Mukund Wagh

@csmssengg.org

Professor
Chhatrapati Shahu College of Engineering, Chh. Sambhajinagar

Mukund Wagh


                     Download Compact PDF  
https://researchid.co/mukund.wagh.81

EDUCATION

Science and Engineering)

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Computer Science, Multidisciplinary, Computer Science Applications
12

Scopus Publications

162

Scholar Citations

5

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Crop Disease Diagnosis Model Using Deep Hybrid Architecture With a New Segnet-Based Segmentation Model
    Dnyandeo Sopan Khemnar, Akash Saxena, Mukund B. Wagh
    Journal of Phytopathology, 2026
    The productivity of common crops in agriculture is declining, leading to increased interest in alternative crops known for their adaptability, nutritional value and resilience to harsh climates. Despite their benefits, these crops face threats from diseases that harm the agricultural economy. Detecting the disease using traditional methods is labor‐intensive, costly, and often questioned for their accuracy. To overcome these challenges, a novel Crop Disease Diagnosis framework using an Enhanced Convolutional Neural Network (CDD‐EC) model has been proposed. In data acquisition, the images from a benchmark dataset are collected using the range of plant disease samples in the initial stage of the CDD‐EC procedure. Gaussian filtering is used as a preprocessing technique on these photos to reduce noise and enhance image quality. A Coordinate Attention module‐assisted SegNet (CASNet) model is then used for segmentation, effectively separating the sick areas from the background. Following segmentation, the Pyramid Histogram of Oriented Gradients (PHOG) is used to analyse the structure and spatial distribution, extract texture features using the Updated Gabor Filter in Local Gabor Directional Pattern (UGF‐LGDP), and derive structural shape information. In a Bidirectional Long Short‐Term Memory network (Bi‐LSTM), the combined features are processed to learn both backward and forward dependencies in the data. The Normalized Average pooling response Batch normalization‐based Convolutional Neural Network (NAB‐CNN) processes the output feature map of Bi‐LSTM for additional refinement and classification. Finally, the system produces a categorized output that identifies the specific type of plant disease present in the image. The CDD‐EC scheme attained the highest accuracy of 0.937, precision of 0.875 and specificity of 0.958. Also, it demonstrated reduced error values with FDR at 0.125 and FPR at 0.042, respectively.
  • Beyond NISQ: Scalable Quantum Algorithms and Architectures for Practical Quantum Advantage
    Dattatray Raghunath Kale, Satish Madhukar Ranbhise, Hemant Shinde, Mukund B. Wagh, Sachin B. Hiranwale, Shilpa Manish Dhopte, Nagesh Jadhav, Rajendra Pawar
    Iet Conference Proceedings, 2026
    Quantum computing has the potential to solve complex problems that are outside the scope of classical systems, making it a standard shifter. Current devices, which are considered as Noisy Intermediate-Scale Quantum (NISQ) systems, are insufficient in their practical utility due to hardware limitations, error rates, and qubit counts. The dynamic growths wanted to exceed the NISQ era and grow scalable quantum algorithms and architectures that can achieve useful quantum benefit in real-world applications are reviewed in this research. This research gives a comprehensive examination of the complications in scaling quantum systems, adjacent resource efficacy, qubit connectivity, and error correction. In addition to connecting architectural innovations like modular quantum computing, error-resilient circuit design, and distributed quantum systems, the paper provides new hybrid quantum-classical algorithms planned to exploit performance in noisy environments. Results from benchmarking in arenas like machine learning, optimization, and quantum chemistry show how these methods can provide short-term quantum benefit. Lastly, this paper proposes a research roadmap for quantum computing that highlights the requirement of co-designing algorithms, software, and hardware. The goal of this effort is to straighten the progress of quantum systems that can resolve problems in the real world at scale by addressing the inadequacies of NISQ devices and emphasizing scalable solutions.
  • Optimized PRNN-ENet for robust IDS in IoV networks
    Mukund B. Wagh, Vishnu A. Suryawanshi, Rambhau B. Lagdive, Surendra K. Waghmare, Suvarna S. Pawar
    Progress in Artificial Intelligence, 2026
  • Crop Disease Diagnosis Model using Hybrid Model
    Dnyandeo Khemnar, Akash Saxena, Mukund Wagh
    2025 3rd Dmiher International Conference on Artificial Intelligence in Healthcare Education and Industry Idicaihei 2025, 2025
    The productivity of common crops in agriculture is declining, leading to increased interest in alternative crops known for their adaptability, nutritional value, and resilience to harsh climates. Despite their benefits, these crops face threats from diseases that harm the agricultural economy. Traditional disease detection methods are labor-intensive, costly, and often questioned for their accuracy. Hence, this paper proposes a novel Crop Disease Diagnosis framework using Enhanced Convolutional neural network (CDD-EC) model. The process of CDD-EC starts with data acquisition, where images are gathered from a benchmark dataset that includes a variety of plant disease samples. These images undergo preprocessing using Gaussian filtering, which helps to diminish noise and improve image quality. Following this step, segmentation is conducted using a Coordinate Attention module assisted SegNet (CASNet) model, which efficiently isolates the diseased regions from the background. After segmentation, extraction of texture features through Updated Gabor Filter in Local Gabor Directional Pattern (UGF-LGDP), analyzing shape and spatial distribution using Pyramid Histogram of Oriented Gradients (PHOG), and obtaining structural shape features are performed. The combined features are then fed into a Bidirectional Long Short-Term Memory network (Bi-LSTM), which learns both backward and forward dependencies in the data. The output feature map from the Bi-LSTM is then passed to a Normalized Average pooling response Batch normalization-based Convolutional Neural Network (NAB-CNN) for additional refinement and classification. Finally, the system produces a categorized output that identifies the specific type of plant disease present in the image.
  • HOTCP: Hybrid Optimal Test Case Prioritisation with Multi-Objective Constraints
    Mukund Baburao Wagh, Vishal V. Puri, Sanjay B. Waykar, Rajesh Kadu
    Journal of Information and Knowledge Management, 2024
    As a result of late detection and resource limitations during any software evaluation, there have been several software-related breakdowns or malfunctions. Many people have begun focussing on the test cases or alternatively the priority of validation suites after identifying the difficulties in the regression testing process of any product. The test case prioritisation technique is presented as a solution to this problem. It increases the fault detection rate. Earlier research studies have been implemented many techniques, but the rate of fault detection is not up to the mark. To overcome this drawback, we proposed HOTCP (Hybrid Optimal Test Case Prioritisation with Multi-Objective Constraints) model, which includes two steps: first is test case generation and the next one is test case prioritisation. The test case is generated from the released software. Consequently, test case prioritisation will be done by the optimisation strategy, in which the multi-objective function will be defined based on the constraints like statement coverage, branch coverage, contribution index and fault exposing potential. For this optimisation process, a new algorithm is proposed termed as CCCOA (Customised Coot and Chimp Optimisation Algorithm). The COOT optimisation and Chimp optimisation algorithms are combined in this algorithm. The system produces prioritised test cases, and the performance of the proposed method is validated in comparison to the traditional methods in terms of several metrics.
  • A Linear Swarm – Based Intelligence for Resource Allocation and Fault Prediction in Cloud
    Suvarna S. Pawar
    Panamerican Mathematical Journal, 2024
    The widespread utilization Cloud Computing (CC) services for hosting real-time applications have led to the appearance of service dependability as an essential issue in both users and Cloud Service Providers (CSP). Two diverse fault tolerant approaches are provided to improve the cloud service reliability known as proactive and reactive model. Various prevailing approaches consider the coordination problem among the Virtual Machines (VMs) executes parallel processing. Devoid of appropriate VM coordination, the parallel processing outcomes are not so appropriate. To handle this issue, a VM-based cluster allocation model is designed to diminish the total resource consumption by the data centers and network resources. Here, the VM migration process is performed for deteriorating PM to certain optimal PMs. At last, the optimal target selection is handled with an improved optimization approach known as linear Swarm-based intelligence. Here, various metrics are evaluated and compared with other approaches. The experimental outcomes illustrate the efficiency of the anticipated model.
  • Improved rider for vehicular adhoc NETwork routing via neural network
    N. Gomathi, Mukund B. Wagh
    Evolutionary Intelligence, 2022
  • Prediction of heart disease using hybrid optimisation techniques in data clustering
    Amolkumar N. Jadhav, Mukund B. Wagh, N. Gomathi
    International Journal of Computational Science and Engineering, 2022
    The disease diagnosis in the medical field enhances better medical service to patients and also leads to a decrease in their mortality rate. The prediction of the survival rate of the patients purely depends on the accurate diagnosis of the diseases, but still, it is a major challenge to the physicians as well as to medical domains. Besides, several researches have experimented related to the prediction and classification of heart diseases, but they are ineffective in providing accurate results. In this research, the performance analysis of the optimal clustering algorithm-based real-world heart dataset is carried out with the developed clustering methods. Here, three developed methods, such as kernel-based exponential grey wolf optimisation (KEGWO), enhanced kernel-based exponential grey wolf optimisation (EKEGWO), and whale grey clustering (WGC) algorithm obtained better performance and provided accurate results about the diagnosis of diseases. Moreover, the performance analysis is done by considering the evaluation metrics like the Jaccard coefficient, F-measure, MSE and Rand coefficient.
  • Optimal route selection for vehicular ad hoc networks using lion algorithm
    Journal of Engineering Research Kuwait, 2019
  • Route discovery for vehicular ad hoc networks using modified lion algorithm
    Mukund B. Wagh, N. Gomathi
    Alexandria Engineering Journal, 2018
    Vehicular Ad hoc Networks (VANETs) are a subdivision of Mobile Ad hoc Networks (MANETs), which take significant responsibility in the Intelligent Transportation System (ITS) domain for providing reliable road safety. Various researchers have dealt with the development under VANET for better routing. Yet, they found great difficulty in providing multi-constrained Quality of Service (QoS) to the network. To tackle the difficulty, the routing cost is determined by considering the congestion cost, travel cost, QoS awareness cost and collision cost, wherein the QoS awareness cost is estimated using fuzzification. In this paper, a renowned optimization algorithm, called Lion Algorithm (LA), is modified to adopt the minimized routing cost under the VANET. Further, the performance of the proposed LA is compared with the existing algorithms like, Genetic algorithm (GA) and LA by analyzing the convergence, routing cost and computational complexity. The proposed LA provides reliable routing with reduced cost and computational complexity.
  • Water wave optimization-based routing protocol for vehicular adhoc networks
    Mukund B. Wagh, N. Gomathi
    International Journal of Modeling Simulation and Scientific Computing, 2018
  • Quantitative and qualitative correlation analysis of optimal route discovery for vehicular ad-hoc networks
    Wagh B. Mukund, N. Gomathi
    Journal of Central South University, 2018

RECENT SCHOLAR PUBLICATIONS

  • NeuralRecon++-Pixel-aligned Neural Implicit Surfaces for Efficient Geometry and Appearance Reconstruction
    A Cholke, P Cholke, M Wagh, BR Devhare, S Aher, S Tambe
    University of Bahrain , 2026
    2026
  • Crop Disease Diagnosis Model Using Deep Hybrid Architecture With a New Segnet‐Based Segmentation Model
    DS Khemnar, A Saxena, MB Wagh
    Journal of Phytopathology 174 (2), e70301 , 2026
    2026
  • Crop Disease Diagnosis Model using Hybrid Model
    D Khemnar, A Saxena, M Wagh
    2025 3rd DMIHER International Conference on Artificial Intelligence in … , 2025
    2025
  • Quantum-Enhanced Big Data Analytics for Climate Change Predictions: A Scalable Solution for Global Challenges
    G Dattatray Kale, Amolkumar Jadhav,Mukund Wagh,Sarang Patil,Shrihari ...
    Journal of Mines, Metals and Fuels 73 (11), 3563-3575 , 2025
    2025
    Citations: 1
  • Beyond NISQ: scalable quantum algorithms and architectures for practical quantum advantage
    DR Kale, SM Ranbhise, H Shinde, MB Wagh, SB Hiranwale, SM Dhopte, ...
    IET Conference Proceedings CP967 2025 (43), 661-667 , 2025
    2025
  • Review Paper On Crop Disease Diagnosis Model Using Deep Hybrid Architecture With A New Segnet-Based Segmentation Model
    MW Dnyandeo Khemnar, Akash Saxena
    International Journal of Environmental Sciences 11 (16), 718-724 , 2025
    2025
  • A Comprehensive Survey on Deep Learning Approaches in Medical Image Diagnosis
    VS Wable, D Hebri, M Wagh
    2025
  • Market Basket Analysis for Product Recommendation: Trends, Techniques, And Applications
    V Dixit, D Hebri, M Wagh
    2025
    Citations: 1
  • HOTCP: Hybrid Optimal Test Case Prioritisation with Multi-Objective Constraints
    MB Wagh, VV Puri, SB Waykar, R Kadu
    Journal of Information & Knowledge Management 23 (03), 2450012 , 2024
    2024
    Citations: 1
  • A Linear Swarm – Based Intelligence for Resource Allocation and Fault Prediction in Cloud
    Suvarna S. Pawar, Mukund B. Wagh, Nandkishor P. Karlekar, Vishal V. Puri, G ...
    Panamerican Mathematical Journal 34 (4), 257-270 , 2024
    2024
  • Automating Machinery with Object Detection using YOLO and Servo Controllers
    R Peter, G Pereira, Y Kamble, MB Wagh
    Asian Journal For Convergence In Technology (AJCT) ISSN-2350-1146 9 (1), 23-29 , 2023
    2023
  • Improved rider for vehicular adhoc NETwork routing via neural network
    N Gomathi, MB Wagh
    Evolutionary Intelligence 15 (2), 1517-1530 , 2022
    2022
    Citations: 2
  • Prediction of heart disease using hybrid optimisation techniques in data clustering
    AN Jadhav, MB Wagh, N Gomathi
    International Journal of Computational Science and Engineering 25 (4), 375-384 , 2022
    2022
    Citations: 1
  • Multimedia Research (MR)
    MB Wagh
    2021
  • Pilot scheduling to mitigate pilot contamination using optimization algorithm in massive MIMO systems
    MB Wagh
    Multimedia Research 4 (1), 24-31 , 2021
    2021
    Citations: 5
  • Design and Development of Optimization based Route Discovery Phase for Effective Routing in VANET
    WM Baburao
    Chennai , 2021
    2021
  • Design and Development of Optimization based Route Discovery Phase for Effective Routing in VANET
    MB Wagh
    Chennai , 2021
    2021
  • Genetic Structure and Markers-Trait Association Analyses for Fe-Toxicity Tolerance, Grain-Fe Content and Yield Component Traits in Rice
    P Arjun, M Wagh, S Pawar, IC Mohanty, E Pandit, J Meher, SK Pradhan
    J Plant Biol Crop Res 3 (1), 1022 , 2020
    2020
    Citations: 4
  • Optimal route selection for vehicular ad hoc networks using lion algorithm
    MB Wagh, N Gomathi
    Journal of Engineering Research 7 (3), 178-199 , 2019
    2019
    Citations: 9
  • Improved GWO-CS algorithm-based optimal routing strategy in VANET
    MB Wagh, N Gomathi
    J Netw Commun Syst 2 (1), 34-42 , 2019
    2019
    Citations: 68

MOST CITED SCHOLAR PUBLICATIONS

  • Improved GWO-CS algorithm-based optimal routing strategy in VANET
    MB Wagh, N Gomathi
    J Netw Commun Syst 2 (1), 34-42 , 2019
    2019
    Citations: 68
  • Route discovery for vehicular ad hoc networks using modified lion algorithm
    MB Wagh, N Gomathi
    Alexandria engineering journal 57 (4), 3075-3087 , 2018
    2018
    Citations: 43
  • Water wave optimization-based routing protocol for vehicular adhoc networks
    MB Wagh, N Gomathi
    International Journal of Modeling, Simulation, and Scientific Computing 9 … , 2018
    2018
    Citations: 19
  • Optimal route selection for vehicular ad hoc networks using lion algorithm
    MB Wagh, N Gomathi
    Journal of Engineering Research 7 (3), 178-199 , 2019
    2019
    Citations: 9
  • Quantitative and qualitative correlation analysis of optimal route discovery for vehicular ad-hoc networks
    MB Wagh, N. Gomathi
    Journal of Central South University 25 (7), 1732-1745 , 2018
    2018
    Citations: 7
  • Pilot scheduling to mitigate pilot contamination using optimization algorithm in massive MIMO systems
    MB Wagh
    Multimedia Research 4 (1), 24-31 , 2021
    2021
    Citations: 5
  • Genetic Structure and Markers-Trait Association Analyses for Fe-Toxicity Tolerance, Grain-Fe Content and Yield Component Traits in Rice
    P Arjun, M Wagh, S Pawar, IC Mohanty, E Pandit, J Meher, SK Pradhan
    J Plant Biol Crop Res 3 (1), 1022 , 2020
    2020
    Citations: 4
  • Improved rider for vehicular adhoc NETwork routing via neural network
    N Gomathi, MB Wagh
    Evolutionary Intelligence 15 (2), 1517-1530 , 2022
    2022
    Citations: 2
  • Quantum-Enhanced Big Data Analytics for Climate Change Predictions: A Scalable Solution for Global Challenges
    G Dattatray Kale, Amolkumar Jadhav,Mukund Wagh,Sarang Patil,Shrihari ...
    Journal of Mines, Metals and Fuels 73 (11), 3563-3575 , 2025
    2025
    Citations: 1
  • Market Basket Analysis for Product Recommendation: Trends, Techniques, And Applications
    V Dixit, D Hebri, M Wagh
    2025
    Citations: 1
  • HOTCP: Hybrid Optimal Test Case Prioritisation with Multi-Objective Constraints
    MB Wagh, VV Puri, SB Waykar, R Kadu
    Journal of Information & Knowledge Management 23 (03), 2450012 , 2024
    2024
    Citations: 1
  • Prediction of heart disease using hybrid optimisation techniques in data clustering
    AN Jadhav, MB Wagh, N Gomathi
    International Journal of Computational Science and Engineering 25 (4), 375-384 , 2022
    2022
    Citations: 1
  • Shared intelligent optimum route selection through traffic management system in VANET– SIRS
    MB Wagh, N Gomathi
    International Journal of Engineering & Technology 7 (No. 1.2), 125-129 , 2018
    2018
    Citations: 1
  • NeuralRecon++-Pixel-aligned Neural Implicit Surfaces for Efficient Geometry and Appearance Reconstruction
    A Cholke, P Cholke, M Wagh, BR Devhare, S Aher, S Tambe
    University of Bahrain , 2026
    2026
  • Crop Disease Diagnosis Model Using Deep Hybrid Architecture With a New Segnet‐Based Segmentation Model
    DS Khemnar, A Saxena, MB Wagh
    Journal of Phytopathology 174 (2), e70301 , 2026
    2026
  • Crop Disease Diagnosis Model using Hybrid Model
    D Khemnar, A Saxena, M Wagh
    2025 3rd DMIHER International Conference on Artificial Intelligence in … , 2025
    2025
  • Beyond NISQ: scalable quantum algorithms and architectures for practical quantum advantage
    DR Kale, SM Ranbhise, H Shinde, MB Wagh, SB Hiranwale, SM Dhopte, ...
    IET Conference Proceedings CP967 2025 (43), 661-667 , 2025
    2025
  • Review Paper On Crop Disease Diagnosis Model Using Deep Hybrid Architecture With A New Segnet-Based Segmentation Model
    MW Dnyandeo Khemnar, Akash Saxena
    International Journal of Environmental Sciences 11 (16), 718-724 , 2025
    2025
  • A Comprehensive Survey on Deep Learning Approaches in Medical Image Diagnosis
    VS Wable, D Hebri, M Wagh
    2025
  • A Linear Swarm – Based Intelligence for Resource Allocation and Fault Prediction in Cloud
    Suvarna S. Pawar, Mukund B. Wagh, Nandkishor P. Karlekar, Vishal V. Puri, G ...
    Panamerican Mathematical Journal 34 (4), 257-270 , 2024
    2024