Ahmet Kayabaşı| Artificial Intelligence | Best Researcher Award

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Prof. Dr. Ahmet Kayabaşı | Artificial Intelligence | Best Researcher Award

Professor | Karamanoglu Mehmetbey University | Turkey

Prof. Dr. Ahmet Kayabaşı is a distinguished academic in electrical-electronics engineering with expertise in artificial intelligence, antennas, biomedical signal processing, image processing, fuzzy logic, and power electronics. He earned his PhD in Electrical-Electronics Engineering from Selcuk University and has since built a strong academic career combining teaching, research, and leadership. His professional experience includes serving as Head of Department, Director of the Institute of Graduate Studies, and Senate Member, along with mentoring numerous MSc and PhD students. His research interests span interdisciplinary fields, applying advanced AI techniques in UAV swarm algorithms, smart agriculture, biomedical diagnostics, and energy-efficient power systems. He has been actively involved in TÜBİTAK and institutional projects, contributing to impactful solutions for both academia and industry. Recognized for his excellence, he has received awards such as Best Presenter Award at ICAT and has played vital roles in academic conferences and scientific communities. His research skills include developing intelligent systems, applying machine learning to engineering challenges, and designing novel antenna and biomedical applications. He has published widely in leading international journals indexed in IEEE, Scopus, and Web of Science, with notable contributions in Applied Thermal Engineering, Swarm and Evolutionary Computation, and Computers and Electronics in Agriculture. His academic excellence is reflected in 609 citations by 522 documents, 47 publications, and an h-index of 13.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

  1. Sabanci, K., Kayabasi, A., & Toktas, A. (2017). Computer vision‐based method for classification of wheat grains using artificial neural network. Journal of the Science of Food and Agriculture, 97(8), 2588–2593.

  2. Yigit, E., Sabanci, K., Toktas, A., & Kayabasi, A. (2019). A study on visual features of leaves in plant identification using artificial intelligence techniques. Computers and Electronics in Agriculture, 156, 369–377.

  3. Kayabasi, A., Toktas, A., Yigit, E., & Sabanci, K. (2018). Triangular quad-port multi-polarized UWB MIMO antenna with enhanced isolation using neutralization ring. AEU-International Journal of Electronics and Communications, 85, 47–53.

  4. Sabanci, K., Toktas, A., & Kayabasi, A. (2017). Grain classifier with computer vision using adaptive neuro‐fuzzy inference system. Journal of the Science of Food and Agriculture, 97(12), 3994–4000.

  5. Yildiz, B., Aslan, M. F., Durdu, A., & Kayabasi, A. (2024). Consensus-based virtual leader tracking swarm algorithm with GDRRT*-PSO for path-planning of multiple-UAVs. Swarm and Evolutionary Computation, 88, 101612.

Reymark Delena | Data Analytics | Best Researcher Award

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Assist. Prof. Dr. Reymark Delena | Data Analytics | Best Researcher Award

Assistant Professor | Mindanao State University – Iligan Institute of Technology | Philippines

Assist. Prof. Dr. Reymark Delena is a dedicated researcher and academic with expertise in machine learning, IoT, climate informatics, data analytics, and smart systems. He earned a Master of Science in Information Technology from De La Salle University and a Bachelor’s degree in Information Systems from the University of Southern Mindanao, further supported by vocational training in ICT. He has served in various academic roles including Instructor, Assistant Professor, and currently contributes as a Senior Consultant in Smart Agriculture at PhilRice, while also engaging in teaching and mentoring at MSU. His professional journey includes roles as a software engineer and developer, providing him with a strong foundation in practical system development alongside research. His research interests cover smart agriculture, early detection systems, educational technologies, and sustainable digital solutions. He has actively participated in national and international conferences, presented research papers, and collaborated on funded projects such as RiceProTek and early fungal detection systems, reflecting his commitment to applied research with societal benefits. He possesses strong skills in programming, mobile and web development, data visualization, AI modeling, and technology management, making him versatile across academic and industry domains. His contributions have been recognized through invited talks, community workshops, and research forums, highlighting his academic leadership and service. His growing research impact is evident with 4 citations by 4 documents, 6 documents, and an h-index of 1.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

  1. Ampuan, A. D., & Deleña, R. D. (2024). A quantitative evaluation of online appointment system at Mindanao State University–Main Campus: Employing the system usability scale (SUS) and technology acceptance model (TAM). Proceedings of the 3rd International Conference on Digital Transformation and Applications (ICDXA).

  2. Delena, R. D., Tangkeko, M. S., & Sieras, J. C. (2023). From climate to crop: Unveiling the impact of agro-climate dataset on rice yield in Cotabato Province. Data in Brief, 51, 109754.

  3. Delena, R. D., Tangkeko, M. S., Ampuan, A. D., & Sieras, J. C. (2023). ARP Cotabato: Exploring seasonal climate and rice production in Cotabato Province through advanced data visualization and rapid analytics. Software Impacts, 17, 100546.

  4. Dia, N. J., Sieras, J. C., Khalid, S. A., Macatotong, A. H. T., Mondejar, J. M., & Delena, R. D. (2025). EduGuard RetainX: An advanced analytical dashboard for predicting and improving student retention in tertiary education. SoftwareX, 29, 102057.

  5. Gulam, S. B., & Delena, R. D. (2024). The development of a web-based Meranaw language lexicon using a rule-based morphological analyzer for Meranaw verbs: Dindiyorobasa App. Proceedings of the 3rd International Conference on Digital Transformation and Applications (ICDXA).

Neven Saleh | Healthcare Engineering Systems | Women Researcher Award

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Assist. Prof. Dr. Neven Saleh | Healthcare Engineering Systems | Women Researcher Award

Associate Professor | Future University in Egypt | Egypt

Assist. Prof. Dr. Neven Saleh is a highly motivated and detail-oriented researcher with extensive experience in biomedical engineering, machine learning, deep learning for disease diagnosis, healthcare technology, hospital design, and assistive communication systems. She holds a Ph.D. in Biomedical Engineering from Politecnico di Torino and a master’s and bachelor’s degree in biomedical and electronics engineering from Egyptian universities. She has served as an associate professor at multiple institutions, supervised numerous Ph.D. and M.Sc. students, and contributed to international research collaborations across Italy, Egypt, and the USA. Her research interests focus on AI-driven diagnostic systems for retinal and neurological disorders, cancer detection using image processing, healthcare technology assessment, hospital workflow optimization, and assistive technologies for patients with disabilities. She has received multiple awards and honors for her innovative projects, teaching excellence, and scientific contributions, including national competitions, best thesis awards, and international conference recognitions. She possesses strong research skills in machine learning, deep learning, computer vision, biomedical signal processing, medical instrumentation, and healthcare technology management. She is a member of professional organizations such as TWAS-OWSD and IFMBE and has completed advanced certifications in machine learning, clinical engineering, and healthcare quality management. Her research impact is reflected in 277 citations by 204 documents, 41 publications, and an h-index of 9.

Profile: Google Scholar | Scopus | ORCID

Featured Publications

  1. Saleh, N., Sharawi, A. A., Abd Elwahed, M., Petti, A., Puppato, D., & Balestra, G. (2014). Preventive maintenance prioritization index of medical equipment using quality function deployment. IEEE Journal of Biomedical and Health Informatics, 19(3), 1029–1035.

  2. Saleh, N., Abdel Wahed, M., & Salaheldin, A. M. (2022). Transfer learning‐based platform for detecting multi‐classification retinal disorders using optical coherence tomography images. International Journal of Imaging Systems and Technology, 32(3), 740–752.

  3. Saleh, N., Farghaly, M., Elshaaer, E., & Mousa, A. (2020). Smart glove-based gestures recognition system for Arabic sign language. In 2020 International Conference on Innovative Trends in Communication and … (pp. 37–…).

  4. Saleh, N., Hassan, M. A., & Salaheldin, A. M. (2024). Skin cancer classification based on an optimized convolutional neural network and multicriteria decision-making. Scientific Reports, 14(1), 17323.

  5. Salaheldin, A. M., Abdel Wahed, M., Talaat, M., & Saleh, N. (2024). Deep learning‐based automated detection and grading of papilledema from OCT images: A promising approach for improved clinical diagnosis and management. International Journal of Imaging Systems and Technology, 34(4), e23133.

Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

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Prof. Fatma Zahra Sayadi | Deep Learning | Best Innovation Award

Associate Professor | University of Sousse | Tunisia

Fatma Elzahra Sayadi is a highly accomplished researcher and academic specializing in electronics and microelectronics, with current research focused on video surveillance systems, real-time processing, and signal compression. She earned her PhD in electronics for real-time systems from the University of Bretagne Sud in collaboration with the University of Monastir and has also completed her engineering and master’s studies in electrical and electronic systems. She has extensive professional experience as a maître de conférences and previously as a maître assistante and assistant technologist, teaching courses in microprocessors, multiprocessors, programming, circuit testing, and industrial electronics. Her research interests include signal processing, parallel architectures, microelectronics, real-time systems, and communication networks. She has actively participated in national and international research projects and collaborations with institutions in France, Italy, Germany, and Morocco. Her work has been published in over 37 journal articles, 40 conference papers, and six book chapters, and she has supervised several doctoral and master’s theses. She has been recognized with awards such as the first prize at the Women in Research Forum at the University of Sharjah and contributes to professional communities as a reviewer, evaluator, and organizer of academic events. She is skilled in research methodologies, signal and data analysis, electronic system design, and digital education innovation. Her academic contributions have been cited by 395 documents, with 69 documents contributing to her citations, and she has an h-index of 13.

Profile: Google Scholar | Scopus | ORCID 

Featured Publications

  1. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2020). CNN-SVM learning approach based human activity recognition. In International Conference on Image and Signal Processing (pp. 271–281). 77 citations.

  2. Bouaafia, S., Khemiri, R., Sayadi, F. E., & Atri, M. (2020). Fast CU partition-based machine learning approach for reducing HEVC complexity. Journal of Real-Time Image Processing, 17(1), 185–196. 53 citations.

  3. Haggui, O., Tadonki, C., Lacassagne, L., Sayadi, F., & Ouni, B. (2018). Harris corner detection on a NUMA manycore. Future Generation Computer Systems, 88, 442–452. 48 citations.

  4. Basly, H., Ouarda, W., Sayadi, F. E., Ouni, B., & Alimi, A. M. (2022). DTR-HAR: Deep temporal residual representation for human activity recognition. The Visual Computer, 38(3), 993–1013. 40 citations.

  5. Bouaafia, S., Khemiri, R., Messaoud, S., Ben Ahmed, O., & Sayadi, F. E. (2022). Deep learning-based video quality enhancement for the new versatile video coding. Neural Computing and Applications, 34(17), 14135–14149. 35 citations.

Ahmad Reza Naghsh Nilchi | Deep Learning | Best Researcher Award

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Prof. Ahmad Reza Naghsh Nilchi | Deep Learning | Best Researcher Award

Faculty Member | University of Isfahan | Iran

Prof. Ahmad Reza Naghsh-Nilchi is a distinguished researcher in computer vision, artificial intelligence, and medical image processing with a strong academic and professional background. He completed his PhD in Electrical and Computer Engineering at Michigan State University, where he specialized in digital image processing, and has since built an influential career in both academia and research. Over the years, he has served in multiple leadership positions including department chair, dean of research, and head of research laboratories, while also supervising numerous PhD and master’s students in advanced AI and imaging topics. His professional experience extends internationally through collaborations with leading institutions such as UC Irvine, University of Toronto, York University, and University of Ireland, contributing significantly to global research initiatives. His research interests span robust deep learning, adversarial defense, trustworthy AI, multimodal action recognition, image captioning, retinal analysis, and robot-camera pose estimation, reflecting both theoretical innovation and practical applications. He has published more than 70 papers in prestigious journals and conferences indexed by IEEE and Scopus, and his work has received more than 2,200 citations. His excellence has been recognized through multiple honors, including awards as University Researcher of the Year and Industrial Researcher of the Year. He possesses advanced research skills in AI model development, medical imaging, digital signal processing, and multimodal data analysis, complemented by editorial roles, conference organization, and active memberships in professional associations such as IEEE and ACM. His career demonstrates a commitment to advancing science, mentoring the next generation, and fostering impactful interdisciplinary collaborations. His Scopus output reflects international impact, with 1,319 citations by 1,214 documents, 65 published documents, and an h-index of 21.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Fathi, A., & Naghsh-Nilchi, A. R. (2012). Noise tolerant local binary pattern operator for efficient texture analysis. Pattern Recognition Letters, 33(9), 1093–1100.

Fathi, A., & Naghsh-Nilchi, A. R. (2012). Efficient image denoising method based on a new adaptive wavelet packet thresholding function. IEEE Transactions on Image Processing, 21(9), 3981–3990.

Fathi, A., & Naghsh-Nilchi, A. R. (2013). Automatic wavelet-based retinal blood vessels segmentation and vessel diameter estimation. Biomedical Signal Processing and Control, 8(1), 71–80.

Amirgholipour, S. K., & Ahmad, R. (2009). Robust digital image watermarking based on joint DWT-DCT. International Journal of Digital Content Technology and its Applications, 3(2), 42–48.*

Kasmani, S. A., & Naghsh-Nilchi, A. (2008). A new robust digital image watermarking technique based on joint DWT-DCT transformation. In 2008 Third International Conference on Convergence and Hybrid Information Technology (pp. 539–544). IEEE.

Benito Farina | Spatio-Temporal CV | Best Researcher Award

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Mr. Benito Farina | Spatio-Temporal CV | Best Researcher Award

Researcher | Universidad Politecnica de Madrid | Spain

Benito Farina is a dedicated researcher in artificial intelligence, machine learning, and biomedical engineering with a strong focus on medical imaging, cancer screening, and predictive modeling. He completed his bachelor’s and master’s degrees in Biomedical Engineering with highest honors at Università degli Studi di Napoli Federico II, where his theses explored machine learning for breast cancer histopathology and deep learning models for lung nodule malignancy detection. He pursued his doctoral studies in Electrical Engineering at Universidad Politécnica de Madrid, graduating with distinction for his research on spatio-temporal image analysis methods to enhance lung cancer screening and therapy response prediction. Professionally, he gained extensive experience as a Junior Research Scientist at Universidad Politécnica de Madrid, where he developed AI-based medical imaging datasets, implemented advanced models including CNNs, RNNs, and transformers, and explored generative models and explainable AI for clinical applications. He later joined the Centro de Investigación Biomédica en Red as a Research Scientist, leading projects in medical image segmentation, classification, and interpretability, managing GPU-based deployments, and contributing to international collaborations and grant proposals. His international exposure includes visiting scientist positions at Harvard University’s Brigham and Women’s Hospital, where he worked on image harmonization techniques to improve consistency in multi-center datasets. His research interests lie in artificial intelligence for healthcare, medical image processing, radiomics, generative models, self-supervised learning, and explainable AI with a vision of translating computational tools into clinical practice. Throughout his career, he has guided undergraduate and master’s students, actively contributed to competitive AI challenges, and engaged in cultural leadership as Vice-President of a community association promoting cultural heritage and development. He has presented his research at reputed conferences, published in indexed journals, and continues to expand his academic contributions through collaborative projects. His research skills include proficiency in Python, R, MATLAB, TensorFlow, PyTorch, and Keras, expertise in GPU cluster computing, dataset development, model deployment with Docker, and technical documentation with LaTeX. Fluent in Italian, Spanish, and English, he thrives in multicultural academic environments and has demonstrated both technical excellence and leadership capabilities. Benito has earned academic distinctions for his outstanding performance in higher education and doctoral research, reflecting his commitment to excellence. With strong foundations in artificial intelligence and biomedical engineering, he aspires to drive advancements in precision medicine, foster global collaborations, and translate AI innovations into impactful healthcare solutions.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Farina, B., Guerra, A. D. R., Bermejo-Peláez, D., Miras, C. P., Peral, A. A., & others. (2023). Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients. Journal of Translational Medicine, 21(1), 174.

Farina, B., Guerra, A. D. R., Miras, C. P., Madueño, G. G., Muñoz-Barrutia, A., & others. (2021). Delta-radiomics signature for prediction of survival in advanced NSCLC patients treated with immunotherapy. In 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) (pp. 886–890). IEEE.

Farina, B., Benito, R. C., Montalvo-García, D., Bermejo-Peláez, D., Maceiras, L. S., & others. (2025). Spatio-temporal deep learning with temporal attention for indeterminate lung nodule classification. Computers in Biology and Medicine, 196, 110813.

Ramos-Guerra, A. D., Farina, B., Rubio Pérez, J., Vilalta-Lacarra, A., & others. (2025). Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data. Cancer Immunology, Immunotherapy, 74(4), 120.

Seijo, L., Bermejo-Peláez, D., Gil-Bazo, I., Farina, B., Domine, M., & others. (2023). Integration of longitudinal deep-radiomics and clinical data improves the prediction of durable benefits to anti-PD-1/PD-L1 immunotherapy in advanced NSCLC patients. Journal of Translational Medicine, 21(1), 174.

Bolaños, M. C., Farina, B., Guerra, A. D. R., Miras, C. P., Madueño, G. G., & others. (2020). Design and implementation of predictive models based on radiomics to assess response to immunotherapy in non-small-cell lung cancer. In XXXVIII Congreso Anual de la Sociedad Española de Ingeniería Biomédica.

Osman Yildirim | Deep Learning | Best Researcher Award

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Prof. Osman Yildirim | Deep Learning | Best Researcher Award

Head of the Department | Istanbul Aydın University | Turkey 

Prof. Osman Yildirim is a distinguished academic and researcher recognized for his contributions at the intersection of engineering, business, sustainability, and biomedical applications. He holds dual doctoral degrees in Engineering and Business Administration, a unique combination that has enabled him to approach research challenges with a strong interdisciplinary perspective. Over the course of his career, he has taken on significant academic leadership roles, including serving as Head of Department at Istanbul Aydin University, while also guiding doctoral students and fostering collaborative research projects. His professional experience spans teaching across engineering and business disciplines, coordinating research initiatives, and contributing to institutional development through mentorship and administrative leadership. His primary research interests focus on green transformation, sustainable supply chains, carbon policy impacts, energy management systems in universities, and AI-based medical imaging applications for improved diagnostics. These areas reflect his commitment to aligning research with both technological advancements and societal needs, particularly in the context of sustainable development and healthcare innovation. He has published widely in reputed Q1 and Q2 indexed journals such as Scopus and SCI, showcasing the impact of his work in both technical and applied fields. His achievements have been recognized through awards and honors that acknowledge his contributions to advancing interdisciplinary research and education. In addition, he has built valuable collaborations with international teams, integrating expertise from engineering, business, and medicine to deliver impactful solutions with global relevance. His research skills include expertise in machine learning, AI-driven image analysis, sustainable system design, and computational modeling for optimization under carbon constraints. These technical strengths, combined with his leadership and mentorship, position him as a leading scholar dedicated to advancing academic excellence and addressing global challenges through innovative and socially relevant research.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

Ozturk, A. I., Yıldırım, O., İdman, E., & İdman, E. (2025). A comparative study of hybrid decision tree–deep learning models in the detection of intracranial arachnoid cysts. Neuroscience Informatics, 100234.

Ozturk, A. I., Yildirim, O., Kaygusuz, K., Idman, E., & Idman, E. (2025). Brain cyst detection using deep learning models. International Journal of Innovative Research and Scientific Studies, 8(5), 8974.

Borhan Elmi, M. M., & Yıldırım, O. (2025). Improve MPPT in organic photovoltaics with chaos-based nonlinear MPC. Balkan Journal of Electrical and Computer Engineering, 13(1), 1418574.

Ozturk, A. I., Yıldırım, O., & Deryahanoglu, O. (2025). A comprehensive strategy for the identification of arachnoid cysts in the brain utilizing image processing segmentation methods. International Journal of Innovative Technology and Exploring Engineering, 14(2), 1031.

Borhan Elmi, M. M., & Yıldırım, O. (2024). Improve LVRT capability of organic solar arrays by using chaos-based NMPC. International Journal of Energy Studies, 4(3), 1449558.

Yildirim, O., Khaustova, V. Y., & Ilyash, O. I. (2023). Reliability and validity adaptation of the hospital safety climate scale. The Problems of Economy, 4(1), 207–216.

Yildirim, O. (2023). Multidimensional and strategic outlook in digital business transformation: Human resource and management recommendations for performance improvement. In Book chapter.

Yildirim, O. (2023). Health professionals’ perspective in the context of social media, paranoia, and working autonomy during the COVID-19 pandemic period. Archives of Health Science Research, 10(1), 30–37.

Yildirim, O. (2023). The personified model for supply chain management. In Multidimensional and strategic outlook in digital business transformation: Human resource and management recommendations for performance improvement.

Yildirim, O., Ilyash, O. I., Khaustova, V. Y., & Celiksular, A. (2022). The effect of emotional intelligence and work-related strain on the employee’s organizational behavior factors. The Problems of Economy, 2(1), 124–131.

Yildirim, O. (2022). Investigation of the electrical conductivity of pernigranilin with carbon monoxide and nitrogen monoxide doping. Mathematical Statistician and Engineering Applications, 9(4).

Yildirim, O. (2022). Cyst segmentation using filtering technique in computed tomography abdominal kidney images. Mathematical Statistician and Engineering Applications, 9(4).

Yildirim, O. (2022). Design of flyback converter by obtaining the characteristics of polymer based R2R organic PV panels. International Journal of Renewable Energy Research, 12(4).

Avdullahi, A., & Yildirim, O. (2021). The mediating role of emotional stability between regulation of emotion and overwork. In Book chapter.

Tunç, P., Yıldırım, O., Göktepe, E. A., & Çapuk, S. (2021). Investigation of the relationship between personality, organizational identification and turnover in competitive flight model. TroyAcademy, 6(1), 894141.

Tunç, P., Yıldırım, O., Göktepe, E. A., & Çapuk, S. (2021). Investigation of the relationship between personality, organizational identification and turnover in competitive flight model. Çanakkale Onsekiz Mart Üniversitesi Uluslararası Sosyal Bilimler Dergisi, 4(1), 804959.

Dimitrios Theodoropoulos | Deep Learning | Best Researcher Award

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Mr. Dimitrios Theodoropoulos | Deep Learning | Best Researcher Award

University of Crete Medical School | Greece

Dimitrios Theodoropoulos is a researcher and AI specialist in medical imaging with expertise in machine learning, deep learning, computer vision, and artificial intelligence applications in healthcare, particularly in radiology and diabetic retinopathy analysis. He is currently pursuing a PhD in Artificial Intelligence in Medical Imaging at the University of Crete, Medical School, building on a master’s degree in computer engineering from Hellenic Mediterranean University and a bachelor’s degree in physics with specialization in microelectronics from the University of Crete, complemented by training as a radiology assistant. Alongside his academic path, he has worked extensively as a radiographer in MRI, CT, X-ray, mammography, DEXA, and EEG imaging, effectively integrating research with clinical practice. He has served as a visiting research fellow at FORTH-CBML and collaborated with institutions such as the Athens Neurotraining Center and Alexandra Hospital, bridging advanced AI research with healthcare innovation. His research focuses on the development of machine learning and deep learning algorithms for classification, segmentation, and detection tasks in medical imaging, with emphasis on diabetic retinopathy, intensive care monitoring, and noninvasive intracranial pressure assessment, while also extending to areas such as pollen analysis. He has published widely in Scopus-indexed journals and conferences, presented at international congresses and academic symposiums, and delivered guest lectures at the National and Kapodistrian University of Athens. Proficient in Python, MATLAB, TensorFlow, PyTorch, Scikit-learn, Linux, and Docker, he has advanced expertise in data preprocessing, model optimization, and AI-driven biomedical solutions. With certifications in Python programming, machine learning, and deep learning, combined with memberships in the Hellenic Artificial Intelligence Society and the Union of Greek Physicists, he demonstrates a rare integration of technical, clinical, and analytical skills, enabling him to advance scientific progress while contributing to patient-centered healthcare innovation.

Profile: Google Scholar | Scopus Profile

Featured Publications

Tsiknakis N., Theodoropoulos D., Manikis G., Ktistakis E., Boutsora O., et al., Deep learning for diabetic retinopathy detection and classification based on fundus images: A review, Comput. Biol. Med., 135, 104599.

Chatziadam P., Dimitriadis A., Gikas S., Logothetis I., Michalodimitrakis M., Theodoropoulos D., et al., TwiFly: A data analysis framework for Twitter, Information, 11(5), 247.

Theodoropoulos D., Karabetsos D.A., Antonios V., Efrosini P., Karantanas A., et al., The current status of noninvasive intracranial pressure monitoring: A literature review, Clin. Neurol. Neurosurg., 108209.

Theodoropoulos D., Sifakis N., Manikis G., Papadourakis G., Armyras K., et al., Semantic segmentation of diabetic retinopathy lesions using deep learning, SN Comput. Sci., 6(7), 782.

Theodoropoulos D., Trivizakis E., Marias K., Xirouchaki N., Vakis A., et al., Predicting intracranial pressure levels: A deep learning approach using computed tomography brain scans, Neurosurgery, 10.1227.

Barat Barati | Artificial Intelligence | Research Impact Award

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Assist. Prof. Dr. Barat Barati | Artificial Intelligence | Research Impact Award

Medical Physics | Shoushtar Faculty of Medical Sciences | Iran

Assist. Prof. Dr. Barat Barati is a distinguished academician and researcher specializing in radiotherapy, artificial intelligence (AI), and computational simulation, with a career dedicated to advancing healthcare diagnostics and treatment through innovative research and teaching. Currently serving as a faculty member at Shoushtar Faculty of Medical Sciences, he integrates deep learning models with biomedical signal processing to address challenges in medical sciences, particularly brain tumor diagnosis and classification. He earned his doctoral degree with a specialization in artificial intelligence and simulation methods, where his PhD research introduced novel approaches by combining machine learning algorithms with Monte Carlo simulation tools such as MCNP, significantly advancing medical physics and diagnostic imaging. With a strong foundation in physics, mathematics, computer science, and biomedical technologies, Dr. Barati bridges engineering and medicine while enhancing his expertise through specialized training in programming, data analysis, and AI-driven healthcare applications. His research focuses on applying AI and computational simulations in radiotherapy and medical imaging, emphasizing brain tumor detection, classification, and radiation treatment modeling, while also extending to biomedical signal processing and machine learning applications for improved diagnostic accuracy and treatment planning. As a faculty member, he contributes to teaching, mentoring, research supervision, and interdisciplinary collaborations, publishing impactful work in Scopus-indexed journals. Recognized for his ability to mentor young researchers and his vision to advance precision medicine, Dr. Barati demonstrates leadership, innovation, and commitment to improving patient outcomes, making him a deserving candidate for the Best Researcher Award and an influential figure in the global scientific community.

Profile: Google Scholar | Scopus Profile | ORCID Profile

Featured Publications

  1. Noorimotlagh, Z., Mirzaee, S. A., Kalantar, M., Barati, B., Fard, M. E., & Fard, N. K. (2021). The SARS-CoV-2 (COVID-19) pandemic in hospital: An insight into environmental surfaces contamination, disinfectants’ efficiency, and estimation of plastic waste production. Environmental Research, 202, 111809.

  2. Mohseni, H., Amini, S., Abiri, B., Kalantar, M., Kaydani, M., Barati, B., Pirabbasi, E., & … (2021). Are history of dietary intake and food habits of patients with clinical symptoms of COVID-19 different from healthy controls? A case–control study. Clinical Nutrition ESPEN, 42, 280–285.

  3. Moghiseh, Z., Xiao, Y., Kalantar, M., Barati, B., & Ghahrchi, M. (2023). Role of bio-electrochemical technology for enzyme activity stimulation in high-consumption pharmaceuticals biodegradation. 3 Biotech, 13(5), 119.

  4. Barati, B., Erfaninejad, M., & Khanbabaei, H. (2025). Evaluation of effect of optimizers and loss functions on prediction accuracy of brain tumor type using a light neural network. Biomedical Signal Processing and Control, 103, 107409.

  5. Akbari, G., Mard, S. A., Savari, F., Barati, B., & Sameri, M. J. (2022). Characterization of diet based nonalcoholic fatty liver disease/nonalcoholic steatohepatitis in rodent models: Histological and biochemical outcomes. Universidad de Murcia, Departamento de Biología Celular e Histología.

Vera Yuk Ying Chung | Light Field Image Processing | Best Researcher Award

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Dr. Vera Yuk Ying Chung | Light Field Image Processing | Best Researcher Award

Senior Lecturer at The University of Sydney | Australia

Dr. Vera Yuk Ying Chung is a renowned researcher in computer science specializing in light field image processing, machine learning, event-based vision, and multimedia processing. She has dedicated her career to advancing computational methods with practical applications in healthcare, virtual reality, prosthetic vision, agriculture, and multimedia technologies. As a faculty member at the University of Sydney, she has made significant contributions through her extensive publications in high-impact journals and conferences, where her research has gained strong recognition and citations. Her work bridges theory and practice, providing solutions that impact both academia and industry. Dr. Chung has also played a key role in mentoring PhD candidates, securing competitive grants, and fostering international collaborations. With her leadership, interdisciplinary expertise, and long-term dedication, she continues to influence the global research community while shaping innovative technologies for the future.

Professional Profiles

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Vera Yuk Ying Chung completed her doctoral studies in computer science at the School of Information Technology, University of Sydney, where she focused on advanced areas of computing and image processing. Her PhD training provided her with a strong foundation in computational techniques, algorithmic design, and data-driven research methodologies. Over the years, she has continued to expand her academic knowledge through active engagement in interdisciplinary studies, including artificial intelligence, deep learning, biomedical computing, and multimedia systems. Her education not only equipped her with technical expertise but also strengthened her ability to address complex real-world challenges through research. In addition, her continuous involvement with students and research projects reflects her dedication to education and knowledge dissemination. By combining her academic background with practical research initiatives, she has established herself as a leader in computer vision and multimedia studies, making significant contributions to both academia and industry.

Professional Experience

Dr. Vera Yuk Ying Chung has built an extensive professional career as a researcher and academic at the University of Sydney, where she has been actively engaged in teaching, mentoring, and advancing cutting-edge research. Her experience includes supervising PhD candidates supported by industry and international grants, coordinating collaborative projects, and publishing widely in prestigious venues such as IEEE Transactions on Image Processing, IEEE Transactions on Visualization and Computer Graphics, and AAAI Conference on Artificial Intelligence. She has also collaborated with diverse teams across different domains, including biomedical imaging, virtual reality, and smart agriculture, showing her adaptability and interdisciplinary reach. Her role has not only been limited to academic research but also extended to project leadership, where she has guided large-scale initiatives and ensured impactful outcomes. With her ability to combine academic rigor with real-world applications, she has earned recognition as a respected leader within the global computer science community.

Research Interest

Dr. Vera Yuk Ying Chung’s research interests span a wide range of areas in computer science, with particular focus on light field image processing, event-based vision, machine learning, and multimedia technologies. She has contributed to developing methods for image quality assessment, super-resolution, 3D reconstruction, and vision systems for visually impaired individuals, reflecting her interest in creating solutions with real societal impact. Her research also extends into biomedical applications, including medical imaging, radiology report generation, and prosthetic vision, which highlight her commitment to health-focused innovation. Additionally, she has explored applications of artificial intelligence in fields such as virtual reality, haptic feedback, smart agriculture, and data-driven environmental monitoring. By bridging computational theory with practical challenges, her research addresses both technical advancements and human-centered needs. Her diverse interests demonstrate a forward-looking approach that continues to push the boundaries of machine learning, computer vision, and multimedia processing.

Research Skill

Dr. Vera Yuk Ying Chung possesses a wide range of research skills that enable her to excel in interdisciplinary areas of computer science. She is highly proficient in machine learning techniques, deep neural networks, and event-based vision processing, which she applies to solve complex challenges in multimedia and image analysis. Her expertise in light field image processing and image quality assessment demonstrates her technical strength in developing models for high-resolution imaging, super-resolution, and 3D reconstruction. She also brings skills in biomedical imaging, virtual reality applications, and smart agricultural solutions, reflecting her versatility and adaptability. Dr. Chung has strong abilities in experimental design, data analysis, algorithm development, and cross-domain integration, which allow her to bridge theory with practical implementations. Furthermore, her experience in supervising research students, managing grants, and coordinating collaborative projects highlights her leadership and organizational skills, making her a well-rounded and impactful researcher.

Publications Top Notes

Title: Light field spatial super-resolution using deep efficient spatial-angular separable convolution
Year: 2018
Citation: 232

Title: A hybrid network intrusion detection system using simplified swarm optimization (SSO)
Year: 2012
Citation: 221

Title: Deep learning in generating radiology reports: A survey
Year: 2020
Citation: 220

Title: Learning implicit credit assignment for cooperative multi-agent reinforcement learning
Year: 2020
Citation: 186

Title: Fast light field reconstruction with deep coarse-to-fine modeling of spatial-angular clues
Year: 2018
Citation: 181

Title: A new hybrid approach for mining breast cancer pattern using discrete particle swarm optimization and statistical method
Year: 2009
Citation: 179

Title: A particle swarm optimization approach based on Monte Carlo simulation for solving the complex network reliability problem
Year: 2010
Citation: 168

Title: CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR
Year: 2021
Citation: 162

Title: Feature selection with intelligent dynamic swarm and rough set
Year: 2010
Citation: 132

Title: Forecasting wind power in the Mai Liao Wind Farm based on the multi-layer perceptron artificial neural network model with improved simplified swarm optimization
Year: 2014
Citation: 104

Title: Artificial bee colony based data mining algorithms for classification tasks
Year: 2011
Citation: 88

Title: Performance analysis of cellular automata Monte Carlo Simulation for estimating network reliability
Year: 2010
Citation: 78

Title: Using transfer learning with convolutional neural networks to diagnose breast cancer from histopathological images
Year: 2017
Citation: 75

Title: A new simplified swarm optimization (SSO) using exchange local search scheme
Year: 2012
Citation: 50

Title: NTIRE 2025 challenge on light field image super-resolution: Methods and results
Year: 2025
Citation: 49

Title: A hybrid gravitational search algorithm with swarm intelligence and deep convolutional feature for object tracking optimization
Year: 2018
Citation: 45

Title: Light field image quality assessment with auxiliary learning based on depthwise and anglewise separable convolutions
Year: 2021
Citation: 44

Title: Human-Computer Interaction. Interaction Design and Usability: 12th International Conference, HCI International 2007, Beijing, China, July 22-27, 2007, Proceedings, Part I
Year: 2007
Citation: 43

Title: Stochastic dual simplex algorithm: A novel heuristic optimization algorithm
Year: 2019
Citation: 35

Title: Fast search block-matching motion estimation algorithm using FPGA
Year: 2000
Citation: 34

Conclusion

Dr. Vera Yuk Ying Chung is a deserving candidate for the Best Researcher Award due to her extensive contributions to light field image processing, machine learning, event-based vision, and multimedia research, along with her impactful publications in prestigious journals and conferences. Her work has advanced both theoretical and applied knowledge, with applications ranging from healthcare and prosthetic vision to virtual reality and smart agriculture, demonstrating meaningful contributions to society. With her strong record of research funding, mentorship, and academic leadership, she continues to inspire and guide future researchers. Her potential for expanding global collaborations, taking on greater leadership roles, and driving innovative interdisciplinary research further underscores her suitability for this recognition.