Nanotechnology, AI, IoT and Applied Machine Learning Research Group (Nano-Bio-AI)

Mission

To advance interdisciplinary research in nanotechnology, photonics, AI/IoT, and applied machine learning by developing intelligent materials, energy-efficient devices, and smart sensing systems that address real-world challenges in healthcare, clean energy, environmental monitoring, precision agriculture, and smart infrastructure — through collaborative, ethical, and application-driven innovation that delivers scalable, sustainable, and socially impactful technologies.

Vision

To become a globally recognized research hub for sustainable, human-centered technological innovation — pioneering advancements in nanophotonics, semiconductor devices, programmable photonic systems, AI-enabled cyber-physical platforms, and intelligent environmental & energy solutions.

We envision shaping a future where intelligent materials, data-driven systems, and next-generation devices transform industries, strengthen knowledge economies, and contribute to a smart, resilient, and sustainable world.

Objectives

Innovative Research

Technological Development

Interdisciplinary Collaboration

Educational Excellence

Community Engagement

Sustainable Solutions

Recent Publications

We are excited to announce the recent 2 publications in two esteemed journals. These significant contributions reflect our ongoing commitment to advancing knowledge in the fields of Internet of Things (IoT), machine learning (ML), and nanotechnology.

1. Automatic Priority Analysis of Emergency Response Systems Using Internet of Things (IoT) and Machine Learning (ML)

Authors: ASM Mohsin, SH Choudhury, MA Muyeed
Published in: Transportation Engineering (2025)
https://doi.org/10.1016/j.treng.2025.100304. (Q1, 89%, Citescore 8.1)

In this paper, the authors present an innovative approach to enhance emergency response systems through the integration of IoT and ML techniques. By automating priority analysis, this research aims to improve the efficiency and effectiveness of emergency responses, potentially saving lives and resources.

2. Quantifying Monomer–Dimer Distribution of Nanoparticles from Uncorrelated Optical Images Using Deep Learning

Authors: ASM Mohsin, SH Choudhury
Published in: ACS Omega (2025)
https://doi.org/10.1021/acsomega.4c07914, Q 1, 76%, Citescore 6.6).

This study explores a novel methodology for quantifying the distribution of nanoparticles using deep learning techniques on uncorrelated optical images. The findings provide valuable insights into nanoparticle behavior, paving the way for advancements in nanotechnology applications.

These publications underscore our research group's dedication to pushing the boundaries of technology and contributing to groundbreaking developments in our fields. We congratulate our authors on their achievements and look forward to their future work.

For more details on the papers, please check the respective journals.

🗓️ 29th April 2025.

Alhamdulliah, I'm delighted to share that I have received the Q1 Quality Journal Publication Award from the Brac University Research Metrics Committee! This award recognizes our achievement of the highest number of Q1 journal papers—12 in total—and our top 10% ranking for journal quality from July 2023 to June 2024.

Despite facing resource challenges, our dedication, hard work and collaborative efforts have led to these remarkable results. I want to express my heartfelt gratitude to everyone who contributed to this achievement:

Research Assistants: Rumman, Shadmani, Shadab, Sujoy, and Atib — your tireless dedication has been invaluable.

Undergraduate FYDP Group (Bristy and others) and Master’s Students: Nishat — your hard work and enthusiasm have been crucial.

Co-Principal Investigators: Dr. Mosaddeque and Dr. Belal, along with collaborators Dr. Aparna, Dr. Jamal and Ehsan — thank you for your trust, expertise, and support throughout our research journey.