http://192.168.40.123:8080/handle/123456789/4 Tue, 10 Nov 2026 05:54:35 GMT 2026-11-10T05:54:35Z http://192.168.40.123:8080/handle/123456789/18491 Examining the influence of deterrent and enhancement factors on QR-code mobile payment continuance intention: insights from PLS-SEM and IPMA analysis Rahman, Ashikur; Farid, Fahmid al; Bashar, Mohammad Abul; Uddin, Jia; Mahmud, Arif; Karim, Hezerul Abdul Introduction: The rise of contactless payment has made quick response (QR) code-mobile payment (QR-MP) platform increasingly popular among mobile financial service (MFS) users, especially in emerging economies. It has been demonstrated that the ongoing use of QR payments can significantly drive the growth of emerging economies. However, despite its importance, the continued use of this technology has not been satisfactory. Thus, this study seeks to explore the modified Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model, including four additional constructs: amotivation (AM), alternative attractiveness (AA), QR transaction anxiety (QTA), and transaction convenience (TC) to examine the MFS users' sustained usage of QR payment. Methods: Data were collected from 247 MFS users in Bangladesh using an online survey and analyzed through SEM-PLS and non-linear analysis of IPMA. Results: The research findings reveal that effort expectancy is the most influential factor, and that both moderator factors, QTA and TC, are significant. However, social influence and hedonic motivation were found to be insignificant. Furthermore, our extended research model explains 76.5% of the variance in CINT without the moderation effect. Discussion: The IPMA findings help to find the best-performing variables and provide practical insights for this study. Theoretical and managerial implications are provided to enrich the existing literature on the study of information technology, indicating how MFS providers in developing countries can retain their existing users. Articles Thu, 22 Jan 2026 00:00:00 GMT http://192.168.40.123:8080/handle/123456789/18491 2026-01-22T00:00:00Z http://192.168.40.123:8080/handle/123456789/18490 Experimental and DFT studies on energy storage efficiency of Ag+ doped barium titanate thin films (Ba1-xAgxTiO3 (x = 0.00, 0.10, 0.20)) Itas, Yahaya Saadu; Lariski, Fatima Musa; Khandaker, Mayeen Uddin; Rayyes, Ali El-; Benabdallah, Faiza The current study investigated the energy storage capacity of Ag+ doped two-dimensional (2D) barium titanate perovskite, using combinations of experimental theoretical techniques. Data from electrochemical impedance spectra and DFT geometrical optimization revealed the existence of +1 valency state by Ag ions. With the obtained parameters, highest efficiency was attributed to 0.10 mol% Ag doped 2D BaTiO3 variant, due to reduced defect and suppressed ferroelectric order. Notably, the piezoelectric response showed slight decreasing trend with Ag+ content, reaching the lower value of 223 pC/N with 0.20 mol% Ag. Reduction in the values of Tc due to Ag + ions indicates reduced polarization and high dielectric permittivity. In all samples used, good relationship existed between recoverable-energy storage and efficiency, owing to remanent polarization, hysteresis loss and dielectric breakdown. Moderate doping improved breakdown strength, leading to reduced Pr which increases Wrec values with relatively 1.12 Jcm−3. The conducted experimental and DFT analysis revealed good alignment between some considered structural and energy storage parameters, such as lattice constants, piezoelectricity, and curie temperature. Articles Thu, 01 Jan 2026 00:00:00 GMT http://192.168.40.123:8080/handle/123456789/18490 2026-01-01T00:00:00Z http://192.168.40.123:8080/handle/123456789/18489 Explainable AI-driven hybrid deep learning framework for accurate skin cancer diagnosis Sakib, Abdullah Al; Swapno, Sm Masfequier Rahman; Ahamed, Fahim; Mohiuddin, Arafath Bin; Bhuiyan, Md Imranul Hoque; Khan, Shakil; Khushbu, Katura Gania; Haque, Rezaul; Alahmadi, Tahani Jaser; Moni, Mohammad Ali Objectives: To develop and evaluate a hybrid, partially interpretable deep learning (DL) approach for multi-class skin cancer classification that improves robustness under varying acquisition conditions and delivers clinically meaningful explanations. Methods: The proposed pipeline starts with preprocessing, including hair artefact removal using the Dull Razor method and anisotropic diffusion filtering for noise reduction while preserving lesion boundaries. Data augmentation is limited to the training set to prevent leakage. Class imbalance is addressed using class-weighted cross-entropy loss. EfficientNetB0 serves as the backbone CNN, and global feature embeddings are used to train a Random Forest (RF) classifier. Predictions are made by combining outputs from the deep model and the RF through probability-level fusion. The framework is evaluated on the HAM10000 dataset (7 classes) and a combined ISIC2019+DermNet dataset (8 classes). Performance metrics are compared against strong Vision Transformer (ViT) and transfer learning baselines. A proof-of-concept web application is developed for explainable decision making. Results: The proposed model achieves 98.61% accuracy and 98.60% F1-score on the combined dataset. It reaches 95.02% accuracy and 95.06% F1-score on HAM10000 using lesion-wise 5-fold cross-validation. For melanoma-specific evaluations, it demonstrates high sensitivity and AUC, indicating strong performance on critical cases. Grad-CAM maps suggest that the network highlights potentially important diagnostic lesion areas. Conclusion: The results indicate that partially interpretable architectures are a promising direction for robust skin cancer classification. The integration of Grad-CAM explanations and a web-based interface indicates that our framework may serve as a useful exploratory clinical decision-support tool. Articles Tue, 01 Dec 2026 00:00:00 GMT http://192.168.40.123:8080/handle/123456789/18489 2026-12-01T00:00:00Z http://192.168.40.123:8080/handle/123456789/18488 Explainable AI for breast cancer detection: Biglycan biomarker classification with transfer learning Islam, Md. Mominul; Akter, Naime; Assaduzzaman, Md.; Shimul, Md. Monir Hossain; Sarker, Rahmatul Kabir Rasel Background Breast cancer is a leading global cause of cancer-related mortality, where early diagnosis is essential for improved survival outcomes. Although deep learning has shown strong performance in pathology image classification, many models remain difficult to interpret, which limits clinical trust and practical adoption. This study aimed to develop an explainable deep learning framework for classifying breast tissue based on Biglycan (BGN) biomarker expression. Methods Immunohistochemical photomicrographs from the publicly available Biglycan Breast Cancer Dataset were processed and classified into cancerous and healthy categories. Three transfer learning models, EfficientNet-B0, DenseNet-161, and ResNet-50, were fine-tuned using ImageNet pre-trained weights under a unified training setting with the Adam optimizer (learning rate = 0.001, batch size = 32, epochs = 50). Model performance was evaluated using accuracy, precision, recall, and F1-score. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to visualize tissue regions that contributed most to the model's predictions. ResultsEfficientNet-B0 achieved the best overall performance with 99 % accuracy, precision of 0.97, recall of 1.00 for the cancerous class, and an F1-score of 0.99. Grad-CAM heatmaps indicated that the model focused on diagnostically relevant tissue regions associated with strong BGN-related staining patterns. The best-performing model was integrated into a lightweight web-based application to enable real-time image upload and prediction. Conclusion This study presents an explainable transfer learning approach for BGN-driven breast tissue classification with strong performance on the Biglycan dataset. The integration of Grad-CAM provides region-level visual explanations that improve transparency, while the web deployment demonstrates a practical pathway for accessible decision support in digital pathology. Articles Sun, 01 Mar 2026 00:00:00 GMT http://192.168.40.123:8080/handle/123456789/18488 2026-03-01T00:00:00Z