scispace - formally typeset
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.

Papers
More filters
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

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.
Journal ArticleDOI

A survey of ontology learning techniques and applications.

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.
Journal ArticleDOI

Deep Unified Model For Face Recognition Based on Convolution Neural Network and Edge Computing

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.
Journal ArticleDOI

Deep learning model integrating features and novel classifiers fusion for brain tumor segmentation

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.
Journal ArticleDOI

Soft Computing-Based EEG Classification by Optimal Feature Selection and Neural Networks

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.