What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Operating system
- Computer network
The scientist’s investigation covers issues in Artificial intelligence, Pattern recognition, Computer vision, Image processing and Pattern recognition.
His Artificial intelligence research incorporates themes from Speech recognition and Natural language processing.
Mohamed Cheriet combines subjects such as Pixel and Machine learning with his study of Pattern recognition.
The Computer vision study which covers Historical document that intersects with Image enhancement, Visualization and Color image.
His Image processing research incorporates elements of Document processing and Process.
His study in Pattern recognition is interdisciplinary in nature, drawing from both Cursive, Active contour model, Probabilistic logic, Time delay neural network and Perceptron.
His most cited work include:
- A recursive thresholding technique for image segmentation (250 citations)
- “One Against One” or “One Against All”: Which One is Better for Handwriting Recognition with SVMs? (173 citations)
- Character Recognition Systems (172 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of investigation include Artificial intelligence, Pattern recognition, Computer vision, Cloud computing and Distributed computing.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Machine learning, Speech recognition and Natural language processing.
His Speech recognition research is multidisciplinary, relying on both Intelligent character recognition and Handwriting recognition.
Pattern recognition and Image are frequently intertwined in his study.
Computer vision is closely attributed to Historical document in his work.
Mohamed Cheriet has included themes like Computer network and Software deployment in his Cloud computing study.
He most often published in these fields:
- Artificial intelligence (49.64%)
- Pattern recognition (29.74%)
- Computer vision (15.15%)
What were the highlights of his more recent work (between 2018-2021)?
- Distributed computing (11.31%)
- Artificial intelligence (49.64%)
- Computer network (10.22%)
In recent papers he was focusing on the following fields of study:
Mohamed Cheriet mainly investigates Distributed computing, Artificial intelligence, Computer network, Cloud computing and Pattern recognition.
His Distributed computing research incorporates themes from Optimization problem, Scalability, Resource allocation and Service.
Artificial intelligence connects with themes related to Machine learning in his study.
His Computer network research includes elements of Wireless and Throughput.
His work deals with themes such as Artificial neural network, Enhanced Data Rates for GSM Evolution and Provisioning, which intersect with Cloud computing.
All of his Pattern recognition and Hyperspectral imaging, Feature extraction and Pattern recognition investigations are sub-components of the entire Pattern recognition study.
Between 2018 and 2021, his most popular works were:
- Efficient Provisioning of Security Service Function Chaining Using Network Security Defense Patterns (37 citations)
- Gabor filter-based texture for ancient degraded document image binarization (12 citations)
- An Improved UAV-PHD Filter-Based Trajectory Tracking Algorithm for Multi-UAVs in Future 5G IoT Scenarios (10 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Computer network
His main research concerns Artificial intelligence, Distributed computing, Computer network, Cloud computing and Pattern recognition.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Stability and Machine learning.
Mohamed Cheriet has researched Distributed computing in several fields, including Optimization problem, Approximation algorithm, Service and Resource allocation.
His work in Computer network tackles topics such as Wireless which are related to areas like Software-defined networking, Frame and Wireless sensor network.
His work carried out in the field of Cloud computing brings together such families of science as Enhanced Data Rates for GSM Evolution and Broadcasting.
His research on Pattern recognition often connects related topics like Image.
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