M
Muhammad Usman Ghani Khan
Researcher at University of Engineering and Technology, Lahore
Publications - 106
Citations - 1460
Muhammad Usman Ghani Khan is an academic researcher from University of Engineering and Technology, Lahore. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 14, co-authored 95 publications receiving 713 citations. Previous affiliations of Muhammad Usman Ghani Khan include Bahauddin Zakariya University & University of Sheffield.
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Journal ArticleDOI
A Deep Learning Approach for Automated Diagnosis and Multi-Class Classification of Alzheimer’s Disease Stages Using Resting-State fMRI and Residual Neural Networks
Farheen Ramzan,Muhammad Usman Ghani Khan,Asim Rehmat,Sajid Iqbal,Sajid Iqbal,Tanzila Saba,Amjad Rehman,Zahid Mehmood +7 more
TL;DR: Analysis of results indicate that classification and prediction of neurodegenerative brain disorders such as AD using functional magnetic resonance imaging and advanced deep learning methods is promising for clinical decision making and have the potential to assist in early diagnosis of AD and its associated stages.
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A survey of ontology learning techniques and applications.
Muhammad Nabeel Asim,Muhammad Wasim,Muhammad Usman Ghani Khan,Waqar Mahmood,Hafiza Mahnoor Abbasi +4 more
TL;DR: The process of ontological learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) is described and many algorithms under each category are discussed.
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Deep Unified Model For Face Recognition Based on Convolution Neural Network and Edge Computing
Muhammad Zeeshan Khan,Saad Harous,Saleet Ul Hassan,Muhammad Usman Ghani Khan,Razi Iqbal,Shahid Mumtaz +5 more
TL;DR: An algorithm for face detection and recognition based on convolution neural networks (CNN), which outperform the traditional techniques, is proposed and a smart classroom for the student’s attendance using face recognition has been proposed.
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Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation
Sajid Iqbal,Sajid Iqbal,Muhammad Usman Ghani Khan,Tanzila Saba,Zahid Mehmood,Nadeem Javaid,Amjad Rehman,Rashid Abbasi +7 more
TL;DR: This research presents deep learning models using long short term memory (LSTM) and convolutional neural networks (ConvNet) for accurate brain tumor delineation from benchmark medical images and uses class weighting to cope with the class imbalance problem.
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Soft Computing-Based EEG Classification by Optimal Feature Selection and Neural Networks
Muhammad Hamza Bhatti,Javeria Khan,Muhammad Usman Ghani Khan,Razi Iqbal,Moayad Aloqaily,Yaser Jararweh,Brij B. Gupta +6 more
TL;DR: The results show that the proposed optimal feature selection and neural network-based classification approach with overlapped frequency bands is an effective method for EEG classification as compared to previous techniques.