Assist. Prof. Dr. Joaquim Casaca | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Joaquim Casaca | Artificial Intelligence | Best Researcher Award

Prof, IADE, European University, Portugal

Joaquim António A. Casaca is an accomplished academic and professional in management, specializing in information security and marketing. He currently serves as an Assistant Professor at IADE, European University, Lisbon. Known for his expertise in management and economics, Joaquim has contributed extensively to research in areas such as entrepreneurial competence, marketing, and information security.

Publication Profile

Scopus

ORCID

🎓 Education Background

Joaquim Casaca holds a PhD in Management (2010) from Universidade Lusíada de Lisboa, with a thesis focusing on information security management in Portuguese SMEs. He earned a Master’s in Management (1999) and an MBA (1997) from ISEG – Lisbon School of Economics and Management, University of Lisbon. Additionally, he completed a Postgraduate degree in Information Sciences and Technologies for Organizations (1996) from ISEG, and holds a BSc in Economics (1982) from the same institution.

💼 Professional Experience

Since 2010, Joaquim has been an Assistant Professor at IADE, European University (Lisbon). Previously, he held academic roles at the University of Lisbon and Lusófona University, and financial positions in notable companies such as PT Multimédia, Portugal Telecom, and Companhia Portuguesa Rádio Marconi. His broad experience spans academia, finance, and management consultancy.

🏆 Awards and Honors

Joaquim received the Banco Espírito Santo Award in 1999 at ISEG for his outstanding Master’s thesis. This recognition highlights his early excellence and research capability in management.

🔍 Research Focus

His research interests center on management, information security, entrepreneurial competence, and marketing. Recent work includes studies on game-based learning’s effect on entrepreneurial skills and the role of neuroscience in economics and marketing. Joaquim’s interdisciplinary approach integrates management theory with emerging technologies and consumer behavior.

🔚 Conclusion

With a strong academic foundation and a versatile professional background, Joaquim A. Casaca is a respected figure in management and information security education. His ongoing contributions advance the understanding of how technology and management intersect in organizational contexts.

📚 Top Publications

  • The effect of game-based learning on the development of entrepreneurial competence among higher education students
    Daniel, A. D., Negre, Y., Casaca, J. A., Patricio, R., & Tsvetcoff, R. (2024). Education + Training.
    DOI: 10.1108/ET-10-2023-0448 — Cited by 3 articles

  • Neuroscience Applied to Economics and Marketing: A bibliometric Review of the Literature
    Casaca, J. A. (2024). International Journal of Business Innovation and Research.
    DOI: 10.1504/ijbir.2024.10066189

  • The determinants of non-consumption of disposable plastic: application of an extended theory of planned behaviour
    Casaca, J. A. (2024). International Journal of Business Environment.
    DOI: 10.1504/IJBE.2024.135693

  • Relational Marketing and Customer Satisfaction: A Systematic Literature Review
    Casaca, J. A. (2023). Estudios Gerenciales.
    DOI: 10.18046/j.estger.2023.169.6218

  • Relationship Marketing and Customer Retention – A Systematic Literature Review
    Casaca, J. A. (2023). Studies in Business and Economics.
    DOI: 10.2478/sbe-2023-0044

 

Prof. Tao Ye | AI Accelerator | Best Researcher Award

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Prof. Tao Ye | AI Accelerator | Best Researcher Award

Prof, The Chinese University of Hong Kong, China

Dr. Terry Tao Ye is a renowned professor and researcher specializing in electrical and electronic engineering, nanotechnology, and smart sensing systems. Currently affiliated with the School of Science and Engineering at The Chinese University of Hong Kong, Shenzhen (CUHK-Shenzhen), he has made significant contributions to the fields of RFID systems, embedded platforms, and wearable electronics. With a rich career spanning academia and industry, Dr. Ye has played pivotal roles in developing foundational technologies and fostering cutting-edge research in China and internationally. 🌏🔬

Publication Profile

Summary of Suitability for Best Researcher Award – Prof. Tao Ye

Dr. Terry Tao Ye is a prolific researcher and leader with groundbreaking contributions in nanoscience, wearable sensors, and SoC design. His extensive high-impact publications, prestigious grants, and interdisciplinary innovations demonstrate exceptional research excellence and influence, making him highly deserving of the Best Researcher Award.

🎓 Education Background

Dr. Ye holds a Ph.D. in Electrical Engineering from Stanford University, California, USA (1995–2004), where he researched Systems-on-Chip and Embedded Systems under the guidance of Dr. Giovanni De Micheli. He earned his B.Eng. from the Department of Electronic Engineering at Tsinghua University, Beijing, China (1988–1993), solidifying a strong foundation in electronics and communication engineering. 📘🎓

💼 Professional Experience

Dr. Ye has held multiple esteemed academic and industrial positions. He is currently a Professor at CUHK-Shenzhen (2025–present) and also at SUSTech (2018–present). He holds an adjunct professorship at Carnegie Mellon University since 2015 and has served in leadership and professorial roles at Sun Yat-Sen University and the Joint Institute of Engineering with CMU. His industry experience includes significant roles at Impinj Inc. in Seattle, where he led the development of RFID Gen2 standards, and Synopsys Inc., where he pioneered ASIC and EDA tools. His early career also includes roles at the Hong Kong LSCM R&D Center and Silicon Architects, contributing to foundational IC design technologies. 🧑‍🏫💻📡

🏅 Awards and Honors

Dr. Ye has secured over 30 competitive research grants as principal investigator or core member, spanning national, provincial, and institutional levels. Notably, his work has been funded by the National Science Foundation of China (NSFC), the Guangdong Provincial Key-Area R&D Program, and Shenzhen Science and Technology Program. His contributions to RFID, smart sensing, and embedded design have earned him widespread recognition in academia and industry. 🏆📑

🔬 Research Focus

Dr. Ye’s research interests include System-on-Chip design, embedded systems, energy-efficient interconnects, wearable electronics, flexible sensors, and e-textiles. He is currently leading projects on electronic skin, wireless medical devices, and high-frequency signal integrity in textile-based circuits. His interdisciplinary work bridges hardware design, signal processing, and biomedical applications. 🧠⚙️📲

🔚 Conclusion

With an outstanding blend of academic excellence and industrial innovation, Dr. Terry Tao Ye stands as a thought leader in electrical engineering and emerging smart technologies. His contributions to research, education, and global collaboration continue to shape the future of intelligent systems and nanotechnology. 🌟📡🔋

📚 Top Publications with Details

RV-SCNN: A RISC-V Processor With Customized Instruction Set for SNN and CNN Inference Acceleration on Edge Platforms, IEEE TCAD, 2025

Cited by: 12

Optimizing CNN Computation Using RISC-V Custom Instruction Sets for Edge Platforms, IEEE Transactions on Computers, 2024

Cited by: 2

Smartphone administered pulsed radio frequency energy therapy for expedited cutaneous wound healing, npj Digital Medicine, 2025

Cited by: 51
Polyelectrolyte-based wireless and drift-free iontronic sensors for orthodontic sensing, Science Advances, 2025

Cited by: 4

Parasitic Capacitance Modeling and Measurements of Conductive Yarns for e-Textile Devices, Nature Communications, 2023

Cited by: 8

Exploring RFID Technology for Wireless Control of Smart Antennas”, IEEE Internet of Things Journal, 2024

Cited by: 24

e-Bandage: Exploiting Smartphone as a Therapeutic Device for Cutaneous Wound Treatment”, Advanced Intelligent Systems, 2024

Cited by: 39

Dr. José Carlos López Figueroa | Digital Leadership | Best Researcher Award

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Dr. José Carlos López Figueroa | Digital Leadership | Best Researcher Award

Teacher, Sonora Institute of Technology, Mexico

Dr. José Carlos López Figueroa is a distinguished academic and researcher in the field of management and organizational studies. Currently serving as a Professor and Coordinator of the Master’s Program in e-Business at the Sonora Institute of Technology, he has made significant contributions to digital leadership, governance, and institutional research. With a Ph.D. in Organizational Studies from the Universidad Autónoma Metropolitana (UAM), he has been recognized with the prestigious third place in the National Award for Doctoral Theses. As an active researcher, he is a Level Candidate Member of the National System of Researchers (SNII-México) and has published extensively in high-quality indexed journals. His collaborations with national and international networks, including REMINEO and Huika Mexihco, further solidify his impact on the academic and business communities.

Publication Profile

🎓 Academic Background

Dr. López Figueroa earned his Ph.D. in Organizational Studies from the Universidad Autónoma Metropolitana (UAM), demonstrating his strong foundation in research and management sciences. His doctoral work was highly acclaimed, securing third place in the National Award for Doctoral Theses. His research expertise spans across leadership, governance, institutions, and digital transformation in SMEs. He has authored numerous research articles and book chapters, contributing significantly to the understanding of managerial and organizational dynamics in various sectors.

💼 Professional Experience

With a profound academic career, Dr. López Figueroa has actively engaged in both teaching and research. As the coordinator of the Master’s Program in e-Business at the Sonora Institute of Technology, he has played a crucial role in shaping future professionals in digital business strategies. His expertise in managerial skills, entrepreneurship, public policy, and marketing strategies has been instrumental in guiding various research projects. He has successfully coordinated studies like “Managerial Management Practices as a Key Factor for the Development of Competitive Advantage in SMEs in Southern Sonora” and “Digital Leadership Skills in SMEs,” producing impactful research outputs, including indexed articles and theses. Additionally, he has contributed as a principal investigator and collaborator in 12 externally funded research projects.

🏆 Awards and Honors

Dr. López Figueroa has been recognized for his scholarly excellence with multiple accolades. His Ph.D. dissertation received third place in the National Award for Doctoral Theses, showcasing the significance of his research. He is a Level Candidate Member of the National System of Researchers (SNII-México), reflecting his growing influence in academic research. Beyond awards, he holds key editorial positions, including serving as General Coordinator and Member of the Academic Committee for the National Congress on Organizational Studies. His contributions to the field have also been acknowledged through invitations as a volunteer reviewer for prestigious indexed journals.

🔬 Research Focus

Dr. López Figueroa’s research interests center around leadership, governance, territory, and institutional studies, with a particular emphasis on digital transformation in SMEs. His work explores how managerial and organizational practices influence competitive advantage in businesses. Through his extensive involvement in research projects, he has analyzed key areas such as digital leadership, organizational culture, entrepreneurship, and institutional logics applied to various economic sectors. His research integrates methodologies like survey-based analysis, statistical modeling, and systematic reviews, ensuring a robust and data-driven approach. His findings have been widely disseminated through journal publications, book chapters, conferences, and podcasts, reinforcing his thought leadership in organizational studies.

🔍 Conclusion

Dr. José Carlos López Figueroa is a pioneering researcher and educator dedicated to advancing knowledge in digital leadership and organizational studies. His impactful research, collaborative projects, and commitment to student mentorship have positioned him as an influential figure in academia. His ability to bridge theoretical research with practical business applications has made significant contributions to SMEs and institutional governance. Through his collaborations with REMINEO, Huika Mexihco, and international institutions, he continues to shape the future of management and digital transformation. His scholarly excellence, extensive publications, and research-driven initiatives make him a strong candidate for the Best Researcher Award.

📚 Top Publications 

La organización digital: comprensión de un campo y tendencias de estudio (2024)

Territorio y organizaciones: un análisis bibliométrico en Web of Science (2024)

Digital Leadership Skills for SMEs in Northwest Mexico (2023)

Governance and Institutions in Organizational Studies (2023)

Managerial Management Practices and Competitive Advantage in SMEs (2022)

Zeshan Khan | Artificial Intelligence| Best Researcher Award

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Assoc. Prof. Dr. Zeshan Khan |Artificial Intelligence| Best Researcher Award

Associate Professor, National Yunlin University of Science and Technology, Taiwan

Dr. Zeshan Aslam Khan is an esteemed Associate Professor at the International Graduate School of Artificial Intelligence, National Yunlin University of Engineering Sciences and Technology. With a strong background in Artificial Intelligence, Image Analysis, and Recommender Systems, he has made significant contributions to academia and industry. As the Director of the PRISM Lab, he actively supervises cutting-edge AI research, fostering innovation in Smart Metering, Fingerprint Recognition, and Alzheimer’s Detection. His work is recognized globally, with prestigious awards, high-impact publications, and collaborations with leading research institutions in the UK, Ireland, Taiwan, and Pakistan. 🌍📚

Publication Profile

Scopus

🎓 Education

Dr. Khan holds a Ph.D. in Electronic Engineering (2020) with a specialization in Learning Machines for Recommender Systems. His academic journey includes an M.Sc. in Computer Systems Engineering from Halmstad University, Sweden (2010), and a B.Sc. in Computer Information Systems Engineering from UET Peshawar, Pakistan (2005). His extensive educational background has laid a strong foundation for his expertise in AI-driven systems and computational intelligence. 🎓🔬

💼 Experience

With over a decade of experience, Dr. Khan has established himself as a leading researcher and educator in Artificial Intelligence. He has served as a Visiting Researcher at the University of Birmingham (UK) and the University of Galway (Ireland). His industry collaborations include partnerships with the National Radio Telecommunication Corporation (NRTC), Pakistan, and the Future Technology Research Center, Taiwan. As an Associate Editor of the Journal of Innovative Technologies (JIT) and a reviewer for top-tier journals like IEEE Transactions on AI, he plays a crucial role in shaping AI research globally. 🌟🔍

🏆 Awards and Honors

Dr. Khan’s excellence in research and academia has been recognized through numerous accolades. He was awarded the prestigious Ph.D. Gold Medal (2020) and the Faculty Research Brilliance Award (2022). In 2023, he received the Productive Researcher Award for his outstanding publications and graduate supervisions. His work has also secured significant research grants, including the Pakistan Engineering Council (PEC) Grant and the Higher Education Commission (HEC) Grant, enabling advancements in AI and IoT applications. 🏅🔬

🔬 Research Focus

Dr. Khan’s research revolves around Artificial Intelligence, Image Classification/Segmentation, Recommender Systems, Embedded Systems, and Fractional Calculus. His groundbreaking work in explainable AI, fractional optimization, and chaotic heuristics has been widely published in high-impact Q1 journals. His innovative contributions include developing AI-powered solutions for healthcare, smart metering, and signature verification, bridging the gap between academia and industry through real-world applications. 🤖📈

📝 Conclusion

Dr. Zeshan Aslam Khan stands as a prominent figure in the field of Artificial Intelligence, with a profound impact on research, education, and industry collaborations. His dedication to AI-driven solutions, student mentorship, and high-impact publications solidifies his reputation as a leader in predictive intelligence and systems modeling. With a global research footprint and numerous accolades, he continues to drive technological advancements that shape the future of AI. 🌍🚀

📚 Publications 

Generalized fractional optimization-based explainable lightweight CNN model for malaria disease classification – Computers in Biology and Medicine, 2025 (Q1, IF: 7.0) [Link] 📖🔬

Fractional Gradient Optimized Explainable CNN for Alzheimer’s Disease Diagnosis – Heliyon, 2024 (Q1, IF: 3.4) [Link] 🧠📊

Design of chaotic Young’s double slit experiment optimization heuristics for nonlinear muscle model identification – Chaos, Solitons & Fractals, 2024 (Q1, IF: 5.3) [Link] 🎯💡

A gazelle optimization expedition for key term separated fractional nonlinear systems applied to muscle modeling – Chaos, Solitons & Fractals, 2024 (Q1, IF: 5.3) [Link] 📉⚙️

Generalized fractional strategy for recommender systems with chaotic ratings behavior – Chaos, Solitons & Fractals, 2022 (Q1, IF: 5.3) [Link] ⭐🔍

Lianbo Ma | Artificial Intelligence | Best Researcher Award

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Prof. Lianbo Ma | Artificial Intelligence | Best Researcher Award

Professor, Northeastern University, China

Dr. Lianbo Ma is a distinguished professor at Northeastern University, China, with expertise in computational intelligence, machine learning optimization, big data analysis, and natural language processing. With a Ph.D. from the University of Chinese Academy of Sciences, he has significantly contributed to bio-inspired computing, multi-objective optimization, and cloud computing resource allocation. As a prolific researcher, Dr. Ma has published over 90 papers in high-impact journals and conferences, earning global recognition for his work. His research has been widely cited, and he has received numerous prestigious awards, making him a key figure in artificial intelligence and optimization.

Publication Profile

Google Scholar

🎓 Education

Dr. Ma holds a Doctorate in Machine-Electronic Engineering from the University of Chinese Academy of Sciences (2014). He earned his Master’s degree (2007) and Bachelor’s degree (2004) in Information Science and Engineering from Northeastern University, China. His academic journey has provided a solid foundation in AI-driven optimization, neural networks, and computational intelligence.

💼 Experience

Dr. Ma has held various esteemed positions in academia and research institutions. Since 2017, he has been a professor at Northeastern University, China, specializing in software engineering and AI. He previously served as an associate professor (2016-2017) and assistant research fellow at the Shenyang Institute of Automation, Chinese Academy of Sciences (2007-2015). His international experience includes a visiting scholar position at Surrey University, UK (2019-2020), under the mentorship of Prof. Yaochu Jin. His extensive professional journey highlights his contributions to AI-driven industrial applications and large-scale optimization.

🏆 Awards and Honors

Dr. Ma has been recognized among the World’s Top 2% Scientists (Elsevier & Stanford, 2022-2023) and has received several prestigious accolades, including the IEEE Best Paper Runner-Up Award (2023), the Best Student Paper Award at the International Conference on Swarm Intelligence (2021), and the Outstanding Reviewer Awards from Elsevier (2016, 2018). His achievements extend to the Liaoning Province Natural Science Academic Award and the BaiQianWan Talents Project Award. His dedication to research and mentorship is further evident in his recognition as an Excellent Master’s Thesis Instructor.

🔬 Research Focus

Dr. Ma’s research spans computational intelligence, large-scale multi-objective optimization, and bio-inspired computing. His expertise extends to cloud computing, edge computing, and social network analysis, where he has worked on cloud resource allocation and influence maximization. He is also actively engaged in multi-modal data processing, focusing on knowledge graphs, entity extraction, and text mining. His research integrates AI with industrial applications, advancing neural architecture search and intelligent data analysis.

🔍 Conclusion

Dr. Lianbo Ma is a pioneering researcher in artificial intelligence, computational intelligence, and machine learning optimization. His contributions to big data analytics, neural architecture search, and evolutionary computation have positioned him as a leading figure in the field. With numerous accolades, high-impact publications, and extensive academic service, Dr. Ma continues to shape the future of AI-driven optimization and intelligent computing. 🚀

📖 Publications

A Hybrid Neural Architecture Search Algorithm Optimized via Lifespan Particle Swarm Optimization for Coal Mine Image Recognition

Truthful Combinatorial Double Auctions for Mobile Edge Computing in Industrial IoT. IEEE Transactions on Mobile Computing, 21(11), 4125-4138. DOI

Single-Domain Generalized Predictor for Neural Architecture Search System. IEEE Transactions on Computers. DOI

One-Step Forward and Backtrack: Overcoming Zig-Zagging in Loss-Aware Quantization Training. AAAI-24 Conference Proceedings.

Pareto-wise Ranking Classifier for Multi-objective Evolutionary Neural Architecture Search. IEEE Transactions on Evolutionary Computation. DOI

An Adaptive Localized Decision Variable Analysis Approach to Large-Scale Multiobjective and Many-objective Optimization. IEEE Transactions on Cybernetics, 52(7), 6684-6696. DOI

Enhancing Learning Efficiency of Brain Storm Optimization via Orthogonal Learning Design. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 51(11), 6723-6742. DOI

 

QIANG QU | Artificial Intelligence Award | Best Researcher Award

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Prof. QIANG QU | Artificial Intelligence Award | Best Researcher Award

PROFESSOR, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

Dr. Qiang Qu is a distinguished professor and a leading researcher in blockchain, data intelligence, and decentralized systems. He serves as the Director of the Guangdong Provincial R&D Center of Blockchain and Distributed IoT Security at the Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS). Additionally, he holds a professorship at Shenzhen University of Advanced Technology and has previously served as a guest professor at The Chinese University of Hong Kong (Shenzhen). Dr. Qu has also contributed as the Director and Chief Scientist of Huawei Blockchain Lab. With a strong international academic presence, he has held research positions at renowned institutions such as ETH Zurich, Carnegie Mellon University, and Nanyang Technological University. His pioneering work focuses on scalable algorithm design, data sense-making, and blockchain technologies, making significant contributions to AI, data systems, and interdisciplinary studies.

Publication Profile

🎓 Education

Dr. Qiang Qu earned his Ph.D. in Computer Science from Aarhus University, Denmark, under the supervision of Prof. Christian S. Jensen. His doctoral research was supported by the prestigious GEOCrowd project under Marie Skłodowska-Curie Actions. He further enriched his academic journey as a Ph.D. exchange student at Carnegie Mellon University, USA. He holds an M.Sc. in Computer Science from Peking University, China, and a B.S. in Management Information Systems from Dalian University of Technology.

💼 Experience

Dr. Qu has a diverse professional background, reflecting his global expertise. Since 2016, he has been a professor at SIAT, leading groundbreaking research in blockchain and distributed IoT security. He also served as Vice Director of Hangzhou Institutes of Advanced Technology (SIAT’s Hangzhou branch). Prior to this, he was an Assistant Professor and the Director of Dainfos Lab at Innopolis University, Russia. His research journey includes being a visiting scientist at ETH Zurich, a visiting scholar at Nanyang Technological University, and a research fellow at Singapore Management University. He also gained industry experience as an engineer at IBM China Research Lab.

🏅 Awards and Honors

Dr. Qu has received several national and international research grants, recognizing his impactful contributions to blockchain and AI-driven data intelligence. He is a prominent editorial board member of the Future Internet Journal and serves as a guest editor for multiple high-impact journals. As an active contributor to the research community, he has been a TPC (Technical Program Committee) member for prestigious conferences and regularly reviews top-tier AI and data systems journals.

🔬 Research Focus

Dr. Qu’s research interests revolve around data intelligence and decentralized systems, with a strong focus on blockchain, scalable algorithm design, and data-driven decision-making. His work has been instrumental in developing efficient data parallel approaches, AI-driven network analysis, and cross-blockchain data migration techniques. His interdisciplinary contributions bridge AI, IoT security, and geospatial analytics, driving innovation in secure and intelligent computing.

🔚 Conclusion

Dr. Qiang Qu stands as a thought leader in blockchain and data intelligence, combining academic excellence with real-world impact. His contributions to AI-driven decentralized systems and scalable data solutions continue to shape the fields of computer science and IoT security. His extensive research collaborations, editorial roles, and international experience make him a key figure in advancing secure and intelligent computing technologies. 🚀

📚 Publications

SNCA: Semi-supervised Node Classification for Evolving Large Attributed Graphs – IEEE Big Data Mining and Analytics (2024). Cited in IEEE 📖

CIC-SIoT: Clean-Slate Information-Centric Software-Defined Content Discovery and Distribution for IoT – IEEE Internet of Things Journal (2024). Cited in IEEE 📖

Blockchain-Empowered Collaborative Task Offloading for Cloud-Edge-Device Computing – IEEE Journal on Selected Areas in Communications (2022). Cited in IEEE 📖

On Time-Aware Cross-Blockchain Data Migration– Tsinghua Science and Technology (2024). Cited in Tsinghua University 📖

Few-Shot Relation Extraction With Automatically Generated Prompts – IEEE Transactions on Neural Networks and Learning Systems (2024). Cited in IEEE 📖

Opinion Leader Detection: A Methodological Review – Expert Systems with Applications (2019). Cited in Elsevier 📖

Neural Attentive Network for Cross-Domain Aspect-Level Sentiment Classification– IEEE Transactions on Affective Computing (2021). Cited in IEEE 📖

Efficient Online Summarization of Large-Scale Dynamic Networks –  IEEE Transactions on Knowledge and Data Engineering (2016). Cited in IEEE 📖

Sikandar Ali | Artificial Intelligence Award | Best Researcher Award

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Dr. Sikandar Ali | Artificial Intelligence Award | Best Researcher Award

Postdoc Fellow, Inje University, South Korea

🎓 Sikandar Ali is a passionate AI researcher and educator specializing in Artificial Intelligence applications in healthcare. Currently pursuing a PhD at Inje University, South Korea, he has a strong academic background and extensive research experience in digital pathology, medical imaging, and machine learning. As a team leader of the digital pathology project, he develops innovative AI algorithms for cancer diagnosis while collaborating with a global team of researchers. Sikandar is a recipient of prestigious scholarships, accolades, and recognition for his contributions to AI and healthcare innovation.

Publication Profile

Google Scholar

Education

📘 Sikandar Ali holds a PhD in Artificial Intelligence in Healthcare (CGPA: 4.46/4.5) from Inje University, South Korea, where his thesis focuses on integrating pathology foundation models with weakly supervised learning for gastric and breast cancer diagnosis. He earned an MS in Computer Science from Chungbuk National University, South Korea (GPA: 4.35/4.5), with research on AI-based clinical decision support systems for cardiovascular diseases. His undergraduate degree is a Bachelor of Engineering in Computer Systems Engineering from Mehran University of Engineering and Technology, Pakistan, with a CGPA of 3.5/4.0.

Experience

💻 Sikandar is an experienced researcher and AI specialist. Currently working as an AI Research Assistant at Inje University, he focuses on cutting-edge projects in digital pathology, cancer detection, and medical imaging. Previously, he worked as a Research Assistant at Chungbuk National University, focusing on cardiovascular disease diagnosis using AI. His industry experience includes roles such as Search Expert at PROGOS Tech Company and Software Developer Intern at Hidaya Institute of Science and Technology.

Awards and Honors

🏆 Sikandar has received multiple awards, including the Brain Korean Scholarship, European Accreditation Council for Continuing Medical Education (EACCME) Certificate, and recognition as an outstanding Teaching Assistant at Inje University. He has also earned full travel grants for international conferences, extra allowances for R&D industry projects, and certificates for reviewing research papers in leading journals. Additionally, he is a Guest Editor at Frontiers in Digital Health.

Research Focus

🔬 Sikandar’s research focuses on developing AI algorithms for medical imaging, with expertise in weakly supervised learning, self-supervised learning, and digital pathology. His projects include designing AI systems for cancer detection, COVID-19 prediction, and IPF severity classification. He also works on object detection applications using YOLO models and wearable sensor-based activity detection for pets. His commitment to explainability and interpretability in AI models ensures their practical utility in healthcare.

Conclusion

🌟 Sikandar Ali is a dedicated AI researcher driving innovation in healthcare through artificial intelligence. With his strong educational foundation, diverse research experience, and impactful contributions, he aims to bridge the gap between AI and medicine, making healthcare more efficient and accessible.

Publications

Detection of COVID-19 in X-ray Images Using DCSCNN
Sensors 2022, IF: 3.4

A Soft Voting Ensemble-Based Model for IPF Severity Prediction
Life 2021, IF: 3.2

Metaverse in Healthcare Integrated with Explainable AI and Blockchain
Sensors 2023, IF: 3.4

Weakly Supervised Learning for Gastric Cancer Classification Using WSIs
Springer 2023

Classifying Gastric Cancer Stages with Deep Semantic and Texture Features
ICACT 2024

Computer Vision-Based Military Tank Recognition Using YOLO Framework
ICAISC 2023

Activity Detection for Dog Well-being Using Wearable Sensors
IEEE Access 2022

Cat Activity Monitoring Using Wearable Sensors
IEEE Sensors Journal 2023, IF: 4.3

Deep Learning for Algae Species Detection Using Microscopic Images
Water 2022, IF: 2.9

Comprehensive Review on Multiple Instance Learning
Electronics 2023

Hybrid Model for Face Shape Classification Using Ensemble Methods
Springer 2021

Cervical Spine Fracture Detection Using Two-Stage Deep Learning
IEEE Access 2024

 

Yunhyung LEE | Computer science| Best Researcher Award

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Prof. Dr. Yunhyung LEE | Computer Science | Best Researcher Award

Professor, Korea Institute of Maritime and Fisheries Technology, South Korea

Dr. Yunhyung Lee is a distinguished professor at the Korea Institute of Maritime and Fisheries Technology and an adjunct professor at Korea Maritime and Ocean University. With an academic journey spanning nearly two decades, Dr. Lee has made significant contributions to marine systems engineering, control systems, and maritime research. A prolific researcher and academician, he is known for his innovative approaches in marine electric systems, fuzzy control, and genetic algorithms. His commitment to fostering maritime education and cutting-edge research has earned him several accolades and a global reputation in his field. 🌐✨

Publication Profile

ORCID

Education 🎓

Dr. Lee graduated summa cum laude with a Bachelor’s degree in Marine System Engineering from Korea Maritime and Ocean University in 2002. He further earned his Master’s degree in 2004 and completed his Ph.D. in Mechatronics Engineering in 2007. His academic excellence is reflected in multiple awards, including the President’s Award for graduating with the highest honors. 🏆📚

Professional Experience 💼

Dr. Lee began his academic career as a part-time lecturer at Korea Maritime and Ocean University and Youngsan University. From 2008 to 2014, he served as a professor at the Korea Port Training Institute before joining the Korea Institute of Maritime and Fisheries Technology in 2014. Simultaneously, he has been an adjunct professor at Korea Maritime and Ocean University since 2015. His practical experience includes spearheading innovative research projects and consulting for industry collaborations. ⚙️🛳️

Awards and Honors 🏅

Dr. Lee’s outstanding achievements have been recognized through numerous awards, including the Albert Nelson Marquis Lifetime Achievement Award (2018) and the Young Researcher Award from the Korean Society of Marine Engineering (2015). He has also been honored for his contributions to education and research with awards such as the Best Paper Award by the Korean Federation of Science and Technology Societies (2006) and the Citation for Excellence in Lecturing by Korea Maritime and Ocean University (2008). 🌟🎖️

Research Focus 🔬

Dr. Lee’s research encompasses control engineering, marine electric systems, genetic algorithms, fuzzy control, and PID control. His studies aim to enhance the safety, efficiency, and reliability of marine propulsion systems and other maritime technologies. Through numerous research projects and innovative solutions, he has significantly advanced the field of marine and fisheries technology. 🌊⚡

Conclusion 🌟

Dr. Yunhyung Lee’s exceptional career reflects his dedication to advancing marine and maritime technology through research, education, and industry collaboration. His passion for innovation and his unwavering commitment to excellence make him a leading figure in his field. 🌏✨

Publications 📚

Application of Real-Coded Genetic Algorithm–PID Cascade Speed Controller to Marine Gas Turbine Engine Based on Sensitivity Function Analysis
Mathematics, 2025 – Cited by: 5

Development of Hull Care for Warships Based on a Manned-Unmanned Hybrid System: Focusing on the Underwater Hull Plate
Journal of the KNST, 2024 – Cited by: 3

Modeling and Parameter Estimation of a 2DOF Ball Balancer System
Journal of the Korea Academia-Industrial Cooperation Society, 2024 – Cited by: 4

Ground-Fault Recognition in Low-Voltage Ships Based on Variation Analysis of Phase-to-Ground Voltage and Neutral-Point Voltage
IEEE Access, 2024 – Cited by: 8

Speed Control for Low Voltage Propulsion Electric Motor of Green Ship through DTC Application
Journal of the Korea Academia-Industrial Cooperation Society, 2023 – Cited by: 6

RCGA-PID Controller Based on ITAE for Gas Turbine Engine in the Marine Field
The Journal of Fisheries and Marine Sciences Education, 2023 – Cited by: 3

PID Controller Design Based on Direct Synthesis for Set Point Speed Control of Gas Turbine Engine in Warships
Journal of the Korean Society of Fisheries Technology, 2023 – Cited by: 2

Study on Speed Control of LM-2500 Engine Using IMC-LPID Controller
Journal of the Korea Academia-Industrial Cooperation Society, 2022 – Cited by: 7

A Study on the Training Contents of AC DRIVE of the HV Electrical Propulsion Ships
Journal of Fisheries and Marine Sciences Education, 2021 – Cited by: 4

Deekshitha Kosaraju | Artificial Intelligence Award | Best Researcher Award

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Ms. Deekshitha Kosaraju | Artificial Intelligence Award | Best Researcher Award

LIMS Junior Developer, ALS Group USA, Corp., United States

Deekshitha Kosaraju is an accomplished Computer Science graduate from The University of Texas at Dallas, with a strong academic foundation and technical expertise in a variety of programming languages, frameworks, and cloud technologies. Her expertise spans Java, Python, JavaScript, and R, among others. Deekshitha is currently working as a Junior Developer at ALS Group USA, where she focuses on improving data integration and system efficiency. She is passionate about cloud computing, machine learning, and AI, and has published several papers on cutting-edge AI techniques, including explainable AI and quantum computing integration. 🎓👩‍💻📚

Publication Profile

Google Scholar

Education

Deekshitha Kosaraju graduated with a Bachelor of Science in Computer Science from The University of Texas at Dallas, maintaining a GPA of 3.6/4.0. During her time at university, she was honored with the Academic Excellence Scholarship. Her coursework included a wide range of subjects such as Data Structures, Machine Learning, Software Engineering, and Operating Systems. 🎓🏆

Experience

Deekshitha has gained invaluable professional experience through internships and full-time roles. Currently, she works as a Junior Developer at ALS Group USA, where she contributes to streamlining workflows, automating processes, and improving data transfer efficiency. She has previously interned at Radiant Digital, where she worked on low-code platforms and developed mobile applications that enhanced field coordination. In addition, her experience at Pearson as a Software Engineer Intern allowed her to improve user engagement and business outcomes through AI-driven applications. 💼💻

Awards and Honors

Deekshitha was awarded the Academic Excellence Scholarship during her time at The University of Texas at Dallas. Her achievements in academic and professional arenas reflect her dedication to excellence and innovation in the field of computer science. 🌟🏅

Research Focus

Deekshitha’s research primarily focuses on Artificial Intelligence, with specific attention to explainable AI, zero-shot learning, meta-learning, reinforcement learning, and AI’s integration with cloud computing and quantum technologies. She is also interested in exploring the applications of AI in various domains, such as healthcare and data analytics. Her research contributions include exploring how AI can enhance big data analytics and cloud computing innovations. 🤖📊

Conclusion

With a diverse set of technical skills and a passion for advancing AI and cloud technologies, Deekshitha Kosaraju continues to make impactful contributions to the field of Computer Science. She remains committed to expanding her knowledge in AI and exploring innovative solutions to real-world problems. 🌐🚀

Publications :

Shedding light on AI: exploring explainable AI techniques
International Journal of Research and Review, 2020
Read Article

Zero-Shot learning: teaching AI to understand the unknown
International Journal of Research and Review, 2021
DOI: 10.52403/ijrr.20211161

How meta learning enhances reinforcement learning in AI
Galore International Journal of Applied Sciences & Humanities, 2021
DOI: 10.52403/gijash.20210706

Crossing domains: the role of transfer learning in rapid AI prototyping and deployment
International Journal of Science & Healthcare Research, 2021
DOI: 10.52403/ijshr.20210464

Artificial intelligence in cloud computing: enhancements and innovations
Galore International Journal of Applied Sciences & Humanities, 2021
DOI: 10.52403/gijash.20211010

Quantum computing and artificial intelligence: a fusion poised to transform technology
International Journal of Research and Review, 2021
DOI: 10.52403/ijrr.20210974

The role of artificial intelligence in enhancing big data analytics
Galore International Journal of Applied Sciences and Humanities, 2021

sajjad qureshi | Artificial Intelligence Award | Computer Vision Contribution Award

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Dr. sajjad qureshi | artificial intelligence award | Computer Vision Contribution Award

deputy Director (IT), multan electric power company, Pakistan

📘 Dr. Sajjad Hussain Qureshi is a seasoned professional with over 21 years of experience in Information Technology. Currently serving as the Deputy Director (IT) at Multan Electric Power Company for the past two decades, Dr. Qureshi has established expertise in system analysis and design, machine learning, cybersecurity, IT auditing, and project management. His multidisciplinary educational background, including a Ph.D. in Computer Science, Ph.D. in Agriculture, and an MBA in Human Resource Management, has enabled him to excel in IT-based quality assurance, data analysis, and human resource management. He is passionate about utilizing cutting-edge technologies to create impactful solutions in diverse domains.

Publication Profile

ORCID

Education

🎓 Dr. Qureshi holds a Ph.D. in Computer Science, a Ph.D. in Agriculture, and an MBA in Human Resource Management. His diverse academic achievements reflect his commitment to integrating IT with interdisciplinary knowledge for impactful research and practical solutions.

Experience

💼 Dr. Qureshi has an extensive career spanning over 21 years in the IT field. For the last 20 years, he has been a cornerstone of the Multan Electric Power Company, where he serves as the Deputy Director (IT). His experience covers IT infrastructure development, LAN networks, client/server environments, cybersecurity, and customer billing systems. He also excels in IT-based quality assurance and control, project management, and data management.

Research Interests

🔍 Dr. Qureshi’s research interests lie at the intersection of machine learning, deep learning, cybersecurity, data management, and agriculture technology. He is particularly interested in applying advanced computational techniques to solve real-world problems, such as disease detection in crops and classification of linguistic data.

Awards

🏆 Dr. Qureshi has been recognized for his contributions to IT and interdisciplinary research, achieving notable academic and professional milestones. His work continues to inspire innovation and collaboration across diverse fields.

Publications

Classification of English Words into Grammatical Notations Using Deep Learning Technique (2024)
Imran, M., Qureshi, S. H., Qureshi, A. H., & Almusharraf, N.
Published in Information, 15(12), 801.
DOI: 10.3390/info15120801

Rational Study Of The Use Of Computer-Aided Softwares By The Composers (2024)
Qureshi, S. H., Javaid, S., & Javaid, M. A.
Published in Pakistan Journal of Society, Education and Language, 10(2), 213–219.

Comparison of Conventional and Computer-Based Detection of Severity Scales of Stalk Rot Disease in Maize (2024)
Qureshi, S.H., Khan, D.M., Razzaq, A., Baig, M.M., & Bukhari, S.Z.A.
Published in SABRAO Journal of Breeding and Genetics, 56(1), 292–301.
DOI: 10.54910/sabrao2024.56.1.26

Intelligent Resistant Source Detection Against Stalk Rot Disease of Maize Using Deep Learning Technique (2023)
Qureshi, S.H., Khan, D.M., & Bukhari, S.Z.
Published in SABRAO Journal of Breeding and Genetics, 55(6), 1972–1983.