Nahiyan Bin Noor

Researcher

UAMS primary UA Little Rock

Also affiliated: Chittagong University of Engineering & Technology (2019)

Faculty Researcher

4 h-index 15 pubs 98 cited

Research Areas

Biomedical Subjects

OverviewAI-generated summary

Nahiyan Bin Noor's research focuses on the application of machine learning algorithms to address critical public health issues, particularly in the realm of substance abuse and chronic disease prediction. His work includes developing and validating models to predict treatment retention, overdose events, and mortality among US military veterans undergoing buprenorphine treatment for opioid use disorder. He has also investigated the use of machine learning for predicting anemia from eye conjunctiva images and for forecasting heart disease using ensembles of models and large language models.

Bin Noor's research extends to analyzing online discourse, examining toxicity in Reddit conversations and comparing toxicity across social media platforms during the COVID-19 pandemic. His work also touches on sustainable healthcare infrastructure, including the design and evaluation of renewable energy microgrids for healthcare facilities. He has a publication record of 15 articles with an h-index of 4 and 95 citations, and collaborates with researchers at the University of Arkansas for Medical Sciences and the University of Arkansas at Little Rock.

Metrics

h-index: 4
Publications: 15
Citations: 98

Selected Publications

Hybrid Transformer-Based Channel Estimation for Massive Multiple-Input Multiple-Output Systems Using Low-Rank Adaptation Optimization (2026)

AI Computer Science and Robotics Technology DOI OpenAlex
Multi-Label Classification of Toxicity in Music Lyrics: Benchmarking Machine Learning Against Deep Learning Approaches (2026)

DOI OpenAlex
Advances in Bangladeshi Cuisine Recognition: A Review of Deep Learning, Vision–Language Models, Fine-Tuning and Parameter-Efficient Adaptation (2026)

AI Computer Science and Robotics Technology DOI OpenAlex
Social determinants of health diagnosis codes in in-person and telehealth visits during pregnancy in the United States, 2020–2025: a repeated cross-sectional and case-control study (2026)

BMC Pregnancy and Childbirth DOI OpenAlex
Evaluating the optimal duration of medication treatment for opioid use disorder (2026)

Addiction 1 citation DOI OpenAlex
Advanced Techniques for Dental Diagnosis Using Multimodal Vision Language Models Approaches with Prompt Engineering & Finetuning (2025)

DOI OpenAlex
Association between different modalities of opioid use disorder-related care delivery and opioid use disorder-related patient outcomes: A retrospective cohort study (2025)

Addictive Behaviors Reports DOI OpenAlex
Development and Validation of Machine-Learning Algorithms Predicting Retention, Overdoses, and All-Cause Mortality Among US Military Veterans Treated with Buprenorphine for Opioid Use Disorder (2025)

Drug and Alcohol Dependence DOI OpenAlex
Development and validation of machine-learning algorithms predicting retention, overdoses, and all-cause mortality among US military veterans treated with buprenorphine for opioid use disorder (2024)

Journal of Addictive Diseases 2 citations DOI OpenAlex
Examining Toxicity’s Impact on Reddit Conversations (2024)

Studies in computational intelligence 12 citations DOI OpenAlex
A Systematic Approach to Predict Anemia from Eye Conjunctiva Images (2023)

3 citations DOI OpenAlex
Towards Carbon-Neutral Healthcare Facilities: Design and Evaluation of a Renewable Energy Microgrid (2023)

1 citation DOI OpenAlex
Strategic Utilization of Dispatchable Loads and Nodal Reserves for Improved Reserve Deliverability (2023)

1 citation DOI OpenAlex
An Efficient Technique of Predicting Toxicity on Music Lyrics Machine Learning (2023)

7 citations DOI OpenAlex
A Survey on Neural and Non-Neural Network Based Approaches to Classify Images and Signals (2023)

2 citations DOI OpenAlex