M
Muhammad Awais Azam
Researcher at Pir Mehr Ali Shah Arid Agriculture University
Publications - 182
Citations - 3774
Muhammad Awais Azam is an academic researcher from Pir Mehr Ali Shah Arid Agriculture University. The author has contributed to research in topics: Activity recognition & Nusselt number. The author has an hindex of 28, co-authored 167 publications receiving 2340 citations. Previous affiliations of Muhammad Awais Azam include Chungbuk National University & University of Engineering and Technology, Lahore.
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Novel centroid selection approaches for KMeans-clustering based recommender systems
Sobia Zahra,Mustansar Ali Ghazanfar,Asra Khalid,Muhammad Awais Azam,Usman Naeem,Adam Prügel-Bennett +5 more
TL;DR: This paper proposes a k-means clustering-based recommendation algorithm, which addresses the scalability issues associated with traditional recommender systems and provides a better quality cluster and converges quicker than existing approaches, which in turn improves accuracy of the recommendation provided.
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Recent Advances in Information-Centric Networking-Based Internet of Things (ICN-IoT)
TL;DR: The potential of ICN for IoT is presented by providing state-of-the-art literature survey on ICN-based caching, naming, security, and mobility approaches for IoT with appropriate classification and operating systems and simulation tools are presented.
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Unsteady heat and mass transfer mechanisms in MHD Carreau nanofluid flow
Masood Khan,Muhammad Awais Azam +1 more
TL;DR: In this article, the authors studied the thermophoresis and Brownian motion in a magnetohydrodynamic (MHD) Carreau nanofluid flow induced by a permeable stretching surface.
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Continuous authentication of smartphone users based on activity pattern recognition using passive mobile sensing
TL;DR: A novel continuous authentication scheme is proposed for smartphone users, which is based on activity pattern recognition, which recognizes smartphone users on the basis of their physical activity patterns using accelerometer, gyroscope, and magnetometer sensors of smartphone.
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Stock market prediction using machine learning classifiers and social media, news
Wasiat Khan,Mustansar Ali Ghazanfar,Muhammad Awais Azam,Amin Karami,Khaled Hamed Alyoubi,Ahmed S. Alfakeeh +5 more
TL;DR: Algorithms on social media and financial news data are used to discover the impact of this data on stock market prediction accuracy for ten subsequent days and Random forest classifier is found to be consistent and highest accuracy is achieved by its ensemble.