Mr. Prathamesh Vijay Lahande

@sicsr.ac.in

Teaching Associate, Symbiosis Institute of Computer Studies and Research
Symbiosis International (Deemed University), Pune, India

Mr. Prathamesh Vijay Lahande


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https://researchid.co/prathameshlahande

RESEARCH INTERESTS

Reinforcement Learning, Cloud Computing
27

Scopus Publications

69

Scholar Citations

4

Scholar h-index

2

Scholar i10-index

Scopus Publications

  • A Quantum Computing Integration of Cuckoo Search with Classification Algorithms for Metaheuristic Optimization
    Prathamesh Vijay Lahande
    Communications in Computer and Information Science, 2026
  • A Novel Hybrid Scheduling Approach for Enhancing Cloud System Performance
    Prathamesh Vijay Lahande
    Lecture Notes in Networks and Systems, 2026
  • Exploring Quantum Computing Algorithms for Effective Task Scheduling in Computing Systems
    Prathamesh Vijay Lahande, Akshay Lokare, Indrajeet Kedari
    Conference Proceedngs Wccst 2026 World Conference on Computational Science and Technology, 2026
    Task scheduling is a critical problem within the modern underlying computing infrastructure, where resource overload causes a direct impact on the cost and time performance metrics, leading to increased latency, excessive financial cost, and overall poor throughput. Traditional scheduling algorithms are normally fixed and lack the strong exploration and exploitation processes, hence limiting their effectiveness when faced with large problems in scheduling, which are NP-hard. The current work investigates Quantum Computing (QC)-algorithms including Grover, the Variational Quantum Eigensolver (VQE) and Quantum Annealing (QA). This research work includes an extensive experiment conducted in the WorkflowSim simulator with heterogeneous task-scheduling performed with diverse Virtual Machine (VM) configurations. The performance-metrics considered for their comparison include cost and time, along with Relative Performance Index (RPI). The experimental results indicate that QC-algorithm QA achieves higher cost-efficiency, with an average cost reduction of 4 % compared to Grover and 25% compared to VQE, with a corresponding average reduction in time of about 14 % compared to Grover. VQE provides competitive behavior with particular VM configurations but provides poor stability behavior with a wider range of parameters. Statistical evaluations using five regression provides the Grover algorithm to have a superior predictive stability and stronger goodness-of-fit concerning cost and time compared to QA and VQE. Future research will be directed towards the design of hybrid quantum-classical scheduling systems and inclusion of additional performance indicators (including energy efficiency and throughput) and testing the mentioned systems on real quantum equipment to evaluate scalability and real-world feasibility in computational environments.
  • The Evolution of Cloud through SJF-ML Hybrid Scheduling
    Prathamesh Vijay Lahande
    Journal of Information and Organizational Sciences, 2025
    Purpose: The author proposes sixteen Shortest Job First - Machine Learning (SJF-ML) hybrid algorithms, combining the cloud's SJF scheduling algorithm with four ML algorithm categories, with cloud evolution through ML intelligence as the primary objective. The four categories include: SJF-CA, SJF-ELA, SJF-PM, and SJF-RA. The developed SJF-ML algorithms by the author perform pattern recognition of the tasks that are to be computed, to improve decision-making during task computations in the cloud. These sixteen SJF-ML algorithms include: SJF-ADAB, SJF-BAY, SJF-DT, SJF-KNN, SJF-LAS, SJF-LDA, SJF-LGB, SJF-LN, SJF-MLP, SJF-NAV, SJF-PLY, SJF-RDG, SJF-RF, SJF-RBST, SJF-SVM, and SJF-XGB. Performance Metrics: Cost, Time, Energy, and LB are utilized to compare the developed algorithms with baseline SJF, along with comparing them within their respective SJF-ML categories. Dataset: The real-time Google Big Data Task (BDT) dataset, comprising tasks ranging from one hundred to one thousand across nineteen files, was computed using the SJF-ML and SJF algorithms. Experiment: Open-source CloudSim simulator with VM counts of 20, 40, 60, 80, and 100 were utilized to compute the BDTs, outputting results across the considered metrics. Results: The algorithms SJF-XGB and SJF-LN provided the best results, with SJF-DT, SJF-LAS, and SJF-LDA providing poor results. Findings: Hybridization of the cloud's scheduling algorithms with ML provides improved intelligence and performance, resulting in the evolution of the cloud.
  • A Blockchain-based Hadoop System for Enhanced IPR Management
    Prathamesh Lahande, Sanjana Lahande
    Journal of Intellectual Property Rights, 2025
    The Intellectual Property Rights (IPRs) management systems used today face issues majorly related to tampering, scalability, and third-party disturbances leading to disputes between the users. These disputes lead to delays and higher costs required to manage the IPRs. To solve these issues, this research proposes an integrated system using the modern digital technologies of Blockchain and Hadoop to form a Blockchain- based Hadoop system and provide an enhanced IPR Management System. A detailed methodology is provided in this research work where what are the Blockchain and Hadoop components involved and how they work together to provide this enhanced platform. For this proposed system, the Blockchain provides an immutable secure manner to store IPRs and provides transparency among all the IPR users. Additionally, Hadoop provides a highly scalable infrastructure along with various analysis and trend identification tools useful for IPR management. With the proposed system, users can manage their innovations through a reliable platform, leading to an increased number of innovations and improving the IPRs revenue worldwide. This paper also provides how theproposed system can be applied to the IPR lifecycle and its activities such as filing, approval, licensing, transfers, monitoring along other activities involved. The proposed system benefits society through its fault-tolerant approach through its immutability and decentralized mechanism providing global access for managing their innovations. Unlike the traditional systems, a faster, better, and enhanced system is provided to lower disputes among stakeholders, further leading to lowered costs and delays in processing them.
  • QC-MLQ - A Novel Quantum Computing-based Multi-Level Queue Scheduler for Hadoop-integrated Fog Computing Environment
    Prathamesh Vijay Lahande
    2025 6th International Conference for Emerging Technology Incet 2025, 2025
    Purpose: This paper introduces a novel Quantum Computing-based Multi-Level Queue (QC-MLQ) Task Scheduler (TS) in the Hadoop-integrated Fog Computing Environment (FCE). The QC-MLQ schedules tasks using MLQs and finds an optimal number of queues needed for best results. Performance Metrics: Metrics related to time, efficiency, and resource consumption are used to find the optimal amount of queues using QC-MLQ. Time parameters include cycle and wait time, measured in seconds (s). Efficiency metrics include the Task Completion Rate (TCR) measured in processes per microsecond (P/μs) and CPU Utilization (CPU-UTZ) percentage. Resource consumption metrics include cost in dollars ($) and Energy-UTZ measured in watts (w). Experiment: The experiment was conducted in an FCE simulator where Google cluster raw big data tasks were computing on ten Virtual Machines (VMs) using the QC-MLQ scheduler with different queues. Results: The QC-MLQ scheduler provides optimal performance using a queue size of five, half the number of VMs, providing cycle and wait time of 121005.6 s and 85405.6 s. Additionally, a TCR of 11.6928 μ/s with CPU-UTZ of 99.86% and cost of $21350 consuming 128100 w of Energy-UTZ was achieved with this queue size. Findings: The results promote using the QC-MLQ scheduler with half the number of queues concerning the number of VMs in the FCE. Using lesser or higher queues yields poor results. Future Work: As a part of future work, an architecture of Hadoop-integrated FCE with QC-MLQ with Neural Networks is presented for improved FCE for offering better performance.
  • Implementing HRRN for Evaluating Cloud Performance Using Reinforcement Learning
    Prathamesh Vijay Lahande, Parag Ravikant Kaveri
    Communications in Computer and Information Science, 2025
  • Novel Hybrid Machine-Learning Algorithms for Resource Optimization in Cloud
    Prathamesh Vijay Lahande, Parag Ravikant Kaveri, Jatinderkumar R. Saini
    Lecture Notes in Networks and Systems, 2025
  • Evaluation of IPRs using Modern Sentimental Analysis Methods in the Law Domain
    Sanjana Lahande, Prathamesh Lahande, Parag Kaveri
    Journal of Intellectual Property Rights, 2025
    Intellectual Property Rights (IPRs) provide a systematic medium to safeguard people's unique ideas. Various authors from all around the globe have contributed to the IPRs in the Law domain by publishing their research articles. Although the literature on the IPRs in the Law domain is found to be several decades old, no Sentimental Analysis has been conducted on it. Identifying this significant research gap, the authors of this research paper have evaluated the IPRs using modern Sentimental Analysis methods in the Law domain. The authors have used Sentimental Analysis methods of Microsoft Azure, Valence Aware Dictionary and Sentiment Reasoner (VADER), and International Business Machines (IBM) Watson to perform this Sentimental Analysis on over six thousand research articles from the past four decades, in which authors from over fifty countries have contributed to the IPR in the Law domain. The authors used significant research paper components, including the Title, Keywords, and overall Contribution of the authors, as input for conducting this Sentimental Analysis. The overall results obtained from Sentimental Analysis methods of Microsoft Azure, VADER, and IBM Watson convey that 83.25 % positive, 13.11 % neutral, and 3.64 % negative research was conducted in the IPRs research in the Law domain. These results convey that, to date, the research in the IPRs of the law domain has been going in a positive direction, thereby providing a solution-oriented positive approach for the upcoming researchers in the IPRs of the law domain.
  • Exponential and Logarithmic Regression Models to Improve Cloud Performance Using Reinforcement Learning
    Prathamesh Vijay Lahande, Parag Ravikant Kaveri, Shirish Chintaman Joshi
    Lecture Notes in Electrical Engineering, 2025
  • Improving VM Scheduling Policies in Cloud Computing Through Quantum-Assisted Reinforcement Learning
    Diksha Subramanyam Iyer, Vidhi Gurudas Pednekar, Yudhaan Kapil Muzumdar, Prathamesh Vijay Lahande
    Proceedings of 8th International Conference on Trends in Electronics and Informatics Icoei 2025, 2025
  • EM-ACO-ARM: An Enhanced Multiple Ant Colony Optimization Algorithm for Adaptive Resource Management in Cloud Environment
    Prathamesh Lahande, Parag Kaveri, Harvinder Singh, Sukhjit Singh Sehra, Jatinderkumar R. Saini
    Procedia Computer Science, 2025
  • Evaluating the Need of Reinforcement Learning by Implementing Heuristic Algorithms with Its Load Balancing and Performance Testing in Cloud
    Prathamesh Vijay Lahande, Parag Ravikant Kaveri, Vinay Chavan, Kishor Dhole, Prashant Awasthi
    Communications in Computer and Information Science, 2025
  • Empirical Analysis of Resource Scheduling Algorithms in Cloud Simulated Environment
    Prathamesh Vijay Lahande, Parag Ravikant Kaveri
    Communications in Computer and Information Science, 2024
  • Mathematical Model for Improving Cloud Load Balancing Using Scheduling Algorithms
    Prathamesh Vijay Lahande, Parag Ravikant Kaveri
    Lecture Notes in Networks and Systems, 2024
  • Improving Service Broker Policy of the Cloud Using Reinforcement Learning Through Equally Spread Current Execution Load Balancing Policy
    Prathamesh Vijay Lahande, Parag Ravikant Kaveri
    Lecture Notes in Networks and Systems, 2024
  • Performance testing of scheduling algorithms for finding the availability factor
    Prathamesh Vijay Lahande, Parag Ravikant Kaveri
    Artificial Intelligence Blockchain Computing and Security Proceedings of the International Conference on Artificial Intelligence Blockchain Computing and Security Icabcs 2023, 2024
  • Performance Evaluation of Service Broker Policies in Cloud Computing Environment Using Round Robin
    Tanishka Hemant Chopra, Prathamesh Vijay Lahande
    Communications in Computer and Information Science, 2024
  • Reinforcement Learning for Reducing the Interruptions and Increasing Fault Tolerance in the Cloud Environment
    Prathamesh Lahande, Parag Kaveri, Jatinderkumar Saini
    Informatics, 2023
  • Fault Tolerance using Reinforcement Learning for Cloud Resource Management: Fault Tolerance using RL for Cloud Resource Management
    Prathamesh Vijay Lahande, Parag Kaveri
    ACM International Conference Proceeding Series, 2023
  • Reinforcement Learning to Improve Resource Scheduling and Load Balancing in Cloud Computing
    Parag Ravikant Kaveri, Prathamesh Lahande
    SN Computer Science, 2023
  • Performance testing of scheduling algorithms for finding the availability factor
    Prathamesh Vijay Lahande, Parag Ravikant Kaveri
    Artificial Intelligence Blockchain Computing and Security Volume 1, 2023
  • Novel Image Cryptography Method to Improve Security in Cloud Computing
    Prathamesh Vijay Lahande, Parag Ravikant Kaveri
    Lecture Notes in Networks and Systems, 2023
  • Reinforcement Learning Algorithms for Effective Resource Management in Cloud Computing
    Prathamesh Vijay Lahande, Parag Ravikant Kaveri
    Communications in Computer and Information Science, 2023
  • Reinforcement Learning Approach for Optimizing Cloud Resource Utilization With Load Balancing
    Prathamesh Vijay Lahande, Parag Ravikant Kaveri, Jatinderkumar R. Saini, Ketan Kotecha, Sultan Alfarhood
    IEEE Access, 2023
  • Reinforcement Learning Applications for Performance Improvement in Cloud Computing—A Systematic Review
    Prathamesh Vijay Lahande, Parag Ravikant Kaveri
    Lecture Notes in Electrical Engineering, 2022
  • Increasing data secrecy in cloud by implementing image cryptography
    International Journal of Scientific and Technology Research, 2020

RECENT SCHOLAR PUBLICATIONS

  • AI-Powered Finite Element Simulation Device for Structural Problem Solving
    P Lahande
    IN Patent 479626-001 , 2026
    2026
  • A Deep Learning Based Evolution of Decision Trees for Cyber Attack Detection
    P Kamble, M Patane, A Chandole, S Dandge, PV Lahande
    INDIACom-2026 , 2026
    2026
  • Exploring Quantum Computing Algorithms for Effective Task Scheduling in Computing Systems
    PV Lahande, A Lokare, I Kedari
    WcCST-2026 , 2026
    2026
  • The Role of Precision Agriculture and Smart Technologies: A Comprehensive Research Study on Drones, Sensors, and AI for Enhancing Crop Management and Food Security
    P Lahande
    Flora and Fauna 32 (1), 26-35 , 2026
    2026
  • A Quantum Computing Integration of Cuckoo
    PV Lahande
    Soft Computing and Its Engineering Applications: 7th International … , 2026
    2026
  • Hybrid Round-Robin - Classification Algorithms for Cost and Time Optimization in Cloud Environment
    PV Lahande
    IITCEE 2026 , 2026
    2026
  • Quantum Equally Spread Current Execution Load Algorithm for Edge-Cloud Environment
    PV Lahande
    IITCEE 2026 , 2026
    2026
  • Integration Of Fuzzy Logic and Graph Theory In Surface Pattern Recognition
    VVSR R. Parvathi, Teena, Kokisa Phorah, Prathamesh Vijay Lahande, Vipin ...
    International Journal of Applied Mathematics 38 (10), 2691 - 2699 , 2025
    2025
  • A Novel Hybrid Scheduling Approach for Enhancing Cloud System Performance
    PV Lahande
    ICDSNE 2025 1668, 291–301 , 2025
    2025
  • AI Based solution of finite element Unstructured problem
    MAK Prof. (Dr.) Anil Kumar, Dr. Gaurav Varshney, Dr. Hambeer Singh, Dr ...
    GB Patent 6,472,235 , 2025
    2025
  • A Blockchain-based Hadoop System for Enhanced IPR Management
    P Lahande, S Lahande
    Journal of Intellectual Property Rights 30 (5), 629-639 , 2025
    2025
  • Novel Hybrid Machine-Learning Algorithms for Resource Optimization
    PV Lahande, PR Kaveri
    ICT Analysis and Applications: Proceedings of ICT4SD 2024, Volume 7 1196, 471 , 2025
    2025
  • QC-MLQ-A Novel Quantum Computing-based Multi-Level Queue Scheduler for Hadoop-integrated Fog Computing Environment
    PV Lahande
    2025 6th International Conference for Emerging Technology (INCET), 1-6 , 2025
    2025
  • Improving VM Scheduling Policies in Cloud Computing Through Quantum-Assisted Reinforcement Learning
    DS Iyer, VG Pednekar, YK Muzumdar, PV Lahande
    2025 8th International Conference on Trends in Electronics and Informatics … , 2025
    2025
  • Implementing HRRN for Evaluating Cloud Performance Using Reinforcement Learning
    PV Lahande, PR Kaveri
    International Conference on Machine Intelligence and Smart Systems, 73-86 , 2025
    2025
    Citations: 1
  • Evaluation of iprs using modern sentimental analysis methods in the law domain
    S Lahande, P Lahande, P Kaveri
    Journal of Intellectual Property Rights (JIPR) 30 (2), 236-245 , 2025
    2025
    Citations: 1
  • Exponential and Logarithmic Regression Models to Improve Cloud Performance Using Reinforcement Learning
    PV Lahande, PR Kaveri, SC Joshi
    International Conference on Information Technology, 501-509 , 2025
    2025
  • The Evolution of Cloud through SJF-ML Hybrid Scheduling
    PV Lahande
    Journal of Information and Organizational Sciences 49 (2), 193-211 , 2025
    2025
  • EM-ACO-ARM: An Enhanced Multiple Ant Colony Optimization Algorithm for Adaptive Resource Management in Cloud Environment
    P Lahande, P Kaveri, H Singh, SS Sehra, JR Saini
    Procedia Computer Science 252, 796-805 , 2025
    2025
    Citations: 7
  • Evaluating the Need of Reinforcement Learning by Implementing Heuristic Algorithms with Its Load Balancing and Performance Testing in Cloud
    PV Lahande, PR Kaveri, V Chavan, K Dhole, P Awasthi
    International Conference on Soft Computing and its Engineering Applications … , 2024
    2024

MOST CITED SCHOLAR PUBLICATIONS

  • Reinforcement learning approach for optimizing cloud resource utilization with load balancing
    PV Lahande, PR Kaveri, JR Saini, K Kotecha, S Alfarhood
    IEEE Access 11, 127567-127577 , 2023
    2023
    Citations: 31
  • Reinforcement Learning to improve Resource Scheduling and Load Balancing in Cloud Computing
    DPRK Prathamesh Vijay Lahande
    SN Computer Science , 2023
    2023
    Citations: 13
  • EM-ACO-ARM: An Enhanced Multiple Ant Colony Optimization Algorithm for Adaptive Resource Management in Cloud Environment
    P Lahande, P Kaveri, H Singh, SS Sehra, JR Saini
    Procedia Computer Science 252, 796-805 , 2025
    2025
    Citations: 7
  • Reinforcement Learning Applications for Performance Improvement in Cloud Computing—A Systematic Review
    PV Lahande, PR Kaveri
    Sustainable Advanced Computing: Select Proceedings of ICSAC 2021, 91-112 , 2022
    2022
    Citations: 4
  • Increasing data secrecy in cloud by implementing image cryptography
    PRK Prathamesh Vijay Lahande
    International Journal of Scientific & Technology Research 9 (4), 26-31 , 2020
    2020
    Citations: 4
  • Implementing FCFS and SJF for finding the need of Reinforcement Learning in Cloud Environment
    P Lahande, P Kaveri
    ITM Web of Conferences 50, 01004 , 2022
    2022
    Citations: 3
  • Mathematical model for improving cloud load balancing using scheduling algorithms
    PV Lahande, PR Kaveri
    International Conference on Network Security and Blockchain Technology, 333-343 , 2023
    2023
    Citations: 2
  • Implementing HRRN for Evaluating Cloud Performance Using Reinforcement Learning
    PV Lahande, PR Kaveri
    International Conference on Machine Intelligence and Smart Systems, 73-86 , 2025
    2025
    Citations: 1
  • Evaluation of iprs using modern sentimental analysis methods in the law domain
    S Lahande, P Lahande, P Kaveri
    Journal of Intellectual Property Rights (JIPR) 30 (2), 236-245 , 2025
    2025
    Citations: 1
  • Performance Evaluation of Service Broker Policies in Cloud Computing Environment Using Round Robin
    TH Chopra, PV Lahande
    International Conference on Soft Computing and its Engineering Applications … , 2023
    2023
    Citations: 1
  • Reinforcement Learning for Reducing the Interruptions and Increasing Fault Tolerance in the Cloud Environment
    P Lahande, P Kaveri, J Saini
    Informatics 10 (3), 64 , 2023
    2023
    Citations: 1
  • Reinforcement learning algorithms for effective resource management in cloud computing
    PV Lahande, PR Kaveri
    International Conference on Soft Computing and its Engineering Applications … , 2022
    2022
    Citations: 1
  • AI-Powered Finite Element Simulation Device for Structural Problem Solving
    P Lahande
    IN Patent 479626-001 , 2026
    2026
  • A Deep Learning Based Evolution of Decision Trees for Cyber Attack Detection
    P Kamble, M Patane, A Chandole, S Dandge, PV Lahande
    INDIACom-2026 , 2026
    2026
  • Exploring Quantum Computing Algorithms for Effective Task Scheduling in Computing Systems
    PV Lahande, A Lokare, I Kedari
    WcCST-2026 , 2026
    2026
  • The Role of Precision Agriculture and Smart Technologies: A Comprehensive Research Study on Drones, Sensors, and AI for Enhancing Crop Management and Food Security
    P Lahande
    Flora and Fauna 32 (1), 26-35 , 2026
    2026
  • A Quantum Computing Integration of Cuckoo
    PV Lahande
    Soft Computing and Its Engineering Applications: 7th International … , 2026
    2026
  • Hybrid Round-Robin - Classification Algorithms for Cost and Time Optimization in Cloud Environment
    PV Lahande
    IITCEE 2026 , 2026
    2026
  • Quantum Equally Spread Current Execution Load Algorithm for Edge-Cloud Environment
    PV Lahande
    IITCEE 2026 , 2026
    2026
  • Integration Of Fuzzy Logic and Graph Theory In Surface Pattern Recognition
    VVSR R. Parvathi, Teena, Kokisa Phorah, Prathamesh Vijay Lahande, Vipin ...
    International Journal of Applied Mathematics 38 (10), 2691 - 2699 , 2025
    2025