Deep Neural Networks (DNNs) have gained unprecedented popularity due to their high-
order performance and automated feature extraction capability. This has encouraged …

Over the past few years, larger and deeper neural network models, particularly convolutional
neural networks (CNNs), have consistently advanced state-of-the-art performance across …

Graph Neural Networks (GNNs) have emerged as the leading deep learning models for
graph-based representation learning. However, the training and inference of GNNs on large …

This paper critically examines model compression techniques within the machine learning
(ML) domain, emphasizing their role in enhancing model efficiency for deployment in …

Biomedical image segmentation (BIS) task is challenging due to the variations in organ
types, position, shape, size, scale, orientation, and image contrast. Conventional methods …

Powered lower-limb prostheses with vision sensors are expected to restore amputees'
mobility in various environments with supervised learning-based environmental recognition …

The emergence of powerful deep learning architectures has resulted in breakthrough
innovations in several fields such as healthcare, precision farming, banking, education, and …

In medical imaging, the effective detection and classification of Breast Cancer (BC) is a
current research important task because of the still existing difficulty to distinguish …

The global mortality rate is known to be the highest due to cardiovascular disease (CVD).
Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as …

Network pruning has become an effective scheme to cut down the network complexity and
speed up the inference. Current work mainly focuses on filter pruning, which deletes filters in …