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.
Papers
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Journal ArticleDOI
Advancements in satellite image classification : methodologies, techniques, approaches and applications
G. A. Fotso Kamga,G. A. Fotso Kamga,Laurent Bitjoka,Tallha Akram,A. Mengue Mbom,S. Rameez Naqvi,Y. Bouroubi +6 more
TL;DR: In this paper, an image is divided into spatially continuous, disjoint, and spatial-temporal segments. But the classification of the segmentations is not addressed in this paper.
Journal ArticleDOI
Prediction of Critical Currents for a Diluted Square Lattice Using Artificial Neural Networks
TL;DR: In this paper, the authors proposed a technique based on artificial neural networks to facilitate extrapolation of these curves for unforeseen values of temperature and magnetic fields, which may be adopted for prediction in other types of regular and diluted lattices.
Journal ArticleDOI
Video Analytics Framework for Human Action Recognition
Usman Tariq Tariq Masood,Muhammad Attique Khan,Majed Alhaisoni,Ammar Armghan,Fayadh Alenezi,Yunyoung Nam,Tallha Akram +6 more
TL;DR: In this article, an entropy-skewness based features reduction technique has been implemented and the reduced features are converted into a codebook by serial based fusion and a custom made genetic algorithm is implemented on the constructed features codebook in order to select the strong and well-known features.
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A probabilistic segmentation and entropy-rank correlation-based feature selection approach for the recognition of fruit diseases
TL;DR: In this paper, an automated system is proposed for the identification and recognition of fruit diseases, which overcomes the challenges like convex edges, inconsistency between colors, irregularity, visibility, scale, and origin.
Proceedings ArticleDOI
Automatic detection of plant diseases; utilizing an unsupervised cascaded design
Zia Ullah Khan,Tallha Akram,Syed Rameez Naqvi,Sajjad Ali Haider,Muhammad Kamran,Nazeer Muhammad +5 more
TL;DR: A machine vision framework that determines the ocular manifestation of plant diseases by analyzing the images in CIELab color space by utilizing a cascaded unsupervised image segmentation technique is described.