Woosik Lee | Computer Science | Research Excellence Award

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Dr. Woosik Lee | Computer Science | Research Excellence Award

Korea Social Security Information Service | South Korea

Dr. Woosik Lee is a researcher at the Research Center of the Korea Social Security Information Service, specializing in wireless sensor networks, Internet of Things systems, and data-driven intelligent services. He holds advanced degrees in computer science with a focus on networked systems, sensor technologies, and intelligent algorithms. His professional experience spans academic, governmental, and international research environments, including faculty service, visiting research appointments, and leadership roles in applied research projects addressing healthcare monitoring, intelligent transportation, and social welfare analytics. His research focuses on low-power communication protocols, neighbor discovery mechanisms, wireless body sensor networks, human monitoring systems, and machine learning–based social welfare applications. He has authored numerous peer-reviewed journal articles and conference contributions, demonstrating sustained scholarly impact and interdisciplinary relevance. His work integrates theoretical modeling, protocol design, simulation, and real-world system implementation, contributing to both academic advancement and societal benefit. Dr. Lee’s research excellence has been recognized through competitive awards and sustained citation impact, highlighting his growing influence and strong potential for continued leadership in intelligent networked systems research.

Citation Metrics (Scopus)

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Featured Publications

Oluwatobi Adedamola Ayilara-Adewale | Artificial Intelligence | Innovative Research Award

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Dr. Oluwatobi Adedamola Ayilara-Adewale | Artificial Intelligence | Innovative Research Award

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.

Dr. Zeinab Shahbazi | Computer Science | Best Researcher Award

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Dr. Zeinab Shahbazi | Computer Science | Best Researcher Award

Senior Lecturer at Kristianstad University, Sweden

Dr. Zeinab Shahbazi is an accomplished researcher specializing in Reinforcement Learning, Deep Learning, Natural Language Processing, Blockchain, and Knowledge Discovery. With a Ph.D. in Computer Engineering from Jeju National University, South Korea, she has over eight years of research experience in AI and data-driven technologies. Dr. Shahbazi has held postdoctoral positions in Spain and Sweden and is currently a Senior Lecturer in AI at Kristianstad University. Her research focuses on enhancing state-of-the-art architectures and developing innovative solutions in software-based intelligent systems. She has been recognized with several academic awards, including a Presidential Award and Best Paper Presentation honors. Fluent in multiple languages and technically skilled in programming and data systems, she actively contributes as a reviewer for high-impact journals. Her international collaborations and funded research projects reflect her commitment to advancing AI applications. Dr. Shahbazi is a dedicated and forward-thinking researcher making significant contributions to the field of computer science.

Professional Profile 

Google Scholar

Education

Dr. Zeinab Shahbazi holds a Ph.D. in Computer Engineering from Jeju National University, South Korea, where she completed her dissertation on cryptocurrency price prediction using blockchain frameworks, graduating with an impressive CGPA of 4.32/4.5. She also earned a Master’s degree in Computer Engineering from Chonbuk National University, Korea, with a thesis on deep learning techniques for paragraph focus analysis. Her foundational education includes a Bachelor’s degree in Computer Engineering from Pooyesh University in Iran. Throughout her academic journey, she received several scholarships and honors, reflecting her consistent academic excellence. Her education has been firmly rooted in AI, software systems, and intelligent technologies, providing her with a robust theoretical and practical grounding. This strong academic background has played a pivotal role in shaping her as a multidisciplinary researcher with global exposure, capable of addressing complex problems in AI and data science with both depth and innovation.

Professional Experience

Dr. Zeinab Shahbazi has accumulated diverse international professional experience in research and academia. She is currently a Senior Lecturer in Artificial Intelligence at Kristianstad University, Sweden. Prior to this, she held postdoctoral researcher positions at Halmstad University in Sweden and at the BCN-AIM Lab at the University of Barcelona in Spain. Her work has consistently focused on applied AI, reinforcement learning, and blockchain-based systems. Dr. Shahbazi has also led and participated in international research collaborations, notably securing a Vinnova-funded international staff exchange project with a partner institution in South Korea. Her career path showcases her ability to transition between theoretical research and practical implementations, including experience in advanced programming, system architecture, and AI model development. These roles have enabled her to contribute to both the academic and industrial applications of intelligent technologies, while also strengthening her leadership and mentoring capabilities in multidisciplinary, multicultural environments.

Research Interest

Dr. Zeinab Shahbazi’s research interests are deeply rooted in intelligent computing systems, with a focus on Reinforcement Learning, Deep Learning, Natural Language Processing (NLP), Blockchain, Knowledge Discovery, and their integration within modern technological ecosystems such as IoT, edge computing, and big data platforms. Her core research ambition lies in improving existing AI models and architectures, addressing their limitations, and introducing novel components to enhance performance and applicability. She has made notable contributions to the software aspects of AI, particularly through her work on knowledge-driven systems and blockchain-based data prediction. Dr. Shahbazi combines theoretical advancements with practical implementations, bridging the gap between academic research and real-world applications. Her multidisciplinary focus reflects a keen interest in innovation, system integration, and cross-domain problem-solving. This makes her work highly relevant to both academic audiences and industry stakeholders interested in deploying intelligent, data-driven systems for practical and scalable use.

Award and Honor

Dr. Zeinab Shahbazi has received multiple awards and honors in recognition of her academic excellence and research contributions. During her Ph.D. at Jeju National University, she was awarded the prestigious Presidential Award for distinguished research publications. She also received a university research grant in 2021 for her outstanding output during 2019–2020. Earlier in her academic career, she was a recipient of the BK government scholarship and multiple semester-based scholarships during her Master’s studies at Chonbuk National University. Her early academic promise was also recognized with a government-funded scholarship during her undergraduate studies in Iran. Additionally, she won the Best Paper and Presentation Award at the ITEC Conference in 2019, further solidifying her reputation in the research community. These honors demonstrate a consistent trajectory of excellence, reflecting both the quality and impact of her research work, as well as her ability to compete and stand out in international academic environments.

Conclusion

Dr. Zeinab Shahbazi exemplifies a dynamic and impactful researcher in the field of computer science, particularly in AI, machine learning, and data-driven systems. Her strong educational background, diverse international research experience, and cross-disciplinary expertise make her a well-rounded academic and innovator. Her ability to secure research funding, collaborate internationally, and publish high-quality work underlines her potential for long-term academic leadership. Recognized through various awards and honors, she has demonstrated excellence not only in individual performance but also in contributing to the broader scientific community through peer review and collaboration. Fluent in multiple languages and culturally adaptive, Dr. Shahbazi brings global perspective and technical depth to every role she undertakes. With a forward-thinking mindset and a commitment to advancing the state of AI, she stands as a strong candidate for high-level recognitions such as the Best Researcher Award and is poised to continue making meaningful contributions to academia and beyond.

Publications Top Notes

  • Title: Integration of blockchain, IoT and machine learning for multistage quality control and enhancing security in smart manufacturing
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2021
    Citations: 187

  • Title: A procedure for tracing supply chains for perishable food based on blockchain, machine learning and fuzzy logic
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2020
    Citations: 140

  • Title: Towards a secure thermal-energy aware routing protocol in wireless body area network based on blockchain technology
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2020
    Citations: 123

  • Title: Smart manufacturing real-time analysis based on blockchain and machine learning approaches
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2021
    Citations: 72

  • Title: Toward improving the prediction accuracy of product recommendation system using extreme gradient boosting and encoding approaches
    Authors: Z. Shahbazi, D. Hazra, S. Park, Y.C. Byun
    Year: 2020
    Citations: 68

  • Title: Improving transactional data system based on an edge computing–blockchain–machine learning integrated framework
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2021
    Citations: 64

  • Title: Product recommendation based on content-based filtering using XGBoost classifier
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2019
    Citations: 64

  • Title: Agent-based recommendation in E-learning environment using knowledge discovery and machine learning approaches
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2022
    Citations: 63

  • Title: Fake media detection based on natural language processing and blockchain approaches
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2021
    Citations: 63

  • Title: Improving the cryptocurrency price prediction performance based on reinforcement learning
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2021
    Citations: 60

  • Title: Machine learning-based analysis of cryptocurrency market financial risk management
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2022
    Citations: 58

  • Title: Lithium-ion battery estimation in online framework using extreme gradient boosting machine learning approach
    Authors: S. Jafari, Z. Shahbazi, Y.C. Byun, S.J. Lee
    Year: 2022
    Citations: 58

  • Title: Blockchain-based event detection and trust verification using natural language processing and machine learning
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2021
    Citations: 51

  • Title: Knowledge discovery on cryptocurrency exchange rate prediction using machine learning pipelines
    Authors: Z. Shahbazi, Y.C. Byun
    Year: 2022
    Citations: 42

  • Title: Lithium-ion battery health prediction on hybrid vehicles using machine learning approach
    Authors: S. Jafari, Z. Shahbazi, Y.C. Byun
    Year: 2022
    Citations: 36

Raoudha Ben Djemaa | Computer science | Best Scholar Award

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Prof. Raoudha Ben Djemaa | Computer science | Best Scholar Award

ISITCOM, university of sousse, Tunisia

Raoudha Ben Djemaa, born on March 6, 1976, in Sfax, Tunisia, is a prominent computer science educator and researcher. She is currently a Maître de Conférences (Associate Professor) at the Department of Networks and Multimedia, ISITCOM, University of Sousse, Tunisia. She has extensive experience in computer science education and research, particularly in the areas of web service adaptation, cloud computing, and context-aware systems. Throughout her career, she has also been dedicated to guiding students at various academic levels and contributing to international conferences and journals. 📚💻

Profile

Google Scholar

Education

Raoudha Ben Djemaa’s educational journey began with her Baccalaureate in Experimental Sciences from Lycée secondaire 15 novembre 1959, Sfax, Tunisia, in 1994. She completed her Maîtrise in Computer Science from the Faculty of Economic Sciences and Management of Sfax in 1998 with honors. She later obtained a Master’s degree in Information Systems and New Technologies in 2004 (with distinction, major of her class). She earned her PhD in Computer Science in 2009, with the highest distinction, under the supervision of Prof. Abdelmajid Ben Hamadou. In 2019, she completed her Habilitation Universitaire in Computer Science at the same faculty. 🎓

Experience

Raoudha has held various teaching positions over the years. She has been a Maître de Conférences at ISITCOM since 2020, where she has contributed to the development of curricula in the areas of distributed systems and web programming. Previously, she served as a Maître Assistante (Assistant Professor) and an assistant in several Tunisian institutions. Her earlier career includes teaching secondary school mathematics and computer science. She has also supervised numerous PhD and master’s students, demonstrating her leadership in academic mentorship. 👩‍🏫

Research Interests

Raoudha’s primary research interests include context-sensitive systems, adaptation in web applications, cloud computing, and pervasive computing. She is particularly focused on enhancing web services through semantic similarity measures and self-adaptation techniques for distributed systems. Her work often integrates cloud technologies and the Internet of Things (IoT), with an emphasis on the development of efficient middleware solutions for self-adaptive systems. Her research aims to create smarter, more responsive computing environments. 🌐🔍

Awards

Raoudha has been recognized for her outstanding contributions to computer science education and research. Notably, she has received the distinction of leading several successful doctoral and master’s research projects. Her research on cloud service discovery and self-adaptation in web services has been published in high-impact journals and has garnered international attention. 🏆

Publications Top Notes

Raoudha Ben Djemaa has published several significant articles in prominent journals. Some of her notable publications include:

Finding Internet of Things Resources: A State-of-the-Art Study, Data & Knowledge Engineering, 2022, DOI: 10.1016/j.datak.2022.102025.

Description, Discovery, and Recommendation of Cloud Services: A Survey, Service Oriented Computing and Applications, 2022.

Cloud Services Description Ontology Used for Service Selection, Service Oriented Computing and Applications, 2022.

A Survey of Middlewares for Self-Adaptation and Context-Aware in Cloud of Things Environment, Procedia Computer Science, 2022, DOI: 10.1016/j.procs.2022.09.338.

Enhanced Semantic Similarity Measure Based on Two-Level Retrieval Model, Journal of Concurrency and Computation: Practice and Experience, 2019.

Reflective Approach to Improve Self-Adaptation of Web Service Compositions, International Journal of Pervasive Computing and Communication, 2019.

Efficient Cloud Service Discovery Approach Based on LDA Topic Modeling, Journal of Systems and Software, 2018.

Arturo Benayas Ayuso | Computer Science | Best Researcher Award

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Prof. Arturo Benayas Ayuso | Computer Science | Best Researcher Award

PhD Candidate, Universidad Politécnica de Madrid, Spain

Arturo Benayas Ayuso is a highly skilled Naval Architect with a distinguished career in naval shipbuilding and digital transformation. He currently leads the integration of the “El Cano” platform at NAVANTIA, spearheading Industry 4.0 innovations in ship design, construction, and management. His expertise in integrating PLM systems and IoT into shipbuilding projects has positioned him as a leader in naval digitization. Fluent in multiple languages, Arturo also serves as a lecturer, sharing his knowledge of statistics at Universidad Complutense de Madrid. 🚢💡

Publication Profile

ORCID

Education

Arturo holds a Master’s in Naval Architecture from Universidad Politécnica de Madrid and is currently pursuing a PhD, focusing on IoT applications in ship design, shipbuilding, and management. His academic background, combined with his professional experience, allows him to seamlessly bridge the gap between theory and practice in the maritime industry. 🎓📚

Experience

As the Integration Lead of NAVANTIA’s “El Cano” platform, Arturo manages the digitization and PLM integration of naval shipbuilding processes. His past roles include overseeing the FORAN-PLM integration for Spain’s S80 submarine and collaborating on several high-profile naval projects, including the Royal Navy’s CVF program. His work has consistently focused on improving digital workflows in naval engineering using systems like Windchill and Teamcenter PLM. 🛠️⚙️

Research Focus

Arturo’s research revolves around applying IoT technology to ship design and manufacturing. His work aims to enhance the efficiency of shipbuilding processes by integrating advanced digital tools and IoT into ship management systems. This focus on Industry 4.0 in naval architecture ensures future-ready solutions in naval engineering. 🔍🌐

Awards and Honors

Arturo has contributed significantly to both industry and academia, sharing his insights at conferences like RINA and publishing in prestigious industry magazines. His thought leadership in naval shipbuilding and PLM system integration has earned him recognition within the maritime and technology sectors. 🏅📜

Publications

Integrated Development Environment in Shipbuilding Computer Systems – ICAS 2011, cited in studies related to shipbuilding digitization

Automated/Controlled Storage for an Efficient MBOM Process in Shipbuilding Managing IoT Technology – RINA, 2018, discussed in articles on smart ship management

Data Management for Smart Ship: Reducing Machine Learning Cost in IoS Applications – RINA, 2018, frequently referenced in works on IoT and machine learning integration

Ali Reza ALAEI | Computer Science | Interdisciplinary Research Excellence Award

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Assist Prof Dr. Ali Reza ALAEI | Computer Science | Interdisciplinary Research Excellence Award

Faculty of Science and Engineering at Southern Cross University, Australia

Dr. Ali Reza Alaei is a PhD graduate specializing in computer science, focusing on Big Data analysis, sentiment extraction, image processing, and biometric systems. With a strong research background and extensive teaching experience, he is currently a Senior Lecturer at Southern Cross University, where he aims to lead impactful research projects and academic initiatives.

Profile 

Scopus profile

Education 🎓

Dr. Alaei obtained his PhD in Computer Science from the University of Mysore, India, in 2012, where his thesis focused on the “Automatic Segmentation of Persian Handwritten Texts Enabling Accurate Recognition.” He also earned a Master’s degree in Computer Science from the same institution in 2007, where he researched the “Recognition of Persian/Arabic Numerals Using Feature Reduction and Distance Measure.”

Experience 🧑‍🏫

With over 20 years of academic experience, Dr. Alaei has held various positions, including Senior Lecturer at Southern Cross University since January 2023 and Lecturer at the same institution from October 2018 to December 2022. His previous roles include Research Fellow at Griffith University, Postdoctoral Research Fellow at LI-RFAI in France, and PhD Scholar at the University of Mysore. His career has been marked by significant contributions to both teaching and research.

Research Interests 🔍

Dr. Alaei’s research interests encompass Big Data analysis, statistical data modeling, human perception modeling, image processing, document image analysis and recognition, and biometric authentication. He aspires to further explore sentiment analysis, human perception understanding, and intelligent technologies through machine learning and vision applications.

Awards 🏆

Dr. Alaei has received several academic honors, including ranking 113th in the national examination of Iranian Universities for B.Sc. entrance and achieving the second rank in his M.Sc. program. He was awarded the best paper award at the International Conference on Cognition and Recognition in 2008 and received accolades for his outstanding performance as a graduate student in India.

Publications 📚

Dr. Alaei has an extensive publication record with 29 journal articles, 39 conference papers, and a total of 70 publications. Some notable peer-reviewed articles include:

  1. Document Image Quality Assessment: A Survey – ACM Computing Survey, 2024. Cited by: 2432.
  2. Review of age and gender detection methods based on handwriting analysis – Neural Computing & Applications, 2023.
  3. Sentiment analysis in tourism: Capitalising on Big Data – Journal of Travel Research, 2019. Cited by: 564.
  4. Revisiting Tourism Destination Image: A Holistic Measurement Framework Using Big Data – Journal of Travel Research, 2022.

Conclusion ✅

Dr. Ali Reza Alaei is an accomplished researcher and educator, dedicated to advancing the fields of Big Data analysis, image processing, and biometrics. With a robust track record of research and teaching, he continues to contribute significantly to academia and the broader scientific community.

Yanming Zhao | Computer Science | Best Researcher Award

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Prof. Yanming Zhao | Computer Science | Best Researcher Award

Professor at Hebei MINZU Normal University, China

Yanming Zhao is a distinguished Professor at Hebei University of Nationalities, specializing in visual computing and deep neural networks. With a commitment to advancing technology and innovation, he has made significant contributions to the field of computer application technology, evidenced by his extensive research and numerous publications. 🌟

Profile 

Scopus Profile

Education🎓

Yanming graduated with a Master’s degree in Computer Application Technology from the School of Information at Shenyang University of Technology in 2010. His academic background laid a solid foundation for his future research endeavors and leadership in academia.

Experience🏛️💼

As a Master’s Supervisor and experienced researcher, Professor Zhao has participated in over nine provincial-level research projects and has consulted on over 500 industry projects. His work not only showcases his expertise but also his dedication to bridging the gap between academia and industry.

Research Interests🔬📈

Professor Zhao’s research primarily focuses on visual computing and deep neural networks. He has developed innovative algorithms, including the visual selectivity-based 3D graph convolutional algorithm (VS-3DGCN), aimed at enhancing point cloud segmentation performance and addressing key challenges in 3D graph convolutional algorithms.

Awards 🏆

Throughout his career, Yanming has received numerous accolades, including the title of Excellent Scientific and Technological Worker in Hebei Province and Outstanding Expert Managed by Chengde City. These awards reflect his significant contributions to the scientific community and his leadership in research.

Publications

Professor Zhao has published more than 30 academic papers in esteemed journals, such as:

  • Multi-channel depth segmentation network based on 3D graph convolution algorithm and its application in point cloud segmentation
    • Authors: Zhao, Y.
    • Journal: Alexandria Engineering Journal
    • Year: 2024
    • Citations: 0
  • The Multi-View Deep Visual Adaptive Graph Convolution Network and Its Application in Point Cloud
    • Authors: Fan, H., Zhao, Y., Su, G., Zhao, T., Jin, S.
    • Journal: Traitement du Signal
    • Year: 2023
    • Citations: 4
  • Graph Convolution Algorithm Based on Visual Selectivity and Point Cloud Analysis Application
    • Authors: Zhao, Y., Su, G., Yang, H., Jin, S., Yang, J.
    • Journal: Traitement du Signal
    • Year: 2022
    • Citations: 2
  • Slow Feature Extraction Algorithm Based on Visual Selection Consistency Continuity and Its Application
    • Authors: Yang, H., Zhao, Y., Su, G., Fan, H., Shang, Y.
    • Journal: Traitement du Signal
    • Year: 2021
    • Citations: 0
  • Design and application of a slow feature algorithm coupling visual selectivity and multiple long short-term memory networks
    • Authors: Zhao, Y., Yang, H., Su, G.
    • Journal: Traitement du Signal
    • Year: 2021
    • Citations: 1

These contributions have garnered a total citation index of 102 times, illustrating the impact of his work on the research community. 📚🔗

Conclusion🌍✨

In summary, Professor Yanming Zhao stands out as a leading figure in the fields of visual computing and deep learning. His extensive research, numerous publications, and accolades make him a deserving candidate for the Best Researcher Award. His ongoing commitment to innovation and excellence continues to inspire colleagues and students alike.

Osama Sohaib | Information Systems | Best Researcher Award

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Dr. Osama Sohaib | Information Systems | Best Researcher Award

Associate Professor, American University of Ras Al Khaimah, United Arab Emirates

Dr. Osama Sohaib is an Associate Professor of Business Analytics at the American University of Ras al Khaimah, UAE. He holds a Ph.D. in Information Systems from the University of Technology Sydney, Australia. With over 15 years of teaching experience, Dr. Sohaib is dedicated to educating and mentoring undergraduate and postgraduate students in information systems, focusing on the intersection of technology and business. 🌍📚

Publication Profile

Google Scholar

Education

Dr. Sohaib earned his Ph.D. in Information Systems in 2015 from the University of Technology Sydney, Australia. He is currently pursuing a Master of Business Analytics at the University of Queensland and holds a Graduate Certificate in Applied Artificial Intelligence from Charles Sturt University. His academic journey also includes a Master of Science in Computer Science, a Postgraduate Diploma in Information Management, and a Bachelor of Science in Software Development. 🎓📖

Experience

With over 15 years of experience in academia, Dr. Sohaib has held various positions, including Associate Professor at the American University of Ras al Khaimah and Lecturer at the University of Technology Sydney. He has also taught at Macquarie University and the University of New South Wales. His roles have included supervising research students, coordinating academic programs, and contributing to funded projects in business information systems. 💼👨‍🏫

Research Focus

Dr. Sohaib’s research interests encompass business information systems, e-services, digital privacy, digital transformation, business intelligence, decision-making, and applied machine learning. His work aims to enhance service effectiveness across various sectors, including digital business, healthcare, education, and government, with a strong emphasis on the ethical and societal implications of technology. 💡🔍

Awards and Honors

Dr. Sohaib has received multiple accolades, including the “Research of the Year” award from the School of Business at AURAK for his exceptional research contributions in 2023 and 2024. He was also honored with the “Best Paper Award” at the 25th International Conference on Information Systems Development in 2016 for his work on web content accessibility. 🏆🌟

Publication Top Notes

Assessing Web Content Accessibility of E-Commerce Websites for People with Disabilities
Best Paper Award, 2016
Link to Publication | 2016 | Journal of Information Systems Development | Cited by: 120

Digital Privacy in the Age of Big Data and Machine Learning: People’s Expectations and Experiences
Link to Publication | 2022 | International Journal of Information Management | Cited by: 85

Factors Influencing Continuance Intention in Augmented Reality Platforms
Link to Publication | 2023 | Journal of Business Research | Cited by: 45

Opportunities and Challenges in the Implementation of AI in Accounting and Auditing Software
Link to Publication | 2024 | International Journal of Accounting Information Systems | Cited by: 10

The Effect of Individual’s Technological Belief and Usage on their Absorptive Capacity towards their Learning Behaviour in the Learning Environment
Link to Publication | 2020 | Computers & Education | Cited by: 30

Alex Mirugwe | Computer Science | Young Scientist Award

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Mr. Alex Mirugwe | Computer Science | Young Scientist Award

Data Scientist at Makerere University, School of Public Health, Uganda

Alex Mirugwe is a highly skilled Data Scientist with over 4 years of experience, specializing in applying machine learning and AI to healthcare challenges, particularly in HIV, cancer, and tuberculosis diagnostics. He has a proven track record of developing data-driven solutions that improve patient outcomes in resource-constrained settings. His research has been published in several peer-reviewed journals, and he is proficient in a wide range of data science tools and methodologies. Alex also contributes to academia as an Assistant Lecturer and is involved in curriculum development and student mentoring in computer science.

Profile:

Strengths for the Award:

  1. Specialized Expertise in Healthcare Data Science: Alex Mirugwe has developed machine learning models and AI tools to solve critical health challenges, such as HIV patient care and cervical cancer detection. His work is not only technically sound but has made tangible impacts on healthcare delivery in resource-constrained environments.
  2. Research Contributions and Publications: Alex has authored multiple peer-reviewed journal articles on healthcare applications of AI, including sentiment analysis of public health data, tuberculosis detection, and cancer screening. These publications demonstrate his commitment to advancing the application of AI in public health and data science.
  3. Experience in Machine Learning and AI: His technical expertise spans a range of relevant tools and techniques, including deep learning, transfer learning, and predictive modeling, which are crucial for impactful healthcare interventions. His experience in both teaching and research also ensures that his knowledge is applied and shared within the academic community.
  4. Proven Success in Real-World Applications: Alex’s work on reducing HIV patient data duplication, predicting HIV patient outcomes, and improving cervical cancer screening speaks to his practical problem-solving skills in high-stakes environments. The use of AI to improve healthcare decision-making is well-aligned with global trends toward technology-driven health solutions.
  5. Cross-Disciplinary and Global Approach: Alex’s education, spanning institutions in Uganda and South Africa, and his research interests in global health issues, reflect his broad outlook. His involvement with international collaborators highlights his ability to bridge different disciplines and apply his knowledge across borders.

Areas for Improvement:

  1. More Diverse Research Focus: While Alex has concentrated on significant healthcare issues, expanding his research beyond HIV, cancer, and tuberculosis may enhance his portfolio. Including more work in diverse fields, such as environmental health or genomics, would add breadth to his achievements.
  2. Leadership in Research Projects: Alex has demonstrated technical prowess and teaching capabilities, but more emphasis on leadership roles in large-scale research projects or interdisciplinary initiatives could elevate his profile. Leading a significant multi-institutional study or directing larger research teams may help solidify his standing.
  3. Policy and Implementation Impact: Though Alex has made practical contributions, more evidence of his work leading to large-scale policy changes or national-level healthcare implementations could further strengthen his application. This would demonstrate how his AI models or algorithms scale to influence public health strategies at a systemic level.
  4. International Research Collaborations: Although his work is impactful within Uganda, expanding collaborations with more international research institutes or global health organizations could further enhance his visibility and contribution to global health initiatives.

 

Education:

Alex Mirugwe holds an MSc in Data Science from the University of Cape Town, South Africa, completed in 2021, where he conducted research on automated bird detection using machine learning. His academic performance was strong, with a GPA of 74.52%. Prior to this, he earned a BSc in Computer Engineering from Makerere University, Uganda, in 2019, graduating with a CGPA of 4.18/5.0. His undergraduate dissertation focused on developing a low-cost wireless TV audio transceiver, reflecting his early interest in applying engineering principles to real-world problems. His educational background combines technical proficiency in computer science with a strong emphasis on data science and machine learning applications.

Experience:

Alex Mirugwe is a highly skilled data scientist with over four years of experience applying machine learning and AI to healthcare challenges, particularly in diagnosing HIV, cancer, and tuberculosis. He has successfully developed predictive models to improve patient care and outcomes in resource-limited settings, such as creating algorithms for cervical cancer screening and reducing HIV patient data duplication. His work spans both practical implementation and academic research, with multiple publications on AI-driven health interventions. In addition to his research, Alex is an experienced educator, teaching data science and machine learning courses at the university level.

Research Focus:

Alex Mirugwe’s research focuses on leveraging data science and machine learning to address critical healthcare challenges, particularly in resource-constrained settings. His work encompasses developing predictive models for patient care in HIV treatment, enhancing cervical cancer screening accuracy through AI algorithms, and analyzing public sentiment during health crises, such as the Ebola outbreak. Additionally, he explores various applications of AI in public health, including improving tuberculosis detection and reducing data duplication in electronic medical records. Overall, his research aims to harness advanced data analytics to improve patient outcomes and inform public health strategies, making significant contributions to the field of healthcare data science.

Publications Top Notes:

  • Automating Bird Detection Based on Webcam Captured Images Using Deep Learning
    • Authors: A. Mirugwe, J. Nyirenda, E. Dufourq
    • Year: 2022
    • Citations: Not specified in the provided information.
  • Restaurant Tipping Linear Regression Model
    • Author: A. Mirugwe
    • Year: 2020
    • Citations: Not specified in the provided information.
    • Link: SSRNPaper
  • Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers
    • Authors: A. Mirugwe, C. Ashaba, A. Namale, E. Akello, E. Bichetero, E. Kansiime, J. Nyirenda
    • Year: 2024
    • Citations: Not specified in the provided information.
    • Journal: Life, 14(6), 708.
  • Adoption of Artificial Intelligence in the Ugandan Health Sector: A Review of Literature
    • Author: A. Mirugwe
    • Year: 2024
    • Citations: Not specified in the provided information.
    • Link: Available at SSRN 4735326.

Conclusion:

Alex Mirugwe presents an impressive and well-rounded portfolio, with extensive experience in applying machine learning and AI to tackle critical healthcare challenges. His achievements, particularly in HIV care and cancer screening, demonstrate his ability to leverage data science for real-world health outcomes. While he has a strong research and technical background, focusing on leadership, broadening his research scope, and contributing to systemic policy changes could bolster his case further. He is a strong candidate for the Best Researcher Award, especially within the domain of AI-driven healthcare solutions in resource-constrained settings.

Miin-Shen Yang | Computer Science | Best Researcher Award

Published on by World Top Scientists

Prof Dr. Miin-Shen Yang | Computer Science | Best Researcher Award

Distinguished Professor,Chung Yuan Christian University, Taiwan

👨‍🏫 Miin-Shen Yang is a distinguished scholar and professor specializing in applied mathematics and artificial intelligence. He has made significant contributions to fuzzy clustering, machine learning, and soft computing. Currently serving as a Life Distinguished Professor at Chung Yuan Christian University (CYCU), Taiwan, Dr. Yang is highly regarded in the scientific community for his innovative research. He is also recognized among the top 0.5% of scholars globally by ScholarGPS and Stanford University’s Top 2% Scientists.

Publication Profile

ORCID

Strengths for the Award:

  1. Extensive Academic Background: Miin-Shen Yang has earned degrees in mathematics and statistics from prestigious institutions, with a Ph.D. from the University of South Carolina, USA. His long-standing association with Chung Yuan Christian University (CYCU), Taiwan, adds to his academic credibility.
  2. Research Impact: His research areas—statistics, clustering algorithms, fuzzy clustering, soft computing, pattern recognition, and machine learning—are crucial in modern scientific and technological advancements, especially in the AI-driven era.
  3. Global Recognition: Miin-Shen Yang’s inclusion in Stanford University’s Top 2% Scientists and ScholarGPS’s global top 0.5% demonstrates the international recognition of his work and significant contributions to artificial intelligence, image processing, and related fields.
  4. Editorial Roles: He served as an Associate Editor for IEEE Transactions on Fuzzy Systems and remains on the Editorial Board of Electronics (MDPI). These roles show his influence in shaping scientific discourse in his fields of expertise.
  5. Leadership in Academia: As a Distinguished Professor and previous Chairperson and Dean of the College of Science at CYCU, he has demonstrated not only research expertise but also leadership in academic governance.

Areas for Improvement:

  1. Broader Collaborations: While Miin-Shen Yang’s contributions are notable in the fields of applied mathematics and artificial intelligence, there could be a stronger emphasis on collaborative projects across interdisciplinary fields such as biostatistics or environmental data science, which are becoming increasingly critical for global research challenges.
  2. Applied Research and Industry Connections: Strengthening connections between his academic research and real-world industrial applications could further enhance the societal impact of his work, especially in sectors like healthcare, energy, or environmental sustainability where AI and machine learning are emerging as transformative tools.
  3. Public Engagement and Outreach: Additional efforts to disseminate his research through public engagement activities, workshops, or conferences that target both academic and non-academic audiences could raise the visibility and practical applicability of his findings.

Education

🎓 Miin-Shen Yang holds a B.S. in Mathematics from Chung Yuan Christian University (1977), an M.S. in Applied Mathematics from National Chiao-Tung University (1980), and a Ph.D. in Statistics from the University of South Carolina, Columbia, USA (1989).

Experience

💼 Dr. Yang joined CYCU in 1989 and became a Professor in 1994. He has held several key positions, including Department Chair, Director of the Chaplain’s Office, and Dean of the College of Science. He also served as a Visiting Professor at the University of Washington from 1997 to 1998.

Research Focus

🔬 Dr. Yang’s research interests span applications of statistics, fuzzy clustering, machine learning, soft computing, pattern recognition, and artificial intelligence. His contributions have significantly advanced clustering algorithms and AI-related technologies.

Awards and Honors

🏅 Dr. Yang has been recognized among Stanford University’s Top 2% Scientists and listed among ScholarGPS global top 0.5% scholars. He has also served as an Associate Editor for IEEE Transactions on Fuzzy Systems and is currently an Editorial Board Member for the journal Electronics.

Publications (Top Notes)

📚 Dr. Yang has published extensively on fuzzy clustering and artificial intelligence in leading journals. His works have been widely cited, marking his influence in the field.

“Fuzzy Clustering Algorithms and Applications” – Published in 2015 in Pattern Recognition Letters. Cited by 100+ articles

Conclusion:

Miin-Shen Yang is an exceptional candidate for the Research for Best Research Award, with a strong and diversified research portfolio in applied mathematics, artificial intelligence, and machine learning. His global recognition, academic leadership, and editorial contributions demonstrate his significant impact on the scientific community. While further strengthening his research collaborations across broader disciplines and emphasizing real-world applications could enhance his overall impact, his current achievements make him a highly competitive and deserving nominee for the award.