Lecturer | Osun State University | Nigeria

Dr. Oluwatobi Adedamola Ayilara-Adewale is a computer science researcher specializing in machine learning, AI-driven cybersecurity and intelligent systems, serving as an academic and research contributor in these domains. With advanced degrees in computer science and a strong foundation in computational methods and digital systems, he has gained professional experience through participation in national and international research projects involving digital resilience, smart agriculture, climate-focused data analytics and secure digital infrastructures, often providing technical leadership in multidisciplinary teams. His research focuses on artificial intelligence, IoT security, intrusion detection, blockchain security, predictive analytics and cyber-resilient architectures, supported by numerous peer-reviewed publications spanning journals, conference outputs and book chapters. He has contributed to the development of machine learning models for security, intelligent decision-support systems and emerging frameworks for digital trust. Dr. Ayilara-Adewale has received recognition for innovative research and holds professional certifications in cloud computing, cybersecurity and penetration testing. He is an active member of multiple professional bodies, reflecting his commitment to advancing knowledge in computing and cybersecurity, and he has engaged in collaborative initiatives that strengthen the ecosystem of applied AI research. His growing scholarly profile, technical versatility and dedication to secure and intelligent systems position him as a valuable contributor to contemporary research and a strong candidate for excellence awards.

Profiles: Google Scholar

Featured Publications

1. Jimoh, K., Ajayi, A., & Ayilara, O. (2014). Intelligent model for manual sorting of plastic wastes. International Journal of Computer Applications, 101(7), 20–26.

2. Jimoh, K. O., Adepoju, T. M., Sobowale, A. A., & Ayilara, O. A. (2018). Offline gesture recognition system for Yorùbá numeral counting. Asian Journal of Research in Computer Science, 1(4), 1–11.

3. Ajayi, A. O., Jimoh, K. A., & Ayilara, O. A. (2016). Evaluation of plastic waste classification systems. British Journal of Mathematics & Computer Science, 16(3), 1–11.

4. Ayilara, M. S., Fasusi, S. A., Ajakwe, S. O., Akinola, S. A., Ayilara-Adewale, O. A., … (2025). Impact of climate change on agricultural ecosystem. In Climate change, food security, and land management: Strategies for a sustainable future.

5. Olanrewaju, A., & Ayilara, O. A. (2024). The effect of data compromises on internet users: A review on financial implication of the elderly in the United States. African Journal of Social Sciences and Humanities Research, 1, 28–37.

Dr. Oluwatobi Adedamola Ayilara-Adewale’s work advances secure and intelligent digital ecosystems by integrating artificial intelligence with resilient cybersecurity frameworks. His research contributes to safer technologies, sustainable data-driven solutions and innovative systems that support societal development, industry transformation and global digital trust.

Huxiong Li | Artificial Intelligence | Artificial Intelligence

Published on 09/10/202509/10/2025 by World Top Scientists

Prof. Dr. Huxiong Li | Artificial Intelligence | Artificial Intelligence

Professor | Shaoxing University | China

Prof. Dr. Huxiong Li is a leading researcher in artificial intelligence, specializing in 3D vision, intelligent perception, urban digital twins, and complex network control. He has made significant contributions through innovative research, demonstrated by his extensive publications, patents, and leadership of multiple national and international projects. His work bridges AI technologies with practical applications in cultural heritage preservation and smart city infrastructure, reflecting a strong interdisciplinary approach. Over the years, he has fostered collaborations with global institutions, enhancing the reach and impact of his research. Prof. Li’s guidance of numerous projects has not only advanced scientific understanding but also facilitated industrial implementation of AI technologies. His research demonstrates consistent excellence, originality, and societal relevance, positioning him as a prominent figure in geospatial artificial intelligence. According to Scopus, his measurable research impact includes 28 citations, 9 documents, and an h-index of 402.

Profiles: Scopus | ORCID

Featured Publications

1. Reducing the clustering challenge in the IoT using two disjoint convex hulls. Scientific Reports, 2025.

2. Integrating InSAR coherence and air pollution detection satellites to study the impact of war on air quality. International Journal of Applied Earth Observation and Geoinformation, 2025.

Bushra Naz | Deep learning | Best Researcher Award

Published on 09/09/202509/09/2025 by World Top Scientists

Dr. Bushra Naz | Deep learning | Best Researcher Award

Associate professor at Mehran University of Engineering and Technology| Pakistan

Dr. Bushra Naz is an accomplished academic and researcher with expertise in artificial intelligence, deep learning, image processing, hyperspectral image classification, and pattern recognition. Serving as an Associate Professor and PhD supervisor, she has made significant contributions to advancing knowledge through impactful research and dedicated mentorship. Her funded projects include innovative solutions in speech emotion recognition, assistive technologies for visually impaired individuals, water quality monitoring, and sustainable agriculture, reflecting a strong focus on societal benefit. She has published widely, reviewed for leading international journals, and actively participated in global conferences as a session chair and committee member. Her achievements are further recognized through prestigious scholarships, research fellowships, and honors that demonstrate her academic excellence and leadership. With a commitment to bridging theory and practice, Dr. Naz continues to drive interdisciplinary collaborations and inspire future researchers, positioning herself as a leader in advancing AI-driven solutions for real-world challenges.

Professional Profile

Google Scholar

Education

Dr. Bushra Naz has a strong academic foundation in computer systems and engineering, beginning with a bachelor’s degree in Computer Systems Engineering, followed by a master’s degree in Communication Systems and Networks. She pursued her doctoral studies at Nanjing University of Science and Technology, China, where she completed a PhD in Computer Science and Engineering with a research focus on machine learning and hyperspectral image classification. Her doctoral thesis explored advanced elastic-net representation methods for image classification, demonstrating her early commitment to innovative AI-driven solutions. She also earned international recognition during her doctoral journey, supported by prestigious scholarships and fellowships, which allowed her to gain global exposure and strengthen her research expertise. With a solid academic trajectory rooted in both national and international institutions, Dr. Naz has combined technical depth with interdisciplinary knowledge, equipping her with the skills to pursue cutting-edge research while training the next generation of scholars and professionals.

Experience

Dr. Bushra Naz brings extensive academic and research experience spanning over a decade. She began her professional journey as a laboratory lecturer, progressively advancing to lecturer, assistant professor, and currently serves as an associate professor in the Department of Computer Systems Engineering at Mehran University of Engineering and Technology, Jamshoro. In these roles, she has taught a diverse range of subjects including microprocessors, operating systems, digital image processing, machine learning, deep learning, and artificial intelligence, shaping the technical skills of numerous students. Beyond teaching, she has taken on leadership roles in departmental committees, project supervision, curriculum review, and outcome-based education implementation. Her responsibilities also include supervising undergraduate, master’s, and doctoral research projects, many of which align with pressing technological and societal challenges. Through her experience, she has built a reputation as a dedicated educator, innovative researcher, and academic leader who seamlessly integrates research and teaching to drive meaningful outcomes.

Research Focus

Dr. Bushra Naz’s research focus lies in the application of artificial intelligence and machine learning to solve complex real-world problems. Her expertise covers deep learning, neural networks, hyperspectral imaging, image classification, object detection, and pattern recognition. She has conducted pioneering research in spectral-spatial methods for image classification, advancing techniques in optimization and sparse representation. Her projects span diverse domains, including speech emotion recognition, augmented reality-based navigation for the visually impaired, IoT-driven water quality monitoring, crop sensing for sustainable agriculture, and accident detection systems. This interdisciplinary approach highlights her commitment to applying AI solutions for societal impact, sustainability, and technological innovation. In addition, she actively contributes as a reviewer for high-impact journals and participates in international conferences as a session chair, strengthening global research dialogue. By integrating technical rigor with practical application, Dr. Naz continues to expand the frontiers of AI research while addressing challenges that directly benefit communities and industries.

Award and Honor

Dr. Bushra Naz’s academic excellence and research contributions have been recognized through numerous awards and honors at national and international levels. She received the prestigious China Scholarship Council award for her PhD studies and was further distinguished with the ELITE Scholarship as the Best Foreign Student during her doctoral program. Her excellence in research was acknowledged with honor certificates and rewards for her publications in IEEE journals. Earlier in her career, she earned the Higher Education Commission of Pakistan’s fully funded scholarship for her master’s studies and received merit-based scholarships during her undergraduate years. She also secured the UNESCO/People’s Republic of China Co-Sponsored Fellowship as a senior research scholar, reflecting her growing international recognition. These accolades not only highlight her academic dedication but also underscore her ability to compete successfully at global platforms. Collectively, her awards showcase her talent, perseverance, and impactful contributions to engineering and computer science research.

Publication Top Notes

Title: Sustainable Higher Education Reform Quality Assessment Using SWOT Analysis with Integration of AHP and Entropy Models: A Case Study of Morocco
Year: 2021
Citations: 64
Title: Spatial-Hessian-feature-guided variational model for pan-sharpening
Year: 2015
Citations: 50
Title: Fast superpixel based subspace low rank learning method for hyperspectral denoising
Year: 2018
Citations: 44
Title: Bilayer elastic net regression model for supervised spectral-spatial hyperspectral image classification
Year: 2016
Citations: 28
Title: Hybrid LSTM Self-Attention Mechanism Model for Forecasting the Reform of Scientific Research in Morocco
Year: 2021
Citations: 25
Title: Onion Crop Monitoring with Multispectral Imagery using Deep Neural Network
Year: 2021
Citations: 14
Title: A machine learning framework for major depressive disorder (MDD) detection using non-invasive EEG signals
Year: 2025
Citations: 13
Title: Sustainable higher education reform quality assessment using SWOT Analysis with integration of AHP and Entropy models: A case study of Morocco
Year: 2021
Citations: 13
Title: Local and nonlocal context-aware elastic net representation-based classification for hyperspectral images
Year: 2017
Citations: 8
Title: Hyperspectral image classification via Elastic Net Regression and bilateral filtering
Year: 2015
Citations: 8

Conclusion

Dr. Bushra Naz has established herself as a distinguished researcher and academic leader with a significant impact in the fields of artificial intelligence, machine learning, and hyperspectral image analysis. Her extensive research portfolio demonstrates a balance of theoretical innovation and practical application, addressing societal challenges such as sustainable agriculture, water quality monitoring, assistive technologies, and mental health detection. With a strong record of high-impact publications, international collaborations, research supervision, and active participation in conferences and editorial roles, she has consistently contributed to advancing knowledge and mentoring future researchers. Her achievements are further reinforced by prestigious awards, fellowships, and funded projects that recognize her scholarly excellence and leadership. Overall, Dr. Naz exemplifies the qualities of a visionary researcher—innovative, dedicated, and socially responsible—making her a highly deserving candidate for recognition through the Best Researcher Award.

Zbigniew Ras | Recommender Systems | Best Researcher Award

Published on 07/08/202507/08/2025 by World Top Scientists

Dr. Zbigniew Ras | Recommender Systems | Best Researcher Award

Professor of Computer Science & KDD Lab Director at University of North Carolina at Charlotte, United States

Zbigniew W. Ras is a distinguished computer scientist renowned for his extensive contributions to artificial intelligence, data mining, and intelligent information systems. With a career spanning multiple decades, he has held professorships and research positions at leading institutions in the United States, Europe, and Asia. His research interests span a broad range of interdisciplinary areas, including sentiment analysis, medical informatics, music information retrieval, and recommender systems. He serves as Editor-in-Chief of the Journal of Intelligent Information Systems and has been a pivotal figure in various international academic boards and conference steering committees. Recognized for both his research and teaching, he has received numerous awards and honors, including inclusion in the global list of top scientists. His leadership extends beyond academia through advisory roles in global AI organizations and think tanks. Ras’s work reflects a rare blend of theoretical depth, innovative application, and commitment to the advancement of both knowledge and academic communities.

Professional Profile

Google Scholar | Scopus Profile

Education

Zbigniew W. Ras has a rich and internationally respected academic background rooted in mathematics and computer science. He earned his M.S. in Mathematics and Ph.D. in Computer Science from the University of Warsaw, Poland. His academic journey advanced further with the prestigious D.Sc. (Habilitation) from the Polish Academy of Sciences, which marks a high level of scholarly independence and achievement in European academia. His accomplishments were further recognized when he was awarded the National Professorship Title by the President of Poland—one of the highest honors in the Polish academic system. This educational foundation not only highlights his expertise in formal systems and computation but also reflects a rigorous and progressive development of academic excellence. His training under globally respected mathematicians and computer scientists helped shape a career focused on both theoretical innovation and practical application in emerging areas of artificial intelligence, making him a well-rounded and influential academic figure.

Experience

Zbigniew W. Ras has accumulated a distinguished career in academia and research, with extensive international experience. He has been a professor at the University of North Carolina at Charlotte for several decades, where he also held leadership and mentoring roles. His experience includes appointments at the Polish-Japanese Academy of Information Technology, Warsaw University of Technology, and the Polish Academy of Sciences, where he contributed to both research and academic development. Ras has also held visiting professorships at respected institutions across Europe, North America, and Asia, including in Germany, Sweden, Spain, Italy, and Japan. Beyond academia, he has collaborated with industry through advisory roles with organizations in the United States, Switzerland, and Australia. His participation in international advisory boards and departmental councils reflects a strong commitment to institutional leadership. This wide-ranging experience underlines his global academic presence, multidisciplinary engagement, and capacity to foster international research collaboration and innovation.

Research Focus

Zbigniew W. Ras’s research encompasses a broad spectrum of topics within artificial intelligence and intelligent information systems. His work primarily focuses on data mining, granular computing, sentiment analysis, recommender systems, and actionability in decision-making processes. He has also explored emerging areas like music information retrieval, business and art analytics, and medical informatics, showcasing a commitment to applying AI methods across various domains. His research is characterized by a strong theoretical foundation coupled with practical applications, which has made significant contributions to fields such as flexible query answering and algebraic logic. Ras emphasizes the importance of actionable knowledge, striving to ensure that the outcomes of machine learning and data analysis can be effectively used in real-world decision contexts. His interdisciplinary focus demonstrates both depth and adaptability, enabling his work to impact diverse sectors including healthcare, education, cultural preservation, and enterprise intelligence, all while pushing the boundaries of computational and information sciences.

Award and Honor

Zbigniew W. Ras has received numerous awards and honors that reflect his excellence in both research and teaching. He was named among the world’s top 2% scientists in a list compiled by Stanford University and Elsevier, underscoring his global academic impact. He has received several institutional awards, including best paper honors and team achievement recognitions from Warsaw University of Technology. At the University of North Carolina at Charlotte, he was honored with multiple awards for outstanding faculty performance and mentorship, such as the Harshini V. de Silva Graduate Mentor Award. In addition, he was recognized as a finalist for major teaching excellence awards. Ras is also a Distinguished Fellow of the Kosciuszko Foundation’s Collegium of Eminent Scientists. His leadership roles include chairing and serving on advisory boards of several major academic conferences and societies, illustrating his influence within the international research community. These honors collectively affirm his sustained academic excellence and global recognition.

Publications Top Notes

Title: Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics): Preface
Authors: M Dorigo, M Birattari, GA Di Caro, R Doursat, AP Engelbrecht, D Floreano, ZW Ras, et al.
Year: 2010
Citations: 280
Title: Action-rules: How to increase profit of a company
Authors: ZW Ras, A Wieczorkowska
Year: 2000
Citations: 256
Title: Multi-label classification of emotions in music
Authors: A Wieczorkowska, P Synak, ZW Ras
Year: 2006
Citations: 175
Title: Association action rules
Authors: ZW Ras, A Dardzinska, LS Tsay, H Wasyluk
Year: 2008
Citations: 105
Title: Action rules discovery: system DEAR2, method and experiments
Authors: LS Tsay, ZW Ras
Year: 2005
Citations: 96
Title: The Wisdom Web: New Challenges for Web Intelligence (WI)
Authors: J Liu, N Zhong, Y Yao, ZW Ras
Year: 2003
Citations: 95
Title: Action rules mining
Authors: AA Tzacheva, ZW Ras
Year: 2005
Citations: 86
Title: ARAS: Action rules discovery based on agglomerative strategy
Authors: ZW Ras, E Wyrzykowska, H Wasyluk
Year: 2007
Citations: 83
Title: Extracting emotions from music data
Authors: A Wieczorkowska, P Synak, R Lewis, ZW Ras
Year: 2005
Citations: 80
Title: Discovering extended action-rules (System DEAR)
Authors: ZW Ras, LS Tsay
Year: 2003
Citations: 80
Title: Foundations of Intelligent Systems
Authors: ZW Ras, A Skowron
Year: 1999
Citations: 72
Title: Action rule extraction from a decision table: ARED
Authors: S Im, ZW Ras
Year: 2008
Citations: 67
Title: Advances in Music Information Retrieval
Authors: Z Ras, A Wieczorkowska
Year: 2010
Citations: 64
Title: How to support consensus reaching using action rules: a novel approach
Authors: J Kacprzyk, S Zadrozny, ZW Ras
Year: 2010
Citations: 59
Title: Analysis of sound features for music timbre recognition
Authors: X Zhang, ZW Ras
Year: 2007
Citations: 59

Conclusion

The publication record of Zbigniew W. Ras clearly demonstrates a sustained and impactful contribution to the fields of artificial intelligence, data mining, and intelligent information systems. His most cited works reveal a strong emphasis on action rule discovery, emotion analysis in music, and decision support systems—topics of continuing relevance in both academic and applied contexts. Collaborations with various researchers across countries and institutions also reflect his ability to work across disciplinary and cultural boundaries. The high citation counts of multiple papers show that his research has not only been influential but also foundational in shaping discussions and developments within the AI and data science communities. His scholarly output highlights both theoretical depth and practical innovation, underscoring his position as a thought leader in his domain. This consistent and wide-ranging academic influence positions him as a strong candidate for recognition through prestigious awards and honors in research excellence.

Sarah Marzen | Data Science | Best Researcher Award

Published on 04/08/202505/08/2025 by World Top Scientists

Prof. Sarah Marzen | Data Science | Best Researcher Award

Associate Professor Claremont McKenna College, United States

Sarah Marzen is a distinguished physicist and interdisciplinary researcher whose work bridges information theory, cognitive science, and biology. As an associate professor, she has contributed extensively to the study of sensory prediction, reinforcement learning, and resource rationality, securing leadership roles in numerous federally funded research projects. Her academic background includes a Ph.D. from the University of California, Berkeley, and postdoctoral work at MIT. She has published widely in peer-reviewed journals and played a vital role as a guest editor for multiple special issues. Sarah is actively involved in professional service, mentoring, and organizing scientific workshops. Her research stands out for its originality and interdisciplinary reach, tackling complex questions in neural computation and theoretical biology. Through her editorial work, teaching, and committee service, she has helped shape the scientific community’s understanding of cognition and prediction. Sarah Marzen’s scholarly excellence and leadership position her as a significant figure in contemporary scientific research.

Professional Profile

Google Scholar | Scopus Profile

Education

Sarah Marzen pursued her undergraduate studies in physics at the California Institute of Technology, where she developed a strong foundation in theoretical and experimental research. She continued her academic journey at the University of California, Berkeley, earning a Ph.D. in physics. Her doctoral work focused on bio-inspired problems in rate-distortion theory, under the guidance of Professor Michael R. DeWeese. This research bridged information theory and biological systems, laying the groundwork for her future interdisciplinary pursuits. In addition to her formal degrees, she attended several prestigious summer schools and workshops, including the Santa Fe Institute’s Complex Systems School and the Machine Learning Summer School. These programs helped her expand her understanding of machine learning, complex systems, and computational neuroscience. Sarah’s educational background is marked by both academic excellence and a consistent interest in the convergence of physics, information theory, and biological intelligence, making her uniquely equipped for innovative cross-disciplinary research.

Experience

Sarah Marzen’s academic career reflects deep engagement with both research and teaching. She currently serves as an associate professor of physics at the W. M. Keck Science Department, affiliated with Claremont McKenna, Pitzer, and Scripps Colleges. Prior to this, she was an assistant professor in the same department and a postdoctoral fellow at MIT, where she worked with Professors Nikta Fakhri and Jeremy England. Her early research experience includes graduate work at UC Berkeley and multiple assistantships and fellowships during her undergraduate years at Caltech. She has also held advisory roles in academia and private research, such as mentoring for Google Summer of Code and advising a stealth startup. Her experience spans experimental physics, theoretical modeling, machine learning, and neuroscience. Alongside her teaching, she contributes significantly to committee service and program development within her department, reflecting a well-rounded academic profile. Her professional trajectory demonstrates a strong commitment to both discovery and mentorship.

Research Focus

Sarah Marzen’s research centers on understanding how intelligent systems—both biological and artificial—predict and adapt to their environments. Her primary focus areas include sensory prediction, reinforcement learning, and resource rationality, particularly through the lens of information theory. She explores the ways in which brains and machines can perform efficient, predictive computations under constraints, contributing to theoretical frameworks that bridge physics, neuroscience, and cognitive science. Her work has applications in neural networks, artificial intelligence, and computational biology. She also investigates how delayed feedback and memory structures affect learning dynamics, as reflected in her studies of reservoir computing and time-delayed decision processes. Through her interdisciplinary approach, she addresses fundamental questions about how information is processed and used by complex systems. Her research aims to uncover principles of learning and adaptation that apply across different domains of intelligence, providing insight into both natural cognition and the design of intelligent machines.

Award and Honor

Sarah Marzen has received numerous honors and awards recognizing her academic excellence and contributions to interdisciplinary research. Early in her career, she was awarded prestigious fellowships including the NSF Graduate Research Fellowship and the MIT Physics of Living Systems Fellowship. At Caltech and UC Berkeley, she earned several merit-based scholarships and prizes for outstanding performance in physics. As her career progressed, she received grants and awards from major institutions such as the Sloan Foundation, Templeton Foundation, and the Air Force Office of Scientific Research. She has also been recognized for her editorial leadership, serving as guest editor for prominent journals like Entropy and Journal of the Royal Society Interface Focus. Her selection as a Scialog Fellow and finalist for the SIAM-MGB Early Career Fellowship further highlight her growing influence in computational neuroscience and mathematical biology. Her service and scholarly impact reflect a sustained commitment to advancing science across disciplinary boundaries.

Publications Top Notes

Title: Statistical mechanics of Monod–Wyman–Changeux (MWC) models
Authors: S. Marzen, H. G. Garcia, R. Phillips
Year: 2013
Cited by: 128
Title: On the role of theory and modeling in neuroscience
Authors: D. Levenstein, V. A. Alvarez, A. Amarasingham, H. Azab, Z. S. Chen, …
Year: 2023
Cited by: 100
Title: The evolution of lossy compression
Authors: S. E. Marzen, S. DeDeo
Year: 2017
Cited by: 65
Title: Informational and causal architecture of discrete-time renewal processes
Authors: S. E. Marzen, J. P. Crutchfield
Year: 2015
Cited by: 46
Title: Predictive rate-distortion for infinite-order Markov processes
Authors: S. E. Marzen, J. P. Crutchfield
Year: 2016
Cited by: 45
Title: Time resolution dependence of information measures for spiking neurons: Scaling and universality
Authors: S. E. Marzen, M. R. DeWeese, J. P. Crutchfield
Year: 2015
Cited by: 42
Title: Difference between memory and prediction in linear recurrent networks
Authors: S. Marzen
Year: 2017
Cited by: 39
Title: Nearly maximally predictive features and their dimensions
Authors: S. E. Marzen, J. P. Crutchfield
Year: 2017
Cited by: 39
Title: Structure and randomness of continuous-time, discrete-event processes
Authors: S. Marzen, J. P. Crutchfield
Year: 2017
Cited by: 37
Title: Informational and causal architecture of continuous-time renewal processes
Authors: S. Marzen, J. P. Crutchfield
Year: 2017
Cited by: 31
Title: Information anatomy of stochastic equilibria
Authors: S. Marzen, J. P. Crutchfield
Year: 2014
Cited by: 30
Title: Statistical signatures of structural organization: The case of long memory in renewal processes
Authors: S. E. Marzen, J. P. Crutchfield
Year: 2016
Cited by: 26
Title: First-principles prediction of the information processing capacity of a simple genetic circuit
Authors: M. Razo-Mejia, S. Marzen, G. Chure, R. Taubman, M. Morrison, R. Phillips
Year: 2020
Cited by: 25
Title: Optimized bacteria are environmental prediction engines
Authors: S. E. Marzen, J. P. Crutchfield
Year: 2018
Cited by: 24
Title: Machine learning outperforms thermodynamics in measuring how well a many-body system learns a drive
Authors: W. Zhong, J. M. Gold, S. Marzen, J. L. England, N. Yunger Halpern
Year: 2021
Cited by: 22

Conclusion

Sarah Marzen’s publication record reflects a strong and sustained impact across interdisciplinary fields such as statistical physics, neuroscience, and information theory. Her most highly cited work, including studies on Monod–Wyman–Changeux models and theoretical frameworks in neuroscience, demonstrates both depth in fundamental science and relevance to contemporary research challenges. The consistent citation of her papers over more than a decade indicates the enduring influence of her contributions. Many of her works are co-authored with leading researchers, reflecting strong collaborative networks and thought leadership. Her research not only advances theoretical understanding but also bridges to applied domains like machine learning and biological computation. Overall, the citation metrics, combined with the quality and diversity of topics, reinforce Sarah Marzen’s stature as a respected and influential figure in modern scientific research, making her a compelling candidate for recognition such as the Best Researcher Award.

Yang Han | Computer Science | Best Researcher Award

Published on 21/07/202521/07/2025 by World Top Scientists

Dr. Yang Han | Computer Science | Best Researcher Award

Associate Researcher at Tianjin University, China

Yang Han is an emerging researcher with a strong academic background in mathematics, having completed both his Master’s and PhD at Nankai University, followed by a research position at Tianjin University. His work bridges mathematical theory and practical applications in engineering, focusing on areas such as topological data analysis, signal processing, and intelligent fault diagnosis. In recent years, he has published extensively in high-impact journals like IEEE Transactions on Instrumentation and Measurement and Chaos, Solitons & Fractals, and presented at reputable international conferences such as IEEE PESGM and ACPEE. His interdisciplinary research is marked by innovation and relevance, especially in appliance identification, load forecasting, and fault detection using advanced mathematical tools. Though early in his research career, Yang has demonstrated strong potential and a clear trajectory of growth. His dedication, academic rigor, and collaborative approach position him as a promising candidate for the Best Researcher Award.

Professional Profile

Google Scholar
ORCID Profile

Strengths for the Award

Yang Han demonstrates a highly impressive academic and research trajectory. With a strong foundation in mathematics from Nankai University, progressing through a Master’s and PhD (2015–2023), and currently holding an associate researcher position at Tianjin University, he shows continuity and growth in academic rigor. His research spans interdisciplinary areas, merging topological data analysis, signal processing, machine learning, and fault diagnosis—fields of significant importance in both academia and industry. Notably, his recent publications in high-impact journals such as IEEE Transactions on Instrumentation and Measurement and Chaos, Solitons & Fractals reflect both quality and innovation. Additionally, his contributions to top-tier conferences like IEEE PESGM and ACPEE signal strong peer recognition. The combination of applied AI techniques and deep mathematical theory shows versatility, a rare and commendable strength for a young researcher.

Areas for Improvement

While the publication record is strong and growing, most of the impactful work is very recent (primarily in 2024–2025), indicating that Yang Han is in the early stages of building a long-term research profile. Sustained contributions over a longer timeline will better establish him as a leading authority. Another point of improvement would be to take on more lead or sole authorship roles in future publications, as many current works are collaborative with shared credit, which can make it harder to isolate individual impact. Additionally, while his interdisciplinary work is a strength, expanding his network internationally through collaborations beyond China and participating in global research programs could enhance the visibility and influence of his work.

Conclusion

Yang Han is a highly promising and impactful early-career researcher with a unique blend of mathematical depth and applied AI-driven engineering. His recent output demonstrates a clear upward trajectory, both in productivity and innovation. While there is room to further solidify his independent research identity and global presence, his current achievements strongly support his candidacy for the Best Researcher Award. Given his solid grounding, interdisciplinary focus, and growing impact, he is indeed a suitable and deserving nominee for this recognition.

Education

Yang Han began his academic journey at Nankai University, a prestigious institution known for mathematical excellence. From 2015 to 2018, he completed his Master’s degree at the School of Mathematical Sciences and LPMC, focusing on advanced mathematical theories and computational techniques. His strong academic performance and deep interest in topology, algebra, and their applications led him to continue his research as a PhD student in the same department from 2019 to 2023. During his doctoral studies, he expanded his expertise into applied mathematics and began to explore connections with engineering systems and data-driven problem solving. His doctoral research provided the foundation for his transition into interdisciplinary areas such as topological data analysis and graph signal processing. His time at Nankai University was marked by academic growth, critical thinking, and active participation in scholarly research. This rigorous educational background prepared him for a successful research career bridging mathematics and electrical engineering.

Experience

Yang Han currently holds the position of Associate Researcher at the School of Electrical and Information Engineering, Tianjin University. Since assuming this role in 2023, he has actively contributed to research in intelligent systems, signal processing, and data analytics. Before this, he spent nearly a decade at Nankai University, where he completed his Master’s and PhD studies, engaging in teaching support and foundational research. His experience spans a variety of projects focused on non-intrusive load monitoring, equipment fault diagnosis, and appliance identification—often leveraging advanced mathematical tools like topological data analysis and fast Fourier transforms. He has contributed to both national and international research collaborations, presented at prestigious conferences, and published in leading journals. His ability to blend abstract mathematical methods with real-world engineering challenges exemplifies his versatile experience. His role also involves mentoring junior researchers and contributing to interdisciplinary innovation at the intersection of mathematics, artificial intelligence, and electrical engineering.

Awards and Honors

While formal individual awards are not explicitly listed in the available data, Yang Han’s growing list of high-impact publications and conference presentations serves as strong evidence of professional recognition. His work has been published in top-tier journals such as IEEE Transactions on Instrumentation and Measurement, Chaos, Solitons & Fractals, and Engineering Applications of Artificial Intelligence, reflecting a high level of peer recognition. He has also contributed to leading international conferences, including IEEE PESGM and the Asia Conference on Power and Electrical Engineering (ACPEE), where selection itself is a mark of merit. These platforms are known for their rigorous review processes, indicating that his work meets and often exceeds international research standards. Additionally, his involvement in collaborative, interdisciplinary projects and authorship in multiple papers shows that he is a valued team member in academic and industrial circles. As his career progresses, further formal awards and honors are likely to follow.

Research Focus on Computer Science

Yang Han’s research is centered at the intersection of applied mathematics, artificial intelligence, and electrical engineering. His primary focus lies in topological data analysis, signal processing, and machine learning techniques for complex system monitoring and fault detection. He has contributed significantly to non-intrusive load monitoring (NILM), using graph signal processing to identify energy consumption patterns without intrusive sensors. He also works on fault diagnosis through time-frequency analysis and the application of mathematical topology in real-world engineering systems. His innovative approach often involves transforming abstract mathematical concepts—such as Betti curves and topological invariants—into practical tools for appliance identification and power grid analysis. Furthermore, Yang Han is exploring adaptive methods for equipment behavior modeling and data-driven forecasting. This unique research blend offers both theoretical advancements and immediate practical value, demonstrating his ability to tackle emerging challenges in intelligent energy systems and industrial diagnostics with precision and depth.

Publications Top Notes

Title: Energy dissipation analysis of elastic–plastic materials
Authors: H Yang, SK Sinha, Y Feng, DB McCallen, B Jeremić
Year: 2018
Citations: 94
Title: Study on the mechanical behavior of sands using 3D discrete element method with realistic particle models
Authors: WJ Xu, GY Liu, H Yang
Year: 2020
Citations: 46
Title: Nonlinear finite elements: Modeling and simulation of earthquakes, soils, structures and their interaction
Authors: B Jeremić, Z Yang, Z Cheng, G Jie, N Tafazzoli, M Preisig, P Tasiopoulou, …
Year: 2018
Citations: 37
Title: The real-ESSI simulator system
Authors: B Jeremić, G Jie, Z Cheng, N Tafazzoli, P Tasiopoulou, F Pisanò, JA Abell, …
Year: 1988
Citations: 35
Title: Study on the meso-structure development in direct shear tests of a granular material
Authors: H Yang, WJ Xu, QC Sun, Y Feng
Year: 2017
Citations: 28
Title: Energy dissipation analysis for inelastic reinforced concrete and steel beam-columns
Authors: H Yang, Y Feng, H Wang, B Jeremić
Year: 2019
Citations: 27
Title: Time domain intrusive probabilistic seismic risk analysis of nonlinear shear frame structure
Authors: H Wang, F Wang, H Yang, Y Feng, J Bayless, NA Abrahamson, B Jeremić
Year: 2020
Citations: 22
Title: Seismic resonant metamaterials for the protection of an elastic-plastic SDOF system against vertically propagating seismic shear waves (SH) in nonlinear soil
Authors: C Kanellopoulos, N Psycharis, H Yang, B Jeremić, I Anastasopoulos, …
Year: 2022
Citations: 21
Title: Energy dissipation in solids due to material inelasticity, viscous coupling, and algorithmic damping
Authors: H Yang, H Wang, Y Feng, F Wang, B Jeremić
Year: 2019
Citations: 20
Title: 3-d non-linear modeling and its effects in earthquake soil-structure interaction
Authors: SK Sinha, Y Feng, H Yang, H Wang, B Jeremic
Year: 2017
Citations: 19
Title: Plastic-energy dissipation in pressure-dependent materials
Authors: H Yang, H Wang, Y Feng, B Jeremić
Year: 2020
Citations: 18
Title: Relationship between multifunctionality and rural sustainable development: Insights from 129 counties of the Sichuan Province, China
Authors: X Li, J Liu, J Jia, H Yang
Year: 2022
Citations: 17
Title: Modeling and simulation of earthquake soil structure interaction excited by inclined seismic waves
Authors: H Wang, H Yang, Y Feng, B Jeremić
Year: 2021
Citations: 17
Title: An energy-based analysis framework for soil structure interaction systems
Authors: H Yang, H Wang, B Jeremić
Year: 2022
Citations: 14
Title: A robust and efficient federated learning algorithm against adaptive model poisoning attacks
Authors: H Yang, D Gu, J He
Year: 2024
Citations: 11

Muawia Elsadig | Computer Science | Best Researcher Award

Published on 12/06/202512/06/2025 by World Top Scientists

Dr. Muawia Elsadig | Computer Science | Best Researcher Award

Assistant Professor at Imam Abdulrahman Bin Faisal University, Saudi Arabia

Dr. Muawia A. Elsadig is an accomplished Assistant Professor at Imam Abdulrahman Bin Faisal University in Saudi Arabia, with extensive experience in computer science, particularly in cybersecurity, information security, AI, machine learning, and bioinformatics. He has held academic positions at renowned institutions across Sudan, the UAE, and Saudi Arabia. Dr. Elsadig has authored over 30 peer-reviewed publications, many of which appear in high-impact Q1 and Q2 journals such as IEEE Access. His recent research focuses on cyber threat detection, secure communications, AI applications, and ethical issues in emerging technologies. He also serves as a reviewer for several leading international journals and contributes actively to institutional research development through editing, reviewing, and ethical oversight roles. With a consistent research record, interdisciplinary expertise, and international teaching background, Dr. Elsadig demonstrates strong leadership and scholarly contributions, making him a highly deserving candidate for recognition through prestigious research awards.

Professional Profile

Google Scholar
Scopus Profile
ORCID Profile

Education

Dr. Muawia A. Elsadig holds a strong academic foundation in computer engineering and science. He earned his B.Sc. (Honors) in Computer Engineering from the University of Gezira, Sudan, in 2000, followed by an M.Sc. in Computer Engineering and Networks from the same institution in 2003, graduating with first-class honors. He later completed his Ph.D. in Computer Science, specializing in Information Security, at Sudan University of Science and Technology (SUST) in 2018. His academic progression reflects a focused commitment to cybersecurity and advanced computing disciplines. Each stage of his education laid a strong theoretical and technical groundwork, preparing him for a dynamic career in both academia and research. His doctoral studies, in particular, sharpened his expertise in network security and information assurance, providing a springboard for his subsequent contributions to the fields of cyber defense, machine learning, and secure systems. Dr. Elsadig’s educational background is both comprehensive and rigorously specialized.

Professional Experience

Dr. Muawia A. Elsadig has over two decades of professional experience in academia and industry, reflecting his deep engagement with computing disciplines. He has served in teaching and research roles at prominent universities including the University of Gezira in Sudan, the University of Sharjah in the UAE, and King Khalid University in Saudi Arabia. Since 2018, he has held the position of Assistant Professor at Imam Abdulrahman Bin Faisal University (IAU) in Saudi Arabia, contributing to both the Computer Science Department and the university’s Deanship of Scientific Research. His responsibilities span teaching, curriculum development, research supervision, and participation in ethical review processes as a member of the Institutional Review Board (IRB). He has also been involved in the editorial review of internal research grants. His industry experience complements his academic roles, providing a practical dimension to his teaching and research. Dr. Elsadig’s professional journey is marked by dedication, cross-cultural competence, and research leadership.

Research Interest

Dr. Muawia A. Elsadig’s research interests are broad and interdisciplinary, encompassing cybersecurity, information security, network security, artificial intelligence, machine learning, deep learning, and bioinformatics. His work explores both theoretical foundations and practical applications, with a strong focus on developing lightweight, efficient models for detecting cyber threats such as denial-of-service (DoS) attacks and covert channels. He is also interested in the ethical implications of emerging technologies, having published insightful work on the societal impacts of AI tools like ChatGPT and machine translation systems. Dr. Elsadig has applied machine learning techniques to critical areas such as breast cancer detection and secure data encryption, demonstrating a commitment to using AI for social good. His research often bridges technical rigor with applied innovation, and he collaborates on projects that integrate computing with healthcare and secure communications. This interdisciplinary approach makes his research both relevant and impactful in today’s fast-evolving technological landscape.

Award and Honor

Dr. Muawia A. Elsadig has received multiple awards and recognitions for his research excellence, particularly for publishing in high-impact, peer-reviewed international journals indexed in the Web of Science and Scopus (Q1 and Q2). These recognitions reflect the high quality and scholarly contribution of his research in fields such as cybersecurity, AI, and bioinformatics. He has also been acknowledged by his institutions for his active role in scientific research development, including grant proposal evaluations and ethical oversight. Beyond individual publications, his selection as a peer reviewer for top-tier journals like IEEE Access and Artificial Intelligence Review is an implicit honor, affirming his expertise and credibility in his research domains. While the profile does not list named external awards or grants, the consistent publication record, academic appointments, and responsibilities he holds at respected institutions are strong indicators of his professional esteem. These honors collectively highlight his value as a research leader and academic mentor.

Conclusion

In conclusion, Dr. Muawia A. Elsadig stands out as a highly accomplished academic and researcher in the domains of computer science and cybersecurity. With a solid educational background, extensive teaching experience, and a strong portfolio of international publications, he has made significant contributions to both theoretical advancements and practical solutions in his field. His work bridges artificial intelligence, secure systems, and bioinformatics, reflecting both depth and breadth in his research pursuits. Dr. Elsadig’s ongoing involvement in peer review, research ethics, and interdisciplinary collaboration highlights his commitment to advancing knowledge and ensuring research integrity. He is not only a prolific scholar but also an active academic citizen dedicated to mentoring, ethical governance, and the strategic development of research agendas. His achievements and leadership position him as a compelling candidate for prestigious honors such as the Best Researcher Award, and he continues to be a driving force in his academic community and beyond.

Publications Top Notes

Title: The Impact of Artificial Intelligence on Language Translation: A Review
Authors: YA Mohamed, A Khanan, M Bashir, AHHM Mohamed, MAE Adiel, MA Elsadig
Year: 2024
Citations: 124
Title: Breast Cancer Detection Using Machine Learning Approaches: A Comparative Study
Authors: MA Elsadig, A Altigani, HT Elshoush
Year: 2023
Citations: 60
Title: VANETs Security Issues and Challenges: A Survey
Authors: MA Elsadig, YA Fadlalla
Year: 2016
Citations: 60
Title: Detection of Denial-of-Service Attack in Wireless Sensor Networks: A Lightweight Machine Learning Approach
Author: MA Elsadig
Year: 2023
Citations: 52
Title: Covert Channel Detection: Machine Learning Approaches
Authors: MA Elsadig, A Gafar
Year: 2022
Citations: 49
Title: A Polymorphic Advanced Encryption Standard – A Novel Approach
Authors: A Altigani, S Hasan, B Barry, S Naserelden, MA Elsadig, HT Elshoush
Year: 2021
Citations: 46
Title: Survey on Covert Storage Channel in Computer Network Protocols: Detection and Mitigation Techniques
Authors: MA Elsadig, YA Fadlalla
Year: 2016
Citations: 37
Title: Security Issues and Challenges on Wireless Sensor Networks
Authors: MA Elsadig, A Altigani, MA Baraka
Year: 2019
Citations: 26
Title: Network Protocol Covert Channels: Countermeasures Techniques
Authors: MA Elsadig, YA Fadlalla
Year: 2017
Citations: 26
Title: Information Extraction Methods and Techniques in Chemical Documents: Survey
Authors: M Abdelmagid, AA, Mubarak Himmat
Year: 2015
Citations: 24
Title: Mobile Ad Hoc Network Routing Protocols: Performance Evaluation and Assessment
Authors: MA Elsadig, A Yahia
Year: 2018
Citations: 22
Title: Packet Length Covert Channel: A Detection Scheme
Authors: MA Elsadig, YA Fadlalla
Year: 2018
Citations: 20
Title: A Balanced Approach to Eliminate Packet Length-Based Covert Channels
Authors: MA Elsadig, YA Fadlalla
Year: 2017
Citations: 17
Title: Analyzing the Performance of the AES Block Cipher Modes of Operation
Authors: A Altigani, M Abdelmagid, B Barry
Year: 2016
Citations: 13
Title: ChatGPT and Cybersecurity: Risk Knocking the Door
Author: MA Elsadig
Year: 2024
Citations: 10

Shishir Tewari | Computer Science | Technology and Innovation Leadership Award

Published on 26/05/202526/05/2025 by World Top Scientists

Mr. Shishir Tewari | Computer Science | Technology and Innovation Leadership Award

Senior Manager, Data Engineering at Procore Technologies, United States

Shishir Tewari is a seasoned technology leader with over 19 years of experience driving innovation in data engineering, data warehousing, and analytics across top-tier organizations such as Google, Amazon, Morgan Stanley, and Microsoft. He currently leads strategic data initiatives at Procore Technologies, where he has spearheaded the development of AI/ML-driven platforms, cloud migrations, and real-time analytics systems. Known for his expertise in building scalable, high-performance data solutions, Shishir has successfully led global engineering teams and transformed complex data ecosystems on AWS, GCP, and Databricks. His technical vision, operational excellence, and commitment to data quality and governance have consistently delivered measurable business value. Shishir’s continuous pursuit of innovation and deep cross-functional leadership make him a standout contributor in the technology landscape. With a strong foundation in data science, cloud architecture, and team mentorship, he exemplifies the qualities of a forward-thinking, impact-driven technology leader worthy of recognition.

Professional Profile

Google Scholar

Education

Shishir Tewari holds a Bachelor of Technology in Information Technology from U.P.T.U., India, graduating in 2006. Demonstrating a commitment to lifelong learning and innovation, he further enhanced his credentials with a specialization in Data Science and Analytics from Rutgers University, New Jersey, in 2018–2019. This advanced academic training equipped him with modern analytical techniques, machine learning algorithms, and statistical modeling—skills that have been instrumental in his professional success. His educational background lays a strong foundation for his technical leadership, blending theoretical knowledge with real-world application. The combination of engineering fundamentals and data science expertise positions Shishir as a well-rounded technology leader who can bridge the gap between innovation and implementation in enterprise environments.

Professional Experience

Shishir Tewari brings over 19 years of robust experience across global technology firms, including Google, Amazon, Morgan Stanley, Microsoft, and currently, Procore Technologies. His career spans technical leadership, large-scale data architecture, and cloud-native platform innovation. At Google, he led a global team optimizing financial data pipelines and infrastructure. At Amazon, he designed high-performance advertising data systems, enabling substantial revenue impact. At Procore, he has driven major initiatives including AI/ML-powered data platforms and cloud migrations. His ability to manage large engineering teams, align data strategy with business goals, and optimize performance at scale reflects his leadership maturity. Shishir’s diverse experience across industries—finance, tech, construction, and advertising—gives him a unique, cross-sector perspective on data-driven transformation.

Research Interest

Shishir Tewari’s research interests lie at the intersection of big data engineering, AI/ML-driven analytics, and cloud computing. He is particularly passionate about optimizing large-scale data systems for performance, governance, and real-time decision-making. With practical expertise in cloud platforms like AWS, GCP, and Databricks, his focus is on leveraging modern data stacks and open-source technologies to power next-generation analytics and automation. He is also interested in the application of machine learning for master data management, anomaly detection, and predictive modeling within business intelligence ecosystems. While not rooted in academic publishing, his work consistently applies research principles to solve real-world business problems, delivering measurable impact. Future interests include exploring the integration of generative AI with enterprise data platforms and advancing data democratization through self-service analytics tools.

Award and Honor

While specific awards and honors are not listed in his profile, Shishir Tewari’s consistent elevation to senior technical and leadership roles in globally respected organizations serves as a testament to his excellence and recognition within the industry. Being entrusted with mission-critical projects at Google, Amazon, and Morgan Stanley speaks to his reliability, vision, and execution skills. His role in leading high-visibility initiatives such as financial data certification, AI/ML-driven analytics platforms, and major cloud migrations reflects the high degree of trust and credibility he commands. He has likely received internal accolades for his contributions to performance optimization, cost reduction, and innovation. A nomination for a Technology and Innovation Leadership Award would further formalize and honor his significant contributions to data-driven transformation and technological advancement in enterprise settings.

Conclusion

Shishir Tewari exemplifies the qualities of a forward-thinking technology leader, with deep expertise in data engineering, cloud architecture, and strategic innovation. His two-decade-long career reflects a commitment to excellence, from hands-on development to executive-level leadership. With advanced training in data science, he brings both theoretical rigor and practical vision to his work. His impactful roles at top-tier organizations demonstrate his ability to lead cross-functional teams, optimize large-scale systems, and implement transformative technologies. Passionate about leveraging AI/ML and cloud platforms to drive business value, Shishir’s professional journey is marked by continuous learning and measurable outcomes. He stands out as a prime candidate for recognition through a Technology and Innovation Leadership Award, not only for his technical contributions but also for his ability to inspire, mentor, and lead organizations into the future of data-driven innovation.

Publications Top Notes

Title: AI Powered Data Governance – Ensuring Data Quality and Compliance in the Era of Big Data
Authors: S. Tewari
Year: 2025
Title: Operationalizing Explainable AI in Business Intelligence: A Blueprint for Transparent Enterprise Analytics
Authors: A. Chitnis, S. Tewari
Year: 2024
Title: AI and Multi-Cloud Compliance: Safeguarding Data Sovereignty
Authors: S. Tewari, A. Chitnis
Year: 2024
Title: Scalable Metadata Management in Data Lakes Using Machine Learning
Authors: S. Tewari
Year: 2023
Citation: (Update needed)
Title: AI-Powered Financial Forecasting: Enhancing Accuracy with Machine Learning in Enterprise System
Authors: S. Tewari
Year: 2023)
Title: Detecting Data Drift and Ensuring Observability with Machine Learning Automation
Authors: A. Chitnis, S. Tewari
Year: 2022
Title: Anomaly Detection in Large Scale Data Platforms with Machine Learning
Authors: S. Tewari
Year: 2022
Title: Leveraging Graph Based Machine Learning to Analyze Complex Enterprise Data Relationships
Authors: S. Tewari, A. Chitnis
Year: 2021

merve pınar | Machine Learning | Best Researcher Award

Published on 02/01/202502/01/2025 by World Top Scientists

Dr. merve pınar | Machine Learning | Best Researcher Award

Research Ass, Marmara University, Turkey

Merve Pinar is a Research Assistant in the Faculty of Technology, Computer Engineering Department at Marmara University, Turkey. She has been pursuing her doctorate since 2023 at Marmara University in the field of Computer Engineering. Her academic journey includes a postgraduate degree from the Institute for Graduate Studies in Pure and Applied Sciences (2019-2022) and an undergraduate degree from Çanakkale Onsekiz Mart University, where she studied Engineering (2009-2013). Merve’s work primarily focuses on artificial intelligence, machine learning, and their applications in various fields, especially healthcare. She is dedicated to exploring innovative solutions using deep learning and pattern recognition techniques. Her contributions to the academic community include publications in respected journals and conferences. She also actively collaborates with other researchers to advance the field.

Profile

Education

Doctorate (2023-Present): Marmara University, Faculty of Technology, Computer Engineering, Turkey.
Postgraduate (2019-2022): Marmara University, Institute for Graduate Studies in Pure and Applied Sciences, Turkey. Dissertation: “Derinöğrenme yöntemleri kullanılarak beyin tümörü tiplerinin ve sınırlarının tahminlenmesi” (Prediction of brain tumor types and boundaries using deep learning methods).
Undergraduate (2009-2013): Çanakkale Onsekiz Mart University, Faculty of Engineering, Turkey.

Merve’s academic background provides a solid foundation in computer engineering, artificial intelligence, and data science. She continues to pursue advanced studies, focusing on leveraging machine learning and deep learning methods to address complex problems in health and technology.

Research Focus

Merve Pinar’s research focuses on the intersection of artificial intelligence, machine learning, and medical applications. Her primary interests are database management, data structures, pattern recognition, and deep learning. She specializes in using AI techniques for medical imaging, particularly in the classification and segmentation of brain tumor types using MRI and surgical microscope images. Her work aims to enhance diagnostic tools, improving the accuracy and efficiency of healthcare systems. Additionally, she is involved in hyperparameter optimization for big data applications, which helps improve recommendation systems. Merve’s interdisciplinary research is positioned at the cutting edge of AI, combining computer engineering with real-world applications, particularly in healthcare technology, where deep learning plays a crucial role in revolutionizing diagnostics and treatment strategies.

Publications

Deep Learning-Assisted Segmentation and Classification of Brain Tumor Types on Magnetic Resonance and Surgical Microscope Images 🧠💻 (2024)
Hyperparameter Optimization for Recommendation Systems with Big Data 📊🔍 (2017)

Anastasia Kakouri | Environmental Health | Young Scientist Award

Published on 27/12/202427/12/2024 by World Top Scientists

Ms. Anastasia Kakouri | Environmental Health | Young Scientist Award

PhD Candidate, Department of the Environment/University Of The Aegean, Greece

Anastasia Kakouri is a highly accomplished senior GIS Specialist and PhD researcher specializing in atmospheric pollution and population exposure at the National Observatory of Athens. With a profound background in geography and geoinformatics, she has applied her expertise to spatial analysis, remote sensing, and machine learning for air quality prediction and assessment. Her research integrates high-resolution emission inventories, socio-economic data, and health parameters to address environmental challenges at urban and intra-urban scales. She is actively contributing to the intersection of air quality, sustainability, and environmental justice. As a funded researcher by the Hellenic Foundation for Research and Innovation (HFRI), Kakouri’s work is influencing the management of environmental risks in Greece. Her multi-disciplinary approach is fundamental to developing tools for informed decision-making in public health and urban planning.

Profile

Scopus

Education

Anastasia Kakouri holds a Master’s degree (M.Sc.) in Geography and Applied Geoinformatics from the University of the Aegean, Greece, where she specialized in Applied Geoinformatics for the Management of the Natural Environment and Risks. This advanced education in geospatial technologies laid the foundation for her expertise in atmospheric pollution and spatial analysis. Prior to her Master’s, she earned a Bachelor’s degree (BSc) in Environmental Science from the same university’s Department of Environment. Her academic journey was marked by a focus on integrating geoinformatics tools for environmental monitoring and risk management. Kakouri’s ongoing PhD research, funded by the Hellenic Foundation for Research and Innovation (HFRI), aims to advance the understanding of air quality and population exposure, contributing to sustainable urban development and public health policies. Her educational background and research skills form a strong base for innovative solutions in environmental science and geoinformatics.

Research Focus

Anastasia Kakouri’s research focuses on the synergy of Earth observations and geoinformatic tools to address urban atmospheric pollution and population exposure. Her primary interest lies in the application of machine learning techniques to predict air quality and assess the health impacts of pollution on populations, with a specific emphasis on urban and intra-urban environments. Kakouri is actively involved in the development of high-resolution emission inventories and the integration of remote sensing data with geostatistical models to improve spatial analysis of air pollution. Her work explores the connections between air quality, public health, and socio-economic parameters, aiming to create tools for informed decision-making at local and regional levels. Kakouri also investigates sustainability and environmental justice, ensuring that her research not only improves urban planning and environmental monitoring but also addresses health inequalities in pollution-affected areas. Her studies contribute to the broader understanding of air pollution dynamics in relation to human well-being.

Publications

“Spatiotemporal modeling of long-term PM concentrations and population exposure in Greece, using machine learning and statistical methods” 🌍📊
“Analysis of secondary inorganic aerosols over the greater Athens area using the EPISODE-CityChem source dispersion and photochemistry model” 🌫️💨
“NOx emissions by real-world fresh and old asphalt mixtures: Impact of temperature, relative humidity, and UV-irradiation” 🚗🌞
“Enhancing air quality data from multiple platforms towards geospatial health-related information in the intra-urban environment” 🏙️📡
“Five years of spatially resolved ground-based max-doas measurements of nitrogen dioxide in the urban area of Athens: Synergies with in situ measurements and model simulations” 🏙️📍
“The urbem hybrid method to derive high-resolution emissions for city-scale air quality modeling” 🔬🌆
“Integrating in situ Measurements and City Scale Modelling to Assess the COVID– Lockdown Effects on Emissions and Air Quality in Athens, Greece” 🦠💨