DEPARTMENTS
Muneeswaran P.
Muneeswaran P.

Assistant Professor

Office Address:

CSE105, Department of CSE

Contact no:

0495-2281309

Home Address:

Thiruthangal, Tamil Nadu

  • Ph.D. (IIT Mandi)

  • Educational Qualifications

    • Ph.D. (IIT Mandi)

    Journals

    Packiaraj, Muneeswaran, and Sriram Kailasam. "HyPar-FCA: a distributed framework based on hybrid partitioning for FCA." The Journal of Supercomputing 78, no. 10 (2022): 12589-12620.

    Packiaraj, Muneeswaran, and Sriram Kailasam. "HyPar-FCA+: an improved workload-aware elastic framework for FCA." The Journal of Supercomputing 79, no. 11 (2023): 11767-11796.

    Non-Enzymatic Flexible Electrochemical Sensor using conducting polymer nanocomposites for Hydrogen Peroxide Detection in Real Samples

    S Selvaraj, S Archana, D Ragupathy, P Muneeswaran, M Eswaran

    IEEE Sensors Journal

    Design and simulation of HEMT-based sensor for Heavy Metal Ion detection with selectivity enhancement using ensemble methods
    M Aravindan, C Periasamy, M Packiaraj, S Raghavan, P Raguraman
    Micro and Nanostructures 211, 208541

    Conferences

    Muneeswaran P, Jyoti, and Sriram Kailasam, A hybrid partitioning strategy for distributed FCA,  in Proceedings of the Fifthteenth International Conference on Concept Lattices and Their Applications, Tallinn, Estonia, June 29-July 1, 2020, pp. 71–82, 2020.

    DASNet: A Dual Adaptive Subtle-Feature Network for Diabetic Retinopathy Detection in Fundus Images.
    YD Sonale, P Raguraman, M Packiaraj
    ICPRAM, 331-338

    L Rishi Raj, Muneeswaran Packiaraj, "Fusion-Based Deep Learning Framework for Inclusive Anxiety Severity Detection", 2025 International Conference on Signal Processing, Computation, Electronics, Power and Telecommunication (IConSCEPT).

    Research Contributions

    • Al-Integrated Wearable Sensor Patch for Continuous Glucose Monitoring and On-Demand Drug Delivery, IIT Mandi iHub and HCi Foundation, Budget 20.57 Lakh, Tenure 2026-2028

    Quantum Computing, Quantum Machine Learning, Federated Learning, Distributed Systems