Subrata

@bauet.ac.bd

Assistant Professor, Department of Computer Science and Engineering, Bangladesh Army University of Engineering & Technology
Department of Computer Science and Engineering, Bangladesh Army University of Engineering & Technology

Subrata


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https://researchid.co/subrata11

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Computer Science, Multidisciplinary, Computer Engineering
20

Scopus Publications

249

Scholar Citations

7

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • COVID-19 Distance Learning Understanding Classification Using Scalogram Based on Transfer Learning and Principal Feature Classifier from EEG Signals
    Md Momenul Haque, Subrata Kumer Paul, Rakhi Rani Paul, Md Kamrul Islam, Mursheda Nusrat Della, et al.
    Machine Learning for Healthcare Informatics Techniques and Applications, 2026
    The COVID-19 pandemic has triggered an abrupt transformation in the way education is delivered, leading to an unexpected transition to distance learning and creating unique challenges in comprehending student engagement and performance. By accurately classifying student brainwave recordings during class lectures, the challenge of understanding and enhancing student participation can be effectively addressed. We propose a solution based on transfer learning and a principle feature classifier using scalogram images from electroencephalogram (EEG) signals to address the understanding. We use an EEG signal distance learning dataset and preprocess it by removing powerline noise using a bandpass filter, removing artifacts using independent component analysis (ICA), and converting the 1D signal to 2D scalogram EEG images using continuous wavelet transform (CWT). We trained the scalogram images using ResNet-50 and MobileNet V1 transfer learning architectures, outperforming other approaches by leveraging pre-existing knowledge. To extract the primary features, we employ principal component analysis (PCA), a technique that reduces the dimensionality of the data while retaining the crucial features. To determine the level of student engagement during distance learning, as the primary feature classifiers, we employed three traditional machine learning (ML) approaches: K-nearest neighbors (KNN), logistic regression (LR), and support vector machine (SVM). Our experimental result proves that, among these algorithms, ResNet-LR achieved the highest accuracy of https://www.w3.org/1998/Math/MathML" display="inline"> 98 % . The results demonstrate the evaluation of our proposed solution in accurately classifying EEG signals during distance learning, which can aid in understanding student engagement and performance.
  • Blockchain-Based Zero-Knowledge Framework for Verifiable and Confidential File Sharing: Integrating NaCl Box Encryption with a Hyperledger Notary
    Subrata Kumer Paul, Md. Ahnaf Muhaimin, Md. Masud Rana, Shirin Sultana Rakhi, Rakhi Rani Paul, Md. Ekramul Hamid, Mirza A.F.M. Rashidul Hasan
    2026 5th International Conference on Electrical Computer and Telecommunication Engineering Icecte 2026, 2026
    The increasing cases of data breaches, data tampering and information loss have posed an immediate demand to secure file sharing systems that guarantee privacy and verifiable integrity. The paper presents an end-to-end (E2E), encrypted file-sharing system based on the combination of client-side encryption and privately hosted, blockchain-based, notary service. All the encryption is done locally with the aid of the Networking and Cryptographic Library (NaCl) box primitive, and only the approved recipients are allowed access to the shared files with valid credentials. A lightweight local backend stores the encrypted data (ciphertext) and operates with public keys and user metadata; however, it does not access plaintext files and cannot decrypt them, keeping the entire data confidential. In order to offer non-repudiation, a hash of every encrypted file is computed with the Secure Hash Algorithm (SHA-256) and the result is stored in a private Hyperledger Fabric network. This provides an audit trail that can never be changed or tampered with and will not reveal the true files or encryption keys. In our prototype, sending to the blockchain ledger creates latency on an order of seconds 2.3 seconds in a 2.35-second total file-send path and file integrity verification by hash requires an average of 183 milliseconds. The suggested architecture is a good way to ensure confidentiality, proof of authenticity and integrity, and is a convenient way to provide the organization with a requirement to exchange files privately.
  • IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for Healthcare
    Subrata Kumer Paul, Abu Saleh Musa Miah, Rakhi Rani Paul, Md. Ekramul Hamid, Jungpil Shin, Md Abdur Rahim
    Computers Materials and Continua, 2025
    The Internet of Things (IoT) and mobile technology have significantly transformed healthcare by enabling real-time monitoring and diagnosis of patients. Recognizing Medical-Related Human Activities (MRHA) is pivotal for healthcare systems, particularly for identifying actions critical to patient well-being. However, challenges such as high computational demands, low accuracy, and limited adaptability persist in Human Motion Recognition (HMR). While some studies have integrated HMR with IoT for real-time healthcare applications, limited research has focused on recognizing MRHA as essential for effective patient monitoring. This study proposes a novel HMR method tailored for MRHA detection, leveraging multi-stage deep learning techniques integrated with IoT. The approach employs EfficientNet to extract optimized spatial features from skeleton frame sequences using seven Mobile Inverted Bottleneck Convolutions (MBConv) blocks, followed by Convolutional Long Short Term Memory (ConvLSTM) to capture spatio-temporal patterns. A classification module with global average pooling, a fully connected layer, and a dropout layer generates the final predictions. The model is evaluated on the NTU RGB+D 120 and HMDB51 datasets, focusing on MRHA such as sneezing, falling, walking, sitting, etc. It achieves 94.85% accuracy for cross-subject evaluations and 96.45% for cross-view evaluations on NTU RGB+D 120, along with 89.22% accuracy on HMDB51. Additionally, the system integrates IoT capabilities using a Raspberry Pi and GSM module, delivering real-time alerts via Twilios SMS service to caregivers and patients. This scalable and efficient solution bridges the gap between HMR and IoT, advancing patient monitoring, improving healthcare outcomes, and reducing costs.
  • An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people
    Subrata Kumer Paul, Md. Abul Ala Walid, Rakhi Rani Paul, Md. Jamal Uddin, Md. Sohel Rana, Maloy Kumar Devnath, Ishaat Rahman Dipu, Md. Momenul Haque
    Bulletin of Electrical Engineering and Informatics, 2024
    Hand gestures and sign language are crucial modes of communication for deaf individuals. Since most people can't understand sign language, it's hard for a mute and an average person to talk to each other. Because of technological progress, computer vision and deep learning can now be used to count. This paper shows two ways to use deep knowledge to recognize sign language. These methods help regular people understand sign language and improve their communication. Based on American sign language (ASL), two separate datasets have been constructed; the first has 26 signs, and the other contains three significant symbols with the crucial sequence of frames or videos for regular communication. This study looks at three different models: the improved ResNet-based convolutional neural network (CNN), the long short-term memory (LSTM), and the gated recurrent unit (GRU). The first dataset is used to fit and assess the CNN model. With the adaptive moment estimation (Adam) optimizer, CNN obtains an accuracy of 89.07%. In contrast, the second dataset is given to LSTM and GRU and a comparison has been conducted. LSTM does better than GRU in all classes. LSTM has a 94.3% accuracy, while GRU only manages 79.3%. Our preliminary models' real-time performance is also highlighted.
  • A Comprehensive Analysis on Skin Cancer Classification Using Transfer Learning
    Israt Zerin Renu, Md. Momenul Haque, Subrata Kumer Paul, Mahmuda Shirin Mou, Md Nahid Rahman, Sudipta Sen Gupta, Rakhi Rani Paul
    2024 3rd International Conference on Advancement in Electrical and Electronic Engineering Icaeee 2024, 2024
    Skin cancer, a prevalent and potentially life-threatening disease, necessitates accurate and timely diagnosis for effective treatment. In addressing this critical healthcare challenge, this research proposes a novel approach to skin cancer classification. The study identifies the need for enhanced diagnostic accuracy and introduces a solution by integrating the efficiency of transfer learning, specifically utilizing the EfficientNet V2 architecture. The proposed methodology leverages pre-trained convolutional neural networks (CNNs) to extract high-level features from dermatological images, which are subsequently input into a sophisticated classifier, forming a robust framework for precise skin cancer classification. The research employs a diverse dataset comprising 3297 images sourced from the publicly available Skin Cancer ISIC archive dataset, ensuring comprehensive coverage of benign and malignant skin lesions. During the training phase, the proposed EfficientNet V2 transfer learning approach outperforms conventional methods, achieving an impressive accuracy of 84%, surpassing both Inception V3 (82%) and a generic CNN model (81%). Interpretability analysis is conducted to elucidate the decision-making process of the EfficientNet V2 model, providing insights into the key features influencing its predictions. This transparency is crucial for building trust in the model’s recommendations and facilitating its seamless integration into clinical workflows. The EfficientNet V2 transfer learning approach emerges as a promising and effective solution for advancing skin cancer classification systems, contributing significantly to the ongoing efforts to improve diagnostic accuracy in dermatology.
  • Deep Learning Techniques for Bangladeshi Coin Detection and Automated Counting System: A Comparative Study of Multiple Algorithms
    Ashraf Hussan Babor, Umme Habiba Choity, Most. Kaspia, Subrata Kumer Paul, Rakhi Rani Paul, Md. Momenul Haque, Md. Ekramul Hamid
    2024 3rd International Conference on Advancement in Electrical and Electronic Engineering Icaeee 2024, 2024
    Efficient currency detection and counting systems are crucial for the smooth operation of any financial ecosystem. This paper explores the development, implementation, and potential impact of a specialized currency detection and counting system for Bangladeshi coin currencies. Through this comprehensive study, this paper addresses the unique challenges and opportunities in Bangladesh’s coin currency management landscape, highlighting the motivations and results of such a system’s introduction. Computer vision technology includes currency detection. In this paper, we present a coin currency detection system that can detect coin currency from images. Designing a CNN-based coin recognition system for the recognition of Bangladeshi coins of denominations ‘1tk’, ‘2tk’, and ‘5tk’. We have created a dataset that contains more than 10,000 images. We photographed coin currencies from both sides, and the system can recognize coin currencies from both sides. For high efficiency, we test on various backgrounds. Experimental results are presented successfully with a training accuracy of 99.99% with VGG16, 99.38% with VGG19, and 99.99% with EfficientNetB0. We also got validation accuracy with VGG16 of 90.90%, 99.96% with VGG19, and 99.51% with EfficientNetB0.
  • Brain Tumor Detection on MRI Images Using a Combination of CNN and Ensemble Learning Approach
    Mahmuda Shirin Mou, Md. Momenul Haque, Subrata Kumer Paul, Rakhi Rani Paul, Israt Zerin Renu, Md Nahid Rahman, Sudipta Sen Gupta, Md. Ekramul Hamid
    International Conference on Recent Progresses in Science Engineering and Technology Icrpset 2024, 2024
    Brain tumors represent a critical neurological threat, disrupting brain function due to abnormal tissue growth arising from uncontrolled cell division. Addressing the need for accurate detection, this study proposes an efficient machine-learning approach for brain tumor diagnosis using Magnetic Resonance Imaging (MRI) data. Recent advancements in deep learning, especially Convolutional Neural Networks (CNNs), have shown substantial promise in medical imaging for disease diagnosis. Leveraging CNN s for feature extraction, our research introduces an innovative framework that combines CNN-based analysis with data augmentation and ensemble learning (Random Forest) to classify brain MRI scans as either cancerous or noncancerous with high precision. Our proposed model achieves significant accuracy, with CNN yielding 86% and the integrated CNN-Random Forest model achieving 96%, demonstrating an improvement over traditional pre-trained models with lower computational demands. This approach presents a practical tool for neurosurgeons, supporting rapid, accurate tumor detection and improving diagnostic outcomes in clinical settings. By integrating data augmentation with ensemble learning, our model not only enhances classification performance but also reduces the computational load, making it suitable for real-time applications. This framework thus represents a promising solution to the challenges of brain tumor diagnosis, providing both efficient and reliable detection that may enhance decision-making in medical practice.
  • Blockchain Based Secure and Decentralized Smart Licensing of Charging Vehicles for Rajshahi City Corporation
    Momenul Haque, Subrata Kumer Paul, Kamrul Islam, Mursheda Nusrat Della, Rakhi Rani Paul, Sultan Fahim
    2023 International Conference on Information and Communication Technology for Sustainable Development Icict4sd 2023 Proceedings, 2023
    The adoption of charging vehicles in Rajshahi has emerged as a significant step towards reducing air pollution and promoting a healthy, green city. However, the current centralized licensing system poses challenges such as time-consuming processes and the potential for fraudulent activities. To address these issues, this paper proposes the implementation of a secure and decentralized smart licensing system for charging vehicles in Rajshahi City Corporation, leveraging the smart capabilities of blockchain technology. By utilizing Hyperledger Fabric, the implementation generates blockchain based smart contracts that automate and streamline the licensing process smartly and efficiently. This approach ensures the immutability, transparency, and integrity of license records, minimizing the risk of fraud and enhancing overall efficiency. Additionally, the system incorporates the use of QR codes and an inquiry system to enhance the credibility of paper-based licenses. Compared to other digital licenses, this blockchain-based method significantly reduces risks and contributes to the development of a greener and more sustainable Rajshahi city environment. Furthermore, the introduction of a sustainable business policy, utilizing the G2C model, facilitates revenue generation and distribution fairly and equitably for all stakeholders involved in the charging vehicle ecosystem. This implementation not only offers cost benefits and efficiency improvements for drivers but also promotes a sustainable and environmentally friendly city.
  • Improving Performance of a Brain Tumor Detection on MRI Images Using DCGAN-Based Data Augmentation and Vision Transformer (ViT) Approach
    Md. Momenul Haque, Subrata Kumer Paul, Rakhi Rani Paul, Nurnama Islam, Mirza A. F. M. Rashidul Hasan, Md. Ekramul Hamid
    Gans for Data Augmentation in Healthcare, 2023
  • Leukemia Cancer Classification from Abnormal Cell Images Using a Deep Learning Approach: A Comprehensive Study
    Sadia Afrin, Subrata Kumer Paul, Rakhi Rani Paul, Md. Momenul Haque, Md Shahriar Kabir, Sultan Fahim, Md. Kamrul Islam
    2023 26th International Conference on Computer and Information Technology Iccit 2023, 2023
    Leukemia, a malignant blood cancer, is characterized by the uncontrolled proliferation of abnormal white blood cells in the bone marrow and blood. It is a life-threatening disease with various subtypes, making precise classification crucial for effective treatment. However, accurate classification of leukemia subtypes from abnormal cell images poses a challenging problem due to the complex and subtle variations in cell morphology. In response to this challenge, this comprehensive study employs an innovative machine learning approach, utilizing the EfficientNet B3 architecture. With this model, we achieved an impressive accuracy rate of 98.95% and a minimal loss of 0.120, demonstrating its efficacy in classifying leukemia subtypes. To conduct our experiments, we utilized the Kaggle Leukemia cancer dataset, ensuring a robust and reliable evaluation of our proposed methodology. This research contributes significantly to the development of efficient tools for early diagnosis and personalized treatment strategies for leukemia.
  • Human Fall Detection System using Long-Term Recurrent Convolutional Networks for Next-Generation Healthcare: A Study of Human Motion Recognition
    Subrata Kumer Paul, Anika Anomi Zisa, Md. Abul Ala Walid, Yeashtaruna Zeem, Rakhi Rani Paul, Md Momenul Haque, Md. Ekramul Hamid
    2023 14th International Conference on Computing Communication and Networking Technologies Icccnt 2023, 2023
  • A 2D Convolution Neural Network Based Method for Human Emotion Classification from Speech Signal
    Rakhi Rani Paul, Subrata Kumer Paul, Md. Ekramul Hamid
    Proceedings of 2022 25th International Conference on Computer and Information Technology Iccit 2022, 2022
  • A Comprehensive Study on Ethereum Blockchain-based Digital Marketplace using NFT Smart Contract Infrastructure
    Md. Momenul Haque, Subrata Kumer Paul, Rakhi Rani Paul, Md. Ekramul Hamid, Sultan Fahim, Shahinur Islam
    Proceedings of 2022 25th International Conference on Computer and Information Technology Iccit 2022, 2022
  • A Blockchain-Based Secure Payment System for Vehicle Fuel Filling Station
    Md. Momenul Haque, Subrata Kumer Paul, Rakhi Rani Paul, Mirza A. F. M. Rashidul Hasan, Sultan Fahim, Shahinur Islam
    Proceedings of 2022 25th International Conference on Computer and Information Technology Iccit 2022, 2022
  • Seasonal analysis of food items and feeding habits of endangered riverine catfish Rita Rita (Hamilton, 1822)
    M. A. Haque, S. Paul, M. A. S. Jewel, U. Atique, A. K. Paul, S. Iqbal, S. Mahboob, K. A. Al-Ghanim, F. Al-Misned, Z. Ahmed
    Brazilian Journal of Biology, 2022
  • Speech Command Recognition System using Deep Recurrent Neural Networks
    Subrata Kumer Paul, Rakhi Rani Paul
    2021 5th International Conference on Electrical Engineering and Information and Communication Technology Iceeict 2021, 2021
  • Throat Microphone Speech Enhancement Using Machine Learning Technique
    Subrata Kumer Paul, Rakhi Rani Paul, Masafumi Nishimura, Md. Ekramul Hamid
    Learning and Analytics in Intelligent Systems, 2021
  • Detecting Diabetes in Human Body using Different Machine Learning Techniques
    S.M. Tahsin Zaman, Subrata Kumer Paul, Rakhi Rani Paul, Md. Ekramul Hamid
    6th International Conference on Computer Communication Chemical Materials and Electronic Engineering Ic4me2 2021, 2021
  • Effective pitch estimation using canonical correlation analysis
    Subrata Kumer Paul, Rakhi Rani Paul
    2020 2nd International Conference on Advanced Information and Communication Technology Icaict 2020, 2020
  • Throat to Acoustic Speech Mapping for Spectral Parameter Correction using Artificial Neural Network Approach
    Subrata Kumer Paul, Rakhi Rani Paul, Masafumi Nishimura, Md. Ekramul Hamid
    International Exchange and Innovation Conference on Engineering and Sciences, 2020

RECENT SCHOLAR PUBLICATIONS

  • Real-Time Face Recognition System for Age and Gender Prediction: A Robust CNN-Ensemble Approach with IMDB-WIKI and UTKFace Datasets
    SK Paul, M Akter, MP Islam, K Ahamed, RR Paul, DNI Noor, ME Hamid, ...
    2025 IEEE International Conference on Signal Processing, Information … , 2026
    2026
  • COVID-19 Distance Learning Understanding Classification Using Scalogram Based on Transfer Learning and Principal Feature Classifier from EEG Signals
    MM Haque, SK Paul, RR Paul, MK Islam, MN Della, S Fahim
    Machine Learning for Healthcare Informatics 10, 127–145 , 2026
    2026
  • DesignMind ML-personalized UI/UX design with adaptive color and simplified features
    RR Paul, S Akter Shanu, M Islam, N Fairuz, SK Paul, M Rahman, ...
    Computer Science and Engineering Research (CSER) 3 (1), 19-27 , 2026
    2026
  • Deep Learning Models for Vision-Based Driver Drowsiness Detection: A Comparative Review
    MFH Ishtiyak, N Ahmed, MS Tasnuva, MM Haque, SK Paul
    2026 5th International Conference on Electrical, Computer … , 2026
    2026
  • Blockchain-Based Zero-Knowledge Framework for Verifiable and Confidential File Sharing: Integrating NaCl Box Encryption with a Hyperledger Notary
    SK Paul, MA Muhaimin, MM Rana, SS Rakhi, RR Paul, ME Hamid, ...
    2026 5th International Conference on Electrical, Computer … , 2026
    2026
  • Hybrid CNN–BiLSTM–GRU architecture for PAMAP2-based human activity recognition in patient monitoring
    SK Paul, RR Paul, DNI Noor, ME Hamid, MAFMR Hasan
    Computer Science and Engineering Research 3 (1), 4-10 , 2026
    2026
  • University Student Stress Dataset
    SK Paul, RR Paul, AS Musa Miah, ME Hamid, MAFM Rashidul Hasan
    Mendeley Data , 2025
    2025
  • Effect of Activation Function and Model Optimizer on the Performance of Human Activity Recognition System Using Various Deep Learning Models
    SK Paul, DNI Noor, RR Paul, ME Hamid, FA Farid, HA Karim, ...
    Computer Vision and Pattern Recognition, https://doi.org/10.48550/arXiv.2512 … , 2025
    2025
    Citations: 1
  • IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for Healthcare
    SK Paul, ASM Miah, RR Paul, ME Hamid, J Shin, MA Rahim
    Computers, Materials & Continua 84 (2), 2513-2530 , 2025
    2025
    Citations: 7
  • Brain Tumor Detection on MRI Images Using a Combination of CNN and Ensemble Learning Approach
    MS Mou, MM Haque, SK Paul, RR Paul, IZ Renu, MN Rahman, SS Gupta, ...
    2024 International Conference on Recent Progresses in Science, Engineering … , 2025
    2025
    Citations: 3
  • IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for Healthcare
    S Kumer Paul, A Saleh Musa Miah, R Rani Paul, M Ekramul Hamid, ...
    arXiv e-prints, arXiv: 2501.07039 , 2025
    2025
  • Deep Learning Techniques for Bangladeshi Coin Detection and Automated Counting System: A Comparative Study of Multiple Algorithms
    AH Babor, UH Choity, M Kaspia, SK Paul, RR Paul, MM Haque, ...
    2024 3rd International Conference on Advancement in Electrical and … , 2024
    2024
    Citations: 2
  • A Comprehensive Analysis on Skin Cancer Classification Using Transfer Learning
    IZ Renu, MM Haque, SK Paul, MS Mou, MN Rahman, SS Gupta, RR Paul
    2024 3rd International Conference on Advancement in Electrical and … , 2024
    2024
    Citations: 7
  • An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people
    SK Paul, MAA Walid, RR Paul, MJ Uddin, MS Rana, MK Devnath, IR Dipu, ...
    Bulletin of Electrical Engineering and Informatics (Q3 Journal) 13 (1), 499-509 , 2024
    2024
    Citations: 48
  • Optimal Features Selection for Human Activity Recognition (HAR) System Using Deep Learning Architectures [J]
    KS Paul, RR Paul, AM Rahman, MDM HAQUE, MDE HAMID
    Journal of Computer and Communications 12 (12), 16-33 , 2024
    2024
    Citations: 1
  • Leukemia Cancer Classification from Abnormal Cell Images Using a Deep Learning Approach: A Comprehensive Study
    S Afrin, SK Paul, RR Paul, MM Haque, MS Kabir, S Fahim, MK Islam
    2023 26th International Conference on Computer and Information Technology … , 2023
    2023
    Citations: 3
  • Improving performance of a brain tumor detection on MRI images using DCGAN-based data augmentation and vision transformer (ViT) approach
    MM Haque, SK Paul, RR Paul, N Islam, MAFM Rashidul Hasan, ...
    GANs for Data Augmentation in Healthcare, 157-186 , 2023
    2023
    Citations: 17
  • GANs for Data Augmentation in Healthcare
    A Solanki, M Naved, S Kumer Paul, R Rani Paul
    Springer International Publishing AG , 2023
    2023
    Citations: 6
  • Blockchain based secure and decentralized smart licensing of charging vehicles for rajshahi city corporation
    M Haque, SK Paul, K Islam, MN Della, RR Paul, S Fahim
    2023 International Conference on Information and Communication Technology … , 2023
    2023
    Citations: 2
  • Human fall detection system using long-term recurrent convolutional networks for next-generation healthcare: A study of human motion recognition
    SK Paul, AA Zisa, MAA Walid, Y Zeem, RR Paul, MM Haque, ME Hamid
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023
    Citations: 7

MOST CITED SCHOLAR PUBLICATIONS

  • An Adam based CNN and LSTM approach for sign language recognition in real time for deaf people
    SK Paul, MAA Walid, RR Paul, MJ Uddin, MS Rana, MK Devnath, IR Dipu, ...
    Bulletin of Electrical Engineering and Informatics (Q3 Journal) 13 (1), 499-509 , 2024
    2024
    Citations: 48
  • Issues and Concepts of Graph Database and a Comparative Analysis on list of Graph Database tools
    A Das, A Mitra, SN Bhagat, SK Paul
    2020 International Conference on Computer Communication and Informatics … , 2020
    2020
    Citations: 41
  • Study of fractional order SEIR epidemic model and effect of vaccination on the spread of COVID-19
    S Paul, A Mahata, S Mukherjee, B Roy, M Salimi, A Ahmadian
    International journal of applied and computational mathematics 8 (5), 237 , 2022
    2022
    Citations: 31
  • A comprehensive study on ethereum blockchain-based digital marketplace using NFT smart contract infrastructure
    MM Haque, SK Paul, RR Paul, ME Hamid, S Fahim, S Islam
    2022 25th International Conference on Computer and Information Technology … , 2022
    2022
    Citations: 19
  • Improving performance of a brain tumor detection on MRI images using DCGAN-based data augmentation and vision transformer (ViT) approach
    MM Haque, SK Paul, RR Paul, N Islam, MAFM Rashidul Hasan, ...
    GANs for Data Augmentation in Healthcare, 157-186 , 2023
    2023
    Citations: 17
  • A 2D convolution neural network based method for human emotion classification from speech signal
    RR Paul, SK Paul, ME Hamid
    2022 25th international conference on computer and information technology … , 2022
    2022
    Citations: 11
  • Speech command recognition system using deep recurrent neural networks
    SK Paul, RR Paul, ME Hamid
    2021 5th International Conference on Electrical Engineering and Information … , 2021
    2021
    Citations: 9
  • IoT-Based Real-Time Medical-Related Human Activity Recognition Using Skeletons and Multi-Stage Deep Learning for Healthcare
    SK Paul, ASM Miah, RR Paul, ME Hamid, J Shin, MA Rahim
    Computers, Materials & Continua 84 (2), 2513-2530 , 2025
    2025
    Citations: 7
  • A Comprehensive Analysis on Skin Cancer Classification Using Transfer Learning
    IZ Renu, MM Haque, SK Paul, MS Mou, MN Rahman, SS Gupta, RR Paul
    2024 3rd International Conference on Advancement in Electrical and … , 2024
    2024
    Citations: 7
  • Human fall detection system using long-term recurrent convolutional networks for next-generation healthcare: A study of human motion recognition
    SK Paul, AA Zisa, MAA Walid, Y Zeem, RR Paul, MM Haque, ME Hamid
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023
    Citations: 7
  • EEG-Based Multi-Class Emotion Recognition using Hybrid LSTM Approach
    SF Md Momenul Haque, Subrata Kumer Paul, Rakhi Rani Paul, Mursheda Nusrat ...
    International Journal of Innovative Research in Computer Science … , 2023
    2023
    Citations: 7
  • GANs for Data Augmentation in Healthcare
    A Solanki, M Naved, S Kumer Paul, R Rani Paul
    Springer International Publishing AG , 2023
    2023
    Citations: 6
  • Throat microphone speech enhancement using machine learning technique
    SK Paul, RR Paul, M Nishimura, ME Hamid
    International Conference on Innovative Computing and Cutting-edge … , 2020
    2020
    Citations: 6
  • Detecting diabetes in human body using different machine learning techniques
    SMT Zaman, SK Paul, RR Paul, ME Hamid
    2021 International Conference on Computer, Communication, Chemical … , 2021
    2021
    Citations: 5
  • Speech Recognition of Throat Microphone using MFCC Approach
    SK Paul, RR Paul
    International Research Journal of Computer Engineering 7 (05), 1940-1943 , 2020
    2020
    Citations: 5
  • A blockchain-based secure payment system for vehicle fuel filling station
    MM Haque, SK Paul, RR Paul, MAFMR Hasan, S Fahim, S Islam
    2022 25th International Conference on Computer and Information Technology … , 2022
    2022
    Citations: 4
  • Brain Tumor Detection on MRI Images Using a Combination of CNN and Ensemble Learning Approach
    MS Mou, MM Haque, SK Paul, RR Paul, IZ Renu, MN Rahman, SS Gupta, ...
    2024 International Conference on Recent Progresses in Science, Engineering … , 2025
    2025
    Citations: 3
  • Leukemia Cancer Classification from Abnormal Cell Images Using a Deep Learning Approach: A Comprehensive Study
    S Afrin, SK Paul, RR Paul, MM Haque, MS Kabir, S Fahim, MK Islam
    2023 26th International Conference on Computer and Information Technology … , 2023
    2023
    Citations: 3
  • Deep Learning Techniques for Bangladeshi Coin Detection and Automated Counting System: A Comparative Study of Multiple Algorithms
    AH Babor, UH Choity, M Kaspia, SK Paul, RR Paul, MM Haque, ...
    2024 3rd International Conference on Advancement in Electrical and … , 2024
    2024
    Citations: 2
  • Blockchain based secure and decentralized smart licensing of charging vehicles for rajshahi city corporation
    M Haque, SK Paul, K Islam, MN Della, RR Paul, S Fahim
    2023 International Conference on Information and Communication Technology … , 2023
    2023
    Citations: 2