Ali Raza | Network Attacks | Best Researcher Award

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Mr. Ali Raza | Network Attacks | Best Researcher Award

Lecturer at The University Of Lahore, Pakistan

Mr. Ali Raza is an accomplished computer science professional and researcher with a strong academic foundation and expertise in machine learning, cybersecurity, and software development. He completed his MS in Computer Science with a high CGPA of 3.93 from Khwaja Fareed University of Engineering and Information Technology (KFUEIT), where he also earned his bachelor’s degree. Mr. Raza has experience as a Lecturer at the University of Lahore, teaching software engineering courses, and as a Visiting Lecturer at KFUEIT, covering subjects like machine learning and data structures. His industry experience as a Full Stack Python Developer at BuiltinSoft involved developing web applications using Python Django and machine learning frameworks. Mr. Raza has published several impactful research articles in high-ranking journals, focusing on network attack detection, health risk prediction, and cyber-attack prevention. His work combines deep technical skills and a commitment to advancing applied research in computer science.

Profile:

Education:

Mr. Ali Raza holds an impressive academic background, having completed his Master of Science (MS) in Computer Science at Khwaja Fareed University of Engineering and Information Technology (KFUEIT) with a remarkable CGPA of 3.93 in 2023. During his studies, KFUEIT achieved a ranking of #258 in the Asian University Rankings for Southern Asia, underscoring the institution’s reputation for academic excellence. Prior to this, he earned his Bachelor of Science (BS) in Computer Science from the same university, graduating with a CGPA of 3.47 in 2021. This solid educational foundation has equipped Mr. Raza with the necessary knowledge and skills to excel in the fields of computer science and machine learning, fostering his commitment to furthering research and innovation in technology.

Professional Experience:

Mr. Ali Raza has built a solid professional background in academia and industry, contributing to both teaching and software development. Currently, he serves as a Lecturer in the Department of Software Engineering at the University of Lahore, ranked #40 in the Asian University Rankings for Southern Asia, where he specializes in Object-Oriented Programming. Prior to this role, he was a Visiting Lecturer at Khwaja Fareed University of Engineering and Information Technology (KFUEIT) from 2021 to 2023, where he taught a wide range of courses, including Introduction to ICT, Programming Fundamentals, Database Systems, Machine Learning, Data Structures, and Algorithms. Complementing his academic roles, Mr. Raza gained valuable industry experience as a Full Stack Python Developer at BuiltinSoft from 2020 to 2022. In this role, he developed business web applications using Python Django and integrated machine learning frameworks, further enhancing his practical expertise in application development. This blend of academic and industry experience has equipped Mr. Raza with both a deep theoretical foundation and hands-on technical skills.

Research Interests:

Mr. Ali Raza’s research interests center on advancing methodologies in machine learning, cybersecurity, computer vision, and signal processing. He is particularly focused on leveraging machine learning algorithms to enhance network security, developing predictive models to detect cyber threats, and optimizing feature engineering for data-driven health risk analysis. Additionally, his work in computer vision, particularly using deep learning techniques, explores novel approaches for identifying genetic disorders from facial images, providing valuable tools in the field of medical diagnostics. His research contributions demonstrate a commitment to developing innovative, practical solutions that address complex challenges in technology and healthcare.

Conclusion:

Ali Raza’s strong academic background, extensive teaching and industry experience, and impactful research contributions make him a highly suitable candidate for the Best Researcher Award. His interdisciplinary approach, particularly in applying machine learning to pressing challenges in cybersecurity and healthcare, demonstrates a commitment to both innovation and societal impact. His work aligns well with the goals of the award, making him a deserving candidate for recognition.

Publication Top Noted:

A novel deep learning approach for deepfake image detection

  • Authors: A. Raza, K. Munir, M. Almutairi
  • Journal: Applied Sciences
  • Volume: 12
  • Issue: 19
  • Article: 9820
  • Year: 2022
  • Citations: 80

Predicting employee attrition using machine learning approaches

  • Authors: A. Raza, K. Munir, M. Almutairi, F. Younas, MMS Fareed
  • Journal: Applied Sciences
  • Volume: 12
  • Issue: 13
  • Article: 6424
  • Year: 2022
  • Citations: 77

Ensemble learning-based feature engineering to analyze maternal health during pregnancy and health risk prediction

  • Authors: A. Raza, H.U.R. Siddiqui, K. Munir, M. Almutairi, F. Rustam, I. Ashraf
  • Journal: Plos One
  • Volume: 17
  • Issue: 11
  • Article: e0276525
  • Year: 2022
  • Citations: 63

A novel approach for polycystic ovary syndrome prediction using machine learning in bioinformatics

  • Authors: S. Nasim, M.S. Almutairi, K. Munir, A. Raza, F. Younas
  • Journal: IEEE Access
  • Volume: 10
  • Pages: 97610-97624
  • Year: 2022
  • Citations: 39

A performance overview of machine learning-based defense strategies for advanced persistent threats in industrial control systems

  • Authors: M. Imran, H.U.R. Siddiqui, A. Raza, M.A. Raza, F. Rustam, I. Ashraf
  • Journal: Computers & Security
  • Volume: 134
  • Article: 103445
  • Year: 2023
  • Citations: 29

Novel class probability features for optimizing network attack detection with machine learning

  • Authors: A. Raza, K. Munir, M.S. Almutairi, R. Sehar
  • Journal: IEEE Access
  • Year: 2023
  • Citations: 28

Effective feature engineering technique for heart disease prediction with machine learning

  • Authors: A.M. Qadri, A. Raza, K. Munir, M.S. Almutairi
  • Journal: IEEE Access
  • Volume: 11
  • Pages: 56214-56224
  • Year: 2023
  • Citations: 27

A novel methodology for human kinematics motion detection based on smartphones sensor data using artificial intelligence

  • Authors: A. Raza, M.R. Al Nasar, E.S. Hanandeh, R.A. Zitar, A.Y. Nasereddin, et al.
  • Journal: Technologies
  • Volume: 11
  • Issue: 2
  • Article: 55
  • Year: 2023
  • Citations: 24

LogRF: An approach to human pose estimation using skeleton landmarks for physiotherapy fitness exercise correction

  • Authors: A. Raza, A.M. Qadri, I. Akhtar, N.A. Samee, M. Alabdulhafith
  • Journal: IEEE Access
  • Year: 2023
  • Citations: 22

A novel ensemble method for enhancing Internet of Things device security against botnet attacks

  • Authors: A. Arshad, M. Jabeen, S. Ubaid, A. Raza, L. Abualigah, K. Aldiabat, H. Jia
  • Journal: Decision Analytics Journal
  • Volume: 8
  • Article: 100307
  • Year: 2023
  • Citations: 21

Boquan Li | Cyber Threat | Best Researcher Award

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Dr. Boquan Li | Cyber Threat | Best Researcher Award

Assistant Professor at College of Computer Science and Technology, Harbin Engineering University, China

Dr. Boquan Li is a Tenure Track Associate Professor at the College of Computer Science and Technology, Harbin Engineering University, where he has served since January 2024. Prior to this, he was a Research Scientist at the School of Computing and Information Systems, Singapore Management University, from April 2022 to December 2023. Dr. Li holds a Ph.D. in Information Engineering from the University of Chinese Academy of Sciences and a Bachelor of Engineering from Harbin Engineering University. His research interests focus on artificial intelligence, cybersecurity, deepfake detection, and speaker recognition, with numerous publications in leading international conferences and journals. Dr. Li is also an active peer reviewer for prestigious journals like IEEE Transactions on Software Engineering.

Profile:

Education:

Dr. Boquan Li holds a Doctor of Philosophy (Ph.D.) from the University of Chinese Academy of Sciences, where he specialized in Information Engineering at the Institute of Information Engineering. He completed his Ph.D. in January 2022, building a strong foundation in artificial intelligence, cybersecurity, and data science. Prior to his doctoral studies, Dr. Li earned a Bachelor of Engineering degree from the School of Software at Harbin Engineering University in June 2016. His comprehensive academic background has equipped him with expertise in cutting-edge technologies, enabling him to contribute significantly to research in AI and cybersecurity.

Professional Experience:

Dr. Boquan Li has a diverse professional background in both academia and research. Since January 2024, he has been serving as a Tenure Track Associate Professor at the College of Computer Science and Technology, Harbin Engineering University, where he contributes to teaching and research in artificial intelligence and cybersecurity. Prior to this role, Dr. Li worked as a Research Scientist at the School of Computing and Information Systems, Singapore Management University, from April 2022 to December 2023. In this capacity, he was involved in cutting-edge research on deepfake detection, speaker recognition, and digital forensics. His professional experience highlights his expertise in developing innovative solutions to cybersecurity challenges and advancing research in AI-driven technologies.

Research Interests:

Dr. Boquan Li’s research interests focus on cutting-edge areas of artificial intelligence, cybersecurity, and multimedia forensics. He is particularly interested in deepfake detection, where he explores the vulnerabilities and robustness of detection systems across various domains. His work also covers speaker recognition, digital forensics, and adversarial attacks, aiming to develop defense mechanisms against cyber threats. Additionally, Dr. Li has a strong interest in cross-modal fusion techniques, particularly in audio-visual speech recognition, and domain adaptation methods for enhancing the accuracy of AI models across diverse datasets. His research contributes to advancing secure and reliable AI systems.

Skills:

Dr. Boquan Li possesses a diverse skill set that encompasses advanced computational techniques and a robust understanding of artificial intelligence and machine learning algorithms. He is proficient in developing and implementing deep learning models, particularly for applications in image and audio processing. His expertise extends to cybersecurity measures, with a focus on identifying vulnerabilities in AI systems and creating effective defense strategies against adversarial attacks. Additionally, Dr. Li is skilled in data analysis and statistical methods, enabling him to interpret complex datasets and derive meaningful insights. His strong programming skills in languages such as Python and proficiency with machine learning frameworks like TensorFlow and PyTorch further enhance his research capabilities in the field of computer science and technology.

Conclusion:

Dr. Boquan Li’s research addresses critical issues in AI security, deepfake detection, and adversarial defenses, areas of increasing importance in today’s technological landscape. His innovative work, combined with his academic and research experience, positions him as a strong candidate for the Best Researcher Award. His contributions have practical applications in cybersecurity and AI ethics, demonstrating both academic excellence and real-world impact.

Publication Top Noted:

  • How Generalizable are Deepfake Image Detectors? An Empirical Study
  • Two-stage Semi-supervised Speaker Recognition with Gated Label Learning
    • Authors: Xingmei Wang, Jiaxiang Meng, Kong Aik Lee, Boquan Li, Jinghan Liu
    • Year: 2024
    • Conference: International Joint Conference on Artificial Intelligence
    • Type: Conference paper
  • Assessing Backdoor Risk in Deepfake Detectors
    • Authors: Jiawen Wang, Boquan Li, Min Yu, Kam-Pui Chow, Jianguo Jiang, Fuqiang Du, Xiang Meng, Weiqing Huang
    • Year: 2024
    • Conference: IFIP WG 11.9 International Conference on Digital Forensics
    • Type: Conference paper
  • CATNet: Cross-Modal Fusion for Audio–Visual Speech Recognition
    • Authors: Xingmei Wang, Jiachen Mi, Boquan Li, Yixu Zhao, Jiaxiang Meng
    • Year: 2024
    • Journal: Pattern Recognition Letters
    • DOI: 10.1016/j.patrec.2024.01.002
  • A Residual Fingerprint-Based Defense Against Adversarial Deepfakes
  • FakeFilter: A Cross-Distribution Deepfake Detection System with Domain Adaptation
    • Authors: Jianguo Jiang, Boquan Li, Baole Wei, Gang Li, Chao Liu, Weiqing Huang, Meimei Li, Min Yu
    • Year: 2021
    • Journal: Journal of Computer Security
    • DOI: 10.3233/jcs-200124
  • Restoration as a Defense Against Adversarial Perturbations for Spam Image Detection
    • Authors: Jianguo Jiang, Boquan Li, Min Yu, Chao Liu, Weiqing Huang, Lejun Fan, Jianfeng Xia
    • Year: 2019
    • Conference: International Conference on Artificial Neural Networks
    • DOI: 10.1007/978-3-030-30508-6_56