M
Muhammad Jaleed Khan
Researcher at Institute of Space Technology
Publications - 25
Citations - 878
Muhammad Jaleed Khan is an academic researcher from Institute of Space Technology. The author has contributed to research in topics: Deep learning & Hyperspectral imaging. The author has an hindex of 8, co-authored 21 publications receiving 429 citations. Previous affiliations of Muhammad Jaleed Khan include National University of Ireland, Galway.
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Journal ArticleDOI
Modern Trends in Hyperspectral Image Analysis: A Review
TL;DR: This review focuses on the fundamentals of hyperspectral image analysis and its modern applications such as food quality and safety assessment, medical diagnosis and image guided surgery, forensic document examination, defense and homeland security, remote sensing applicationssuch as precision agriculture and water resource management and material identification and mapping of artworks.
Proceedings ArticleDOI
Breast cancer detection in mammograms using convolutional neural network
TL;DR: Promising experimental results have been obtained which depict the efficacy of deep learning for breast cancer detection in mammogram images and further encourage the use ofDeep learning based modern feature extraction and classification methods in various medical imaging applications especially in breast cancer Detection.
Journal ArticleDOI
Deep learning for automated forgery detection in hyperspectral document images
TL;DR: The proposed method effectively identifies different ink types in a hyperspectral document image for forgery detection and achieves an overall accuracy of 98.2% for blue and 88% for black inks, which is the highest accuracy among the latest techniques of ink mismatch detection on theWIHSI database.
Proceedings ArticleDOI
Deep Face Recognition for Biometric Authentication
TL;DR: A convolutional neural network based face recognition system which detects faces in an input image using Viola Jones face detector and automatically extracts facial features from detected faces using a pre-trained CNN for recognition.
Journal ArticleDOI
Comparative analysis of svm, ann and cnn for classifying vegetation species using hyperspectral thermal infrared data
TL;DR: This study concludes that thermal infrared hyperspectral emissivity data has the potential to discern vegetation species using state of the art machine learning and deep learning methods.