Jinglin Li | Computer Science | Best Researcher Award

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Mr. Jinglin Li | Computer Science | Best Researcher Award

Engineer | China National Nuclear Corporation | China

Li Jinglin is a researcher specializing in intelligent systems, reinforcement learning, and energy-efficient technologies for industrial and service applications. He holds advanced degrees in Instrument Science and Technology, Electrical Engineering, and Vehicle Engineering with a focus on new energy systems. His research encompasses the development of intelligent interactive service technologies for elderly care, optimization of energy-harvesting wireless sensor networks, and multi-task scheduling for energy-secured unmanned vehicles. He has led projects on digital twin platform technologies and vertical displacement control of nuclear fusion plasma, applying deep reinforcement learning to enhance system performance and replace traditional control methods. Li has extensive experience in algorithm design, including MATLAB-based reinforcement learning, adaptive dynamic programming, and multi-level exploration deep Q-network scheduling, with applications in optimal microgrid transmission, mobile charging sequence scheduling, and network monitoring. His work has resulted in multiple first-author publications in high-impact journals covering reinforcement learning, wireless sensor networks, and energy management, as well as conference contributions in control and automation. Beyond his technical expertise, he demonstrates strong analytical, problem-solving, and team collaboration skills, with experience in summarizing complex research findings and implementing practical solutions. Li actively engages in academic presentations and has earned recognition for his research achievements. In addition to his research, he maintains leadership roles in university sports teams, reflecting his commitment to teamwork, discipline, and resilience. His professional approach combines a proactive mindset, logical thinking, and a dedication to advancing intelligent and sustainable technological solutions across both industrial and service domains.

Profile: Scopus

Featured Publications

Li, J. (2024). A deep reinforcement learning approach for online mobile charging scheduling with optimal quality of sensing coverage in wireless rechargeable sensor networks. Ad Hoc Networks, 156, 103431.

Li, J. (2024). A reinforcement learning based mobile charging sequence scheduling algorithm for optimal sensing coverage in wireless rechargeable sensor networks. Journal of Ambient Intelligence and Humanized Computing, 15(6), 2869–2881.

Li, J. (2023). Mobile charging sequence scheduling for optimal sensing coverage in wireless rechargeable sensor networks. Applied Sciences, 13(5), 2840.

Li, J. (2024). A reinforcement learning based mobile charging sequence scheduling algorithm for optimal stochastic event detection in wireless rechargeable sensor networks. IEEE Transactions on Network and Service Management.

Li, J. (2024). A swarm deep reinforcement learning based on-demand mobile charging-scheduling and charging-time control joint algorithm for optimal stochastic event detection in wireless rechargeable sensor networks. Expert Systems with Applications.

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:

Eiichiro Fukusaki | Data Science and Analytics | Best Researcher Award

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Prof. Eiichiro Fukusaki | Data Science and Analytics | Best Researcher Award

Professor Osaka University Japan

Prof. Eiichiro Fukusaki is a prominent figure in the field of biotechnology and metabolomics, currently serving as a Professor at the Department of Biotechnology, Graduate School of Engineering, Osaka University, Japan. He also holds the role of Director for the Industrial Biotechnology Initiative at the Institute for Open and Transdisciplinary Research Initiatives. With a career spanning academia, industry, and leadership roles, Prof. Fukusaki is known for his innovative contributions to both fundamental science and its practical applications.

Profile

Scopus

Google Scholar

Orcid

Education 🎓

Prof. Fukusaki’s academic journey began at Osaka University, where he earned his Bachelor’s degree in Engineering in 1983, followed by a Master’s degree from the Graduate School of Engineering in 1985. His passion for research led him to complete his Ph.D. at the same institution in 1993, setting the stage for his distinguished career in biotechnology.

Experience 🧑‍🔬

Prof. Fukusaki’s career started in 1985 as a researcher at Nitto Denko Corporation, where he advanced to Deputy Chief Researcher. Transitioning to academia in 1995, he joined Osaka University as an Associate Professor before being promoted to full Professor in 2007. He has also held leadership positions, including President of the Society for Biotechnology, Japan (2021-2023), and Director of the Industrial Biotechnology Initiative since 2020. Additionally, he was honored as an Honorary Professor at the Institute of Technology Bandung in 2019.

Research Interest 🔬

Prof. Fukusaki’s research revolves around the development and application of metabolomics technologies, with over 300 published papers and 50 patents to his name. His work bridges fundamental science and industry applications in diverse fields, including food, pharmaceuticals, and chemicals. He actively fosters international collaborations and has spearheaded double degree programs between Osaka University and global institutions.

Awards 🏆

Prof. Fukusaki has received numerous accolades throughout his career, such as:

  • Excellent Paper Awards from the Society for Biotechnology, Japan (multiple years: 1993-2020).
  • Encouragement of Young Scientists Award from the Japanese Society for Chemical Regulation of Plants (2001).
  • Saito Award (2004) and Achievement Award (2015) from the Society for Biotechnology, Japan.
  • Biotechnology Award (2024) and the prestigious Honorary Fellow of the Metabolomics Society (2019) for his groundbreaking work in food metabolomics.
  • ITB Award (2022) for advancing food metabolomics in Asia.

Publications Top Notes: 📚

Prof. Fukusaki has published extensively, with over 300 original papers. Below are some notable works:

“Metabolomics technology development for food analysis”

Published in Food Chemistry, 2015. Cited by 150 articles. Read here

“Innovations in chemical profiling using metabolomics”

Published in Journal of Biotechnology, 2019. Cited by 180 articles. Read here

“Applications of metabolomics in pharmaceuticals”

Published in Analytical Chemistry, 2021. Cited by 230 articles. Read here

Time-course metabolic profiling in Arabidopsis thaliana cell cultures after salt stress treatment

Chloroplast-mediated activation of plant immune signalling in Arabidopsis

Prediction of Japanese green tea ranking by gas chromatography/mass spectrometry-based hydrophilic metabolite fingerprinting

Flower color modulations of Torenia hybrida by downregulation of chalcone synthase genes with RNA interference

Development of a method for comprehensive and quantitative analysis of plant hormones by highly sensitive nanoflow liquid chromatography–electrospray ionization-ion trap mass …

High-throughput technique for comprehensive analysis of Japanese green tea quality assessment using ultra-performance liquid chromatography with time-of-flight mass …