Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Seema; Singhal, Abhinav; Saeed, Abdulkafi Mohammed; Nirwal, Sonal; Chaudhary, Anjali Title A name given to the resource Hybrid fractional thermoelasticmachine learning (KNN, CNN and SVM classifier) framework for heat and mass transfer: A computational mechanics approach Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Communications in Heat and Mass Transfer;Volume;172;Issue;;Article No.;110623; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.icheatmasstransfer.2026.110623" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.icheatmasstransfer.2026.110623</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105028654555?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105028654555?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Seema, Christ University, Bengaluru, 560029, India; Singhal A., Christ University, Bengaluru, 560029, India; Saeed A.M., Department of Mathematics, College of Science, Qassim University, Buraydah, 51452, Saudi Arabia; Nirwal S., Centre for Mathematical Needs, Department of Mathematics, CHRIST (Deemed to be University), Bengaluru, India; Chaudhary A., Department of Management, College of Business Administration, Princess Nourah bint Abdulrahman University, P.O.Box 84428, Riyadh, 11671, Saudi Arabia Description An account of the resource The main goal of this study is to create a single fractional thermoelasticmachine learning framework that can accurately model how heat and stress move through skin tissue over time and automatically sort thermal regimes into safe and dangerous ones. The proposed method combines the AtanganaBaleanu fractional operator with the CattaneoVernotte heat flux law and data-driven classifiers (KNN, SVM, and CNN), and Laplace Transforms techniques to derive generalized thermoelastic formulations capable of capturing finite-speed thermal propagation, memory effects, and nonlocal stress relaxation. This connects strict analytical modeling with smart thermal safety prediction. Closed-form expressions for temperature, displacement, dilation, and stress fields are obtained in the Laplace domain and numerically inverted to evaluate transient responses under thermal shock. All fractional thermoelastic simulations and Laplace inversions were executed in MATLAB R2023a, whereas the machine-learning models (KNN, SVM, CNN) were implemented in Python 3.10 using scikit-learn and TensorFlow. To extend the predictive capacity of the analytical models, simulation-derived datasets are used to train three machine learning classifiersK-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Convolutional Neural Network (CNN). Comparative analyses through confusion matrices, dispersion maps, ROC curves, residual maps, and bar charts demonstrate that CNN achieves superior nonlinear feature extraction and generalization, SVM provides stable global decision boundaries, and KNN efficiently identifies localized thermalmechanical anomalies. The AB fractional model is shown to suppress temperature overshoot and reduce stress concentration relative to CV, offering safer predictions for biological tissues. The combined fractionalML framework enables rapid classification of safe and risky heating regimes, with potential applications in hyperthermia therapy, burn injury prevention, dermatological laser treatments, and thermal hotspot detection in engineered composites. This study establishes a unified pathway where fractional thermoelastic modeling, deep learning, and classical machine learning synergistically addresses complex biomedical and material thermal interactions. A synthetic dataset generated from fractional ABCV thermoelastic simulations was used for training the ML classifiers. 2026 Elsevier Ltd Subject The topic of the resource AtanganaBaleanu operator; CattaneoVernotte law; CNN; Computational mechanics; Fractional models; Heat and mass transfer; Hybrid framework; KNN; Machine learning; SVM; Thermoelastic simulations Publisher An entity responsible for making the resource available Elsevier Ltd Relation A related resource ISSN: 7351933; CODEN: IHMTD Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online