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Md. Nahiduzzaman

মোঃ নাহিদুজ্জামান

Assistant Professor

  •  Room No:  3rd Floor, Dept. Of ECE
  •  Phone:   880-1763591843
  •  Email:   nahiduzzaman@ece.ruet.ac.bd
Md. Nahiduzzaman, a highly accomplished and passionate researcher from Bangladesh, has consistently demonstrated his commitment to academic excellence and innovation throughout his career. A proud alumnus of Rajshahi University of Engineering and Technology (RUET), he graduated with a Bachelor of Science in Computer Science and Engineering, earning an impressive CGPA of 3.85 out of 4.00 and ranking 5th among a competitive cohort of 120 students.

In addition to his research pursuits, Nahiduzzaman has been sharing his expertise as a lecturer in the Department of Electrical and Computer Engineering at RUET since November 5, 2019. His dedication to educating the next generation of engineers showcases his commitment to academia and the broader community.

Driven by a profound interest in leveraging artificial intelligence for the betterment of society, Nahiduzzaman has focused his research on developing state-of-the-art automated disease detection systems. These cutting-edge solutions employ explainable AI to analyze medical images through the advanced machine and deep learning models. Additionally, Nahiduzzaman is dedicated to refining existing deep learning models, enhancing their applicability to a wide array of real-world scenarios while simultaneously reducing their complexity and bolstering interpretability.

A prolific scholar, Nahiduzzaman has authored 19 high-impact Q1 journal articles and numerous papers presented at prestigious IEEE conferences. Among his notable achievements is the successful completion of a University Grants Commission-funded project centered on developing a deep learning model for detecting COVID-19 in CT scan images through a sophisticated ensemble-based machine learning approach. Furthermore, he has collaborated with esteemed international institutions, such as Manchester Metropolitan University, in order to expand his research horizons.

Nahiduzzaman is a research assistant at the esteemed Qatar University Machine Learning Lab, where he continues exploring his research interests. These include machine learning applications in areas such as disease detection, medical image analysis, and the power sector. With his relentless pursuit of knowledge, a keen eye for innovation, and dedication to educating future engineers, Md. Nahiduzzaman is a shining example of the potential at the intersection of computer science and real-world problem-solving.
Field of Research
  •  Object Detection and Classification
  •  Deep Learning
  •  Machine Learning
  •  Medical Image Analysis
  •  First Joined: 05th Nov, 2019
  •      Dept. of Electrical & Computer Engineering

Position

Education

More about Md. Nahiduzzaman
Book / Book Chapter
2
Journal Articles
28
Conference Papers
2
Standard List of Bibliography
SL Authors Title Publisher Details Publication Year Type
1 Md. Omaer Faruq Goni, BSc; Md. Nahiduzzaman, BSc; Dr Muhammad E. H. Chowdhury, Ph.D; Abdulrahman Alqahtani, Ph.D.; S. M. Riazul Islam, Ph.D. Explainable Malaria Detection from Red Blood Cell Images using CNN-ELM Model Expert Systems with Applications (Elsevier, Q1, IF: 8.5) (Under Review) 2023 Journal
2 Md. Faysal Ahamed; Md. Munawar Hossain; Md. Nahiduzzaman; Md. Rabiul Islam; Md. Robiul Islam; Mominul Ahsan; Julfikar haider A Review on Brain Tumor Segmentation Based on Deep Learning Methods with Federated Learning Techniques Computerized Medical Imaging and Graphics (Q1, IF: 5.7) (Under Review) 2023 Journal
3 Md Nahiduzzaman; Md. Faysal Ahamed; Norah Saleh Alghamdi; S. M. Riazul Islam SHAP-Guided Gastrointestinal Disease Classification with Lightweight Parallel Depthwise Separable CNN and Ridge Regression ELM Expert Systems with Applications (Elsevier, Q1, IF: 8.5) (Under Review) 2023 Journal
4 Md. Omaer Faruq Goni, Md.Nahiduzzaman, Md. Shamim Anower, Md. Mahabubur Rahman, Md. Robiul Islam, Mominul Ahsan, Julfikar Haider, and Mohammad Shahjalal Fast and Accurate Fault Detection and Classification in Transmission Lines using Extreme Learning Machine ELSEVIER e-Prime 2023 Journal
5 Md. Nahiduzzaman et al. A Novel framework for Lung Cancer Classification using Lightweight Convolutional Neural Networks and Hybrid Ridge-ELM Model with SHAP Interpretability Expert Systems with Applications (Elsevier, Q1, IF: 8.5) (Under Review) 2023 Journal
6 Md. Faysal Ahmed, Md. Nahiduzzaman et al. Malaria Parasite Classification from RBC Smears Using Lightweight Parallel Depthwise Separable CNN and Ridge Regression ELM by Integrating SHAP Techniques Cognitive Computation (Under Review) 2023 Journal
7 Md Nahiduzzaman, Md. Robiul Islam, Md. Omaer Faruq Goni, Md. Shamim Anower, Mominul Ahsan, Julfikar Haider, and Marcin Kowalski Diabetic Retinopathy Identification Using Parallel Convolutional Neural Network Based Feature Extractor and ELM Classifier Expert Systems with Applications (Elsevier, Q1, IF: 8.5) 2023 Journal
8 Md. Nahiduzzaman; Hafsa Binte Kibria; Muhammad E.H. Chowdhury; Amith Khandakar; Abdulrahman Alqahtani; Julfikar Haider; Sakib Mahmud; Anwarul Hasan A Novel Deep Learning Framework for Detection and Classification of Lung Adenocarcinoma and Squamous Cell Carcinoma from Computed Tomography Images Cognitive Computation (Submitted) 2023 Journal
9 Abdullah Al Rafi, Rakibul Hassan, Md Rabiul Islam, Md Nahiduzzaman Real-Time Lightweight Bangla Sign Language Recognition Model Using Pre-trained MobileNetV2 and Conditional DCGAN Springer Nature Singapore 2023 Book Chapter
10 Md. Nahiduzzaman; Lway Faisal Abdulrazak; Hafsa Binte Kibria; Amith Khandakar; Mominul Ahsan;Julfikar Haider; Mohammad Ali Moni A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images Biocybernetics and Biomedical Engineering (Under Review) 2023 Journal
11 Muntakim Mahmud Khan, Muhammad E.H. Chowdhury *, A S M Shamsul Arefin, Kanchon Kanti Podder, Md. Sakib Abrar Hossain, Abdulrahman Alqahtani, M. Murugappan *, Amith Khandakar, Adam Mushtak, Md Nahiduzzaman A Deep Learning Based Automatic Segmentation and 3D Visualization Technique for Intracranial Hemorrhage Detection using Computed Tomography Images Diagnostic (MDPI) 2023 Journal
12 Md. Nahiduzzaman, Muhammad E. H. Chowdhury, Abdus Salam, Emama Nahid, Faruque Ahmed, Nasser Al-Emadi, Mohamed Arselene Ayari, Amith Khandakar and Julfikar Haider Explainable Deep Learning Model for Automatic Mulberry Leaf Disease Detection Frontiers in Plant Science (Q1, IF: 5.6) 2023 Journal
13 Abida Sultana, Md Nahiduzzaman, Sagor Chandro Bakchy, Saleh Mohammed Shahriar, Hasibul Islam Peyal, Muhammad E. H. Chowdhury *, Amith Khandakar, Mohamed Arslane Ayari, Mominul Ahsan *, Julfikar Haider A Real Time Method for Distinguishing COVID-19 Utilizing 2D-CNN and Transfer Learning Sensors (Q1, IF: 3.576), MDPI 2023 Journal
14 Md. Nahiduzzaman, Md. Omaer Faruq Goni, Rakibul Hassan, Md. Robiul Islam, Md Khalid Syfullah, Saleh Mohammed Shahriar, Md. Shamim Anower, Mominul Ahsan, Julfikar Haider Parallel CNN-ELM: A Multiclass Classification of Chest X-Ray Images to Identify Seventeen Lung Diseases Including COVID-19 Expert Systems with Applications (Elsevier (IF 8.5)) 2023 Journal
15 Hasibul Islam Peyal, Md Nahiduzzaman, Md Abu Hanif Pramanik, Md Khalid Syfullah, Saleh Mohammed Shahriar, Abida Sultana, Mominul Ahsan, Julfikar Haider, Amith Khandakar, Muhammad EH Chowdhury Plant Disease Classifier: Detection of Dual-Crop Diseases using Lightweight 2D CNN Architecture IEEE Access (Q1, IF: 3.367) 2023 Journal
16 Md. Nahiduzzaman et al. Detection of Various Lung Diseases including COVID-19 Using Extreme Learning Machine Algorithm Based on the Features Extracted from a Lightweight CNN Architecture Elsevier (Biocybernetics and Biomedical Engineering (Q1, IF: 6.4)) 2023 Journal
17 Md. Faysal Ahmed, Md. Khalid Syfullah, Ovi Sarkar, Md. Tohidul Islam, Md Nahiduzzaman, Md. Rabiul Islam, Amith Khandakar, Mohamed Arselene Ayari, Muhammad E. H. Chowdhury IRv2-Net: A Deep Learning Framework for Enhanced Polyp Segmentation Performance Integrating InceptionResNetV2 and UNet Architecture with Test Time Augmentation Techniques Sensors (Q1, IF: 3.9), MDPI 2023 Journal
18 Amith Khandakar; Md. Nahiduzzaman; Rusab Sarmun; Md Ahsan Atick Faisal; Mohammad Kaoser Alam; Tawsifur Rahman; Nasser Al-Emadi; Mohamed Arselene Ayari; Muhammad E. H. Chowdhury Deep Learning-based Real-time Detection and Classification of Tomato Ripeness Stages using YOLOv8 on Raspberry Pi International Journal of Machine Learning and Cybernetics (Q1, Springer) (Submitted) 2023 Journal
19 Md. Robiul Islam, Md. Nahiduzzaman, Md. Omaer Faruq Goni, Abu Sayeed, Md. Shamim Anower, Mominul Ahsan, Julfikar Haider Explainable Transformer-based Deep Learning Model for the Detection of Malaria Parasite from the Blood Cell-Images Sensors (Q1, IF: 3.9), MDPI 2022 Journal
20 Md. Omaer Faruq Goni, Md. Nazrul Islam Mondal, S. M. Riazul Islam, Md Nahiduzzaman, Md. Robiul Islam, Md. Shamim Anower, Kyung-Sup Kwak Diagnosis of Malaria using Double Hidden Layer Extreme Learning Machine Algorithm with CNN Feature Extraction and Parasite Inflator IEEE Access (Q1, IF: 3.367) 2022 Journal
21 Md. Nahiduzzaman, Md. Rabiul Islam and Rakibul Hassan ChestX-Ray6: Prediction Of Multiple Diseases Including COVID-19 From Chest X-ray Images Using Convolutional Neural Network Expert Systems with Applications (Elsevier, Q1, IF: 8.5) 2022 Journal
22 Hafsa Binte Kibria *, Md Nahiduzzaman, Md. Omaer Faruq Goni, Mominul Ahsan, Julfikar Haider * An Ensemble Approach for Prediction of Diabetes Mellitus using Soft Voting Classifier with Explainable AI Sensors (Q1, IF: 3.9), MDPI 2022 Journal
23 Saleh Mohammed Shahriar *, Erphan A. Bhuiyan, Md Nahiduzzaman, Mominul Ahsan, Julfikar Haider * State of Charge Estimation for EV Battery Management Systems using Hybrid Recurrent Learning Approach with Explainable AI Energies (MDPI (Q1, IF: 3.252)) 2022 Journal
24 Md. Robiul Islam, Md. Nahiduzzaman Complex Features Extraction with Deep Learning Model for the Detection of COVID19 from CT Scan Images Using Ensemble Based Machine Learning Approach Expert Systems with Applications (Q1, IF: 8.5) 2022 Journal
25 Md Robiul Islam, Lway Faisal Abdulrazak, Md Nahiduzzaman, Md Omaer Faruq Goni, Md Shamim Anower, Mominul Ahsan, Julfikar Haider, Marcin Kowalski Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images Computers in Biology and Medicine (Q1, IF: 7.7) 2022 Journal
26 Md Omaer Faruq Goni, Md Nahiduzzaman, Md Shamim Anower, S M Muyeen, Innocent Kamwa Integration of Machine Learning with Economic Energy Scheduling International Journal of Electrical Power and Energy Systems (Q1, IF: 5.5) 2022 Journal
27 Md Nahiduzzaman, Md. Omaer Faruq Goni, Md. Shamim Anower, Md. Robiul Islam, Mominul Ahsan, Julfikar Haider, Saravanakumar Gurusamy, Rakibul Hassan, Md. Rakibul Islam A Novel Method for Multivariant Pneumonia Classification based on Hybrid CNN-PCA Based Feature Extraction using Extreme Learning Machine with Chest X-Ray Images IEEE Access (Q1, IF: 3.367) 2021 Journal
28 Md Nahiduzzaman, Md. Robiul Islam, S. M. Riazul Islam, Md. Omaer Faruq Goni, Md. Shamim Anower, Kyung-Sup Kwak Hybrid CNN-SVD Based Prominent Feature Extraction and Selection for Grading Diabetic Retinopathy using Extreme Learning Machine Algorithm IEEE Access (Q1, IF: 3.367) 2021 Journal
29 Md Rakibul Islam, Abdul Matin, Md Nahiduzzaman, Md Saifullah Siddiquee, Fahim Md Sifnatul Hasnain, SM Shovan, Tonmoy Hasan A Novel Deep Convolutional Neural Network Model for Detection of Parkinson Disease by Analysing the Spiral Drawing Springer, Singapore 2021 Book Chapter
30 Saleh Mohammed Shahriar, Hasibul Islam Peyal, Md Nahiduzzaman, Md Abu Hanif Pramanik An IoT-Based Real-Time Intelligent Irrigation System using Machine Learning ICTS 2021 Conference
31 Md. Robiul Islam, Md. Nahid Hasan, Md. Nahiduzzaman Severity Grading of Diabetic Retinopathy using Deep Convolutional Neural Network IJISRT 2020 Journal
32 Md. Nahiduzzaman, Md. Julker Nayeem, Md. Toukir Ahmed, Md. Shahid Uz Zaman Prediction of Heart Disease Using Multi-Layer Perceptron Neural Network and Support Vector Machine IEEE 2020 Conference
Phd Students
IdNameThesis workCurrent Position
Masters Students
IdNameThesis workCurrent Position
Undergraduate Students
IdNameThesis workCurrent Position
1610004Saleh Mohammed ShahriarState of Charge Estimation for EV Battery Management Systems using Hybrid Recurrent Learning Approach with Explainable AIPost Graduate Student at University of Victoria, Canada
1610037Hasib PeyalPlant Disease Classifier: A SmartAndroid Application for Detection of Multi-crop Diseases Using Lightweight CNN ArchitectureLecturer at Varendra University
1610024Abida SultanaA Real Time Method for Distinguishing COVID-19 Utilizing 2D-CNN and Transfer LearningLive in Canada
Activity Description