M
Michael Kisangiri
Researcher at National Institute of Advanced Industrial Science and Technology
Publications - 25
Citations - 307
Michael Kisangiri is an academic researcher from National Institute of Advanced Industrial Science and Technology. The author has contributed to research in topics: Electrical capacitance tomography & Tanzania. The author has an hindex of 5, co-authored 22 publications receiving 203 citations.
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A Survey on Non-Intrusive Load Monitoring Methodies and Techniques for Energy Disaggregation Problem
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.
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A Survey of Machine Learning Applications to Handover Management in 5G and Beyond
Michael S. Mollel,Attai Ibrahim Abubakar,Metin Ozturk,Shubi Kaijage,Michael Kisangiri,Sajjad Hussain,Muhammad Imran,Qammer H. Abbasi +7 more
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.
Journal Article
Comparison of Empirical Propagation Path Loss Models for Mobile Communication
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.
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Intelligent handover decision scheme using double deep reinforcement learning
Michael S. Mollel,Michael S. Mollel,Attai Ibrahim Abubakar,Metin Ozturk,Shubi Kaijage,Michael Kisangiri,Ahmed Zoha,Muhammad Imran,Qammer H. Abbasi +8 more
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.
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The Challenges of Adopting M-Learning Assistive Technologies for Visually Impaired Learners in Higher Learning Institution in Tanzania
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.