Umar Islam | Computer Science | Best Researcher Award

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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 ⛓️💹🔮

 

 

 

Thai Ha Dang | Computers and Electronics in Agriculture | Best Researcher Award

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Mr. Thai Ha Dang | Computers and Electronics in Agriculture | Best Researcher Award

Researcher, University of North Texas, United States

Thai Ha Dang is a passionate graduate student currently pursuing his Ph.D. in Electrical Engineering at the University of North Texas. With over 2 years of experience in wearable embedded devices and wireless sensing systems, he specializes in RF energy harvesting and machine learning for signal processing. His research spans human and animal models, and he has worked on projects related to cow behavior classification, energy harvesting systems, and underwater monitoring. Thai’s commitment to research has led him to present at various international conferences and publish in high-impact journals. He has honed his skills in embedded system design, programming, and data analysis, making him a key player in the field of agricultural technology and sensor networks. His strong academic background and innovative contributions have made him a respected researcher among peers and mentors alike.

Profile

Orcid

Education

Thai Ha Dang’s educational journey began at Hanoi University of Science and Technology, Vietnam, where he earned his Degree of Engineer in Electrical Engineering, ranking in the top 15% of his class. He further advanced his studies by pursuing a Master’s degree in Electrical Computer Engineering at Pukyong National University in South Korea, where he graduated with a GPA of 4.12/4.5. This rigorous academic background provided a strong foundation in embedded systems, machine learning, and wireless sensor networks. Currently, he is enrolled in the Ph.D. program in Electrical Engineering at the University of North Texas, where his research focuses on wearable embedded devices and RF energy harvesting. His dedication to academia is reflected in his continued pursuit of knowledge and excellence in his research endeavors, particularly in the application of machine learning techniques for signal processing in embedded systems.

Experience

Thai Ha Dang has built a solid foundation in research and industry through diverse experiences. As a Research Assistant in the Embedded Sensing & Processing Systems (ESPS) Lab at the University of North Texas, he is currently working on developing an underwater monitoring system, combining his interests in wireless sensing and energy harvesting. Before this, he contributed to a wide array of projects at the AIOT Lab, Pukyong National University, where he designed a multi-channel embedded device for monitoring cow behavior. This involved system design, firmware development, and experimentation. He also gained hands-on experience during his tenure as an engineer in Samsung Display Vietnam’s AI group, where he worked on training neural networks for computer vision tasks related to defect detection. His strong technical skills, combined with a practical understanding of industry needs, make him well-equipped to tackle complex research challenges in embedded systems and machine learning applications.

Awards and Honors

Thai Ha Dang has been recognized for his contributions to the research community through several prestigious awards. Notably, he received the Best Paper Award at the Korea Institute of Convergence Signal Processing (KICSP) in December 2021 for his work on deep learning approaches for food quality assessment using hyperspectral sensors. Additionally, he was honored with the Brain Korea 21 Scholarship for the years 2021-2023, further validating his potential as a leader in his field. Thai’s academic excellence has been supported by research assistantships at both Pukyong National University and the University of California Irvine. These honors reflect his continuous pursuit of knowledge and the impact his work has had on advancing technology in agriculture and embedded systems. His recognition through these awards underscores his talent, dedication, and potential to drive innovation in his research.

Research Focus

Thai Ha Dang’s research primarily focuses on developing and applying wearable embedded systems for low-powered monitoring and energy harvesting, with a strong emphasis on machine learning techniques. His work includes creating self-powered systems, such as his wireless sensor network for monitoring cow behavior, which uses 915 MHz radio frequency energy harvesting. Another key area of his research is food quality monitoring, where he explores battery-free systems powered by RF energy harvesting to detect freshness in food products. Additionally, Thai has delved into underwater monitoring and aquaculture, with applications for shrimp larvae counting using multi-scale feature networks. His multidisciplinary research blends electrical engineering, machine learning, and sensor technology to address real-world challenges in agriculture, food safety, and environmental monitoring. Thai is particularly interested in developing sustainable and efficient systems that are capable of operating in challenging and remote environments, offering a glimpse into the future of intelligent, energy-efficient devices.

Publication Top Notes

  • “Self-Powered Cattle Behavior Monitoring System Using 915 MHz Radio Frequency Energy Harvesting,” IEEE Access, 2024.
  • “VAE-LSTM Data Augmentation for Cattle Behavior Classification Using a Wearable Inertial Sensor,” IEEE Sensor Letters, 2024.
  • “Radio-Frequency Energy Harvesting-based Self-Powered Dairy Cow Behavior Classification System,” IEEE Sensors Journal, 2023.
  • “A LoRaWAN-Based Smart Sensor Tag for Cow Behavior Monitoring,” IEEE Sensors Conference, 2022.
  • “B2EH: Batteryless BLE Sensor Network Using RF Energy Harvesting,” IEEE Applied Sensing Conference, 2023.
  • “Shrimp Larvae Counting in Dense Environments Using Size-Adaptive Density Map Estimation and Multi-scale Feature Network,” IEEE Transactions on Agrifood Electronics (accepted).