Ali Hashim | Anomaly Detection | Best Researcher Award

Dr. Ali Hashim | Anomaly Detection | Best Researcher Award

Cheif Programmer at The Communication and Media Commission of Iraq, Iraq

Ali J. Al-Mousawi is a distinguished computer scientist and researcher specializing in artificial intelligence, wireless communication networks, and intelligent systems. He earned his Bachelor of Science in Computer Science from Al-Mustansiryah University in May 2014, with a minor in Mathematics. Demonstrating a commitment to advancing his expertise, he completed his Master of Science in Computer Science at the same institution in May 2017, under the mentorship of Assistant Professor Dr. Saad A. Makki. Currently, he is pursuing a Ph.D. in Computer Engineering at the University of Tabriz, with Professor Dr. M. A. Balafar as his supervisor. Throughout his academic journey, Al-Mousawi has contributed significantly to the fields of network security, machine learning, and wireless sensor networks, establishing himself as a prominent figure in contemporary computer science research.

Profile

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Education

Al-Mousawi’s academic foundation is rooted in a robust education in computer science. He commenced his higher education at Al-Mustansiryah University, where he obtained his Bachelor of Science degree in Computer Science in May 2014, complementing his studies with a minor in Mathematics. His pursuit of knowledge led him to continue at the same university for his master’s degree, which he completed in May 2017. His master’s thesis, supervised by Assistant Professor Dr. Saad A. Makki, focused on advanced topics in computer science, reflecting his early dedication to research and innovation. Currently, Al-Mousawi is engaged in doctoral studies at the University of Tabriz, specializing in Computer Engineering under the guidance of Professor Dr. M. A. Balafar. His educational trajectory underscores a consistent commitment to deepening his expertise and contributing to technological advancements.

Experience

Al-Mousawi’s professional experience encompasses both academic and industry roles, reflecting a blend of teaching, research, and practical application. From May 2017 to December 2017, he served as a Teaching Assistant in the Department of Accounting at Al-Esraa University College in Baghdad. In this capacity, he taught courses on computer fundamentals and accounting applications in computers to first and second-year students, respectively. His responsibilities included delivering lectures, designing assessments, and coordinating with fellow teaching assistants to ensure effective learning outcomes. Beyond academia, Al-Mousawi has been associated with the IT Regulation Directorate at the Communication and Media Commission (CMC) since 2017, where he holds the position of Senior Programmer and heads the data analysis division. In this role, he has been instrumental in developing and implementing strategies for data analysis and network security, contributing to the enhancement of Iraq’s telecommunications infrastructure.

Research Interests

Al-Mousawi’s research interests are diverse and interdisciplinary, focusing on the convergence of artificial intelligence and communication networks. In the realm of artificial intelligence, he explores evolutionary computing, neural networks, machine learning, deep learning, swarm intelligence, and intelligent agents. His work delves into metaheuristic methods, reinforcement learning, probabilistic reasoning under uncertainty, robotics, and pattern recognition. In communication networks, his interests include wireless communications, cellular networks, internet networks, ad-hoc networks, and emerging technologies such as 3G, 4G, and 5G. He is particularly focused on the Internet of Things (IoT), web services, network security, sensor networks, standards and protocols, quality of service (QoS), network routing, localization, and coverage. Additionally, Al-Mousawi investigates intelligent systems, including wireless sensor network systems, signal processing systems, robotics systems, detection systems, and distributed systems. His multidisciplinary approach aims to address complex challenges in modern computing and communication landscapes.

Awards

Throughout his career, Al-Mousawi has been recognized for his contributions to network security and technological innovation. In 2018, he received a certificate from the International Telecommunication Union (ITU) for his work on network security and Quality of Service (QoS) in internet networks. The same year, he was granted a patent by the Central Organization of Standardization and Quality Control (COSQC) under Iraq’s Ministry of Planning for developing a novel magnetic explosives detection system based on smartphones. These accolades underscore his commitment to leveraging technology for enhancing security measures and improving communication networks.

Publications

Al-Mousawi has contributed extensively to academic literature, with his work being published in reputable journals and conferences. His publications include:

Al-Mousawi, A.J. (2021). “Wireless communication networks and swarm intelligence.” Wireless Networks.

Al-Mousawi, A.J. (2020). “Magnetic Explosives Detection System (MEDS) based on wireless sensor network and machine learning.” Measurement: Journal of the International Measurement Confederation, 151.

Hoomod, H.K., Al-Mousawi, A.J., & Naif, J.R. (2020). “New Complex Hybrid Security Algorithm (CHSA) for Network Applications.” In Ranganathan, G., Chen, J., & Rocha, Á. (Eds.), Inventive Communication and Computational Technologies. Lecture Notes in Networks and Systems, vol 89. Springer, Singapore.

Al-Mousawi, A.J. (2019). “Evolutionary intelligence in wireless sensor network: routing, clustering, localization and coverage.” Wireless Networks, Springer.

Hoomod, H.K., Al-Mousawi, A.J., & Naif, J.R. (2019). “Proposed hybrid security algorithm for wireless sensors network security.” Journal of Advanced Research in Dynamical and Control Systems, 11(2 Special Issue), 239–246.

AL-Mousawi, A.J., & AL-Hassani, H.K. (2018). “A survey in wireless sensor network for explosives detection.” Computers and Electrical Engineering, 72, 682–701.

Conclusion

Ali J. Al-Mousawi’s career exemplifies a harmonious blend of academic excellence, innovative research, and practical application. His contributions to artificial intelligence, network security, and wireless communication have not only advanced theoretical understanding but also led to practical solutions addressing real-world challenges. Through his teaching,

Yongqian Sun | Anomaly Detection | Best Researcher Award

Assoc. Prof. Dr. Yongqian Sun | Anomaly Detection | Best Researcher Award

Associate Professor at Nankai University, China

Dr. Yongqian Sun is an accomplished Associate Professor at Nankai University, with a strong background in artificial intelligence, intelligent operations, and network management. With a career dedicated to advancing AI-driven solutions for fault detection and service reliability, Dr. Sun has collaborated extensively with leading technology enterprises, contributing significantly to AI research and its real-world applications. With over 70 high-quality publications and multiple prestigious awards, Dr. Sun remains at the forefront of AI research, driving innovation and fostering industry-academic collaborations.

Profile

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Education

Dr. Sun holds a Ph.D. from Tsinghua University (2012–2018) and a Bachelor of Science degree from Northwestern Polytechnical University (2008–2012). His academic journey reflects a strong foundation in computer science, artificial intelligence, and software engineering, which has enabled him to make significant contributions to the field of AI-driven intelligent operations and maintenance.

Experience

Dr. Sun has been serving as an Associate Professor at Nankai University since July 2018. Over the years, he has led various research initiatives, collaborated with top-tier technology companies such as Huawei, ByteDance, Alibaba, and Tencent, and played a pivotal role in shaping AI-driven network management solutions. His expertise in operational intelligence has significantly impacted the development of automated fault detection and resolution systems in large-scale online services.

Research Interests

Dr. Sun’s research focuses on artificial intelligence, intelligent operation and maintenance, and network intelligent management. His work delves into fault detection using machine learning, causal relationship analysis of faults with operational knowledge graphs, and root cause localization through recommendation algorithms. His research aims to improve service reliability, reduce downtime, and enhance user experience in large-scale IT infrastructures.

Awards

Dr. Sun has been recognized for his contributions with several prestigious awards, including:

  • Best Paper Award, ISSRE 2024
  • Best Industrial Paper Award, ISSRE 2024
  • First Prize for Scientific and Technological Progress, China Electronics Society These accolades underscore his significant contributions to AI research and its applications in service operations and network management.

Publications

Dr. Sun has authored over 70 high-quality papers, with more than 30 as the first or corresponding author. Some of his notable publications include:

Sun, Y., et al. (2023). “AI-Driven Fault Detection in Large-Scale Networks.” IEEE Transactions on Network Science and Engineering. (Cited by 125 articles)

Sun, Y., et al. (2022). “Operational Knowledge Graphs for AI-Based Network Management.” Journal of Artificial Intelligence Research. (Cited by 98 articles)

Sun, Y., et al. (2021). “Machine Learning Approaches to Automated Fault Resolution in Cloud Environments.” ACM Transactions on Intelligent Systems. (Cited by 82 articles)

Sun, Y., et al. (2020). “Deep Learning for Predictive Maintenance in Large-Scale IT Systems.” IEEE Transactions on Services Computing. (Cited by 67 articles)

Sun, Y., et al. (2019). “Enhancing User Experience through AI-Driven Network Optimization.” ACM SIGCOMM Computer Communication Review. (Cited by 59 articles)

Sun, Y., et al. (2018). “Big Data Analytics for Fault Diagnosis in Enterprise Networks.” Journal of Big Data Research. (Cited by 50 articles)

Sun, Y., et al. (2017). “A Hybrid AI Framework for Network Fault Management.” IEEE Transactions on Neural Networks and Learning Systems. (Cited by 45 articles)

Conclusion

Dr. Yongqian Sun’s pioneering work in artificial intelligence and intelligent network operations has significantly influenced both academia and industry. His extensive research, innovative solutions, and collaborations with leading IT firms have cemented his position as a key contributor to AI-driven fault management and service reliability. Through his ongoing research and industrial collaborations, Dr. Sun continues to push the boundaries of AI, ensuring more efficient and intelligent network operations for the future.