Dr. JOONSOO KIM | Digital Transformation | Best Researcher Award

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Dr. JOONSOO KIM | Digital Transformation | Best Researcher Award

Postdoctoral Researcher, Korea Institute of Construction Technology (KICT), South Korea.

Dr. Joon-Soo Kim is an innovative and skilled researcher in Construction Management, with a strong command of advanced data analytics, machine learning, and environmental sustainability. His academic and research work uniquely integrates engineering with cutting-edge technologies such as text mining and big data to enhance construction safety and efficiency. Currently serving as a Postdoctoral Researcher at the Korea Institute of Civil Engineering and Building Technology (KICT), Dr. Kim is highly regarded for developing reliable decision-making models tailored to complex engineering challenges. His expertise spans from eco-friendly engineering solutions to smart waste management systems, making him a vital contributor to sustainable civil engineering research. πŸ—οΈπŸ“ŠπŸŒΏ

Publication Profile

Scopus

πŸŽ“ Education Background

Dr. Kim earned his Ph.D. in Construction Environment and Energy Engineering from Kyungpook National University, where his dissertation explored road construction environmental load and cost estimation using machine learning. He also holds a Master’s in Construction Systems Engineering with a specialization in Geotechnical and Road Engineering, and a Bachelor’s degree in Civil Engineeringβ€”all from the same university. His academic path reflects a strong commitment to applying data-driven insights to real-world infrastructure problems. πŸŽ“πŸ“š

πŸ’Ό Professional Experience

Dr. Kim is currently a Postdoctoral Researcher at KICT, contributing to advancements in civil infrastructure and environmental solutions. Prior to this, he worked at the Intelligent Construction Automation Research Center, Kyungpook National University, for over three years. He also shared his expertise as a lecturer at Daegu University, teaching courses such as Basic Statistics and Construction Management. His combined academic and field experience empowers him to lead high-impact research in civil engineering. πŸ’πŸ‘¨β€πŸ«

πŸ… Awards and Honors

Dr. Kim holds registered intellectual property rights, including a software-based Construction Waste Information Management (CWIM) system using QR codes (2023), and a patented Eco-Friendly Value Engineering Decision Analysis System (Patent No. 10-1745567, registered in 2017). These innovations underscore his commitment to enhancing efficiency and sustainability in construction engineering through smart technologies. πŸ†πŸ“œπŸ’‘

πŸ” Research Focus

Dr. Kim’s primary research areas include construction safety, environmental load management, and project efficiency, with a strong focus on big data, machine learning, and geospatial analysis. He specializes in Value Engineering (VE), Life Cycle Assessment (LCA), and advanced image processing techniques like YOLO for object detection. His multidisciplinary approach supports disaster prevention and promotes green building practices. πŸ”ŽπŸŒ±πŸ“ˆ

πŸ“˜ Conclusion

Combining strong academic foundations with hands-on innovation, Dr. Joon-Soo Kim continues to make significant strides in the civil and construction engineering fields. His work not only enhances safety and environmental responsibility but also sets a benchmark in leveraging AI-driven methodologies for engineering problem-solving. πŸ‘πŸŒπŸš§

πŸ“š Top Publications withΒ 

  1. Image Processing and QR Code Application Method for Construction Safety Management
    Kim, J.-S., Yi, C.-Y., & Park, Y.-J., 2021, Applied Sciences
    πŸ“‘ Cited by: 18 articles (as per Google Scholar)

  2. Impact Evaluation of Water Footprint on Stages of Drainage Works
    Chen Di, Kim, J.-S., Batagalle V., & Kim, B.-S., 2020, Journal of the Korean Society of Civil Engineers
    πŸ“‘ Cited by: 6 articles

  3. Characteristics Analysis of Seasonal Construction Site Fall Accident using Text Mining
    Kim, J.-S., & Kim, B.-S., 2019, Journal of the Korea Institute of Construction Engineering and Management
    πŸ“‘ Cited by: 11 articles

  4. Analysis of the Environmental Load Impact Factors for IPC Girder Bridge Using PCA
    Kim, J.-S., Jeon, J.-G., & Kim, B.-S., 2018, Journal of the Korea Institute of Construction Engineering and Management
    πŸ“‘ Cited by: 9 articles

  5. Analysis of Fire-Accident Factors Using Big-Data in Construction Areas
    Kim, J.-S., & Kim, B.-S., 2017, KSCE Journal of Civil Engineering
    πŸ“‘ Cited by: 34 articles

  6. Eco-Friendly Design Evaluation Model Using PEI for Construction Facilities
    Kim, J.-S., & Kim, B.-S., 2017, Journal of the Korean Society of Civil Engineers
    πŸ“‘ Cited by: 10 articles

  7. Definition of Environmental Cost and Eco-VE Model for Construction Facility
    Kim, M.-J., Kim, J.-S., & Kim, B.-S., 2016, Journal of the Korean Society of Civil Engineers
    πŸ“‘ Cited by: 7 articles

  8. A Proposal of NIA Model for Eco VE Decision of Construction Facilities
    Kim, J.-S., & Kim, B.-S., 2015, Journal of the Korean Society of Civil Engineers
    πŸ“‘ Cited by: 12 articles

 

Prof. Dr. Metin Zontul | Machine Learning | Best Researcher Award

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Prof. Dr. Metin Zontul | Machine Learning | Best Researcher Award

Dean, Sivas University of Science and Technology, Turkey

Prof. Dr. Metin Zontul is a seasoned academic and researcher in the fields of machine learning, data mining, and intelligent systems, currently serving as Professor and Dean at the Faculty of Engineering and Natural Sciences, Sivas University of Science and Technology, Turkey. With over 30 years of academic experience, he has held various esteemed positions at several universities in Turkey and contributed significantly to national-level research projects, innovation in artificial intelligence, and academic leadership.

Publication Profile

Google Scholar

ORCID

πŸŽ“ Education Background

He earned his Ph.D. in Quantitative Methods in Business Administration (2004) from the Institute of Social Sciences, focusing his dissertation on clustering countries trading with Turkey using SOM-type artificial neural networks. He holds an M.Sc. in Computer-Aided Design, Manufacturing, and Programming (1996), where he analyzed local area network access protocols, and a B.Sc. in Computer Engineering (1993) from Middle East Technical University.

πŸ’Ό Professional Experience

Prof. Zontul has held multiple academic ranks, starting as a Lecturer at Cumhuriyet University (1994–2005) and advancing to Assistant, Associate, and then Professor at institutions such as Istanbul AydΔ±n University, Arel University, Ayvansaray University, and Topkapi University. He has been a key academic leader, serving as Dean and Department Chair across several faculties. Since 2023, he has led the Faculty of Engineering and Natural Sciences at Sivas UST. He also supervises graduate theses and collaborates on research with TUBITAK and other industry-linked projects.

πŸ† Awards and Honors

Prof. Zontul has received Publication Incentive Awards from Istanbul AydΔ±n University in 2014 and 2016 for his scholarly contributions. He is a former member of IEEE and holds a 2024 patent for a Personnel Assignment and Routing System related to unit failure and maintenance operations.

πŸ”¬ Research Focus

His research interests span machine learning, deep learning, data mining, signal processing, natural language processing, and intelligent systems. He has contributed extensively to the scientific community through 25+ peer-reviewed journal articles, 20+ conference papers, and collaborative projects involving academia and industry. His supervision of numerous theses and his involvement in over 30 national research projects reflect his commitment to practical and academic advancements in AI.

πŸ”š Conclusion

Prof. Dr. Metin Zontul stands as a multifaceted academician blending research, leadership, and innovation. His significant contributions to AI, education, and national research initiatives have cemented his reputation as a leading scholar in his field.

πŸ“š Top PublicationsΒ 

  1. Application of artificial intelligence neural network modeling to predict the generation of domestic, commercial and construction wastes (2021)
    Journal: Waste Management & Research
    Cited by: 92
    Co-authors: G. Coskuner, M.S. Jassim, S. Karateke

  2. Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation (2022)
    Journal: Waste Management & Research
    Cited by: 49
    Co-authors: M.S. Jassim, G. Coskuner

  3. Urban bus arrival time prediction: A review of computational models (2013)
    Journal: International Journal of Recent Technology and Engineering (IJRTE)
    Cited by: 123
    Co-author: M. Altinkaya

  4. Measuring the efficiency of telecommunication sectors of OECD countries using data envelopment analysis (2005)
    Journal: CU Journal of Economics and Administrative Sciences
    Cited by: 41
    Co-authors: O. Kaynar, H. Bircan

  5. Wind speed forecasting using reptree and bagging methods in Kirklareli-Turkey (2013)
    Journal: Journal of Theoretical and Applied Information Technology
    Cited by: 35
    Co-authors: F. Aydin, G. Dogan, S. Sener, O. Kaynar

  6. The prediction of the ZnNi thickness and Ni% of ZnNi alloy electroplating using a machine learning method (2021)
    Journal: Transactions of the IMF
    Cited by: 34
    Co-authors: R. Katirci, H. Aktas

  7. A smart and mechanized agricultural application: From cultivation to harvest (2022)
    Journal: Applied Sciences
    Cited by: 31
    Co-authors: F. Kiani, G. Randazzo, I. Yelmen, A. Seyyedabbasi, S. Nematzadeh, F.A. Anka, et al.

 

 

Assoc. Prof. Dr. Feng Xie | intelligence systems | Best Researcher Award

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Assoc. Prof. Dr. Feng Xie | intelligence systems | Best Researcher Award

School of Information Science and Technology / Sanda University, China

Dr. Feng Xie is an accomplished Associate Professor at the School of Information Science and Technology, Sanda University, China . With a career that bridges academia and industry, he has been at the forefront of intelligent transportation systems, urban mobility, and smart city innovations. As a tech entrepreneur and researcher, he has led over 500 consultancy projects globally and holds numerous patents and software copyrights. His expertise spans traffic management, AI applications, IoT, and big data analytics, with significant contributions that have earned him prestigious awards and talent program recognitions.

Publication Profile

ORCID

πŸŽ“ Education Background:

Dr. Xie earned his Ph.D. from Nanyang Technological University, Singapore , in 2002 and completed his postdoctoral research at Tongji University, China , in 2005. His academic foundation is rooted in transportation engineering, computer science, and intelligent systems, providing the basis for his interdisciplinary approach to research and technology deployment.

πŸ’Ό Professional Experience:

Currently serving as an Associate Professor at Shanghai Shanda University, Dr. Xie has also been the founder of Shanghai Van-Chance Trans. Technologies (2010–2022), where he led large-scale smart transportation projects across Asia. He worked extensively with government and industry partners, such as Singapore’s Land Transport Authority and IKEA, and directed projects like the world’s largest underground parking facility. He has also held leadership roles in cross-border technology associations and has developed systems used in cities like Beijing, Hangzhou, and Wuhan.

πŸ† Awards and Honors:

Dr. Feng Xie has been recognized with several prestigious awards, including the IES Engineering Achievement Award in 2004 for his contributions to Singapore’s i-Transport project and the Shanghai Science Progress Award in 2013. He has also been selected for elite talent programs such as the Shanghai “3310” Overseas High-level Talent Program and Nanjing “321” Leading Technology Entrepreneurship Talent Program. His innovative work has resulted in 5 patents and 9 software copyrights, solidifying his impact in both academic and applied research domains.

🧠 Research Focus:

Dr. Xie’s research is centered on Intelligent Transportation Systems (ITS), AI-driven traffic management, smart parking, indoor positioning, urban planning, and emerging tech applications in IoT and quantitative finance. His efforts in traffic simulation, traveler behavior modeling, and data-driven urban development have influenced policies and technologies in smart mobility across multiple major cities. He has collaborated with Tongji University, published in Transportation Research Board journals, and contributed to key projects with global relevance.

βœ… Conclusion:

With a unique blend of academic rigor and entrepreneurial innovation, Dr. Feng Xie exemplifies leadership in intelligent systems and sustainable urban technology 🌍. His work has profoundly shaped how modern cities approach mobility, data analytics, and smart infrastructure development. He continues to push the boundaries of AI, transportation science, and cross-border collaboration, earning him a rightful nomination for the Best Researcher Award.

πŸ“š Top Publications :

PDCG-Enhanced CNN for Pattern Recognition in Time Series Data
Journal: Elsevier – Expert Systems with Applications
Year: 2022 | Cited by: 38 articles

Modeling Traveler Behavior Using Hybrid RP/SP Data and Path-Size Logit Models
Journal: Transportation Research Record: Journal of the Transportation Research Board
Year: 2012 | Cited by: 65 articles

AI-Based Traffic Incident Management Systems: A Case Study of Singapore’s i-Transport Project
Journal: IEEE Transactions on Intelligent Transportation Systems
Year: 2014 | Cited by: 79 articles

Urban Traffic Simulation Using GPS Data Fusion and Adaptive Signal Optimization
Journal: Journal of Transportation Engineering, ASCE
Year: 2016 | Cited by: 45 articles

Smart Parking Systems Powered by IoT and AI: A Case Study of Guinness Record Facility
Journal: Sensors (MDPI)
Year: 2020 | Cited by: 54 articles

Prof. Dr. YiSheng Huang | Computer Science | Best Researcher Award

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Prof. Dr. YiSheng Huang | Computer Science | Best Researcher Award

Professor, National Ilan University/EE, Taiwan

Dr. Yi-Sheng Huang is a distinguished Professor at National Ilan University, Taiwan, with a remarkable career in Electrical and Electronic Engineering. Holding a Ph.D. from National Taiwan University of Science and Technology (NTUST), he has significantly contributed to Discrete Event Dynamic Systems, Petri Nets, and Intelligent Transportation Systems (ITS). Recognized among the World’s Top 2% Scientists (1960–2023), his expertise spans both theoretical advancements and real-world applications, shaping the future of smart mobility and automation. With leadership roles in academia and international collaborations, Dr. Huang continues to drive impactful research and innovation in the field of intelligent systems. πŸš€

Publication Profile

πŸ“š Academic Background

Dr. Yi-Sheng Huang earned his Ph.D. in Electrical Engineering from NTUST in 2001, laying the foundation for his expertise in dynamic systems and intelligent transportation. His commitment to academic excellence led him to various leadership positions, including Chairman of the Department of Electrical Engineering at National Ilan University (2015–2019) and Dean of the Office of Research and Development (2023–2024). He also enriched his global academic experience as a Visiting Professor at the New Jersey Institute of Technology (2008, 2014), fostering cross-border collaborations in intelligent automation. πŸŽ“

🏒 Professional Experience

With over two decades of academic and research contributions, Dr. Huang has completed 30 major research projects and collaborated with industry leaders such as CECI Engineering Consultants, INC., Taiwan. His consultancy work spans 6 completed and 7 ongoing industry projects, demonstrating his ability to bridge theoretical research with practical applications. He is an esteemed member of the IEEE SMC Society and IEEE ITS Society, further solidifying his influence in cutting-edge technological advancements. His extensive publication record includes 114 journal papers indexed in Scopus and SCI, reflecting his commitment to advancing the field of intelligent systems. ⚑

πŸ† Awards and Honors

Dr. Huang’s research excellence has earned him a place among the World’s Top 2% Scientists (1960–2023), highlighting his profound impact on electrical engineering and intelligent systems. His work has significantly influenced transportation optimization, making urban mobility smarter and more efficient. As a leading researcher, he continues to push the boundaries of Discrete Event Dynamic Systems and Petri Nets applications, setting new standards in system modeling and control. His contributions have been acknowledged globally, reinforcing his reputation as a pioneering scientist in his field. πŸ…

πŸ”¬ Research Focus

Dr. Huang’s research revolves around Discrete Event Dynamic Systems, Petri Nets, and Intelligent Transportation Systems (ITS). His work has led to advanced methodologies in traffic flow management, congestion control, and system optimization, improving urban transport networks. By integrating theoretical models with real-world applications, he has contributed to automated decision-making frameworks, enhancing efficiency and sustainability in transportation. His research is instrumental in shaping smart cities and next-generation mobility solutions, fostering safer and more efficient transport ecosystems worldwide. 🚦

πŸ” Conclusion

Dr. Yi-Sheng Huang stands at the forefront of intelligent transportation research, driving significant innovations in automation and dynamic systems. His global academic presence, extensive research contributions, and impactful industry collaborations establish him as a leading figure in electrical engineering and intelligent mobility solutions. With a legacy of over 114 publications, numerous research projects, and an enduring influence in academia, Dr. Huang continues to shape the future of intelligent transportation and automated systems. πŸŒπŸš€

πŸ“ Top Publications

A Petri net-based model for real-time traffic control in urban networks.

Optimization of discrete event systems using hybrid Petri nets.

Modeling and performance analysis of ITS using dynamic Petri nets.

Smart transportation planning with discrete event system simulation.

Enhancing automated transport systems using intelligent Petri nets.

 

Dr. Uddalak Mitra | Machine learning | Best Researcher Award

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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 πŸ§¬πŸ€–. 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

πŸŽ“ 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.

πŸ’Ό 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.

πŸ† 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.

πŸ”¬ 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.

πŸ” 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.

πŸ“š 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

Rab Nawaz Bashir | Machine Learning | Best Researcher Award

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Dr. Rab Nawaz Bashir | Machine Learning | Best Researcher Award

Assistant Professor, COMSATS University, Pakistan

Dr. Rab Nawaz Bashir πŸŽ“ is a distinguished computer scientist and Assistant Professor at COMSATS University Islamabad, Vehari Campus. He holds a Ph.D. in Computer Science from Islamia University Bahawalpur and has been instrumental in shaping research and education in artificial intelligence, machine learning, and the Internet of Things (IoT). With a passion for academic excellence, he has mentored numerous undergraduate, graduate, and Ph.D. students, guiding them toward impactful research and industry readiness. His dedication to scholarly contributions and innovative teaching has made him a respected figure in the field of computer science.

Publication Profile

Education πŸŽ“

Dr. Bashir has a strong academic foundation, earning his Ph.D. in Computer Science (2021) from Islamia University Bahawalpur. Prior to that, he completed an MS in Computer Science (2015) from the same institution. His educational journey began with a Master in Computer Science (MCS) from Pir Mehr Ali Shah University of Arid Agriculture (2008). To further enhance his expertise, he is currently pursuing a Fellowship in Computer Science at Prince Sultan University, Saudi Arabia.

Experience πŸ‘¨β€πŸ«

With over 15 years of experience, Dr. Bashir has significantly contributed to academia and research. Since 2022, he has served as an Assistant Professor at COMSATS University Islamabad, Vehari Campus, where he leads curriculum development, organizes seminars, and supervises undergraduate and Ph.D. research. Previously, he was a Lecturer at COMSATS University (2015–2022), University of Agriculture Faisalabad (2014–2015), and Institute of Southern Punjab, Multan (2010–2014). Before transitioning fully into academia, he worked in software development at the University of Agriculture Faisalabad (2008–2010), specializing in secure and scalable web applications using ASP.NET and SQL Server.

Awards and Honors πŸ†

Dr. Bashir has been recognized multiple times for his outstanding contributions to research and academic excellence. He has received several Research Awards (2021, 2022, 2023, 2024) and Annual Performance Awards (2022, 2023, 2024) from his institution. Additionally, his leadership in securing National Standard for Education Accreditation Council (NSEAC) accreditation for the university in 2021 and 2024 highlights his commitment to academic quality and institutional development.

Research Focus πŸ”¬

Dr. Bashir’s research spans various cutting-edge domains, including machine learning, IoT, and computer programming. His work emphasizes real-world applications, such as IoT-enabled smart agriculture, machine learning-based fraud detection, and deep learning for natural image processing. His interdisciplinary collaborations have resulted in high-impact research, contributing to advancements in federated learning, fog computing, and agricultural technology.

Conclusion 🌟

Dr. Rab Nawaz Bashir is a leading academic and researcher in computer science, with a strong focus on machine learning, IoT, and programming. His dedication to mentoring students, publishing impactful research, and advancing academic excellence has earned him numerous awards and recognitions. With a vision for future innovations, he continues to push the boundaries of computer science, contributing to both academia and industry through groundbreaking research and leadership.

Publications πŸ“š

A Novel 1-Dimensional Cosine Chaotic Equation and Digital Image Encryption TechniqueΒ (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3447889

Federated Learning (FL) Model of Wind Power PredictionΒ (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3415781

IoT-Enabled Firmness Grades of Tomato in Cold Supply Chain Using Fusion of Whale Optimization Algorithm and Extreme Learning MachineΒ (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3379327

Mushroom Species Classification in Natural Habitats Using Convolutional Neural Networks (CNN)Β (IEEE Access, 2024) – DOI: 10.1109/ACCESS.2024.3502543

Machine Learning and Fog Computing-Enabled Sensor Drift Management in Precision AgricultureΒ (IEEE Sensors Journal, 2024) – DOI: 10.1109/JSEN.2024.3451662

Principal Component Analysis (PCA) and Feature Importance-Based Dimension Reduction for Reference Evapotranspiration (ET0) Predictions of Taif, Saudi Arabia (Computers and Electronics in Agriculture, 2024) – DOI: 10.1016/j.compag.2024.109036

Ensemble Deep Learning-Based Prediction of Fraudulent Cryptocurrency TransactionsΒ (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3310576

Internet of Things Based Weekly Crop Pest Prediction by Using Deep Neural Network Β (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3301504

Stacked Ensemble Model for Tropical Cyclone Path PredictionΒ (IEEE Access, 2023) – DOI: 10.1109/ACCESS.2023.3292907

Smart Reference Evapotranspiration Using Internet of Things and Hybrid Ensemble Machine Learning ApproachΒ (Internet of Things, 2023) – DOI: 10.1016/j.iot.2023.100962

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

πŸŽ“ 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

πŸ“˜ 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

πŸ’» 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

πŸ† 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

πŸ”¬ 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

 

Tesfay Gidey | Artificial Intelligence | Best Researcher Award

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Dr. Tesfay Gidey | Artificial Intelligence | Best Researcher Award

Lecturer, Addis Ababa Science and Technology University, Ethiopia

Tesfay Gidey Hailu is a highly skilled Information and Communication Engineer and data scientist with a passion for leveraging data to drive innovation and business insights. With expertise in computer science, software engineering, machine learning, and data analytics, he excels in problem-solving, leadership, and technology project management. Tesfay’s work focuses on indoor localization, signal processing, and health data applications, making him a forward-thinking leader in his field. His dedication to continuous learning and delivering actionable results underscores his impressive career in academia and industry. πŸ’ΌπŸ”§πŸ“Š

Publication Profile

ORCID

Strengths for the Award:

  1. Diverse Expertise: Tesfay’s expertise spans across critical areas such as signal processing, indoor localization, machine learning, data fusion, and health informatics, aligning well with cutting-edge research areas.
  2. Impressive Academic Qualifications: Holding a Ph.D. in Information and Communication Engineering, along with two MSc degrees, he possesses deep knowledge in interdisciplinary fields.
  3. Research Contributions: He has authored numerous peer-reviewed publications in high-impact journals such as Sensors, Intelligent Information Management, and Journal of Biostatistics. His work in Wi-Fi indoor positioning, predictive modeling, and health informatics shows a broad application of research across industries.
  4. Leadership in Academia: His roles as Associate Dean and Head of Department demonstrate his leadership in driving research, improving curriculum quality, and promoting technology transfer.
  5. Innovative Research Focus: His Ph.D. dissertation on transfer learning for fingerprint-based indoor positioning and various data fusion methods reflect his innovative contributions to solving real-world problems with advanced technologies.

Areas for Improvement:

  1. Broader Industry Impact: While his research is highly academic, incorporating more industry-driven collaborations or commercial applications could strengthen the practical impact of his work.
  2. Public Engagement: Increasing public outreach and collaboration with non-academic sectors or public talks could elevate his visibility and expand the impact of his research findings.
  3. Global Collaboration: Expanding his research collaborations beyond local and regional levels, particularly with international industries, could further showcase the global relevance of his work.

Education πŸŽ“

Tesfay holds a Ph.D. in Information and Communication Engineering from the University of Electronic Science and Technology of China (2023), where his research centered on signal and information processing applied to indoor positioning using machine learning algorithms. He also earned an MSc in Software Engineering from HILCOE School of Computer Science and Information Technology (2018) and an MSc in Health Informatics and Biostatistics from Mekelle University (2013). Additionally, he completed his BSc in Statistics with a minor in Computer Science at Addis Ababa University (2006). πŸ“šπŸ’»πŸ“ˆ

Experience πŸ’Ό

Tesfay has held several leadership positions, including Associate Dean at Addis Ababa Science and Technology University (AASTU), where he led research, technology transfer, student recruitment, and faculty training initiatives. He was also the Head of Department and Coordinator at Jimma University, contributing to curriculum enhancement and student retention programs. His experience spans research in manufacturing industries, project management, and academic administration. πŸ«πŸ“ŠπŸ‘¨β€πŸ«

Research Focus πŸ”¬

Tesfay’s research focuses on signal processing, indoor localization, machine learning, data mining, and information fusion. He specializes in developing advanced models for indoor positioning systems, predictive modeling, and statistical quality control, aiming to solve complex problems in health informatics, manufacturing industries, and public health. His work integrates cutting-edge technologies to advance both theoretical and applied fields. πŸ“‘πŸ“‰πŸ€–

Awards and Honors πŸ†

Tesfay has been recognized for his contributions to the fields of information and communication engineering and data science. He has received multiple awards and honors for his research and leadership roles in academia, particularly in driving innovative projects that bridge the gap between technology and industry. πŸŒπŸŽ–οΈ

Publications Highlights πŸ“š

Tesfay has published extensively in top-tier journals, with a focus on indoor positioning systems, data fusion, and health informatics. His research includes the development of novel machine learning models and statistical analysis tools. His works have been widely cited, showcasing his impact in the academic community. πŸ“ŠβœοΈ

MultiDMet: Designing a Hybrid Multidimensional Metrics Framework to Predictive Modeling for Performance Evaluation and Feature Selection (2023). Intelligent Information Management, 15, 391-425. Cited by 2 articles. Link

Data Fusion Methods for Indoor Positioning Systems Based on Channel State Information Fingerprinting (2022). Sensors, 22, 8720. Cited by 15 articles. Link

Heterogeneous Transfer Learning for Wi-Fi Indoor Positioning Based Hybrid Feature Selection (2022). Sensors, 22, 5840. Cited by 10 articles. Link

OHetTLAL: An Online Transfer Learning Method for Fingerprint-Based Indoor Positioning (2022). Sensors, 22, 9044. Cited by 5 articles. Link

A Multilevel Modeling Analysis of the Determinants and Cross-Regional Variations of HIV Testing in Ethiopia (2016). J Biom Biostat, 7, 277. Cited by 8 articles. Link

Conclusion:

Tesfay Gidey Hailu’s robust academic background, extensive research portfolio, and leadership roles make him a strong candidate for the Best Research Award. His work in signal processing, machine learning, and data-driven innovation in health informatics and communication systems demonstrates a clear commitment to advancing technology and solving societal problems. While his impact could be enhanced by deeper industry collaborations and global outreach, his current achievements already reflect substantial contributions to the field, making him deserving of recognition.