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Prof. Pradeep Kumar Das

Assistant Professor Gr-II

Department of Electronics and Communication Engineering

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Information

Room No: 309 +91-870-246 pradeepkd@nitw.ac.in Bio Sketch

    Key Notes

    • Journal(s): 22
    • Conference(s): 5
    • Book Chapter(s): 4
    • PhD: Current - 2

    31

    PUBLICATIONS

    2

    DOCTORAL STUDENTS

    4

    PROJECTS

    Research Areas

    Biomedical Signal Processing
    Computer Vision
    Deep Learning
    Edge AI
    Image Processing
    Machine Learning
    Medical Image Processing
    Signal Processing
    EDUCATION QUALIFICATION
    Degree Institute Year
    Doctor of Philosophy National Institute of Technology Rourkela 2022
    COURSES HANDLED
    Course L-T-P Credit Degree Level
    Computer Organization and Architecture(EC1263) 3-0-2 4 UG
    Internet of Things(EC317) 3-0-0 3 UG
    Computer Programming(MA1163) 3-0-2 4 UG
    Computer Architecture(CS253) 3-0-0 3 UG
    Image Processing(ECM15) 3-0-0 3 UG
    Cloud Computing(EC472) 3-0-0 3 UG
    Computer Vision(EC417) 3-0-0 3 UG
    Linear IC Applications(EC251) 3-0-0 3 UG
    RESEARCH IDs
    ORCID
    ORC ID
    Google Scholar
    Google Scholar ID
    PUBLICATIONS
    Journal(s)
    Enhanced detection of acute leukemia: A hybrid machine learning framework with adaptive weight-optimized level set evolution, By Pradeep Kumar Das, Adyasha Sahu, Sukadev Meher, Rutuparna Panda, Ajith Abraham, Elsevier, Engineering Applications of Artificial Intelligence, vol.161, pp.112244, 2025
    A Hybrid Deep Learning Framework for Automatic Detection of Brain Tumours using Different Modalities, By A. Sahu, P. K. Das, I. Paul, and S. Meher, IEEE, IEEE Transactions on Emerging Topics in Computational Intelligence, vol.9, pp.1216-1225, 2025
    An Integrated Framework for Infectious Disease Control Using Mathematical Modelling and Deep Learning, By M. Salman, P. K. Das, and S. Mohanty, IEEE, IEEE Open Journal of Engineering in Medicine and Biology, vol.6, pp.41-53, 2025
    An efficient deep learning system for automatic detection of Acute Lymphoblastic Leukemia, By P. K. Das, S. Meher, A. Rath, and G. Panda, Elsevier, ISA Transactions, vol., pp., 2025
    OSLTBDNet: Orthogonal softmax layer-based tuberculosis detection network with small dataset, By Pradeep Kumar Das, S Sreevatsav, Adyasha Sahu, Shah Arpan Hasmukh Mayuri, Pareshkumar Ramanbhai Sagar, Elsevier, Biomedical Signal Processing and Control, vol., pp., 2025
    An efficient deep learning scheme to detect breast cancer using mammogram and ultrasound breast images, By A. Sahu, P. K. Das, and S. Meher, Elsevier, Biomedical Signal Processing and Control, vol.87, pp.105377, 2024
    ACDSSNet: Atrous Convolution-based Deep Semantic Segmentation Network for Efficient Detection of Sickle Cell Anemia, By P. K. Das, A. Dash, and S. Meher, IEEE, IEEE Journal of Biomedical and Health Informatics, vol.28, pp.5676-5684, 2024
    A Deforestation Detection Network Using Deep Learning-based Semantic Segmentation, By P. K. Das, A. Sahu, D. Xavy, and S. Meher, IEEE, IEEE Sensors Letters, vol.8, pp.1-4, 2024
    An Efficient Deep Learning Network with Orthogonal Softmax Layer for Automatic Detection of Tuberculosis, By P. K. Das, S. Sreevatsav, and A. Abraham, Elsevier, Engineering Applications of Artificial Intelligence, vol.133, pp.108116, 2024
    An automatic sparse-based deep cascade framework with multilayer representation for detecting breast cancer, By A. Sahu, P. K. Das, and S. Meher, Elsevier, Measurement, vol.228, pp.114375, 2024
    SBCDNet: An Efficient Sparse-Based Deep Cascade Blood Cancer Detection Network, By P. K. Das, A. Sahu, D. V A, and S. Meher, IEEE, IEEE Sensors Letters, vol.8, pp.1-4, 2024
    A Systematic Review on Recent Advancements in Deep Learning and Mathematical Modeling for Efficient Detection of Glioblastoma, By M. Salman, P. K. Das, and S. Mohanty, IEEE, IEEE Transactions on Instrumentation and Measurement, vol.73, pp.1-34, 2024
    Recent Advancements in Machine Learning and Deep Learning-based Breast Cancer Detection using Mammograms, By A. Sahu, P. K. Das, and S. Meher, Elsevier, Physica Medica, vol.114, pp.103138, 2023
    AWOLSE: Adaptive Weight Optimized Level Set Evolution-based Blood Cell Segmentation, By P. K. Das and S. Meher, IEEE, IEEE Transactions on Instrumentation and Measurement, vol.73, pp.5003212, 2023
    High Accuracy Hybrid CNN Classifiers for Breast Cancer Detection using Mammogram and Ultrasound Datasets, By A. Sahu, P. K. Das, and S. Meher, Elsevier, Biomedical Signal Processing and Control, vol.80, pp.104292, 2023
    An Efficient Detection and Classification of Acute Leukemia using Transfer Learning and Orthogonal Softmax Layer-based Model, By P. K. Das, B. Sahoo, and S. Meher, IEEE, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol.20, pp.1817-1828, 2023
    A lightweight deep learning system for automatic detection of blood cancer, By P. K. Das, B. Nayak, and S. Meher, Elsevier, Measurement, vol.191, pp.110762, 2022
    A Systematic Review on Recent Advancements in Deep and Machine Learning based Detection and Classification of Acute Lymphoblastic Leukemia, By P. K. Das, Diya V. A., S. Meher, R. Panda, and A. Abraham, IEEE, IEEE Access, vol.10, pp.81741-81763, 2022
    An Efficient Blood-Cell Segmentation for the Detection of Hematological Disorders, By P. K. Das, S. Meher, R. Panda, and A. Abraham, IEEE, IEEE Transactions on Cybernetics, vol.52, pp.10615-10626, 2022
    An efficient deep convolutional neural network based detection and classification of acute lymphoblastic leukemia, By P. K. Das and S. Meher, Elsevier, Expert Systems with Applications, vol.183, pp.115311, 2021
    A Review of Automated Methods for the Detection of Sickle Cell Disease, By P. K. Das, S. Meher, R. Panda, and A. Abraham, IEEE, IEEE Reviews in Biomedical Engineering, vol.13, pp.309-324, 2020
    Design of optimal high pass and band stop FIR filters using adaptive Cuckoo search algorithm, By S. K. Sarangi, R. Panda, P. K. Das, and A. Abraham, Elsevier, Engineering Applications of Artificial Intelligence, vol.70, pp.67-80, 2018
    Conference(s)
    Efficient Detection of Brain Tumor Using Deep Learning with Small Dataset By A. Kumar, P. K. Das, 2024 3rd IEEE International Conference on Artificial Intelligence for Internet of Things (AIIoT 2024), 2024
    An Efficient Deep Learning-based Breast Cancer Detection Scheme with Small Datasets By A. Sahu, P. K. Das, S. Meher, R. Panda, and A. Abraham, Intelligent Systems Design and Applications, 2023
    Deep Convolutional Neural Network-based Automatic Detection of Brain Tumour By I. Paul, A. Sahu, P. K. Das, and S. Meher, In 2023 2nd International Conference for Innovation in Technology (INOCON), 2023
    Transfer Learning-Based Automatic Detection of Acute Lymphocytic Leukemia By P. K. Das and S. Meher, 2021 National Conference on Communications (NCC), 2021
    Detection and Classification of Acute Lymphocytic Leukemia By P. K. Das, P. Jadoun, and S. Meher, 2020 IEEE-HYDCON, 2020
    Book Chapter(s)
    State-of-the-art methods for diagnosis of acute leukemia: A survey By L. Samantaray, R. Panda, P. K. Das, S. Agrawal, A. Abraham in AI-Driven Human-Machine Interaction for Biomedical Engineering, Academic Press, Research, 2026
    An Efficient Deep CNN-based AML Detection: Overcoming small Database Limitations in Medical Applications By P. K. Das, A. Sahu, and S. Meher in Computational Intelligence for Oncology and Neurological Disorders, CRC Press, Research, 2024
    A Deep Hybrid System for Effective Diagnosis of Breast Cancer By A. Sahu, P. K. Das, and S. Meher in Computational Intelligence for Oncology and Neurological Disorders, CRC Press, Research, 2024
    Detection of Acute Lymphoblastic Leukemia using Machine Learning Techniques By P. K. Das, A. Pradhan, and S. Meher in Machine Learning, Deep Learning and Computational Intelligence for Wireless Communication, Springer, Research, 2021
    PROJECT / CONSULTANCY
    AI-guided Smart Point-of-Care Device for Early Detection of Acute Lymphoblastic Leukemia in Rural and Urban Areas
    Role: Principal Investigator
    Type: Research
    Sponsor: Anusandhan National Research Foundation (ANRF)
    Project Cost (INR): 5940960
    Date of Commencement: 13-05-2026  
    Duration: 36 Months
    Status: Ongoing
    Federated Multimodal Deep learning Framework for Privacy-Preserving and Context Awareness in Early Cardiovascular Disease Prediction
    Role: Co-Principal Investigator
    Type: SPARC Project
    Sponsor: MHRD
    Project Cost (INR): 9344000
    Date of Commencement: 01-12-2025  
    Duration: 23 Months
    Status: Ongoing
    Artificial intelligence-based epileptic seizure detection framework using EEG signal
    Role: Co-Principal Investigator
    Type: Research
    Sponsor: Indian Council of Medical Research (ICMR)
    Project Cost (INR): 3010885
    Date of Commencement: 01-03-2024  
    Duration: 35 Months
    Status: Ongoing
    Development of Deep Learning Schemes for Automatic Detection of Tuberculosis
    Role: Principal Investigator
    Type: Research
    Sponsor: Research Seed Grant, MHRD, NIT Warangal
    Project Cost (INR): 500000
    Date of Commencement: 07-05-2025   Date of Completion: 06-05-2026
    Duration: 12 Months
    Status: Completed
    RESEARCH FELLOWS / PhD STUDENTS
    Current PhD Students
    Chukka Ranjith
    Area of Research: Development of Deep Learning Schemes for Automatic Disease Detection
    Suresh Kallepalli (Part-Time Ph.D)
    Area of Research: Medical Image Processing using Deep Learning
    CONFERENCE / WORKSHOP / SYMPOSIUM / SHORT TERM COURSE / FACULTY DEVELOPMENT PROGRAMME / GIAN
    AWARDS AND ACCOLADES
    2025
    Associate Editor, IEEE Transactions on Instrumentation and Measurement
    ADDITIONAL RESPONSIBILITIES
    •  Faculty Advisor, B.Tech Third Year, ECE, ECE(VLSI) (Continuing from August, 2025)
    •  Faculty In-Charge (Co-Incharge), IC Applications Lab (Continuing from August, 2025)
    •  PIC Industry Interaction Curriculum-PG, EMLS (Continuing from August, 2025)
    •  PIC-Information, ECE Department (May, 2025 - August, 2025)
     Last updated on May 13, 2026