heterogeneous systems remains a major challenge …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 238–254 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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Advances in Deep Learning for Medical Image Analysis in the Era of Precision Medicine
Abstract: Medical imaging is fundamental to modern healthcare but analyzing the high-dimensional data requires advanced techniques. Manual image interpretation is time-consuming, subjective and limited in detecting complex patterns and minute details. Recent breakthroughs in Deep Learning offer transformative advances for unlocking clinically relevant information from medical images. This paper provides a comprehensive 6000+ word review of the current state-of-the-art Deep Learning techniques for medical image analysis including detailed coverage of key …
Published in Research and Reviews : Journal of Computational Biology · Vol. 12, Issue 2, 2023 · pp. 10–23 Read article
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Fertilizer Prediction Using Machine Learning
Abstract: Fertilizer prediction is a critical aspect of modern agriculture, aimed at optimizing resource utilization while maximizing crop yields. In recent years, machine learning (ML) techniques have emerged as powerful tools for addressing this challenge by leveraging data-driven approaches to predict the optimal type and quantity of fertilizer required for different crops and soil conditions. This research paper provides a comprehensive review of the existing literature and methodologies employed in fertilizer …
Published in Journal of Mechatronics and Automation · Vol. 11, Issue 2, 2024 · pp. 26–35 Read article
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Greener 3D Printing: The Role of Artificial Intelligence in Sustainable Polymer and Composite Manufacturing
Abstract: The integration of sustainable materials with additive manufacturing (AM) technologies marks a significant step towards environmentally responsible production. Biodegradable polymers, recycled thermoplastics, and bio-based composites, when used in 3D printing, offer the potential to reduce the ecological footprint of manufacturing. However optimizing the interplay between material properties process parameters, and product performance remains a complex challenge. This review examines how artificial intelligence (AI) is being applied to address these challenges …
Published in Journal of Polymer & Composites · Vol. 14, Issue 1, 2026 · pp. 288–300 Read article
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ML-Based Predictive Modeling of Mechanical Properties in 3D-Printed Polymer Composites for IoT Applications
Abstract: This study aims to develop an interpretable and high-accuracy machine learning framework for predicting the mechanical properties of 3D-printed fiber-reinforced polymer composites, with a focus on structure–property correlations relevant to polymer processing and functional performance. Composite specimens based on PLA and ABS matrices were fabricated using FDM with varying weight fractions (5–20 wt%) of carbon and glass fibers. Standardized mechanical testing (ASTM D638, D256, D790) was performed to evaluate tensile …
Published in Journal of Polymer & Composites · Vol. 13, Issue 4, 2025 · pp. 61–78 Read article
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Advancements in Machine Learning: A Comprehensive Review of Algorithms, Applications, and Future Directions
Abstract: Gaining knowledge of Machine learning (ML)-guided format algorithms leverage predictive models to generate novel devices with optimized properties across several domains, which include drug discovery, fabric synthesis, and biomolecular engineering. Selecting an effective format set of policies consists of identifying appropriate hyperparameters, predictive models, and generative mechanisms to maximize format fulfilment. This study introduces an established method for set of policies requirements, ensuring that generated designs meet predefined fulfilment criteria, …
Published in Recent Trends in Programming languages · Vol. 12, Issue 2, 2025 · pp. 17–33 Read article
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Leveraging Generative AI for Test Case Creation in Complex Systems
Abstract: Modern software systems exhibit increasing complexity, demanding sophisticated testing methodologies to ensure reliability and functionality. Traditional manual testing approaches often struggle to keep pace with this complexity, leading to inadequate test coverage and increased risk of unforeseen issues. This study explores the potential of Generative AI (GAI) in revolutionizing test case creation for complex systems. We delve into the practical application of GAI techniques, such as Variational Autoencoders (VAEs) and …
Published in Recent Trends in Programming languages · Vol. 12, Issue 3, 2025 · pp. 16–22 Read article