Dr. Amol K. Kadam
@bvucoep.edu.in
RESEARCH, TEACHING, or OTHER INTERESTS
Computer Engineering
Scopus Publications
- Role of deep learning and machine learning algorithms in spinal cord injury (SCI): A comprehensive review-current insights and future directions
Dhanashree V. Patil, Amol K. Kadam
Aip Conference Proceedings, 2026 - Hybrid Convolutional Neural Networks–Transformer Architecture Optimized with Gooseneck Barnacle Algorithm for Grape Leaf Disease Classification
, Amol S. Suryawanshi, , Amol K. Kadam, , Mahesh P. Wankhade, , Ganesh Vitthal Kadam, , Govinda B. Sambare, , Sumit Arun Hirve, , Dattatray G Takale, and
Es Food and Agroforestry, 2026
The proposed DCNN-GBOA framework combines dynamic CLAHE, U-Net segmentation, and metaheuristic optimization, achieving superior grape leaf disease detection with 0.98 accuracy, 0.94 F1-score, and 0.96 Kappa, outperforming benchmark models such as DCNN-CMPA and VGGNet-16. - Quantum-Inspired Optimization Algorithms for Large-Scale Machine Learning Tasks
Satyawan Changadev Hembade, Sagarkumar S. Badhiye, Madhuri Ninawe, Piyush Pal, Payal Sunil Lahane, Amol K Kadam
2026 International Conference on Emerging Smart Computing and Informatics Esci 2026, 2026 - Advances in Crop Disease Detection: A Survey of Machine Learning Methods
Amol S. Suryawanshi, Amol K. Kadam
Lecture Notes in Networks and Systems, 2025 - Improving Software Defect Prediction Accuracy through Modified Entropy Calculation in the Random Forest Algorithm
Ranjeetsingh Suryawanshi
Communications on Applied Nonlinear Analysis, 2025
Assume the scenario in which you are attempting to categorize software defects for a broad dataset. Which algorithm do you think will be the most effective for accomplishing that? Random Forest, Support Vector Machine, Neural Networks, Naive Bayes, K-Nearest Neighbours, Decision Tree, Logistic Regression, and other techniques are among those that can be utilized to solve the problem described above. The Random Forest technique, which allows for the generation of predictions through the utilization of many Decision Trees, is one of the most often utilized methods. Entropy, a complicated computation that examines the degree of uncertainty in the data, is the foundation upon which this algorithm is built. It is possible that the calculation of entropy, which is a function that uses natural logarithm, will take a significant amount of time. Does one know of a more accurate method for calculating entropy? The Taylor series expression was utilized in this investigation to investigate a different approach to calculating the natural logarithm. Any function may be approximated by utilizing its derivatives, and this series, which is made up of the sum of infinite terms, is what it is. Also, we updated the Random Forest algorithm by substituting the natural logarithm with the Taylor series equation in the Entropy calculation. This was done in order to achieve these modifications. Following the implementation of our improved algorithm on the dataset, we examined its performance in comparison to the Entropy formula that was first developed. An improvement in the algorithm's accuracy in predicting software defects was discovered by us as a result of our update to the technique - JIT Software Bug Prediction using DBST Cleanup Mechanism with Explainable Backfit Transformer and Deep Kendall Analysis
Veena Janardhan Jadhav, Prakash Devale, Amol Kadam
Proceedings of the 2025 12th International Conference on Computing for Sustainable Global Development Indiacom 2025, 2025
Just-in-Time (JIT) bug prediction is crucial for improving software quality by identifying defects at the commit level. However, existing methods face significant challenges, including imbalanced datasets, inadequate representation of code modification structures, and limited explainability in fault models. To address these limitations, this study proposes a novel framework for JIT bug prediction, comprising three key components: a Density-aware Borderline Synthetic Tomek (DBST) cleanup mechanism, an Explainable Backfit structural transformer, and Deep Kendall Analysis. The DBST cleanup mechanism processes imbalanced JIT datasets by integrating Borderline SMOTE with Tomek links. It prioritizes boundary instances and enhances class separation using relative density and the taxicab metric. For feature extraction, the Explainable Backfit structural transformer captures syntactic and semantic features from code changes through dependency parsing, constituency parsing, and an equivariant semantic attention mechanism. Additionally, Deep Kendall Analysis employs a Deep Loopy Bi-LSTM to model long-term dependencies and Kendall rank correlation to discern relationships within the data. Finally, a global-local interpretable model-agnostic technique generates synthetic neighbours based on global and local relationships, providing transparent explanations for bug predictions. The proposed framework addresses critical gaps in JIT bug prediction by improving dataset processing, enhancing feature representation, and ensuring model explainability. This study contributes to the field by introducing a robust and interpretable approach to JIT bug prediction, with potential implications for advancing software quality assurance practices. - Vehicle Registration Plate Recognition Using YoloV8 and IOT
Juber Nadaf, Amol Kadam, Pramod Jadhav, Prasad Kadam, Vinod Patil, Milind Gaikwad
2025 1st International Conference on Aiml Applications for Engineering and Technology Icaet 2025, 2025
Number Plate recognition or the vehicle License Plate recognition is an interesting topic of research in smart cities which utilizes Image Processing techniques. Since the number of cars is rising at an exponential rate, automated systems are supposed to store vehicle data for a variety of applications. In this intended algorithm, an efficient technique has been identified for recognition of different vehicle number registration plates. Through this technique, we can easily deal with problems like low light, irregular font number plates, wrong angles. We use the method of Optical Character Recognition to identify characters that are extracted from the registration plate. Even though this project works efficiently for common use cases, it has its own drawbacks and limitations even though we are not using deep learning or any other machine learning algorithms. One of the important steps in vehicle plate recognition is the character recognition which is a technique to obtain individual text characters. YOLOv8 (You Only Look Once), and Darknet- 53 are the methods that is used to extract the features of the data. In this model, the input data is the image of the vehicle that is captured from the camera sensors or cell phones and later cropped to display only the registration plate. We undergo data-pre-processing to improve the quality or the pixels of image on the number plate. Testing is done using two different models one is the model which is obtained with more pre-processing data and the other obtained without any pre-processing data. We evaluate two datasets of number plate images: one with no interference and one where the colour intensity of the image has been decreased to test, the accuracy of the model - Towards Sustainable Farming: An Intelligent Plant Disease Detection System Using Machine Learning
Amol Kadam, Amol Suryawanshi, Sumati Jagdale, Shankar N Kadam, Sana M Bagban, Jyoti P Kharade
2025 2nd International Conference on Integration of Computational Intelligent System Icicis 2025, 2025
The objective of the project “Farm Defender Using Machine Learning” is to bring a simple, effective, automated method to detect plant diseases to help maintain the health of plants and contribute to sustainable agricultural practices. Access to data over the years has enabled the development of various plant detection systems for use in agriculture, environmental management, and biodiversity conservation. Plant identification using traditional methods is often tedious, requiring considerable manual labor and expert knowledge, and is therefore both time and cost- intensive. Utilizing ML techniques, image recognition models specifically, this plant identification system seeks to automate the identification and quality assessment of the plants. The system implements the use of a conventional neural networks (CNN's) that has been trained on mega sets of plant images, which merely capture salient visual features like leaf shape, color and texture to identify plant species and even recognize symptoms of diseases. Transfer learning enables the fine-tuning of pre-trained models to perform plant detection, reducing the time and resources needed for training. Data augmentation methods (rotation, scaling, color adjustment, etc.) can help the model be more robust and adaptable to different environmental conditions at the same time, which ensures higher model accuracy and reliability. Detecting diseases in crops is a crucial but often time-consuming and challenging part of farming. It demands great time and skilled labor. In this paper, an intelligent and effective method is proposed to detect crop disease based upon computer vision and machine learning techniques. To create a project that is different from the others, 3 datasets were combined to create one large dataset. The proposed system is capable of recognizing 38 different diseases across 5 commonly grown plants, with an impressive accuracy of 93%. The dataset is divided into 70,295 images for training, 17,572 images for validation, and 33 images for testing. - Facial Emotion Prediction Using Deep Learning Algorithm
Amol Kadam, Dhanashree V. Patil, Prasad D. Kadam, Prashant D. Yadav, Mahavir K. Beldar, Roheshkumar S. Lavate
2025 9th International Conference on Computing Communication Control and Automation Icccbea 2025, 2025
Research on the analysis and prediction of human emotions with deep learning methods has emerged as a critical field. Understanding emotions facilitates understanding human behavior. To anticipate emotions and analyse human emotions from face photos, this study explores the use of various techniques, including deep learning. The primary goal is to investigate various Convolutional Neural Networks (CNNs), which are essential for effectively extracting spatial data from pictures. To measure model performance, a variety of evaluation measures will be used. In addition, CNNs will be integrated with Recurrent Neural Network models to maximise optimisation, such as Long Short-Term Memory (LSTM) and Deep Neural Networks (DNN). - An Empirical Study on Heart and Liver Disease Prediction Using Supervised Learning Algorithms
Amol Kadam, Devdatta Mokashi, Dhanashree V. Patil, Mohan Mali, Vaibhav Pawar, Pramod A. Kharade
2025 2nd International Conference on Integration of Computational Intelligent System Icicis 2025, 2025
Heart disease that are referred as cardiovascular disease is one of the main causes of death worldwide and frequently leads to heart attacks. It restricts blood flow. Incorporating AI and machine learning into the healthcare industry could greatly improves predicting and diagnosing diseases. In this study, we compare different machine learning classifiers like Random Forest, Logistic Regression, Support Vector Machine, Naive Bayes, Decision Tree, and K-Nearest Neighbors for predicting liver and heart diseases. Accuracy of these classifiers are evaluated using four Kaggle datasets that are openly accessible. With 82.35 % on Heart Disease dataset, 74.59% Heart Disease 2020 dataset, 68.6% on the Framingham dataset, and 83.33 % on Liver Disease dataset, Results show that Random Forest classifier has the best accuracy. Findings demonstrate the potential of machine learning algorithms for early disease diagnosis, allow for prompt medical intervention. Future research concentrates on enhancing predicted accuracy through use of advanced feature selection techniques and deep learning approaches. - Mathematical Techniques in the Design of Robust Control Systems
Jubber Salim Nadaf
Panamerican Mathematical Journal, 2025 - Crime Investigation Tracker with Suspect Prediction
Amol Kadam, Devdatta Mokashi, Priti P Yadav, Kiran Bibhishan Naikwadi, Sagar Baburao Patil, Vinayak N Patil
2025 2nd International Conference on Integration of Computational Intelligent System Icicis 2025, 2025 - A MATHEMATICAL MODELING PERSPECTIVE FOR AUTOMATION ON IDEAL SELF-REGULATING VIDEO SURVEILLANCE SYSTEMS
Jubber Nadaf
International Journal of Applied Mathematics, 2025 - Software Defect Prediction by Logistic Regression with Gradient Descent Cost Computation
Ranjeetsingh Suryawanshi, Amol Kadam
2024 International Conference on Emerging Smart Computing and Informatics Esci 2024, 2024 - Enhancing Software Defect Projections Performance by Class Rebalancing
International Journal of Intelligent Systems and Applications in Engineering, 2024 - FSCM Quality Control using Blockchain & IOT
Amol Kadam, Dhanashree V. Patil, Vaibhav Pawar, Mohan Mali, Sandip Chavan, Prashant D. Yadav
2024 8th International Conference on Computing Communication Control and Automation Iccubea 2024, 2024 - Design of Software Reliability Growth Model for Improving Accuracy in the Software Development Life Cycle (SDLC)
International Journal of Intelligent Systems and Applications in Engineering, 2024 - Diabetes prediction and drug administration using knowledge engineering approach
Netra Patil, Naveenkumar Jayakumar, Sheetal S. Patil, Avinash M. Pawar, Amol Kadam
Aip Conference Proceedings, 2023 - An Innovative Approach for Predicting Software Defects by Handling Class Imbalance Problem
Ranjeetsingh Suryawanshi, Amol Kadam, Devata Anekar, Vinayak Patil
International Journal on Recent and Innovation Trends in Computing and Communication, 2023 - Calories Burned Prediction Using Machine Learning
Amol Kadam, Anurag Shrivastava, Sonali K. Pawar, Vinod H Patil, Jacob Michaelson, Ashish Singh
Proceedings of International Conference on Contemporary Computing and Informatics Ic3i 2023, 2023 - Tackling Climate Change with Artificial Intelligence
Amol Kadam, Lakshmi Namratha Vempaty, Durga Prasanna Kumar Melam, Dinesh Kumar Vairavel, A. Deepak, Vinod H Patil
Proceedings of International Conference on Contemporary Computing and Informatics Ic3i 2023, 2023 - Music Generation Using RNN-LSTM with GRU
Sheetal S. Patil, Suhas H. Patil, Avinash M. Pawar, Rudreshwar Shandilya, Amol K. Kadam, Rohini B. Jadhav, Mrunal S. Bewoor
2023 International Conference on Integration of Computational Intelligent System Icicis 2023, 2023 - Vehicle Number Plate Detection using YoloV8 and EasyOCR
Sheetal S. Patil, Suhas H. Patil, Avinash M. Pawar, Mrunal S. Bewoor, Amol K. Kadam, Uday C. Patkar, Kiran Wadare, Siddhant Sharma
2023 14th International Conference on Computing Communication and Networking Technologies Icccnt 2023, 2023 - A Systematic Ensemble Approach for Concept Drift Detector Selection in Data Stream Classifiers
Rucha Chetan Samant, Suhas H. Patil, Rahul Nand Sinha, Amol K. Kadam
International Journal of Engineering Trends and Technology, 2022 - Credibility Analysis of User‐Designed Content Using Machine Learning Techniques
Milind Gayakwad, Suhas Patil, Amol Kadam, Shashank Joshi, Ketan Kotecha, Rahul Joshi, Sharnil Pandya, Sudhanshu Gonge, Suresh Rathod, Kalyani Kadam, Maya Shelke
Applied System Innovation, 2022 - Software defect prediction: A survey with machine learning approach
International Journal of Advanced Science and Technology, 2020 - Unsupervised extraction of common product attributes from E-commerce websites by considering client suggestion
Ms. Amruta A. Kore*, , Dr. D. M. Thakore, Dr. Amol K. Kadam, , and
International Journal of Innovative Technology and Exploring Engineering, 2019 - An experimental on top-k high utility itemset mining by efficient algorithm Tkowithtku
International Journal of Innovative Technology and Exploring Engineering, 2019 - An experimental technique for efficient selection of test case prioritization methods
International Journal of Innovative Technology and Exploring Engineering, 2019 - Test case ranking with rate of fault finding
International Journal of Innovative Technology and Exploring Engineering, 2019 - Efficient algorithm TKO with TKU for mining top-K item set
Journal of Advanced Research in Dynamical and Control Systems, 2019 - Novel approach for efficient choice of test case prioritization technique
Journal of Advanced Research in Dynamical and Control Systems, 2019 - Improving efficiency of test case prioritization approach with rate of fault detection
Journal of Advanced Research in Dynamical and Control Systems, 2019 - Software superiority achievement through functional point and test point analysis
Amol K. Kadam, S.D. Joshi, Debnath Bhattacharyya
International Journal of Software Engineering and Its Applications, 2016
RECENT SCHOLAR PUBLICATIONS
- Role of deep learning and machine learning algorithms in spinal cord injury (SCI): A comprehensive review-current insights and future directions
DV Patil, AK Kadam
AIP Conference Proceedings 3410 (1), 020023 , 2026
2026 - Hybrid Convolutional Neural Networks–Transformer Architecture Optimized with Gooseneck Barnacle Algorithm for Grape Leaf Disease Classification
AS Suryawanshi, AK Kadam, MP Wankhade, GV Kadam, GB Sambare, ...
ES Food and Agroforestry 23, 1957 , 2025
2025 - An Empirical Study on Heart and Liver Disease Prediction Using Supervised Learning Algorithms
A Kadam, D Mokashi, DV Patil, M Mali, V Pawar, PA Kharade
2025 2nd International Conference on Integration of Computational … , 2025
2025 - Crime Investigation Tracker with Suspect Prediction
A Kadam, D Mokashi, PP Yadav, KB Naikwadi, SB Patil, VN Patil
2025 2nd International Conference on Integration of Computational … , 2025
2025 - Towards Sustainable Farming: An Intelligent Plant Disease Detection System Using Machine Learning
A Kadam, A Suryawanshi, S Jagdale, SN Kadam, SM Bagban, ...
2025 2nd International Conference on Integration of Computational … , 2025
2025 - Facial Emotion Prediction Using Deep Learning Algorithm
A Kadam, DV Patil, PD Kadam, PD Yadav, MK Beldar, RS Lavate
2025 9th International Conference on Computing, Communication, Control and … , 2025
2025 - JIT Software Bug Prediction using DBST Cleanup Mechanism with Explainable Backfit Transformer and Deep Kendall Analysis
VJ Jadhav, P Devale, A Kadam
2025 12th International Conference on Computing for Sustainable Global … , 2025
2025 - Vehicle registration plate recognition using YOLOv8 and IoT
J Nadaf, A Kadam, P Jadhav, P Kadam, V Patil, M Gaikwad
2025 1st International Conference on AIML-Applications for Engineering … , 2025
2025
Citations: 3 - Advances in Crop Disease Detection: A Survey of Machine Learning Methods
AS Suryawanshi, AK Kadam
International Conference on Information and Communication Technology for … , 2024
2024 - FSCM Quality Control using Blockchain & IOT
A Kadam, DV Patil, V Pawar, M Mali, S Chavan, PD Yadav
2024 8th International Conference on Computing, Communication, Control and … , 2024
2024 - AI-Driven Green Space Optimization for Sustainable Urban Parks: Enhancing Biodiversity and Resource Efficiency.
V Patil, J Patil, A Kadam, AR Patil, D Mokashi, GM Lonare
Library of Progress-Library Science, Information Technology & Computer 44 (3) , 2024
2024
Citations: 8 - Evaluating Awareness and Impact of Government Schemes in India: A Comprehensive Study.
A Kadam, VH Patil, M Mali, S Shinde, S Madkar, AR Patil, A Talale
Library of Progress-Library Science, Information Technology & Computer 44 (3) , 2024
2024
Citations: 9 - Software Defect Prediction by Logistic Regression with Gradient Descent Cost Computation
R Suryawanshi, A Kadam
2024 International Conference on Emerging Smart Computing and Informatics … , 2024
2024
Citations: 12 - Novel perceptive approach for automation on Ideal Self-Regulating Video Surveillance Model
AK Juber Nadaf
International Journal of intelligent systems and applications in engineering … , 2024
2024 - 5Hemant Singh Pokhariya, Dr
AK Kadam, DKH Krishna, N Varshney, A Deepak
Sandip Kumar Hegde, Dr. Vinod H. Patil,“Design of Software Reliability … , 2024
2024
Citations: 6 - Enhancing Software Defect Projections Performance by Class Rebalancing
S Ranjeet, A Kadam
International Journal of Intelligent Systems and Applications in Engineering … , 2024
2024
Citations: 7 - Design of software reliability growth model for improving accuracy in the software development life cycle (SDLC)
AK Kadam, KH Krishna, N Varshney, A Deepak, HS Pokhariya, SK Hegde, ...
International Journal of Intelligent Systems and Applications in Engineering … , 2024
2024
Citations: 16 - Music generation using RNN-LSTM with GRU
SS Patil, SH Patil, AM Pawar, R Shandilya, AK Kadam, RB Jadhav, ...
2023 International Conference on Integration of Computational Intelligent … , 2023
2023
Citations: 9 - Calories burned prediction using machine learning
A Kadam, A Shrivastava, SK Pawar, VH Patil, J Michaelson, A Singh
2023 6th International Conference on Contemporary Computing and Informatics … , 2023
2023
Citations: 19 - Tackling climate change with artificial intelligence
A Kadam, LN Vempaty, DPK Melam, DK Vairavel, A Deepak, VH Patil
2023 6th International Conference on Contemporary Computing and Informatics … , 2023
2023
Citations: 6
MOST CITED SCHOLAR PUBLICATIONS
- Vehicle number plate detection using yolov8 and easyocr
SS Patil, SH Patil, AM Pawar, MS Bewoor, AK Kadam, UC Patkar, ...
2023 14th International conference on computing communication and networking … , 2023
2023
Citations: 30 - Credibility analysis of user-designed content using machine learning techniques
M Gayakwad, S Patil, A Kadam, S Joshi, K Kotecha, R Joshi, S Pandya, ...
Applied System Innovation 5 (2), 43 , 2022
2022
Citations: 22 - Calories burned prediction using machine learning
A Kadam, A Shrivastava, SK Pawar, VH Patil, J Michaelson, A Singh
2023 6th International Conference on Contemporary Computing and Informatics … , 2023
2023
Citations: 19 - Design of software reliability growth model for improving accuracy in the software development life cycle (SDLC)
AK Kadam, KH Krishna, N Varshney, A Deepak, HS Pokhariya, SK Hegde, ...
International Journal of Intelligent Systems and Applications in Engineering … , 2024
2024
Citations: 16 - Detecting Eliminating Rogue Access Point in IEEE 802.11 WLAN
SB Vanjal, AK Kadam, PA Jadhav
International Journal of Smart Sensor and Adhoc Network., 108-112 , 2011
2011
Citations: 14 - Automated online college admission management system
M Gupta, KK Iyer, MR Singh, AK Kadam
International Journal of Computer Science Trends and Technology 5 (3), 1-4 , 2017
2017
Citations: 13 - Software Superiority Achievement through Functional Point and Test Point Analysis
AK Kadam, SD Joshi, D Bhattacharyya, HJ Kim
Int. J. Softw. Eng. Its Appl 10 (11), 181-192 , 2016
2016
Citations: 13 - Software Defect Prediction by Logistic Regression with Gradient Descent Cost Computation
R Suryawanshi, A Kadam
2024 International Conference on Emerging Smart Computing and Informatics … , 2024
2024
Citations: 12 - Software defect prediction: A survey with machine learning approach
RS Suryawanshi, A Kadam, DR Anekar
Int. J. Adv. Sci. Technol 29 (5), 330-335 , 2020
2020
Citations: 12 - A Systematic Ensemble Approach for Concept Drift Detector Selection in Data Stream Classifiers
AKK Rucha Chetan Samant , Suhas H. Patil , Rahul Nand Sinha
International Journal of Engineering Trends and Technology 70 (9), 119-131 , 2022
2022
Citations: 11 - Unsupervised extraction of common product attributes from E-commerce websites by considering client suggestion
AA Kore, DM Thakore, AK Kadam
Int. J. Innov. Technol. Explor. Eng 8 (11), 1199-1203 , 2019
2019
Citations: 11 - Evaluating Awareness and Impact of Government Schemes in India: A Comprehensive Study.
A Kadam, VH Patil, M Mali, S Shinde, S Madkar, AR Patil, A Talale
Library of Progress-Library Science, Information Technology & Computer 44 (3) , 2024
2024
Citations: 9 - Music generation using RNN-LSTM with GRU
SS Patil, SH Patil, AM Pawar, R Shandilya, AK Kadam, RB Jadhav, ...
2023 International Conference on Integration of Computational Intelligent … , 2023
2023
Citations: 9 - Test case ranking with rate of fault finding
A Magdum, SD Joshi, AK Kadam, A Sarda
Int. J. Innov. Technol. Explor. Eng 8 (8), 462-464 , 2019
2019
Citations: 9 - AI-Driven Green Space Optimization for Sustainable Urban Parks: Enhancing Biodiversity and Resource Efficiency.
V Patil, J Patil, A Kadam, AR Patil, D Mokashi, GM Lonare
Library of Progress-Library Science, Information Technology & Computer 44 (3) , 2024
2024
Citations: 8 - An Innovative Approach for Predicting Software Defects by Handling Class Imbalance Problem
R Suryawanshi, A Kadam, D Anekar, V Patil
International Journal on Recent and Innovation Trends in Computing and … , 2023
2023
Citations: 8 - Efficient Algorithm TKO with TKU for Mining Top-K Item Set
A Kurhade, J Naveenkumar, AK Kadam
vol 11, 1566-1570 , 2019
2019
Citations: 8 - Enhancing Software Defect Projections Performance by Class Rebalancing
S Ranjeet, A Kadam
International Journal of Intelligent Systems and Applications in Engineering … , 2024
2024
Citations: 7 - An experimental on top-k high utility itemset mining by efficient algorithm Tkowithtku
A Kurhade, J Naveenkumar, AK Kadam
Int. J. Innov. Technol. Explor. Eng 8 (8), 519-522 , 2019
2019
Citations: 7 - 5Hemant Singh Pokhariya, Dr
AK Kadam, DKH Krishna, N Varshney, A Deepak
Sandip Kumar Hegde, Dr. Vinod H. Patil,“Design of Software Reliability … , 2024
2024
Citations: 6
