Umar Islam | Computer Science | Best Researcher Award

Mr. Umar Islam | Computer Science | Best Researcher Award

Senior Lecturer, IQRA National University Swat Campus, Pakistan

Mr. Umar Islam is a passionate and accomplished educator and researcher in the field of Computer Science, currently serving as a Lecturer at Iqra National University (INU) Swat Campus, Pakistan. With an impressive academic background spanning 18 years in Computer Science, Mr. Islam has become a recognized expert in AI, machine learning, blockchain security, IoT, bioinformatics, and financial analytics. His work has been published in over 15 research articles, including several in top-tier journals. A dedicated researcher, he focuses on real-time AI solutions, particularly in healthcare and cybersecurity. Mr. Islam is also a committed mentor, providing supervision and guidance to students in advanced topics such as Python programming, machine learning, and AI applications. His contributions to the academic community and his research endeavors demonstrate his commitment to pushing the boundaries of knowledge and solving real-world problems.

Profile

Education

Mr. Umar Islam has an extensive academic journey, earning 18 years of education in Computer Science. His academic path began with a Bachelor’s degree in Computer Science, followed by a Master’s degree, where he built the foundation of his knowledge in various aspects of computing. Mr. Islam’s thirst for knowledge and his passion for research led him to pursue advanced studies in areas like AI, machine learning, IoT, and cybersecurity, with a strong focus on applying these technologies to solve real-world challenges. His educational journey has equipped him with the skills to lead cutting-edge research projects and to innovate in fields like bioinformatics and financial analytics. Currently, he is working toward a PhD, which will further deepen his understanding and expertise in these areas. Through his education, Mr. Islam has gained a comprehensive understanding of theoretical and applied Computer Science, which he integrates into both his teaching and research.

Experience

With six years of teaching experience at the higher education level, Mr. Umar Islam has played a pivotal role in shaping the future of numerous students at Iqra National University (INU) Swat Campus. As a lecturer, he has delivered comprehensive lessons in Computer Science topics such as AI, machine learning, and cybersecurity. His commitment to academic excellence is reflected in his success as a supervisor, guiding students through complex topics like Python programming, e-learning analytics, and AI-driven applications. In addition to teaching, Mr. Islam has gained four years of extensive research experience, with a focus on AI applications in healthcare, cybersecurity, and blockchain security. He has led multiple research projects, producing groundbreaking results, and has contributed significantly to the academic community with over 15 published research articles. His academic experience extends beyond teaching, positioning him as a thought leader in his field.

Research Focus

Mr. Umar Islam’s research is deeply focused on the intersection of artificial intelligence (AI), cybersecurity, healthcare, and financial analytics. One of his key research areas includes AI-driven solutions in healthcare, particularly the development of federated learning-based intrusion detection systems and epileptic seizure prediction models. He is also actively exploring AI in cybersecurity, specifically in blockchain security, to mitigate data tampering risks. His work in financial analytics uses AI and machine learning to predict market trends, including cryptocurrency values, demonstrating his interdisciplinary approach to solving real-world problems. In addition to these topics, Mr. Islam is involved in pioneering research in IoT security and bioinformatics. His research aims to address key global challenges such as healthcare delivery, data security, and economic stability through cutting-edge AI applications. His innovative contributions to various fields have resulted in multiple published articles in prestigious journals, demonstrating the far-reaching impact of his work.

Publication Top Notes

  • Detection of distributed denial of service (DDoS) attacks in IoT-based monitoring system of banking sector using machine learning models 🌐🔐📊
  • IOTA-Based Mobile Crowd Sensing: Detection of Fake Sensing Using Logit-Boosted Machine Learning Algorithms 🤖📱💡
  • Real-time detection schemes for memory DoS (M-DoS) attacks on cloud computing applications ☁️💻🛡️
  • Detection of renal cell hydronephrosis in ultrasound kidney images: a study on the efficacy of deep convolutional neural networks 🏥🧠📸
  • A novel anomaly detection system on the internet of railways using extended neural networks 🚆🔍⚙️
  • NeuroHealth guardian: A novel hybrid approach for precision brain stroke prediction and healthcare analytics 🧠💓📈
  • An intelligent approach for preserving the privacy and security of a smart home based on IoT using LogitBoost techniques 🏠🔐💡
  • Enhancing Economic Stability with Innovative Crude Oil Price Prediction and Policy Uncertainty Mitigation in USD Energy Stock Markets 💰📊📉
  • Investigating the Effectiveness of Novel Support Vector Neural Network for Anomaly Detection in Digital Forensics Data 💾🔎👨‍💻
  • Empowering global ethereum price prediction with EtherVoyant: a state-of-the-art time series forecasting model ⛓️💹🔮

 

 

 

Ming Yuan | Model Compression Award | Best Researcher Award

Mr Ming Yuan | Model Compression Award | Best Researcher Award

Mr Ming Yuan, City University of Hong Kong , Hong Kong

Ming Yuan is a distinguished scholar in the field of applied mathematics, currently pursuing a Master of Applied Mathematics at Northwestern Polytechnical University (2021-2024). He holds a Bachelor of Science in Statistics from Shandong University (2015-2019). Yuan has made significant contributions to the areas of nonlinear dynamical systems, model compression, and optimization. His research has been published in prestigious journals such as Neurocomputing and Discrete Applied Mathematics. Notable works include a systematic DNN weight pruning framework and studies on the α-index of minimally connected graphs. Yuan has received several accolades, including the 2024 Outstanding Master’s Graduate Award and multiple scholarships. His work reflects a blend of theoretical innovation and practical application, positioning him as a promising researcher in his field.

Publication Profile

Scopus

Education

Ming Yuan is currently completing his Master of Applied Mathematics at Northwestern Polytechnical University, a program he commenced in September 2021 and is set to finish in April 2024. Prior to this, Yuan earned his Bachelor of Science in Statistics from Shandong University, China, where he studied from September 2015 to June 2019. During his undergraduate studies, he developed a strong foundation in statistical theories and methodologies, which has been instrumental in his advanced research. Yuan’s academic journey is marked by a commitment to excellence and a passion for exploring complex mathematical concepts. His educational background has provided him with the skills and knowledge necessary to contribute significantly to the field of applied mathematics, particularly in nonlinear dynamical systems, model compression, and optimization.

Experience 

Ming Yuan has accumulated substantial experience in mathematical research and academia. As a Master’s student at Northwestern Polytechnical University, he has been deeply involved in various research projects since September 2021. Yuan’s work primarily focuses on nonlinear dynamical systems, model compression, and optimization, areas in which he has published extensively. His notable publications include a systematic DNN weight pruning framework based on symmetric accelerated stochastic ADMM and studies on the α-index of minimally connected graphs. Additionally, Yuan has collaborated with renowned researchers, contributing to high-impact journals like Neurocomputing and Discrete Applied Mathematics. His practical experience is further enriched by his undergraduate tenure at Shandong University, where he engaged in several scientific research projects and mathematical contests. This blend of rigorous academic training and hands-on research experience positions Yuan as a capable and innovative researcher in his field.

Research Focus 

Ming Yuan’s research focuses on several critical areas within applied mathematics, including nonlinear dynamical systems, model compression, and optimization. His work aims to develop innovative solutions and methodologies that address complex mathematical problems. One of his key contributions is a systematic DNN weight pruning framework based on symmetric accelerated stochastic ADMM, which has been published in Neurocomputing. Additionally, Yuan has explored the α-index of minimally connected graphs, contributing to the field of graph theory. His research is characterized by a rigorous analytical approach and a commitment to advancing theoretical understanding while ensuring practical applicability. Yuan’s studies often involve interdisciplinary collaboration, enhancing the impact and relevance of his findings. His dedication to exploring new frontiers in mathematics positions him as a forward-thinking researcher with a promising future in academia and beyond.

Publication Top Notes

A systematic DNN weight pruning framework based on symmetric accelerated stochastic ADMM

On the α-index of minimally 2-connected graphs with given order or size