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.

This study proposes a deep learning-based framework for the automated detection of asthma and chronic obstructive pulmonary disease (COPD) using respiratory sound analysis. Breath sound recordings are preprocessed through resampling, silence trimming, and Wiener filtering to enhance signal quality. Short-Time Fourier Transform (STFT) is employed to convert audio signals into spectrogram representations, which are further augmented using time masking, frequency masking, and time warping to improve model generalization. The proposed model utilizes EfficientNet with multi-scale feature fusion to capture both local and global patterns in respiratory sounds. Experimental results demonstrate that the proposed approach achieves superior performance, with an accuracy of 99.21%, sensitivity of 99.56%, and specificity of 100%, outperforming existing CNN, ResNet, and SVM-based methods. The findings indicate that the proposed method is a reliable and efficient tool for non-invasive respiratory disease diagnosis.