Mohammad Abrar

About

Dr. Mohammad Abrar is a distinguished researcher in the field of computer science, specializing in artificial intelligence, neural networks, classification, and federated learning. He earned his Ph.D. in computer science in 2013 from the prestigious University Technology Malaysia.

With a keen interest in cutting-edge technologies, Dr. Abrar has made remarkable contributions to the field of AI. His expertise lies in the development and application of neural networks, specifically in the area of classification. His research work has been published in reputable conferences and high-impact research journals, making a significant impact on the scientific community.

Dr. Abrar's dedication and expertise in federated learning have paved the way for advancements in distributed machine learning algorithms, ensuring privacy-preserving and efficient training processes across decentralized data sources. His research findings have practical implications for various domains, including healthcare, finance, and telecommunications.

Employment

• Arab Open University Assistant Professor
  2023 - Present
• Bacha Khan University Charsadda Assistant Professor
  2018 - Present
• Universiti Teknologi Malaysia Penerbit Research Associate
  2013 - 2017
• Kardan University Assistant Professor
  2009 - 2012
• Agricultural University Peshawar Lecturer
  2006 - 2009

Education

Education history is unavailable.

Projects & Funding

Projects & funding information is unavailable.

Publications (19)

• Brain Tumor Segmentation from MRI Images Using Handcrafted Convolutional Neural Network
  Diagnostics 2023 DOI: 10.3390/diagnostics13162650
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• Deep Learning Techniques for Web-Based Attack Detection in Industry 5.0: A Novel Approach
  Technologies 2023 DOI: 10.3390/technologies11040107
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• A Convolutional Neural Network Model for Wheat Crop Disease Prediction
  Computers, Materials and Continua 2023 DOI: 10.32604/cmc.2023.035498
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• Brain Tumor Segmentation Using a Patch-Based Convolutional Neural Network: A Big Data Analysis Approach
  Mathematics 2023 DOI: 10.3390/math11071635
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• Efficient Data Collaboration Using Multi-Party Privacy Preserving Machine Learning Framework
  IEEE Access 2023 DOI: 10.1109/ACCESS.2023.3339750
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• Evolutionary Model for Brain Cancer-Grading and Classification
  IEEE Access 2023 DOI: 10.1109/ACCESS.2023.3330919
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• Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with Advanced Data Privacy and Security Measures
  Mathematics 2023 DOI: 10.3390/math11194189
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• Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data
  IEEE Access 2022 DOI: 10.1109/ACCESS.2021.3077592
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• Machine health surveillance system by using deep learning sparse autoencoder
  Soft Computing 2022 DOI: 10.1007/s00500-022-06755-z
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• Machine health surveillance system by using deep learning sparse autoencoder
  Research Square 2021 DOI: 10.21203/rs.3.rs-911385/v1
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