Jayateertha Diwan | Genetics | Best Researcher Award

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Prof. Dr. Jayateertha Diwan | Genetics | Best Researcher Award

University of Agricultural Sciences, Dharwad, Karnataka | India

Prof. Dr. Jayateertha Diwan is a distinguished agricultural scientist recognized for his extensive contributions to plant breeding, genetics, and crop improvement, with a strong focus on rice and other major field crops. His research integrates classical breeding, molecular biology, and biotechnology to enhance crop performance, develop stress-tolerant varieties, and improve resource-use efficiency. He has led and contributed to innovative studies on early vigor traits, yield-enhancing QTLs, nutrient-use efficiency, salinity tolerance, and direct-seeded rice systems, employing advanced tools such as SSR markers, MAS, genomic resources, and next-generation sequencing. His work spans germplasm characterization, multi-location trials, A/B/R line development, trait-specific molecular analysis, and breeding strategy design aligned with farmer and market needs. Professionally, he has held progressive leadership roles involving teaching, research, extension, academic management, and guidance of postgraduate and doctoral scholars across plant breeding, molecular breeding, crop genomics, and biotechnology. His experience also includes collaborative research in national and international programs, participation in multi-institutional projects, and contributions to genome sequencing initiatives involving wild rice and microbial species. He has served as a breeder, scheme head, research project investigator, and expert trainer in various advanced workshops and capacity-building programs, contributing to the scientific community through high-impact publications, project leadership, academic services, and active involvement in research on integrated farming systems, stress physiology, molecular diagnostics, and precision crop improvement. His professional journey reflects a deep commitment to advancing agricultural science, improving crop productivity, and translating research innovations for sustainable farming and societal benefit.

Profiles: Scopus | Orcid

Featured Publications

Diwan, J. R., Mahadevaswamy,., Patil, S., Bhanu, D., Devi, K. L., Hegde, S. N., Krishnaraj, P. U., Namitha, R., Pradhan, B. B., & Vashisht, D. (2025). Genome characterization of Acinetobacter species from the rice rhizosphere: A potential plant growth promoting rhizobacteria (PGPR). Current Genetics.

Kavyashree, N. M., Diwan, J. R., Mahantashivayogayya, K., Lokesha, R., & Naik, N. M. (2022). Micro-morphological diversity of rice (Oryza sativa L.) as seen under foldscope. Environment Conservation Journal.

Kariyanna, B., Prabhuraj, A., Mohan, M., Bheemanna, M., Kalmath, B., Pampanna, Y., & Diwan, J. R. (2020). Insecticide usage pattern and evolution of resistance in eggplant shoot and fruit borer, Leucinodes orbonalis Guenée (Lepidoptera: Crambidae) in India. Plant Archives. (EID: 2-s2.0-85090445715)

Umar Farooq, M. S., Diwan, J. R., Mahantashivayogayya, K., Kulkarni, V. V., & Shakuntala, N. M. (2019). Genetic evaluation of rice (Oryza sativa L.) genotypes for yield and nutritional quality traits. Journal of Experimental Biology and Agricultural Sciences, 7(2), 117–127.

Muniswamy, S., Lokesha, R., Yamanura, R., Ramesh,., & Diwan, J. R. (2017). Stability for disease, genotype × environment interaction for yield and its components in pigeonpea [Cajanus cajan (L.) Millsp.]. Legume Research.

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: