M
Monagi H. Alkinani
Researcher at Information Technology University
Publications - 49
Citations - 676
Monagi H. Alkinani is an academic researcher from Information Technology University. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 7, co-authored 38 publications receiving 171 citations. Previous affiliations of Monagi H. Alkinani include University of Western Ontario.
Papers
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Journal ArticleDOI
Factors impacting consumers’ intention toward adoption of electric vehicles in Malaysia
Shahla Asadi,Mehrbakhsh Nilashi,Sarminah Samad,Rusli Abdullah,Marwan Mahmoud,Monagi H. Alkinani,Elaheh Yadegaridehkordi +6 more
TL;DR: In this article, a model has been developed based on two theoretical models called the Norm Activation Model and the Theory of Planned Behaviour to identify the influencing factors on consumers' intention to use electric vehicles.
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Myocardial infarction detection based on deep neural network on imbalanced data
TL;DR: The proposed CNN is an end-to-end model without requiring any stages of machine learning and requires only one stage to detect MI from the input signals and improves the detection accuracy by 9% for detecting MI signals.
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Patch-based models and algorithms for image denoising: a comparative review between patch-based images denoising methods for additive noise reduction
TL;DR: The use of the state-of-the-art patch-based denoising methods for additive noise reduction is investigated and fast patch similarity measurements produce fast patch- based image denoizing methods.
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Detecting Human Driver Inattentive and Aggressive Driving Behavior Using Deep Learning: Recent Advances, Requirements and Open Challenges
TL;DR: An in-depth investigation of most recent deep learning-based systems, algorithms, and techniques for the detection of Distraction, Fatigue/Drowsiness, and Aggressiveness of a human driver.
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Detection of lung nodule and cancer using novel Mask-3 FCM and TWEDLNN algorithms
Laxmikant Tiwari,Rohit Raja,Vineet Kumar Awasthi,Rohit Miri,G. R. Sinha,Monagi H. Alkinani,Kemal Polat +6 more
TL;DR: An efficient Deep Learning methodology is proposed for lung cancer detection utilizing Target based Weighted Elman DL Neural Network (TWEDLNN), and Mask Unit (MU) based 3FCM algorithm and the performance of proposed MC-CLAHE is authenticated by contrasting the proposed technique’s performance with prevailing techniques.