Zhaozhen Jiang | Computer Science | Best Research Article Award

Published on by NESIN Awards

Dr. Zhaozhen Jiang | Computer Science | Best Research Article Award

Assistant Researcher | Naval Submarine Academy | China

Dr. Zhaozhen Jiang is a distinguished researcher at the Navy Submarine Academy in Qingdao, China, specializing in intelligent systems, maritime navigation, and dynamic target search. His research focuses on the development of advanced path-planning algorithms and neural network–based optimization techniques for complex maritime environments. He has published extensively and collaborated widely with researchers across multiple disciplines, reflecting a strong commitment to interdisciplinary innovation. His recent work on GBNN-based maritime dynamic target search demonstrates a focus on enhancing operational decision-making and situational awareness in challenging naval contexts. Through his research, he aims to advance autonomous maritime systems and contribute to safer, more efficient naval operations, while fostering technological progress with meaningful societal impact.

Citation Metrics (Scopus)

40
30
20
10
0

Citations

37

Documents

15

h-index

4

Citations

Documents

h-index

View Scopus Profile

Featured Publications

Sarbajit Paul Bappy | Computer Science | Research Excellence Award

Published on by NESIN Awards

Mr. Sarbajit Paul Bappy | Computer Science | Research Excellence Award

Teaching Assistant | Daffodil International University | Bangladesh

Sarbajit Paul Bappy is an emerging researcher in computer science with a growing focus on applied machine learning, medical image analysis, and agricultural informatics. He is currently serving as a Teaching Assistant in the Department of Computer Science and Engineering at Daffodil International University, Bangladesh, where he has been contributing to academic instruction and research support since 2025. Alongside his professional role, he is pursuing his undergraduate degree in Computer Science and Engineering at the same institution, demonstrating a strong integration of academic excellence and early-career research productivity. His scholarly work includes peer-reviewed publications and openly accessible datasets that address critical challenges in healthcare diagnostics and smart agriculture. Notably, he co-authored SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images (MAKE, 2025), which combines deep learning with explainable AI techniques to enhance early disease detection. He also contributed significantly to the dataset Jackfruit AgroVision, a comprehensive benchmark for disease detection in jackfruit and its leaves, supporting advancements in precision agriculture and food-security research. His collaborations span multidisciplinary teams involving experts such as Amir Sohel, Rittik Chandra Das Turjy, Md Assaduzzaman, Ahmed Al Marouf, Jon George Rokne, and Reda Alhajj, illustrating his ability to contribute within diverse international research groups. Through his ongoing work in AI-driven health diagnostics, dataset development, and sustainable agricultural technology, Bappy aims to advance research that supports societal well-being, improves disease detection accuracy, and contributes to innovation within global machine learning communities.

Profiles: Google Scholar | ORCID | LinkedIn

Featured Publications

1. Sohel, A., Turjy, R. C. D., Bappy, S. P., Assaduzzaman, M., Marouf, A. A., Rokne, J. G., & Alhajj, R. (2025). SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images. Machine Learning and Knowledge Extraction, 7(4), 157. https://doi.org/10.3390/make7040157  MDPI+1

2. Sohel, A., Bijoy, M. H. I., Turjy, R. C. D., & Bappy, S. P. (2025). Jackfruit AgroVision: A Extensive Dataset for Jackfruit Disease and Leaf Disease Detection using Machine Learning. Mendeley Data. https://doi.org/10.17632/pt647jfn52.1

Mona Almutairi | Artificial Intelligence | Best Researcher Award

Published on by NESIN Awards

Ms. Mona Almutairi | Artificial Intelligence | Best Researcher Award

Shaqra University | Saudi Arabia

Ms. Mona Almutairi is a highly motivated computer science graduate with a strong academic foundation and practical experience in system engineering and data management. She completed her Bachelor’s degree in Computer Science from Shaqra University in 2019 with an impressive GPA of 4.19 out of 5, demonstrating consistent academic excellence. Her professional experience includes serving as a System Engineer at the Ministry of Economy and Planning, where she contributed to optimizing systems operations and enhancing digital workflows, as well as volunteering as a Data Entry Assistant at the Ministry of Health, where she efficiently managed and organized large datasets with accuracy and confidentiality. She further enriched her technical expertise through professional courses in Software Engineering from the Saudi Digital Academy and Web Development from the Ministry of Communications and Information Technology, equipping her with up-to-date industry knowledge and coding proficiency. Her research interests lie in software development, data analysis, and emerging technologies that integrate innovation with societal advancement. Ms. Almutairi’s research skills include proficiency in data analysis tools, problem-solving, and the ability to apply algorithmic thinking to real-world challenges. She is also adept at using Microsoft Office and has strong communication, teamwork, and adaptability skills, making her a collaborative and reliable professional. Her dedication to learning and excellence has been recognized through various academic and professional achievements, reflecting her commitment to continuous improvement. Overall, Ms. Almutairi is a forward-thinking computer scientist who combines technical knowledge, analytical capabilities, and professional experience to drive innovation in the field of information technology.

Profiles: Google Scholar | ORCID

Featured Publications

Almutairi, M., & Dardouri, S. (2025). Intelligent hybrid modeling for heart disease prediction. Information, 16(10), 869. Citations: 1

Mohammed Alenazi | Computer Engineering | Best Researcher Award

Published on by NESIN Awards

Mr. Mohammed Alenazi | Computer Engineering | Best Researcher Award

Assistant Professor | University of Tabuk | Saudi Arabia

Mr. Mohammed M. Alenazi is an accomplished academic and researcher with expertise in electrical and electronics engineering, computer engineering, and artificial intelligence applications in energy-efficient networks. He earned his Ph.D. in Electrical and Electronics Engineering from the University of Leeds, UK (2018–2022), focusing on energy efficiency in AI-powered communication systems. Prior to this, he completed his M.Eng. in Computer Engineering at Florida Institute of Technology, USA (2016–2017), and a B.Eng. in Computer Engineering from University Sultan Bin Fahad (2007–2011), along with an Associate’s degree in Electrical/Electronics Equipment Installation and Repair from Tabuk College of Technology (2002–2004). Professionally, Mr. Alenazi began his career as a Senior Engineer at Saudi Telecom Company (2006–2011), where he gained practical experience in optical fiber networks, before transitioning to academia as a Teaching Assistant at Northern Border University (2012–2013) and later at the University of Tabuk, where he continues to serve since 2013, eventually advancing into an assistant professorship. His research interests include machine learning, IoT networks, energy optimization, and intelligent systems, with key contributions in developing models for energy-efficient ML-based service placement, neural network embedding in IoT, and intelligent sterilization systems, reflected in several IEEE and Scopus-indexed publications. In addition to publications, he has contributed innovative patents, such as systems for vehicle communication during accidents. His research skills encompass advanced AI modeling, simulation of communication networks, and interdisciplinary problem-solving in sustainable technologies. Mr. Alenazi is an active member of IEEE, AAAI (USA), AISB (UK), PMI, and the Saudi Council of Engineers, and he holds prestigious certifications including CCNA, CompTIA Security+ CE, and PMP. He has consistently demonstrated leadership in academia and professional communities, bridging industry and research while mentoring students. With a growing academic profile of 28 citations, 7 documents, and an h-index of 3, he is well-positioned for continued impact and recognition in his field.

Profiles: Google Scholar | Scopus | ORCID  | ResearchGate

Featured Publications

  1. Alenazi, M. M., Yosuf, B. A., El-Gorashi, T., & Elmirghani, J. M. H. (2020). Energy efficient neural network embedding in IoT over passive optical networks. 2020 22nd International Conference on Transparent Optical Networks (ICTON), 1–6. Cited by: 13

  2. Yosuf, B. A., Mohamed, S. H., Alenazi, M. M., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient AI over a virtualized cloud fog network. Proceedings of the Twelfth ACM International Conference on Future Energy Systems. Cited by: 11

  3. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient distributed machine learning in cloud fog networks. 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), 935–941. Cited by: 9

  4. Banga, A. S., Alenazi, M. M., Innab, N., Alohali, M., Alhomayani, F. M., Algarni, M. H., & others. (2024). Remote cardiac system monitoring using 6G-IoT communication and deep learning. Wireless Personal Communications, 136(1), 123–142. Cited by: 4

  5. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2022). Energy efficient placement of ML-based services in IoT networks. 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). Cited by: 4

Trong Nhan Nguyen | Artificial Intelligence | Best Researcher Award

Published on by NESIN Awards

Mr. Trong Nhan Nguyen | Artificial Intelligence | Best Researcher Award

Biomedical Engineering at Pukyong National University, South Korea

Nhan T. Nguyen, a Master’s student at Pukyong National University, is a promising early-career researcher specializing in biomedical engineering, computer vision, and artificial intelligence. His research focuses on non-destructive testing, low-level vision, and automated inspection systems using advanced AI techniques such as GANs, transformers, and diffusion models. Nhan has contributed to multiple peer-reviewed publications in prestigious journals like IEEE Transactions and MDPI Applied Sciences, with additional manuscripts under review and in preparation. His work demonstrates strong practical relevance, with AI models deployed in industrial applications including semiconductor inspection, robotic automation, and smart city infrastructure. He has received several academic honors and awards, reflecting his dedication and innovation. Despite being at the master’s level, he serves as a peer reviewer for international journals and conferences, highlighting his scholarly maturity. With interdisciplinary expertise, a growing publication record, and impactful real-world applications, Nhan is highly suitable for the Best Researcher Award in the early-career category.

Professional Profile 

Education🎓

Nhan T. Nguyen has built a strong educational foundation in engineering and artificial intelligence across reputable institutions in Vietnam and South Korea. He earned his Bachelor of Science degree in Information Technology Engineering from Ho Chi Minh University of Technology, where he was actively involved in undergraduate research and received multiple academic awards and scholarships. During his undergraduate years, he developed projects integrating AI with OCR and chatbot systems. Currently, he is pursuing a Master’s degree in the Industry 4.0 Convergence Bionics Engineering program at Pukyong National University in South Korea under the supervision of Professor Junghwan Oh. His graduate research focuses on non-destructive testing, specifically in scanning acoustic microscopy systems, and applying AI to industrial inspection tasks. Through this academic journey, Nhan has gained in-depth knowledge and hands-on experience in computer vision, machine learning, and robotics, forming a strong educational background that supports his innovative contributions to research and industry applications.

Professional Experience📝

Nhan T. Nguyen has gained diverse professional experience in the fields of artificial intelligence, computer vision, and industrial automation. He served as an AI Engineer at the Artificial Intelligence Center of FPT Software in Vietnam, where he worked on optimizing dehumidification processes for the Chicago Art Museum and enhancing defect detection in steel production using machine learning algorithms. His role involved data analysis, predictive modeling, and AI deployment in real-world environments. He also contributed to a deep learning-based search engine enhancement project for a pharmaceutical retail company. In addition, at FPT Information System’s Smart City Department, he developed camera-based systems for sidewalk encroachment detection, which were integrated into Ho Chi Minh City’s traffic management system. Currently, as a Graduate Research Assistant at Pukyong National University, he is involved in automating weld inspection systems and developing AI models for defect detection in scanning acoustic microscopy. His experience bridges academic research and practical industrial implementation.

Research Interest🔎

Nhan T. Nguyen’s research interests lie at the intersection of artificial intelligence, computer vision, and industrial automation, with a particular focus on low-level vision tasks and non-destructive testing. He is passionate about developing advanced AI models such as Generative Adversarial Networks (GANs), transformers, and diffusion models for applications in image restoration, super-resolution, and defect detection. His work emphasizes enhancing the performance and reliability of automated inspection systems used in semiconductor manufacturing, steel production, and other industrial settings. Nhan is also interested in integrating AI with robotic systems, using tools like 3D scanners, lasers, and cameras to automate surface inspection processes. Additionally, he explores exploratory data analysis across multiple domains, including medical, environmental, and industrial datasets. His goal is to bridge the gap between theoretical research and practical implementation, contributing to more intelligent, accurate, and efficient inspection and monitoring systems in smart manufacturing and biomedical engineering environments.

Award and Honor🏆

Nhan T. Nguyen has received numerous awards and honors in recognition of his academic excellence, innovative research, and technical achievements. He was awarded a scholarship by Pukyong National University in 2023 for his outstanding performance as a graduate student. During his undergraduate studies at Ho Chi Minh University of Technology, he received the prestigious KMS Technology Scholarship in 2022, as well as the City Now Company Scholarship and the Impressive Award in the HUTECT Start-up Wing competition in 2021. He also earned a Consolation Prize in the university’s AI Hackathon in 2020 and was recognized for his undergraduate research contributions. Nhan consistently demonstrated academic excellence, earning the Outstanding Undergraduate Student Scholarship in 2018. These honors reflect his dedication to research, creativity in problem-solving, and strong commitment to applying AI technologies to real-world challenges. His consistent recognition throughout his academic career underscores his potential as a leading researcher in his field.

Research Skill🔬

Nhan T. Nguyen possesses a robust set of research skills that span artificial intelligence, computer vision, and industrial automation. He is highly proficient in data processing, exploratory data analysis, and model development using Python and advanced machine learning frameworks. His expertise includes designing and implementing deep learning models, particularly using Generative Adversarial Networks (GANs), transformers, and diffusion models for image super-resolution, denoising, and defect detection. Nhan is skilled in integrating AI models with hardware systems such as robotic arms, 3D scanners, lasers, and industrial cameras to build intelligent inspection systems. He has hands-on experience with non-destructive testing methods, particularly scanning acoustic microscopy, and is adept at handling real-world industrial datasets. Additionally, Nhan is capable of deploying AI solutions into operational environments, enhancing automation processes in sectors like semiconductor manufacturing, smart cities, and healthcare. His ability to bridge theoretical models with practical applications showcases his strong technical and problem-solving capabilities as a researcher.

Conclusion💡

Nhan T. Nguyen demonstrates exceptional promise and proven capability in applied AI and biomedical inspection research, with practical impact, strong publications, and academic service. For a master’s-level researcher, this profile is outstanding.

Publications Top Noted✍️

📄 1. GAN-Based Super-Resolution in Linear R-SAM Imaging for Enhanced Non-Destructive Semiconductor Measurement

  • Authors: Thi Thu Ha Vu, Tan Hung Vo, Trong Nhan Nguyen, Jaeyeop Choi, Le Hai Tran, Vu Hoang Minh Doan, Van Bang Nguyen, Wonjo Lee, Sudip Mondal, Junghwan Oh

  • Year: 2025

  • Citation (DOI): 10.3390/app15126780

  • Source: Applied Sciences, Published on June 17, 2025

📄 2. Transformer-Based Super-Resolution Scanning Acoustic Imaging for Industrial Inspection

  • Authors: Trong Nhan Nguyen, Vu Hoang Minh Doan, Tan Hung Vo, Jaeyeop Choi, Junghwan Oh

  • Year: 2025

  • Citation (DOI): 10.1109/icit63637.2025.10965207

  • Source: 2025 IEEE International Conference on Industrial Technology (ICIT), Published on March 26, 2025

📄 3. Optimizing Scanning Acoustic Tomography Image Segmentation With Segment Anything Model for Semiconductor Devices

  • Authors: Thi Thu Ha Vu, Tan Hung Vo, Trong Nhan Nguyen, Jaeyeop Choi, Sudip Mondal, Junghwan Oh

  • Year: 2024

  • Citation (DOI): 10.1109/TSM.2024.3444850

  • Source: IEEE Transactions on Semiconductor Manufacturing, Published in November 2024

Afeez Soladoye | Machine learning | Young Scientist Award

Published on by NESIN Awards

Mr. AfeezSoladoye | Machine learning | Young Scientist Award

Lecturer at Federal university Oye-Ekiti, Nigeria

Soladoye Afeez Adekunle is a promising young scholar in Computer Engineering, currently pursuing his Ph.D. at the Federal University Oye-Ekiti. With a Master’s degree earned with distinction, he has demonstrated strong academic and research capabilities. His work spans machine learning, artificial intelligence, and applied computing, including the development of medical prediction systems and fake news detection using deep learning. In addition to his teaching responsibilities at undergraduate and postgraduate levels, he actively contributes as a peer reviewer for reputable journals such as BMJ Open and serves as a technical editor. His involvement in academic committees and university-level projects reflects his leadership and dedication to institutional development. While his practical projects are impactful, the inclusion of more peer-reviewed publications and measurable research outcomes would further enhance his profile. Overall, his commitment to innovation, education, and research makes him a suitable and competitive candidate for the Young Scientist Award.

Professional Profile

Education🎓

Soladoye Afeez Adekunle has a solid educational background in Computer Engineering, reflecting his dedication to academic excellence and continuous professional development. He is currently pursuing a Ph.D. in Computer Engineering at the Federal University Oye-Ekiti, Nigeria, with a research focus on advanced computing and intelligent systems. He previously earned a Master of Engineering (M.Eng) in Computer Engineering from the same university, graduating with distinction in 2023. His undergraduate studies were completed at Ladoke Akintola University of Technology, Ogbomosho, where he obtained a Bachelor of Technology (B.Tech) degree in Computer Engineering in 2016. His foundational education includes a Senior School Leaving Certificate from Foundation Model College, Ikirun, in 2009, and a Primary School Leaving Certificate from Al-hilal Nursery and Primary School, Ikirun, in 2003. His academic journey reflects a consistent commitment to learning, skill acquisition, and growth in the field of computer science and engineering, preparing him for a successful career in research and education.

Professional Experience📝

Soladoye Afeez Adekunle has amassed valuable professional experience across academia, research, and industry. He currently serves as a Lecturer II in the Department of Computer Engineering at the Federal University Oye-Ekiti, where he teaches both undergraduate and postgraduate courses, supervises student projects, and mentors young researchers. In addition to his teaching role, he is the Assistant Examination Officer and Level Advisor, playing a vital role in exam coordination and academic advising. He also contributes as a Technical Editor for the FUOYE Journal of Engineering and Technology and reviews scholarly articles for esteemed journals like BMJ Open and the Nigerian Journal of Technological Development. As a freelance Machine Learning Engineer, he has developed predictive systems for medical diagnosis and fake news detection, showcasing his ability to apply research in practical contexts. His previous roles include network engineering trainee and peer tutor, reflecting a versatile and well-rounded professional path in computer science and engineering.

Research Interest🔎

Soladoye Afeez Adekunle has earned recognition for his dedication to academic excellence, professional service, and contributions to the field of computer engineering. He graduated with distinction in his Master’s degree in Computer Engineering from the Federal University Oye-Ekiti, a testament to his academic strength and commitment to excellence. He has also been entrusted with key roles within the university, such as Assistant Examination Officer, Level Advisor, and member of several strategic committees, including the Artificial Intelligence Committee and departmental accreditation teams. These roles highlight the trust placed in him by his peers and institutional leadership. Additionally, his active involvement as a reviewer for respected international and national journals such as BMJ Open and the Nigerian Journal of Technological Development reflects recognition of his scholarly competence and critical thinking. Although formal awards are not explicitly listed, his growing responsibilities, editorial roles, and consistent academic performance collectively reflect a strong professional honor and recognition within his academic community.

Award and Honor🏆

Soladoye Afeez Adekunle has earned recognition for his dedication to academic excellence, professional service, and contributions to the field of computer engineering. He graduated with distinction in his Master’s degree in Computer Engineering from the Federal University Oye-Ekiti, a testament to his academic strength and commitment to excellence. He has also been entrusted with key roles within the university, such as Assistant Examination Officer, Level Advisor, and member of several strategic committees, including the Artificial Intelligence Committee and departmental accreditation teams. These roles highlight the trust placed in him by his peers and institutional leadership. Additionally, his active involvement as a reviewer for respected international and national journals such as BMJ Open and the Nigerian Journal of Technological Development reflects recognition of his scholarly competence and critical thinking. Although formal awards are not explicitly listed, his growing responsibilities, editorial roles, and consistent academic performance collectively reflect a strong professional honor and recognition within his academic community.

Research Skill🔬

Soladoye Afeez Adekunle possesses a diverse and practical set of research skills that align with cutting-edge developments in computer engineering and artificial intelligence. His expertise includes data analysis, machine learning model development, deep learning, and natural language processing. He has applied these skills in various impactful projects such as medical prediction systems for cancer and stroke, fake news detection, and object measurement using computer vision techniques. Adept at data preprocessing, model training, performance evaluation, and algorithm optimization, he ensures high-quality and accurate research outcomes. He is also skilled in using tools and frameworks such as Python, TensorFlow, Keras, and MATLAB for simulation and modeling. His experience in peer reviewing academic journals and formatting manuscripts further demonstrates his understanding of scientific writing and research ethics. Soladoye’s ability to merge academic research with practical application, along with his commitment to innovation, positions him as a capable and forward-thinking researcher in the technology domain.

Conclusion💡

Soladoye, Afeez Adekunle presents a strong case for the Young Scientist Award, especially in the areas of emerging technologies, machine learning, and applied computing. His academic excellence, teaching versatility, peer-review contributions, and practical ML project development demonstrate his passion and potential.

Publications Top Noted✍️

  • Title: IMPACT OF SOCIAL MEDIA ON POLICE BRUTALITY AWARENESS IN NIGERIA

    • Authors: OJOA, SOLADOYE Afeez A.

    • Year: 2020

    • Citations: 24

  • Title: Detection of Cervical Cancer Using Deep Transfer Learning

    • Authors: B.A. Omodunbi, A.A. Soladoye, A.O. Esan, N.S. Okomba, T.G.O.O.M. Ojelabi

    • Year: 2024

    • Citations: 4*

  • Title: Optimizing Stroke Prediction Using Gated Recurrent Unit and Feature Selection in Sub-Saharan Africa

    • Authors: A.A. Soladoye, D.B. Olawade, I.A. Adeyanju, O.M. Akpa, N. Aderinto, et al.

    • Year: 2025

    • Citations: 2

  • Title: E-learning: Significance on Federal Unity Schools Students’ in Nigeria Amidst COVID-19 Lockdown

    • Authors: A.A. Soladoye

    • Year: 2020

    • Citations: 2

  • Title: Development of a Medical Condition Prediction Model Using Natural Language Processing with K-Nearest Neighbour

    • Authors: B.A. Omodunbi, A.A. Soladoye, N.S. Okomba, M.O. Ayinla, C.S. Odeyemi

    • Year: [Year not specified]

    • Citations: 2*

  • Title: Smart Hospitality: Leveraging Technological Advances to Enhance Customer Satisfaction

    • Authors: O.O. Osadare, O.N. Akande, A.A. Soladoye, P.O. Sobowale

    • Year: 2024

    • Citations: 1

  • Title: Internet of Things (IoT) Based Remote Surveillance Camera for Supervision of Examinations

    • Authors: C. Segun Odeyemi, B.A. Omodunbi, O.M. Olaniyan, A.A. Soladoye

    • Year: 2024

    • Citations: 1

  • Title: Prediction of Customer Satisfaction in Airline Hospitality Services for Improved Service Delivery Using Support Vector Machine

    • Authors: A.A. Sobowale, O.O. Osadare, A.A. Soladoye, P.O. Sobowale

    • Year: 2024

    • Citations: 1

  • Title: Development of an Interactive Android-Based Ayo-Olopon Game

    • Authors: E.Y. Bolaji Abigail Omodunbi, Afeez Adekunle Soladoye, Opeyemi Asaolu

    • Year: 2023

    • Citations: 1

CHENGZU DONG | Computer Science | Best Researcher Award

Published on by NESIN Awards

Prof . CHENGZU DONG | Computer Science | Best Researcher Award

Assistant Professor at Lingnan University , Hong Kong

Dr. Chengzu Dong is a highly accomplished early-career researcher specializing in cybersecurity, AI, blockchain, IoT, UAVs, and edge computing. Currently an Assistant Professor at Lingnan University, he has published over 30 peer-reviewed papers in prestigious Q1 journals and Core A/A* conferences, showcasing his strong research productivity and interdisciplinary expertise. His collaborations with CSIRO and industry partners reflect a robust blend of academic rigor and applied impact. Dr. Dong has received multiple accolades, including best paper awards, hackathon honors, and scholarships, highlighting his innovation and leadership potential. In addition to research, he has made significant contributions to teaching, mentoring, and curriculum development across several international institutions. While he could further enhance his profile through principal investigator roles and broader international visibility, his achievements and contributions make him a strong candidate for the Best Researcher Award, particularly in emerging areas of intelligent systems, secure computing, and next-generation network technologies.

Professional Profile 

Education🎓

Dr. Chengzu Dong has a strong and diverse educational background in computer science and information technology. He earned his Ph.D. from Deakin University, Australia, specializing in blockchain, AI, UAVs, edge computing, IoT, Web3, and the Metaverse. During his Ph.D. studies (2021–2024), he conducted extensive research in next-generation technologies, contributing significantly to academic and applied fields. Prior to that, he completed a Bachelor of Computer Science (Honours) at Swinburne University of Technology in 2020, where he deepened his expertise in software development and systems engineering. He also holds a Bachelor of Information Technology from Deakin University (2016–2019), which laid the foundation for his interests in cybersecurity and emerging technologies. His education reflects a consistent focus on interdisciplinary innovation and a strong grounding in both theoretical knowledge and practical applications. Dr. Dong’s academic journey across top Australian universities has prepared him well for a career in high-impact, technology-driven research and teaching.

Professional Experience📝

Dr. Chengzu Dong brings a rich and diverse professional background in academia, research, and industry. He is currently serving as an Assistant Professor at Lingnan University, Hong Kong, where he teaches and develops courses in blockchain, data mining, machine learning, and cybersecurity. Prior to this, he held multiple academic roles at Deakin University, including seminar lecturer, academic tutor, course developer, and capstone mentor, contributing significantly to curriculum innovation and student mentorship. His research experience includes collaborations with CSIRO, Australia’s leading scientific agency, where he worked on blockchain and AI projects. He also held positions as a research assistant at Swinburne University and Deakin University, and worked in software development roles at Artchain Global, Creative Geelong, and FPT Software. These roles highlight his strong technical skills and ability to bridge academia and industry. Dr. Dong’s professional journey reflects a well-rounded portfolio of teaching, research, and applied innovation in emerging technologies.

Research Interest🔎

Dr. Chengzu Dong’s research interests lie at the intersection of emerging technologies and intelligent systems, with a strong focus on cybersecurity, blockchain, artificial intelligence (AI), Internet of Things (IoT), unmanned aerial vehicles (UAVs), edge computing, Web3, and the Metaverse. His work aims to address critical challenges in data privacy, secure communication, and decentralized systems through the integration of blockchain and federated learning frameworks. He is particularly passionate about developing secure and efficient architectures for UAV delivery systems and smart edge networks, making his research highly relevant to real-world applications. Dr. Dong’s interdisciplinary approach combines theoretical advancements with practical implementations, as demonstrated by his collaborations with CSIRO and numerous industry partners. His contributions not only advance academic knowledge but also provide innovative solutions to pressing technological issues in digital security and autonomous systems. This diverse and forward-looking research portfolio positions him as a thought leader in next-generation computing and intelligent infrastructure.

Award and Honor🏆

Dr. Chengzu Dong has received numerous awards and honors in recognition of his academic excellence, research impact, and innovative contributions to emerging technologies. He was the recipient of the prestigious Deakin University Postgraduate Research Scholarship and a CSIRO Top-up Scholarship, supporting his advanced research in blockchain and AI. His work has earned best paper awards at international conferences such as IEEE IAS GLOBCONHT 2023, and he has achieved first runner-up prizes in blockchain hackathons in both Thailand and Australia. Dr. Dong is a certified member of the Australian Computer Society and holds various professional certifications, including Cisco CCNET and Certificate IV in Training and Assessment. His academic excellence was further recognized with the Golden Key Top 15% Student Award. He has also received recognition as a journal and conference reviewer, including a free ACM membership. These accolades collectively highlight his leadership, innovation, and dedication to research excellence and professional development.

Research Skill🔬

Dr. Chengzu Dong possesses a comprehensive set of research skills that span theoretical development, applied experimentation, and interdisciplinary collaboration. He is highly proficient in blockchain technology, artificial intelligence, federated learning, and cybersecurity frameworks, with a particular focus on secure systems for UAVs and edge computing environments. Dr. Dong demonstrates strong technical expertise in programming languages such as Python, Node.js, and React, along with experience in data analytics, machine learning model development, and system architecture design. His ability to design privacy-preserving frameworks and implement decentralized solutions reflects his strength in combining research theory with practical outcomes. Additionally, his experience working with organizations like CSIRO showcases his capability to collaborate on large-scale, real-world projects. Dr. Dong is also skilled in academic writing and publishing, with over 30 high-quality publications in top-tier journals and conferences. His strong analytical mindset, problem-solving ability, and innovation make him a highly capable and impactful researcher in advanced computing domains.

Conclusion💡

Dr. Chengzu Dong exemplifies the qualities of an outstanding researcher and academic, making him a highly suitable candidate for the Best Researcher Award. His extensive contributions to cutting-edge areas such as blockchain, AI, cybersecurity, and UAV systems reflect both depth and breadth in research expertise. With over 30 high-impact publications, multiple international awards, and active collaborations with renowned institutions like CSIRO, Dr. Dong has demonstrated consistent research excellence and innovation. His ability to translate theoretical knowledge into practical solutions for real-world challenges, especially in emerging technologies, underscores his relevance and leadership in the field. Additionally, his dedication to teaching and mentoring at multiple universities enhances his influence in shaping future researchers and professionals. Dr. Dong’s interdisciplinary skills, academic achievements, and forward-thinking research agenda not only position him as a leader in his domain but also affirm his deserving candidacy for this prestigious recognition.

Publications Top Noted✍

  • Title: BBM: A Blockchain-Based Model for Open Banking via Self-Sovereign Identity
    Authors: C. Dong, Z. Wang, S. Chen, Y. Xiang
    Year: 2020
    Citations: 32

  • Title: A Novel Security Framework for Edge Computing Based UAV Delivery System
    Authors: A. Yao, F. Jiang, X. Li, C. Dong, Y.X. Jia, X.L. Gang Li
    Year: 2021
    Citations: 28

  • Title: Enhancing Quality of Service Through Federated Learning in Edge-Cloud Architecture
    Authors: J. Zhou, S. Pal, C. Dong, K. Wang
    Year: 2024
    Citations: 22

  • Title: A Blockchain-Aided Self-Sovereign Identity Framework for Edge-Based UAV Delivery System
    Authors: C. Dong, F. Jiang, X. Li, A. Yao, G. Li, X. Liu
    Year: 2021
    Citations: 20

  • Title: Optimizing Performance in Federated Person Re-Identification Through Benchmark Evaluation for Blockchain-Integrated Smart UAV Delivery Systems
    Authors: C. Dong, J. Zhou, Q. An, F. Jiang, S. Chen, L. Pan, X. Liu
    Year: 2023
    Citations: 15

  • Title: Continuous Authentication for UAV Delivery Systems Under Zero-Trust Security Framework
    Authors: C. Dong, F. Jiang, S. Chen, X. Liu
    Year: 2022
    Citations: 15

  • Title: A Privacy-Preserving Location Data Collection Framework for Intelligent Systems in Edge Computing
    Authors: A. Yao, S. Pal, X. Li, Z. Zhang, C. Dong, F. Jiang, X. Liu
    Year: 2024
    Citations: 11

  • Title: A Framework for User Biometric Privacy Protection in UAV Delivery Systems with Edge Computing
    Authors: A. Yao, S. Pal, C. Dong, X. Li, X. Liu
    Year: 2024
    Citations: 11

Xiang Li | Computer Science | Best Researcher Award

Published on by NESIN Awards

Ms. Xiang Li | Computer Science | Best Researcher Award

PHD candidate at University of Chinese Academy of Sciences, China

Xiang Li, a Ph.D. candidate at the University of Chinese Academy of Sciences, demonstrates exceptional potential for the Best Researcher Award. With a solid academic foundation—ranking in the top 5–7% throughout his studies—he has excelled in areas such as deep learning, stochastic processes, and pattern recognition. His research focuses on cross-domain few-shot learning, addressing real-world challenges like medical lesion detection and remote sensing scene classification. He has published in the prestigious Knowledge-Based Systems journal and submitted another to IEEE Transactions on Geoscience and Remote Sensing. Xiang has also earned accolades, including the Second Prize in the National Mathematical Modeling Competition and a top-tier finish in the Huawei Software Elite Challenge. His future interests in class-incremental learning and prompt tuning highlight a clear vision for impactful research. Overall, Xiang Li’s innovative contributions, academic excellence, and commitment to advancing AI technologies make him a strong and deserving candidate for this recognition.

Professional Profile 

Education

Xiang Li has demonstrated outstanding academic performance throughout his educational journey. He earned his Bachelor’s degree in Information and Computer Science from Shandong University, graduating in July 2021 with an impressive GPA of 91.73/100, placing him in the top 7.46% of his class. His coursework included high-level subjects such as Mathematical Statistics, Operations Research, and Advanced Algebra, in which he consistently achieved top scores. Following this, he was admitted to the University of Chinese Academy of Sciences, where he completed foundational Ph.D. training from September 2021 to July 2022, ranking in the top 5% with a GPA of 87.13/100. His advanced studies covered critical areas like Matrix Analysis, Deep Learning, and Pattern Recognition. Currently, he is conducting doctoral research at the Institute of Optics and Electronics, Chinese Academy of Sciences, focusing on cross-domain few-shot learning. His educational background reflects strong technical competence and a solid foundation for innovative research.

Professional Experience

Xiang Li has accumulated valuable professional research experience during his Ph.D. studies at the Institute of Optics and Electronics, Chinese Academy of Sciences. His primary research focuses on cross-domain few-shot learning, a vital area in artificial intelligence that addresses challenges in data-scarce environments. He has led and contributed to key projects, including the development of a dynamic representation enhancement framework to improve model generalization across different domains, and the fine-tuning of general pre-trained models for few-shot remote sensing scene classification. In addition to research, Xiang has actively participated in national competitions, winning third prize in the Huawei Software Elite Challenge for designing a traffic scheduling plan and contributing to infrared small target detection strategies in another competition. These experiences highlight his strong technical problem-solving skills, teamwork, and ability to apply theoretical knowledge to real-world challenges. His professional work reflects both depth and versatility, positioning him as a highly capable and innovative young researcher.

Research Interest

Xiang Li’s research interests lie at the forefront of artificial intelligence, with a strong focus on cross-domain few-shot learning, computer vision, and representation learning. He is particularly interested in developing algorithms that enable models to perform effectively in data-scarce scenarios, addressing the challenges posed by domain shifts and limited labeled data. His current work involves enhancing the representational capacity of models to learn diverse and meaningful features across domains, with applications in medical image analysis and remote sensing. Xiang is also exploring techniques for fine-tuning general pre-trained models to adapt to new tasks without extensive retraining. Looking ahead, he is keen on advancing research in few-shot class-incremental learning, where models continuously adapt to new classes with minimal data, and in prompt tuning for vision-language pre-trained models, which integrates natural language processing with visual recognition. His interests reflect a forward-thinking approach to building intelligent systems capable of learning efficiently and generalizing across tasks.

Award and Honor

Xiang Li has received several prestigious awards and honors in recognition of his academic excellence and research capabilities. During his undergraduate and doctoral studies, he was consistently awarded scholarships from both Shandong University and the University of Chinese Academy of Sciences, reflecting his outstanding academic performance and dedication. In June 2022, he was named a Merit Student at the University of Chinese Academy of Sciences, an honor reserved for top-performing students. His strong analytical and problem-solving skills were further recognized in national competitions, where he earned the Second Prize in the National College Students’ Mathematical Modeling Competition in 2019. Additionally, he played a key role in a team that won third prize in the Huawei Software Elite Challenge, a highly competitive event involving over 300 teams. These honors highlight his ability to excel both academically and practically, reinforcing his position as a promising and accomplished young researcher in the field of computer science.

Research skill

Xiang Li possesses a strong set of research skills that make him a capable and innovative scholar in the field of artificial intelligence and computer vision. His expertise spans advanced areas such as cross-domain few-shot learning, deep learning, and representation learning. He demonstrates exceptional analytical abilities, evident in his design and implementation of dynamic representation frameworks to enhance model generalization across diverse domains. Xiang is proficient in applying theoretical concepts to practical problems, as seen in his work on fine-tuning pre-trained models for remote sensing scene classification. His skill set includes programming, algorithm development, statistical analysis, and critical thinking, which he has effectively applied in both solo research and collaborative projects. Furthermore, his ability to publish in top-tier journals, such as Knowledge-Based Systems, reflects his competence in scientific writing, experimental design, and result interpretation. These research skills enable him to tackle complex challenges and contribute meaningfully to the advancement of intelligent systems.

Conclusion

Xiang Li is a highly promising young researcher with a solid academic foundation, well-defined research focus, and impactful contributions in the field of computer vision and machine learning. His achievements in cross-domain few-shot learning, publication in a top-tier journal, and award-winning competition experience clearly demonstrate excellence in research and innovation.

Publications Top Noted

  • Title: RSGPT: A remote sensing vision language model and benchmark
    Authors: Y. Hu, Yuan; J. Yuan, Jianlong; C. Wen, Congcong; Y. Liu, Yu; X. Li, Xiang
    Year: 2025

  • Title: Uni3DL: A Unified Model for 3D Vision-Language Understanding
    Authors: X. Li, Xiang; J. Ding, Jian; Z. Chen, Zhaoyang; M. Elhoseiny, Mohamed
    Year: 2025 (Conference Paper)

  • Title: 3D Shape Contrastive Representation Learning With Adversarial Examples
    Authors: C. Wen, Congcong; X. Li, Xiang; H. Huang, Hao; Y.S. Liu, Yu Shen; Y. Fang, Yi
    Year: 2025
    Journal: IEEE Transactions on Multimedia
    Citations: 4

  • Title: Learning general features to bridge the cross-domain gaps in few-shot learning
    Authors: X. Li, Xiang; H. Luo, Hui; G. Zhou, Gaofan; M. Li, Meihui; Y. Liu, Yunfeng
    Year: 2024
    Journal: Knowledge-Based Systems
    Citations: 1

Shayesteh Tabatabaei | Computer Science | Women Researcher Award

Published on by NESIN Awards

Assoc. Prof. Dr. Shayesteh Tabatabaei | Computer Science | Women Researcher Award

Doctored at University of Saravan, Iran

Assoc. Prof. Dr. Shayesteh Tabatabaei is a distinguished computer engineering researcher, ranked among the top 2% of scientists worldwide in 2024. She holds a Ph.D. in Computer Engineering and specializes in Wireless Sensor Networks, Mobile Ad-Hoc Networks, IoT, and Optimization Algorithms. With numerous high-impact journal publications, she has significantly contributed to intelligent routing protocols and energy-efficient networking solutions. As an Associate Professor, she teaches advanced courses in Artificial Intelligence, Fuzzy Logic, and Distributed Systems while mentoring students and researchers. Recognized as a top researcher multiple times, she has also led workshops on ISI article writing, IoT, and wireless networks. Her expertise in computational methodologies and commitment to knowledge dissemination make her a key figure in her field. Dr. Tabatabaei’s research excellence, leadership, and dedication to innovation make her a strong candidate for prestigious academic awards, with potential for further global collaborations and industry-driven research initiatives.

Professional Profile 

Education

Assoc. Prof. Dr. Shayesteh Tabatabaei holds a Ph.D. in Computer Engineering from Tehran Science and Research University, Iran, earned in 2015 with an outstanding GPA of 18.63/20. Her doctoral research focused on developing intelligent routing protocols for mobile ad-hoc networks under the supervision of Dr. M. Teshnehlab. She completed her M.Sc. in Computer Engineering at Islamic Azad University of Shabestar in 2009, where she improved the AODV routing protocol using reinforcement learning, achieving a GPA of 18.69/20. Her academic journey began with a B.Sc. in Computer Engineering from the same university, graduating in 2006 with a GPA of 17.12/20. Throughout her education, Dr. Tabatabaei demonstrated excellence in research and innovation, particularly in wireless networks and intelligent algorithms. Her strong academic background has shaped her expertise in computer engineering, making her a leading researcher and educator in the field of network optimization, IoT, and artificial intelligence.

Professional Experience

Assoc. Prof. Dr. Shayesteh Tabatabaei is a highly accomplished academic and researcher in computer engineering, currently serving as an Associate Professor in the Department of Computer Engineering at the Higher Education Complex of Saravan, Iran. With extensive teaching experience, she has instructed both undergraduate and postgraduate courses in Artificial Intelligence, Fuzzy Logic, Distributed Systems, Advanced Database Systems, and Programming Languages such as C, C++, Python, and SQL. Her research focuses on Wireless Sensor Networks, Mobile Ad-Hoc Networks, IoT, and Optimization Algorithms, with numerous high-impact journal publications and conference presentations. She has been recognized multiple times as a top researcher and has actively contributed to academic development by organizing workshops on ISI article writing, IoT, and wireless networks. Dr. Tabatabaei’s expertise extends to computational simulations and algorithm development, making her a leading figure in her field. Her dedication to education, research, and innovation continues to influence the next generation of computer engineers.

Research Interest

Assoc. Prof. Dr. Shayesteh Tabatabaei’s research interests lie at the intersection of intelligent computing and network optimization, focusing on Wireless Sensor Networks (WSNs), Mobile Ad-Hoc Networks (MANETs), Internet of Things (IoT), and Intelligent Algorithms. Her work aims to enhance the efficiency, reliability, and security of communication networks through advanced routing protocols, optimization algorithms, and artificial intelligence techniques. She has contributed significantly to energy-aware clustering, fault tolerance mechanisms, and adaptive routing in WSNs, utilizing machine learning, fuzzy logic, and evolutionary computing. Additionally, her research explores optimization algorithms such as Genetic Algorithms, Bee Colony Optimization, and Social Spider Optimization to improve network performance. Through her extensive publications in high-impact journals and conferences, Dr. Tabatabaei continues to advance the field of computational intelligence and networked systems. Her passion for innovation drives her to develop cutting-edge solutions for real-world challenges in modern communication technologies.

Award and Honor

Assoc. Prof. Dr. Shayesteh Tabatabaei has received multiple awards and honors in recognition of her outstanding contributions to research and academia. She has been ranked among the top 2% of scientists worldwide in 2024, highlighting her global impact in computer engineering. She has been recognized as the Top Researcher at various institutions multiple times, including Islamic Azad University of Malekan Branch in 2011, 2016, and 2017, and the Higher Education Complex of Saravan in 2019, 2021, and 2022. Her achievements reflect her dedication to advancing knowledge in wireless sensor networks, optimization algorithms, and artificial intelligence. In addition to her research excellence, she has led training workshops and mentored young scholars, further solidifying her reputation as a leader in her field. Her numerous accolades demonstrate her commitment to innovation, making her a strong candidate for prestigious academic and scientific awards on both national and international levels.

Research Skill

Assoc. Prof. Dr. Shayesteh Tabatabaei possesses strong research skills in computer engineering, wireless communication, and intelligent systems. Her expertise spans algorithm design, network optimization, artificial intelligence, and data analysis, with a particular focus on Wireless Sensor Networks (WSNs), Mobile Ad-Hoc Networks (MANETs), IoT, and optimization techniques. She is proficient in developing energy-efficient routing protocols, fault-tolerant clustering methods, and machine learning-based optimization algorithms. Dr. Tabatabaei has extensive experience with simulation tools such as MATLAB, R, Opnet, and GloMoSim, which she utilizes to validate her research findings. Additionally, she is skilled in multiple programming languages, including C, C++, Python, JavaScript, SQL, and Oracle, enabling her to implement and test computational models effectively. Her ability to integrate fuzzy logic, evolutionary algorithms, and artificial intelligence into network solutions showcases her innovative approach to problem-solving, making her a highly capable and influential researcher in the field.

Conclusion

Dr. Shayesteh Tabatabaei is highly qualified for the Women Researcher Award, given her global recognition, extensive research contributions, leadership in academia, and dedication to advancing knowledge in computer engineering. Strengthening international collaborations and industry partnerships could further elevate her impact.

Publications Top Noted

  • A novel fault tolerance energy-aware clustering method via social spider optimization (SSO) and fuzzy logic and mobile sink in wireless sensor networks (WSNs).

    • Cited by: 65
    • Year: 2020

  • A novel energy-aware clustering method via Lion Pride Optimizer Algorithm (LPO) and fuzzy logic in wireless sensor networks (WSNs).

    • Cited by: 50
    • Year: 2019

  • Proposing an energy-aware routing protocol by using fish swarm optimization algorithm in WSN (wireless sensor networks).

    • Cited by: 47
    • Year: 2021

  • A new method to find a high reliable route in IoT by using reinforcement learning and fuzzy logic.

    • Cited by: 36
    • Year: 2020

  • Reliable routing algorithm based on clustering and mobile sink in wireless sensor networks.

    • Cited by: 30
    • Year: 2019

  • A novel method for clustering in WSNs via TOPSIS multi-criteria decision-making algorithm.

    • Cited by: 23
    • Year: 2020

  • Improved routing vehicular ad-hoc networks (VANETs) based on mobility and bandwidth available criteria using fuzzy logic.

    • Cited by: 20
    • Year: 2020

  • A new routing protocol to increase throughput in mobile ad hoc networks.

    • Cited by: 20
    • Year: 2015

  • Provide energy-aware routing protocol in wireless sensor networks using bacterial foraging optimization algorithm and mobile sink.

    • Cited by: 19
    • Year: 2022

Hussain A. Younis | Computer Science | Best Researcher Award

Published on by NESIN Awards

Mr. Hussain A. Younis | Computer Science | Best Researcher Award

College of Education at University of Basrah, Iraq

Hussain A. Younis is a dedicated researcher specializing in Artificial Intelligence, Security, Digital Image Processing, and Robotics. With a strong academic background from India and Malaysia and an affiliation with the University of Basrah, he has published impactful research in high-ranking journals and IEEE conferences. His work demonstrates interdisciplinary expertise, particularly in AI applications, human-robot interaction, and digital security. As an active IEEE member and potential reviewer, he is engaged in professional research communities. While his contributions are commendable, completing his Ph.D., increasing Q1/Q2 journal publications, securing research grants, and enhancing international collaborations would further strengthen his research profile. His growing citation impact and involvement in digital transformation research make him a strong candidate for the Best Researcher Award. With continued contributions in leadership, industry collaborations, and high-impact research, Hussain A. Younis is well-positioned to make significant advancements in the field of computer science and engineering.

Professional Profile 

Education

Hussain A. Younis has a strong academic background in computer science, with a Master’s degree earned in 2012 from India and ongoing Ph.D. studies since 2019 in Malaysia. His educational journey reflects a commitment to advanced research in Artificial Intelligence, Security, Digital Image Processing, and Robotics. His affiliation with the University of Basrah further strengthens his academic and research foundation, allowing him to contribute significantly to the field. Throughout his studies, he has focused on interdisciplinary research, exploring innovative solutions in AI-driven security systems, pattern recognition, and human-robot interaction. His academic pursuits have been complemented by active participation in professional organizations like IEEE, where he is a member and a prospective reviewer. While his research credentials are impressive, completing his Ph.D. will further solidify his expertise and credibility. His educational background positions him as a promising researcher with the potential to make impactful contributions to the scientific community.

Professional Experience

Hussain A. Younis has extensive professional experience in research and academia, with a focus on Artificial Intelligence, Security, Digital Image Processing, and Robotics. He is affiliated with the University of Basrah, where he contributes to both teaching and research in computer science. His work spans various interdisciplinary areas, including AI-driven security systems, pattern recognition, and human-robot interaction. As an IEEE member, he actively participates in academic conferences and serves as a prospective reviewer, further demonstrating his engagement in the global research community. His publications in high-impact journals and IEEE conferences highlight his contributions to advancing technology, particularly in robotics education, cybersecurity, and digital transformation. While his professional experience is commendable, taking on leadership roles in research projects, securing grants, and fostering international collaborations would further enhance his impact. His commitment to innovation and academic excellence makes him a valuable contributor to the scientific and technological landscape.

Research Interest

Hussain A. Younis’s research interests lie at the intersection of Artificial Intelligence, Security, Digital Image Processing, Pattern Recognition, and Robotics. His work explores innovative AI-driven solutions for enhancing security, improving human-robot interaction, and advancing digital transformation. He is particularly interested in speech recognition models, robotics in education, and secure cryptographic systems, contributing to cutting-edge developments in these fields. His research also addresses challenges in cybersecurity, focusing on encryption techniques and stream cipher systems to enhance data protection. Additionally, he investigates distinguishable patterns in image processing, applying AI techniques to optimize pattern recognition for various applications. Through his active participation in IEEE conferences and high-impact journal publications, he continuously contributes to technological advancements. His interdisciplinary approach and commitment to innovation position him as a promising researcher in AI and security, with the potential to make significant contributions to both academic research and real-world applications.

Award and Honor

Hussain A. Younis has been recognized for his contributions to research in Artificial Intelligence, Security, Digital Image Processing, and Robotics through various academic achievements and honors. His publications in high-impact journals and IEEE conferences reflect his dedication to advancing knowledge in these fields. As an active IEEE member, he has gained recognition within the global research community and has been invited to serve as a reviewer for IEEE conferences in Iraq. His work on robotics in education, cybersecurity, and encryption systems has earned significant attention, highlighting his expertise in interdisciplinary research. While his achievements are commendable, securing prestigious research grants, international fellowships, and industry collaborations would further enhance his profile. His commitment to innovation and scientific excellence makes him a strong contender for research awards, and with continued contributions, he is poised to receive greater recognition for his impact on the technological and academic landscape.

Research Skill

Hussain A. Younis possesses strong research skills in Artificial Intelligence, Security, Digital Image Processing, Pattern Recognition, and Robotics. His expertise lies in developing AI-driven solutions for security, speech recognition, and human-robot interaction, showcasing his ability to integrate multiple disciplines. He is proficient in data analysis, algorithm development, cryptographic security, and digital transformation technologies, enabling him to conduct high-quality research with practical applications. His experience in publishing in high-impact journals and IEEE conferences reflects his ability to conduct rigorous academic research and communicate findings effectively. As an active IEEE member and prospective reviewer, he demonstrates critical analysis and evaluation skills essential for scholarly contributions. Additionally, his research involves problem-solving, programming, and system design, particularly in robotics education and cybersecurity. To further enhance his research impact, focusing on international collaborations, advanced machine learning techniques, and securing research grants would strengthen his expertise and academic contributions.

Conclusion

Hussain A. Younis demonstrates strong research potential with impactful publications in AI, Robotics, and Security. His IEEE membership, interdisciplinary research, and international exposure make him a strong candidate for the Best Researcher Award. However, completing the Ph.D., increasing high-impact publications, and engaging in leadership roles would significantly enhance his eligibility for this prestigious award.

Publications Top Noted

  1. Hussain A. Younis, TAE Eisa, M Nasser, TM Sahib, AA Noor, OM Alyasiri, … (2024)

    • A systematic review and meta-analysis of artificial intelligence tools in medicine and healthcare: applications, considerations, limitations, motivation and challenges
    • Citations: 114

  2. Hussain A. Younis, NIR Ruhaiyem, W Ghaban, NA Gazem, M Nasser (2023)

    • A systematic literature review on the applications of robots and natural language processing in education
    • Citations: 48

  3. IM Hayder, TA Al-Amiedy, W Ghaban, F Saeed, M Nasser, GA Al-Ali, HA Younis, … (2023)

    • An intelligent early flood forecasting and prediction leveraging machine and deep learning algorithms with advanced alert system
    • Citations: 40

  4. OM Alyasiri, K Selvaraj, Hussain A. Younis, TM Sahib, MF Almasoodi, IM Hayder (2024)

    • A survey on the potential of artificial intelligence tools in tourism information services
    • Citations: 38

  5. S Salisu, NIR Ruhaiyem, TAE Eisa, M Nasser, F Saeed, HA Younis (2023)

    • Motion capture technologies for ergonomics: A systematic literature review
    • Citations: 25

  6. IM Hayder, GANA Ali, Hussain A. Younis (2023)

    • Predicting reaction based on customer’s transaction using machine learning approaches
    • Citations: 20

  7. Hussain A. Younis, ASA Mohamed, R Jamaludin, MNA Wahab (2021)

    • Survey of robotics in education, taxonomy, applications, and platforms during COVID-19
    • Citations: 20

  8. OM Alyasiri, AM Salman, S Salisu (2024)

    • ChatGPT revisited: Using ChatGPT-4 for finding references and editing language in medical scientific articles
    • Citations: 18

  9. Hussain A. Younis, OM Alyasiri, Muthmainnah, TM Sahib, IM Hayder, S Salisu, … (2023)

    • ChatGPT Evaluation: Can It Replace Grammarly and Quillbot Tools
    • Citations: 16

  10. MA Hussain, Hussain A. Younis, Iznan H. Hasbullah, Ghofran Kh. Shraida, Hameed A … (2023)

  • An Efficient Color-Image Encryption Method Using DNA Sequence and Chaos Cipher
  • Citations: 14
  1. Hussain A. Younis, ASA Mohamed, MN Ab Wahab, R Jamaludin, S Salisu (2021)
  • A new speech recognition model in a human-robot interaction scenario using NAO robot: Proposal and preliminary model
  • Citations: 11
  1. Hussain A. Younis, TY Abdalla, AY Abdalla (2009)
  • Vector quantization techniques for partial encryption of wavelet-based compressed digital images
  • Citations: 11