M
Muhammad Shaheen
Researcher at Foundation University, Islamabad
Publications - 40
Citations - 446
Muhammad Shaheen is an academic researcher from Foundation University, Islamabad. The author has contributed to research in topics: Cluster analysis & Association rule learning. The author has an hindex of 10, co-authored 38 publications receiving 321 citations. Previous affiliations of Muhammad Shaheen include National University of Computer and Emerging Sciences & University of Engineering and Technology, Lahore.
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Context Based Positive and Negative Spatio-Temporal Association Rule Mining
TL;DR: An approach to spatial association rule mining from datasets projected at a temporal bar in which the contextual situation is considered while generating positive and negative frequent itemsets and the numerical evaluation shows that the algorithm is more efficient at generating specific, reliable and robust information than traditional algorithms.
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Face recognition under varying expressions and illumination using particle swarm optimization
TL;DR: A computationally intelligent and efficient method based on particle swarm optimization (PSO) is developed that utilizes the features extracted from texture and wavelet domain to select informative wavelet sub-band in social networks.
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Prosperous Human Gait Recognition: an end-to-end system based on pre-trained CNN features selection
Asif Mehmood,Muhammad Attique Khan,Muhammad Sharif,Sajid Ali Khan,Muhammad Shaheen,Tanzila Saba,Naveed Riaz,Imran Ashraf +7 more
TL;DR: A novel fully automated method is proposed for HGR under various view angles using deep learning using supervised learning methods and shows significant improvement in accuracy and recall rate as compared to the existing state-of-the-art techniques.
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A method of data mining for selection of site for wind turbines
TL;DR: A data mining framework which will help in selection of suitable site for wind turbine installation and the prediction of the model developed for the wind energy site has been found to be significantly accurate when compared with expert opinion and previous studies.
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Data mining applications in hydrocarbon exploration
TL;DR: The review reveals the suitability of existing techniques to data collected from diverse sources in addition to the use of analytical techniques for the process of hydrocarbon exploration.