M
Mohamed Elhoseny
Researcher at Mansoura University
Publications - 287
Citations - 11252
Mohamed Elhoseny is an academic researcher from Mansoura University. The author has contributed to research in topics: Computer science & Wireless sensor network. The author has an hindex of 49, co-authored 240 publications receiving 7044 citations. Previous affiliations of Mohamed Elhoseny include Maharaja Agrasen Institute of Technology & Cairo University.
Papers
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Journal ArticleDOI
An efficient hierarchical clustering protocol for multihop Internet of vehicles communication
TL;DR: The experimental values ensured the superior performance of the EHCP over the compared methods such as n‐hop and distributed multihop clustering using a neighborhood follow algorithm in a significant manner.
Journal ArticleDOI
Recognizing human activity in mobile crowdsensing environment using optimized k-NN algorithm
TL;DR: Experimental results proved that the PSO-kNN algorithm is able to find the optimal or near optimal value(s) of the k parameter which enhances the accuracy of k-NN classifier.
Journal ArticleDOI
Optimizing robot path in dynamic environments using Genetic Algorithm and Bezier Curve
TL;DR: Compared to the state-of-the-art methods, GADPP improves the performance of robot based applications in terms of the path length, the smoothness of the course, and the required time to get the optimum path.
Journal ArticleDOI
Deep learning with LSTM based distributed data mining model for energy efficient wireless sensor networks
TL;DR: A deep learning based distributed data mining (DDM) model to achieve energy efficiency and optimal load balancing at the fusion center of WSN is presented and results indicated that the RNN-LSTM reduces the signaling overhead, average delay and maximizes the overall throughput compared to other methods.
Book ChapterDOI
Computer-Aided Acute Lymphoblastic Leukemia Diagnosis System Based on Image Analysis
TL;DR: This chapter presents Computer-Aided Acute Lymphoblastic Leukemia (ALL) diagnosis system based on image analysis to identify the cells ALL by segmenting each cell in the microscopic images, and then classify each segmented cell to be normal or affected.