This study presents the exploration of unsteady magnetohydrodynamic (MHD) free convection flow of tangent hyperbolic nano-fluid flow on a moving cylinder with Brownian motion and thermophoresis effects. The current flow analysis yields nonlinear partial differential equations. The governing equations such as continuity, momentum, temperature and concentration are converted into dimensionless form and then solved numerically by adopting explicit finite difference method where Compaq Visual FORTRAN 6.6.a was also used for simulating the fluid flow system. The numerical outcomes are showed graphically to understand the result clearly. For the accurateness of the numerical technique a stability and convergence analysis was carried out. The aim was to illustrate the physical impacts of chemical reaction parameter, thermal radiation and viscous dissipation on various fluid fields along with the advanced visualization through streamlines. By comparing with the previous studies it was found that this fluid influenced the mass and heat properties more significantly rather than the other fluid. Additionally, this model predicts the shear thinning attitude significantly and describes the blood flow accurately. It has also applications in biological sciences, bio-engineering maneuver, and petroleum industries. Eventually the obtained outcomes were validated with previously published articles.

This research introduces “XMP‐Net,” a modified Xception–based deep learning architecture constructed for the categorization of skin conditions, with a particular focus on identifying monkeypox. The study recognizes skin images of four categories: normal, chickenpox, measles, and monkeypox. To enhance interpretability and foster confidence in the model′s predictions, Grad‐CAM (gradient‐weighted class activation mapping) and LIME (local interpretable model‐agnostic explanations) were employed to illustrate the model′s thinking manner. The model demonstrated impressive classification performance, attaining an accuracy of 98.33% for normal skin, 98.25% for monkeypox, 84.21% for measles, and 77.27% for chickenpox. Precision, recall, and F 1‐score values were also analyzed for each class, with monkeypox achieving a precision of 91.80%, a recall of 98.25%, and an F 1‐score of 94.92%. The visual explanations generated by Grad‐CAM and LIME highlighted critical parts in the input images that affected the model′s likelihoods, offering clinicians valuable insights into the diagnostic process. This research underscores the potential of explainable artificial intelligence (XAI) in augmenting traditional diagnostic methods, particularly for emerging communicable maladies like monkeypox, and provides a foundation for developing reliable, interpretable, and accessible diagnostic tools for resource‐constrained settings.

Alzheimer’s disease (AD) is a degenerative brain disorder affecting millions worldwide, making early diagnosis crucial for effective management. Conventional diagnostic procedures, like neuropsychological exams and biomarker-based studies, are sometimes costly, intrusive, and not broadly accessible. This paper presents a strong deep learning (DL) architecture for classifying AD stages using MRI data. Our methodology employs transfer learning with pre-trained models such as InceptionV3, DenseNet169, and ResNet50V2, in conjunction with the synthetic minority over-sampling technique to mitigate class imbalance and improve model efficacy. We further use gradient-weighted class activation mapping to enhance the explainability and interpretability of predictions from the model, providing visual insights into the principal brain areas influencing classification judgments. Our suggested technique is assessed utilizing a publicly accessible dataset of MRI scans, yielding outstanding results in multiclass dementia stage classification, with an accuracy of 99.76%. The suggested approach surpasses current methodologies and offers a clear, medically interpretable approach for early Alzheimer’s diagnosis, which has possible applicability in resource-limited healthcare settings. These results underscore the efficacy of DL for non-invasive, affordable, and dependable AD diagnosis. However, external validation on datasets such as ADNI and OASIS is necessary to confirm generalizability before clinical application.