T
Tallha Akram
Researcher at COMSATS Institute of Information Technology
Publications - 77
Citations - 2675
Tallha Akram is an academic researcher from COMSATS Institute of Information Technology. The author has contributed to research in topics: Feature extraction & Feature selection. The author has an hindex of 23, co-authored 68 publications receiving 1390 citations. Previous affiliations of Tallha Akram include Canara Engineering College & Chongqing University.
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CCDF: Automatic system for segmentation and recognition of fruit crops diseases based on correlation coefficient and deep CNN features
Muhammad Attique Khan,Tallha Akram,Muhammad Sharif,Muhammad Awais,Kashif Javed,Hashim Ali,Tanzila Saba +6 more
TL;DR: The proposed technique incorporates two major steps of infected regions detection and finally feature extraction and classification, and outperforms several existing methods in terms of greater precision and improved classification accuracy.
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Attributes based skin lesion detection and recognition: A mask RCNN and transfer learning-based deep learning framework
TL;DR: A fully automated computerized aided diagnosis (CAD) system is proposed based on the deep learning framework and a fair comparison with other state-of-the-art is provided to further increase confidence in the proposed framework.
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Brain tumor detection and classification: A framework of marker-based watershed algorithm and multilevel priority features selection.
Muhammad Attique Khan,Ikram Ullah Lali,Amjad Rehman,Mubashar Ishaq,Muhammad Sharif,Tanzila Saba,Saliha Zahoor,Tallha Akram +7 more
TL;DR: An automated system is developed for tumor extraction and classification from MRI based on marker‐based watershed segmentation and features selection that outperforms existing methods with greater precision and accuracy.
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A framework of human detection and action recognition based on uniform segmentation and combination of Euclidean distance and joint entropy-based features selection
Muhammad Sharif,Muhammad Attique Khan,Tallha Akram,Muhammad Younus Javed,Tanzila Saba,Amjad Rehman +5 more
TL;DR: This research proposes a hybrid strategy for efficient classification of human activities from a given video sequence by integrating four major steps: segment the moving objects by fusing novel uniform segmentation and expectation maximization, extract a new set of fused features using local binary patterns with histogram oriented gradient and Harlick features, and feature classification using multi-class support vector machine.
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License number plate recognition system using entropy-based features selection approach with SVM
Muhammad Attique Khan,Muhammad Sharif,Muhammad Younus Javed,Tallha Akram,Mussarat Yasmin,Tanzila Saba +5 more
TL;DR: Simulation results reveal that the proposed method performs exceptionally better compared with existing works, and different performance measures are considered.