Eveline Bezerra | Computational Chemistry | Best Researcher Award

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Prof. Dr. Eveline Bezerra | Computational Chemistry | Best Researcher Award

Professor | Federal Rural University of Semi-Arid | Brazil

Dr. Eveline M. Bezerra is a Brazilian researcher at the Universidade Federal Rural do Semi-Árido (UFERSA), Mossoró, Brazil. With an established record in computational and theoretical chemistry, her scientific work explores molecular modeling, quantum biochemistry, and the electronic properties of organic and biomolecular systems. She has authored 20 peer-reviewed publications and has been cited in 285 academic documents, accumulating a total of 391 citations and maintaining an h-index of 13, reflecting the sustained influence of her research within the scientific community. Her recent contributions include advanced investigations into optical and electronic behavior in π-extended conjugated systems and the molecular interactions relevant to neurodegenerative disorders such as Alzheimer’s disease. Notably, her study on “Red-shifted optical absorption induced by donor–acceptor–donor p-extended dibenzalacetone derivatives” (RSC Advances, 2025) highlights her expertise in computational design of organic chromophores, while her work on “Alzheimer’s Disease Immunotherapy and Mimetic Peptide Design” (ACS Chemical Neuroscience, 2024) demonstrates her interdisciplinary engagement in biomedical chemistry through mutation screening, molecular dynamics, and quantum biochemistry approaches. Dr. Bezerra collaborates extensively with national and international researchers—her co-authorship network spanning more than 80 collaborators—strengthening cross-disciplinary connections between chemistry, materials science, and molecular medicine. Her computational insights contribute to the design of advanced functional materials and therapeutic biomolecules, aligning fundamental theory with applied innovation. Through her scientific outputs and collaborative endeavors, Dr. Bezerra exemplifies the growing impact of theoretical and computational methodologies in addressing global challenges in health, energy, and nanotechnology.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

  1. da Costa, R. F., Freire, V. N., Bezerra, E. M., Cavada, B. S., Caetano, E. W. S., & others. (2012). Explaining statin inhibition effectiveness of HMG-CoA reductase by quantum biochemistry computations. Physical Chemistry Chemical Physics, 14(4), 1389–1398. [Citations: 98]

  2. Dantas, D. S., Oliveira, J. I. N., Neto, J. X. L., da Costa, R. F., Bezerra, E. M., Freire, V. N., & others. (2015). Quantum molecular modelling of ibuprofen bound to human serum albumin. RSC Advances, 5(61), 49439–49450. [Citations: 52]

  3. Zanatta, G., Nunes, G., Bezerra, E. M., da Costa, R. F., Martins, A., Caetano, E. W. S., & others. (2014). Antipsychotic haloperidol binding to the human dopamine D3 receptor: Beyond docking through QM/MM refinement toward the design of improved schizophrenia medicines. ACS Chemical Neuroscience, 5(10), 1041–1054. [Citations: 46]

  4. Tessarolo, L. D., Mello, C. P., Lima, D. B., Magalhães, E. P., Bezerra, E. M., & others. (2018). Nanoencapsulation of benznidazole in calcium carbonate increases its selectivity to Trypanosoma cruzi. Parasitology, 145(9), 1191–1198. [Citations: 34]

  5. Zanatta, G., Barroso-Neto, I. L., Bambini-Junior, V., Dutra, M. F., Bezerra, E. M., & others. (2012). Quantum biochemistry description of the human dopamine D3 receptor in complex with the selective antagonist eticlopride. Journal of Proteomics & Bioinformatics, 5, 155–162. [Citations: 32]

Nur Intan Raihana Ruhaiyem | Machine Learning | Best Researcher Award

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Dr. Nur Intan Raihana Ruhaiyem | Machine Learning | Best Researcher Award

Senior Lecturer | Universiti Sains Malaysia | Malaysia

Dr. Nur Intan Raihana Ruhaiyem is a highly accomplished researcher and Senior Lecturer at the School of Computer Sciences, Universiti Sains Malaysia, with notable expertise in computational biology, image processing, data visualization, and artificial intelligence applications. Her research spans deep learning, computer vision, and biomedical informatics, focusing on developing intelligent systems that enhance healthcare diagnostics, cultural heritage preservation, and data-driven decision-making. She has authored over 50 scholarly publications in reputable international journals and conferences, including IEEE Access, Biomedical Signal Processing and Control, Intelligence-Based Medicine, Diagnostics (Basel), Image and Vision Computing, and Scientific Reports. Her works have collectively garnered more than 230 citations and an h-index of 7, underscoring her growing impact in the computational and data science research community. Recent contributions such as the development of Mamba-based UNet architectures for medical image segmentation and hybrid restoration models for historical murals reflect her capacity to integrate advanced AI models into multidisciplinary domains. Dr. Ruhaiyem’s collaborative research extends internationally, with partnerships involving scholars from Australia, China, and the broader ASEAN region. Her role as a technical committee member for several prominent conferences—such as the International Visual Informatics Conference and Soft Computing in Data Science—demonstrates her leadership in promoting innovation and research excellence in data science and visual analytics. A Certified Professional Trainer recognized by Malaysia’s Human Resources Development Fund, she has also played a key role in professional education, serving as a lead instructor for national Data Science Certification programs. Through her research, mentorship, and active academic engagement, Dr. Ruhaiyem contributes significantly to advancing digital transformation, fostering analytical literacy, and bridging computational intelligence with societal needs.

Profiles: Google Scholar | Scopus | ORCID | ResearchGate

Featured Publications

1. Younis, H. A., Ruhaiyem, N. I. R., Ghaban, W., Gazem, N. A., & Nasser, M. (2023). A systematic literature review on the applications of robots and natural language processing in education. Electronics, 12(13), 2864. Citations: 75

2. Salisu, S., Ruhaiyem, N. I. R., Eisa, T. A. E., Nasser, M., Saeed, F., & Younis, H. A. (2023). Motion capture technologies for ergonomics: A systematic literature review. Diagnostics, 13(15), 2593. Citations: 63

3. Goni, M. R., Ruhaiyem, N. I. R., Mustapha, M., Achuthan, A., & Nassir, C. M. N. C. M. (2022). Brain vessel segmentation using deep learning—A review. IEEE Access, 10, 111322–111336. Citations: 42

4. Yang, J., & Ruhaiyem, N. I. R. (2024). Review of deep learning-based image inpainting techniques. IEEE Access, 12, 138441–138482. Citations: 17

5. Younis, H. A., Ruhaiyem, N. I. R., Badr, A. A., Abdul-Hassan, A. K., Alfadli, I. M., & others. (2023). Multimodal age and gender estimation for adaptive human-robot interaction: A systematic literature review. Processes, 11(5), 1488. Citations: 16