Michael Kisangiri

TL;DR: An up to date overview of NILM system and its associated methods and techniques for energy disaggregation problem is presented and the review of the state-of-the art NILm algorithms are reviewed.

TL;DR: In this paper, the authors provide an extensive tutorial on HO management in 5G networks accompanied by a discussion on machine learning (ML) applications to HO management, where two broad categories are considered; namely, visual data and network data.

TL;DR: Few empirical models suitable for path loss prediction in mobile communication are presented, showing that in general the SUI, COST-231, ERICSSON, and Hata-Okumura under-predict the path loss in all environments, while the ECC-33 model shows the best results, especially in suburban and over-p predict pathloss in urban area.

TL;DR: In this article, the authors proposed an offline scheme based on double deep reinforcement learning (DDRL) to minimize the frequency of HOs in mm-wave networks, which subsequently mitigates the adverse QoS.

TL;DR: The awareness and usage levels of existing mobile assistive technologies for visual impairment, and the remaining challenges that visually impaired students face, when using such tools on smartphones to access m-learning content from HLIs in Tanzania are determined.