Nooshin Nemati | Computer Science | Best Researcher Award

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Ms. Nooshin Nemati | Computer Science | Best Researcher Award

Ankara University, Turkey

Dr. Nooshin Nemati is a dedicated researcher in the fields of Artificial Intelligence, Deep Learning, and Medical Image Processing, currently pursuing her PhD in Computer Engineering at Ankara University, where she also contributes to multiple AI-based cancer detection projects. She holds a Master’s degree in Electrical and Electronics Engineering from Yuzuncu Yıl University and a Bachelor’s from Qazvin Azad University.

Profile:

Educational Background:

Nooshin Nemati is currently a PhD candidate in Computer Engineering at Ankara University. She earned her Master’s degree in Electrical and Electronics Engineering from Yuzuncu Yıl University with a completed her undergraduate studies at Qazvin Azad University in Iran.

Research Areas:

Her main research interests lie in Artificial Intelligence, Deep Learning, Medical Image Processing, and Computer Vision, particularly applied to cancer detection in histopathology images. She focuses on segmentation, classification, and detection tasks using advanced deep learning frameworks.

Projects and Contributions:

She has actively contributed to significant research initiatives such as the TUBITAK 1001 Project, focused on deep learning methodologies for breast cancer detection, and the BAP Project, which deals with cancer region detection in histopathology images. She has also been involved in the development of important datasets such as NuSeC and MiDeSeC, aimed at supporting machine learning in medical imaging. In addition, she has applied her technical skills in software development projects including system analysis and automation tools for banks.

Technical Skills:

Nooshin is proficient in AI, Machine Learning, Deep Learning, and programming frameworks such as ASP.NET and WordPress. She also holds certifications like Network+ and CCNA, showcasing her broad technical competence.

Citation Metrics:

  • Total Citations: 75

  • Citations Since 2020: 71

  • h-index: 6

  • h-index Since 2020: 5

  • i10-index: 3

  • i10-index Since 2020: 3

Publication Top Notes:

  • An imbalance-aware nuclei segmentation methodology for H&E stained histopathology images
    2023
    Citations: 22

  • Detection of colorectal cancer with vision transformers
    2022
    Citations: 11

  • Effect of color normalization on nuclei segmentation problem in H&E stained histopathology images
    2022
    Citations: 10

  • A hybridized deep learning methodology for mitosis detection and classification from histopathology images
    2023
    Citations: 8

  • CompSegNet: An enhanced U-shaped architecture for nuclei segmentation in H&E histopathology images
    2024
    Citations: 7

 

Belal Hamed | Computer Science | Best Researcher Award

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Dr. Belal Hamed | Computer Science | Best Researcher Award

Assistant Lecturer at Department of Computer Science, Faculty of Science, Minia University, Egypt

Belal Ahmed Mohammed Hamed is an Assistant Lecturer at the Department of Computer Science, Faculty of Science, Minia University, and at the Department of Artificial Intelligence, Minia National University, Egypt. He holds a master’s degree in Computer Science, with research expertise in bioinformatics, machine learning, and graph-based disease prediction models. His work focuses on developing advanced algorithms for pattern recognition in DNA sequences and medical data analysis. He has published in Scopus and SCI-indexed journals, and contributed to six research projects, including two funded ones. He also serves as a reviewer for journals such as Scientific Reports and The Journal of Supercomputing. His notable contributions include a high-accuracy Graph Convolutional Network model for Alzheimer’s gene prediction.

Profile:

Academic Background:

Belal holds a Master’s degree in Computer Science. His academic training and research work are rooted in computer science, with a focus on interdisciplinary applications in healthcare and genomics.

Research Areas:

  • Bioinformatics

  • Machine Learning

  • SNP-based Disease Prediction

  • Graph Neural Networks

  • DNA Pattern Matching Algorithms

Research Contributions:

Belal developed a deep learning model that integrates SNP data and Graph Convolutional Networks (GCNs) to predict gene-disease associations, specifically in Alzheimer’s disease. The model achieved 98.04% accuracy and AUROC of 0.996, identifying both known and novel genes. His framework is adaptable for use in other diseases, supporting personalized medicine and clinical research.

Publications & Impact:

  • 4 research papers in SCI/Scopus-indexed journals (Springer Nature, Wiley)

  • Google Scholar Citations: 73

  • h-index: 3

Research & Projects:

  • Participated in 6 research projects, including 2 funded

  • Contributed to 1 industry-academic collaboration in medical data analysis

Editorial Roles:

  • Reviewer for The Journal of Supercomputing, Scientific Reports, and Medical Data Mining Journal

  • Young Scientist – Medical Data Mining Journal

Collaborations:

Active in interdisciplinary research teams, particularly in genomics and artificial intelligence.

Publication Top Notes: