LOS ANGELES: Md Shahnawaj, a researcher at Pacific States University (PSU), has led an international team to the prestigious “Best Research Paper Award” at the 3rd IEEE Technology & Innovation Conference (TIC-2026), held in Kuala Lumpur, Malaysia, on June 5–6, 2026. The project was conducted under the academic supervision of PSU Adjunct Professor Roise Uddin.
As lead author, Md Shahnawaj spearheaded the award-winning paper, titled “Digital Twin-Based Optimization of Production Processes in Smart Factories,” which was recognized for its significant contributions to Industry 4.0. He guided an internationally collaborative team—comprising Bangladeshi scholars and partners—that includes Md Abu Kawsar Prodhan Hemal, Mohammad Hasibul Hasan, Hamim Islam Hellol, Kelly Andrea Zarate Contreras, and Md Al Ridwan. Together they developed a pioneering framework that merges Digital Twin technology with Machine Learning to advance smart manufacturing. The full paper has been published on IEEE Xplore and is indexed in Scopus.
The research was carried out under the academic supervision of Professor Roise Uddin, an adjunct faculty member at Pacific States University and a graduate of the California State University system. A specialist in cybersecurity and artificial intelligence, Professor Uddin mentored Md Shahnawaj and the team, guiding the study’s research design, methodology, and validation from concept to publication. “Md Shahnawaj showed exceptional leadership and technical depth throughout this project,” said Professor Uddin. “This award reflects the team’s dedication and the strength of applied AI research at PSU.”
“Winning the Best Paper Award is a tremendous honor for our entire team,” said Md Shahnawaj, lead author of the study. “Under Professor Uddin’s guidance, we were able to turn a bold idea into a validated framework that can make a real difference for modern manufacturing.”
Impact on Modern Industry
The study provides a solution to critical manufacturing inefficiencies by integrating real-time data and predictive analytics, helping factories transition from traditional methods to autonomous, intelligent operations.
Key Findings
Operational Efficiency: The system achieved up to a 25% improvement in production efficiency.
Cost Savings: Organizations can expect an 18% reduction in both maintenance and operational expenses.
Downtime Reduction: The research demonstrated a 40% decrease in equipment downtime, significantly boosting manufacturing reliability.
The researchers utilized Deep Neural Networks (DNN) to achieve 95.2% accuracy, marking a major milestone in predictive analytics for smart factories. “The system enables a shift from reactive to proactive manufacturing, contributing to the development of intelligent and autonomous industrial environments,” the team noted.
About the Conference
The 3rd IEEE Technology & Innovation Conference (TIC-2026) is a premier global event supported by the World Research Union and Spectrum International University College, dedicated to advancing technology for humanity. Accepted papers from the conference are published on IEEE Xplore and indexed in Scopus, ensuring broad visibility within the international research community.
About Pacific States University
Pacific States University, located in Los Angeles, California, offers programs in business, engineering, and information technology, with a growing portfolio of applied research in artificial intelligence, cybersecurity, and emerging technologies. PSU faculty and students actively contribute to international scholarship through IEEE conferences and peer-reviewed publications.
