H-Index & Metrics Best Publications
Mazin Abed Mohammed

Mazin Abed Mohammed

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 33 Citations 3,460 95 World Ranking 6903 National Ranking 1

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Machine learning
  • Computer network

Artificial neural network, Artificial intelligence, Pattern recognition, Nasopharyngeal carcinoma and Scalability are his primary areas of study. Mazin Abed Mohammed integrates many fields in his works, including Artificial neural network, Fractal dimension, Ultrasound, Breast ultrasound, Breast cancer and Computerized system. Mazin Abed Mohammed incorporates Artificial intelligence and Breast cancer classification in his research.

His Pattern recognition study integrates concerns from other disciplines, such as Object detection and Backpropagation. Among his research on Nasopharyngeal carcinoma, you can see a combination of other fields of science like Local binary patterns, Region growing, Histogram of oriented gradients, Feature selection and Computer-aided. His Scalability research spans across into subjects like Digital library, Fault tolerance, Shared resource and Data science.

His most cited work include:

  • Enabling technologies for fog computing in healthcare IoT systems (175 citations)
  • Enabling technologies for fog computing in healthcare IoT systems (175 citations)
  • Solving vehicle routing problem by using improved genetic algorithm for optimal solution (73 citations)

What are the main themes of his work throughout his whole career to date?

Mazin Abed Mohammed focuses on Artificial intelligence, Pattern recognition, Machine learning, Artificial neural network and Support vector machine. His work on Segmentation, Region growing and Feature selection as part of general Artificial intelligence research is frequently linked to Nasopharyngeal carcinoma, bridging the gap between disciplines. In his work, Noise is strongly intertwined with Region of interest, which is a subfield of Segmentation.

His research in the fields of Naive Bayes classifier, Random forest and Extreme learning machine overlaps with other disciplines such as Parkinson's disease and Heart disease. His work in Artificial neural network tackles topics such as Local binary patterns which are related to areas like Histogram of oriented gradients. As a member of one scientific family, Mazin Abed Mohammed mostly works in the field of Support vector machine, focusing on Grayscale and, on occasion, Image texture.

He most often published in these fields:

  • Artificial intelligence (72.92%)
  • Pattern recognition (35.42%)
  • Machine learning (22.92%)

What were the highlights of his more recent work (between 2019-2021)?

  • Artificial intelligence (72.92%)
  • Machine learning (22.92%)
  • Artificial neural network (28.12%)

In recent papers he was focusing on the following fields of study:

Mazin Abed Mohammed mostly deals with Artificial intelligence, Machine learning, Artificial neural network, Pattern recognition and Support vector machine. Artificial intelligence is often connected to Computer vision in his work. His Feature, Feature selection, Naive Bayes classifier and Extreme learning machine study in the realm of Machine learning interacts with subjects such as Heart disease.

His research investigates the connection between Artificial neural network and topics such as Local binary patterns that intersect with issues in Histogram of oriented gradients and Region growing. His work on Pattern recognition as part of general Pattern recognition study is frequently linked to Fully automatic, Bioelectrical impedance analysis, Materials science and Spectrum analyzer, bridging the gap between disciplines. As a part of the same scientific study, Mazin Abed Mohammed usually deals with the Support vector machine, concentrating on Random forest and frequently concerns with Benchmark.

Between 2019 and 2021, his most popular works were:

  • Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network (41 citations)
  • Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network (41 citations)
  • Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks (29 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Machine learning
  • Computer network

Mazin Abed Mohammed mainly investigates Artificial intelligence, Artificial neural network, Machine learning, Local binary patterns and Pattern recognition. His research in the fields of Entropy, Entropy and Entropy overlaps with other disciplines such as Ligament and Partial tear. His work on Support vector machine as part of his general Machine learning study is frequently connected to Economic shortage and Key issues, thereby bridging the divide between different branches of science.

His Support vector machine research integrates issues from Grayscale, Histogram, Benchmark, Random forest and Feature selection. Mazin Abed Mohammed has included themes like Histogram of oriented gradients, Region growing, Image texture and Feature in his Local binary patterns study. He integrates many fields, such as Pattern recognition, Fully automatic, Brain tumor, Model based segmentation and Mri brain, in his works.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Enabling technologies for fog computing in healthcare IoT systems

Ammar Awad Mutlag;Mohd Khanapi Abd Ghani;N. Arunkumar;Mazin Abed Mohammed;Mazin Abed Mohammed.
Future Generation Computer Systems (2019)

324 Citations

Solving vehicle routing problem by using improved genetic algorithm for optimal solution

Mazin Abed Mohammed;Mazin Abed Mohammed;Mohd Khanapi Abd Ghani;Raed Ibraheem Hamed;Salama A. Mostafa.
Journal of Computational Science (2017)

143 Citations

Neural network and multi-fractal dimension features for breast cancer classification from ultrasound images

Mazin Abed Mohammed;Mazin Abed Mohammed;Belal Al-Khateeb;Ahmed Noori Rashid;Dheyaa Ahmed Ibrahim.
Computers & Electrical Engineering (2018)

111 Citations

Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease

Salama A. Mostafa;Aida Mustapha;Mazin Abed Mohammed;Raed Ibraheem Hamed.
Cognitive Systems Research (2019)

96 Citations

Benchmarking Methodology for Selection of Optimal COVID-19 Diagnostic Model Based on Entropy and TOPSIS Methods

Mazin Abed Mohammed;Karrar Hameed Abdulkareem;Alaa S. Al-Waisy;Salama A. Mostafa.
IEEE Access (2020)

85 Citations

K -Means clustering and neural network for object detecting and identifying abnormality of brain tumor

N Arunkumar;Mazin Abed Mohammed;Mazin Abed Mohammed;Mohd Khanapi Abd Ghani;Dheyaa Ahmed Ibrahim.
soft computing (2019)

75 Citations

Decision-level fusion scheme for nasopharyngeal carcinoma identification using machine learning techniques

Mohd Khanapi Abd Ghani;Mazin Abed Mohammed;Mazin Abed Mohammed;N. Arunkumar;Salama A. Mostafa.
Neural Computing and Applications (2020)

70 Citations

Fully automatic model‐based segmentation and classification approach for MRI brain tumor using artificial neural networks

N. Arunkumar;Mazin Abed Mohammed;Salama A. Mostafa;Dheyaa Ahmed Ibrahim.
Concurrency and Computation: Practice and Experience (2020)

67 Citations

A real time computer aided object detection of nasopharyngeal carcinoma using genetic algorithm and artificial neural network based on Haar feature fear

Mazin Abed Mohammed;Mazin Abed Mohammed;Mohd Khanapi Abd Ghani;N. Arunkumar;Raed Ibraheem Hamed.
Future Generation Computer Systems (2018)

66 Citations

A fuzzy logic control in adjustable autonomy of a multi-agent system for an automated elderly movement monitoring application.

Salama A. Mostafa;Aida Mustapha;Mazin Abed Mohammed;Mohd Sharifuddin Ahmad.
International Journal of Medical Informatics (2018)

65 Citations

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