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Emmanuel Gbenga Dada
Researcher at University of Maiduguri
Publications - 42
Citations - 557
Emmanuel Gbenga Dada is an academic researcher from University of Maiduguri. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 4, co-authored 28 publications receiving 168 citations. Previous affiliations of Emmanuel Gbenga Dada include University of Malaya & Information Technology University.
Papers
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Journal ArticleDOI
Machine learning for email spam filtering: review, approaches and open research problems
Emmanuel Gbenga Dada,Joseph Stephen Bassi,Haruna Chiroma,Shafi’i Muhammad Abdulhamid,Adebayo Olusola Adetunmbi,Opeyemi Emmanuel Ajibuwa +5 more
TL;DR: A systematic review of some of the popular machine learning based email spam filtering approaches and recommended deep leaning and deep adversarial learning as the future techniques that can effectively handle the menace of spam emails.
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Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems
Stephen Bassi Joseph,Emmanuel Gbenga Dada,Afeez Abidemi,David Opeoluwa Oyewola,Ban Mohammed Khammas +4 more
TL;DR: A thorough review of state-of-the-art and classical strategies for PID controller parameters tuning using metaheuristic algorithms can be found in this article , where the primary objectives of PID control parameters are to achieve minimal overshoot in steady state response and lesser settling time.
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Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing.
TL;DR: In this paper, a deep residual convolution neural network (DRNN) was proposed for CMD detection in cassava leaf images with the aid of distinct block processing, which can counterbalance the imbalanced image dataset of the cassava diseases and increase the number of images available for training and testing.
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Systematic literature review and metadata analysis of ransomware attacks and detection mechanisms
Abdullahi Mohammed Maigida,Shafi’i Muhammad Abdulhamid,Morufu Olalere,John K. Alhassan,Haruna Chiroma,Emmanuel Gbenga Dada +5 more
TL;DR: This review can serve as a benchmark for researchers in proposing a novel ransomware detection methodology and starting point for novice researchers in getting access to ransomware datasets.
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Exploring machine learning: a scientometrics approach using bibliometrix and VOSviewer
TL;DR: In this article , the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) was applied to clustering prediction of authors dominance ranking.