Assist. Prof. Dr. Yuchae Jung, Open Cyber University of Korea , South Korea.

Dr. Yuchae Jung is an accomplished Affiliated Professor at KAIST School of Computing, Seoul, South Korea. With an interdisciplinary background spanning computer science, medical sciences, and artificial intelligence, she brings a unique integration of biomedical knowledge and computational innovation to her research. Over the years, Dr. Jung has held key academic and research roles in prestigious institutions, including Harvard Medical School and State University of New York. Her professional journey reflects a strong commitment to advancing digital healthcare, AI-driven diagnostics, and computational biology.

Professional Profile

Google Scholar

Education Background

Dr. Jung earned her Ph.D. and M.S. in Medical Science from The Catholic University of Korea (2008, 2002), following her undergraduate degree in Computer Science from Sookmyung Women’s University in 2000. This solid academic foundation has enabled her to contribute innovatively to both computer science and medical informatics.

Professional Experience

Dr. Jung is currently affiliated with KAIST’s School of Computing as a professor. She has previously held significant roles at The Catholic University of Korea, Boin IT, Seoul National University, and Sookmyung Women’s University. She has also conducted postdoctoral research at Brigham & Women’s Hospital (Harvard Medical School) and State University of New York. Her professional engagements include lectures, research leadership, and AI-based system development across medical and computing fields.

Awards and Honors

Dr. Jung has been the Principal Investigator of several prestigious grants from organizations such as the Ministry of SMEs and Startups, National Library of Korea, Ministry of Science, and Ministry of Education. Her projects span from NLP-based clinical dialogue systems to cancer therapy algorithms and bioinformatics applications in glioblastoma research. She was also honored as a keynote speaker by The Korean Society of Pathologists.

Research Focus

Her core research interests lie in Medical AI, including deep transfer learning for digital pathology image analysis, clinical Natural Language Processing (Bio-NLP), and cancer genomics (TFs, repeat sequences, miRNAs). She also explores gene expression network analysis in cancer and functional informatics for precision diagnostics. Her work bridges cutting-edge AI with real-world healthcare applications.

Conclusion

Dr. Yuchae Jung is a pioneering figure in interdisciplinary AI and bioinformatics, contributing impactful research to cancer genomics and healthcare AI. With a dynamic academic trajectory and a clear focus on translational science, she continues to be a driving force in computational medicine and smart health systems. Her extensive contributions position her as a deserving candidate for recognition in digital healthcare innovation.

Top Publications Highlights

Classification of Diffuse Glioma Subtype from Clinical-Grade Pathological Images Using Deep Transfer Learning
Published: 2021 in MDPI Sensors
Cited by: 39 articles (Google Scholar)
A groundbreaking study applying deep transfer learning for pathology image classification.
Impact of tumor purity on immune gene expression and clustering analyses across multiple cancer types
Published: 2018 in Cancer Immunology Research
Cited by: 107 articles
Investigates how tumor purity affects gene expression in cancer immunology.
Hybrid-Aware Model for Senior Wellness Service in Smart Home
Published: 2017 in MDPI Sensors
Cited by: 25 articles
Explores smart health monitoring using a hybrid AI model in smart homes.
Aneuploidy meets network analysis: leveraging copy number alterations
Published: 2017 in Translational Cancer Research
Cited by: 15 articles
Integrates systems biology with cancer genomics.
Cancer stem cell targeting: Are we there yet?
Published: 2015 in Archives of Pharmacal Research
Cited by: 55 articles
Reviews strategies to target elusive cancer stem cells.
Systemic approaches identify Z-ajoene as a GBM stem cell-specific targeting agent
Published: 2014 in Molecules and Cells
Cited by: 40+ articles
Identifies garlic-derived compound with anti-glioblastoma activity.
Numb regulates glioma stem cell fate and growth
Published: 2012 in Stem Cells
Cited by: 100+ articles
A critical study in stem cell regulation in glioma.
GEAR: Genomic Enrichment Analysis of Regional DNA Copy Number Changes
Published: 2008 in Bioinformatics
Cited by: 80+ articles
Proposes a novel method for regional DNA copy number analysis.
DNA methylation patterns of ulcer-healing genes in gastric cancers
Published: 2010 in Journal of Korean Medical Science
Cited by: 35 articles
Connects epigenetics with cancer pathology.
PathCluster: a framework for gene set-based hierarchical clustering
Published: 2008 in Bioinformatics
Cited by: 90+ articles
Presents a tool widely adopted in gene expression analysis.

Seyed Abolfazl Aghili | Deep Learning | Best Review Paper Award

Posted on 16/04/202516/04/2025 by sciencefather

Dr. Seyed Abolfazl Aghili | Deep Learning | Best Review Paper Award

lecturer, iran university of science and technology, Iran

Seyed Abolfazl Aghili is a passionate civil engineer with a strong focus on construction engineering and management. With a Ph.D. in Civil Engineering from the prestigious Iran University of Science and Technology (IUST), he specializes in using artificial intelligence for enhancing the resilience of HVAC systems in hospitals. His research integrates cutting-edge technologies such as machine learning and deep learning to optimize building systems and improve decision-making in construction projects. Seyed’s dedication to his field has earned him a reputation as a driven academic and professional in the civil engineering community.

Publication Profile

ORCID

Education Background

Seyed Abolfazl Aghili completed his Ph.D. in Civil Engineering with a specialization in Construction Engineering and Management from Iran University of Science and Technology (IUST) between 2019 and 2024. His doctoral thesis focused on developing a framework to assess the long-term resilience of hospital air conditioning systems using artificial intelligence. Prior to that, he earned his M.Sc. in Civil Engineering with a focus on Construction Engineering and Management at IUST, where he investigated employee selection methods in construction firms. He also holds a B.Sc. in Civil Engineering from Isfahan University of Technology (IUT).

Professional Experience

Seyed Abolfazl Aghili has extensive experience in both academic research and practical applications of civil engineering, particularly in construction management. He has worked on various projects involving energy management, risk management, and resilience within the construction industry. His academic journey has seen him contribute significantly to the research community, particularly in the areas of AI in construction systems and HVAC performance. Furthermore, he has been an integral part of various conferences and publications, sharing his insights on improving construction management processes through technology.

Awards and Honors

Seyed Abolfazl Aghili has earned several prestigious awards throughout his academic journey. He was ranked 5th among 2200 participants in the Nationwide University Entrance Exam for the Ph.D. program in Iran in 2019. Additionally, he ranked 2nd among all construction management students at Iran University of Science and Technology during his M.Sc. studies. He was also ranked in the top 1% (220th out of 32,663) in the Nationwide University Entrance Exam for the M.Sc. program in Iran in 2013.

Research Focus

Seyed’s primary research interests lie in the application of machine learning and deep learning techniques in construction engineering. His work focuses on enhancing the resilience of building systems, especially HVAC systems in healthcare settings. He also explores risk management, sustainability, lean construction, and decision-making systems for project managers. His interdisciplinary research combines civil engineering with advanced AI methodologies, driving innovations in construction management and systems optimization.

Conclusion

Seyed Abolfazl Aghili’s academic and professional journey reflects his commitment to advancing civil engineering through innovative solutions. His focus on integrating artificial intelligence into construction systems is helping to create smarter, more sustainable, and resilient built environments. Through his work, he continues to contribute valuable insights to both the academic world and the practical sector of construction engineering.

Publications Top Notes

Artificial Intelligence Approaches to Energy Management in HVAC Systems: A Systematic Review. Journal of Buildings, 15.7 (2025).

Data-driven approach to fault detection for hospital HVAC system. Journals of Smart and Sustainable Built Environment, ahead-of-print (2024).

Feasibility Study of Using BIM in Construction Site Decision Making in Iran. International Conference on Civil Engineering, Architecture and Urban Infrastructure, July 2015.

Review of digital imaging technology in safety management in the construction industry. 1st National Conference on Development of Civil Engineering, Architecture, Electricity and Mechanical in Iran (December, 2014).

The role of insurance companies in managing the crisis after earthquake. 1st National Congress of Engineering, Construction, and Evaluation of Development Projects, May 2013.

The need for a new approach to pre-crisis and post-crisis management of earthquake. 1st National Conference on Seismology and Earthquake, February 2013.

Dr. Aiai Wang | Machine Learning | Best Researcher Award

Posted on 12/04/202512/04/2025 by sciencefather

Dr. Aiai Wang | Machine Learning | Best Researcher Award

Doctoral student, University of Science and Technology Beijing, China

Ai-Ai Wang is a passionate and dedicated young researcher born in March 1998 in Langfang, Hebei Province, China. A proud member of the Communist Party of China (CPC), she is currently based at the University of Science and Technology Beijing (USTB), where she serves as the Secretary of the 16th Party Branch, 4 Zhaizhai. With a solid academic foundation in mining and civil engineering, Ai-Ai has excelled in both academic and research spheres, contributing significantly to digital and intelligent mining technologies. Her work emphasizes physical dynamics in tailings sand cementation and filling, showing strong potential for innovation in sustainable mining practices.

Publication Profile

Scopus

Education Background:

Ai-Ai Wang completed her Bachelor of Science in Mining Engineering from North China University of Science and Technology in 2021. She further pursued her Master’s degree in Civil Engineering at the University of Science and Technology Beijing (2021.09–2024.06), affiliated with the School of Civil and Resource Engineering.

Professional Experience:

Alongside her academic journey, Ai-Ai has undertaken significant responsibilities, currently serving as Secretary of the Party Branch at USTB. Her leadership extends beyond administration into collaborative research projects, software development, and patent contributions under renowned mentors such as Prof. Cao Shuai. She has played vital roles in developing intelligent systems for mining operations, reinforcing her multidisciplinary strengths.

Awards and Honors:

Ai-Ai Wang has been recognized extensively for her academic and research excellence. Notable accolades include the “Top Ten Academic Stars” at USTB (2023), a National Scholarship for Master’s Degree Students (2022), the prestigious Taishan Iron and Steel Scholarship (2023), and multiple First-Class Academic Scholarships from USTB. She was twice named an Outstanding Three-Good Graduate Student and honored by her school as an outstanding individual. Moreover, she has received scientific awards such as the First Prize from the China Gold Association and the Second Prize from the China Nonferrous Metals Industry for her impactful contributions to green and safe mining.

Research Focus:

Ai-Ai Wang’s research is rooted in advanced techniques of tailings sand cementation, intelligent filling systems, and digital mining. She explores the structural stability of backfills, application of nanomaterials, and CT-based 3D modeling of internal structures. Her work blends civil engineering, environmental safety, and digital innovation, aiming to enhance sustainability and efficiency in modern mining. She also contributes to cutting-edge software systems and patented technologies for mining design and operation support.

Conclusion:

Ai-Ai Wang stands out as a promising engineer and researcher whose academic achievements, professional dedication, and innovative research in intelligent mining set a high standard for future civil and mining engineers. Her trajectory reflects not just technical mastery but a deep commitment to sustainable and smart engineering solutions in the mining industry.

Top Publications with Details

Effect of height to diameter ratio on dynamic characteristics of cemented tailings backfills with fiber reinforcement through impact loading – Construction and Building Materials, 2022
Cited by: 26 articles
Influence of types and contents of nano cellulose materials as reinforcement on stability performance of cementitious tailings backfill – Construction and Building Materials, 2022
Cited by: 20 articles
Quantitative analysis of pore characteristics of nanocellulose reinforced cementitious tailings fills using 3D reconstruction of CT images – Journal of Materials Research and Technology, 2023
Cited by: 12 articles

Prof. Dr. Otilia Manta | Artificial Intelligence | Innovative Research Award

Posted on 07/04/202507/04/2025 by sciencefather

Prof. Dr. Otilia Manta | Artificial Intelligence | Innovative Research Award

Professor PhD, Romanian Academy, Romania

Prof. Dr. habil. Elena Otilia Manta is a renowned economist, academic, and scientific researcher, currently serving as a Professor at the Romanian-American University and a Scientific Researcher at the Romanian Academy. With over two decades of experience in economic sciences, she is widely respected for her expertise in finance, FinTech, AI integration, and sustainable development. She also holds various leadership roles in international organizations, including Vice President of EUExperts in Brussels, reflecting her strong influence in policy and academic circles globally.

Publication Profile

Scopus
ORCID

Education Background

Prof. Manta holds a Ph.D. in Economics and the academic title of “habilitation,” enabling her to supervise doctoral research. Her education has been complemented by continuous development in areas like finance, international economic relations, and technological integration, laying a solid academic foundation for her contributions in both national and international academic communities.

Professional Experience

Since 2014, Prof. Manta has been a Scientific Researcher at the Romanian Academy and since 2019, a full Professor at the Romanian-American University in Bucharest. She also serves as Vice President of the European Union Experts (EUExperts) and the International Research Institute for Economics and Management (IRIEM), among others. As founder and CEO of The Romanian Group for Investments and Consultancy (RGIC), she combines academic depth with real-world financial leadership. She has also served as an EU Expert and Rapporteur with the European Commission.

Awards and Honors

Prof. Manta has received numerous honors, including the “2006 Woman of the Year Commemorative Medal” by the American Biographical Institute. She is a respected honorary member of the Romanian-Italian Chamber of Commerce and holds various affiliations with prestigious academic, financial, and developmental organizations such as the UN HLPF Mechanism and the Financial Stability Oversight Council (NY).

Research Focus

Her research focuses on FinTech, artificial intelligence integration in banking, sustainable economic development, academic transparency, and financial innovation. As an active participant in multidisciplinary fields, Prof. Manta is a frequent contributor to leading journals and conferences, continuously shaping discussions on the future of finance and digital transformation.

Publications – Top Notes

Banking Transformation Through FinTech and the Integration of Artificial Intelligence in Payments – FinTech, 2025 | DOI: 10.3390/fintech4020013 – A highly impactful paper exploring AI’s role in revolutionizing payment systems.

FinTech and AI as Opportunities for a Sustainable Economy – FinTech, 2025 | DOI: 10.3390/fintech4020010 – Widely cited for linking technology to green and inclusive finance.

The Transfer of Managerial Expertise in Romanian Companies through the Application of the DEMATEL Method – Journal for Future Society and Education, 2025 – Emphasizes decision science in corporate governance.

Ensuring Academic Integrity: Tools and Mechanisms for a Transparent Educational Environment – Preprints, 2025 – Focuses on digital tools fostering academic ethics.

Conclusion

Prof. Dr. habil. Otilia Manta is a distinguished leader, bridging the worlds of academia, finance, and international consultancy. Her career reflects a strong commitment to innovation, transparency, and global collaboration in economics. Through her scholarly research, institutional leadership, and consultancy, she continues to inspire future generations in both Romania and abroad.

Dr. Uddalak Mitra | Machine learning | Best Researcher Award

Posted on 27/03/202527/03/2025 by sciencefather

Dr. Uddalak Mitra | Machine learning | Best Researcher Award

Assistant Professor, JIS College of Engineering, India

Dr. Uddalak Mitra is an esteemed Assistant Professor at JIS College of Engineering, specializing in bioinformatics, machine learning, and deep learning . With a strong academic foundation and a passion for research, he has significantly contributed to the intersection of computational intelligence and biological sciences. His expertise lies in decoding DNA, RNA, and protein sequences using cutting-edge AI techniques, paving the way for advancements in healthcare and genomics. Dr. Mitra’s work seamlessly blends theoretical knowledge with real-world applications, making impactful strides in both academia and industry.

Publication Profile

Google Scholar

Education:

Dr. Mitra has built a solid academic background in computational sciences, equipping himself with the expertise required to address complex biological challenges. His educational journey has provided him with the technical prowess to integrate artificial intelligence into biomedical research.

Experience:

As an Assistant Professor at JIS College of Engineering, Dr. Mitra actively engages in research and mentoring, shaping the next generation of scientists. His work focuses on applying machine learning models to analyze biological data, improving early disease detection methodologies. Additionally, he has authored several research papers and contributed to the academic community through his innovative studies.

Awards and Honors:

Dr. Mitra has made commendable contributions to scientific research, earning recognition for his published works. He holds 9 patents, showcasing his dedication to innovation. His memberships in esteemed organizations like IFERP and ISTE reflect his commitment to professional development and research excellence.

Research Focus:

His research primarily revolves around bioinformatics, machine learning, and deep learning. He explores information-theoretic tools for biological sequence analysis, integrating artificial intelligence to derive meaningful insights from genomic data. His recent studies focus on clinical applications of AI, particularly in disease diagnosis and personalized medicine.

Conclusion:

Dr. Uddalak Mitra is a pioneering researcher dedicated to bridging the gap between bioinformatics and artificial intelligence. His contributions to genomic research, coupled with his expertise in AI-driven disease diagnosis, make him a valuable asset to the scientific community. With a strong foundation in computational biology, he continues to push the boundaries of research, striving for innovations that benefit both medicine and technology.

Publications:

Leveraging AI and Machine Learning for Next-Generation Clinical Decision Support Systems (CDSS) – Published in AI-Driven Innovation in Healthcare Data Analytics, 2025.

Cognitive Handwriting Insights for Alzheimer’s Diagnosis: A Hybrid Framework – Information, 2025

Integrated System for Disease Detection Using Semiconductor-Based Gas Sensors and AI/ML – IN Patent A61B0005080000, 2025

Significance of AI/ML Wearable Technologies for Education and Teaching – Wearable Devices and Smart Technology for Educational Teaching Assistance, 2025

Integrating AI/ML With Wearable Devices for Monitoring Student Mental Health – Wearable Devices and Smart Technology for Educational Teaching Assistance, 2025

The Evolution of Entrepreneurship in the Age of AI – Advanced Intelligence Systems and Innovation in Entrepreneurship, 2024

A Novel Algorithm for Genomic STR Mining: Application to Phylogeny Reconstruction and Taxa Identification – International Journal of Bioinformatics Research and Applications, 2024

Ulas Bagci | Artificial Intelligence | Outstanding Scientist Award

Posted on 07/03/202507/03/2025 by sciencefather

Assoc. Prof. Dr. Ulas Bagci | Artificial Intelligence | Outstanding Scientist Award

Assoc. Prof., Northwestern University, United States

Dr. Ulas Bagci is a distinguished researcher and tenured Associate Professor at Northwestern University, specializing in Radiology, Electrical and Computer Engineering, and Biomedical Engineering. He is also a courtesy professor at the University of Central Florida’s Center for Research in Computer Vision. As the Director of the Machine and Hybrid Intelligence Lab, Dr. Bagci focuses on the integration of artificial intelligence, deep learning, and medical imaging. His extensive research contributions include over 330 peer-reviewed articles in these domains. Previously, he was a staff scientist and lab co-manager at the National Institutes of Health (NIH), where he played a pivotal role in advancing AI-driven medical imaging applications. Dr. Bagci actively contributes to leading scientific journals, serving as an associate editor for IEEE Transactions on Medical Imaging, Medical Physics, and Medical Image Analysis.

Publication Profile

Education

Dr. Ulas Bagci holds a Ph.D. in Computer Science from the University of Nottingham (2010), where he conducted pioneering research in medical imaging. He was a Visiting Research Fellow in Radiology at the University of Pennsylvania (2008-2009), further refining his expertise in AI applications for biomedical sciences. He earned his M.Sc. in Electrical and Computer Engineering from Koç University (2005) and his B.Sc. in Electrical and Computer Engineering from Bilkent University (2003).

Experience

Dr. Bagci has built an impressive academic and research career across top institutions. Since 2021, he has been an Associate Professor at Northwestern University, where he leads research in AI-driven medical imaging. Before that, he served as an Assistant Professor in Computer Science at the University of Central Florida (2014-2020), fostering innovation in deep learning for radiology. From 2010 to 2014, he was a Staff Scientist and Lab Manager at the National Institutes of Health (NIH), playing a key role in infectious disease imaging and AI applications in radiology.

Awards and Honors

Dr. Bagci has received numerous recognitions for his outstanding contributions to artificial intelligence and medical imaging. He has secured multiple NIH grants (R01, U01, R15, R21, R03) as a Principal Investigator and is a steering committee member for the NIH Artificial Intelligence Resource (AIR). Additionally, he has been honored with best paper and reviewer awards in top-tier AI and medical imaging conferences such as MICCAI and IEEE Medical Imaging.

Research Focus

Dr. Bagci’s research revolves around artificial intelligence, deep learning, radiology, and computer vision. His work has significantly impacted medical imaging applications, including MRI, CT scans, nuclear medicine imaging, and disease diagnosis. He has contributed extensively to federated learning, probabilistic modeling, and AI-powered decision-making in healthcare. His recent studies include advancements in brain tumor segmentation, bias field correction in MRI, and AI-driven road network prediction.

Conclusion

Dr. Ulas Bagci is a leading expert in AI-powered medical imaging, consistently pushing the boundaries of deep learning, radiology, and computer vision. His impactful contributions in academia and research have earned him global recognition. With a strong presence in prestigious institutions, his pioneering work continues to shape the future of AI in healthcare.

Publications

Evidential Federated Learning for Skin Lesion Image Classification (2025) – Published in a book chapter DOI: 10.1007/978-3-031-78110-0_23

Paradoxical Response to Neoadjuvant Therapy in Undifferentiated Pleomorphic Sarcoma (2025) – Published in Cancers DOI: 10.3390/cancers17050830

Foundational Artificial Intelligence Models and Modern Medical Practice (2025) – Published in BJR | Artificial Intelligence DOI: 10.1093/bjrai/ubae018

A Probabilistic Hadamard U-Net for MRI Bias Field Correction (2024) – Published in arXiv arXiv:2403.05024

AI-Powered Road Network Prediction with Fused Low-Resolution Satellite Imagery and GPS Trajectory (2024) – Published in Earth Science Informatics

Beyond Self-Attention: Deformable Large Kernel Attention for Medical Image Segmentation (2024) – Presented at the IEEE/CVF Winter Conference on Applications of Computer Vision

Brain Tumor Segmentation (BraTS) Challenge 2024: Meningioma Radiotherapy Planning Automated Segmentation (2024) – Published in arXiv arXiv:2405.18383

QIANG QU | Artificial Intelligence Award | Best Researcher Award

Posted on 04/02/202504/02/2025 by sciencefather

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

Posted on 23/01/202523/01/2025 by sciencefather

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

Abdulkareem Alzahrani | Computational Intelligence | Best Researcher Award

Posted on 17/01/202517/01/2025 by sciencefather

Assoc. Prof. Dr. Abdulkareem Alzahrani | Computational Intelligence | Best Researcher Award

Associate Professor of Artificial Intelligence at CS Dept. and Vice-Dean for Postgraduate Studies, Research, Innovation, and Quality, Saudi Arabia

Dr. Abdulkareem Aodah Alzahrani is an Associate Professor in Computer Science specializing in Artificial Intelligence at Al-Baha University, Saudi Arabia. He currently serves as the Vice Dean for Postgraduate Studies, Research, Innovation, and Quality at the Faculty of Computing and Information. With a career spanning over 16 years, Dr. Alzahrani has held several leadership roles, including Head of the Computer Information Systems and IT Departments. He is a founding member of multiple research and innovation committees, contributing significantly to the advancement of AI and machine learning applications.

Publication Profile

Google Scholar

Education

Dr. Alzahrani earned his Ph.D. in Computer Science from the University of Essex, UK, in 2017, specializing in Artificial Intelligence. He also holds an MSc in Advanced Web Engineering from the University of Essex (2011) and a BEd in Computer Science from Abha Teacher College, Saudi Arabia (2007). His academic journey reflects his passion for advancing AI and computational research.

Experience

Dr. Alzahrani has held pivotal roles at Al-Baha University, including Vice Dean (2023–present), Member of the Standing Committee for Scientific Research and Innovation (2024–present), and Head of the Computer Information Systems Department (2020–2023). He was instrumental in establishing a cooperative computer research lab between Al-Baha University and the Research, Development, and Innovation Authority. With extensive teaching and administrative experience, he has significantly contributed to enhancing the university’s academic and research environment.

Awards and Honors

Dr. Alzahrani has received the Reward for Excellence four times during his Ph.D. studies, awarded by the Saudi Arabian Cultural Bureau in London. Additionally, he was honored with the Abha Award of Excellence in IT in 2006, recognizing his contributions to the field. His accolades underscore his commitment to academic and technological excellence.

Research Focus

Dr. Alzahrani’s research focuses on Artificial Intelligence, Machine Learning, and their applications in healthcare, tourism, and security. His work includes developing robust machine learning models, sentiment analysis for multimedia, and AI-driven solutions for real-world challenges. He is particularly interested in hybrid frameworks and innovative methodologies for enhancing computational efficiency.

Conclusion

Dr. Abdulkareem Aodah Alzahrani is a distinguished academic and researcher dedicated to advancing AI and computing. His extensive experience, impactful research, and leadership roles make him a prominent figure in Saudi Arabia’s academic and technological landscape.

Publications

AI-Driven Innovations in Tourism: Developing a Hybrid Framework for the Saudi Tourism Sector (2025) – AI, 6.1, DOI:10.3390/ai6010007.
Cited by: 7.

A Novel Outlier-Robust Accuracy Measure for Machine Learning Regression Based on Hassanat Distance Metric (2024) – DOI:10.21203/rs.3.rs-4492948/v1.
Cited by: 10.

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Ching-Lung Fan | Deep Learning | Best Researcher Award

Posted on 11/01/202511/01/2025 by sciencefather

Assoc. Prof. Dr. Ching-Lung Fan | Deep Learning | Best Researcher Award

Associate Professor, ROC Military Academy, Taiwan

Ching-Lung Fan is an associate professor in Civil Engineering at the Republic of China Military Academy. He completed his Ph.D. in 2019 from the National Kaohsiung University of Science and Technology. His professional journey reflects a strong dedication to advancing technology in the construction and civil engineering sectors, particularly through the application of machine learning and deep learning methods.

Publication Profile

Education

Dr. Fan holds a Master of Science (M.S.) from National Taiwan University (2006) and a Ph.D. from National Kaohsiung University of Science and Technology (2019). His academic background underscores his commitment to both theoretical and practical contributions to the field.

Experience

Dr. Fan started his academic career as an assistant professor at the Republic of China Military Academy in January 2019 and was promoted to associate professor in June 2022. His teaching and research experience has significantly impacted the study of civil engineering, especially through the integration of machine learning and data mining.

Awards and Honors

Ching-Lung Fan has received several prestigious awards, including the Phi Tau Phi Scholastic Honor (2019), Outstanding Paper Award (2021), Excellent Paper Award (2022), and Best Researcher Award (2024). In 2023, he was honored with membership in Sigma Xi, an esteemed scientific organization.

Research Focus

Dr. Fan’s research interests are primarily centered around machine learning, deep learning, data mining, construction performance evaluation, and risk management. His work integrates cutting-edge computational methods with civil engineering applications to enhance the quality and efficiency of construction projects.

Conclusion

Dr. Fan’s innovative contributions to civil engineering, particularly in the realm of AI-driven solutions, continue to shape the future of construction and infrastructure development. His ongoing research and recognition in the academic community highlight his expertise and impact in the field.