Zaid Bin Mahbub

@northsouth.edu

Associate Professor, Department of Mathematics & Physics, School of Engineering and Physical Sciences
North South University

Zaid Bin Mahbub


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https://researchid.co/zaidbin

EDUCATION

Doctor of Philosophy
School of Physics & Astronomy, University of Nottingham, UK, 2014

Master of Science in Physics (1st Class)
Department of Physics, University of Dhaka, Bangladesh, 2006

Bachelor of Science in Physics (1st Class)
Department of Physics, University of Dhaka, Bangladesh, 2004

Higher Secondary Certificate Science Group (1st Division)
Government Science College, Dhaka, 1996

Secondary School Certificate Science Group(1st Division)
Government Science College, Dhaka, 1994

RESEARCH INTERESTS

Biomedical Physics and Engineering; MRI, Quantitative MRI, pulse sequence; nerve conduction studies
30

Scopus Publications

5128

Scholar Citations

20

Scholar h-index

28

Scholar i10-index

Scopus Publications

  • FootSegONN: an ensemble of Self-ONN-based models for diabetic foot ulcer segmentation
    Md. Shaheenur Islam Sumon, Saadia Binte Alam, Rashedur Rahman, Rusab Sermun, Md. Mezbah Ahmed Mahedi, et al.
    Neural Computing and Applications, 2026
  • Effect of stretch on conduction in myelinated nerve due to wrist movement: An experimental and analytical study
    Sabrina Sharmin, Mohammad Abu Sayem Karal, Zaid Bin Mahbub, Khondkar Siddique-e Rabbani
    Plos One, 2025
    Based on related measurements by others, an earlier publication suggested increased nerve conduction velocity (NCV) with stretch in myelinated fibers, an anomaly based on existing knowledge, and hypothesized that widening of narrow zigzag gaps between structures of interdigitated Schwann cell processes at the node affected saltatory conduction to produce this increased NCV. A new nodal resistance Rne between the axonal membrane and extracellular fluid was introduced into the century old cable theory. Later, a direct and careful measurement of ulnar NCV across a 10 cm segment around the elbow by another publication appeared to support the suggestion of increased NCV with stretch. However, in order to eliminate the possibility of slacks of ulnar nerve in the upper arm affecting the measurements, the present work was taken up on a shorter 5 cm segment which again supported the suggestion, increasing confidence in the Rne hypothesis. Furthermore, wrist flexion or extension was also observed to affect the ulnar NCV at the elbow to some extent, revealing a new phenomenon. While attempting to formulate an analytical treatment of Rne, the earlier work found it very challenging as the physical structure was extremely complex. Proposing an alternative physical model to simulate the variation in Rne suggested earlier, the current study presents an analytical treatment that relates Rne and a corresponding effective resistivity value to increases in stretch, and relates these quantitatively to stretch values based on the measured values of NCV. This then provided the basis of a quantitative analysis which could be useful for future research. While appreciating that other microstructures in the node at or near the axonal membrane may also contribute to the observed anomaly, but lack of direct experimental evidence related to nerve stretch tends to weigh more on the Rne hypothesis in explaining the anomaly.
  • Restoration of magnetohydrodynamic-corrupted 12-lead electrocardiogram to enhance cardiac monitoring during magnetic resonance imaging
    Sakib Mahmud, Muhammad E.H. Chowdhury, Moajjem Hossain Chowdhury, Abdulrahman Alqahtani, Zaid Bin Mahbub, et al.
    Engineering Applications of Artificial Intelligence, 2024
  • NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern
    Md. Ahasan Atick Faisal, Muhammad E. H. Chowdhury, Zaid Bin Mahbub, Shona Pedersen, Mosabber Uddin Ahmed, et al.
    Applied Intelligence, 2023
  • Fetal ECG extraction from maternal ECG using deeply supervised LinkNet++ model
    Arafat Rahman, Sakib Mahmud, Muhammad E.H. Chowdhury, Huseyin Cagatay Yalcin, Amith Khandakar, et al.
    Engineering Applications of Artificial Intelligence, 2023
  • Signer-Independent Arabic Sign Language Recognition System Using Deep Learning Model
    Kanchon Kanti Podder, Maymouna Ezeddin, Muhammad E. H. Chowdhury, Md. Shaheenur Islam Sumon, Anas M. Tahir, et al.
    Sensors, 2023
    Every one of us has a unique manner of communicating to explore the world, and such communication helps to interpret life. Sign language is the popular language of communication for hearing and speech-disabled people. When a sign language user interacts with a non-sign language user, it becomes difficult for a signer to express themselves to another person. A sign language recognition system can help a signer to interpret the sign of a non-sign language user. This study presents a sign language recognition system that is capable of recognizing Arabic Sign Language from recorded RGB videos. To achieve this, two datasets were considered, such as (1) the raw dataset and (2) the face–hand region-based segmented dataset produced from the raw dataset. Moreover, operational layer-based multi-layer perceptron “SelfMLP” is proposed in this study to build CNN-LSTM-SelfMLP models for Arabic Sign Language recognition. MobileNetV2 and ResNet18-based CNN backbones and three SelfMLPs were used to construct six different models of CNN-LSTM-SelfMLP architecture for performance comparison of Arabic Sign Language recognition. This study examined the signer-independent mode to deal with real-time application circumstances. As a result, MobileNetV2-LSTM-SelfMLP on the segmented dataset achieved the best accuracy of 87.69% with 88.57% precision, 87.69% recall, 87.72% F1 score, and 99.75% specificity. Overall, face–hand region-based segmentation and SelfMLP-infused MobileNetV2-LSTM-SelfMLP surpassed the previous findings on Arabic Sign Language recognition by 10.970% accuracy.
  • BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data
    Tawsifur Rahman, Muhammad E. H. Chowdhury, Amith Khandakar, Zaid Bin Mahbub, Md Sakib Abrar Hossain, et al.
    Neural Computing and Applications, 2023
    Nowadays, quick, and accurate diagnosis of COVID-19 is a pressing need. This study presents a multimodal system to meet this need. The presented system employs a machine learning module that learns the required knowledge from the datasets collected from 930 COVID-19 patients hospitalized in Italy during the first wave of COVID-19 (March–June 2020). The dataset consists of twenty-five biomarkers from electronic health record and Chest X-ray (CXR) images. It is found that the system can diagnose low- or high-risk patients with an accuracy, sensitivity, and F1-score of 89.03%, 90.44%, and 89.03%, respectively. The system exhibits 6% higher accuracy than the systems that employ either CXR images or biomarker data. In addition, the system can calculate the mortality risk of high-risk patients using multivariate logistic regression-based nomogram scoring technique. Interested physicians can use the presented system to predict the early mortality risks of COVID-19 patients using the web-link: Covid-severity-grading-AI. In this case, a physician needs to input the following information: CXR image file, Lactate Dehydrogenase (LDH), Oxygen Saturation (O2%), White Blood Cells Count, C-reactive protein, and Age. This way, this study contributes to the management of COVID-19 patients by predicting early mortality risk.
  • MultiResUNet3+: A Full-Scale Connected Multi-Residual UNet Model to Denoise Electrooculogram and Electromyogram Artifacts from Corrupted Electroencephalogram Signals
    Md Shafayet Hossain, Sakib Mahmud, Amith Khandakar, Nasser Al-Emadi, Farhana Ahmed Chowdhury, et al.
    Bioengineering, 2023
    Electroencephalogram (EEG) signals immensely suffer from several physiological artifacts, including electrooculogram (EOG), electromyogram (EMG), and electrocardiogram (ECG) artifacts, which must be removed to ensure EEG’s usability. This paper proposes a novel one-dimensional convolutional neural network (1D-CNN), i.e., MultiResUNet3+, to denoise physiological artifacts from corrupted EEG. A publicly available dataset containing clean EEG, EOG, and EMG segments is used to generate semi-synthetic noisy EEG to train, validate and test the proposed MultiResUNet3+, along with four other 1D-CNN models (FPN, UNet, MCGUNet, LinkNet). Adopting a five-fold cross-validation technique, all five models’ performance is measured by estimating temporal and spectral percentage reduction in artifacts, temporal and spectral relative root mean squared error, and average power ratio of each of the five EEG bands to whole spectra. The proposed MultiResUNet3+ achieved the highest temporal and spectral percentage reduction of 94.82% and 92.84%, respectively, in EOG artifacts removal from EOG-contaminated EEG. Moreover, compared to the other four 1D-segmentation models, the proposed MultiResUNet3+ eliminated 83.21% of the spectral artifacts from the EMG-corrupted EEG, which is also the highest. In most situations, our proposed model performed better than the other four 1D-CNN models, evident by the computed performance evaluation metrics.
  • Machine learning-based classification of healthy and impaired gaits using 3D-GRF signals
    Md Nazmul Islam Shuzan, Muhammad E.H. Chowdhury, Mamun Bin Ibne Reaz, Amith Khandakar, Farhan Fuad Abir, et al.
    Biomedical Signal Processing and Control, 2023
  • Increase in conduction velocity in myelinated nerves due to stretch – An experimental verification
    Sabrina Sharmin, Mohammad Abu Sayem Karal, Zaid Bin Mahbub, Khondkar Siddique-e Rabbani
    Frontiers in Neuroscience, 2023
    BackgroundBased on published experimental evidence, a recent publication revealed an anomalous phenomenon in nerve conduction: for myelinated nerves the nerve conduction velocity (NCV) increases with stretch, which should have been the opposite according to existing concepts and theories since the diameter decreases on stretching. To resolve the anomaly, a new conduction mechanism for myelinated nerves was proposed based on physiological changes in the nodal region, introducing a new electrical resistance at the node. The earlier experimental measurements of NCV were performed on the ulnar nerve at different angles of flexion, focusing at the elbow region, but left some uncertainty for not reporting the lengths of nerve segments involved so that the magnitudes of stretch could not be estimated.AimsThe aim of the present study was to relate NCV of myelinated nerves with different magnitudes of stretch through careful measurements.MethodEssentially, we duplicated the earlier published NCV measurements on ulnar nerves at different angles of flexion but recording appropriate distances between nerve stimulation points on the skin carefully and assuming that the lengths of the underlying nerve segment undergoes the same percentages of changes as that on the skin outside.ResultsWe found that the percentage of nerve stretch across the elbow is directly proportional to the angle of flexion and that the percentage increase in NCV is directly proportional to the percentage increase in nerve stretch. Page’s L Trend test also supported the above trends of changes through obtained p values.DiscussionOur experimental findings on myelinated nerves agree with those of some recent publications which measured changes in CV of single fibres, both myelinated and unmyelinated, on stretch. Analyzing all the observed results, we may infer that the new conduction mechanism based on the nodal resistance and proposed by the recent publication mentioned above is the most plausible one to explain the increase in CV with nerve stretch. Furthermore, interpreting the experimental results in the light of the new mechanism, we may suggest that the ulnar nerve at the forearm is always under a mild stretch, with slightly increased NCV of the myelinated nerves.
  • Design and Implementation of a Smart Insole System to Measure Plantar Pressure and Temperature
    Amith Khandakar, Sakib Mahmud, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Serkan Kiranyaz, et al.
    Sensors, 2022
  • Thermal Change Index-Based Diabetic Foot Thermogram Image Classification Using Machine Learning Techniques
    Amith Khandakar, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, Sawal Hamid Md Ali, Tariq O. Abbas, et al.
    Sensors, 2022
  • Bangla Sign Language (BdSL) Alphabets and Numerals Classification Using a Deep Learning Model
    Kanchon Kanti Podder, Muhammad E. H. Chowdhury, Anas M. Tahir, Zaid Bin Mahbub, Amith Khandakar, et al.
    Sensors, 2022
  • Recent developments in the kinetics of ruptures of giant vesicles under constant tension
    Mohammad Abu Sayem Karal, Md. Kabir Ahamed, Marzuk Ahmed, Zaid Bin Mahbub
    Rsc Advances, 2021
  • A new purification technique to obtain specific size distribution of giant lipid vesicles using dual filtration
    Mohammad Abu Sayem Karal, Tawfika Nasrin, Marzuk Ahmed, Md. Kabir Ahamed, Shareef Ahammed, et al.
    Plos One, 2021
  • A Novel Non-Invasive Estimation of Respiration Rate from Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model
    Md. Nazmul Islam Shuzan, Moajjem Hossain Chowdhury, Md. Shafayet Hossain, Muhammad E. H. Chowdhury, Mamun Bin Ibne Reaz, et al.
    IEEE Access, 2021
  • Analysis of continuous motor nerve conduction velocity distribution from compound muscle action potential
    Zakaria Shams Siam, Rubyat Tasnuva Hasan, Mohammad Abu Sayem Karal, M A Masud, Zaid Bin Mahbub
    Proceedings of 2020 11th International Conference on Electrical and Computer Engineering Icece 2020, 2020
  • Molecular dynamics study in diffusion weighted MRI - A computational model approach
    Raiyan Chowdhury, Ehtesamul Azim, Mohammad Abu Sayem Karal, Asiful Islam, Zaid Bin Mahbub
    Proceedings of 2020 11th International Conference on Electrical and Computer Engineering Icece 2020, 2020
  • Deformation and poration of giant unilamellar vesicles induced by anionic nanoparticles
    Mohammad Abu Sayem Karal, Shareef Ahammed, Victor Levadny, Marina Belaya, Md. Kabir Ahamed, et al.
    Chemistry and Physics of Lipids, 2020
  • Location of Peptide-Induced Submicron Discontinuities in the Membranes of Vesicles Using ImageJ
    Mohammad Abu Sayem Karal, Md. Kabir Ahamed, Marzuk Ahmed, Shareef Ahamed, Zaid Bin Mahbub
    Journal of Fluorescence, 2020
  • Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques
    Moajjem Hossain Chowdhury, Md Nazmul Islam Shuzan, Muhammad E.H. Chowdhury, Zaid B. Mahbub, M. Monir Uddin, et al.
    Sensors Switzerland, 2020
  • Transfer learning with deep Convolutional Neural Network (CNN) for pneumonia detection using chest X-ray
    Tawsifur Rahman, Muhammad E. H. Chowdhury, Amith Khandakar, Khandaker R. Islam, Khandaker F. Islam, et al.
    Applied Sciences Switzerland, 2020
  • Nanosensors in biomedical and environmental applications: Perspectives and prospects
    Mamun Rabbani, Md Enamul Hoque, Zaid Bin Mahbub
    Nanofabrication for Smart Nanosensor Applications, 2020
  • Study of molecular transport through a single nanopore in the membrane of a giant unilamellar vesicle using COMSOL simulation
    Mohammad Abu Sayem Karal, Md. Kamrul Islam, Zaid Bin Mahbub
    European Biophysics Journal, 2020
  • Can AI Help in Screening Viral and COVID-19 Pneumonia?
    Muhammad E. H. Chowdhury, Tawsifur Rahman, Amith Khandakar, Rashid Mazhar, Muhammad Abdul Kadir, et al.
    IEEE Access, 2020

RECENT SCHOLAR PUBLICATIONS

  • FootSegONN: an ensemble of Self-ONN-based models for diabetic foot ulcer segmentation
    RHMEHC Md. Shaheenur Islam Sumon, Saadia Binte Alam, Rashedur Rahman, Rusab ...
    Neural Computing and Applications 38 (121) , 2026
    2026
  • Effect of stretch on conduction in myelinated nerve due to wrist movement: An experimental and analytical study
    S Sharmin, MAS Karal, ZB Mahbub, KS Rabbani
    Plos one 20 (10), e0333925 , 2025
    2025
  • Restoration of magnetohydrodynamic-corrupted 12-lead electrocardiogram to enhance cardiac monitoring during magnetic resonance imaging
    S Mahmud, MEH Chowdhury, MH Chowdhury, A Alqahtani, ZB Mahbub, ...
    Engineering Applications of Artificial Intelligence 133, 108483 , 2024
    2024
    Citations: 2
  • NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern: NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern
    MAA Faisal, MEH Chowdhury, ZB Mahbub, S Pedersen, MU Ahmed, ...
    Applied Intelligence 53 (17), 20034-20046 , 2023
    2023
    Citations: 41
  • Signer-independent arabic sign language recognition system using deep learning model
    KK Podder, M Ezeddin, MEH Chowdhury, MSI Sumon, AM Tahir, ...
    Sensors 23 (16), 7156 , 2023
    2023
    Citations: 68
  • Fetal ECG extraction from maternal ECG using deeply supervised LinkNet++ model
    A Rahman, S Mahmud, MEH Chowdhury, HC Yalcin, A Khandakar, ...
    Engineering Applications of Artificial Intelligence 123, 106414 , 2023
    2023
    Citations: 34
  • BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data
    T Rahman, MEH Chowdhury, A Khandakar, ZB Mahbub, MSA Hossain, ...
    Neural Computing and Applications 35 (24), 17461-17483 , 2023
    2023
    Citations: 38
  • MultiResUNet3+: a full-scale connected multi-residual UNet model to denoise electrooculogram and electromyogram artifacts from corrupted electroencephalogram signals
    MS Hossain, S Mahmud, A Khandakar, N Al-Emadi, FA Chowdhury, ...
    Bioengineering 10 (5), 579 , 2023
    2023
    Citations: 32
  • Increase in conduction velocity in myelinated nerves due to stretch–An experimental verification
    S Sharmin, MAS Karal, ZB Mahbub, KS Rabbani
    Frontiers in Neuroscience 17, 1084004 , 2023
    2023
    Citations: 5
  • Machine learning-based classification of healthy and impaired gaits using 3D-GRF signals
    MNI Shuzan, MEH Chowdhury, MBI Reaz, A Khandakar, FF Abir, ...
    Biomedical Signal Processing and Control 81, 104448 , 2023
    2023
    Citations: 36
  • Fiber Bragg Gratings based smart insole to measure plantar pressure and temperature
    S Mahmud, A Khandakar, MEH Chowdhury, M AbdulMoniem, MBI Reaz, ...
    Sensors and Actuators A: Physical 350, 114092 , 2023
    2023
    Citations: 27
  • Design and implementation of a smart insole system to measure plantar pressure and temperature
    A Khandakar, S Mahmud, MEH Chowdhury, MBI Reaz, S Kiranyaz, ...
    Sensors 22 (19), 7599 , 2022
    2022
    Citations: 73
  • Design, Implementation, and Performance Evaluation of a Fiber Bragg Gratings (FBG) based Smart Insole to Measure Plantar Pressure and Temperature
    S Mahmud, A Khandakar, MEH Chowdhury, M AbdulMoniem, MBI Reaz, ...
    arXiv preprint arXiv:2208.12201 , 2022
    2022
    Citations: 4
  • Design and implementation of a complete wearable smart insole solution to measure plantar pressure and temperature
    A Khandakar, S Mahmud, MEH Chowdhury, MBI Reaz, S Kiranyaz, ...
    arXiv preprint arXiv:2206.07779 , 2022
    2022
    Citations: 12
  • Thermal change index-based diabetic foot thermogram image classification using machine learning techniques
    A Khandakar, MEH Chowdhury, MBI Reaz, SHM Ali, TO Abbas, T Alam, ...
    Sensors 22 (5), 1793 , 2022
    2022
    Citations: 62
  • Bangla sign language (bdsl) alphabets and numerals classification using a deep learning model
    KK Podder, MEH Chowdhury, AM Tahir, ZB Mahbub, A Khandakar, ...
    Sensors 22 (2), 574 , 2022
    2022
    Citations: 91
  • Recent developments in the kinetics of ruptures of giant vesicles under constant tension
    ZBM Mohammad Abu Sayem Karal, Md. Kabir Ahamed, Marzuk Ahmed
    RSC Advances , 2021
    2021
    Citations: 16
  • A new purification technique to obtain specific size distribution of giant lipid vesicles using dual filtration
    ZBM Mohammad Abu Sayem Karal, Tawfika Nasrin, Marzuk Ahmed, Md. Kabir Ahamed ...
    PLOS ONE , 2021
    2021
    Citations: 10
  • A novel non-invasive estimation of respiration rate from motion corrupted photoplethysmograph signal using machine learning model
    MNI Shuzan, MH Chowdhury, MS Hossain, MEH Chowdhury, MBI Reaz, ...
    IEEE Access 9, 96775-96790 , 2021
    2021
    Citations: 67
  • A novel non-invasive estimation of respiration rate from photoplethysmograph signal using machine learning model
    MNI Shuzan, MH Chowdhury, MEH Chowdhury, MM Uddin, A Khandakar, ...
    arXiv preprint arXiv:2102.09483 , 2021
    2021
    Citations: 6

MOST CITED SCHOLAR PUBLICATIONS

  • Can AI help in screening viral and COVID-19 pneumonia?
    MEH Chowdhury, T Rahman, A Khandakar, R Mazhar, MA Kadir, ...
    Ieee Access 8, 132665-132676 , 2020
    2020
    Citations: 2369
  • Transfer learning with deep convolutional neural network (CNN) for pneumonia detection using chest X-ray
    T Rahman, MEH Chowdhury, A Khandakar, KR Islam, KF Islam, ...
    Applied Sciences 10 (9), 3233 , 2020
    2020
    Citations: 794
  • Reliable tuberculosis detection using chest X-ray with deep learning, segmentation and visualization
    T Rahman, A Khandakar, MA Kadir, KR Islam, KF Islam, R Mazhar, ...
    Ieee Access 8, 191586-191601 , 2020
    2020
    Citations: 771
  • Estimating blood pressure from the photoplethysmogram signal and demographic features using machine learning techniques
    MH Chowdhury, MNI Shuzan, MEH Chowdhury, ZB Mahbub, MM Uddin, ...
    Sensors 20 (11), 3127 , 2020
    2020
    Citations: 308
  • Bangla sign language (bdsl) alphabets and numerals classification using a deep learning model
    KK Podder, MEH Chowdhury, AM Tahir, ZB Mahbub, A Khandakar, ...
    Sensors 22 (2), 574 , 2022
    2022
    Citations: 91
  • Design and implementation of a smart insole system to measure plantar pressure and temperature
    A Khandakar, S Mahmud, MEH Chowdhury, MBI Reaz, S Kiranyaz, ...
    Sensors 22 (19), 7599 , 2022
    2022
    Citations: 73
  • Signer-independent arabic sign language recognition system using deep learning model
    KK Podder, M Ezeddin, MEH Chowdhury, MSI Sumon, AM Tahir, ...
    Sensors 23 (16), 7156 , 2023
    2023
    Citations: 68
  • A novel non-invasive estimation of respiration rate from motion corrupted photoplethysmograph signal using machine learning model
    MNI Shuzan, MH Chowdhury, MS Hossain, MEH Chowdhury, MBI Reaz, ...
    IEEE Access 9, 96775-96790 , 2021
    2021
    Citations: 67
  • Thermal change index-based diabetic foot thermogram image classification using machine learning techniques
    A Khandakar, MEH Chowdhury, MBI Reaz, SHM Ali, TO Abbas, T Alam, ...
    Sensors 22 (5), 1793 , 2022
    2022
    Citations: 62
  • Nanosensors in biomedical and environmental applications: Perspectives and prospects
    M Rabbani, ME Hoque, ZB Mahbub
    Nanofabrication for smart nanosensor applications, 163-186 , 2020
    2020
    Citations: 47
  • NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern: NDDNet: a deep learning model for predicting neurodegenerative diseases from gait pattern
    MAA Faisal, MEH Chowdhury, ZB Mahbub, S Pedersen, MU Ahmed, ...
    Applied Intelligence 53 (17), 20034-20046 , 2023
    2023
    Citations: 41
  • BIO-CXRNET: a robust multimodal stacking machine learning technique for mortality risk prediction of COVID-19 patients using chest X-ray images and clinical data
    T Rahman, MEH Chowdhury, A Khandakar, ZB Mahbub, MSA Hossain, ...
    Neural Computing and Applications 35 (24), 17461-17483 , 2023
    2023
    Citations: 38
  • Machine learning-based classification of healthy and impaired gaits using 3D-GRF signals
    MNI Shuzan, MEH Chowdhury, MBI Reaz, A Khandakar, FF Abir, ...
    Biomedical Signal Processing and Control 81, 104448 , 2023
    2023
    Citations: 36
  • Study of molecular transport through a single nanopore in the membrane of a giant unilamellar vesicle using COMSOL simulation
    MAS Karal, MK Islam, ZB Mahbub
    European Biophysics Journal 49 (1), 59-69 , 2020
    2020
    Citations: 35
  • Fetal ECG extraction from maternal ECG using deeply supervised LinkNet++ model
    A Rahman, S Mahmud, MEH Chowdhury, HC Yalcin, A Khandakar, ...
    Engineering Applications of Artificial Intelligence 123, 106414 , 2023
    2023
    Citations: 34
  • MultiResUNet3+: a full-scale connected multi-residual UNet model to denoise electrooculogram and electromyogram artifacts from corrupted electroencephalogram signals
    MS Hossain, S Mahmud, A Khandakar, N Al-Emadi, FA Chowdhury, ...
    Bioengineering 10 (5), 579 , 2023
    2023
    Citations: 32
  • Fiber Bragg Gratings based smart insole to measure plantar pressure and temperature
    S Mahmud, A Khandakar, MEH Chowdhury, M AbdulMoniem, MBI Reaz, ...
    Sensors and Actuators A: Physical 350, 114092 , 2023
    2023
    Citations: 27
  • Bangla sign language alphabet recognition using transfer learning based convolutional neural network
    KK Podder, M Chowdhury, ZB Mahbub, M Kadir
    Bangladesh J. Sci. Res 31 (1), 20-26 , 2020
    2020
    Citations: 25
  • Can AI help in screening viral and COVID-19 pneumonia? arXiv 2020
    ME Chowdhury, T Rahman, A Khandakar, R Mazhar, MA Kadir, ...
    arXiv preprint arXiv:2003.13145 , 2020
    2020
    Citations: 24
  • Deformation and poration of giant unilamellar vesicles induced by anionic nanoparticles
    MAS Karal, S Ahammed, V Levadny, M Belaya, MK Ahamed, M Ahmed, ...
    Chemistry and Physics of Lipids 230, 104916 , 2020
    2020
    Citations: 23