U
Usman Akram
Researcher at University of the Sciences
Publications - 24
Citations - 293
Usman Akram is an academic researcher from University of the Sciences. The author has contributed to research in topics: Support vector machine & Signal. The author has an hindex of 8, co-authored 24 publications receiving 187 citations. Previous affiliations of Usman Akram include National University of Sciences and Technology & National University of Science and Technology.
Papers
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Proceedings ArticleDOI
Glaucoma detection through optic disc and cup segmentation using K-mean clustering
TL;DR: This article focuses on automated detection of glaucoma from fundus images using CDR, region of interest (ROI) extraction through intensity weighted centroid method which is followed by preprocessing and recursively applied k-mean clustering segmentation for the detection of Optic cup (OC) and optic disc (OD).
Journal ArticleDOI
Localization and classification of heart beats in phonocardiography signals —a comprehensive review
TL;DR: A comprehensive survey of different methods proposed for automatic analysis of PCG signals in time, frequency, and time-frequency domains is presented to evaluate the current state of the art and to determine the potential domains of effective analysis.
Journal ArticleDOI
Diabetic retinopathy detection through novel tetragonal local octa patterns and extreme learning machines.
TL;DR: A novel technique to precisely detect the various stages of the DR by extending the research of the content-based image retrieval domain and the experimental results confirm the significance ofThe DR-detection scheme to serve as a stand-alone solution for providing the precise information of the severity of theDR in an efficient manner.
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Heart rate tracking in photoplethysmography signals affected by motion artifacts: a review
TL;DR: In this article, the authors presented state-of-the-art techniques in both areas of the automated analysis, i.e., motion artifacts removal and heart rate tracking, and concluded that adaptive filtering and multi-resolution decomposition techniques are better for MA removal and machine learning-based approaches are future perspective of heart rate tracker.
Proceedings ArticleDOI
Wireless Sensor Network for Distributed Event Detection Based on Machine Learning
TL;DR: Negative pressure wave (NPW) coupled with intelligent machine learning techniques integrated in distributed wireless sensor network (WSN) to identify specific events beased on raw data gathered by individual sensor nodes reduces communication overhead to minimum by processing raw data on sensor nodes directly and reporting the detected events only.