HMIMSA Younes | Application of Artificial Intelligence in Agriculture | Best Researcher Award

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Prof. HMIMSA Younes | Application of Artificial Intelligence in Agriculture | Best Researcher Award

Abdelmalek Essaadi University | Morocco

Professor Younes Hmimsa is a distinguished Moroccan academic and researcher serving as a Full Professor at the Polydisciplinary Faculty of Larache, Abdelmalek Essaadi University. His academic background spans animal biology, plant biotechnology, ecology, and wealth management, reflecting a multidisciplinary approach to environmental and agricultural sciences. With advanced degrees from the University of Tetouan and the University of Alicante, he has developed extensive expertise in plant biology, agroecology, and biodiversity conservation. Professionally, he has been a key figure in multiple international research programs such as PRIMA, ARIMNET, and EVOlea, focusing on agrobiodiversity, climate resilience, and sustainable agricultural systems. His research interests include plant phenology, genetic diversity, agroecosystem sustainability, and the socio-ecological dynamics of traditional farming systems in Mediterranean regions. He possesses strong research skills in agromorphological characterization, environmental data modeling, and interdisciplinary collaboration. Professor Hmimsa has coordinated numerous international conferences, seminars, and research networks that bridge scientific innovation and rural development. His scholarly achievements are complemented by prestigious roles as a reviewer, evaluator, and collaborator with global research institutions. A recipient of several academic honors and author of influential publications, he continues to advance sustainable agricultural practices and ecological research in Morocco and beyond, inspiring both scientific and community engagement.

Profile: ORCID

Featured Publications

Olubunmi Kayode AYANWOYE | Generative Artificial Intelligence | Best Researcher Award

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Dr. Olubunmi Kayode AYANWOYE | Generative Artificial Intelligence| Best Researcher Award

Federal University Oye-Ekiti | Nigeria

Dr. Olubunmi Kayode Ayanwoye is a distinguished Nigerian scholar and educator whose academic and professional pursuits are rooted in mathematics education, pedagogy, and curriculum innovation. He holds advanced degrees in mathematics education from the University of Ibadan and the University of Ado-Ekiti, complemented by a diploma in computer applications, reflecting his strong interdisciplinary foundation. His career spans teaching and lecturing roles at leading Nigerian institutions, including the Oyo State Teaching Service Commission, Emmanuel Alayande College of Education, and the Federal University Oye-Ekiti, where he currently serves as a lecturer. Dr. Ayanwoye’s research interests encompass general education, mathematics pedagogy, gender issues in learning, curriculum design, and research analytics, with a particular focus on integrating technology and artificial intelligence in education. His research skills include meta-analysis, systematic review, statistical interpretation, and instructional design. A regular participant and presenter at national and international academic conferences, he contributes to advancing educational methodologies and digital readiness in teacher education. Throughout his career, Dr. Ayanwoye has received recognition for his academic excellence, leadership, and commitment to innovative teaching practices. Dedicated to fostering critical thinking and inclusivity in education, he continues to inspire future educators through impactful research, mentorship, and a steadfast dedication to academic and professional excellence.

Profile: ORCID

Featured Publications

Ayanwoye, O. K. (2025). Aims and objectives of teaching mathematics as a school subject. In The Methodology of Science Teaching.

Falebita, O. S., Abah, J. A., Asanre, A. A., Abiodun, T. O., Ayanwale, M. A., & Ayanwoye, O. K. (2025, October). Determinants of chatbot brand trust in the adoption of generative artificial intelligence in higher education. Education Sciences, 15(10), Article 1389.

Ayanwoye, O. K. (2025, October 3). Influence of artificial intelligence tool perceptions on mathematics undergraduates’ academic engagement: Role of attitudes and usage intentions. International Journal of Didactic Mathematics in Distance Education, 2(2), 207–224.

Ayanwoye, O. K. (2025, September 15). Assessment of media capture and ethical challenges in reporting corruption in Nigeria. Journal of African Films and Diaspora Studies (JAFDIS), 8(3).

Zhengyi Yao | Artificial Intelligence | Best Researcher Award

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Mr. Zhengyi Yao | Artificial Intelligence | Best Researcher Award

Sichuan Normal University |China

Zhengyi Yao, from Neijiang, Sichuan, China, is a dedicated researcher affiliated with Sichuan Normal University, holding both bachelor’s and master’s degrees in Computer Science and Technology. His work primarily focuses on the Internet of Things (IoT), cybersecurity, cryptography, and artificial intelligence (AI). With a growing presence in academic publishing, he has contributed to several high-impact journals indexed in SCI and Scopus. Mr. Yao has demonstrated a strong commitment to advancing secure, intelligent systems, particularly in logistics and industrial applications. His interdisciplinary approach blends theoretical research with practical implementation, contributing to emerging technologies such as blockchain-enabled IIoT and quantum cryptography. In addition to publishing five journal articles and securing seven patents, he actively contributes to the field through applied innovations aimed at enhancing privacy protection and data security. As a passionate technologist, Mr. Yao is continually exploring transformative solutions in smart systems, emphasizing the ethical and secure integration of AI in modern digital infrastructure.

Profile

Education

Zhengyi Yao completed his academic training at Sichuan Normal University, earning both his bachelor’s and master’s degrees in Computer Science and Technology. His undergraduate studies provided a solid foundation in software development, algorithms, and system architecture, while his postgraduate work emphasized advanced topics such as artificial intelligence, cybersecurity, and cryptographic methods. During his graduate years, he engaged deeply with interdisciplinary studies, aligning computer science with real-world applications in logistics, IoT, and secure communication systems. His academic performance has been marked by consistent excellence and a proactive engagement in research-driven projects. While enrolled, he also explored the practical aspects of emerging technologies, developing tools and frameworks to support digital transformation in industrial systems. His education has been instrumental in shaping his scientific outlook, fostering a commitment to ethical innovation and robust digital security. These academic experiences continue to inform his contributions to academic research and patent development in the tech and security domains.

Experience

Zhengyi Yao has gained substantial experience as a researcher and innovator in the fields of IoT, cybersecurity, cryptography, and AI. While at Sichuan Normal University, he actively participated in multiple collaborative research efforts that examined the integration of blockchain with IIoT systems and privacy-focused AI applications in logistics. Despite limited consultancy and editorial appointments, his practical contributions are demonstrated through five SCI/Scopus-indexed journal publications and seven patents. He has co-authored research tackling challenges in smart logistics security, 5G-based blockchain sensors, and quantum cryptography, showcasing his capability to bridge theoretical and applied computing. Through independent and team-driven efforts, Mr. Yao has contributed to designing secure systems that support data integrity and user privacy in dynamic industrial environments. His hands-on research experience, supported by solid academic training, underpins his drive to innovate in secure computing technologies and has positioned him as a promising young professional in China’s growing digital research landscape

Research Focus

Zhengyi Yao’s research centers on the intersection of emerging technologies like IoT, blockchain, AI, and cybersecurity, with a strong focus on intelligent logistics systems. He explores secure device communication, privacy-preserving data protocols, and cryptographic models for industrial systems. His work on blockchain-enabled IIoT platforms aims to fortify command operations against cyber threats, while his investigations into quantum cryptography are pushing the boundaries of next-generation digital security. One of his key contributions is the development of 5G-based universal blockchain smart sensors, combining speed, scalability, and trust for dynamic logistics applications. His research also examines how AI can be ethically and securely integrated into cyber-physical environments to optimize data flow, user privacy, and system integrity. Through published works and patented innovations, he is shaping solutions to critical security challenges facing smart logistics and industrial platforms. His forward-thinking approach promotes safer, more resilient infrastructures in an increasingly connected digital world.

Publications

Sensitive Data Privacy Protection of Carrier in Intelligent Logistics System
Year: 2024
Citation:2

Blockchain-enabled device command operation security for Industrial Internet of Things
Year: 2023
Citation:12

5G-BSS: 5G-Based Universal Blockchain Smart Sensors
Year: 2022
Citation:2

Conclusion

Zhengyi Yao exemplifies the qualities of a dedicated and innovative researcher, with notable contributions to smart logistics, cybersecurity, and cryptographic technologies. His blend of academic rigor and applied invention positions him as a rising leader in secure digital systems.

Dae Hyeob Yoon | Cognitive Robotics | Best Researcher Award

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Mr. Dae Hyeob Yoon | Cognitive Robotics | Best Researcher Award

Chungbuk National University (CBNU) | South Korea

Dae Hyeob Yoon is an emerging undergraduate researcher in the field of mechanical engineering at Chungbuk National University (CBNU), South Korea. Passionate about micro/nanotechnology, sensors, and MEMS, he has already made noteworthy contributions to the scientific community despite being at an early stage of his academic journey. His research focuses on flexible and conductive heating membranes, culminating in his co-authorship of a peer-reviewed article published in Applied Sciences. His efforts were recognized with an award at the Undergraduate Research Opportunities Program (UROP) at CBNU’s College of Engineering. He has also presented his research at the Korean Society of Mechanical Engineers (KSME) and is set to showcase his work at the European Korean Conference (EKC) in Austria. Yoon’s drive and innovative mindset position him as a promising young talent in the world of research and engineering innovation.

Profile

ORCID

Education

Dae Hyeob Yoon is currently pursuing a Bachelor of Science degree in Mechanical Engineering at Chungbuk National University (CBNU). His education integrates fundamental engineering principles with advanced topics such as micro/nanotechnology, sensor systems, and microelectromechanical systems (MEMS). As part of his academic training, he has engaged deeply with interdisciplinary research methods, particularly focusing on the application of electrospinning and electroless plating technologies in developing flexible electronics. Yoon has also participated in institutional programs such as the Undergraduate Research Opportunities Program (UROP), where he honed his skills in scientific inquiry and innovation. Through his coursework and research experiences, he has demonstrated strong analytical thinking, lab competence, and a deep commitment to problem-solving in modern engineering challenges. His ongoing academic development is supported by hands-on research exposure and international presentation opportunities, laying a strong foundation for future graduate studies or industry-based R&D roles.

Experience

Despite being an undergraduate, Dae Hyeob Yoon has gained substantial research experience through academic and applied projects at CBNU. He played a pivotal role in developing a flexible and conductive heating membrane via BSA-assisted electroless plating on electrospun PVDF-HFP nanofibers, a project that led to his first co-authored publication in a Scopus-indexed journal (Applied Sciences). He has participated in CBNU’s UROP, receiving recognition for his work among peers and faculty. His research contributions have also been shared in the KSME poster sessions, with further international exposure expected at the European Korean Conference (EKC) in Austria. In addition to academic research, Yoon has engaged in a consultancy/industry-related project, integrating real-world needs with lab-scale innovation. His proactive involvement in project design, material synthesis, and result interpretation reflects a strong aptitude for research. His experience signifies readiness for more complex roles in research labs or industry innovation teams.

Awards and Honors

Dae Hyeob Yoon has already earned notable academic recognition at an early stage in his career. He was awarded for his outstanding contribution during the Undergraduate Research Opportunities Program (UROP) achievement presentation hosted by the College of Engineering at Chungbuk National University. This accolade highlighted his scientific contribution and presentation skills in a competitive research environment. Additionally, his co-authored paper was published in the peer-reviewed journal Applied Sciences, an achievement that underscores his ability to contribute to high-quality research outputs. His research work has also been selected for presentation at leading scientific gatherings, including KSME (Korean Society of Mechanical Engineers) and the upcoming European Korean Conference (EKC) in Austria. These honors reflect both national and international acknowledgment of his scientific potential. His early achievements position him as a strong candidate for awards recognizing young researchers or best research articles in the engineering domain.

Research Focus

Dae Hyeob Yoon’s research centers on the integration of micro/nanotechnology, sensors, and MEMS to address modern engineering challenges. His recent work involves the development of a flexible and conductive heating membrane using electrospun nanofibers and electroless plating—technologies poised to impact wearable electronics and smart textiles. By employing Bovine Serum Albumin (BSA)-assisted plating techniques, his research introduces a scalable and cost-effective method for producing mechanically stable, low-voltage heating devices. This innovation aims to overcome performance, flexibility, and integration issues found in existing thermal management systems. His focus on applying advanced materials and fabrication techniques in real-world scenarios reflects his goal of contributing to the next generation of personalized and adaptive electronic technologies. With continued work, Yoon’s research can pave the way for biocompatible, energy-efficient, and multifunctional devices suited for health monitoring, environmental sensing, and human-centric smart systems.

Conclusion

Dae Hyeob Yoon exemplifies a highly motivated undergraduate researcher whose early contributions to flexible electronics and MEMS demonstrate exceptional promise, with strong academic grounding, peer-reviewed publication, research awards, and international presentations validating his suitability for the Best Researcher Article Award.

Publications

Title: Development of a Flexible and Conductive Heating Membrane via BSA‑Assisted Electroless Plating on Electrospun PVDF‑HFP Nanofibers
Year : 2025

Kihwan Nam | Artificial Intelligence | Best Faculty Award

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Prof. Kihwan Nam | Artificial Intelligence | Best Faculty Award

Dr. Kihwan Nam is an Assistant Professor in the Department of Management of Technology at Korea University and the founder of Aimtory, a high-technology AI company. With a unique blend of academic expertise and entrepreneurial insight, he specializes in Artificial Intelligence (AI), particularly Generative AI, Explainable AI, and Digital Transformation. He earned his Ph.D. in Information Systems from KAIST and holds degrees in Industrial Engineering and Statistics from Korea University and Yonsei University, respectively. Dr. Nam has an extensive research record, with publications in top-tier journals such as Journal of Marketing Research, Decision Support Systems, and Knowledge-Based Systems. His professional journey includes leadership roles in startups and significant AI industry contributions. He is passionate about bridging the gap between academia and industry through impactful, data-driven solutions that transform business strategies, smart factories, and healthcare systems. Dr. Nam is a leading figure in the fusion of cutting-edge AI technologies with business innovation.

Profile

🎓 Education

Dr. Kihwan Nam’s academic background spans statistics, engineering, and management. He holds a Ph.D. in Information Systems and Management Engineering from the prestigious KAIST College of Business, where he honed his expertise in AI-driven decision support and business analytics. Prior to his doctorate, he completed his M.S. in Industrial Engineering at Korea University, acquiring strong analytical and system optimization skills. His academic journey began with a B.A. in Statistics from Yonsei University, which laid a solid foundation in data analysis and quantitative modeling. This interdisciplinary academic training enables Dr. Nam to approach complex problems from technical, managerial, and data-driven perspectives. Throughout his studies, he cultivated a deep interest in predictive modeling, econometrics, and the integration of AI technologies in organizational contexts, which continues to shape his academic and industrial research today. His educational path reflects a consistent commitment to excellence and innovation across disciplines.

🧪 Experience

Dr. Nam has a dynamic career in both academia and industry. He currently serves as Assistant Professor in the Management of Technology at Korea University, following a faculty role in Management Information Systems at Dongguk University. In industry, he is the founder of Aimtory, a company focused on cutting-edge AI solutions, and previously led Basbai, an AI solution firm, as CEO. He also co-founded Sentience, reflecting his commitment to tech entrepreneurship. His dual roles have enabled him to conduct collaborative research with top-tier companies, implement AI in real-world applications, and train future innovators. Dr. Nam’s expertise extends across AI project development, big data analytics, and digital business transformation. His work in areas like smart factories, healthcare, and financial markets underscores his versatility. His diverse experience positions him as a thought leader at the intersection of research, innovation, and enterprise AI deployment.

🏅 Awards and Honors

Dr. Kihwan Nam has received numerous prestigious accolades for his impactful research and innovation. He was honored with the Best Paper Award from the Korea Intelligent Information System Society (2017) for his work on recommender systems in retail, and again in 2019 by the Information Systems Review Society for a field experiment in recommendation design. His deep learning-based financial distress prediction study was a Best Paper Nominee at the INFORMS Data Science Workshop (2020). In 2022, he secured top honors at the Korea Gas Corporation Big Data Competition and received an innovation award from the Startup Promotion Agency for the Big-Star Solution Platform. In 2023, he earned the Best Researcher Award at Dongguk University. These recognitions reflect his excellence in both theoretical contributions and practical applications of AI, reinforcing his role as a leading figure in AI-driven business analytics and intelligent systems research.

🔬 Research Focus

Dr. Nam’s research lies at the intersection of Artificial Intelligence, Business Analytics, and Digital Transformation. He specializes in Generative AI, Explainable AI, LLMs, NLP, and Computer Vision, aiming to drive intelligent decision-making in sectors like healthcare, finance, and manufacturing. His core research explores predictive analytics, recommender systems, robot advisory, and econometric modeling applied to real-world business and technological challenges. By incorporating econometrics with data mining and machine learning, he investigates user behavior, personalization strategies, and large-scale business optimization. His recent projects include stock and cryptocurrency prediction, smart factory optimization, and curated recommendation engines. He is also advancing research in digital transformation (DX) and blockchain-based token economies. Dr. Nam emphasizes bridging theory and application by applying AI innovations to actual business environments, often in collaboration with international enterprises. His work is deeply rooted in the integration of robust statistical methods with scalable, real-world AI systems.

✅ Conclusion

Dr. Kihwan Nam is a visionary academic and AI entrepreneur who merges deep theoretical knowledge with practical applications, shaping the future of AI-driven digital transformation across industries through innovative research, impactful teaching, and real-world solutions

Publications

Chan-Uk Yeom | Artificial Intelligence | Best Researcher Award

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Dr. Chan-Uk Yeom | Artificial Intelligence | Best Researcher Award

Dr. Chan-Uk Yeom is a Research Professor at the Research Institute of IT, Chosun University, Korea. He specializes in time series data analysis using deep learning, granular computing, adaptive neuro-fuzzy inference systems, high-dimensional data clustering, and biosignal-based biometrics. Dr. Yeom has held several research positions, including at the Division of AI Convergence College at Chosun University and the Center of IT-BioConvergence System Agriculture at Chonnam National University. His work integrates artificial intelligence, fuzzy systems, and granular models for practical applications such as healthcare, biometrics, and energy efficiency. Dr. Yeom has published extensively in high-impact journals and conferences, holds multiple patents, and has received numerous awards for his innovative research contributions. He actively teaches courses related to AI healthcare applications and electronic engineering. His collaboration and problem-solving skills have been demonstrated through his involvement in competitive AI research challenges and global innovation camps.

Professional Profile

Education

Dr. Yeom completed his entire higher education at Chosun University, Korea. He earned his Ph.D. in Engineering (2022) from the Department of Control and Instrumentation Engineering, with a dissertation on fuzzy-based granular model design using hierarchical structures under the supervision of Prof. Keun-Chang Kwak. Prior to this, he obtained his M.S. in Engineering (2017), focusing on ELM predictors using TSK fuzzy rules and random clustering, and his B.S. in Engineering (2016) in Control and Instrumentation Robotics. His academic work laid a strong foundation in machine learning, granular computing, and fuzzy inference systems, which became the core of his future research trajectory. Throughout his education, Dr. Yeom demonstrated academic excellence, leading to multiple thesis awards, and developed expertise in AI-driven applications for healthcare, energy optimization, and biometrics.

Experience

Currently, Dr. Yeom serves as a Research Professor at the Research Institute of IT, Chosun University (since January 2025). Previously, he was a Research Professor at Chosun University’s Division of AI Convergence College (2023–2024) and a Postdoctoral Researcher at the Center of IT-BioConvergence System Agriculture, Chonnam National University (2022–2023). His extensive research spans user authentication technologies using multi-biosignals, brain-body interface development using AI multi-sensing, and optimization of solar-based thermal storage systems. In addition to research, Dr. Yeom has contributed to teaching undergraduate courses, including AI healthcare applications, electronic experiments, capstone design, and open-source software. He is also experienced in mentorship, student internships, and providing special employment lectures. His active participation in national and international research projects and conferences reflects his global engagement and multidisciplinary expertise in artificial intelligence, healthcare, biometrics, and advanced fuzzy models.

Research Interests

Dr. Yeom’s research integrates deep learning, granular computing, and adaptive neuro-fuzzy systems to solve complex problems in healthcare, biometrics, energy efficiency, and time series data analysis. His innovative work focuses on designing hierarchical fuzzy granular models, developing incremental granular models with particle swarm optimization, and applying AI-driven methods to biosignal-based biometric authentication. Dr. Yeom has developed cutting-edge models for predicting energy efficiency, vehicle fuel consumption, water purification processes, and disease classification from ECG signals. His contributions also extend to explainable AI, emotion recognition, and non-contact biosignal acquisition using 3D-CNN. In addition to academic publications, he has secured multiple patents related to ECG-based personal identification methods, intelligent prediction systems, and granular neural networks. His interdisciplinary approach combines theoretical modeling, real-world applications, and collaborative AI system design, advancing the fields of biomedical informatics, neuro-fuzzy computing, and healthcare convergence technologies.

Awards

Dr. Yeom has received numerous awards recognizing his academic excellence. He earned multiple Excellent Thesis Awards from prestigious conferences, including the International Conference on Next Generation Computing (ICNGC 2024), the Korea Institute of Information Technology (KIIT Autumn Conference 2024), and the Annual Conference of Korea Information Processing Society (ACK 2024). His doctoral work was recognized at Chosun University’s 2021 Graduate School Doctoral Degree Award Ceremony. He also received the Outstanding Presentation Paper Award at the 2020 Korean Smart Media Society Spring Conference and the Excellent Thesis Award at the Korea Information Processing Society 2018 Spring Conference. Earlier, his problem-solving capabilities were showcased as a finalist and top 9 team at the 2018 AI R&D Challenge and during participation in the 2016 Global Entrepreneurship Korea Camp. These honors highlight his sustained contributions to AI research, innovation, and applied technological development.

Conclusion

Dr. Chan-Uk Yeom is a dynamic researcher whose pioneering contributions to granular computing, neuro-fuzzy systems, and AI healthcare applications demonstrate his exceptional expertise, innovative thinking, and global scientific impact, making him a valuable contributor to the advancement of next-generation intelligent systems.

 Publications

  • A Design of CGK-Based Granular Model Using Hierarchical Structure

    Applied Sciences
    2022-03 | Journal article | Author
    DOI: 10.3390/app12063154
    CONTRIBUTORS: Chan-Uk Yeom; Keun-Chang Kwak

  • Adaptive Neuro-Fuzzy Inference System Predictor with an Incremental Tree Structure Based on a Context-Based Fuzzy Clustering Approach

    Applied Sciences
    2020-11 | Journal article | Author
    DOI: 10.3390/app10238495
    CONTRIBUTORS: Chan-Uk Yeom; Keun-Chang Kwak

Hong Wang | Memristors | Best Researcher Award

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Prof. Dr. Hong Wang | Memristors | Best Researcher Award

Dr. Hong Wang is an accomplished Associate Professor at Hebei University, China, specializing in the field of neuromorphic electronics and low-dimensional ferroelectric materials. With a strong academic foundation in Physics, Integrated Circuits, and Optical Engineering, she has rapidly advanced in her field since earning her doctorate in 2021. Her research has led to 15 SCI-indexed publications as a first author, 8 patents, and over 1300 citations, underscoring her scientific impact. Dr. Wang actively collaborates with leading researchers from institutions such as the National University of Singapore, the Chinese Academy of Sciences, and Jilin University, achieving multiple experimental firsts in ferroelectricity and memristor behavior. Her innovative work bridges material science and cognitive computing, making significant contributions to optoelectronic sensing and neuromorphic systems. She is a member of several prestigious scientific societies, including the Chinese Optical Society. Dr. Wang’s dedication and research excellence make her a standout in cognitive science innovations.

Profile

🎓 Education

Dr. Hong Wang’s academic journey began with a Bachelor’s degree in Physics from Beihua University in 2016, which laid the foundation for her interdisciplinary approach to electronic materials. She then earned her Master’s degree in Integrated Circuits from Hebei University in 2018, further refining her expertise in semiconductor and electronic system design. Driven by a passion for optical and neuromorphic technologies, she pursued a PhD in Optical Engineering at Hebei University, completing it in 2021. Her doctoral research focused on the application of low-dimensional ferroelectric materials, contributing valuable insight into the behavior of memristive systems and their implications for artificial neural networks. This strong educational background has enabled her to explore innovative technologies in cognitive sensing and computing, bridging physics, materials science, and neural engineering. Her academic training not only exemplifies depth and rigor but also reflects a unique ability to translate theoretical research into applied cognitive systems.

🧪 Experience

Since 2021, Dr. Hong Wang has served as an Associate Professor at the School of Electronic Information and Engineering, Hebei University. In this role, she has taken on responsibilities spanning research leadership, mentoring graduate students, and leading interdisciplinary projects at the frontier of neuromorphic computing. She has directed five major research projects and collaborated internationally with scholars from Singapore, the Chinese Academy of Sciences, and Jilin University. Her work has provided novel insights into ferroelectricity in materials like SnSe and ReSe₂, and its application in memristive devices. In addition to her academic duties, Dr. Wang has contributed to two industry consultancy projects, aligning academic innovation with technological advancement. Her ability to bridge material innovation with neural system architecture distinguishes her as a versatile and future-oriented cognitive scientist. Her professional experience is marked by innovation, collaboration, and a commitment to enhancing cognitive systems through novel material applications.

🏅 Awards and Honors

While specific awards are not explicitly listed, Dr. Hong Wang’s impressive research metrics and collaborations signify her recognition within the global scientific community. With 15 SCI-indexed publications as first author and over 1365 citations, her work has garnered significant academic attention. Her successful collaborations with leading institutions like the National University of Singapore and the Chinese Academy of Sciences validate her contributions through groundbreaking experimental confirmations in ferroelectric behavior. Additionally, she holds 8 patents, reflecting the originality and applied potential of her research in neuromorphic computing. Her memberships in the Chinese Optical Society, the Chinese Institute of Electronics, and the Chinese Society for Optical Engineering indicate peer recognition and professional trust. These accomplishments, coupled with her high-impact research output, suggest that Dr. Wang is a strong contender for prestigious awards in cognitive science and materials research, and she is an exemplary nominee for the Best Researcher Award in Cognitive Science.

🔬 Research Focus

Dr. Hong Wang’s research centers on the design and application of neuromorphic memristors using low-dimensional ferroelectric materials. She explores how novel quantum dots and two-dimensional semiconductors, such as SnSe and ReSe₂, can mimic synaptic behavior for brain-like computing. A notable achievement includes her demonstration of robust dual-mode optical sensing using ferroelectric quantum dots, enabling both short-range and remote synapse-like responses, leading to high-accuracy image recognition systems. Her experimental work debunks traditional notions in electronics, such as the inertness of Pd electrodes, and provides novel insights into conductive filament formation. Her research has practical implications in artificial vision systems, optoelectronic sensing, and cognitive learning circuits. She is pioneering the application of ferroelectric polarization for neuromorphic behavior, with implications for smart sensing and adaptive cognitive devices. Through multidisciplinary collaborations and material innovations, Dr. Wang is shaping the future of neuromorphic computing, advancing cognitive technologies toward higher efficiency and closer brain mimicry.

✅ Conclusion

Dr. Hong Wang is an emerging leader in neuromorphic computing, merging ferroelectric material innovation with cognitive system design, making her a strong candidate for the Best Researcher Award.

Publications

Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

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Dr. Yuanming Zhang | Intelligent data processing and analysis | Best Researcher Award

Yuanming Zhang is an Associate Professor at the College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China. He earned his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His research focuses on data processing, graph neural networks, knowledge graphs, prognostics, health management, and condition monitoring. With expertise in deep learning and artificial intelligence, he has contributed significantly to neural network advancements. His work integrates cutting-edge technologies for intelligent data analysis and predictive maintenance. 📊🧠🔍

Profile

Education 🎓

Yuanming Zhang obtained his Ph.D. in Information Science from Utsunomiya University, Japan, in 2010. His academic journey emphasized computational intelligence, machine learning, and advanced data analytics. He developed expertise in deep learning models, including convolutional and graph neural networks. His education laid a strong foundation for interdisciplinary research, integrating artificial intelligence with real-world applications. 📚🧑‍🎓📈

Experience 👨‍🏫

Yuanming Zhang has been an Associate Professor at Zhejiang University of Technology since completing his Ph.D. in 2010. His professional journey spans over a decade in academia, focusing on AI, neural networks, and knowledge graphs. He has supervised research projects, collaborated on industry applications, and contributed to advancements in predictive analytics and condition monitoring. His expertise extends to teaching, mentoring, and interdisciplinary AI applications. 🏫🤖📡

Research Interests 🔬

Yuanming Zhang specializes in deep learning, attention mechanisms, graph neural networks, and AI-driven predictive analytics. His research explores neural architectures for data processing, knowledge representation, and condition monitoring. His expertise spans convolutional networks, LSTMs, GRUs, and deep belief networks. His work contributes to advancements in AI-driven diagnostics, intelligent systems, and real-time health monitoring applications. 🧠📊🖥️

Awards & Recognitions 🏅

Yuanming Zhang has received recognition for his contributions to AI, machine learning, and data analytics. His work in deep learning and knowledge graphs has earned him accolades from research institutions and conferences. His papers in neural networks and predictive maintenance have been highly cited, solidifying his impact in the field. His research excellence has been acknowledged through grants and academic distinctions. 🎖️📜🔬

Publications 

 

Chunyu Liu | Cognitive Computing | Best Researcher Award

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Dr. Chunyu Liu | Cognitive Computing | Best Researcher Award

Chunyu Liu is a Lecturer at North China Electric Power University, specializing in machine learning, neural decoding, and visual attention. 📚 She earned her B.S. in Mathematics and Applied Mathematics from Henan Normal University, an M.S. in Applied Mathematics from Northwest A&F University, and a Ph.D. in Computer Application Technology from Beijing Normal University. 🎓 She completed postdoctoral training at Peking University. 🔬 Her research integrates AI methodologies with cognitive neuroscience, focusing on neural encoding, decoding, and attention mechanisms. 🧠 She has published over 10 research papers, including six SCI-indexed publications as the first author. 📝 Her work aims to bridge artificial intelligence with human cognitive function understanding, contributing significantly to computational neuroscience. 🌍 Liu has also been involved in several major research projects, furthering advancements in neural signal analysis and cognitive computing. 🚀

Profile

Education 🎓

Chunyu Liu holds a strong academic background in mathematics and computational sciences. She obtained her B.S. degree in Mathematics and Applied Mathematics from Henan Normal University. ➕ She pursued her M.S. in Applied Mathematics at Northwest A&F University, where she deepened her expertise in mathematical modeling. 🔢 Continuing her academic journey, she earned a Ph.D. in Computer Application Technology from Beijing Normal University. 🖥️ Her doctoral research explored advanced AI techniques applied to neural decoding and cognitive processing. 🧠 To further refine her skills, she completed postdoctoral training at Peking University, focusing on integrating artificial intelligence with neural mechanisms. 🔬 Her academic pathway reflects a multidisciplinary approach, merging mathematics, computer science, and cognitive neuroscience to address complex challenges in brain science and AI. 📊 Liu’s education laid the foundation for her contributions to machine learning, visual attention studies, and neural encoding research.

Experience 👨‍🏫

Dr. Chunyu Liu is currently a Lecturer at North China Electric Power University, where she teaches and conducts research in cognitive computing and machine learning. 🎓 She has led and collaborated on multiple projects related to neural encoding and decoding, investigating how the brain processes object recognition, emotions, and attention. 🧠 Prior to her current role, she completed postdoctoral research at Peking University, where she worked on advanced AI-driven models for neural signal analysis. 🔍 Over the years, Liu has gained extensive experience in analyzing multimodal neural signals, including magnetoencephalography (MEG) and functional MRI (fMRI). 📡 She has also served as a reviewer for esteemed scientific journals and collaborated with interdisciplinary research teams on AI and brain science projects. 🔬 Her expertise extends to both academia and industry, where she has contributed to the development of novel computational models for decoding brain activity. 🚀

Research Interests 🔬

Dr. Chunyu Liu’s research integrates artificial intelligence and brain science to understand cognitive functions through neural decoding. 🧠 She employs multi-modal neural signals such as magnetoencephalography (MEG) and functional magnetic resonance imaging (fMRI) to analyze brain activity. 📡 Her work explores neural encoding and decoding, focusing on object recognition, emotion processing, and multiple-object attention. 🎯 She develops AI-based models to extract human brain features and gain insights into cognitive mechanisms. 🤖 By integrating psychological experimental paradigms with AI, Liu aims to advance computational neuroscience. 🏆 Her research also inspires the development of new AI theories and algorithms based on principles of brain function. 📊 She has led major projects in cognitive computing, contributing significantly to both theoretical advancements and practical applications in neural signal processing. 🚀 Through her work, she bridges the gap between human cognition and artificial intelligence, driving innovations in brain-computer interface research. 🏅

 

Awards & Recognitions 🏅

Dr. Chunyu Liu has received recognition for her outstanding contributions to cognitive computing and AI-driven neuroscience research. 🏅 She has been nominated for the prestigious International Cognitive Scientist Award for her pioneering work in neural decoding and visual attention mechanisms. 🎖️ Liu’s research publications have been featured in high-impact journals, earning her accolades from the scientific community. 📜 Her first-author papers in IEEE Transactions on Neural Systems and Rehabilitation Engineering, Science China Life Sciences, and IEEE Journal of Biomedical and Health Informatics have been widely cited. 📝 She has also been honored with research grants and funding for AI-driven cognitive studies. 🔬 Her innovative work in decoding brain signals has been recognized in international AI and neuroscience conferences. 🌍 Liu’s academic excellence and contributions continue to shape the field of computational neuroscience and machine learning applications in cognitive science. 🚀

Publications 📚

Pritpal Singh | Ambiguous set theory | Best Researcher Award

Published on by Cognitive Scientist Awards

Dr. Pritpal Singh | Ambiguous set theory | Best Researcher Award

Pritpal Singh is an Assistant Professor at the Department of Data Science and Analytics, Central University of Rajasthan, India. He earned his Ph.D. in Computer Science and Engineering from Tezpur (Central) University in 2015 and has held various academic and research positions in India, Taiwan, and Poland. His expertise includes soft computing, optimization algorithms, time series forecasting, image analysis, and machine learning. He has published extensively in high-impact journals like IEEE Transactions, Elsevier, and Springer. His research focuses on advanced computational techniques, including quantum-based optimization and fMRI data analysis. Dr. Singh has received prestigious research fellowships, including a Postdoctoral Fellowship from Taiwan’s Ministry of Science and Technology and an International Visiting Research Fellowship from Poland’s Foundation for Polish Science. His work significantly contributes to artificial intelligence, data science, and computational modeling, making him a key figure in these fields. 🚀📊📚

Profile

Education 🎓

Dr. Pritpal Singh obtained his Ph.D. in Computer Science and Engineering from Tezpur (Central) University, Assam, India, in 2015, specializing in soft computing applications for time series forecasting. He completed his Master in Computer Applications (MCA) from Dibrugarh University, Assam, in 2008, following a B.Sc. in Physics, Chemistry, and Mathematics from the same university in 2005. His academic journey began with Higher Secondary (2002) from the Assam Higher Secondary Education Council and HSLC (1999) from the Secondary Education Board of Assam. His doctoral dissertation focused on improving fuzzy time series forecasting models through hybridization with neural networks and optimization techniques like particle swarm optimization. His strong foundation in computational sciences, mathematics, and engineering has shaped his research in AI-driven predictive modeling, optimization, and data analytics. 🎓📚🔬

Experience 👨‍🏫

Dr. Singh has extensive academic and research experience. He is currently an Assistant Professor at the Central University of Rajasthan (since June 2022). Previously, he was an Assistant Professor at CHARUSAT University, Gujarat (2015-2019), and a Lecturer at Thapar University, Punjab (2013-2015). His research experience includes serving as an Adjunct Professor (Research) at Jagiellonian University, Poland (2020-2022) and a Postdoctoral Research Fellow at National Taipei University of Technology, Taiwan (2019-2020). Throughout his career, he has mentored students, led research projects, and contributed significantly to data science, artificial intelligence, and computational modeling. His global exposure has enriched his expertise in optimization, machine learning, and interdisciplinary AI applications. 🌍📊

Research Interests 🔬

Dr. Singh’s research revolves around ambiguous set theory, optimization algorithms, time series forecasting, image analysis, and machine learning. He specializes in hybrid computational techniques, particularly quantum-based optimization and soft computing applications. His work extends to fMRI data analysis, mathematical modeling, and simulation. His research has been published in leading journals such as IEEE Transactions on Systems, Elsevier’s Information Sciences, and Artificial Intelligence in Medicine. His focus on interdisciplinary AI applications, particularly in healthcare and data science, has positioned him as a key contributor to advancing machine learning methodologies. 🧠📊🤖Awards & Recognitions 🏅

Dr. Singh has received multiple prestigious fellowships and recognitions. In 2019, he was awarded a Postdoctoral Research Fellowship by the Ministry of Science and Technology, Taiwan. In 2020, he received the International Visiting Research Fellowship from the Foundation for Polish Science, Poland. His contributions to artificial intelligence, optimization, and data science have been recognized globally through research grants, invited talks, and publications in top-tier journals. His work in soft computing and AI-driven predictive modeling continues to impact both academic and industrial research. 🏅🎖️📜

Publications 📚

  • Scopus 1-2023: P. Singh, An investigation of ambiguous sets and their application to
    decision-making from partial order to lattice ambiguous sets. Decision Analytics
    Journal (Elsevier), 08, 100286, 2023.
  • Scopus 2-2023: P. Singh, A general model of ambiguous sets to a single-valued ambiguous numberswith aggregation operators. Decision Analytics Journal (Elsevier), 08,
    100260, 2023.
  • Scopus 3-2023: P. Singh, Ambiguous set theory: A new approach to deal with unconsciousness and ambiguousness of human perception. Journal of Neutrosophic and
    Fuzzy Systems (American Scientific Publishing Group), 05(01), 52–58, 2023.
  • Scopus 4-2022: P. Singh, Marcin W ˛atorek, Anna Ceglarek, Magdalena F ˛afrowicz, and
    Paweł O´swi˛ecimka, Analysis of fMRI Time Series: Neutrosophic-Entropy Based
    Clustering Algorithm. Journal of Advances in Information Technology, 13(3), 224–
    229, 2022.