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.
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A survey of the recent architectures of deep convolutional neural networks
TL;DR: Deep Convolutional Neural Networks (CNNs) as mentioned in this paper are a special type of Neural Networks, which has shown exemplary performance on several competitions related to Computer Vision and Image Processing.
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Recent Progress on Generative Adversarial Networks (GANs): A Survey
TL;DR: The basic theory of GANs and the differences among different generative models in recent years were analyzed and summarized and the derived models of GAns are classified and introduced one by one.
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Wind power prediction using deep neural network based meta regression and transfer learning
TL;DR: The effectiveness of the proposed, DNN-MRT technique is expressed by comparing statistical performance measures in terms of root mean squared error (RMSE), mean absolute error (MAE), and standard deviation error (SDE) with other existing techniques.
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A recent survey of reversible watermarking techniques
TL;DR: A major focus of this survey is on prediction-error expansion based reversible watermarking techniques, whereby the secret information is hidden in the prediction domain through error expansion.
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Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks
TL;DR: An effective short term wind power prediction methodology is presented, which uses an intelligent ensemble regressor that comprises Artificial Neural Networks and Genetic Programming, which is implemented and tested on data taken from five different wind farms located in Europe.