S
Syed Omer Gilani
Researcher at University of the Sciences
Publications - 86
Citations - 1253
Syed Omer Gilani is an academic researcher from University of the Sciences. The author has contributed to research in topics: Computer science & Control theory. The author has an hindex of 15, co-authored 77 publications receiving 698 citations. Previous affiliations of Syed Omer Gilani include Dalarna University & National University of Singapore.
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Journal ArticleDOI
Multiday EMG-Based Classification of Hand Motions with Deep Learning Techniques
Muhammad Zia ur Rehman,Asim Waris,Syed Omer Gilani,Mads Jochumsen,Imran Khan Niazi,Mohsin Jamil,Mohsin Jamil,Dario Farina,Ernest Nlandu Kamavuako +8 more
TL;DR: CNN significantly improved performance and increased robustness over time compared with standard LDA with associated handcrafted features, and this data-driven features extraction approach may overcome the problem of the feature calibration and selection in myoelectric control.
Journal ArticleDOI
Skin Lesion Segmentation from Dermoscopic Images Using Convolutional Neural Network.
Kashan Zafar,Syed Omer Gilani,Asim Waris,Ali Ahmed,Mohsin Jamil,Muhammad Nasir Khan,Amer Kashif +6 more
TL;DR: An automated method for segmenting lesion boundaries that combines two architectures, the U-Net and the ResNet, collectively called Res-Unet is proposed, which achieves comparable results to the current available state-of-the-art techniques.
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Performance evaluation of convolutional neural network for hand gesture recognition using EMG
Ali Raza Asif,Asim Waris,Syed Omer Gilani,Mohsin Jamil,Mohsin Jamil,Hassan Ashraf,Muhammad Shafique,Imran Khan Niazi +7 more
TL;DR: The convolutional neural network was implemented to decode hand gestures from the sEMG data recorded from 18 subjects to investigate the effect of hyper-parameters on each hand gesture and showed that regardless of network configuration some motions performed better.
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Improving fuel cell performance via optimal parameters identification through fuzzy logic based-modeling and optimization
Waqas Hassan Tanveer,Waqas Hassan Tanveer,Hegazy Rezk,Hegazy Rezk,Ahmed M. Nassef,Ahmed M. Nassef,Mohammad Ali Abdelkareem,Mohammad Ali Abdelkareem,Mohammad Ali Abdelkareem,Ben W. Kolosz,K. Karuppasamy,Jawad Aslam,Syed Omer Gilani +12 more
TL;DR: In this article, the particle swarm optimization technique is used for obtaining the best parameters of the cell that maximizes its power density, which is almost two times higher than the maximum power density obtained experimentally.
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Brain Tumour Image Segmentation Using Deep Networks
TL;DR: This paper proposes an ensemble of two segmentation networks: a 3D CNN and a U-Net, in a significant yet straightforward combinative technique that results in better and accurate predictions.