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Edward Yellakuor Baagyere

Publications (4)

Efficient Elliptic Curve Arithmetic for Lightweight Cryptographic Schemes for IoT Applications

Zakaria Abukari, Edward Yellakuor Baagyere & Mohammed Muniru Iddrisu · Asian Journal of Research in Computer Science · 2022

The Internet of Things’ (IoT) market is expected to grow exponentially at the global level in the coming years, due to the proliferation of more reliable and faster networks resulting from the extensive rollout of 5 to 10 G mobile networks. By 2025, it is expected that worldwide...

Open access Research Article 10.9734/ajrcos/2022/v14i4307

Spiking Neural Network Learning Models for Spike Sequence Learning and Data Classification

Moses Apambila Agebure, Edward Yellakuor Baagyere & Elkanah Olaosebikan Oyetunji · Asian Journal of Research in Computer Science · 2020

Supervised learning in Spiking Neural Network (SNN) is a hotbed for researchers due to the advantages temporal coded networks provide over that of rate-coded networks with respect to efficiency in information processing and transfer rates. Supervised learning in rate-coded networ...

Open access Research Article 10.9734/ajrcos/2020/v6i430163

Single and Multiple Error Detection and Correction using Redundant Residue Number System for Cryptographic and Stenographic Schemes

Peter Awon-natemi Agbedemnab, Edward Yellakuor Baagyere & Mohammed Ibrahim Daabo · Asian Journal of Research in Computer Science · 2020

The possibility of errors being propagated during the encoding process of cryptographic and steganographic schemes is real due to the introduction of noise by ciphering the data from stage to stage. This real possibility therefore requires that an efficient scheme is proposed suc...

Open access Research Article 10.9734/ajrcos/2019/v4i430123

A Survey of Supervised Learning Models for Spiking Neural Network

Moses Apambila Agebure, Paula Aninyie Wumnaya & Edward Yellakuor Baagyere · Asian Journal of Research in Computer Science · 2021

There has been a significant attempt to derive supervised learning models for training Spiking Neural Networks (SNN), which is the third and most recent generation of Artificial Neural Network (ANN). Supervised SNN learning models are considered more biologically plausible and th...

Open access Research Article 10.9734/ajrcos/2021/v9i430228