Enhancing energy distribution through dynamic multi-hop for heterogeneous WSNs dedicated to IoT-enabled smart grids.

This paper examines the impact of hetterogeneous wireless sensor networks (WSNs) on wireless communication systems, with a focus in Internet of Things (IoT) enabled smart grids. It introduces a novel approach for the fair distribution of energy and computational resources among sensor nodes (SNs), which is crucial for extending network lifespan, enhancing performance, and ensuring SG stability. The research highlights the role of initial energy and processing capacities of SNs.

An efficient computer vision-based approach for acute lymphoblastic leukemia prediction.

Leukemia (blood cancer) diseases arise when the number of White blood cells (WBCs) is imbalanced in the human body. When the bone marrow produces many immature WBCs that kill healthy cells, acute lymphocytic leukemia (ALL) impacts people of all ages. Thus, timely predicting this disease can increase the chance of survival, and the patient can get his therapy early.

Machine Learning for 5G MIMO Modulation Detection.

Modulation detection techniques have received much attention in recent years due to their importance in the military and commercial applications, such as software-defined radio and cognitive radios. Most of the existing modulation detection algorithms address the detection dedicated to the non-cooperative systems only. In this work, we propose the detection of modulations in the multi-relay cooperative multiple-input multiple-output (MIMO) systems for 5G communications in the presence of spatially correlated channels and imperfect channel state information (CSI).