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Amr Tolba
Researcher at King Saud University
Publications - 162
Citations - 2609
Amr Tolba is an academic researcher from King Saud University. The author has contributed to research in topics: Computer science & Engineering. The author has an hindex of 24, co-authored 104 publications receiving 1537 citations. Previous affiliations of Amr Tolba include Menoufia University.
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LoTAD: long-term traffic anomaly detection based on crowdsourced bus trajectory data
TL;DR: This paper proposes LoTAD to explore anomalous regions with long-term poor traffic situations with crowdsourced bus data into TS-segments (Temporal and Spatial segments) to model the traffic condition and combines anomalous TS-Segments detected in different lines to mine anomalous areas.
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A greedy model with small world for improving the robustness of heterogeneous Internet of Things
TL;DR: The two greedy criteria used in GMSW are presented, based on which the concept of local importance of nodes is defined, and the algorithm that transforms a network to possess small world properties by adding shortcuts between certain nodes according to their local importance is presented.
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Multiple cloud storage mechanism based on blockchain in smart homes
Yongjun Ren,Yan Leng,Jian Qi,Pradip Kumar Sharma,Jin Wang,Jin Wang,Zafer Al-Makhadmeh,Amr Tolba,Amr Tolba +8 more
TL;DR: An identity-based proxy aggregate signature (IBPAS) scheme is proposed to improve the efficiency of signature verification, as well as compress the storage space and reduce the communication bandwidth.
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Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks
TL;DR: The effective and optimized neural computing and soft computing techniques to minimize the difficulties and issues in the feature set of lung cancer features are introduced.
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Utilizing IoT wearable medical device for heart disease prediction using higher order Boltzmann model: A classification approach
TL;DR: An Internet of Things-based medical device for collecting patients’ heart details before and after heart disease is introduced and the HOBDBNN method and IoT-based analysis recognize heart disease with 99.03% accuracy with minimum time complexity.