What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Computer network
- Machine learning
Mohamed Elhoseny spends much of his time researching Genetic algorithm, Artificial intelligence, Bézier curve, Mathematical optimization and Wireless sensor network.
Mohamed Elhoseny has researched Genetic algorithm in several fields, including Loan, Actuarial science and Crossover.
His work carried out in the field of Artificial intelligence brings together such families of science as Computer vision and Pattern recognition.
His work on Mutation operator as part of general Mathematical optimization study is frequently linked to Start point, therefore connecting diverse disciplines of science.
The concepts of his Wireless sensor network study are interwoven with issues in Key distribution in wireless sensor networks, Real-time computing and Base station.
He works mostly in the field of Motion planning, limiting it down to topics relating to Shortest path problem and, in certain cases, Particle swarm optimization.
His most cited work include:
- Secure Medical Data Transmission Model for IoT-Based Healthcare Systems (178 citations)
- Feature selection based on artificial bee colony and gradient boosting decision tree (137 citations)
- Balancing Energy Consumption in Heterogeneous Wireless Sensor Networks Using Genetic Algorithm (134 citations)
What are the main themes of his work throughout his whole career to date?
Mohamed Elhoseny focuses on Artificial intelligence, Computer network, Wireless sensor network, Distributed computing and Pattern recognition.
His work deals with themes such as Machine learning and Computer vision, which intersect with Artificial intelligence.
His work on Cluster analysis expands to the thematically related Computer network.
His Wireless sensor network research incorporates themes from Key distribution in wireless sensor networks, Genetic algorithm and Cryptography.
His Genetic algorithm study is concerned with the larger field of Mathematical optimization.
His Encryption research is multidisciplinary, relying on both Digital image, Key and Computer security model.
He most often published in these fields:
- Artificial intelligence (33.60%)
- Computer network (15.81%)
- Wireless sensor network (15.81%)
What were the highlights of his more recent work (between 2020-2021)?
- Artificial intelligence (33.60%)
- Machine learning (8.70%)
- Deep learning (6.72%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Machine learning, Deep learning, Process and Identification.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Computer vision and Pattern recognition.
His study in the fields of Segmentation and Convolutional neural network under the domain of Pattern recognition overlaps with other disciplines such as Abnormality and Fully automatic.
His research in Machine learning intersects with topics in Home automation, Feature extraction and Heart disease.
He has included themes like Ontology, Ubiquitous computing and Domain knowledge in his Identification study.
His Feature selection research integrates issues from Classifier, Feature and Selection.
Between 2020 and 2021, his most popular works were:
- Energy-Aware Marine Predators Algorithm for Task Scheduling in IoT-Based Fog Computing Applications (18 citations)
- An efficient radix trie‐based semantic visual indexing model for large‐scale image retrieval in cloud environment (15 citations)
- An evolutionary lion optimization algorithm‐based image compression technique for biomedical applications (7 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Machine learning
- Computer network
Mohamed Elhoseny mostly deals with Artificial intelligence, Machine learning, Algorithm, Artificial neural network and Deep learning.
His research integrates issues of Computer vision and Pattern recognition in his study of Artificial intelligence.
Many of his research projects under Machine learning are closely connected to Data modeling with Data modeling, tying the diverse disciplines of science together.
His study in Algorithm is interdisciplinary in nature, drawing from both Evolutionary algorithm, Optimization algorithm, Image compression and Quality of service.
The study incorporates disciplines such as Distributed computing, Wearable technology, Activity recognition, The Internet and Scheduling in addition to Artificial neural network.
His study in Deep learning is interdisciplinary in nature, drawing from both Home automation, Feature extraction and Intrusion detection system.
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