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.

Naga Nithin Katta | Image Processing | Best Researcher Award

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Mr. Naga Nithin Katta | Image Processing | Best Researcher Award

Employee at Oppo | India

Naga Nithin Katta is a highly motivated computer science and engineering professional with a strong focus on innovation, research, and problem-solving. His expertise spans artificial intelligence, machine learning, computer vision, and full stack development, areas in which he has applied his skills to impactful projects. He has gained industrial exposure as a software engineer at OPPO, where he contributed to projects involving video stream analysis, automation of testing frameworks, and mobile AI deployment. Alongside his industry experience, he has been an active mentor in data structures and algorithms, helping students strengthen their problem-solving abilities. His leadership has been recognized through international competitions, including selection among the Top 100 teams globally in the Google Solution Challenge and multiple hackathon victories. With a balance of technical knowledge, practical implementation, and a passion for community contribution, he is steadily building a strong foundation as an emerging researcher with promising leadership potential.

Professional Profile

Scopus Profile

Education

Naga Nithin Katta is pursuing a Bachelor of Technology in Computer Science and Engineering at VNR Vignana Jyothi Institute of Engineering and Technology, where he has been developing a strong academic background in computing principles, software engineering, and applied technologies. Prior to this, he successfully completed a diploma in computer science from the Government Institute of Electronics, which provided him with a solid technical base in programming, database management, and system design. His educational journey has been complemented by active participation in research-oriented projects, hackathons, and collaborative learning platforms that encouraged innovation and problem-solving. He has consistently demonstrated academic excellence by integrating classroom knowledge with practical applications, which is evident in his project work and international recognition through competitive platforms. This strong educational foundation has equipped him with both theoretical and applied perspectives, allowing him to bridge the gap between academia and industry while nurturing his passion for research and development.

Professional Experience

Naga Nithin Katta has gained valuable professional experience as a software engineer at OPPO, where he contributed to significant projects aimed at improving efficiency and automation in mobile technologies. His work involved developing web applications using Vue.js and MySQL for managing project statuses, implementing video stream analysis through OpenCV and Python, and deploying AI models on mobile devices using ONNX and Beeware. He played a key role in creating a UI automation system powered by large language models, reducing manual testing efforts and enhancing accuracy. Additionally, he contributed to building a network operator testing automation tool, streamlining processes and reducing workforce requirements. Alongside his industry work, he served as a student mentor at SmartInterviews, guiding learners in data structures and algorithms and preparing them for technical challenges. This blend of industrial expertise and teaching experience reflects his versatility, ability to collaborate across teams, and passion for applying research in practical contexts.

Research Interest

Naga Nithin Katta’s research interests lie primarily in the fields of artificial intelligence, computer vision, natural language processing, and software engineering, with a particular focus on developing innovative solutions that bridge academic research and real-world applications. He has worked on projects such as sign language converters that integrate computer vision with generative AI and cloud technologies, reflecting his interest in human-computer interaction and accessibility-focused applications. His engagement with large language models and UI automation tools demonstrates his curiosity in advancing human-machine interaction and automated testing frameworks. Additionally, his focus on video stream analysis and frame detection highlights his inclination towards multimedia research and visual computing. He is also keen on exploring areas such as deep learning optimization, mobile AI deployment, and cloud-integrated intelligent systems. His vision is to contribute to impactful solutions that enhance everyday technologies while simultaneously pursuing scholarly outputs that advance scientific knowledge.

Research Skill

Naga Nithin Katta has developed strong research skills that enable him to design, implement, and evaluate innovative solutions across different domains of computer science. He is proficient in programming languages such as C, C++, Python, and Java, and demonstrates advanced knowledge in full stack development with tools like ReactJs, Vue.js, and MySQL. His expertise in AI and machine learning is reflected in projects involving computer vision, natural language processing, and model deployment on mobile devices. He has practical experience in research-driven software development, having implemented algorithms for video frame detection, gesture recognition, and UI automation powered by large language models. His familiarity with tools like OpenCV, ONNX, Flask, and cloud-based APIs allows him to conduct applied research efficiently. He also possesses strong problem-solving abilities, demonstrated by his role as a mentor in data structures and algorithms. His skills in bridging theoretical concepts with industrial applications showcase his potential as a future research leader.

Publications Top Notes

Title: Optical Motion Detection Language Generator: A Survey

Year: 2025

Conclusion

Naga Nithin Katta is a deserving candidate for the Best Researcher Award as he has consistently demonstrated innovation, technical expertise, and leadership in both academic and industrial settings. His impactful projects, including advancements in computer vision, automation, and AI-driven solutions, showcase contributions that address real-world challenges and benefit society. With proven recognition in global competitions, mentorship roles, and industry research experience, he has already made meaningful strides as an emerging researcher. With a continued focus on publishing in reputed venues and building stronger international collaborations, he holds significant potential to become a future leader in the research and technology community.

Sa Zhou | Human Machine Interface | Best Researcher Award

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Dr. Sa Zhou | Human Machine Interface | Best Researcher Award

Postdoc at Stanford University | United States

Dr. Sa Zhou is a dedicated researcher in the fields of biomedical engineering, neuroscience, and psychiatry, currently working as a postdoctoral scholar at Stanford University. His research emphasizes multimodal neuroimaging, brain-machine interfaces, stroke rehabilitation, cognitive enhancement, and neuromodulation, bridging engineering and medicine to improve human health outcomes. He has published extensively in internationally recognized journals and contributed to conferences with global visibility. His innovative contributions extend beyond academic research into patents, translational projects, and clinical applications, demonstrating his ability to turn theory into practice. Through his involvement in teaching, mentoring, and editorial activities, he has shown leadership and commitment to advancing science and supporting the next generation of researchers. His global collaborations across Asia and the United States reflect his adaptability and international impact. With a strong foundation and innovative approach, he continues to make meaningful contributions with high potential for future leadership in research and society.

Professional Profiles 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Sa Zhou pursued his higher education with a strong focus on engineering and biomedical sciences, which provided him with a multidisciplinary foundation for his research career. He earned his Bachelor and Master of Philosophy degrees in Electrical Engineering from Yanshan University, where he gained in-depth knowledge of signal processing, system development, and computational approaches to neural data. He then advanced his academic journey by completing his PhD in Biomedical Engineering at The Hong Kong Polytechnic University, where he developed expertise in neuroengineering, multimodal neuroimaging, and stroke rehabilitation. His doctoral research explored neural reorganization in sensorimotor impairments and recovery, involving systematic neurological evaluations, electrophysiological analyses, and clinical trials. This educational background not only honed his analytical and technical skills but also laid the groundwork for his interdisciplinary approach, bridging engineering principles with neuroscience and clinical applications. His academic training has shaped his ability to conduct impactful research at the interface of technology and medicine.

Professional Experience

Dr. Sa Zhou’s professional experience reflects a blend of academic research, teaching, and applied innovation in biomedical engineering and neuroscience. He is currently a postdoctoral scholar at Stanford University in the Department of Psychiatry and Behavioral Sciences, contributing to projects focused on personalized cognitive enhancement and digital interventions for aging-related disorders. Prior to this role, he worked extensively at The Hong Kong Polytechnic University, where he participated in pioneering projects on stroke rehabilitation, neuromodulation, and brain-machine interfaces. His experience also includes collaboration on international research initiatives that integrate engineering, neuroscience, and clinical practice, leading to high-impact publications and translational applications. Alongside research, he has actively contributed to education as a teaching assistant in neuroengineering, applied electrophysiology, and digital signal processing, mentoring undergraduate and postgraduate students. His diverse professional background demonstrates his ability to conduct innovative research, translate findings into practical solutions, and inspire future researchers through academic leadership.

Research Interest

Dr. Sa Zhou’s research interests span a wide spectrum of neuroscience, engineering, and clinical applications, with a particular emphasis on developing innovative technologies for human health and rehabilitation. His work focuses on multimodal neuroimaging techniques, including structural and functional MRI, DTI, and EEG, combined with advanced signal processing and machine learning approaches to understand brain networks. He is also deeply engaged in brain-machine interfaces, stroke rehabilitation, neuromotor interfaces, and robotic systems that enhance motor recovery and cognitive function. His interests extend to non-pharmacological interventions for preclinical Alzheimer’s disease and mild cognitive impairments, reflecting his commitment to addressing aging-related neurological disorders. He also explores neuromodulation methods, including electrical and ultrasound stimulation, to optimize therapeutic outcomes. These diverse interests demonstrate his interdisciplinary approach, integrating engineering innovations with clinical neuroscience to create personalized solutions. His research aims not only to advance scientific knowledge but also to deliver real-world impact in improving patient care and well-being.

Award and Honor

Dr. Sa Zhou has been recognized with numerous awards and honors that highlight his academic excellence, research achievements, and leadership potential. He has received prestigious fellowships, including support from international neuroscience and brain aging associations, acknowledging his contributions to advancing cognitive enhancement research. During his doctoral studies, he was awarded the PolyU Research Postgraduate Scholarship for outstanding performance, along with national-level scholarships that placed him among the top-performing postgraduates in China. He has also earned multiple competitive awards in research and innovation competitions, such as the Hong Kong Medical and Healthcare Device Industries Association Student Research Award and the Champion Award in the Three-Minute Thesis Competition. His teaching excellence was recognized with Best Teaching Assistant Awards, demonstrating his impact in both research and education. These accolades reflect his consistent pursuit of excellence, his ability to compete at international levels, and his dedication to advancing science while inspiring peers and students.

Research Skill

Dr. Sa Zhou possesses a wide range of research skills that integrate advanced engineering techniques with clinical neuroscience applications. His expertise includes real-time robotic control, rehabilitation system design, and multimodal neuroimaging analysis, enabling him to develop and test innovative technologies for stroke rehabilitation and cognitive enhancement. He is proficient in conducting clinical trials with stroke patients, performing neuroimaging scans such as fMRI, DTI, and structural MRI, and analyzing electrophysiological signals including EEG, EMG, and LFP. His skillset also extends to neuromodulation experiments using transcranial ultrasound stimulation and neuromuscular electrical stimulation, combined with advanced kinematic signal recording systems. In addition, he has strong programming and analytical abilities in machine learning, Matlab, Python, and C/C++, which support his work in neural decoding and brain network analyses. These skills, coupled with experience in mentoring, peer review, and system development, demonstrate his ability to design, implement, and translate research into impactful clinical and technological outcomes.

Publications Top Notes

Title: Pathway-specific cortico-muscular coherence in proximal-to-distal compensation during fine motor control of finger extension after stroke
Year: 2021
Citation: 32

Title: Corticomuscular integrated representation of voluntary motor effort in robotic control for wrist-hand rehabilitation after stroke
Year: 2022
Citation: 24

Title: Effect of pulsed transcranial ultrasound stimulation at different number of tone-burst on cortico-muscular coupling
Year: 2018
Citation: 20

Title: Optimization of relative parameters in transfer entropy estimation and application to corticomuscular coupling in humans
Year: 2018
Citation: 18

Title: Low-intensity pulsed ultrasound modulates multi-frequency band phase synchronization between LFPs and EMG in mice
Year: 2019
Citation: 17

Title: Impairments of cortico-cortical connectivity in fine tactile sensation after stroke
Year: 2021
Citation: 15

Title: Medical image segmentation using deep semantic-based methods: A review of techniques, applications and emerging trends
Year: 2022
Citation: 5

Title: Automatic theranostics for long-term neurorehabilitation after stroke
Year: 2023
Citation: 4

Title: Estimation of corticomuscular coherence following stroke patients
Year: 2017
Citation: 4

Title: Decoding Visual Experience and Mapping Semantics through Whole-Brain Analysis Using fMRI Foundation Models
Year: 2024
Citation: 1

Title: Personalized cognitive enhancement for older adults: An aging-friendly closed-loop human-machine interface framework
Year: 2025

Title: Relationships between neuropsychiatric symptoms, subtypes of astrocyte activities, and brain pathologies in Alzheimer’s disease and Parkinson’s disease
Year: 2025

Title: Neural Correlates of Dual‐Functional Local Dynamic Stability in Older Adults
Year: 2024

Title: Profiles of brain topology for dual-functional stability in old age
Year: 2024

Title: Neuromuscular networking connectivity in sensorimotor impairments after stroke
Year: 2023

Conclusion

Dr. Sa Zhou is highly deserving of the Best Researcher Award for his outstanding contributions at the intersection of biomedical engineering, neuroscience, and psychiatry, with impactful research in neuroimaging, brain-machine interfaces, stroke rehabilitation, and cognitive enhancement for aging populations. His work has advanced both theoretical understanding and practical applications, supported by high-quality publications, patents, and international collaborations that bridge engineering and medicine. Beyond research, his leadership in teaching, mentoring, and reviewing reflects a strong commitment to the scientific community and knowledge dissemination. With his growing expertise, innovative approaches, and dedication to addressing critical health challenges, Dr. Zhou shows great promise for future research breakthroughs and leadership in shaping the fields of neuroengineering and translational neuroscience.

Lipeng Jiao | Vegetation Disturbance Detection | Best Researcher Award

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Dr. Lipeng Jiao | Vegetation Disturbance Detection | Best Researcher Award

Lecturer at Henan Normal University | China

Lipeng Jiao is a dedicated researcher and academic specializing in deep learning-based remote sensing, with a strong focus on vegetation time-series modeling and disturbance detection. Currently serving as a lecturer at the School of Tourism, Henan Normal University, China, he has developed expertise in integrating advanced computational methods with environmental monitoring and ecological analysis. His career reflects a balance of theoretical knowledge and practical applications, demonstrated by his active role in large-scale national research projects and collaborations with international institutions. With publications in highly regarded journals such as IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing and GIScience & Remote Sensing, he has established himself as a promising scholar in his field. His research contributions address global environmental challenges, particularly in sustainable land use and ecological monitoring. Through his work, he continues to contribute to both academic advancement and societal well-being.

Professional Profiles 

Scopus Profile | ORCID Profile 

Education

Lipeng Jiao has pursued a strong educational foundation in surveying, mapping, and geographic information systems, building a career rooted in both technical depth and interdisciplinary applications. He earned his bachelor’s degree in surveying and mapping engineering from Shangqiu Normal University, which provided him with the fundamental skills for spatial data analysis and geoscience research. He further advanced his expertise with a master’s degree in surveying and mapping engineering from the China University of Mining and Technology in Beijing, where he specialized in advanced mapping technologies and environmental data interpretation. He then completed his doctoral studies in cartography and geographic information systems at Beijing Normal University, focusing on remote sensing and ecological monitoring. In addition to his domestic education, he broadened his academic perspective through an international visiting scholar program at Virginia Tech in the United States, where he collaborated on advanced research in vegetation dynamics and remote sensing applications.

Professional Experience

Lipeng Jiao is currently serving as a lecturer at the School of Tourism, Henan Normal University, where he is actively engaged in teaching, research, and mentoring students in areas related to remote sensing and environmental studies. His professional journey is marked by extensive involvement in major research initiatives, including participation in national key research and development programs in China. He has contributed to projects that focus on global remote sensing monitoring, land use change, and ecological simulations, establishing himself as an integral member of multidisciplinary research teams. His international exposure as a visiting scholar at Virginia Tech in the United States allowed him to collaborate with leading experts and enhance his research perspective. In addition to his teaching and research responsibilities, he actively contributes to the dissemination of knowledge through publications in recognized journals. His professional experience reflects a commitment to combining scientific innovation with practical applications in environmental sustainability.

Research Interest

Lipeng Jiao’s research interests are centered on the application of deep learning techniques in remote sensing, with a particular emphasis on vegetation time-series modeling and the detection of ecological disturbances. He is passionate about developing advanced computational methods that can improve the monitoring and interpretation of environmental changes across diverse ecosystems. His studies focus on vegetation disturbance detection, attribution of change agents, and mapping of ecological processes, which are critical for understanding the impacts of climate change and human activities on natural resources. He is also interested in synergizing multi-source satellite data to achieve near real-time monitoring of phenomena such as burned areas and vegetation degradation. By integrating cutting-edge artificial intelligence methods with remote sensing data, his research contributes to the improvement of global ecological monitoring systems. His interests extend toward practical applications, aiming to support sustainable resource management and policy-making for environmental conservation.

Research Skill

Lipeng Jiao possesses a diverse set of research skills that enable him to address complex challenges in remote sensing and environmental monitoring. He is proficient in applying deep learning algorithms to process and analyze large-scale vegetation time-series data, allowing for the detection and attribution of ecological disturbances with high accuracy. His expertise extends to multi-source satellite data integration, enhancing the capability to conduct near real-time environmental assessments. He is skilled in geographic information systems, cartography, and advanced data analysis methods that support spatial and temporal modeling. His contributions to national research projects highlight his ability to work within interdisciplinary teams, manage data-intensive tasks, and produce impactful outcomes. Additionally, his international research exposure has strengthened his adaptability to diverse scientific approaches and collaborative environments. These skills position him as a researcher capable of advancing both theoretical innovations and practical applications in ecological monitoring and sustainability science.

Publications Top Notes

Title: Robust Identification of Vegetation Change Using Shapelet-Based Temporal Segmentation of Landsat Time-Series Stacks: A Case Study in the Qilian Mountains
Authors: Lipeng Jiao; Randolph H. Wynne
Year: 2025

Title: Near real-time mapping of burned area by synergizing multiple satellites remote-sensing data
Authors: Lipeng Jiao; Yanchen Bo
Year: 2022

Conclusion

Lipeng Jiao is a deserving candidate for the Best Researcher Award due to his significant contributions in applying deep learning to vegetation remote sensing, advancing the understanding of ecological changes and land use impacts. His work on vegetation disturbance detection, participation in major research projects, and high-quality publications demonstrate both scientific excellence and societal relevance. With his strong research foundation, international experience, and potential for leadership in collaborative and innovative projects, he is well-positioned to continue making impactful contributions to his field and the broader research community.

 

 

Zahra Yahyaoui | Deep Learning | Women Researcher Award

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Dr. Zahra Yahyaoui | Deep Learning | Women Researcher Award

Teacher-Researcher at Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University | Tunisia

Dr. Zahra Yahyaoui is a dedicated researcher and educator with expertise in electronics, microelectronics, renewable energy systems, and artificial intelligence. She has established herself as an active contributor to the advancement of intelligent fault detection and diagnosis methods for photovoltaic and wind energy conversion systems. Her work bridges theory and practice, combining advanced machine learning techniques with embedded hardware implementation, ensuring her research is both academically rigorous and industrially relevant. Alongside her research activities, she has been deeply involved in teaching, supervision, and mentoring, helping to shape the academic and professional development of students in electronics and applied sciences. Her publications in high-impact journals and participation in international conferences highlight her growing recognition in the global research community. With technical versatility, adaptability, and strong teamwork skills, she continues to contribute to sustainable solutions in energy systems while promoting innovation, academic excellence, and interdisciplinary collaboration.

Professional Profiles 

Scopus Profile | ORCID Profile 

Education

Dr. Zahra Yahyaoui pursued her academic path in Tunisia, beginning with a bachelor’s degree in industrial computing with a specialization in embedded systems. She then advanced to a master’s research degree in nanomaterials and embedded electronics, where she specialized in embedded electronics and conducted important research on fault detection and diagnosis in wind energy systems using machine learning. Building on this foundation, she completed her doctoral studies in electronics and microelectronics at the Higher Institute of Applied Sciences and Technology of Kasserine, Kairouan University. Her PhD research focused on developing enhanced intelligent data-driven paradigms for fault detection and diagnosis in power systems, with practical applications on embedded architectures. She carried out her doctoral work within the Research Unit of Advanced Materials and Nanotechnologies, furthering her expertise at the intersection of artificial intelligence, renewable energy, and electronic systems. This strong academic background reflects her commitment to innovative, multidisciplinary research.

Professional Experience

Dr. Zahra Yahyaoui has built a solid academic and professional career through her teaching and research activities. She started as a part-time teacher at the Higher Institute of Applied Sciences and Technology of Kasserine, where she gained experience delivering courses and tutorials in electronics, microprocessor and microcontroller architectures, and embedded systems. Her role expanded to contractual teacher at the same institute under Kairouan University, where she was responsible for teaching system-on-chip design, combinational and sequential logic circuits, and analog signal processing, covering both theoretical and practical sessions. In addition to her teaching duties, she has co-supervised master’s theses on advanced topics such as interval-valued machine learning, deep learning for fault detection in renewable systems, and photovoltaic installation design. Through her academic contributions, she has combined teaching excellence with mentoring, ensuring students receive both theoretical knowledge and practical insights. Her professional journey highlights her commitment to education, innovation, and applied research.

Research Interest

Dr. Zahra Yahyaoui’s research interests lie at the intersection of electronics, artificial intelligence, and renewable energy systems. She focuses on developing intelligent data-driven approaches for fault detection and diagnosis, aiming to enhance the reliability and efficiency of power systems such as photovoltaic and wind energy converters. Her work emphasizes the use of advanced machine learning and deep learning techniques, including BiLSTM, GRU, and optimization algorithms, to address uncertainty in renewable energy conversion and monitoring. She is also interested in the implementation of these algorithms on embedded architectures, integrating software with hardware platforms like FPGA, Raspberry Pi, and microcontrollers for real-world applications. Beyond fault diagnosis, she explores forecasting methods for solar irradiance and power output, contributing to the broader field of sustainable energy management. By combining theoretical modeling, algorithm development, and embedded system integration, her research supports innovation in intelligent renewable energy technologies.

Research Skill

Dr. Zahra Yahyaoui has developed a diverse set of research skills that enable her to carry out impactful and interdisciplinary work. She is proficient in programming languages such as MATLAB and Python, which she uses extensively for data analysis, machine learning model development, and algorithm implementation. She is skilled in simulation tools like ISE and Simplorer, supporting her expertise in circuit and system design. Her hardware-related skills include working with Siemens S7-1200, FPGA boards, Raspberry Pi, and Arduino microcontrollers, allowing her to translate theoretical models into practical embedded system solutions. She has strong problem-solving abilities, adaptability, and teamwork skills, which contribute to successful research collaborations and academic projects. Her research methodology combines theoretical analysis with experimental validation, ensuring robust and application-oriented results. With certifications in artificial intelligence and embedded systems, she brings an advanced skillset for developing intelligent monitoring and diagnostic systems, particularly for renewable energy applications.

Publications Top Notes

Title: Fault detection and diagnosis in grid-connected PV systems under irradiance variations
Authors: Hajji, M.; Yahyaoui, Z.; Mansouri, M.; Nounou, H.; Nounou, M.
Year: 2023

Title: One-Class Machine Learning Classifiers-Based Multivariate Feature Extraction for Grid-Connected PV Systems Monitoring under Irradiance Variations
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Bouzrara, K.
Year: 2023

Title: Effective Fault Detection and Diagnosis for Power Converters in Wind Turbine Systems Using KPCA-Based BiLSTM
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Abodayeh, K.; Bouzrara, K.; Nounou, H.
Year: 2022

Title: Kernel PCA based BiLSTM for Fault Detection and Diagnosis for Wind Energy Converter Systems
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Bouzrara, K.; Nounou, H.; Nounou, M.
Year: 2022

Title: Efficient fault detection and diagnosis of wind energy converter systems
Authors: Yahyaoui, Z.; Hajji, M.; Mansouri, M.; Harkat, M.-F.; Kouadri, A.; Nounou, H.; Nounou, M.
Year: 2020

Conclusion

Dr. Zahra Yahyaoui is a deserving candidate for the Best Researcher Award due to her significant contributions in advancing intelligent data-driven techniques for renewable energy systems, fault detection, and embedded architectures. Her research has produced valuable publications in reputed international journals and conferences, with practical applications that support sustainable energy and technological innovation. Through her teaching, mentorship, and active participation in the academic community, she has demonstrated a strong commitment to knowledge sharing and capacity building. With her proven expertise, dedication, and potential for future leadership, she is well positioned to continue making impactful contributions to both research and society.

Sheilla Ann Pacheco | Machine Learning | Best Researcher Award

Published on by Computer Vision Awards

Assist. Prof. Dr. Sheilla Ann Pacheco | Machine Learning | Best Researcher Award

Faculty at North Eastern Mindanao State University | Philippines

Dr. Sheilla Ann Pacheco is an accomplished academic and researcher with extensive experience in computer science, particularly in the fields of machine learning, image processing, and adversarial defense. With over nine years of academic service, she has established herself as a dedicated educator, mentor, and innovator who contributes significantly to both research and teaching. Her work spans practical and theoretical domains, addressing challenges in privacy-preserving AI, biometrics, and medical applications such as breast cancer prediction. Dr. Pacheco is actively involved in presenting her research at national and international conferences, where she has received recognition for her contributions. She is also engaged with professional organizations such as IEEE and ACM, which allows her to remain connected with global advancements in her field. Combining strong technical expertise, leadership in research, and dedication to academic growth, she continues to advance computer science while inspiring students and peers alike.

Professional Profiles 

Google Scholar | Scopus Profile | ORCID Profile 

Education

Dr. Sheilla Ann Pacheco has pursued her academic journey with determination and excellence in the field of computer science. She earned her Bachelor of Science in Computer Science from Surigao del Sur State University, laying the foundation for her career in research and academia. She further advanced her studies by completing her Master of Science in Computer Science at the same institution, where she deepened her knowledge of programming, data processing, and research methodologies. To further enhance her expertise, she is currently completing her Doctor of Philosophy in Computer Science at the Technological Institute of the Philippines, focusing on advanced topics such as machine learning, adversarial defense, and computational intelligence. Her academic path highlights her continuous commitment to lifelong learning and growth in her field. Through her education, she has developed the strong theoretical and practical background that now underpins her teaching, supervision, and impactful research contributions.

Professional Experience

Dr. Sheilla Ann Pacheco has built a solid professional career as an academic and researcher in the field of information technology. She currently serves as an Assistant Professor at North Eastern Mindanao State University, where she teaches a variety of courses, supervises research, and contributes to the development of the academic community. Over the years, she has guided students in their research projects, emphasizing innovation and practical applications of computer science in areas such as artificial intelligence and data processing. Her experience is not only limited to classroom teaching but also extends to participation in academic conferences, workshops, and seminars where she presents her work and collaborates with other professionals. Her professional journey demonstrates a balance of academic leadership, technical expertise, and a commitment to advancing knowledge. Through her role, she continues to inspire students and colleagues while contributing to the university’s mission of research and innovation.

Research Interest

Dr. Sheilla Ann Pacheco’s research interests lie in the fields of machine learning, image processing, adversarial defense, and privacy-preserving artificial intelligence. She has a particular focus on developing intelligent solutions that enhance the security and accuracy of biometric recognition systems, as reflected in her work on SARGAN-based face recognition and hidden adversarial attacks on facial biometrics. In addition, she explores federated learning models that aim to protect user privacy while enabling effective AI applications. Her research also extends to healthcare, where she has contributed to studies such as breast cancer prediction using ensemble techniques. These areas highlight her commitment to addressing real-world challenges through innovative technologies. By integrating theoretical models with applied solutions, her research contributes both to the scientific community and to society at large. Her future directions aim to expand collaborations in international research networks and further explore secure, ethical, and intelligent AI applications.

Research Skill

Dr. Sheilla Ann Pacheco possesses a wide range of research skills that enable her to excel in both academic and applied studies. Her expertise includes image processing, machine learning, and adversarial defense, which she has applied in developing innovative solutions for biometric recognition and healthcare prediction models. She is proficient in programming, data analysis, and the use of advanced computational tools, allowing her to conduct rigorous and high-quality research. Her skills in academic writing and presentation have enabled her to publish and present her work at reputable conferences and to effectively communicate her findings to diverse audiences. She is also skilled in research supervision, guiding students through the research process and fostering a culture of inquiry and innovation. Combined with strong organizational and leadership skills, she demonstrates the ability to collaborate with peers, contribute to multidisciplinary projects, and advance knowledge in her field through impactful and practical research outcomes.

Publications Top Notes

Title: Enhanced content-based image retrieval using multivisual features fusion
Authors: SAB Pacheco, M Goyani, ZG Rehman, SF Rehman, T Champaneria, …
Year: 2025
Citation: 1

Title: Robust Face Recognition Under Adversarial Attack Using SARGAN Model and Improved Cross Triple MobileNetV1
Authors: SAB Pacheco, JE Estrada, MM Goyani
Year: 2025

Title: Least Variance based Modeling of Heart Disease Prediction System using Ensemble Technique
Authors: SA Pacheco, JP Bangoy, ZG Rehman, SF Rehman, SV Goyani, …
Year: 2025

Title: Hidden adversarial attack on facial biometrics – a comprehensive survey
Authors: MMG Sheilla Ann Bangoy Pacheco, Jheanel Espiritu Estrada
Year: 2025

Title: A Comprehensive Survey on Federated Learning and Its Applications in Health Care
Authors: SAB Pacheco
Year: 2024

Title: Performance of Students in Computer Programming: An Analysis
Authors: IB Christian, SA Pacheco
Year: 2023

Title: Breast Cancer Prediction using Ensemble Technique
Authors: SAB Pacheco
Year: 2022

Title: Trends and Analysis of Graduate Programs
Authors: SAB Pacheco
Year: 2022

Conclusion

Dr. Sheilla Ann Pacheco is a deserving candidate for the Best Researcher Award due to her impactful contributions in machine learning, image processing, and adversarial defense, which address critical challenges in biometrics, privacy-preserving AI, and healthcare applications. Her research outputs, academic leadership, and active involvement in professional organizations highlight her commitment to advancing both scientific knowledge and the research community. With her strong academic foundation, proven dedication, and potential for expanding her influence through future international collaborations and innovative projects, she is well-positioned to make even greater contributions to research and society in the years ahead.

Imran Riaz | Human Recognition System | Best Researcher Award

Published on by Computer Vision Awards

Mr. Imran Riaz | Human Recognition System | Best Researcher Award

PhD Student at University Sains Malaysia | Malaysia

Imran Riaz is an accomplished researcher and academic with expertise in biometrics, image processing, machine learning, medical image analysis, and pattern recognition. He has made significant contributions to the advancement of biometric security systems and medical imaging techniques through impactful publications and international collaborations. His research is supported by industrial projects focusing on cybersecurity applications, such as presentation attack detection and face spoofing detection. In addition to his research, he has demonstrated leadership in academia through teaching undergraduate and postgraduate courses, supervising student projects, and serving as a focal person for artificial intelligence initiatives at his institution. He has actively engaged with the research community as a reviewer for reputable journals and as a member of international professional bodies. His involvement in student leadership, voluntary organizations, and technical workshops reflects a strong balance of professional dedication and community service, establishing him as a well-rounded and forward-looking researcher.

Professional Profiles

Google Scholar | Scopus Profile | ORCID Profile 

Education

Imran Riaz has pursued a comprehensive academic journey in the field of electrical engineering with a focus on advanced computing and biometrics. He is currently completing his PhD in Electrical Engineering at Universiti Sains Malaysia, where he has worked on projects related to multimodal biometric recognition systems and medical image analysis. His doctoral research has been supported by scholarships and has involved both academic and industrial applications, particularly in the areas of cybersecurity and biometric authentication. Prior to his PhD, he earned a Master of Science in Electrical Engineering from Mirpur University of Science and Technology, where he achieved a high academic standing with a strong focus on digital signal processing and image processing techniques. He began his academic path with a Bachelor of Science in Electrical Engineering from the University of Azad Jammu and Kashmir, which provided him with a solid foundation in electrical systems, electronics, and circuit design.

Professional Experience

Imran Riaz has extensive professional experience that combines academia, industry, and research. He is currently serving as a Lecturer at Mirpur University of Science and Technology, where he teaches both undergraduate and postgraduate courses in areas such as machine learning and medical image processing while also acting as the focal person for artificial intelligence initiatives. Earlier, he worked as a Graduate Research Assistant at Universiti Sains Malaysia, contributing to high-level research projects funded by the Ministry of Cyber Security Malaysia, focusing on biometric authentication and face spoofing detection. Prior to that, he held a long-term academic position at Mirpur University of Science and Technology as a Lecturer, supervising student projects, guiding research, and contributing to departmental responsibilities. His industry experience includes serving as Assistant Manager at Transfopower Industries in Lahore, Junior Engineer at the National Physical and Standards Laboratory, and an internship at a hydropower station, reflecting his versatile professional background.

Research Interest

Imran Riaz’s research interests lie in the interdisciplinary domains of biometrics, image processing, medical image analysis, machine learning, and pattern recognition. His work primarily focuses on developing secure, efficient, and accurate biometric authentication systems, exploring new modalities such as dorsal finger creases and finger knuckle print recognition. He has also contributed to advancements in medical imaging through deep learning-based diagnostic systems, particularly in cancer detection and histopathology image analysis. His studies aim to address real-world challenges, including overcoming fingerprint recognition failures caused by physiological factors and enhancing face recognition robustness against spoofing attacks. He also shows a keen interest in applying artificial intelligence and deep learning techniques across broader fields such as agriculture, energy optimization, and healthcare applications. His approach integrates theoretical advancements with practical implementation, bridging the gap between academic research and industry needs, while also contributing to emerging global challenges in smart technologies and intelligent systems.

Research Skill

Imran Riaz possesses strong research skills that span programming, data analysis, and advanced modeling techniques relevant to biometrics and machine learning. He is proficient in Python and MATLAB, with expertise in implementing machine and deep learning algorithms for classification, prediction, and pattern recognition tasks. His skills also extend to using statistical tools such as SPSS, along with simulation and circuit design software like SPICE and EWB, enabling him to integrate computational methods with engineering applications. Additionally, he has experience with tools such as Blender, Pepakura, and Anima8or, reflecting versatility in visualization and modeling. His technical report writing proficiency using LaTeX has supported the development of high-quality publications. These skills have been applied in multiple funded research projects, including biometric spoofing detection and medical image analysis. His ability to combine theoretical knowledge with practical implementation highlights his capacity to design, test, and validate innovative systems for real-world applications.

Publications Top Notes

Title: Automatic grading of palsy using asymmetrical facial features: a study complemented by new solutions
Authors: M Sajid, T Shafique, MJA Baig, I Riaz, S Amin, S Manzoor
Year: 2018
Citation: 55

Title: Data augmentation‐assisted makeup‐invariant face recognition
Authors: M Sajid, N Ali, SH Dar, N Iqbal Ratyal, AR Butt, B Zafar, T Shafique, …
Year: 2018
Citation: 53

Title: SA-GAN: stain acclimation generative adversarial network for histopathology image analysis
Authors: T Kausar, A Kausar, MA Ashraf, MF Siddique, M Wang, M Sajid, …
Year: 2021
Citation: 24

Title: Facial asymmetry-based anthropometric differences between gender and ethnicity
Authors: M Sajid, T Shafique, I Riaz, M Imran, M Jabbar Aziz Baig, S Baig, …
Year: 2018
Citation: 23

Title: Deep learning in age-invariant face recognition: a comparative study
Authors: M Sajid, N Ali, NI Ratyal, M Usman, FM Butt, I Riaz, U Musaddiq, …
Year: 2022
Citation: 17

Title: Demographic-assisted age-invariant face recognition and retrieval
Authors: M Sajid, T Shafique, S Manzoor, F Iqbal, H Talal, U Samad Qureshi, I Riaz
Year: 2018
Citation: 16

Title: Circular shift combination local binary pattern (CSC-LBP) method for dorsal finger crease classification
Authors: I Riaz, AN Ali, H Ibrahim
Year: 2023
Citation: 9

Title: Convolution neural network based approach for breast cancer type classification
Authors: T Kausar, MA Ashraf, A Kausar, I Riaz
Year: 2021
Citation: 8

Title: Loss of fingerprint features and recognition failure due to physiological factors-a literature survey
Authors: I Riaz, AN Ali, H Ibrahim
Year: 2024
Citation: 5

Title: Biometric classification system for dorsal finger creases utilizing multi-block circular shift combination local binary pattern
Authors: I Riaz, AN Ali, H Ibrahim, IA Huqqani
Year: 2024
Citation: 1

Title: Enhanced Parameter Estimation of Solar Photovoltaic Models Using QLESCA Algorithm
Authors: QS Hamad, SAM Saleh, SA Suandi, H Samma, YS Hamad, I Riaz
Year: 2024
Citation: 1

Title: A Finger Knuckle Print Classification System Using SVM for Different LBP Variants
Authors: I Riaz, AN Ali, IA Huqqani
Year: 2024
Citation: 1

Title: Enhancing the Dorsal Side of Fingers Using An Image Enhancement Technique with FPGA Output Comparison
Authors: TS Han, I Riaz, AN Ali
Year: 2024
Citation: 1

Title: Advanced technologies for smart fertilizer management in agriculture: A Review
Authors: JJ Liu, H Wu, I Riaz
Year: 2025

Title: Multimodal Biometric Recognition System Based on Feature-Level Fusion of Dorsal Finger Crease and Finger Knuckle Print
Authors: I Riaz, AN Ali, H Ibrahim, IA Huqqani
Year: 2025

Title: A novel sub-windowing local binary pattern approach for dorsal finger creases based biometric classification system
Authors: I Riaz, AN Ali, H Ibrahim
Year: 2024

Title: Dorsal Finger crease classification system using local binary pattern and its variants-A new finger biometric modality
Authors: I Riaz, AN Ali, H Ibrahim, IA Huqqani
Year: 2024

Title: Evaluation of Three Variants of LBP for Finger Creases Classification
Authors: NAA Salihin, I Riaz, AN Ali
Year: 2024

Title: Training Needs Assessment of Rice Growers in Rice Zone of the Punjab, Pakistan
Authors: I Riaz
Year: 2021

Conclusion

Imran Riaz is a deserving candidate for the Best Researcher Award due to his significant contributions in biometrics, medical image analysis, and machine learning, supported by a strong academic background and impactful research collaborations. His extensive publications in reputed journals and involvement in industrial projects highlight both academic excellence and real-world relevance. Alongside his research, he has contributed to society through teaching, mentoring, and volunteer services, demonstrating a balance of professional and community engagement. With his dedication to advancing biometric security and medical imaging, and his potential to lead future international collaborations and high-impact projects, he stands out as a researcher with both current achievements and promising leadership potential.

Saulius Baskutis | Manufacturing Applications | Excellence in Computer Vision Award

Published on by Computer Vision Awards

Prof. Dr. Saulius Baskutis | Manufacturing Applications | Excellence in Computer Vision Award

Professor at Kaunas University of Technology | Lithuania

Prof. Dr. Saulius Baskutis is a distinguished academic and researcher whose career spans decades of teaching, research, and industrial engagement in the field of engineering sciences. He has contributed significantly to the advancement of materials testing, welding processes, coatings, renewable energy, logistics, and device diagnostics, while also actively participating in collaborative international projects. His professional journey reflects a balance between academic excellence and industrial practice, providing him with the ability to develop innovative, practical solutions for technological challenges. With numerous publications in reputable, indexed journals and presentations at international conferences, he has built a strong scholarly reputation. His leadership in EU-funded projects and membership in professional associations highlights his influence in both academic and professional communities. Recognized for his commitment to advancing science and technology, he continues to inspire students, researchers, and industry experts, while shaping the future of sustainable engineering practices through impactful research and collaborations.

Professional Profiles 

Scopus Profile | ORCID Profile 

Education

Prof. Dr. Saulius Baskutis began his academic journey at Kaunas Polytechnical Institute, where he earned an engineering qualification and later pursued doctoral studies in technical sciences, which shaped the foundation of his research career. His academic training combined deep theoretical knowledge with applied aspects of mechanical and material sciences, enabling him to explore innovative solutions in manufacturing and energy systems. During his postgraduate years, he developed expertise in welding, coatings, and materials testing, areas that would remain central to his research. He later expanded his knowledge through continuous professional development and specialized training programs across Europe, focusing on Industry 4.0, renewable energy, mechatronics, and advanced manufacturing technologies. This commitment to lifelong learning has strengthened his academic portfolio and ensured that his teaching and research remain aligned with evolving industrial needs. His education reflects a continuous pursuit of excellence, integrating traditional engineering knowledge with modern approaches to meet current and future challenges.

Professional Experience

Prof. Dr. Saulius Baskutis has built an impressive professional career combining academia and industry, demonstrating strong leadership and applied expertise in engineering. He has served at Kaunas University of Technology in various roles, progressing from assistant and lecturer to associate professor and professor, contributing extensively to teaching and curriculum development in manufacturing and mechanical engineering. His professional journey is enriched by industrial experience with organizations in Lithuania, Denmark, and Finland, where he worked as an engineer, manager, and project leader. These positions enabled him to apply academic insights to solve real-world engineering problems while fostering stronger connections between research and practice. He has also held leadership roles in European and national research projects, demonstrating the ability to guide interdisciplinary teams and achieve impactful outcomes. His active participation in Erasmus programs and scientific exchanges further broadened his expertise, while his engagement in professional associations has extended his influence within the engineering community.

Research Interest

Prof. Dr. Saulius Baskutis has a diverse range of research interests that reflect his commitment to addressing technological and societal challenges through engineering innovations. His primary focus lies in materials testing, welding processes, and coatings, where he investigates methods to improve durability, performance, and cost-effectiveness in industrial applications. He is also actively engaged in exploring renewable energy sources, with particular attention to sustainable energy production, district heating systems, and the integration of green technologies into industrial processes. His work in logistics and device diagnostics complements these efforts by improving efficiency, safety, and reliability in manufacturing and energy systems. He is keenly interested in the intersection of Industry 4.0 and smart manufacturing, contributing to projects that integrate robotics, automation, and advanced digital technologies. These research interests demonstrate his vision of combining scientific discovery with practical applications to promote sustainable development and enhance global competitiveness in engineering and technological industries.

Research Skill

Prof. Dr. Saulius Baskutis possesses a comprehensive set of research skills that reflect his long-standing academic and industrial experience in engineering. He is highly skilled in experimental methods, particularly in the testing of materials, welding techniques, and coating technologies, where precision and innovation are essential. His ability to design, conduct, and analyze experiments has led to impactful findings published in internationally recognized journals. He demonstrates strong competencies in project management, having successfully led and contributed to national and international research initiatives, including EU-funded programs. His skills extend to collaborative research, working with multidisciplinary teams across Europe to address complex technological challenges. Additionally, he is adept in applying digital tools such as CAD, CAM, and CNC systems to align with Industry 4.0 practices. His ability to bridge theoretical knowledge with applied solutions highlights his strength as both a researcher and innovator, ensuring his contributions remain relevant and forward-looking.

Publications Top Notes

Title: Numerical Method for Internal Structure and Surface Evaluation in Coatings
Authors: Tomas Kačinskas, Saulius Baskutis
Year: 2025

Title: Modeling of Vibrational-Centrifugal Strengthening for Functional Surfaces of Machine Parts
Authors: Vadym Stupnytskyy, Yaroslav Kusyi, Egidijus Dragašius, Saulius Baskutis, Rafal Chatys
Year: 2024

Title: Investigation of the Properties of Anti-Friction Coatings Deposited with Different Casting Methods
Authors: Tomas Kačinskas, Saulius Baskutis, Jolanta Baskutienė, Lina Kavaliauskienė
Year: 2024

Title: Analytical Model of Tapered Thread Made by Turning from Different Machinability Workpieces
Authors: Oleh Onysko, Volodymyr Kopei, Cristian Barz, Yaroslav Kusyi, Saulius Baskutis, Michal Bembenek, Predrag Dašić, Vitalii Panchuk
Year: 2024

Title: Control of the Parameters of the Surface Layer of Steel Parts During Their Processing Applying the Material Homogeneity Criterion
Authors: Kusyi Yaroslav, Stupnytskyy Vadym, Kostiuk Olha, Oleh Onysko, Egidijus Dragašius, Saulius Baskutis, Rafał Chatys
Year: 2024

Title: Simulation and Analytical Studies of Chip Formation Processes in the Cutting Zone of Titanium Alloys
Authors: Vadym Stupnytskyy, Xianning She, Egidijus Dragašius, Saulius Baskutis, Oleh Prodanchuk
Year: 2023

Title: Pool Boiling of Ethanol on Copper Surfaces with Rectangular Microchannels
Authors: Robert Kaniowski, Robert Pastuszko, Egidijus Dragašius, Saulius Baskutis
Year: 2023

Title: Influence of Additives on the Mechanical Characteristics of Hardox 450 Steel Welds
Authors: Saulius Baskutis, Jolanta Baskutiene, Egidijus Dragašius, Lina Kavaliauskiene, Neringa Keršiene, Yaroslav Kusyi, Vadym Stupnytskyy
Year: 2023

Title: Modeling and Simulation of Machined Surface Layer Microgeometry Parameters
Authors: Vadym Stupnytskyy, Egidijus Dragašius, Saulius Baskutis, She Xianning
Year: 2022

Title: Agent-Based Modelling Approach for Autonomous Vehicle Influence on Countries’ Welfare
Authors: Saulius Baskutis, Valentas Gružauskas, Peter Leibl, Linas Obcarskas
Year: 2022

Title: Effect of Chemical Composition of Clay on Physical-Mechanical Properties of Clay Paving Blocks
Authors: Rolandas Avizovas, Saulius Baskutis, Valentinas Navickas, László Tamándl
Year: 2022

Title: Perspectives and Problems of Using Renewable Energy Sources and Implementation of Local “Green” Initiatives: A Regional Assessment
Authors: Saulius Baskutis, Jolanta Baskutiene, Valentinas Navickas, Yuriy Bilan, Wojciech Cieśliński
Year: 2021

Title: Mechanical Properties and Microstructure of Aluminium Alloy AW6082-T6 Joints Welded by Double-Sided MIG Process Before and After Aging
Authors: Saulius Baskutis
Year: 2019

Title: Monitoring of Welding Process Parameters in Gas Tungsten Arc Welding of Carbon Steel Weldments
Authors: Saulius Baskutis
Year: 2019

Title: Effect of Weld Parameters on Mechanical Properties and Tensile Behavior of Tungsten Inert Gas Welded AW6082-T6 Aluminium Alloy
Authors: Saulius Baskutis
Year: 2019

Title: Research on Mechanical Properties of TIG Welded Aluminum Alloy
Authors: Bendikiene R., Baskutis S., Baskutiene J., Ciuplys A.
Year: 2018

Title: Comparative Study of TIG Welded Commercially Pure Titanium
Authors: Saulius Baskutis
Year: 2018

Title: Minimizing the Trade-Off Between Sustainability and Cost Effective Performance by Using Autonomous Vehicles
Authors: Saulius Baskutis
Year: 2018

Title: The Analysis of Dissimilar Metal Weld Joints
Authors: Saulius Baskutis
Year: 2018

Title: Experimental Study of Welded Joints of Aluminium Alloy AW6082
Authors: Baskutis S., Baskutiene J., Bernotaitis E.
Year: 2017

Title: The Vibratory Alignment of the Parts in Robotic Assembly
Authors: Saulius Baskutis
Year: 2017

Title: Influence of Welding Modes on Weldability of Structural Steel Lap Joints in Laser Welding
Authors: Saulius Baskutis
Year: 2017

Title: Warehouses Consolidation in the Logistic Clusters: Food Industry’s Case
Authors: Saulius Baskutis
Year: 2016

Title: Nano and Microhardness Testing of Heterogeneous Structures
Authors: Saulius Baskutis
Year: 2016

Title: The Temperature Control Impact to the Food Supply Chain
Authors: Baskutis S., Navickas V., Gružauskas V., Olencevičiute D.
Year: 2015

Conclusion

Prof. Dr. Saulius Baskutis is highly deserving of the Best Researcher Award for his longstanding dedication to advancing engineering science, particularly in the fields of coatings, welding technologies, renewable energy, and sustainable manufacturing. His significant contributions through impactful publications, leadership in international research projects, and strong collaboration between academia and industry highlight his role in driving innovation and practical solutions for society. With his expertise, professional recognition, and commitment to mentoring future generations, he demonstrates both past excellence and strong potential for continued research leadership and global impact in the years ahead.