Khan Muhammad

TL;DR: In this paper, the authors proposed an effective fall detection framework based on DL algorithms and mobile edge computing (MEC) within 5G wireless networks, the aim being to empower IoMT-based healthcare applications.

TL;DR: A novel clustering algorithm at the edge of the network and an efficient message routing approach, which is known as Group’n Route (GnR) are proposed, which resort to machine learning and graph metrics that reflect the social relationships between the nodes.

TL;DR: This work aims to develop a novel deep learning-based computationally efficient medical imaging framework for effective modeling and early diagnosis of COVID-19 from chest x-ray and computed tomography images by exploiting efficient convolutional neural network to extract high-level features, followed by classification mechanisms for CO VID-19 diagnosis in medical image data.

TL;DR: In this article, the authors focus on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation and highlight open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.