Dr. Jiaheng Peng | Data Science | Best Researcher Award

Dr. Jiaheng Peng | Data Science | Best Researcher Award

PhD Candidate, East China Normal University, China

Jiaheng Peng is a dedicated Ph.D. candidate at East China Normal University, specializing in Open Source Ecosystem, Natural Language Processing, and Evaluation Science. With a strong academic record and a passion for research, he has contributed significantly to understanding Open Source dataset evaluation. His work bridges the gap between academic research and real-world Open Source applications, earning him recognition in the field.

Publication Profile

Google Scholar

🎓 Academic Background

Jiaheng Peng is pursuing his Ph.D. at East China Normal University, focusing on innovative methods to assess Open Source datasets. His research emphasizes citation network analysis, evaluating long-term dataset usage, and developing advanced Natural Language Processing (NLP) models. His academic journey is marked by high-impact publications in top-tier journals and international conferences, reflecting his expertise in computational analysis and data evaluation.

👨‍💼 Professional Experience

Although Jiaheng does not have industry consultancy or ongoing research projects, his scholarly contributions have made a substantial impact on Open Source ecosystem analysis. He actively publishes in high-impact scientific journals and conferences, ensuring that his findings help enhance dataset evaluation metrics. His commitment to advancing data-driven methodologies sets a solid foundation for future research in Open Source analysis.

🏆 Awards and Honors

Jiaheng Peng’s research excellence has been acknowledged with the Best Paper Award at the 1st Open Source Technology Academic Conference (2024). His publications in Q1-ranked journals further highlight his academic impact. His continuous contributions to the Open Source community demonstrate his dedication to advancing research and innovation in Open Source evaluation.

🔬 Research Focus

Jiaheng’s research primarily addresses the limitations of traditional Open Source data insight metrics. His work connects Open Source datasets with their corresponding academic papers, evaluating their significance through citation network mining. By bridging Open Source data with academic insights, he introduces novel evaluation methodologies that enhance dataset usability and long-term impact analysis. His research also extends into Aspect-Based Sentiment Classification, employing advanced Graph Attention Networks and NLP models to extract meaningful insights.

📌 Conclusion

Jiaheng Peng is a rising scholar in the Open Source and NLP domains, with a keen focus on dataset evaluation, citation network analysis, and sentiment classification. His academic contributions, recognized through prestigious awards and top-tier publications, establish him as a promising researcher dedicated to advancing Open Source dataset analytics. With a commitment to scientific excellence, his work continues to influence the global research community.

📚 Publication Top Notes

Evaluating long-term usage patterns of open source datasets: A citation network approach
BenchCouncil Transactions on Benchmarks, Standards and Evaluations (2025)
Cited by: Pending

DRGAT: Dual-relational graph attention networks for aspect-based sentiment classification
Information Sciences (2024)
Cited by: Pending

Data Driven Visualized Analysis: Visualizing Global Trends of GitHub Developers with Fine-Grained Geo-Details
International Conference on Database Systems for Advanced Applications (2024)
Cited by: Pending

ASK-RoBERTa: A pretraining model for aspect-based sentiment classification via sentiment knowledge mining”
Knowledge-Based Systems (2022)
Cited by: Multiple researchers in NLP and sentiment analysis

Mr. Md Tanvir rahman Tarafder | Information Technology | Best Researcher Award

Mr. Md Tanvir rahman Tarafder | Information Technology  | Best Researcher Award

Data analysis, Westcliff university, United States

Tanvir Rahman Tarafder is a passionate and results-driven cloud computing professional with a strong foundation in software development and IT solutions. With expertise in AWS services, including EC2, S3, Lambda, and RDS, he thrives in building scalable and efficient cloud-based architectures. His journey from a Computer Science graduate to a cloud enthusiast reflects his commitment to innovation and problem-solving. Beyond his technical expertise, Tanvir is a team player and excellent communicator, always eager to explore new technological advancements and contribute to impactful projects.

Publication Profile

Google Scholar

Academic Background 🎓

Tanvir is currently pursuing a Master’s in Information Technology (Cloud Computing) at Westcliff University, USA, maintaining an impressive CGPA of 3.96 (expected 2025). He earned his Bachelor of Science in Computer Science & Engineering from American International University-Bangladesh (AIUB) with a CGPA of 3.23 (2018-2021). His strong academic performance is complemented by a solid foundation in programming, databases, and cloud infrastructure. His early education includes a Higher Secondary Certificate from Dhaka City College and a Secondary School Certificate from Bogura Cantonment Public School & College, where he excelled with top grades.

Professional Experience 💼

Tanvir has gained diverse industry experience in various technical and consultancy roles. As an IT Officer at SM Fintech Technologies Ltd., he managed website maintenance, configured email servers, reviewed vendor contracts, and coordinated IT purchases to optimize business operations. His passion for academia led him to work as a Teaching Assistant at AIUB, where he supported students in Computer Graphics courses. Additionally, his role as an International Student Consultant at Revolution Student Consultancy allowed him to guide over 50 students in securing admissions to American universities. His expertise spans cloud computing, software development, and IT consultancy, making him a versatile professional.

Awards and Honors 🏆

Tanvir has demonstrated his technical excellence through multiple industry-recognized certifications. He holds the AWS Certified Solutions Architect Associate (Valid till 2030) and AWS Certified Cloud Practitioner (Valid till 2029), showcasing his deep expertise in cloud computing. Additionally, he has earned certifications in Python programming and front-end web development from prestigious platforms. These achievements highlight his continuous learning mindset and dedication to staying ahead in the tech industry.

Research Focus 🔬

Tanvir’s research focuses on leveraging Artificial Intelligence (AI) and Machine Learning (ML) in cloud computing, predictive analytics, and smart systems. His work includes forecasting Electric Vehicle adoption, AI-driven smart grid optimization, and transformative AI applications in healthcare. His passion for exploring AI’s role in solving real-world problems reflects his commitment to advancing technology for societal benefits. He has contributed to multiple peer-reviewed publications, addressing challenges in water quality analysis, synthetic e-commerce data insights, and medical imaging advancements.

Conclusion 🌟

With a strong technical foundation, hands-on cloud computing experience, and a keen research interest in AI-driven solutions, Tanvir Rahman Tarafder stands out as a forward-thinking innovator in the field of cloud technology and AI. His ability to bridge academic knowledge with practical applications makes him a valuable asset in any technology-driven organization. His continuous pursuit of excellence and eagerness to contribute to groundbreaking research and development mark him as a promising professional in the ever-evolving tech landscape.

Top Publications 📚

Forecasting Electric Vehicle Adoption in the USA Using Machine Learning Models
Published in: Journal of Computer Science and Technology Studies (2024)
Cited by: 12 articles

Discoverable Hidden Patterns in Water Quality through AI, LLMs, and Transparent Remote SensingPublished in: 2024 17th International Conference on Security of Information and Networks (2024)
Cited by: 9 articles

Integrating Transformative AI for Next-Level Predictive Analytics in Healthcare
Published in: IEEE Conference on Engineering Informatics (ICEI) (2024)
Cited by: 9 articles

Optimizing Load Forecasting in Smart Grids with AI-Driven Solutions
Published in: IEEE International Conference on Data and Software Engineering (ICoDSE) (2024)
Cited by: 7 articles

A Novel Diagnostic Framework with an Optimized Ensemble of Vision Transformers and Convolutional Neural Networks for Enhanced Alzheimer’s Disease Detection in Medical Imaging
Published in: Diagnostics Journal (2025)
Cited by: (Pending)

Leveraging Machine Learning for Insights and Predictions in Synthetic E-commerce Data in the USA: A Comprehensive Analysis
Published in: (Journal details pending)
Cited by: (Pending)

Zhe PENG | Data Analytics | Best Researcher Award

Prof. Zhe PENG | Analytics | Best Researcher Award

Assistant Professor, The Hong Kong Polytechnic University, Hong Kong

Dr. Zhe Peng  is a dedicated Research Assistant Professor at the Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University. With a strong background in computer science and engineering, he specializes in intelligent supply chains, AI for manufacturing, and blockchain technologies. His contributions to blockchain, federated learning, and decentralized identity systems have earned him global recognition. With extensive academic and industry experience, Dr. Peng has made a significant impact on cutting-edge technological advancements.

Publication Profile

🎓 Education

Dr. Peng holds a Ph.D. in Computer Science from The Hong Kong Polytechnic University (2018), under the supervision of Prof. Bin Xiao (IEEE Fellow). He earned his M.E. in Information and Communication Engineering from the University of Science and Technology of China (2013) and a B.E. in Communication Engineering from Northwestern Polytechnical University (2010). His academic journey reflects his deep expertise in computing, communication, and AI-driven systems.

💼 Experience

Dr. Peng has held multiple research and industry positions. He is currently a Research Assistant Professor at The Hong Kong Polytechnic University. Previously, he served as a Research Assistant Professor at Hong Kong Baptist University (2020-2023) and as an R&D Manager at the Blockchain and FinTech Lab. In the industry, he worked as the Blockchain Technical Director at SF Technology in Shenzhen (2018-2019). Additionally, he was a Visiting Scholar at Stony Brook University, USA, working under Distinguished Prof. Yuanyuan Yang (IEEE Fellow).

🏆 Awards and Honors

Dr. Peng has received several prestigious awards, including the World’s Top 2% Scientists by Stanford University (2024) and the Award for High SFQ Score at PolyU ISE (2024). He was recognized with an ESI Highly Cited Paper (2023) and received the DASFAA-MUST Best Paper Award (2021). His work was also nominated for THE Awards Asia – Technological or Digital Innovation of the Year (2021). His numerous accolades highlight his contributions to academia, research, and technological innovation.

🔬 Research Focus

Dr. Peng’s research revolves around intelligent supply chains, AI-driven manufacturing, blockchain applications, and autonomous systems. His work on verifiable decentralized identity management, privacy-aware federated learning, and blockchain security has set new benchmarks in these fields. He continues to explore innovative solutions to improve efficiency, transparency, and security in digital ecosystems.

🔚 Conclusion

Dr. Zhe Peng is a visionary researcher at the intersection of AI, blockchain, and smart logistics. His groundbreaking research, academic excellence, and industry experience make him a leading expert in his field. Through his contributions to intelligent systems, federated learning, and blockchain security, he continues to shape the future of technological innovation. 🚀

🔗 Publications 

Lightweight Multimodal Defect Detection at the Edge via Cross-Modal Distillation

VDID: Blockchain-Enabled Verifiable Decentralized Identity Management for Web 3.0 

SymmeProof: Compact Zero-Knowledge Argument for Blockchain Confidential Transactions 

The Impact of Life Cycle Assessment Database Selection on Embodied Carbon Estimation of Buildings 

EPAR: An Efficient and Privacy-Aware Augmented Reality Framework for Indoor Location-Based Services

VFChain: Enabling Verifiable and Auditable Federated Learning via Blockchain Systems 

VQL: Efficient and Verifiable Cloud Query Services for Blockchain Systems 

Mrs. Edna Rocio Bernal Monroy | Machine Learning | Best Researcher Award

Mrs. Edna Rocio Bernal Monroy | Machine Learning | Best Researcher Award

UNAD, Colombia

Dr. Edna Rocío Bernal Monroy is an accomplished computer scientist and researcher specializing in informatics, machine learning, and healthcare technologies. With a strong academic background and diverse international experience, she has contributed significantly to health informatics, wearable sensors, and intelligent systems. Dr. Bernal Monroy has worked across multiple institutions in Colombia, France, and Spain, engaging in teaching, research, and project management. Her work in artificial intelligence (AI) for healthcare has earned her prestigious awards and recognition in the global scientific community.

Publication Profile

🎓 Education

Dr. Bernal Monroy holds a Ph.D. in Information & Communication Technology from the University of Jaén, Spain (2017–2021), focusing on informatics and AI applications in healthcare. She completed a Master of Engineering in Information Systems and Networks at Claude Bernard Lyon 1 University, France (2010–2012). Additionally, she pursued a Specialization in Management of Innovative Health Projects at INCAE Business School, Nicaragua (2016–2017) and earned a Bachelor of Engineering in Computer Science & Technology from the Pedagogical and Technological University of Colombia (2005–2010).

💼 Experience

Dr. Bernal Monroy has held teaching and research roles in various universities. She served as a Full-Time Teacher at the National Open and Distance University, Bogotá (2014–2020) and worked at the San Gil University Foundation (2013–2014) as a Systems Engineering Lecturer. She was also a faculty member at the Pedagogical and Technological University of Colombia (2014–2015). Additionally, she gained international experience as a Project Manager in Informatics at CALYDIAL, France (2011–2012).

🏆 Awards and Honors

Dr. Bernal Monroy has received several prestigious distinctions for her research contributions. She was awarded the Google LARA 2018 Google Research Award for Latin America for her doctoral project on innovation. She also served as a European Project Researcher for REMIND – H2020 – MSCA-RISE-2016 under the European Union’s research initiative. Additionally, she received the CAHI Research Fellowship from the Central American Healthcare Initiative (CAHI) in 2016 for her contributions to healthcare technology and informatics.

🔬 Research Focus

Dr. Bernal Monroy’s research interests lie at the intersection of AI, machine learning, healthcare informatics, and wearable technologies. She specializes in intelligent monitoring systems for healthcare applications, particularly in preventing pressure ulcers through wearable inertial sensors and using AI-driven analytics for healthcare improvements. Her work also extends to human activity recognition, telemedicine, and IoT solutions for health applications.

🏁 Conclusion

Dr. Edna Rocío Bernal Monroy is a leading researcher in AI-driven healthcare solutions with extensive experience in informatics, machine learning, and wearable technologies. Her pioneering research has contributed significantly to intelligent monitoring systems, earning her global recognition and prestigious awards. Through her academic contributions, research projects, and international collaborations, she continues to drive innovation in healthcare informatics and AI applications. 🚀

📚 Publications

Implementation of Machine Learning Techniques to Identify Patterns that Affect the Social Determinants of the Municipality of Tumaco – Nariño (2024) – Published in Encuentro Internacional de Educación en Ingeniería, this paper focuses on using AI to analyze social determinants of health.

Fuzzy Monitoring of In-Bed Postural Changes for the Prevention of Pressure Ulcers Using Inertial Sensors Attached to Clothing (2020) – Published in the Journal of Biomedical Informatics, this research has been cited 31 times and explores AI-driven healthcare monitoring solutions.

Intelligent System for the Prevention of Pressure Ulcers by Monitoring Postural Changes with Wearable Inertial Sensors (2019) – Published in Proceedings, this work highlights wearable sensor-based intelligent systems for healthcare and has been cited 11 times.

UJA Human Activity Recognition Multi-Occupancy Dataset (2021) – A dataset publication in collaboration with other researchers, cited 3 times.

Finite Element Method for Characterizing Microstrip Antennas with Different Substrates for High-Temperature Sensors (2017) – Explores sensor technologies for high-temperature environments.

Estudio de Apoyo para la Implementación de un Sistema de Telemedicina en Lyon, Francia (2013) – Discusses telemedicine systems and their applications in France.

Chunling Bao | Data Science | Best Researcher Award

Ms. Chunling Bao | Data Science | Best Researcher Award

PhD Candidates, Shanghai Normal University, China

Chunling Bao is a dedicated Ph.D. candidate at Shanghai Normal University, specializing in environmental and geographical sciences 🌍. With a strong academic background and research focus on dust storms, climate change, and land surface interactions, she has contributed significantly to understanding environmental dynamics in East Asia. Her scholarly work is widely recognized, with multiple publications in high-impact journals 📚.

Publication Profile

ORCID

🎓 Education

Chunling Bao embarked on her academic journey at Inner Mongolia Normal University, earning her undergraduate degree (2014-2018) and later obtaining her master’s degree (2018-2021) 🎓. She expanded her expertise through an exchange program at the Center for Agricultural Resources Research, Chinese Academy of Sciences (2023), before pursuing her doctoral studies at Shanghai Normal University (2023-present) 🏫.

💼 Experience

With a deep passion for environmental research, Chunling Bao has explored dust storms, vegetation interactions, and land-atmosphere processes. Her experience includes field studies, satellite data analysis, and interdisciplinary research collaborations 🌪️. Her academic training at leading Chinese institutions has enriched her expertise in remote sensing, environmental monitoring, and climate analysis.

🏆 Awards and Honors

Chunling Bao has been recognized for her outstanding research contributions in environmental science 🏅. Her work has been published in top-tier journals, and she has actively participated in academic exchanges and research collaborations. Her efforts in studying dust storm dynamics have positioned her as an emerging scholar in the field 🌿.

🔬 Research Focus

Her research primarily focuses on the spatial and temporal dynamics of dust storms, their drivers, and their environmental impacts in East Asia 🌫️. Using remote sensing and geospatial analysis, she investigates the effects of land surface changes on atmospheric conditions. Her studies contribute to climate adaptation strategies and sustainable environmental management.

📌 Conclusion

As an emerging environmental researcher, Chunling Bao is making significant strides in understanding dust storm dynamics and their broader ecological implications. With her growing academic contributions and research excellence, she continues to shape the field of environmental science and atmospheric studies 🌏.

📚 Publications

Dust Intensity Across Vegetation Types in Mongolia: Drivers and Trends. Remote Sensing, 17(3), 410. 🔗 DOI

Analyses of the Dust Storm Sources, Affected Areas, and Moving Paths in Mongolia and China in Early Spring. Remote Sensing, 14, 3661. 🔗 DOI

Impacts of Underlying Surface on Dusty Weather in Central Inner Mongolian Steppe, China. Earth and Space Science, 8, e2021EA001672. 🔗 DOI

Regional Spatial and Temporal Variation Characteristics of Dust in East Asia. Geographical Research, 40(11), 3002-3015. 🔗 DOI (in Chinese)

Analysis of the Movement Path of Dust Storms Affecting Alxa. Journal of Inner Mongolia Normal University (Natural Science Mongolian Edition), 04, 39-47.

Evaluation of the Impact of Coal Mining on Soil Heavy Metals and Vegetation Communities in Bayinghua, Inner Mongolia. Journal of Inner Mongolia Normal University (Natural Science Mongolian Edition), 40(1), 32-38.

 

 

Yunhyung LEE | Computer science| Best Researcher Award

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

Cyruss Tsurgeon | Data Visualization | Bioinformatics Contribution Award

Mr. Cyruss Tsurgeon | Data Visualization | Bioinformatics Contribution Award

PhD Student, Meharry Medical College, United States

🌟 Cyruss Tsurgeon is a dedicated Biomedical Data Science graduate student and seasoned clinical scientist based in Rancho Cucamonga, CA. With a wealth of experience in diagnostic data interpretation and clinical medicine, Cyruss combines his technical acumen with a passion for advancing healthcare through data science. His impressive journey spans decades of leadership, research, and healthcare administration, making him a valuable contributor to the scientific and medical communities.

Publication Profile

Education

🎓 Cyruss holds an MS in Biomedical Data Science (2022–2023) from Meharry Medical College, Nashville, TN. He also earned an MS in Molecular Biotechnology (2000–2003) from Johns Hopkins University and dual BS degrees in Biochemistry and Microbiology (1987–1992) from the University of Washington, Seattle. Additionally, he has completed certifications such as the Google Data Analytics Professional Certificate and the Executive Data Science Specialization from Coursera, equipping him with expertise in data analytics, R programming, and visualization tools.

Experience

💼 Cyruss boasts a diverse professional background, including over a decade as a Clinical Laboratory Manager/Scientific Director in the US Army, where he led medical laboratories and implemented protocols for risk management and quality improvement. His tenure as a Biologist/Research Scientist at the NIH involved DNA sequencing and genome analysis. Earlier in his career, he served as a Healthcare Administrator in the US Army and a Research Associate at prominent institutions, contributing to molecular biology and comparative genomic studies.

Awards and Honors

🏆 Cyruss has achieved prestigious laboratory certifications, including DLM(ASCP)CM and MLS(ASCP)CM, showcasing his expertise in laboratory medicine. His contributions to clinical data science and diagnostics have been recognized through impactful research and publications in leading journals like Nature and Genome Research.

Research Focus

🔬 Cyruss’s research interests lie at the intersection of biomedical data science, molecular biology, and clinical medicine. He focuses on leveraging data visualization techniques, RNA-Seq analysis, and genome sequencing for clinical applications. His work emphasizes addressing real-world healthcare challenges, including multidrug-resistance surveillance and comparative genomic analyses.

Conclusion

✨ As a lifelong learner and experienced scientist, Cyruss Tsurgeon is committed to advancing healthcare innovation through data science and clinical research. His blend of expertise, leadership, and passion makes him a key player in the biomedical field, shaping the future of medicine and science.

Publications

Exploring RNA-Seq Data Analysis Through Visualization Techniques and Tools: A Systematic Review of Opportunities and Limitations for Clinical Applications
Bioengineering, 2025-01-12
DOI: 10.3390/bioengineering12010056

A Multidrug-Resistance Surveillance Network: 1 Year On
The Lancet Infectious Diseases, 2012-08
DOI: 10.1016/s1473-3099(12)70149-4

An Intermediate Grade of Finished Genomic Sequence Suitable for Comparative Analyses
Genome Research, 2004-10-12
DOI: 10.1101/gr.2648404

Comparative Analyses of Multi-Species Sequences from Targeted Genomic Regions
Nature, 2003-08-14
DOI: 10.1038/nature01858

 

 

Zari Farhadi | Analytics | Best Researcher Award

Dr. Zari Farhadi | Analytics | Best Researcher Award

Lecturer, University of Tabriz, Iran

Dr. Zari Farhadi is a dedicated lecturer and researcher at the University of Tabriz, Iran, with expertise in Data Science, Machine Learning, and Predictive Modeling. Her passion for academic excellence is evident in her work, particularly in the development of hybrid models to enhance data analysis accuracy. With a Ph.D. in Data Science, she has contributed extensively to advancing predictive models through innovative techniques like ensemble learning and deep regression. 🌟📚

Publication Profile

Google Scholar

Education

Zari Farhadi holds a Ph.D. in Data Science, specializing in machine learning, deep learning, and statistical techniques, from the University of Tabriz. Her academic foundation supports her pioneering work in hybrid machine learning models. 🎓

Experience

As a lecturer and researcher, Dr. Farhadi has contributed to various research papers, focusing on machine learning and deep learning. She teaches at both the Computerized Intelligence Systems Laboratory and the Department of Statistics at the University of Tabriz. Her research experience spans across several high-impact areas of data science, including predictive modeling and statistical learning. 🧑‍🏫

Awards and Honors

Though not currently affiliated with professional organizations, Dr. Farhadi’s work has been recognized in academic circles through the citation of her research in top journals, underlining her growing impact in the field of data science. 🏅

Research Focus

Dr. Farhadi’s research centers on Machine Learning, Predictive Modeling, Ensemble Learning Methods, Statistical Learning, and Hybrid Models like ADeFS, which integrate deep learning with statistical shrinkage methods. She strives to improve model performance in real-world applications, including gold price prediction and real estate valuation. 🤖📊

Conclusion

Zari Farhadi continues to innovate and drive research in the fields of machine learning and data science. Through her groundbreaking work in hybrid models, she is shaping the future of predictive analytics and advancing the boundaries of artificial intelligence in academic and industrial applications. 🌍

Publications

An Ensemble Framework to Improve the Accuracy of Prediction Using Clustered Random-Forest and Shrinkage Methods,
Appl. Sci., vol. 12, no. 20, 2022, doi: 10.3390/app122010608
Cited by: 15 articles.

Improving random forest algorithm by selecting appropriate penalized method
Commun. Stat. Simul. Comput., vol. 0, no. 0, pp. 1–16, 2022, doi: 10.1080/03610918.2022.2150779
Cited by: 10 articles.

ERDeR: The combination of statistical shrinkage methods and ensemble approaches to improve the performance of deep regression,
IEEE Access, DOI: 10.1109/ACCESS.2024.3368067
Cited by: 3 articles.

ADeFS: A deep forest regression-based model to enhance the performance based on LASSO and Elastic Net,
Mathematics and Computer Science, MDPI, 13 (1), 118, 2024.
Cited by: Pending.

Combining Regularization and Dropout Techniques for Deep Convolutional Neural Network,
IEEE Glob. Energy Conf. GEC 2022, pp. 335–339, 2022, doi: 10.1109/GEC55014.2022.9986657
Cited by: 5 articles.

Analysis of Penalized Regression Methods in a Simple Linear Model on the High-Dimensional Data,
American Journal of Theoretical and Applied Statistics, 8 (5), 185, 2019.
Cited by: 2 articles.

An Ensemble-Based Model for Sentiment Analysis of Persian Comments on Instagram Using Deep Learning Algorithms,
IEEE Access, DOI: 10.1109/ACCESS.2024.3473617
Cited by: Pending.

Hybrid Model for Visual Sentiment Classification Using Content-Based Image Retrieval and Multi-Input Convolutional Neural Network,
International Journal of Intelligent Systems (Under review).

 

PETROS PATIAS | Data science | Best Researcher Award

Prof. PETROS PATIAS | Data science | Best Researcher Award

CEO, KIKLO – GEOSPATIAL INFORMATION TECHNOLOGIES P.C., Greece

Prof. Petros Patias is a prominent expert in photogrammetry and remote sensing, serving as Professor and Director at the Laboratory of Photogrammetry & Remote Sensing at Aristotle University of Thessaloniki (AUTH), Greece. A leader in his field, he has held esteemed roles, including Vice Rector at the University of Western Macedonia and former President of the Hellenic Society for Photogrammetry & Remote Sensing. Prof. Patias has made groundbreaking contributions internationally through the ISPRS and CIPA, cementing his legacy as an Honorary President and Fellow of these global scientific communities. His impact continues through extensive research, teaching, and scientific guidance worldwide.

Publication Profile

ORCID

Education 🎓📚

Prof. Patias holds a MEng from Aristotle University (1981), an MSc (1985), and a PhD (1987) in Geodetic Science and Surveying from The Ohio State University, USA. His extensive education laid the foundation for his international recognition and contributions in geospatial sciences.

Experience 🏛️🌍

Prof. Patias has held numerous prestigious academic and leadership roles, such as ex-Chairman of the School of Rural and Surveying Engineering at AUTH, board member of the Department of Urban Planning, and Vice Rector at the University of Western Macedonia. He served as President of the Hellenic Society for Photogrammetry & Remote Sensing and led ISPRS Working Groups and Commissions. His experience extends globally as a Visiting Professor at renowned institutions like TU Delft, ETH Zurich, and Universidad del País Vasco.

Research Focus 🔍🌐

Prof. Patias’s research focuses on photogrammetry, remote sensing, and geospatial sciences, with applications in architectural photogrammetry and urban planning. He collaborates internationally, advising institutions such as ETH Zurich, University of Maine, Politecnico di Milano, and IIT Roorkee, and leads impactful projects through European and National organizations.

Awards and Honors 🏆🌟

Prof. Patias has received numerous honors, including an ISPRS Fellowship (2016) and lifetime honorary presidencies with both CIPA and ISPRS. His leadership contributions have earned him esteemed positions, reflecting his commitment to advancing photogrammetry and remote sensing worldwide.

Publications Top Notes 📝📅

“Aerial Photogrammetry for Urban Planning” (2020) published in Remote Sensing; cited by 48 articles.

“Geospatial Data Applications in Urban Development” (2018) published in Geodetic Science Journal; cited by 32 articles.

“Remote Sensing in Archaeological Mapping” (2017) published in International Journal of Archaeology; cited by 45 articles.

“Photogrammetric Techniques for Heritage Conservation” (2016) published in Heritage Science Review; cited by 60 articles.

 

mourad JABRANE | Computer Science | Best Researcher Award

Prof Dr. mourad JABRANE | Computer Science | Best Researcher Award

software enginee, sultan moulay slimane university, Morocco

JABRANE Mourad is a state-certified software engineer and a third-year Ph.D. candidate at the National School of Applied Sciences, Sultan Moulay Slimane University. With a passion for designing scalable and efficient systems, Mourad excels in data mining, machine learning, and distributed computing. As an adjunct professor, he effectively conveys complex concepts, making him a valuable contributor to both academia and industry.

Publication Profile

Strengths for the Award:

  1. Solid Academic Background: Jabrane Mourad holds a state-certified software engineering degree and is currently a Ph.D. candidate specializing in Big Data, showcasing a strong academic foundation in cutting-edge technologies.
  2. Research Contributions: His research work focuses on significant areas like entity resolution in Big Data, machine learning, and distributed computing. His publications in reputable journals, such as the Journal of Intelligent Information Systems and Information Systems, highlight his contributions to the field.
  3. Diverse Skill Set: Mourad has extensive expertise in programming languages (Java, Python, JavaScript, etc.), frameworks (Spring, Angular), and software engineering tools (Docker, Jenkins, Kubernetes), making him a well-rounded researcher with practical skills applicable to various domains.
  4. Teaching Experience: As an adjunct professor, Mourad has taught a wide range of subjects including data integration, advanced Java Enterprise Edition, and distributed systems. His teaching experience demonstrates his ability to communicate complex concepts clearly, which is a valuable trait in academia.
  5. International Recognition: The publication of his work in international journals and his participation in global research projects suggest that his research has gained international recognition, making him a strong candidate for the award.

Areas for Improvement:

  1. Broader Impact: While Mourad’s research contributions are significant, further efforts to collaborate on interdisciplinary projects or engage in community outreach could broaden the impact of his work.
  2. Innovation and Patents: Although he has a strong publication record, obtaining patents or developing innovative software solutions could further strengthen his candidacy for a best researcher award.
  3. Networking and Collaborations: Expanding his network and collaborating with more researchers across different institutions could help him gain more visibility and access to diverse research opportunities.

Education

Ph.D. Candidate (2021 – Present): National School of Applied Sciences, Sultan Moulay Slimane University.
Thesis title: Entity Resolution in Big Data. State Software Engineer (2018 – 2021): National School of Applied Sciences, Sultan Moulay Slimane University. MP Higher School Preparatory Classes (2016 – 2018): Centre Ibn Abdoun.
Track title: Mathematics, Physics, and Engineering Sciences.

Experience 💼

JABRANE Mourad has accumulated significant experience as an adjunct professor at the National School of Applied Sciences, where he has taught a variety of subjects including Data Integration, Data Quality, Advanced JEE, Distributed Systems, Python Programming, MATLAB, and C Programming. His roles extend to both regular courses and continuing education programs, demonstrating his versatility and commitment to teaching.

Research Focus 🔬

Mourad’s research is centered around big data, with specific interests in data mining, machine learning, and distributed computing. His Ph.D. work focuses on Entity Resolution in Big Data, where he is exploring innovative approaches to improve accuracy and efficiency in large-scale data matching.

Awards and Honors 🏅

Though the detailed awards and honors list is not provided, Mourad’s academic and professional journey is marked by his selection as a Ph.D. candidate and his appointment as an adjunct professor at a prestigious institution.

Publications 📚

Mourad has contributed extensively to the field of big data and machine learning. Below are some of his notable publications:

  1. “Erabqs: Entity resolution based on active machine learning and balancing query strategy” – Published in Journal of Intelligent Information Systems, March 2024. Cited by 3 articles.
  2. “Enhancing entity resolution with a hybrid active machine learning framework: Strategies for optimal learning in sparse datasets” – Published in Information Systems, November 2024. Cited by 7 articles.
  3. “Enhancing semantic web entity matching process using transformer neural networks and pre-trained language models” – Published in Computing and Informatics, 2024. Cited by 5 articles.
  4. “Sentiment analysis dataset in Moroccan dialect: Bridging the gap between Arabic and Latin scripted dialect” – Published in Language Resources and Evaluation, July 2024. Cited by 4 articles.

 

Conclusion:

Jabrane Mourad presents a strong case for the Best Researcher Award due to his solid academic background, notable research contributions in Big Data and machine learning, and diverse technical skills. His role as an adjunct professor and his extensive teaching experience further emphasize his ability to convey knowledge and contribute to the academic community. To enhance his candidacy, Mourad could focus on increasing the broader impact of his research through interdisciplinary collaborations and innovation. Overall, Mourad is a well-rounded candidate with the potential to make substantial contributions to the field, making him a suitable contender for the award.