The colossal increase in environmental pollution and degradation, resulting in ecological imbalance, is an eye-catching concern in the contemporary era. Moreover, the proliferation in the development of smart cities across the globe necessitates the emergence of a robust smart waste management system for proper waste segregation based on its biodegradability. The present work investigates a novel approach for waste segregation for its effective recycling and disposal by utilizing a deep learning strategy. The YOLOv3 algorithm has been utilized in the Darknet neural network framework to train a self-made dataset. The network has been trained for 6 object classes (namely: cardboard, glass, metal, paper, plastic and organic waste). Moreover, for comparative assessment, the detection task has also been performed using YOLOv3-tiny to validate the competence of the YOLOv3 algorithm. The experimental results demonstrate that the proposed YOLOv3 methodology yields satisfactory generalization capability for all the classes with a variety of waste items.

Obstructive sleep apnea (OSA) is a severe sleep-associated respiratory disorder, caused due to periodic disruption of breath during sleep. It may cause a number of serious cardiovascular complications, including stroke. Generally, OSA is detected by polysomnography (PSG), a costly procedure and may cause discomfort to the patient. Nowadays, electrocardiogram (ECG) signal-based detection techniques have been explored as an alternative to PSG for OSA detection. Usual linear and nonlinear machine learning techniques are mainly focused on hand-crafted feature extraction and classification which are time-consuming and may not be suitable for huge data. Therefore, in this work, a deep learning model (DLM) using smoothed Gabor spectrogram (SGS) of ECG signals is proposed for automated OSA detection to obtain high performance. The proposed frame-work fed Gabor spectrogram and SGS of ECG signals as input to the pre-trained Squeeze-Net, Res-Net50, and developed DLM called Obstructive sleep apnea convolutional neural network (OSACN-Net). The proposed OSACN-Net achieved an average classification accuracy of 94.81% with SGS using a ten-fold cross-validation strategy. Compared to Squeeze-Net and Res-Net50 developed OSACN-Net is more accurate and lightweight as it requires few learnable parameters which makes it computationally fast and efficient. The comparison results showed that the proposed framework outperformed all existing state-of-the-art methodologies.