Dr. Zhaoyang Wang | Cybersecurity | Best Researcher Award

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Dr. Zhaoyang Wang | Cybersecurity | Best Researcher Award

Institute of Information Engineering, Chinese Academy of Sciences, China.

Wang Zhaoyang is a Ph.D. student at the Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering, Chinese Academy of Sciences, and the School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China. His research focuses on differential privacy, data security, privacy protection in big data systems, and machine learning, contributing to cutting-edge advancements in cybersecurity and data privacy.

Profile

Scopus

🎓 Education

Wang Zhaoyang is currently pursuing a Ph.D. in Cyber Security at the University of Chinese Academy of Sciences, Beijing, China. His research focuses on differential privacy, data security, and privacy protection in big data systems, with an emphasis on developing secure and efficient solutions for modern cybersecurity challenges.

💼 Experience

Wang Zhaoyang is a researcher at the Key Laboratory of Cyberspace Security Defense, Institute of Information Engineering, where he explores advanced topics in cybersecurity, differential privacy, and data security. As a Graduate Research Assistant specializing in cyber security and data privacy, he actively contributes to cutting-edge research on privacy protection in big data systems, secure machine learning, and distributed storage solutions. His work aims to enhance the security and efficiency of modern computing environments, addressing critical challenges in data protection and cyber defense.

🔬 Research Interests

🛡️ Differential Privacy – Ensuring data protection while preserving utility.

🔏 Data Security – Developing secure storage and transmission solutions.

🔐 Privacy Protection in Big Data – Enhancing privacy measures in large-scale data systems.

🤖 Machine Learning & Privacy – Securing AI models against adversarial attacks.

📚 Selected Publications

TurboLog: A Turbocharged Lossless Compression Method for System Logs via Transformer – IJCNN 2024

A Distributed Storage System for System Logs Based on Hybrid Compression Scheme – ISPA/BDCloud/SocialCom/SustainCom 2023| Cited by 1

PRISPARK: Differential Privacy Enforcement for Big Data Computing in Apache Spark – IEEE SRDS 2023

A General Backdoor Attack to Graph Neural Networks Based on Explanation Method – TrustCom 2022 | Cited by 2

Deepro: Provenance-based APT Campaigns Detection via GNN – TrustCom 2022.

 

 

Prof. Dr. Saleh Albahli | Artificial Intelligence | Best Researcher Award

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Prof. Dr. Saleh Albahli | Artificial Intelligence | Best Researcher Award

Qassim University, Saudi Arabia.

Dr. Saleh Albahli is a highly accomplished academic and researcher specializing in Digital Transformation, Data Science, and Artificial Intelligence. Currently an Associate Professor and Vice-Dean of Information Technology Deanship at Qassim University, he is known for spearheading transformative digital initiatives, leading enterprise architecture projects, and contributing to cutting-edge research in machine learning and deep learning. His work is globally recognized, ranking him among the top 2% of scientists in AI research worldwide.

Profile

Scopus

Orcid

🎓 Education

Dr. Saleh Albahli holds a Ph.D. in Computer Science with distinction from Kent State University, USA (2016), showcasing his expertise in advanced computational methodologies and research excellence. He earned a Master’s degree in Information Technology with distinction from The University of Newcastle, Australia (2010), highlighting his dedication to mastering cutting-edge IT solutions. His academic journey began with a Bachelor’s degree in Computer Science from King Saud University, Saudi Arabia (2004), laying a strong foundation for his accomplished career in technology and innovation.

💼 Experience

Dr. Saleh Albahli has built an illustrious career, currently serving as an Associate Professor in the Department of IT at Qassim University since 2020, where he contributes to advancing education and research. Concurrently, he holds dual leadership roles as Vice-Dean of IT Deanship and Director of Enterprise Architecture & Digital Transformation at Qassim University, spearheading transformative initiatives to enhance technological frameworks and drive digital innovation.

Previously, Dr. Albahli gained international experience as a Senior System Analyst at Cleveland Clinic, USA (2015–2016), where he developed cutting-edge systems to optimize healthcare operations. He also served as a Lecturer at Kent State University, USA (2015–2016), imparting knowledge and fostering academic growth. Earlier in his career, he worked as an Oracle Developer and Apps DBA at Riyadh Bank and Integrated Telecom Company in Saudi Arabia (2005–2007), honing his technical expertise in database systems and enterprise applications.

🔬 Research Interests

Digital Transformation and its integration with enterprise architecture

Machine Learning and Deep Learning Pipelines

Big Data Analytics, Data Governance, and Predictive Analytics

Artificial Intelligence Applications in healthcare and business

Process Optimization in technology-driven environments

🏆 Awards & Recognitions

Ranked among the top 2% of scientists globally in AI research (2022)

First Place in Digital Transformation (Qiyas) – Qassim University (2022, 2023)

ISO certifications in 22301, 20000, and 27001 for excellence in IT management

📚 Selected Publications 

Efficient Hyperparameter Tuning for Predicting Student Performance with Bayesian Optimization
Albahli, S.
Multimedia Tools and Applications, 2024, 83(17), pp. 52711–52735.
This study introduces a Bayesian optimization approach to enhance hyperparameter tuning for predictive models in educational datasets, achieving improved accuracy and efficiency. (Citations: 4)

MedNet: Medical Deepfakes Detection Using an Improved Deep Learning Approach
Albahli, S., Nawaz, M.
Multimedia Tools and Applications, 2024, 83(16), pp. 48357–48375.
This paper presents MedNet, a novel deep learning framework tailored to detect medical deepfakes, ensuring the integrity of critical healthcare data. (Citations: 4)

Opinion Mining for Stock Trend Prediction Using Deep Learning
Albahli, S., Nazir, T.
Multimedia Tools and Applications, 2024.
Leveraging deep learning techniques, this research focuses on sentiment analysis to predict stock trends, demonstrating robust performance metrics. (Citations: 0)

An Improved DenseNet Model for Prediction of Stock Market Using Stock Technical Indicators
Albahli, S., Nazir, T., Nawaz, M., Irtaza, A.
Expert Systems with Applications, 2023, 232, 120903.
This work proposes enhancements to DenseNet architectures for stock market predictions based on technical indicators, achieving notable predictive accuracy. (Citations: 10)

A Circular Box-Based Deep Learning Model for the Identification of Signet Ring Cells from Histopathological Images
Albahli, S., Nazir, T.
Bioengineering, 2023, 10(10), 1147.
This open-access study develops a circular box-based deep learning model for the accurate detection of signet ring cells in histopathological images, aiding cancer diagnosis.

 

 

 

Mrs. Golshid Ranjbaran | Artificial Intelligence | Best Researcher Award

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Mrs. Golshid Ranjbaran | Artificial Intelligence | Best Researcher Award

University of Saskatchewan, Canada.

Golshid Ranjbaran is a PhD Candidate in Computer Science at the University of Saskatchewan (USASK), specializing in Artificial Intelligence, Machine Learning, and Interpretability. With a Bachelor's degree in Software Engineering and a Master's in Artificial Intelligence from the Science and Research Branch in Iran, he has accumulated several awards, including the Best Paper Award at the IKT Conference in 2021 and Best Researcher at ITRC in 2022. Golshid's research is aimed at advancing AI methodologies and improving machine learning models for real-world applications. He was also a research associate at the Data Science & Big Data Lab in Seville, Spain, in 2023. 🌐

Profile

Google Scholar

Education 🎓

Golshid holds a Bachelor's degree in Software Engineering and a Master's degree in Artificial Intelligence from the Science and Research Branch in Iran. He is currently pursuing a Ph.D. in Computer Science at the University of Saskatchewan (USASK), Canada, where he focuses on AI, machine learning, and interpretability, aiming to bridge the gap between theoretical advancements and practical applications.

Experience 🏢

Golshid has been awarded several prestigious positions and accolades, including a research position at the Data Science & Big Data Lab in Seville, Spain (2023), and was recognized as the Best Researcher at ITRC (2022). He has also contributed to various consultancy projects and industry collaborations, such as working on AI systems at ITRC, smart meters algorithms, and data governance in Iran.

Research Interests 🔍

Enhancing model interpretability through methods like SHAP.

Exploring sentiment analysis for stock market prediction.

Developing augmented techniques for unbalanced tasks in the financial domain.

Improving network security through Moving Target Defense technology.

Investigating Federated Learning for wearable health devices and ontology-based text summarization for efficient information processing.

Awards 🏆

Best Paper Award at the IKT Conference (2021)

Best Researcher Award at the Iran Telecommunication Research Center (ITRC) (2022)

Research Position at the Data Science & Big Data Lab in Seville, Spain (2023)

Nomination for the Gala GSA Award at the University of Saskatchewan (2025).

Selected Publications 📚

C-SHAP: A Hybrid Method for Fast and Efficient Interpretability – Applied Sciences (Q2 Journal), Published 2025.

Leveraging Augmentation Techniques for Tasks with Unbalancedness within the Financial Domain – EPJ Data Science (Q1 Journal), Published 2023.

Investigating Sentiment Analysis of News in Stock Market Prediction – International Journal of Information and Communication Technology Research, Published 2024.

Unsupervised Learning Ontology-Based Text Summarization Approach with Cellular Learning Automata – Journal of Theoretical and Applied Information Technology, Published 2023.

Analyzing the Effect of News Polarity on Stock Market Prediction – Proceedings of the 12th International Conference on Information and Knowledge Technology (IKT), Published 2021.

 

 

Prof. Junyu Zhou | Medical and Prevention | Young Scientist Award

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Prof. Junyu Zhou | Medical and Prevention | Young Scientist Award

Peking University, China.

Prof. Junyu Zhou is a pioneering researcher in nutritional genomics, metabolic disorders, and personalized medicine. He leverages bioinformatics, computational biology, and experimental approaches to explore gene-diet interactions, focusing on Asian populations. His innovative work includes developing AI models for bioactive compound prediction and uncovering gut microbiota's role in metabolic health. Junyu's recent research extends to natural compound discovery for neurodegenerative diseases, emphasizing computational screening with advanced AI techniques.

Profile

Scopus

Orcid

Education 🎓

Junyu Zhou holds advanced degrees in fields related to nutritional genomics and computational biology. His academic training provides a strong foundation for his cutting-edge research in metabolic disorders and personalized medicine.

Experience 💼

As an Assistant Researcher at Peking University, Junyu Zhou has led and collaborated on multiple groundbreaking projects. His experience spans computational modeling, experimental validations, and interdisciplinary collaborations with global research teams in metabolic diseases and nutritional genomics.

Research Interests 🔬

Nutritional Genomics 🧬

Junyu Zhou investigates gene-diet interactions to understand how genetic variations influence nutritional responses, particularly in Asian populations. His work aims to develop personalized dietary recommendations to improve health outcomes.

Metabolic Diseases 🩺

Focusing on conditions such as diabetes and obesity, Junyu studies the underlying mechanisms of metabolic disorders. His research integrates genetics and gut microbiota to unveil new therapeutic targets.

Computational Biology & Bioinformatics 💻

Junyu applies advanced computational tools to analyze genetic data and predict drug-target interactions. His expertise in bioinformatics helps bridge data science and biology for innovative discoveries.

Gut Microbiota 🌱

Exploring the role of gut microbiota in metabolic health, Junyu's research uncovers microbial contributions to diseases and identifies probiotic strategies for improved metabolic functions.

Natural Product Research 🌿

Junyu's work includes computational screening of natural compounds, focusing on their potential to treat diseases such as Alzheimer's and neurodegenerative disorders.

Personalized Medicine 🩹

By integrating genomics and computational biology, Junyu develops precision healthcare approaches, tailoring interventions to individual genetic and metabolic profiles.

Machine Learning in Drug Discovery 🤖

Junyu employs AI-driven techniques to streamline drug discovery processes. His work includes predictive models for bioactive compounds, enhancing efficiency in identifying new therapeutic agents.

Publications Top Notes 📚

Microbial Dysbiosis Linked to Metabolic Dysfunction-Associated Fatty Liver Disease in Asians: Prevotella copri Promotes Lipopolysaccharide Biosynthesis and Network Instability in the Prevotella Enterotype, Published in: International Journal of Molecular Sciences, 2024, Contributors: Yuan, H.; Wu, X.; Wang, X.; Zhou, J.-Y.; Park, S. Link

Predicting structure-targeted food bioactive compounds for middle-aged and elderly Asians with myocardial infarction: insights from genetic variations and bioinformatics-integrated deep learning analysis, Published in: Food & Function, 2024, Contributors: Junyu Zhou; Heng Yuan; Sunmin Park. Link

Association of Metabolic Diseases and Moderate Fat Intake with Myocardial Infarction Risk, Published in: Nutrients, December 11, 2024, Contributors: Junyu Zhou; Meiling Liu; Sunmin Park. Link

 

 

 

 

 

 

 

 

 

 

 

 

Panjit Musik | Computing science | Best Researcher Award

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🌟Assoc Prof Dr. Panjit Musik. Computing science, Best Researcher Award🏆

Associate Professor at Panjit Musik walailak university, Thailand

Assoc. Prof. Dr. Panjit Musik, born on July 4, 1961, is a distinguished academic in the fields of Physics, Computational Science, and Smart Farming. He currently teaches at the School of Science, Walailak University in Thailand. His academic journey and professional accomplishments reflect a commitment to advancing education and research in scientific and technological innovations.

Author Metrics

Scopus Profile

Dr. Musik has authored numerous research papers published in international and national journals, contributing significantly to the fields of Physics, Computational Science, and Smart Farming. His works are frequently cited, reflecting his influence in these research areas.

Panjit Musik is associated with Walailak University in Tha Sala, Thailand. His academic profile on Scopus shows a modest yet emerging research output, with 4 documents and 5 citations, resulting in an h-index of 1.

Education

Dr. Musik earned his Doctor of Philosophy in Computational Science from Walailak University in 2005. He holds a Master of Science in Teaching Physics from Chiang Mai University, obtained in 1990, and a Bachelor of Education in Physics from Thaksin University, completed in 1983. This strong educational foundation underpins his extensive research and teaching career.

Research Focus

Dr. Musik’s research interests are diverse and interdisciplinary, encompassing Physics Teaching, Real-Time Physics Labs, Computational Modeling and Simulation, and Smart Farming. His work aims to integrate technological advancements with educational practices to enhance learning outcomes and develop innovative solutions for agricultural challenges.

Professional Journey

Dr. Musik’s professional journey began with a focus on physics education and has evolved to include computational modeling and smart farming technologies. He has developed numerous computer-based experimental sets and simulations, contributing to both academic and practical advancements in his fields of expertise.

Honors & Awards

Throughout his career, Dr. Musik has received several accolades for his contributions to science and education. His innovative work in developing experimental sets and integrating computational methods in education has been recognized by academic and professional institutions.

Publications Noted & Contributions

Dr. Musik has published extensively in international journals such as the Turkish Online Journal of Educational Technology and the International Journal on Smart Sensing and Intelligent Systems. His publications address key issues in computational physics, real-time experimental learning, and smart farming technologies, contributing to the academic discourse and practical applications in these areas.

Development of a Computer-Based Simple Pendulum Experiment Set for Teaching and Learning Physics

Authors: Sukmak, W., & Musik, P.
Journal: International Journal on Smart Sensing and Intelligent Systems, 2021, 14(1), pp. 1–8
Citations: 1

Abstract: This article presents the development of a computer-based experiment set designed to enhance the teaching and learning of physics through a simple pendulum experiment. The set aims to provide real-time data acquisition and analysis, making physics concepts more accessible and engaging for students. The development process, implementation, and educational benefits are discussed in detail.

Development of an Automated Water Management System in Orchards in Southern Thailand

Author: Musik, P.
Journal: International Journal on Smart Sensing and Intelligent Systems, 2020, 13(1), pp. 1–7
Citations: 2

Abstract: Dr. Musik explores the design and implementation of an automated water management system tailored for orchards in southern Thailand. This system leverages smart sensing technologies to optimize water usage, ensuring efficient irrigation and enhancing crop yields. The article details the system’s components, operational mechanisms, and the positive impact on orchard management.

Development of Computer-Based Experiment Set on Simple Harmonic Motion of Mass on Springs

Author: Musik, P.
Journal: Turkish Online Journal of Educational Technology, 2017, 16(4), pp. 1–11
Citations: 1

Abstract: This study describes the creation of an experimental set for investigating the simple harmonic motion of a mass on a spring. The set integrates computer-based tools to facilitate real-time data collection and visualization, aiming to improve students’ understanding of oscillatory motion through interactive and hands-on learning experiences.

Large-Scale Simulation Using Parallel Computing Toolkit and Server Message Block

Authors: Musik, P., & Jaroensutasinee, K.
Journal: WSEAS Transactions on Mathematics, 2007, 6(2), pp. 369–372
Citations: 1

Abstract: This paper discusses a large-scale simulation approach using a parallel computing toolkit and server message block. The simulation targets complex mathematical models, enhancing computational efficiency and accuracy. The authors highlight the methodology, computational framework, and potential applications in scientific research.

These articles reflect Dr. Panjit Musik’s extensive work in developing innovative educational tools and applying computational methods to solve practical problems in agriculture and physics education. His research contributes significantly to enhancing teaching methodologies and improving resource management in various domains.

Research Timeline

Dr. Musik’s research timeline spans over three decades, beginning with his master’s research in 1990 on computer control of humidity in experimental greenhouses. His doctoral research in 2005 focused on large-scale water flow simulation using Mathematica. In the years following, he has conducted numerous studies on integrating remote sensing data, developing computer-based experiments, and smart farming solutions.

Collaborations and Projects

Dr. Musik has collaborated with various researchers and institutions on projects aimed at developing innovative educational tools and smart farming technologies. His projects include the development of watershed and hydrologic process modeling for flood forecasting, automated water management systems in orchards, and GIS applications for agricultural water management.

Contributions to the Field

Dr. Musik’s contributions to the field include the development of computer-based experimental sets for physics education, large-scale simulations for environmental modeling, and smart farming technologies. His work has provided valuable insights and practical tools for educators, researchers, and farmers, advancing both academic knowledge and real-world applications.