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Aderemi A. Atayero
Researcher at Covenant University
Publications - 192
Citations - 2170
Aderemi A. Atayero is an academic researcher from Covenant University. The author has contributed to research in topics: Path loss & Wireless network. The author has an hindex of 20, co-authored 189 publications receiving 1411 citations.
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COVID-19 Pandemic and Emerging Plastic-based Personal Protective Equipment Waste Pollution and Management in Africa.
TL;DR: In this article, a Fourier transform infrared (FTIR) spectral fingerprint indicates that face masks were characterised by natural and artificial fibres including polyester fibres, polypropylene, natural latex resin.
Security Issues in Cloud Computing: The Potentials of Homomorphic Encryption
TL;DR: The security issues affecting cloud computing are presented and the use of homomorphic encryption as a panacea for dealing with these serious security concerns vis-a-vis the access to cloud data is proposed.
Journal ArticleDOI
Determination of Neural Network Parameters for Path Loss Prediction in Very High Frequency Wireless Channel
Segun I. Popoola,Abigail Jefia,Aderemi A. Atayero,Ogbeide Kingsley,Nasir Faruk,Olasunkanmi F. Oseni,Robert O. Abolade +6 more
TL;DR: An extensive investigation was conducted to determine the most appropriate neural network parameters for path loss prediction in Very High Frequency (VHF) band and showed that ANN-based path loss model has better prediction accuracy and generalization ability than the empirical models.
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Optimal model for path loss predictions using feed-forward neural networks
Segun I. Popoola,Emmanuel Adetiba,Emmanuel Adetiba,Aderemi A. Atayero,Nasir Faruk,Carlos T. Calafate +5 more
TL;DR: Compared to the Hata, COST 231, ECC-33, and Egli models, the developed ANN model performed better in terms of prediction accuracy and generalization ability.
Journal ArticleDOI
An Overview of Machine Learning within Embedded and Mobile Devices-Optimizations and Applications.
TL;DR: A comprehensive overview of key application areas of EML technology is given, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain.