Cloud platforms generate massive API access logs, where abnormal patterns may indicate security breaches, insider threats, or compromised credentials, demanding intelligent and automated anomaly detection mechanisms. Conventional approaches employ segmentation, statistical profiling, clustering, recurrent networks, and supervised classifiers to model sequential API behavior and distinguish normal activities from malicious deviations. These techniques generally achieve high detection accuracy and improved threat visibility, enhancing cybersecurity monitoring systems while reducing manual auditing efforts in large-scale distributed cloud environments. However, they struggle with evolving attack patterns, high false-positive rates, limited temporal dependency modelling, data imbalance, and poor generalization across heterogeneous cloud infrastructures. This study proposes a self-supervised Temporal Convolutional Network with adaptive anomaly scoring, achieving robust sequential modelling, reduced false alarms, and improved detection stability in cloud APIs. A self-supervised Temporal Convolutional Network models sequential API behavior using causal dilated convolutions and adaptive scoring, enabling accurate, scalable, and real-time cloud anomaly detection.

Accurate classification of kidney abnormalities from computed tomography (CT) images is essential for early diagnosis and clinical decision-making. However, distinguishing kidney stones, cysts, tumours, and normal kidneys remains challenging because of variations in lesion size, appearance, and imaging conditions. This study proposes a multi-resolution attention-fused, calibration-aware deep learning framework for four-class kidney CT image classification. The framework integrates convolutional neural networks and a Vision Transformer to extract complementary local and global features, which are combined through an adaptive attention-based fusion mechanism. Calibration-aware learning, incorporating focal loss, multi-class Brier loss, and feature-level orthogonality regularization, is employed to improve prediction reliability and confidence estimation. The model was trained and evaluated using an image-level data split on a publicly available kidney CT dataset containing normal, cyst, stone, and tumour images. Experimental results demonstrate an overall classification accuracy of 99.3%, a Macro-F1 score of 99.1%, an AUROC of 0.999, and an AUPRC of 0.998 on the independent test set. Robustness analysis under synthetic image corruptions showed only a modest reduction in performance, with the AUROC remaining at 0.995 under severe motion blur conditions. External validation on two independent cohorts (External Site-A and External Site-B) achieved AUROC values of 0.993 and 0.988, respectively, while post-hoc temperature scaling improved calibration by reducing the Expected Calibration Error (ECE) from 0.028 to 0.012 and 0.034 to 0.015. These results demonstrate that the proposed framework provides accurate, robust, and well-calibrated predictions, supporting its potential application in computer-aided kidney CT image analysis.

Wireless sensor networks enable real-time monitoring in critical applications such as industrial automation, environmental sensing, healthcare, and infrastructure management. However, sensor node faults caused by hardware degradation, communication failures, and harsh operating conditions can significantly reduce data reliability and system performance. Existing anomaly detection approaches often depend on large amounts of labelled fault data and suffer from class imbalance, noise sensitivity, computational complexity, and limited generalization in dynamic environments, highlighting the need for adaptive and scalable fault diagnosis frameworks. To address these limitations, this study proposes a self-supervised deep anomaly detection framework for wireless sensor network node fault diagnosis. The proposed method integrates preprocessing and sliding-window segmentation with a Deep Dual Masked Transformer (DDMT) architecture to capture temporal dependencies and learn discriminative features without extensive labelled data. A self-supervised reconstruction objective with a 15% masking strategy is employed to optimize latent representations, while an adaptive anomaly-scoring mechanism generates reliable fault predictions. Experimental results on the Zidi wireless sensor network dataset demonstrate that the proposed method achieves an accuracy of 99.0%, a precision of 98.5%, a recall of 98.3%, an F1-score of 98.4%, and an ROC-AUC of 99.5%, outperforming existing baseline methods. By combining temporal modelling and representation learning within a unified framework, the proposed approach improves anomaly detection accuracy, robustness, scalability, and generalization capability while maintaining low false alarm rates in challenging wireless sensor network environments.