PRASAD PURUSHOTTAM PURNAYE

@mitwpu.edu.in

Computer Engineering and Technology
Dr. Vishwanath Karad MIT World Peace University

PRASAD PURUSHOTTAM PURNAYE


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https://researchid.co/prasadpp17

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Multidisciplinary
8

Scopus Publications

123

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • Early Investigations into Music-Induced Neural and Cardiovascular Responses
    Prasad Purnaye, Sanket Salvi
    Lecture Notes in Networks and Systems, 2025
  • Advancing Business Security in Cloud for Fileless Malware Detection Using Machine Learning
    Inderjeet Balotia, Prasad Purnaye, Vrushali Kulkarni
    Smart Innovation Systems and Technologies, 2025
  • Advancing Subsea Reservoir Management through Digital Twin
    Abdul Faisal, Iliyan Moosani, Abnoor Singh Cheema, Prasad Purnaye, Sheetal A Kulkarni, Mangesh V Bedekar
    2024 IEEE Pune Section International Conference Punecon 2024, 2024
    When designing an intricate system, it's crucial to apply system concepts, principles, and rules to gain a comprehensive understanding of the challenge at hand. The subsea system's architecture refers to the organization of a system, including its components, relationships with the environment, and structure defined by elements, interfaces, processes, restrictions, and behaviors. One of its components, a reservoir, is an underground deposit that is intended to hold hydrocarbons such as oil or gas for extraction. Additionally, digital twin (DT) technology allows for extensive monitoring, analysis, and optimization of assets and processes in a variety of industries, including construction, engineering, architecture, and subsea maintenance. Overall, this paper presents an approach to using Autodesk Maya software to create a digital twin (DT) of the reservoir, which is a vital part of subsea systems.
  • Blockchain-Driven Automation for Degree Certificate Authentication While Preserving Data Integrity
    Mamta Bhamare, Pradnya V. Kulkarni, Prasad Purnaye, Vrushali Kulkarni, Kaustubh Patil, Vedangi Thokal
    Proceedings 2024 International Conference on Progressive Innovations in Intelligent Systems and Data Science Icpids 2024, 2024
    Academic records are constantly at risk of varying attacks which impede traditional methods because they implement manual processes and web services, something that SAC has already underlined as a threat. In this research, an endeavor has been made to investigate the way blockchain technology is benefiting certificate authentication and verification of degree certificates. This research paper develops a solution for using smart contracts on the Ethereum blockchain to prove the security, transferability, and validity of advanced certificates. This helps to validate student academic records securely. The information about the same is kept in a hashed pattern on a blockchain for the verification process so that it is tamper-free. Application of this method delivers a safe, reliable and scalable answer for schools to authenticate & handle academic credentials.
  • Mental Health Analyzer for Depression Detection Based on Textual Analysis
    Pranav Bhat, Alwin Anuse, Rupali Kute, R. S. Bhadade, Prasad Purnaye
    Journal of Advances in Information Technology, 2022
    The global coronavirus pandemic and lockdown has had negative impacts on individuals’ mental health and well-being. The crisis has generated symptoms of depression in many, which may last even after the lockdown is over. To provide support to individuals in terms of counseling and psychiatric treatment, it is necessary to identify such depressive symptoms in a timely fashion. To address this problem, an artificial intelligence-based system is proposed to assess the changes, if any, in the mental health of an individual as a function of time, starting from the pre-lockdown period (in India from 20 April 2020). A Mental Health Analyzer has been implemented to automatically detect whether an individual is trending toward a state of depression based on his or her tweets over time. The deep learning models of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM have been implemented and compared for the emotion classification task, specifically to detect the emotions of sadness, fear, anger, and joy present in a person’s tweets. The system identifies the emotion of sadness present in tweets to detect depression. An ensemble maximizing model using CNN, LSTM, and Bidirectional LSTM is proposed to maximize the recall metric to improve the performance for the task of depression detection. The implemented system was tested using the dataset provided for the SemEval-2018 semantic evaluation tasks and achieves better results than previous models for the task of emotion classification and, further, can detect depression when tested on real Twitter data.
  • BiSHM: Evidence detection and preservation model for cloud forensics
    Prasad Purnaye, Vrushali Kulkarni
    Open Computer Science, 2022
    The cloud market is growing every day. So are cloud crimes. To investigate crimes that happen in a cloud environment, an investigation is carried out adhering to the court of law. Forensics investigations require evidence from the cloud. Evidence acquisition in the cloud requires formidable efforts because of physical inaccessibility and the lack of cloud forensics tools. Time is very crucial in any forensic investigation. If the evidence is preserved before the cloud forensic investigation, it can give the investigators a head start. To identify and preserve such potential evidence in the cloud, we propose a system with an artificial intelligence (AI)-based agent, equipped for binary classification that monitors and profiles the virtual machine (VM) from hypervisor level activities. The proposed system classifies and preserves evidence data generated in the cloud. The evidence repository module of the system uses a novel blockchain model approach to maintain the data provenance. The proposed system works at the hypervisor level, which makes it robust for anti-forensics techniques in the cloud. The proposed system identifies potential evidence reducing the effective storage space requirement of the evidence repository. Data provenance incorporated in the proposed system reduces trust dependencies on the cloud service provider (CSP).
  • A Comprehensive Study of Cloud Forensics
    Prasad Purnaye, Vrushali Kulkarni
    Archives of Computational Methods in Engineering, 2022
  • Information Retrieval for Cloud Forensics
    Prasad Purnaye, Vrushali Kulkarni
    Smart Innovation Systems and Technologies, 2022

RECENT SCHOLAR PUBLICATIONS

  • Advancing Subsea Reservoir Management through Digital Twin
    A Faisal, I Moosani, AS Cheema, P Purnaye, SA Kulkarni, MV Bedekar
    2024 IEEE Pune Section International Conference (PuneCon), 1-6 , 2025
    2025.0
  • Blockchain-Driven Automation for Degree Certificate Authentication While Preserving Data Integrity
    M Bhamare, PV Kulkarni, P Purnaye, V Kulkarni, K Patil, V Thokal
    2024 International Conference on Progressive Innovations in Intelligent … , 2024
    2024.0
    Citations: 1
  • Early Investigations into Music-Induced Neural and Cardiovascular Responses
    P Purnaye, S Salvi
    International Conference on Artificial Intelligence on Textile and Apparel … , 2024
    2024.0
  • Applying Machine Learning to Detect and Prevent Credit Card Fraud
    A Dahale, S Naik, P Purnaye
    Journal of Intelligent Decision Technologies and Applications 1 (2), 29-34 , 2024
    2024.0
  • Advancing Business Security in Cloud for Fileless Malware Detection Using Machine Learning
    I Balotia, P Purnaye, V Kulkarni
    International Conference on Business Intelligence and Data Analytics, 605-618 , 2024
    2024.0
  • Hypervisor-Level Ransomware Detection in Cloud Using Machine Learning
    P Purnaye, A Singh, M Sing, S Nair, D Mehta
    International Journal on Recent and Innovation Trends in Computing and … , 2024
    2024.0
    Citations: 2
  • BiSHM: Evidence detection and preservation model for cloud forensics
    P Purnaye, V Kulkarni
    Open Computer Science 12 (1), 154-170 , 2022
    2022.0
    Citations: 9
  • Information retrieval for cloud forensics
    P Purnaye, V Kulkarni
    Intelligent Data Engineering and Analytics: Proceedings of the 9th … , 2022
    2022.0
    Citations: 2
  • Mental health analyzer for depression detection based on textual analysis
    R Kute
    J. Adv. Inf. Technol. Vol 13 (1) , 2022
    2022.0
    Citations: 21
  • A comprehensive study of cloud forensics
    P Purnaye, V Kulkarni
    Archives of Computational Methods in Engineering 29 (1), 33-46 , 2022
    2022.0
    Citations: 75
  • Memory Dumps of Virtual Machines for Cloud Forensics
    P Purnaye, V Kulkarni
    IEEE Dataport , 2020
    2020.0
    Citations: 4
  • OpenNebula Virtual Machine Profiling for Intrusion Detection System
    P Purnaye, V Kulkarni
    IEEE Dataport , 2020
    2020.0
    Citations: 4
  • A review of the applications of Machine Learning in Genomics
    M Bhoite, ...
    International Conference on Advances in Usability Engineering for Computing … , 2018
    2018.0
  • Cloud forensics: Volatile data preservation
    P Purnaye, V Jyotinagar
    International Journal of Computer Science Engineering (IJCSE) 2, 41-42 , 2015
    2015.0
    Citations: 5
  • Credit Card Fraud Detection System Using Machine Learning
    A Dahale, S Naik, P Purnaye
  • Performance Enhancement of Android Application Testing using Android Devices as a Service Cloud Model
    NN Pise, PP Purnaye, M Dube, MP Mishra, M Pandey, R Jain
    International Journal on Recent and Innovation Trends in Computing and … , 0

MOST CITED SCHOLAR PUBLICATIONS

  • A comprehensive study of cloud forensics
    P Purnaye, V Kulkarni
    Archives of Computational Methods in Engineering 29 (1), 33-46 , 2022
    2022.0
    Citations: 75
  • Mental health analyzer for depression detection based on textual analysis
    R Kute
    J. Adv. Inf. Technol. Vol 13 (1) , 2022
    2022.0
    Citations: 21
  • BiSHM: Evidence detection and preservation model for cloud forensics
    P Purnaye, V Kulkarni
    Open Computer Science 12 (1), 154-170 , 2022
    2022.0
    Citations: 9
  • Cloud forensics: Volatile data preservation
    P Purnaye, V Jyotinagar
    International Journal of Computer Science Engineering (IJCSE) 2, 41-42 , 2015
    2015.0
    Citations: 5
  • Memory Dumps of Virtual Machines for Cloud Forensics
    P Purnaye, V Kulkarni
    IEEE Dataport , 2020
    2020.0
    Citations: 4
  • OpenNebula Virtual Machine Profiling for Intrusion Detection System
    P Purnaye, V Kulkarni
    IEEE Dataport , 2020
    2020.0
    Citations: 4
  • Hypervisor-Level Ransomware Detection in Cloud Using Machine Learning
    P Purnaye, A Singh, M Sing, S Nair, D Mehta
    International Journal on Recent and Innovation Trends in Computing and … , 2024
    2024.0
    Citations: 2
  • Information retrieval for cloud forensics
    P Purnaye, V Kulkarni
    Intelligent Data Engineering and Analytics: Proceedings of the 9th … , 2022
    2022.0
    Citations: 2
  • Blockchain-Driven Automation for Degree Certificate Authentication While Preserving Data Integrity
    M Bhamare, PV Kulkarni, P Purnaye, V Kulkarni, K Patil, V Thokal
    2024 International Conference on Progressive Innovations in Intelligent … , 2024
    2024.0
    Citations: 1
  • Advancing Subsea Reservoir Management through Digital Twin
    A Faisal, I Moosani, AS Cheema, P Purnaye, SA Kulkarni, MV Bedekar
    2024 IEEE Pune Section International Conference (PuneCon), 1-6 , 2025
    2025.0
  • Early Investigations into Music-Induced Neural and Cardiovascular Responses
    P Purnaye, S Salvi
    International Conference on Artificial Intelligence on Textile and Apparel … , 2024
    2024.0
  • Applying Machine Learning to Detect and Prevent Credit Card Fraud
    A Dahale, S Naik, P Purnaye
    Journal of Intelligent Decision Technologies and Applications 1 (2), 29-34 , 2024
    2024.0
  • Advancing Business Security in Cloud for Fileless Malware Detection Using Machine Learning
    I Balotia, P Purnaye, V Kulkarni
    International Conference on Business Intelligence and Data Analytics, 605-618 , 2024
    2024.0
  • A review of the applications of Machine Learning in Genomics
    M Bhoite, ...
    International Conference on Advances in Usability Engineering for Computing … , 2018
    2018.0
  • Credit Card Fraud Detection System Using Machine Learning
    A Dahale, S Naik, P Purnaye
  • Performance Enhancement of Android Application Testing using Android Devices as a Service Cloud Model
    NN Pise, PP Purnaye, M Dube, MP Mishra, M Pandey, R Jain
    International Journal on Recent and Innovation Trends in Computing and … , 0