A
Asifullah Khan
Researcher at Pakistan Institute of Engineering and Applied Sciences
Publications - 232
Citations - 7325
Asifullah Khan is an academic researcher from Pakistan Institute of Engineering and Applied Sciences. The author has contributed to research in topics: Digital watermarking & Computer science. The author has an hindex of 38, co-authored 192 publications receiving 5109 citations. Previous affiliations of Asifullah Khan include Gwangju Institute of Science and Technology & Ghulam Ishaq Khan Institute of Engineering Sciences and Technology.
Papers
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Journal ArticleDOI
WRF-TMH: predicting transmembrane helix by fusing composition index and physicochemical properties of amino acids
Maqsood Hayat,Asifullah Khan +1 more
TL;DR: WRF-TMH model might play a substantial role, and will provide essential information for further structural and functional studies on membrane proteins, and are the best reported so far on the same datasets.
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Predicting lattice constant of complex cubic perovskites using computational intelligence
TL;DR: This study has used support vector regression, random forest, generalized regression neural network, and multiple linear regression based CI approaches to predict lattice constants (LCs) of complex cubic perovskites and observed that the larger prediction error provided by the CI models is correlated with the structure deviation of the compounds from its ideal cubic symmetry.
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Optical screening of nasopharyngeal cancer using Raman spectroscopy and support vector machine
TL;DR: In this article, the authors presented the application of Raman spectroscopy combined with support vector machine (SVM) for the characterization of Nasopharyngeal Cancer (NPC) in the human sera.
Posted Content
A New Channel Boosted Convolution Neural Network using Transfer Learning.
TL;DR: The proposed work validates the concept observed from the evolution of recent CNN architectures that the innovative restructuring of a CNN architecture may increase the networks representative capacity.
Posted Content
Transfer Learning and Meta Classification Based Deep Churn Prediction System for Telecom Industry
TL;DR: A solution to the inherent problems of churn prediction, using the concept of Transfer Learning (TL) and Ensemble-based Meta-Classification, and the performance of the proposed TL-DeepE system is compared with existing techniques.