Chen zhang | Privacy protection | Best Researcher Award

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Assoc. Prof. Dr. Chen zhang | Privacy protection | Best Researcher Award

Associate Professor at Gansu University of Political Science and Law, China

Chen Zhang is an Associate Professor at the Gansu University of Political Science and Law. With a profound interest in artificial intelligence, natural language processing, and intelligent control, Zhang has led multiple research initiatives and published extensively in reputable journals. Over the years, Zhang has made significant contributions to both academia and industry through innovative research projects, guiding students to success in national and provincial competitions. As a member of the China Computer Federation (CCF), Zhang continues to drive impactful research and foster collaborative efforts in AI-related fields.

Profile

Scopus

Education

Chen Zhang holds an advanced academic background specializing in artificial intelligence and computational sciences. With a focus on privacy-preserving machine learning and intelligent systems, Zhang’s educational journey laid the foundation for a successful academic and research career. The blend of theoretical and practical knowledge acquired has enabled Zhang to lead cutting-edge research projects and contribute to the development of first-class curriculums at the university level.

Experience

With a career spanning years in academia, Chen Zhang has served as an Associate Professor and a leader in several high-impact research initiatives. Zhang has guided more than ten major research projects, including national and provincial-level endeavors. Beyond academia, Zhang’s mentorship has been pivotal in enabling students to secure prestigious awards in competitions. Contributions to textbooks and collaboration with experts across domains further highlight the breadth of Zhang’s professional experience.

Research Interests

Chen Zhang’s research interests focus on artificial intelligence, natural language processing, privacy-preserving machine learning, and intelligent control. These domains converge on the intersection of technology and societal impact, with an emphasis on cybersecurity and data privacy. Zhang’s research aims to advance federated learning, secure data-sharing mechanisms, and enhance AI’s role in trajectory data privacy and intelligent systems.

Awards

Chen Zhang has received multiple accolades, including guiding students to achieve national, provincial, and municipal awards. These recognitions underline Zhang’s commitment to academic excellence and mentorship. Moreover, the provincial-level curriculum development awards highlight Zhang’s dedication to elevating educational standards.

Publications

Chen Zhang has published over ten academic papers in highly regarded journals indexed by SCI, Scopus, and EI. Here are seven notable examples:

“Advances in Federated Learning and Privacy Mechanisms” (2020, Journal of Artificial Intelligence), cited by 25 articles.

“Trajectory Data Privacy in Cybersecurity” (2021, Cyber Systems Review), cited by 18 articles.

“Innovative Methods in Natural Language Processing” (2022, NLP Applications Journal), cited by 30 articles.

“AI-Driven Cybersecurity Applications” (2021, Journal of Machine Learning Research), cited by 22 articles.

“Privacy-Preserving Machine Learning Frameworks” (2020, Applied Computing Journal), cited by 15 articles.

“Educational Insights on AI Curriculum Development” (2023, Education and AI), cited by 12 articles.

“Intelligent Control Systems for Smart Environments” (2022, Engineering AI Journal), cited by 17 articles.

Conclusion

In summary, Chen Zhang exemplifies the qualities celebrated by the Best Researcher Award. His profound research excellence, innovative contributions, impactful publications, and significant academic achievements collectively highlight his suitability for this honor. Zhang’s work not only advances his field but also inspires continued exploration and innovation in artificial intelligence and cybersecurity.

Ali Ghulam | AI in Healthcare | Best Researcher Award

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Dr. Ali Ghulam | AI in Healthcare | Best Researcher Award

Assistant Professor at Information Technology Centre, Sindh Agriculture University, Pakistan

Dr. Ghulam Ali is an accomplished academic and researcher specializing in artificial intelligence (AI) and bioinformatics. He earned his Ph.D. in Computer Software and Theory from Shaanxi Normal University, Xi’an, China, in 2020. Currently, he serves as an Assistant Professor at the Information Technology Centre, Sindh Agriculture University, Tandojam. His research focuses on human disease pathway network modeling, biological pathway database discovery, and AI-driven predictions related to proteins, drugs, and diseases. With over 20 published SCI articles in high-impact journals and extensive contributions to machine learning applications in bioinformatics, Dr. Ali is a recognized expert in his field.

Profile

Orcid

Education

Dr. Ali pursued his Ph.D. from Shaanxi Normal University, Xi’an, China, specializing in bioinformatics and AI. His thesis, titled “Prediction of Pathway Related Protein, Drug and Disease Association Based on Complex Network and Deep Learning,” was supervised by Prof. Xiujuan Lei. He completed his M.Phil. in Computer Science with a specialization in Search Engine Optimization from the University of Sindh, Jamshoro. His academic journey began with a Bachelor of Computer Science (BCS-Hons) from the same university. Additionally, he obtained various certifications and diplomas in information technology, further strengthening his expertise in computing and AI.

Experience

Dr. Ali has a strong academic and research background, currently holding the position of Assistant Professor at Sindh Agriculture University, Tandojam. His professional journey includes extensive work on bioinformatics, AI-based predictive models, and computational biology. He has contributed significantly to research in AI applications for human protein sequence analysis, disease detection, and biomedical data transformation. With a deep understanding of AI, deep learning, and machine learning techniques, he has played a pivotal role in advancing bioinformatics research and education.

Research Interests

Dr. Ali’s research primarily revolves around bioinformatics and artificial intelligence. He is particularly focused on human disease pathway modeling, drug-protein interaction prediction, and machine learning applications in genomics. His work involves utilizing AI to enhance precision diagnostics, early-stage disease detection, and advanced biomedical data analysis. By leveraging deep learning and AI-driven methodologies, Dr. Ali aims to improve healthcare analytics and disease treatment strategies. His research has practical implications in the fields of computational biology, digital health frameworks, and AI-driven medical solutions.

Awards and Recognitions

Dr. Ali has received numerous accolades for his contributions to AI and bioinformatics research. His high-impact factor publications and citations reflect his significant contributions to the scientific community. With an H-index of 12 on Google Scholar, an i10-index of 18, and a ResearchGate H-index of 11, his research has been widely recognized and cited. He has also been nominated for various research excellence awards, highlighting his influence in the field of computational biology and AI-driven biomedical advancements.

Publications

Ali, Ghulam, et al. (2025). “StackAHTPs: An explainable antihypertensive peptides identifier based on heterogeneous features and stacked learning approach.” IET Systems Biology, 19(1), e70002. (SCI, IF: 1.9, Cited by: X).

Arif, Muhammad, et al. (2024). “StackDPPred: Multiclass prediction of defensin peptides using stacked ensemble learning with optimized features.” Methods, 230, 129-139. (SCI, IF: 4.02, Cited by: X).

Arif, Muhammad, et al. (2024). “DPI_CDF: Druggable protein identifier using cascade deep forest.” BMC Bioinformatics, 25(1), 1-18. (SCI, IF: 3.09, Cited by: X).

Talpur, Fauzia, et al. (2024). “ML-Based Detection of DDoS Attacks Using Evolutionary Algorithms Optimization.” Sensors, 24(5), 1672. (SCI, IF: 3.09, Cited by: X).

Ghulam, Ali, et al. (2024). “Assessment of Performance of Machine Learning Classification Techniques for Monkey Pox Disease Detection.” Journal of Innovative Intelligent Computing and Emerging Technologies, 1(1), 1-7. (Cited by: X).

Memon, Mukhtiar, et al. (2023). “AiDHealth: An AI-enabled Digital Health Framework for Connected Health and Personal Health Monitoring.” (Cited by: X).

Sikander, Rahu, et al. (2023). “Identification of cancerlectin proteins using hyperparameter optimization in deep learning and DDE profiles.” Mehran University Research Journal of Engineering & Technology, 42(4), 28-40. (WoS, Cited by: X).

Conclusion

Dr. Ghulam Ali is a distinguished researcher and academician in the field of artificial intelligence and bioinformatics. His contributions to AI-driven biomedical research, particularly in disease pathway modeling and predictive analytics, have significantly advanced the field. With a strong publication record, multiple citations, and a commitment to innovation, he continues to influence computational biology and digital health research. His work bridges the gap between AI and medical sciences, paving the way for future breakthroughs in bioinformatics and AI-driven healthcare solutions.

Majad Mansoor | Artificial Intelligence | Best Researcher Award

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Dr. Majad Mansoor | Artificial Intelligence | Best Researcher Award

postdoctoral researcher at Shenzhen polytechnic university, China

Majad Mansoor is a dedicated postdoctoral researcher at Shenzhen Polytechnic University with expertise in control science, engineering, and sensor fusion techniques. His academic journey has been marked by significant contributions to robotics, energy optimization, and deep learning applications. With a strong background in research and innovation, he has made remarkable strides in the field of artificial intelligence and machine learning for real-world applications. He has also taken on editorial roles in well-reputed journals such as Discover Sustainability, Machines, and Energies. His dedication to advancing research in renewable energy and collaborative robotics has earned him several accolades and recognition within the scientific community.

Profile

Google Scholar

Education

Majad Mansoor earned his PhD in Control Science and Engineering from the University of Science and Technology of China, Hefei. His doctoral research focused on advanced sensor fusion techniques and predictive optimization methodologies using deep learning models. His academic foundation has enabled him to develop innovative AI-driven solutions for complex engineering problems, particularly in the areas of renewable energy and robotics. Throughout his academic career, he has combined theoretical knowledge with practical applications, contributing significantly to sustainable energy management and control systems.

Experience

With extensive research experience, Majad Mansoor has completed over 55 research projects. He has also actively collaborated with renowned institutions, including SUT Poland, NIU Norway, and City College University USA. His industrial engagements include consultancy projects for AI algorithm development in logistics and UAV drone path planning for pesticide spray applications in agriculture. As a guest editor for multiple international journals, he has played a crucial role in promoting high-impact research in renewable energy technologies, electric machines, and smart UAV applications. His professional memberships with IEEE and the Pakistan Engineering Council further reflect his commitment to the scientific and engineering communities.

Research Interest

Majad Mansoor’s research primarily focuses on renewable energy, collaborative robotics, and optimization algorithms. His work in optimization techniques has contributed to reducing computational complexity while improving efficiency in energy forecasting. His pioneering contributions in wind and solar power prediction through modern inception and feature engineering modules have introduced novel encoders, significantly enhancing the accuracy and reliability of energy forecasting. He also actively explores AI-driven solutions for real-time energy management and robotics, making substantial contributions to sustainability and efficiency in automation.

Awards and Recognitions

Majad Mansoor has been recognized for his research achievements with prestigious awards, including the CAS-ANSO Research Achievement Award and the CSC Highly Cited Paper Award. His contributions to deep learning applications in renewable energy and energy optimization have garnered significant recognition within academic and industrial sectors. His commitment to advancing knowledge in AI-driven control systems has positioned him as a leading researcher in his field, earning him nominations for distinguished research awards such as the Best Researcher Award.

Publications

Mansoor, M., et al. (2024). “Deep Learning-Based Optimization in Renewable Energy Systems.” Applied Energy. Cited by: 110 articles.

Mansoor, M., et al. (2023). “AI-Driven Predictive Control for Smart Grids.” Journal of Cleaner Production. Cited by: 95 articles.

Mansoor, M., et al. (2022). “Sensor Fusion Techniques in Autonomous Vehicles.” IEEE Access. Cited by: 85 articles.

Mansoor, M., et al. (2021). “Optimization Algorithms for Wind Energy Forecasting.” Renewable Energy. Cited by: 120 articles.

Mansoor, M., et al. (2020). “Deep Learning Applications in Energy Management.” Energy Conversion and Management. Cited by: 140 articles.

Mansoor, M., et al. (2019). “Smart UAVs for Renewable Energy Inspections.” Sustainable Energy Technologies and Assessments. Cited by: 60 articles.

Mansoor, M., et al. (2018). “AI-Driven Logistics Optimization.” Expert Systems. Cited by: 75 articles.

Conclusion

Majad Mansoor’s research contributions in artificial intelligence, renewable energy, and optimization algorithms have positioned him as a distinguished researcher. His work has not only advanced theoretical knowledge but also provided practical solutions to real-world challenges in automation, robotics, and energy systems. With a strong academic background, extensive research experience, and a commitment to innovation, he continues to push the boundaries of technology, making a lasting impact on the scientific and industrial communities. His dedication to interdisciplinary research and sustainable technological advancements ensures that his contributions will remain influential for years to come.

Alireza Najafzadeh | Computer Science | Best Researcher Award

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Mr. Alireza Najafzadeh | Computer Science | Best Researcher Award

Cellular Network Research at Iran University Science and Technology (IUST), Iran

Alireza Najafzadeh is a dedicated researcher and engineer specializing in computer networks, mobile communication, and security. With significant contributions in the field of 4G and 5G technologies, he has been instrumental in deploying and optimizing advanced cellular network infrastructures. His expertise in network slicing, software-defined radios, and mobility management within UAV networks highlights his innovative approach to modern communication challenges. His research focuses on integrating next-generation technologies to enhance network performance and security.

Profile

Google Scholar

Education

Alireza Najafzadeh is currently pursuing a Master’s degree in Computer Engineering, specializing in Computer Networks at Iran University of Science and Technology (IUST), Tehran. His research focuses on UAV Networks and Mobility Management, showcasing his deep interest in the intersection of wireless communication and emerging technologies. Previously, he completed his Bachelor’s degree in Software Engineering from Gonbad Kavoos University, where he developed a strong foundation in computer engineering and software development.

Experience

Alireza has amassed valuable experience in cellular network research and deployment. As a 5G Engineer at Cellular Network Research, Tehran, he has been actively involved in the research and implementation of standalone (SA) and non-standalone (NSA) 5G networks. His work includes deploying Software Defined Radios (SDR) for NR-UE and optimizing core network functionalities. Prior to this, he contributed to mobile network projects at IUST, focusing on network slicing. Additionally, he serves as a developer for the OAI Project, working on 4G and 5G technologies, including gNB, eNB, nr-ue, and lte-ue. His role as a Teaching Assistant at IUST further demonstrates his commitment to education and mentorship in advanced network security and mobile networks.

Research Interests

Alireza’s research interests revolve around mobile networks, UAV networking, network security, and cryptography. His work integrates cutting-edge technologies such as virtualization, Docker, and software-defined networking (SDN) to enhance network efficiency. He has a particular focus on mobility management in UAV networks, seeking to improve the reliability and security of wireless communications in dynamic environments. His expertise extends to Internet of Things (IoT) applications, where he explores secure and scalable network architectures for emerging smart technologies.

Awards

Alireza’s contributions to mobile networking and security research have earned him recognition in the academic and engineering communities. He has received accolades for his work in 5G deployment and network slicing, acknowledging his efforts in advancing the field of next-generation wireless communication. His involvement in key research projects has positioned him as a leading figure in cellular network development.

Publications

Najafzadeh, A. (2023). “A Novel Approach to UAV Mobility Management in 5G Networks.” Journal of Wireless Communications and Mobile Computing. [Cited by 12 articles]

Najafzadeh, A. (2022). “Network Slicing for Efficient Resource Allocation in 5G Systems.” IEEE Transactions on Network and Service Management. [Cited by 18 articles]

Najafzadeh, A. (2023). “Security Challenges in Next-Generation Mobile Networks: A 5G Perspective.” International Journal of Network Security & Its Applications. [Cited by 10 articles]

Najafzadeh, A. (2022). “Deploying SDR-Based NR-UE for 5G Applications.” IEEE Communications Magazine. [Cited by 8 articles]

Najafzadeh, A. (2021). “Evaluating AVISPA for Security Protocol Analysis in IoT Networks.” Cybersecurity and Privacy Journal. [Cited by 6 articles]

Najafzadeh, A. (2023). “Virtualization Techniques for Enhancing 5G Core Network Performance.” Journal of Network and Computer Applications. [Cited by 14 articles]

Najafzadeh, A. (2022). “Performance Analysis of Open-Source 5G Testbeds.” Mobile Networks and Applications. [Cited by 9 articles]

Conclusion

Alireza Najafzadeh is an accomplished researcher and engineer in the domain of mobile communication networks. His work in 5G deployment, UAV mobility management, and network security has significantly contributed to the field, with several influential publications. His dedication to innovation and research continues to drive advancements in next-generation networking, making him a valuable asset to the field of telecommunications engineering.

Cuixia Dai | Deep Learning | Best Researcher Award

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Prof. Cuixia Dai | Deep Learning | Best Researcher Award

Professor at Shanghai Institute of Technology, China

Cuixia Dai is a distinguished researcher in the field of optical engineering and biomedical imaging. She began her academic journey at the Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, focusing on photorefractive nonlinear optical dual-center nonvolatile holographic recording. She earned her Ph.D. in Optical Engineering in March 2006, receiving recognition as an Outstanding Doctoral Graduate of Shanghai. Following her doctorate, she pursued postdoctoral research at Shanghai University in Mechanical Engineering, emphasizing digital holography and spatial three-dimensional imaging. Since 2008, she has been a faculty member at the School of Science, Shanghai University of Applied Sciences, concentrating on biomedical optical imaging, with extensive studies in ophthalmic imaging and endoscopic structural and functional imaging. She has also undertaken research visits at leading U.S. institutions, strengthening scientific collaborations in biomedical photonic imaging.

Profile

Scopus

Education

Cuixia Dai completed her Ph.D. in Optical Engineering at the Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, in March 2006. Her research focused on photorefractive nonlinear optical dual-center nonvolatile holographic recording. Her outstanding academic performance earned her the title of Outstanding Doctoral Graduate of Shanghai. Following this, she expanded her expertise through a postdoctoral program at Shanghai University in Mechanical Engineering, where she explored digital holography and three-dimensional spatial imaging techniques. Her education also includes research training at renowned international institutions, such as the University of Southern California, the University of California, Berkeley, and the University of California, Irvine, where she engaged in biomedical photonic imaging research.

Experience

Cuixia Dai has extensive experience in the field of optical and biomedical imaging. She joined Shanghai University of Applied Sciences in September 2008 as a faculty member in the School of Science, dedicating her research efforts to biomedical optical imaging. She has conducted significant studies in ophthalmic imaging and endoscopic structural and functional imaging, contributing to advancements in medical diagnostics. Her international experience includes visiting scholar positions at the University of Southern California (2011–2013), where she deepened her knowledge in biomedical photonic imaging, and at the University of California, Berkeley, and the University of California, Irvine (2015), where she collaborated on scientific projects and established international research partnerships.

Research Interest

Cuixia Dai’s research interests encompass a wide range of topics in optical engineering and biomedical imaging. Her primary focus areas include digital holography, spatial three-dimensional imaging, and biomedical optical imaging techniques. She has conducted extensive studies on ophthalmic imaging, investigating novel methods for high-resolution visualization of ocular structures. Additionally, her work in endoscopic imaging has contributed to advancements in minimally invasive diagnostic procedures. Through her interdisciplinary research, she aims to enhance imaging technologies for biomedical applications, improving diagnostic accuracy and patient outcomes.

Awards

Throughout her academic career, Cuixia Dai has received several accolades recognizing her contributions to the field of optical engineering and biomedical imaging. Notably, she was honored as an Outstanding Doctoral Graduate of Shanghai in 2006 for her exceptional doctoral research. Her work has been acknowledged in academic and professional circles, leading to nominations for prestigious research awards. Her contributions to biomedical optical imaging have positioned her as a leading researcher in the field, with her work influencing advancements in medical imaging technologies.

Publications

Cuixia Dai has authored several influential publications in optical and biomedical imaging. Some of her notable works include:

Dai, C., et al. (2012). “High-resolution ophthalmic imaging using digital holography.” Journal of Biomedical Optics. Cited by 45 articles.

Dai, C., et al. (2015). “Advancements in three-dimensional endoscopic imaging.” Optics Express. Cited by 60 articles.

Dai, C., et al. (2018). “Nonlinear optical properties in biomedical imaging applications.” Applied Optics. Cited by 35 articles.

Dai, C., et al. (2020). “Enhancing digital holography techniques for medical diagnostics.” Journal of Optical Society of America B. Cited by 50 articles.

Dai, C., et al. (2022). “Functional imaging techniques for real-time endoscopic visualization.” Scientific Reports. Cited by 40 articles.

Dai, C., et al. (2023). “Machine learning approaches in biomedical imaging.” Nature Communications. Cited by 55 articles.

Dai, C., et al. (2024). “Recent trends in holographic imaging for medical applications.” IEEE Transactions on Medical Imaging. Cited by 30 articles.

Conclusion

Cuixia Dai has made significant contributions to optical engineering and biomedical imaging through her research, education, and international collaborations. Her work has advanced digital holography, spatial three-dimensional imaging, and biomedical optical imaging, leading to improved diagnostic techniques in ophthalmology and endoscopy. With numerous prestigious publications and recognition for her research excellence, she continues to drive innovation in biomedical imaging technologies. Her academic and professional achievements underscore her impact on the field, positioning her as a leading researcher dedicated to advancing medical imaging science.

Daemin Shin | Computer Science | Academic Luminary Achievement Award

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Dr. Daemin Shin | Computer Science | Academic Luminary Achievement Award

Manager at Financial Security Institute (FSI), South Korea

Daemin Shin is a Manager at the Financial Security Institute, where he has been actively involved in advancing financial security measures since April 2015. With expertise in cloud security, Zero Trust security models, and data security, he has played a significant role in shaping secure financial infrastructures. Before his current role, he was a Senior Researcher at the Financial Security Research Institute from July 2012 to April 2015. His contributions to financial cybersecurity research have been instrumental in addressing security threats and enhancing the resilience of financial institutions. Shin continues to lead innovative research and development in financial security.

Profile

Scopus

Education

Daemin Shin earned his Master of Science in Engineering from the Graduate School of Information Security at Korea University, South Korea, in February 2009. He further pursued his Ph.D. in Engineering at the Department of Information Security, Soonchunhyang University, South Korea, which he successfully completed in February 2020. His academic journey reflects a strong foundation in cybersecurity, particularly focusing on financial security, cloud computing, and data protection. Throughout his education, he has been deeply engaged in research on securing financial transactions and developing security frameworks for modern digital finance ecosystems.

Experience

Shin has over a decade of experience in the field of financial security, with a strong emphasis on cloud security, data protection, and Zero Trust architectures. He started his career as a Senior Researcher at the Financial Security Research Institute, where he contributed to innovative research projects on financial cybersecurity from 2012 to 2015. Since April 2015, he has been serving as a Manager at the Financial Security Institute, where he continues to work on financial security infrastructure, cybersecurity policies, and security compliance strategies. His professional experience has significantly contributed to the development of robust security measures for the financial sector.

Research Interests

Shin’s research interests primarily focus on cloud security, financial security, and Zero Trust security models. He has conducted extensive research on securing cloud-based financial infrastructures, ensuring compliance with regulatory requirements, and mitigating security threats in digital finance. His recent works include studies on security considerations for DevSecOps software supply chains and Zero Trust evaluation frameworks tailored for financial institutions. His expertise in these domains has positioned him as a thought leader in enhancing cybersecurity resilience in the financial industry.

Awards and Recognitions

Shin has been recognized for his outstanding contributions to financial security and cybersecurity research. He has been nominated for the Best Researcher Award in recognition of his groundbreaking research on cloud security and financial security frameworks. His efforts in improving security compliance policies and implementing Zero Trust methodologies in financial institutions have gained widespread recognition. Shin’s work has had a substantial impact on the cybersecurity domain, making financial transactions and data storage more secure against emerging threats.

Publications

D. Shin, V. Sharma, J. Kim, S. Kwon, and I. You (2017). “Secure and Efficient Protocol for Route Optimization in PMIPv6-Based Smart Home IoT Networks,” IEEE Access, vol. 5, pp. 11100-11117, DOI: 10.1109/ACCESS.2017.2710379. Cited by 200+ articles.

D. Shin, K. Yun, J. Kim, P. V. Astillo, J.-N. Kim, and I. You (2019). “A Security Protocol for Route Optimization in DMM-Based Smart Home IoT Networks,” IEEE Access, vol. 7, pp. 142531-142550, DOI: 10.1109/ACCESS.2019.2943929. Cited by 150+ articles.

Shin, Daemin, Kim, Jiyoon, & You, Ilsun (2023). “국내 금융구득 클라우드 전환 동형 및 보안,” REVIEW OF KIISC, 33(5), 57-68. Cited by 50+ articles.

Shin, Daemin, You, Ilsun, and Kim, Jiyoon (2024). “국내 금융구득 클라우드 보안 위험 및 보안 요구사항에 관한 연구,” Journal of Next-Generation Computing, 20(4), 77-96, DOI: 10.23019/kingpc.20.4.202408.007. Cited by 30+ articles.

Daemin Shin, Jiyoon Kim, I Wayan Adi Juliawan Pawana, Ilsun You (2025). “Enhancing Cloud-Native DevSecOps: A Zero Trust Approach for the Financial Sector,” Computer Standards & Interfaces, DOI: 10.1016/j.csi.2025.103975. Cited by 20+ articles.

Conclusion

Daemin Shin’s dedication to advancing financial security and cybersecurity has been instrumental in shaping modern security frameworks for financial institutions. His research on cloud security, Zero Trust models, and DevSecOps methodologies continues to drive innovation in securing financial infrastructures. With a strong academic and professional background, he remains committed to developing secure financial ecosystems and mitigating cybersecurity risks in an ever-evolving digital landscape. His contributions have earned him significant recognition, making him a leading figure in financial security research.

yang Li | AI in Healthcare | Best Researcher Award

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Prof. yang Li | AI in Healthcare | Best Researcher Award

Chief physician at First Hospital of Shanxi Medical University, China

Dr. Yang Li is a distinguished Chief Neurologist at the First Hospital of Shanxi Medical University, with over three decades of experience in cognitive disorder research and clinical practice. He holds a Doctor of Medicine (M.D.) degree and serves as a doctoral advisor. As the head of the Core Advanced Cognitive Center, he has played a pivotal role in advancing cognitive health initiatives in China. His contributions include the establishment of Shanxi Province’s first memory clinic in 2009, which received national recognition in subsequent years. Dr. Li has spearheaded multiple projects focused on Alzheimer’s disease (AD) and Parkinson’s disease (PD), significantly enhancing early detection and patient care strategies. Recognized for his exceptional contributions, he has been awarded the Second Prize of the Shanxi Provincial Science and Technology Progress Award and was selected as a leading talent under the “San Jin Talents” Support Program.

Profile

Scopus

Education

Dr. Yang Li obtained his Doctor of Medicine (M.D.) degree, equipping him with the expertise necessary for his extensive work in neurology and cognitive disorders. As a dedicated academic, he has mentored numerous doctoral candidates, guiding them in clinical research. His academic journey reflects a strong commitment to advancing neurological science, particularly in memory and cognitive function research. His efforts have contributed significantly to the development of national health policies and innovative diagnostic techniques for neurodegenerative disorders.

Experience

With more than 30 years in the field, Dr. Li has played a transformative role in neurology, specializing in cognitive disorders. His leadership at the First Hospital of Shanxi Medical University has resulted in numerous breakthroughs in early detection and treatment methodologies for conditions such as Alzheimer’s and Parkinson’s disease. Dr. Li has also been instrumental in establishing national training programs, including the Cognitive Specialty Capacity Building Project initiated by the National Health Commission. His expertise extends beyond clinical practice to impactful policy formulation and implementation. His work in digital screening tools and community-based healthcare projects underscores his innovative approach to neurological health.

Research Interests

Dr. Li’s research is primarily centered on cognitive disorders, particularly Alzheimer’s disease and other neurodegenerative conditions. He has pioneered advancements in early screening tools and interventions, integrating digital diagnostics such as neuroimaging assessments, PET-CT scans, and gait analysis. His recent initiatives focus on community-based screening, aiming to develop scalable and efficient methods for detecting mild cognitive impairment (MCI) and dementia in aging populations. His work contributes significantly to global research in cognitive health, emphasizing preventive strategies and innovative therapeutic approaches.

Awards

Dr. Li’s contributions to cognitive neurology have earned him numerous accolades. He was honored with the Second Prize of the Shanxi Provincial Science and Technology Progress Award in recognition of his pioneering research in neurodegenerative disorders. In 2018, he was selected as a leading talent under the “San Jin Talents” Support Program. His memory clinic, established in 2009, was recognized as a “National Outstanding Memory Clinic” in both 2013 and 2014. His dedication to advancing early screening and intervention methods for cognitive impairments has positioned him as a key figure in neurological research and healthcare innovation.

Publications

Dr. Li has contributed extensively to the scientific community with high-impact publications in leading journals. Some of his notable works include:

Qin Y, Han H, Li Y, et al. (2023). “Estimating Bidirectional Transitions and Identifying Predictors of Mild Cognitive Impairment.” Neurology, 100(3), e297-e307. [Cited by 120 articles].

Jia J, Zhao T, Liu Z, et al. (2023). “Association between Healthy Lifestyle and Memory Decline in Older Adults: 10-Year Prospective Cohort Study.” BMJ, 380, e072691. [Cited by 95 articles].

Wu H, Ren Z, Gan J, et al. (2022). “Blood Pressure Control and Risk of Post-Stroke Dementia.” Front Neurol, 13, 1069667. [Cited by 87 articles].

Zhang X, Lv L, Min G, Wang Q, Zhao Y, Li Y. (2021). “Complex Figure Test and Its Clinical Application in Neuropsychiatric Disorders.” Front Neurol, 12, 680474. [Cited by 78 articles].

Xu SY, Song MM, Liu DY, et al. (2024). “Contrast-Induced Encephalopathy with Elevated Cerebrospinal Fluid Protein.” Br J Neurosurg, 38(4), 963-967. [Cited by 56 articles].

Wang F, Fei M, Hu WZ, et al. (2022). “Prevalence of Constipation in Elderly and Its Association with Dementia.” Front Neurosci, 15, 821654. [Cited by 102 articles].

Xing Y, Zhu Z, Du Y, et al. (2020). “COG-REAGENT: Cognitive Training in Amnestic Mild Cognitive Impairment.” J Alzheimers Dis, 75(3), 779-787. [Cited by 112 articles].

Conclusion

Dr. Yang Li has made remarkable contributions to cognitive neurology through his pioneering research, clinical expertise, and commitment to early detection of neurodegenerative disorders. His leadership in community-based screening projects and digital health interventions has significantly advanced the field of cognitive disorders. With numerous prestigious awards, high-impact publications, and dedicated mentorship, Dr. Li continues to shape the landscape of Alzheimer’s and dementia research. His work not only enhances diagnostic methodologies but also fosters preventive healthcare strategies, making a lasting impact on the global fight against cognitive decline.

said boumaraf | Computer Vision | Best Researcher Award

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Dr. said boumaraf | Computer Vision | Best Researcher Award

Postdoctoral Fellow at Khalifa University, Algeria

Dr. Said Boumaraf is a dedicated researcher and academic in the field of computer science, specializing in artificial intelligence, machine learning, and computer vision. With a strong background in biomedical imaging, industrial applications, and networking, his work focuses on developing innovative AI-driven solutions for real-world challenges. He has contributed significantly to both academia and industry, holding various research positions and publishing extensively in high-impact journals. His expertise spans deep learning, feature selection, transfer learning, and anomaly detection, with applications in healthcare, oil and gas industries, and satellite communication systems.

Profile

Orcid

Education

Dr. Boumaraf earned his Ph.D. in Computer Science and Technology from the Beijing Institute of Technology, China, where he worked under the guidance of Prof. Xiabi Liu. His doctoral thesis, titled “Research on Machine Learning Methods for Breast Cancer Classification,” contributed significantly to AI applications in medical diagnosis. Prior to this, he completed his M.Sc. and B.Sc. degrees in Computer Science at Abbes Laghrour University of Khenchela, Algeria. His master’s research focused on wireless sensor network localization, while his bachelor’s thesis explored ontology-based contextual information search. These foundational studies provided him with extensive knowledge in data-driven decision-making and intelligent systems.

Professional Experience

Dr. Boumaraf has accumulated extensive research and professional experience across multiple roles. Currently, he is a postdoctoral fellow at Khalifa University of Science and Technology, UAE, where he is engaged in advanced AI projects such as “Vision-based Flare Analytics” for the oil and gas industry and “AI for Digital Pathology” for healthcare applications. Previously, he was a postdoctoral researcher at the University of Malta, working on AI-driven document analysis and classification. His industrial experience includes serving as a Chief Engineer and Researcher at the Algerian Space Agency, where he contributed to satellite control operations and AI-based anomaly detection in satellite telemetry data. Additionally, he has experience in IT management and government administration, further broadening his expertise in system optimization and software development.

Research Interests

Dr. Boumaraf’s research interests encompass artificial intelligence, deep learning, and computer vision, with applications in biomedical imaging, industrial analytics, and network security. He has focused extensively on machine learning-based medical image analysis, including thyroid nodule detection, histopathology classification, and dermoscopy. His industrial research includes AI-based combustion efficiency monitoring in oil and gas flares and satellite-based remote sensing. Additionally, he is interested in optimization techniques, dynamic knowledge networks, and cross-domain methodologies for enhancing model generalization. His work integrates AI-driven solutions into critical sectors, improving both operational efficiency and scientific innovation.

Awards and Recognitions

Dr. Boumaraf has been recognized for his contributions to AI and computer vision research through various academic and professional honors. He has received multiple nominations and accolades for his work in biomedical imaging and industrial AI applications. His research has been featured in prominent conferences and journals, and he has been actively involved in interdisciplinary collaborations that have garnered recognition from scientific and industrial communities.

Publications

Said Boumaraf, Xiabi Liu, Chokri Ferkous, Xiaohong Ma (2020) – “A New Computer-aided Diagnosis System with Modified Genetic Feature Selection for BI-RADS Classification of Breast Masses in Mammograms,” Biomedical Research International (DOI: 10.1155/2020/7695207). Cited by 50+ articles.

Said Boumaraf, Xiabi Liu, Zhongshu Zheng, Xiaohong Ma, Chokri Ferkous (2020) – “A New Transfer Learning Based Approach to Magnification Dependent and Independent Classification of Breast Cancers in Histopathological Images,” Biomedical Signal Processing and Control (DOI: 10.1016/j.bspc.2020.102192). Cited by 60+ articles.

Said Boumaraf, Xiabi Liu, Yuchai Wan, Zhongshu Zheng, Chokri Ferkous, Xiaohong Ma (2021) – “Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation,” Diagnostics (DOI: 10.3390/diagnostics11030528). Cited by 40+ articles.

Yuchai Wan, Zhongshu Zheng, Ran Liu, Zheng Zhu, Hongen Zhou, Xun Zhang, Said Boumaraf (2021) – “A Multi-Scale and Multi-Level Fusion Approach for Deep Learning-Based Liver Lesion Diagnosis in Magnetic Resonance Images with Visual Explanation,” Life (DOI: 10.3390/life11060582). Cited by 30+ articles.

Al Radi, Muaz, Pengfei Li, Said Boumaraf, Jorge Dias, Naoufel Werghi (2024) – “AI-Enhanced Gas Flares Remote Sensing and Visual Inspection: Trends and Challenges,” IEEE Access. Cited by 20+ articles.

Xiaodong Qin, Xiabi Liu, Said Boumaraf (2019) – “A New Feature Selection Method based on Monarch Butterfly Optimization and Fisher Criterion,” International Joint Conference on Neural Networks (IJCNN). Cited by 25+ articles.

Huiyu Li, Xiabi Liu, Said Boumaraf, Weihua Liu, Xiaopeng Gong, Xiaohong Ma (2020) – “A New Three-stage Curriculum Learning Approach for Deep Network Based Liver Tumor Segmentation,” International Joint Conference on Neural Networks (IJCNN). Cited by 35+ articles.

Conclusion

Dr. Said Boumaraf is a distinguished researcher whose work bridges the gap between artificial intelligence and real-world applications. His contributions to biomedical imaging, industrial AI, and satellite communication have significantly advanced the fields of machine learning and deep learning. With an extensive background in academia and industry, he continues to push the boundaries of AI-driven innovation. Through his research, publications, and professional engagements, Dr. Boumaraf remains at the forefront of cutting-edge AI applications, making meaningful contributions to scientific and technological advancements.

Ji-Soo Jang | Internet of Things (IoT) Data | Best Researcher Award

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Dr. Ji-Soo Jang | Internet of Things (IoT) Data | Best Researcher Award

Senior Research Scientist at Korea Institute of Science and Technology (KIST), South Korea

Dr. Ji-Soo Jang is a distinguished Senior Research Scientist at the Korea Institute of Science and Technology (KIST) in the Electronic Materials Research Center. With extensive expertise in material science and engineering, he has made significant contributions to the fields of nanomaterials, sensors, and energy applications. Dr. Jang has been recognized with numerous prestigious awards and honors for his innovative research. His work has been widely cited, reflecting its impact in advancing technology in chemical sensing, nanomaterials, and environmental applications.

Profile

Scopus

Education

Dr. Jang earned his Ph.D. in Material Science and Engineering from the Korea Advanced Institute of Science and Technology (KAIST) in 2020, where he conducted groundbreaking research on chemical sensors using organic/inorganic nanomaterials. Prior to this, he completed his M.S. in Material Science and Engineering from KAIST in 2016, focusing on nanocatalysts for biomarker detection. He obtained his B.S. from Hanyang University, graduating summa cum laude in 2014. His early academic excellence was evident through his participation in an honors program and his early graduation from Incheon Science High School.

Professional Experience

Dr. Jang has accumulated extensive research experience across globally renowned institutions. Before joining KIST, he was a Postdoctoral Associate at Yale University in Chemical and Environmental Engineering, collaborating with distinguished researchers on membrane technology. He also held a postdoctoral position at KAIST, further developing his expertise in material science. Additionally, he has served as a visiting researcher at the University of California, Irvine, and the Massachusetts Institute of Technology (MIT), contributing to advanced studies in nanotechnology and chemical engineering. His professional journey has been marked by significant collaborations and leadership roles in international research projects and conferences.

Research Interests

Dr. Jang’s research primarily focuses on the development of nanomaterials for environmental and energy applications. His key interests include chemical sensing, functional nanomaterials, membrane technology, and energy storage devices. He has been actively working on designing innovative materials for gas sensors, water purification membranes, and bio-electronic applications. His interdisciplinary approach has led to breakthroughs in highly sensitive and selective chemical sensors, enabling real-world applications in pollution control, biomedical diagnostics, and sustainable energy solutions.

Awards and Honors

Dr. Jang has received numerous accolades in recognition of his research contributions. He was awarded the KIST Young Fellow Award in 2024, demonstrating his leadership in scientific innovation. His doctoral work earned him the Excellence Doctorate Thesis Award at KAIST in 2020. Additionally, he has received prestigious awards such as the ICAE Student Award (2019), the Silver Award in the Samsung Human Tech Paper Competition (2019), and the Trade, Industry, and Energy Ministry Award (2018). His groundbreaking patents have also led to significant technology transfers, with multiple high-value agreements with leading enterprises.

Publications

Jiwon Park, Sang-Mi Chang, Ji-Soo Jang et al., “Bio-Physicochemical Dual Energy Harvesting Fabrics for Self-Sustainable Smart Electronic Suits,” Advanced Energy Materials, 2023. Cited by 50+ articles.

Gwang Su Kim, Ji-Soo Jang et al., “Breathable MOFs Layer on Atomically Grown 2D SnS2 for Stable and Selective Surface Activation,” Advanced Science, 2023. Cited by 40+ articles.

Joonchul Shin, Ji-Soo Jang et al., “Atomically Mixed Catalysts on a 3D Thin-Shell TiO2 for Dual-Modal Chemical Detection and Neutralization,” JMCA, 2023. Cited by 30+ articles.

Ji-Soo Jang, Yunsung Lim, Jihan Kim, “Bi-directional Water-Stream Behavior on Multifunctional Membrane for Simultaneous Energy Generation and Water Purification,” Advanced Materials, 2023. Cited by 100+ articles.

Hyung-Jin Choi, Ji-Soo Jang et al., “Epitaxial Growth of β-Ga2O3 Thin Films on Si with YSZ Buffer Layer,” ACS Omega, 2022. Cited by 25+ articles.

Ji-Soo Jang, Menachem Elimelech, “High Precision Separation Membranes for Selective Environmental Gas Sensors,” Trends in Chemistry, 2021. Cited by 75+ articles.

Ji-Soo Jang, Il-Doo Kim, “Dopant-Driven Positive Reinforcement in an Ex-Solution Process: New Strategy for Highly Durable Catalytic Materials,” Advanced Materials, 2020. Cited by 120+ articles.

Conclusion

Dr. Ji-Soo Jang has established himself as a leading researcher in material science and engineering, particularly in nanomaterials and sensor technology. His work has been instrumental in advancing chemical sensing, environmental sustainability, and energy-efficient technologies. Through his prolific research output, numerous prestigious awards, and impactful collaborations, he continues to shape the future of advanced materials. His contributions to academia and industry demonstrate his commitment to innovation, making him a prominent figure in the scientific community.

Ercan Nurcan Yilmaz | Feature Engineering | Best Paper Award

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Prof. Dr. Ercan Nurcan Yilmaz | Feature Engineering | Best Paper Award

Professor at Gazi University, Turkey

Prof. Ercan Nurcan Yilmaz is a distinguished academic and researcher in the field of electrical and electronics engineering. With a career spanning several decades, he has contributed significantly to research and education, focusing on cybersecurity, smart grid systems, and industrial control systems. His work is widely recognized in academic circles, and he has played a pivotal role in mentoring postgraduate and doctoral students. Prof. Yilmaz has been a professor at Gazi University, where he continues to advance research in algorithms, software development, and energy systems. His expertise and contributions to various research projects have established him as a leading figure in his domain.

Profile

Google Scholar

Education

Prof. Yilmaz completed his undergraduate studies in Electrical Education at Gazi University’s Technical Education Faculty in 1995. He further pursued his postgraduate studies in Electrical Education at Gazi University’s Institute of Science from 1995 to 1998, where he focused on alternators’ parallel connection in a computer-based environment. He earned his doctorate in 2003 from the same institution with his research on SCADA system design using internet networks. His academic journey has provided him with a robust foundation in electrical and electronic systems, allowing him to make meaningful contributions to academia and industry.

Experience

Prof. Yilmaz has held various academic positions at Gazi University. He served as an Assistant Professor from 2007 to 2011, an Associate Professor from 2011 to 2019, and was promoted to Professor in 2019 in the Department of Electrical-Electronics Engineering at the Faculty of Technology. In addition to his teaching and research roles, he has actively participated in administrative and departmental responsibilities. His experience extends beyond academia, encompassing consultancy and project leadership in cybersecurity and industrial automation.

Research Interests

Prof. Yilmaz’s research interests cover a wide spectrum of topics, including cybersecurity threats in industrial control systems, artificial intelligence applications in security, smart grids, and SCADA system automation. His work is deeply rooted in technological advancements, particularly in securing IoT-based applications and integrating machine learning into cybersecurity frameworks. He has also explored renewable energy systems and optimization techniques in microgrid designs. His interdisciplinary approach has contributed to innovative solutions for modern engineering challenges.

Awards

Throughout his career, Prof. Yilmaz has received numerous recognitions for his outstanding contributions to research and education. His work in cybersecurity and smart grid systems has been acknowledged through several academic awards and grants. He has also been nominated for prestigious accolades in engineering and technology research, reflecting the impact of his contributions to the field.

Publications

Prof. Yilmaz has authored numerous research articles in high-impact journals. Some of his notable publications include:

“Machine learning-based identification of cybersecurity threats affecting autonomous vehicle systems” – Published in Computers and Industrial Engineering, 2024, cited by several cybersecurity researchers.

“Real-Time Cyber Attack Detection Over HoneyPi Using Machine Learning” – Published in TEHNICKI VJESNIK-TECHNICAL GAZETTE, 2022.

“False data injection attacks and the insider threat in smart systems” – Published in COMPUTERS & SECURITY, 2020.

“Design and Implementation of Fuel Cell and Photovoltaic Panel-Supported Ozonation System” – Published in OZONE-SCIENCE & ENGINEERING, 2019.

“Design of an off-grid model of micro-smart grid connection of an asynchronous motor fed with LUO converter” – Published in ELECTRICAL ENGINEERING, 2018.

“Design and implementation of real-time monitoring and control system supported with IOS/Android application for industrial furnaces” – Published in IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING, 2018.

“Attack detection/prevention system against cyber attack in industrial control systems” – Published in COMPUTERS & SECURITY, 2018.

Conclusion

Prof. Ercan Nurcan Yilmaz has significantly contributed to the fields of electrical engineering, cybersecurity, and smart grid technologies. His research has paved the way for new methodologies in securing industrial control systems and integrating AI-driven approaches into cybersecurity frameworks. His commitment to education and mentoring has influenced many students and researchers, fostering the next generation of engineering professionals. With an extensive body of published work and ongoing research projects, he continues to drive innovation in engineering and technology.