EXTERNAL PROFILES
Professor
Prof. Dr. Abidur Rahaman
Born in 1984, Joined in Dept. of ICE, NSTU as a Lecturer in 26th May 2013, Post-doc and PhD in Information Display from the Kyung Hee University, South Korea.
Information and Communication Engineering
BIOGRAPHY
Born in 1984, Joined in Dept. of ICE, NSTU as a Lecturer in 26th May 2013, Post-doc and PhD in Information Display from the Kyung Hee University, South Korea.
RESEARCH INTERESTS
2023 - 2024
Post-Doc
Information Display
Kyung Hee University, South Korea
2016 - 2020
PhD
Information Display
Kyung Hee University, South Korea
2008 - 2009
MSc (Thesis) in Applied Physics Electronics and Communication Engineering
Applied Physics Electronics and Communication Engineering
Dhaka University
2003 - 2006
B.Sc (Hons) in Applied Physics Electronics and Communication Engineering
Applied Physics Electronics and Communication Engineering
Dhaka University
2001 - 2002
HSC
Science
Dhaka College
1996 - 2000
SSC
Science
Bamni High School
Last updated on 2025-09-23 10:31:45
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AWARDS AND ACHIEVEMENTS
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High Speed Level-Down Shifter Using LTPO TFTs for Low Power and Interface Electronics
Design and analysis of performance parameters for achieving high efficient ITO/PEDOT: PSS/P3HT: PCBM/Al organic solar cell
A Study on High Performance, Dual-Gate a-IZO/a-IGZTO TFTs with Excellent Stability
A compact DC–DC converter using low-temperature poly-Si oxide thin-film transistors
Bioinformatics Approach to Identify Significant Biomarkers, Drug Targets Shared Between Parkinson’s Disease and Bipolar Disorder: A Pilot Study
Performance evaluation of micro lens arrays: Improvement of light intensity and efficiency of white organic light emitting diodes
Light intensity and efficiency enhancement of n-ZnO/NiO/p-GaN heterojunction-based white light-emitting diodes using micro-pillar array
Prediction on Domestic Violence in Bangladesh during the COVID-19 Outbreak Using Machine Learning Methods
Enhanced current mirror circuit by dual-gate coplanar amorphous InGaZnO TFTs
A High-Gain CMOS Operational Amplifier Using Low-Temperature Poly-Si Oxide TFTs
A High Performance Operational Amplifier Using Coplanar Dual Gate a-IGZO TFTs
High Speed and Wider Swing, Level Shifter Using Low-Temperature Poly-Silicon Oxide TFTs
Effect of Doping Fluorine in Offset Region on Performance of Coplanar a-IGZO TFTs
Book Name: Lightning Protection
Chapter Title: Lightning protection practices in Bangladesh: An Overview (Chapter 2)
High Current Stress Analysis for p-type LTPS Thin Film Transistor with Split Active Layer for High Current Driving AMOLED Display
Highly Stable Dual Gate a- InGaZnO Thin Film Transistor with Top Gate to Drain Offset for AC Gate Pulse Stress
25-2: LTPO TFT Technology for Level Shifter Integrated Gate Driver in UHD 4K Displays
(Invited) Channel Length Dependence of Mechanical Strain for Flexible (a-IGZO) TFT
Excellent Temperature Sensing Device with Coplanar a-IGZO TFT Ring Oscillator
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Piata Binte Nahar
IT Officer, Sonali Bank PLC
Thesis Title: Machine learning–based prediction model for oxygen requirement in hospitalized COVID-19 patients
Overview: Enter ProjeThe COVID-19 pandemic has created severe global shortages of essential resources, particularly oxygen, which is critical for saving lives in patients with respiratory complications. This study develops predictive models to forecast the need for oxygen therapy among hospitalized COVID-19 patients, aiming to ensure timely and adequate oxygen supply within healthcare facilities. We utilized real-world data from 1000 PCR-positive patients admitted to three hospitals in Dhaka, Bangladesh. The dataset included demographics, symptoms, laboratory results, and comorbidities. Six machine learning (ML) models were implemented, including logistic regression (LR), eXtreme gradient boosting (XGBoost), random forest (RF), decision tree (DT), K-nearest neighbors (KNN), and Naïve Bayes (NB). After data preprocessing and tenfold cross-validation, model performance was evaluated on a separate test set. Key predictors identified were oxygen saturation (SpO₂), respiratory distress, blood clots, pneumonia, hypertension, cardiovascular disease, kidney disease, lung infection, and age. Among the models tested, random forest demonstrated the best performance, achieving an accuracy of 93.69%, a recall of 97.5%, a precision of 93.9%, an F1-score of 97.8% and an AUC of 0.919. These results underscore the potential of machine learning techniques to enhance healthcare planning and resource management. By enabling early identification of oxygen needs, predictive models can reduce preventable deaths and strengthen healthcare resilience in future pandemics.
- Institutional Email: abidur@nstu.edu.bd
- Personal Email: abid317509@gmail.com
- Mobile number: 01914317509
- Emergency Contact: 01954260136
- PABX:N/A
- Website:N/A
SOCIAL PROFILES
Department
Information and Communication Engineering
Noakhali Science and Technology University