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 Singhal, Abhinav; Seema; Saeed, Abdulkafi Mohammed; Tiwari, Rakhi; Chaudhary, Anjali Title A name given to the resource Hybrid fractional thermoelasticmachine learning framework for heat and mass transfer in skin tissue: Enhanced simulations using AtanganaBaleanu, CattaneoVernotte models, and KNNSVM classifiers 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;171;Issue;;Article No.;110074; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.icheatmasstransfer.2025.110074" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.icheatmasstransfer.2025.110074</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105021951870?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105021951870?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 Singhal A., Christ University, Bengaluru, 560029, India; Seema, Christ University, Bengaluru, 560029, India; Saeed A.M., Department of Mathematics, College of Science, Qassim University, Buraydah, 51452, Saudi Arabia; Tiwari R., University Department of Mathematics, Babasaheb Bhimrao Ambedkar Bihar University, Muzaffarpur, 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 This study presents a hybrid computational framework that couples advanced fractional thermoelastic modeling with machine-learning-based safety classification for heat and mass transfer in skin tissue. The classical CattaneoVernotte (CV) non-Fourier heat conduction law is extended through the AtanganaBaleanu (AB) fractional operator to capture memory-driven thermal responses, finite thermal wave propagation, and nonlocal biological effects more accurately than traditional Fourier-type formulations. Closed-form expressions are derived using Laplace transforms and inverted numerically to obtain transient temperature, displacement, dilation, and stress fields within the tissue. The AB fractional model demonstrates smoother thermal evolution, reduced overshoot, and lower stress concentrations relative to the CV model, reflecting improved biomedical safety margins during rapid thermal exposure. To enable real-time risk assessment, synthetic datasets generated from the thermoelastic simulations are used to train Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) classifiers. The ML models reliably distinguish safe and risky thermal regimes, with SVM offering superior generalization and KNN capturing localized variations. The novelty of this work lies in directly integrating fractional physics-based modeling with machine-learning classification for thermal safety diagnosticsestablishing a unified paradigm for predictive biomedical heat transfer. The framework advances thermal therapy planning, burn-injury prevention, implant design, and smart clinical monitoring. While the current study is based on idealized geometry and simulated data, future extensions will incorporate in-vivo tissue characteristics, complex skin layers, and deep learning models to further enhance clinical applicability. 2025 Elsevier Ltd Subject The topic of the resource AtanganaBaleanu operator; Bioheat transfer; Biomedical applications; CattaneoVernotte law; Fractional models; Heat and mass transfer; Hybrid simulations; KNN; Laplace transform; Machine learning; SVM; Thermal stress; Thermoelasticity 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