Abdallah Al-Zubi | Data Science | Best Researcher Award

Published on by Network Awards

Mr. Abdallah Al-Zubi | Data Science | Best Researcher Award

Abdallah Al-Zubi at University Of Nebraska Lincoln | United States

Mr. Abdallah Alzubi is an accomplished AI engineer and researcher with over eight years of experience in machine learning, data science, and software engineering. Currently pursuing a Ph.D. in AI Engineering at the University of Nebraska-Lincoln, his research focuses on developing MEMS-based analog computing architectures for real-time signal processing, human activity recognition, and structural health monitoring. His contributions span both academic research and industry innovation, including the establishment of the AI department at John Wiley and Sons in Jordan, as well as collaborations on cutting-edge projects funded by the Intelligence Advanced Research Projects Activity (IARPA). He is recognized for bridging theoretical AI research with impactful business and healthcare applications.

Professional Profile:

Education: 

Mr. Abdallah Alzubi is a proficient AI engineer and researcher specializing in data science, machine learning, and software engineering, with extensive academic and professional experience. He is currently pursuing a Ph.D. in AI Engineering at the University of Nebraska-Lincoln, USA, focusing on MEMS-based Analog Computing. He also holds an M.S. in AI Engineering from the same institution, where he completed his thesis on Gradient-Based Multi-Time-Scale Trainable Continuous Time Recurrent Networks, as well as an M.S. in Data Science from Princess Sumaya University for Technology, Jordan, with research on Pathfinder Optimization clustering techniques. His academic journey began with a B.S. in Computer Engineering from Jordan University of Science & Technology, where he developed an automated Arabic optical character recognition system.

Experience:

Mr. Alzubi serves as a Research Assistant at the University of Nebraska-Lincoln, where he develops MEMS-based hardware simulations for structural health monitoring and signal denoising using TensorFlow and Keras, while also designing AI models for seismic structural assessments and human activity detection. Previously, as an AI Engineer at John Wiley & Sons (NJ), he pioneered the establishment of their AI Department in Jordan, enhancing speech recognition systems, building big data-driven article recommendation engines, and improving sentiment analysis accuracy. Earlier in his career, he worked as a Software Engineer at Globitel, Jordan, where he created mobile proximity matching services for taxi dispatching and developed secure authentication solutions (Mobile Connect) for telecom clients. As a Solution Developer at ILS Saudi Co. Ltd, he implemented ERP systems to optimize operations across manufacturing, HR, and finance. At SEDCO, Jordan, he further contributed by enhancing customer queuing management systems—reducing communication latency sevenfold—and integrating smart advertising and multilingual functionalities.

Research Interest:

His research interests span across MEMS-based analog computing for low-power AI applications, machine learning for structural health monitoring and earthquake response, human activity recognition in healthcare, natural language processing for speech recognition and sentiment analysis, and big data analytics for real-time AI system design.

Publications Top Noted:

  • Automated System for Arabic Optical Character Recognition with Lookup Dictionary
    Year: 2012
    Citations: 21

  • Automated System for Arabic Optical Character Recognition
    Year: 2012
    Citations: 9

  • G-CTRNN: A Trainable Low-Power Continuous-Time Neural Network for Human Activity Recognition in Healthcare Applications
    Year: 2025

  • A Novel MEMS Reservoir Computing Approach for Classifying Human Acceleration Activity Signal
    Year: 2025

  • Distributed and Automated Machine Learning in Big Data Stream Analytics
    Year: 2019
    Citations: 1

Conclusion:

Mr. Abdallah Al-Zubi exemplifies the qualities of a forward-thinking researcher in AI and Data Science. His innovative work on MEMS-based analog computing, coupled with contributions to structural health monitoring, human activity recognition, and big data-driven AI, positions him as a global leader in next-generation artificial intelligence research. His unique blend of academic rigor, industry leadership, and impactful real-world applications makes him a highly deserving candidate for the Best Researcher Award. With his ongoing contributions, he is poised to play a critical role in shaping the future of low-power AI systems and intelligent infrastructure solutions.

Arifur Rahman | Machine Learning | Best Researcher Award

Published on by Network Awards

Arifur Rahman | Machine Learning | Best Researcher Award

Mr. Arifur Rahman, NAGAD Digital Financial Service, Bangladesh

Arifur Rahman 🎓 is a passionate researcher and software engineer from Bangladesh 🇧🇩, specializing in Machine Learning 🤖, Deep Learning 🧠, NLP 📚, and Bioinformatics 🧬. A graduate of KUET in Computer Science and Engineering 💻, he has excelled in both academia and industry. Currently, he serves as a Full Stack Developer 🧑‍💻 at NAGAD Digital Financial Service, contributing to innovative supply chain projects. Arifur is also an active researcher with several IEEE and Elsevier publications 📝, and has earned recognition in programming contests 🏆. His dedication to applied AI and system development showcases a unique blend of technical and research excellence 🚀.

🌍 Professional Profile

Google Scholar

🎓 Education

  • 🎓 B.Sc. in Computer Science and Engineering, KUET (2018 – 2023)

    • 📊 CGPA: 3.35/4.00; Final Two Years CGPA: 3.73/4.00

  • 🏫 Noakhali Govt. College (2015 – 2017)

    • 🌟 GPA: 5.00/5.00 (Cumilla Board Scholarship Winner)

👨‍💼 Experience

  • 🧑‍💻 Software Engineer, NAGAD Digital Financial Service (Feb 2024 – Present)

    • 💼 Full Stack Developer in PRISM (Supply Chain Management) using Flutter, Java Spring Boot, PHP

  • 🔬 Research Engineer (NLP), AIMS Lab, United International University (Oct 2023 – Feb 2024)

    • 📚 Worked on Recommender Systems and published in IEEE Access

  • 👨‍💻 Software Engineer, Nazihar IT Solution Ltd. (May 2023 – Sep 2023)

    • 💻 Developed subroutines using Temenos Java Framework for banking solutions

🏆 Suitability for Best Researcher Award

Mr. Arifur Rahman is an exceptional candidate for the Best Researcher Award, demonstrating strong potential and proven excellence in research and innovation across emerging domains such as Machine Learning, Deep Learning, Natural Language Processing (NLP), Health Informatics, and Biomedical Engineering. His impactful research, hands-on development skills, and academic contributions distinguish him as a rising leader in computational science and applied AI.

🔹 Professional Development 

Arifur Rahman 🚀 is actively involved in both industry-driven software engineering and cutting-edge academic research 📖. His journey has been marked by continuous professional growth, serving in roles that merge development and innovation 💼. At NAGAD, he contributes as a Full Stack Developer 🌐, while his time at AIMS Lab sharpened his NLP and recommender system expertise 🧠. He has also contributed as a reviewer in IEEE conferences 📑, showcasing his engagement with the global research community. Arifur’s hands-on experience with technologies like Flutter, Java Spring Boot, ReactJS, and blockchain 🔗 highlights his dynamic skill set and commitment to excellence ⭐.

🔍 Research Focus

Arifur Rahman’s research focuses on a diverse range of AI-powered technologies 🧠, with core interests in Machine Learning, Deep Learning, and Natural Language Processing 🤖📚. His work explores real-world applications such as health informatics 🏥, bioinformatics 🧬, fake news detection, and blockchain security 🔐. Through his IEEE and Elsevier publications, he has addressed critical problems in diabetic retinopathy diagnosis, DNA sequence classification, and higher education recommendation systems 🎓. His blend of theoretical innovation and practical solutions ensures his research contributes to both scientific progress and societal impact 🌍.

🏅 Awards and Honors

  • 🎖️ Dean’s List Award at KUET for outstanding academic performance (2019–2020)

  • 🥇 Intra-KUET Programming Contest 2021 – 3rd Place 🧠💡

  • 🥈 Intra-KUET Programming Contest 2019 – 6th Place 🧠

  • 🥉 Divine IT Qualification Round – Rank 10 (Nov 2023) 💻

  • 🏆 TechnoNext Technical Coding Test 2023 (Fresher) – Rank 7 🔢

📊 Publication Top Notes

  1. Recommender system in academic choices of higher education – IEEE Access (2024) 📚5 🎓🤖
  2. Advancements in breast cancer diagnosis… with PCA, VIF – 6th Int. Conf. on Electrical Engineering and Info (2024) 📚2 🧬🩺📊
  3. Optimizing SMS Spam Detection… Voting Ensembles & Bi-LSTM – 5th Int. Conf. on Data Intelligence and Cognitive (2024) 📚1 📱📩🧠
  4. Cracking the Genetic Codes: DNA Sequence Classification… – Int. Conf. on Advances in Computing, Communication (2024) 📚1 🧬🧪🧠
  5. Secure Land Purchasing using… Multi-Party Skyline Queries – 26th Int. Conf. on Computer and Info Tech (2023) 📚1 🌍🏠🔐
  6. Fake News Detection… Soft and Hard Voting Ensemble – Procedia Computer Science (2025) 📚– 📰❌🗳️

Dr. Tee Connie | Machine Learning Awards | Best Researcher Award

Published on by Network Awards

Dr. Tee Connie | Machine Learning Awards | Best Researcher Award

Dr. Tee Connie , Multimedia University , Malaysia

Dr. Tee Connie is a distinguished academic and researcher in the field of Information Technology, specializing in machine learning, pattern recognition, computer vision, and biometrics. She is currently a Professor at the Faculty of Information Science and Technology, Multimedia University, Malaysia, where she also serves as Dean of the Institute for Postgraduate Studies. Dr. Tee holds a Ph.D. and Master’s in Information Technology from Multimedia University, and a Bachelor’s degree in Information Technology with First Class Honours from the same institution. Her research is widely recognized, evidenced by numerous funded projects and publications, including notable grants for innovative applications in gait analysis, vehicle traffic analysis, and computer vision solutions. She has also contributed to the field with a patent for a hand geometry and palm print verification system. Her extensive experience and leadership in both research and academic administration underscore her significant impact in advancing information technology.

Professional Profile:

Scopus

Orcid

Summary of Suitability for the Research for Best Researcher Award: Tee Connie

Introduction: Dr. Tee Connie, a Professor at Multimedia University, is a distinguished candidate for the Research for Best Researcher Award. Her extensive background in machine learning, computer vision, and biometrics, coupled with her leadership roles and significant research contributions, positions her as a highly suitable nominee.

🎓Education:

Dr. Tee Connie completed her Doctor of Philosophy in Information Technology at Multimedia University, Malaysia, in 2015. Prior to this, she earned a Master of Science in Information Technology from the same institution in 2005. She also holds a Bachelor of Information Technology, with a major in Information System Engineering, graduating with First Class Honours and a CGPA of 3.92/4.00 in 2003.

🏢Work Experience:

Dr. Tee Connie has held several academic and administrative positions at Multimedia University, Malaysia. She has been a Professor at the Faculty of Information Science and Technology since 2023 and currently serves as the Dean of the Institute for Postgraduate Studies, a role she has held since April 2022. Prior to this, she was the Deputy Dean of the Institute for Postgraduate Studies from April 2021 to April 2022. Dr. Tee’s career at the university began as a Lecturer in 2005, and she was promoted to Senior Lecturer in 2008, a position she held until 2021. She has also worked as an Associate Professor at the Faculty of Information Science and Technology since 2021 and served as a Tutor from 2003 to 2005.

🏆Awards and Grants:

Dr. Tee Connie has been awarded several significant research grants. She is leading the Malaysia-Jordan Matching Grant project on “A Non-Invasive Gait Analysis for Parkinson’s Disease Screening Using Computer Vision and Machine Learning Techniques,” which runs from September 2024 to August 2026, with a funding amount of RM 23,000. She is also a project member for the TM R&D Fund’s “Smart-VeTRAN: Smart Vehicle Traffic Impact Analysis Using 4G/5G Network” (RM 678,453) and the “Machine Learning Based Distributed Acoustic Sensing (DAS) for Fiber Break Prevention” projects (Sub-project 1: RM 638,731; Sub-project 2: RM 599,061), both running from August 2022 to July 2024. Other notable grants include the Fundamental Research Grant Scheme’s “Confined Parking Spaces and Congestion Prediction using Deep Q-Learning Strategy” (RM 89,093) and the “Few-shot Learning Approach for Human Activity Recognition and Anomaly Detection” (RM 113,850), both spanning from September 2022 to April 2024. Additionally, she has secured funding for projects such as the “Cryptographically Secure Cloud-Based Infrastructure (CryptCloud)” (RM 917,504), the IR Fund’s “Gender and Age Estimation using Human Gait for Smart Cities Surveillance” (RM 24,000), and the Multimedia University-Telkom University Joint Research Grant for “Gait Analysis for Neurodegenerative Disorders using Computer Vision and Deep Learning Approaches” (RM 20,000). Her past projects include contributions to the International Collaboration Fund’s “Design and Development of A Drone Based Hyperspectral Imaging System for Precision Agriculture” (RM 264,660) and several other notable grants in fields related to computer vision, biometrics, and security surveillance.

Publication Top Notes:

  • Visual-based vehicle detection with adaptive oversampling
  • A Robust License Plate Detection System Using Smart Device
  • Review on Digital Signal Processing (DSP) Algorithm for Distributed Acoustic Sensing (DAS) for Ground Disturbance Detection
  • A Review of AI Techniques in Fruit Detection and Classification: Analyzing Data, Features and AI Models Used in Agricultural Industry
  • Boosting Vehicle Classification with Augmentation Techniques across Multiple YOLO Versions