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277 articles for “model interpretability”
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Total Quality Management Initiatives for Enhancing the Performance of Manufacturing Companies: An Empirical Investigation
Abstract: Total quality management is the industrial management approach of reducing defects by improving the processes. This study has been carried out in SMEs of northern India which have implemented TQM initiatives and faced different barriers in its successful implementation. Questionnaire survey has been performed followed by Qualitative and Quantitative modeling including interpretive structural modeling, fuzzy set theory, and structural equation modeling. Results of investigation recommended that resistance from employees is …
Published in Journal of Production Research & Management · Vol. 9, Issue 3, 2019 · pp. 41–55 Read article
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Artificial Intelligence in Trigonometry: Innovations, Applications, and Future Prospects
Abstract: Artificial Intelligence (AI) has transformed numerous scientific fields, yet its integration with classical mathematics such as trigonometry is still emerging. This paper explores how AI enhances trigonometric problem solving, learning, and real-world applications. We analyse AI-driven tools for teaching trigonometry, AI in geometric and spatial reasoning, usage in robotics and computer vision, and future directions for research. Key challenges, methodologies, and case studies are discussed to provide a comprehensive overview …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Artificial Intelligence in Healthcare for Implants and Tissue Regeneration: Advances, Challenges, and Future Directions
Abstract: Artificial intelligence (AI) has been a revolutionary influence in contemporary healthcare, especially in implant design, biomaterials research, and tissue regeneration. In regenerative medicine, AI facilitates predictive modeling, optimization, and decision-making via the analysis of intricate biological, material, and clinical information. This study analyzes current research on AI applications in implant technologies and tissue regeneration, specifically addressing scaffold engineering, biomaterial characterisation, stem cell and gene treatments, smart biomaterials, and implant planning. …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 13, Issue 3, 2025 Read article
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Early Disease Detection Using Artificial Intelligence
Abstract: Growth in artificial intelligence and machine learning now make it possible for the healthcare sector to be totally transformed by a new chapter, particularly in the era of medical image analysis. This study focuses on harnessing these advancements to develop a sophisticated model for early disease detection across diverse medical domains, majorly in skin disease. By integrating diverse datasets and leveraging advanced algorithms, our methodology aims to identify subtle disease …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 14, Issue 3, 2024 · pp. 11–19 Read article
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Geophysical Determination of Construction Clay Deposit Using 2-Dimensional Resistivity Imaging In Ologbo Area of Edo State
Abstract: AbstractGeophysical exploration based on a two-dimensional electrical resistivity survey was carried out to determine the presence of construction clay deposits in Ologbo Area of Edo State, Nigeria. Begin and end co-ordinates for line 1 were N6°05.585′, E005°39.686′ and N6°05.541′, E005°39.652′ and N6°03.754′, E005°39.029′ and N6°03.700′, E005°39.054′ for line 2. The survey technique utilized was Wenner-Schlumberger array in the two explored areas to justify the operation of assembling apparent resistivity data. …
Published in Research & Reviews : Journal of Physics · Vol. 5, Issue 2, 2016 · pp. 1–12 Read article
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A Comprehensive Investigation of Bagging-Based Ensemble Methods for Improving Machine Learning Model Robustness
Abstract: Machine learning models such as Decision Trees, Logistic Regression, and K-Nearest Neighbors are widely used for classification tasks due to their simplicity and interpretability. However, these models often suffer from high variance, overfitting, and poor generalization when applied to real-world datasets, particularly those that are small, noisy, or imbalanced, as commonly encountered in healthcare, finance, and cybersecurity applications. To address these limitations, this research proposes a Bagging (Bootstrap Aggregating)-based ensemble …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 24–34 Read article
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A Lightweight Cost-Sensitive Explainable Ensemble Framework for Early Heart Disease Risk Prediction
Abstract: Cardiovascular disease is still one of the leading causes of death, and hence, the early prediction of risk is a very important task in preventive medicine. Although recent studies have shown encouraging results in the application of machine learning algorithms to the prediction of heart disease, it has been noticed that most of the algorithms are more concerned with accuracy-driven optimization than the concerns of safety and false negatives. In …
Published in International Journal of Bioinformatics and Computational Biology · Vol. 4, Issue 2, 2026 · pp. 26–40 Read article
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Remote Healthcare Diabetic Retinopathy Detection Using Deep Learning
Abstract: High blood glucose levels are a hallmark of diabetes mellitus (DM), a metabolic disease. This can give rise to a range of complications, with Diabetic Retinopathy (DR) being among them. DR can impair vision and, if not addressed, may lead to a loss of eyesight. Symptoms include aberrant blood vessels, fluid leaks, exudates, haemorrhages, and retinal microaneurysms. With the advancement of technology, medical imaging has become one of the most …
Published in Journal of Open Source Developments · Vol. 10, Issue 2, 2023 · pp. 42–47 Read article
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Image-Based Quantitative Mapping of Structure Property Relationships in Polymer Composite Materials
Abstract: The performance of polymer composite materials is intrinsically governed by their microstructural architecture, which is shaped by manufacturing conditions and constituent interactions. Despite extensive experimental characterization efforts, establishing transparent and quantitative structure–property relationships from microstructural images remains a challenge. In this study, an explainable image-driven framework is developed to systematically correlate microstructural features with composite property indicators. Microstructure images are processed to identify voids, fibers, and filler phases, from which …
Published in Journal of Polymer & Composites · Vol. 14, Issue 3, 2026 · pp. 188–196 Read article
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Presenting the Model and Prioritization of Lean Production Success Drivers (Case study: Iran Khodro Company)
Abstract: The purpose of the current research is to present a model and prioritize the drivers of lean production success (case study: Iran Khodro Company). The statistical population and statistical sample of this study consists of 20 experts (managers) of Iran Khodro production department. In this research, first, the most important key success factors of lean production were determined using the validity technique of the content validity ratio, then the interpretative …
Published in International Journal of Sustainability · Vol. 1, Issue 1, 2024 · pp. 30–42 Read article
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Groundwater Darcy Velocity from underground Temperature: A Focus on the City of Turin (NW Italy)
Abstract: We report on an application of a methodology for inferring water transfer in a shallow aquifer by using analytical models of interpretation of heat transport by advection and conduction in permeable horizons. In this research, we highlight as the statistical interpretation of thermal data is a tool to obtain a quantitative estimation of the horizontal component of the Darcy velocity in a shallow aquifer. This study is based on the …
Published in Journal of Water Resource Engineering and Management · Vol. 8, Issue 3, 2021 · pp. 19–29 Read article
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Strategy for Improving Software Maintenance Using Machine Learning for Security Requirements: A Review
Abstract: Within the area of software technical education, the significance of software defect discovery has increased as a research focus to enhance program reliability. By maximizing testing resources and assisting developers in identifying potential problems using program defect predictions, program dependability is increased. Applying software engineering (SE) techniques to critical and intricate systems, like networking and security systems, is imperative. Traditional methods of predicting software maintainability have limitations, particularly in balancing …
Published in International Journal of Information Security Engineering · Vol. 2, Issue 2, 2024 · pp. 36–48 Read article
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Analyzing Barriers to Digital Procurement in Polymer Composites Supply Chain Using ISM for Sustainable Transformation
Abstract: The adoption of e-procurement in the polymer and composites industry presents a transformative opportunity to enhance supply chain efficiency, reduce material waste, and support sustainable engineering practices. However, industries face significant barriers in transitioning from traditional procurement to digital systems, particularly in sourcing specialized materials such as epoxy resins, bio-based polymers, and hybrid composites. This study employs Interpretive Structural Modeling (ISM) to identify, analyze, and prioritize eleven critical barriers affecting …
Published in Journal of Polymer & Composites · Vol. 13, Issue 6, 2025 · pp. 512–521 Read article
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Explainable Machine Learning for Process Parameter Optimization in Gradient 3D-Printed Polymer Composites
Abstract: The explainable machine learning-based structure may be employed to achieve a favorable process parameter of the graduate 3D-printed polymer composite structures to improve the mechanical and thermal properties without compromising the transparency of the decisions made during the fabrication process. Gradient composite specimens were made by systematically varied process parameters like nozzle temperature, raster orientation, deposition speed, gradient transition rate and fused filament fabrication. A predictive model of tensile strength …
Published in Journal of Polymer & Composites · Vol. 14, Issue 2, 2026 · pp. 847–866 Read article
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Automated Healthcare Support System with AI
Abstract: The Automated Healthcare Support System with Artificial Intelligence (AI) presents a smart and scalable digital solution aimed at improving the accessibility and efficiency of healthcare services. The system is designed to provide preliminary medical guidance, perform symptom-based analysis, and deliver health-related insights through an intuitive user interface. By enabling early identification of potential health conditions, it assists users in determining the necessity of professional medical consultation. The proposed platform utilizes …
Published in Research and Reviews : A Journal of Medical Science and Technology · Vol. 15, Issue 1, 2026 Read article
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A Review of Recent Advancements in Machine Learning and Deep Learning Approaches for Pet Diseases Prediction
Abstract: This systematic study assesses recent developments in Machine Learning (ML) and Deep Learning (DL) approaches to predict pet diseases. With the increasing role of Artificial Intelligence (AI) in pet healthcare, this study identifies recent research trends, limitations, and future directions. A comprehensive search was done using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines in selecting 20 relevant studies from over 300 articles published between 2020 and …
Published in Research and Reviews : Journal of Veterinary Science and Technology · Vol. 14, Issue 3, 2025 · pp. 1–6 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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Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Method: A Comprehensive Review
Abstract: Structural health monitoring (SHM) has a critical role in ensuring civil infrastructure safety, reliability, and durability through real-time, condition-based monitoring. Traditional SHM systems employ hundreds of sensors such as accelerometers, strain gauges, and displacement transducers for monitoring vast amounts of data for structural inspection, but do not effectively manage complicated nonlinear data. This research paper, “Optimization of Structural Health Monitoring Using Artificial Neural Network and Comparison with Traditional Methods,” investigates …
Published in Journal of Structural Engineering and Management · Vol. 13, Issue 1, 2026 · pp. 23–33 Read article
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Evolution: Boosters, Drivers and plausible Models
Abstract: Evolution has often been interpreted as the positive natural selection, paving the path for adaptive improvement in biological genetic information. Evolution yielding functionally adapted protein molecules is much more complex than DNA. Constraints mediating the rate, path and altered convergence of functionally evolved proteins are justified to elaborate their functional role in evolution. All additional stress factors as per the constrained micro-environment imposed on a cell system delineate the evolutionary …
Published in Research and Reviews : A Journal of Life Sciences · Vol. 2, Issue 2, 2012 · pp. 10–15 Read article
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Econometric Modeling for Oil Sectors Stock Prices Time Series Data
Abstract: This study examines the dynamic relationships of stock oil prices of different Indian oil sectors viz., Bharat Petroleum, Hindustan Oil Corporation and Oil and Natural Gas Corporation based on the different econometric models. Empirical result shows that the monthly oil prices are non-stationary and integrated of order one. Johanson procedure to test for the possibility of co-integration relationships result shows that there is no co-integration relationship. Since the variables are …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 2, 2018 · pp. 27–34 Read article