HaiTian Chen | Computer Science | Best Researcher Award

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Ms. HaiTian Chen | Computer Science | Best Researcher Award

College of Science, North China University of Science and Technology, China

Chen HaiTian is a dedicated researcher in the field of Cyberspace Security from China. Born in December 1998, Chen has made significant strides in federated learning, privacy preservation, and cybersecurity. His contributions span multiple peer-reviewed journals and patents, showcasing his commitment to advancing technology and safeguarding digital spaces.

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ORCID

 

Education

Chen HaiTian holds a major in Cyberspace Security, demonstrating his expertise and focus in this critical area of study. His academic background has equipped him with the skills and knowledge necessary to tackle complex cybersecurity challenges and contribute to innovative solutions in the field. 🎓

Research Interests

Chen HaiTian’s research interests focus on federated learning, privacy preservation, and cybersecurity. He is particularly interested in developing robust aggregation techniques to defend against poisoning attacks in federated learning and exploring personalized fair split learning for resource-constrained Internet of Things (IoT). 🔍

Awards

Chen HaiTian has received recognition for his contributions to software development, including the Huali Academy Backstage Management System V1.0 and the DC Early Warning System V1.0. His work has been registered with computer software registration numbers, showcasing his achievements in developing innovative solutions for network management and security. 🏆

Publications

Chen, H.; Chen, X.; Peng, L. (2023). FLRAM: Robust Aggregation Technique for Defense Against Byzantine Poisoning Attacks in Federated Learning. Electronics. Cited by Electronics.

Chen, H.; Chen, X.; Peng, L. (2024). Personalized Fair Split Learning for Resource-Constrained Internet of Things. Sensors, 24, 88. Cited by Sensors.

Chen, H., Chen, X., Ma R., et al. (2024). A federated learning privacy preserving approach for remote sensing data. Computer Applications. Cited by Computer Applications.

Chen, H., Chen, X. (2023). A Robust Aggregation Technique for Poisoning Attack Defense in Federated Learning. Cited by Journal.

Xu C., Zhang S., Chen H., et al. (2024). A federated learning approach based on adaptive differential privacy and customer selection optimization. Computer Applications. Cited by Computer Applications.

Peng L., Zhang S., Chen H., et al. (2023). Clustered federated learning based on improved CFSFDP algorithm. Journal of North China University of Science and Technology (Natural Science Edition). Cited by NCUST.

Qiang Li | Computer Science | Best Researcher Award

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Mr. Qiang Li | Computer Science | Best Researcher Award

Lecturer, Qingdao University, China

Dr. Li Qiang is an experienced lecturer in computer science with a PhD in Engineering. He specializes in high-performance computing and has a strong background in both teaching and research. Committed to fostering academic excellence and technological innovation, Dr. Li has been a dedicated educator and researcher at Qingdao University since 2015.

Profile

ORCID

 

Education 🎓

PhD in Engineering: University of the Chinese Academy of Sciences, Computer Network Information Center (2010-2014), Advisor: Lu Zhonghua. Master’s in Information Science and Engineering: Shandong University of Science and Technology (2007-2010), Advisor: Zhao Maoxian. Bachelor’s in Education: Qingdao University (2003-2007).

Experience 👨‍🏫

Lecturer at Qingdao University, School of Computer Science and Technology (January 2015-Present). Teaching undergraduate and graduate courses in computer science. Supervising student research projects and theses. Conducting research in high-performance computing. Published 12 research papers in journals and conferences. Granted 2 patents.

Research Interests 🔬

Dr. Li Qiang’s research interests lie in high-performance computing, particularly in the optimization and parallel implementation of numerical simulations and the development of new computational frameworks. His work focuses on enhancing computational efficiency and scalability in large-scale scientific computations.

Awards 🏆

Dr. Li Qiang has been recognized for his contributions to the field of high-performance computing through multiple publications and patents. His innovative work has led to advancements in computational methods and has garnered attention in the academic community.

Publications 📄

Optimization Research of Heterogeneous 2D-Parallel Lattice Boltzmann Method Based on Deep Computing Unit. Appl. Sci. 2024, 14, 6078.

Heterogeneous Parallel Implementation of Large-Scale Numerical Simulation of Saint-Venant Equations. Appl. Sci. 2022, 12, 5671. Cited by 6

The Study of Parallelization of SWAT Hydrology Cycle. The 32nd ACM International Conference on Supercomputing, Beijing, 2018. [Cited by 3]

A New Parallel Framework of Distributed SWAT Calibration. Journal of Arid Land, 2015, 7(1): 122-131. [Cited by 7]

Parallel Simulation of High-Dimensional American Option Pricing Based on CPU VS MIC. Concurrency and Computation: Practice and Experience, 2014, 27(5): 1110-1121. [Cited by 5]

Jihyeon Ryu | Computer Science | Best Researcher Award

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Assist Prof Dr. Jihyeon Ryu | Computer Science | Best Researcher Award

Professor, Kwangwoon University, South Korea

👩‍🏫 Jihyeon Ryu is an Assistant Professor at the School of Computer and Information Engineering, Kwangwoon University. She specializes in split learning, convolutional neural networks, and user authentication, contributing significantly to the field of computer science with her extensive research and numerous publications.

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Google Scholar

 

Education

Ph.D. in Software (Integrated Master’s and Doctorate Course) from Sungkyunkwan University (Mar. 2018 – Feb. 2023), advised by Prof. Dongho Won and Prof. Hyoungshick Kim. B.S. in Mathematics and Computer Science and Engineering from Sungkyunkwan University (Mar. 2013 – Feb. 2018). Early Graduation from Sejong Science High School (Mar. 2011 – Feb. 2013).

 

Research Interests:

Split Learning. Convolutional Neural Networks. User Authentication

Awards

Family Company Workshop Lecture, Hoseo University (Feb. 2022). Invited Lecturer, Mental Women’s High School (Nov. 2021). Excellence Prize, Software Department Excellence Research Awards (Feb. 2021). Graduate Merit Scholarship, Sungkyunkwan University (Mar. 2018 – Feb. 2021). SimSan Scholarship, Multiple instances (Mar. 2018 – Sep. 2020). Excellence Award, 국가 암호기술 전문인력 양성과정 (Nov. 2019). Grand Prize, Software Department Excellence Research Awards (Feb. 2019). Samsung Science Scholarship (Mar. 2013 – Feb. 2018)

Publications

Lightweight Hash-Based Authentication Protocol for Smart Grids. Sensors, 2024. Sangjin Kook, Keunok Kim, Jihyeon Ryu, Youngsook Lee, Dongho Won.

Enhanced Lightweight Medical Sensor Networks Authentication Scheme Based on Blockchain. IEEE ACCESS, 2024. Taewoong Kang, Naryun Woo, Jihyeon Ryu.

Secure and Anonymous Authentication Scheme for Mobile Edge Computing Environments. IEEE Internet of Things Journal, 2024. Hakjun Lee, Jihyeon Ryu, Dongho Won.

Distributed and Federated Authentication Schemes Based on Updatable Smart Contracts. Electronics, 2023. Keunok Kim, Jihyeon Ryu, Hakjun Lee, Youngsook Lee, Dongho Won.

An Improved Lightweight User Authentication Scheme for the Internet of Medical Things. Sensors, 2023. Keunok Kim, Jihyeon Ryu, Youngsook Lee, Dongho Won.

Nabi Mehri Khansari | Machine Learning | Best Researcher Award

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Dr. Nabi Mehri Khansari | Machine Learning | Best Researcher Award

University Professor, Sahand University of Technology, Iran

Dr. Nabi Mehri-Khansari is an esteemed Assistant Professor at the Sahand University of Technology. With a rich academic background in Mechanical and Aerospace engineering from prestigious institutions like Iran University of Science and Technology and the University of Tehran, he has made significant contributions to the field. His research spans failure analysis, damage and fracture mechanics in lightweight composite structures, leveraging machine learning and deep learning. Dr. Mehri-Khansari has collaborated with various international research centers and industries, enhancing his expertise and impact in the field.

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Scopus

Education

🎓 Dr. Nabi Mehri-Khansari obtained his B.Sc. degree in Mechanical Engineering from the Iran University of Science and Technology in 2011. He pursued his M.Sc. and Ph.D. degrees in Aerospace Engineering from the University of Tehran, completing them in 2014 and 2018, respectively. His academic excellence is marked by being ranked 2nd in M.Sc. and 1st in Ph.D., earning acceptance with quotas for talented students. He also served as a research fellow at NTNU University, Trondheim, Norway, further broadening his academic horizons.

Experience

🔧 Dr. Mehri-Khansari has an extensive professional background. He has been a faculty member at the Sahand University of Technology since January 2019. Prior to this, he was a lecturer at the University of Tehran – North Branch, a research assistant at NTNU University in Norway, and a technical expert at the Iranian Space Institute. His diverse roles reflect his versatile expertise and commitment to advancing engineering education and research.

Research Interests

🔬 Dr. Mehri-Khansari’s research interests are vast and interdisciplinary. They include wind turbine technology, multi-scale fracture mechanics of composites and inhomogeneous media, multi-scale damage mechanics, aeroelasticity, and defect detection methods. His innovative work often incorporates machine learning and deep learning techniques, pushing the boundaries of traditional engineering research.

Awards

🏅 Dr. Mehri-Khansari has received numerous accolades throughout his career. These include the prestigious Ph.D. acceptance with quotas for talented students, being ranked 1st in his Ph.D. program at the University of Tehran, and the Best Teacher Award from the Sahand University of Technology in June 2024. His membership in professional organizations such as the American Society of Mechanical Engineering and the Iranian Composites Scientific Association further underscores his professional excellence.

Publications

Orthotropic failure criteria based on machine learning and micro-mechanical matrix adapting coefficient
Mixed-modes (I/III) fracture of aluminum foam based on micromechanics of damage
Micro-mechanical damage diagnosis methodologies based on machine learning and deep learning models
Numerical & experimental assessment of mixed-modes (I/II) fracture of PMMA/hydroxyapatite nanocomposite

Ali Raza | artificial intelligence | Best Researcher Award

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Mr. Ali Raza | artificial intelligence | Best Researcher Award

Lecturer, The University of Lahore, Pakistan

Ali Raza is a dedicated research scholar specializing in data science, known for his expertise in machine learning and deep learning applications. With a strong academic background and extensive professional experience in software development, he has contributed significantly to research in artificial intelligence and health informatics.

Profile

Google Scholar

📚 Education:

Ali completed his Bachelor of Science in Computer Science at KFUEIT after graduating from Iqra Degree College with a degree in Pre-Engineering. He further pursued his passion for computer science by earning a Master’s degree in Computer Science from KFUEIT, where his research focused on novel approaches in deep learning for image detection.

💼 Experience:

Ali’s professional journey includes roles as a Research Assistant at KFUEIT, where he published research articles on artificial intelligence. He has also worked as a Desktop App Developer at DexDevs Company and as a Full Stack Python Developer at BuiltinSoft Company, gaining expertise in business application development and machine learning frameworks.

🔬 Research Interests:

Ali’s research interests revolve around data science, particularly in machine learning model optimization, health informatics, and artificial intelligence applications in diverse domains such as pregnancy health analysis and network security.

🏆 Awards:

Ali has contributed significantly to research, evident from his publications and contributions as a peer reviewer for IEEE Access and PLOS ONE, highlighting his recognition in the academic community.

📄 Publications:

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction, Plos one, 2022 (cited 46 times)

A novel deep learning approach for deepfake image detection, Applied Sciences, 2022 (cited 58 times)

Predicting employee attrition using machine learning approaches, Applied Sciences, 2022 (cited 44 times)

A novel methodology for human kinematics motion detection based on smartphones sensor data using artificial intelligence, Technologies, 2023 (cited 23 times)

Novel class probability features for optimizing network attack detection with machine learning, IEEE Access, 2023