Rahul Rajendra Papalkar

@vupune.ac.in

Assistant Professor in Computer Engineering
vishwkarma University

Rahul Rajendra Papalkar


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https://researchid.co/rahulpapalkar
Rahul Papalkar earned his BE, MTech, and CDAC degrees from State University in India. He is now the Head of the System Department and an Assistant Professor in the Computer Engineering Department at Vishwakarma University in Pune, where he has been teaching for 16 years. He has 15 patents, has published 25 research articles in Scopus and SCI-indexed international journals, and has written 8 book chapters for CRC Taylor Francis. He has also filed his PhD at SGBAU Amravati University.

EDUCATION

PhD IT

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Networks and Communications, Computer Engineering, Artificial Intelligence, Computer Science
19

Scopus Publications

231

Scholar Citations

10

Scholar h-index

11

Scholar i10-index

Scopus Publications

  • Dist-PGQBM: Distributed Pyramidal Graph Attention-Enabled Deep Learning Model for Manipulated Timestamp Detection From Raw Disk Image
    Rahul Rajendra Papalkar, Yogesh Haridas Jadhav, Preeti Vinayakrao Dudhe, Maithili Sanjay Deshmukh, Harish Sadashiv Motekar, Trupti Patil, Reshma Ramakant Kanse, Apurva Bhalchandra Parandekar
    Concurrency and Computation Practice and Experience, 2026
    Timestamp manipulation affects the reliability of the digital forensic investigation process. Hence, numerous traditional approaches are utilized to perform meaningful investigation, but they are limited in their inability to eradicate the occurrence of redundant details, increased computational complexity, and reduced interpretability. Thus, an effective Distributed Pyramidal Graph attention‐enabled Quantum Bidirectional Long short term Memory (Dist‐PGQBM) is proposed. The extraction of the optimal features mitigates the occurrence of redundant data, thereby enhancing the accurate detection of the manipulated timestamp. The distributed learning strategy followed by the research aids in the parallel processing of complex features with the effective reduction in the computational resource requirement. Moreover, the Pyramidal Graph attention (PGA) incorporated in the research enhanced the ability of the proposed model to handle complex temporal patterns via the effective handling of the noisy inputs with increased interpretability. Nevertheless, the quantum layer integrated with the Dist‐PGQBM made the manipulated timestamp detection a faster process. Experimental results show that the proposed Dist‐PGQBM method outperforms the other existing methods by attaining the high accuracy of 97.76%, precision of 98.18%, recall of 97.34%, and Mean Absolute Error (MAE) of 1.67 for 90% of training using the msuhanov/dfir_ntfs dataset.
  • Artificial Intelligence for Brain Tumor Diagnosis: A Review of Models, Techniques, and Clinical Implications
    Anushka Sonti, Kunjal Ahuja, Harshal Nilesh Bhankele, Rohan Kumar Sinha, Maithili Mahadik, Rahul Papalkar
    Iet Conference Proceedings, 2026
    The use of artificial intelligence (AI) and deep learning has made a big difference in finding and sorting out brain tumors. This paper talks about various AI methods, like specially made Convolutional Neural Networks (CNNs) and already trained models such as ResNet50, EfficientNet, DenseNet121, and InceptionV3. These models have shown great results when used with MRI images to detect tumors. Moreover, techniques such as Grad-CAM, SHAP, and LIME assist in making AI models more transparent, thereby enabling doctors to comprehend the output of the model more easily. Improved image quality is attained by processes such as equalization, homomorphic filtering, and segmentation to aid in obtaining more precise results while detecting and classifying tumors. In addition, models that combine several AI models together, or combine CNNs with conventional approaches such as Support Vector Machines (SVMs) and Random Forests, have been found to perform well in detecting various types of tumors. Despite all these developments, challenges such as limited datasets, variations in types of tumors, and regulations that must be adhered to before these models are employed in actual hospitals still exist. To deal with these issues and help use AI in brain tumor treatment, techniques like data expansion and transfer learning can help. The paper shows how AI is changing the way brain tumors are diagnosed early, how treatment is planned, and how care is provided, which helps patients get better results.
  • AI-Driven Dermatology: A Study on Skin Disease Identification Through Advanced Medical Imaging
    Mayur Prakash Deshmukh, Rajkumar Jagdale, Rahul Papalkar
    Lecture Notes in Networks and Systems, 2026
  • WACSO: Wolf Crow Search Optimizer for Convolutional Neural Network Hyperparameter Optimization
    Rahul Rajendra Papalkar, Jayendra Jadhav, Tareek Pattewar, Vivek Thorat, Pallavi Morey, Mayur Deshmukh, Rajkumar Jagdale
    Neural Processing Letters, 2025
    Convolutional Neural Networks (CNNs) experience performance and training efficiency changes according to the selection of correct hyperparameters. The research presents WACSO which combines Crow Search Optimization with Grey Wolf Optimizer to improve Convolutional Neural Networks hyperparameter selection through a hybrid metaheuristic algorithm. The hybrid algorithm WACSO uses exploration parts from CSO together with GWO exploitation mechanics to obtain optimized performance. WACSO reaches higher classification accuracy than traditional optimization algorithms when performing tests on the MNIST and CIFAR-10 datasets along with Random Search and particle swarm optimization and genetic algorithms and standalone CSO and standalone GWO. The best classification results reached 98.9% accuracy levels on MNIST along with 91.5% accuracy levels on CIFAR-10. The final outcomes of this system depend on the combination of model structure along with dataset challenges and available computational power. The investigation demonstrates that mixing algorithms drawn from nature can lead to successful CNN hyperparameter optimization. The promising outcomes of WACSO depend on multiple variables including computation expenses and sensitive parameter adjustments and universal result adaptability between different datasets and network setups. Research into WACSO should expand to involve longer evaluations across multiple datasets and various models to confirm widespread usage.
  • Insights into Women’s Sentiments on Breast Cancer Detection, Causes, and Treatments: A Comprehensive Analysis
    Kavita Kumavat, Jayendra Jadhav, Trupti Shinde, Rahul Papalkar, Sulbha Yadhav, Sonal Bankar
    Artificial Intelligence in Oncology Cancer Diagnosis and Treatment Medical Imaging and Personalized Medicine, 2025
  • Translating Hybrid ANN-ARIMA Diagnostic Models for Early Detection of Oncological Biomarkers
    Rahul Rajendra Papalkar, Jayendra Jadhav, Harish Motekar, Pravin Nerkar, Snehal H. Kuche, Nikhil S. Band, Vinod M. Rathod
    Artificial Intelligence in Oncology Cancer Diagnosis and Treatment Medical Imaging and Personalized Medicine, 2025
  • Enhancing IoT security: A Creative Swagger Optimization algorithm for DDoS defence
    Rahul Rajendra Papalkar, Abrar S. Alvi
    Network Computation in Neural Systems, 2024
    In the Internet of Things (IoT), the security of information between network transmissions is very important since the system stores data in data storage and is performed by the exchange of network information about things. DDoS in an IoT network is an attack that targets the availability of the servers by flooding the communication channel with impersonated requests coming from distributed IoT devices. To overcome the above-mentioned issue, this research proposed a Creative Swagger (CS) Optimized Deep Convolutional Neural Network (DeepCNN) that detects and mitigates DDoS attacks. The CS algorithm is designed by fusing the distinctive behaviour of the Swagger with the innovative concepts of the civilized creature, which is used to effectively tune the parameters of Deep CNN to improve the detection accuracy of DDoS attacks. For initial verification, a blacklist table is used and the verification includes checking IP address and other pertinent attributes. The proposed CS-optimized Deep CNN model obtains high effectiveness by attaining an accuracy of 97.07%, sensitivity of 97.23%, and specificity of 96.91% at 80% of the training data for utilizing UNSW-NB15 Dataset. Moreover, this method provides the best solution for detecting DDoS attacks in IoT platforms with higher robustness.
  • Review of unknown attack detection with deep learning techniques
    Rahul Rajendra Papalkar, Abrar S. Alvi
    Artificial Intelligence Blockchain Computing and Security Proceedings of the International Conference on Artificial Intelligence Blockchain Computing and Security Icabcs 2023, 2024
    Zero-day attacks, which are also known as unknown attacks, are a major threat to computer networks because they take advantage of weaknesses that no one knew about before. Researchers have looked into using convolutional neural networks (CNN) to find zero-day threats and stop them. The use of deep learning methods to the detection of emerging threats in the realm of network security is rapidly growing in importance. The goal of this study is to come up with a general way for creating and training a convolutional neural network (CNN) model for identifying unexpected threats using the KDD Cup 1999 and BoT IoT datasets. To use the suggested method, first prepare the data, then extract features, make a CNN design, train and test models, and then release them. The method could make breach detection systems more effective and efficient and help protect computer networks from security risks. This would be a very good thing. In addition, This paper gives an overview of current study on detecting zero-day attacks with CNN, including methods for collecting and preparing data, CNN structures, training and testing strategies, and evaluation measures. The poll shows the pros and cons of using CNN to find zero-day attacks and points out key research holes and goals for the future. But this method needs more research to figure out how to deal with its limitations and problems and to see if it works in real-world network settings.
  • An optimized feature selection guided light-weight machine learning models for DDoS attacks detection in cloud computing
    Rahul R. Papalkar, A.S. Alvi, Shabir Ali, Mohan Awasthy, Reshma Kanse
    Artificial Intelligence Blockchain Computing and Security Proceedings of the International Conference on Artificial Intelligence Blockchain Computing and Security Icabcs 2023, 2024
    The investigation highlights the need of lightweight machine learning models for efficient and effective cloud based VNF intrusion detection. The study suggests utilising lightweight machine learning models guided by feature selection to spot distributed denial of service (DDoS) attacks in the cloud. The proposed strategy uses the Extreme Gradient Boosting (XGBoost) approach to create a lightweight machine learning model, and a genetic algorithm to choose relevant characteristics for it. We put the proposed technique through its paces by simulating DDoS attacks on the cloud with a data set. The results showed that out of all the machine learning models evaluated, XGBoost's optimised feature selection guided performance was the most effective and efficient. This study contributes to the field of cybersecurity by examining the necessity of intrusion detection in conjunction with the increasing significance of cloud-based VNF systems for high-speed network operations. The proposed lightweight machine learning approach may direct the development of more efficient and effective intrusion detection systems to protect cloud based VNFs from security threats. To evaluate the method's usefulness in real-world cloud-based VNF systems and to explore its potential application to other kinds of cyber threat, more research is necessary.
  • A speech emotion recognition system using machine learning
    Reshma Kanse, Supriya Ajagekar, Trupti Patil, Harish Motekar, Vinod Rathod, Rahul Papalkar, Shabir Ali
    Artificial Intelligence Blockchain Computing and Security Proceedings of the International Conference on Artificial Intelligence Blockchain Computing and Security Icabcs 2023, 2024
    As we know speech is very direct way of expression our emotions. When it comes to human to human talks we can easily recognize each other emotions, feelings and views but when it comes to human to machine communication, it's difficult for system or machine to get exact emotions of the person. So here is when SERtion comes into picture. The SERtion is a machine learning project built on various classifiers and feature extraction techniques. SERtion aims to create a machine learning model that will recognize the exact emotions hidden in the audio signals. It follows various processing steps to accomplish the goal of creating a successful model. SERtion can be used in call centers where machine can predict the emotions of the customer that calls and gives feedback of their service or those who enquiry and even fire complaints for their service.
  • WeSafe: A safety app for all
    Reshma Kanse, Supriya Ajagekar, Trupti Patil, Harish Motekar, Vinod Rathod, Rahul Papalkar, Shabir Ali
    Artificial Intelligence Blockchain Computing and Security Proceedings of the International Conference on Artificial Intelligence Blockchain Computing and Security Icabcs 2023, 2024
  • Enhanced-honey bee based load balancing algorithm for cloud environment
    Saurabh Singhal, Shabir Ali, Dhirendra Kumar Shukla, Arvind Dagur, Rahul Papalkar, Vinod Rathod, Mohan Awasthy
    Artificial Intelligence Blockchain Computing and Security Proceedings of the International Conference on Artificial Intelligence Blockchain Computing and Security Icabcs 2023, 2024
  • Review of unknown attack detection with deep learning techniques
    Rahul Rajendra Papalkar, Abrar S. Alvi
    Artificial Intelligence Blockchain Computing and Security Volume 1, 2023
  • An optimized feature selection guided light-weight machine learning models for DDoS attacks detection in cloud computing
    Rahul R. Papalkar, A.S. Alvi, Shabir Ali, Mohan Awasthy, Reshma Kanse
    Artificial Intelligence Blockchain Computing and Security Volume 1, 2023
  • Enhanced-honey bee based load balancing algorithm for cloud environment
    Saurabh Singhal, Shabir Ali, Dhirendra Kumar Shukla, Arvind Dagur, Rahul Papalkar, Vinod Rathod, Mohan Awasthy
    Artificial Intelligence Blockchain Computing and Security Volume 1, 2023
  • WeSafe: A safety app for all
    Reshma Kanse, Supriya Ajagekar, Trupti Patil, Harish Motekar, Vinod Rathod, Rahul Papalkar, Shabir Ali
    Artificial Intelligence Blockchain Computing and Security Volume 1, 2023
  • A speech emotion recognition system using machine learning
    Reshma Kanse, Supriya Ajagekar, Trupti Patil, Harish Motekar, Vinod Rathod, Rahul Papalkar, Shabir Ali
    Artificial Intelligence Blockchain Computing and Security Volume 1, 2023
  • Analysis of Defense Techniques For Ddos Attack In Iot -Review
    Rahul Rajendra Papalkar, Abrar S Alvi
    Ecs Transactions, 2022
  • Issues of concern in storage system of IoT based big data
    Rahul R. Papalkar, Pravin R Nerkar, C.A. Dhote
    IEEE International Conference on Information Communication Instrumentation and Control Icicic 2017, 2017

RECENT SCHOLAR PUBLICATIONS

  • HC-LSTM: A Hybrid Deep Learning Model for Robust Intrusion Detection in IoMT Systems
    RR Papalkar, SK Singh
    Sustainable Global Societies Initiative 1 (4) , 2026
    2026
  • Enhancing IoT security: a creative swagger optimization algorithm for DDoS defence
    RR Papalkar, AS Alvi
    Network: Computation in Neural Systems 37 (2), 273-311 , 2026
    2026
    Citations: 3
  • Dist-PGQBM: Distributed Pyramidal Graph Attention-Enabled Deep Learning Model for Manipulated Timestamp Detection From Raw Disk Image
    RR Papalkar, YH Jadhav, PV Dudhe, MS Deshmukh, HS Motekar, T Patil, ...
    CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 38 (7) , 2026
    2026
  • Intelligent Intrusion Detection Systems for Mitigating Cyber Attacks: A Comprehensive Review
    RR Papalkar, SK Singh
    Sustainable Global Societies Initiative 1 (1) , 2026
    2026
  • EduChain: Secure Academic Credentialing with Blockchain
    RR Papalkar, K Kumavat, T Shinde, H Motekar, A Sardhara, K Katake
    International Conference on Sustainable Innovation with Artificial … , 2026
    2026
  • Federated ensemble learning framework for symptom-based lung cancer detection
    JS Jadhav, V Thorat, VG Ingole, D Bhise, P Landge, S Ali
    Artificial Intelligence and Sustainable Innovation, 308-313 , 2026
    2026
  • Optimized AI Security for IoMT: Hybrid PSO-ACO with CNN
    R Papalkar, VG Ingole, S Sarda, H Motekar, N Gawande, S Ali
    Artificial Intelligence and Sustainable Innovation, 461-468 , 2026
    2026
  • A robust deep learning approach for detecting COVID-19 and pneumonia in chest X-ray scans
    JS Jadhav, RR Papalkar, SN More, AM Pawar, SA Shinde, RV Kadam
    Artificial Intelligence and Sustainable Innovation, 352-356 , 2026
    2026
  • AI-Driven Dermatology: A Study on Skin Disease Identification Through Advanced
    MP Deshmukh, R Jagdale, R Papalkar
    Proceedings of International Conference on AI Systems and Sustainable … , 2025
    2025
  • Artificial intelligence for brain tumor diagnosis: a review of models, techniques, and clinical implications
    A Sonti, K Ahuja, HN Bhankele, RK Sinha, M Mahadik, R Papalkar
    IET Conference Proceedings CP967 2025 (43), 192-198 , 2025
    2025
  • Translating Hybrid ANN-ARIMA Diagnostic Models for Early Detection of Oncological Biomarkers
    RR Papalkar, J Jadhav, H Motekar, P Nerkar, SH Kuche, NS Band, ...
    Artificial Intelligence in Oncology: Cancer Diagnosis and Treatment, Medical … , 2025
    2025
  • Insights into Women’s Sentiments on Breast Cancer Detection, Causes, and Treatments: A Comprehensive Analysis
    K Kumavat, J Jadhav, T Shinde, R Papalkar, S Yadhav, S Bankar
    Artificial Intelligence in Oncology: Cancer Diagnosis and Treatment, Medical … , 2025
    2025
  • Optimizing key performance indicators in cloud computing: Scheduling techniques
    B Kanchalwar, R Papalkar, J Jadhav, P Bhagat, S Hiremath, SV Mahajan
    Intelligent Computing and Communication Techniques, 282-287 , 2025
    2025
  • Enhancing cloud coverage detection in remote sensing imagery through deep learning and advanced feature extraction
    J Jadhav, R Papalkar, M Pal, P Morey, V Thorat, P Bhagat
    Intelligent Computing and Communication Techniques, 425-431 , 2025
    2025
  • AI-Driven Dermatology: A Study on Skin Disease Identification Through Advanced Medical Imaging
    MP Deshmukh, R Jagdale, R Papalkar
    International Conference on AI Systems and Sustainable Technologies, 325-337 , 2025
    2025
    Citations: 1
  • WACSO: Wolf crow search optimizer for convolutional neural network hyperparameter optimization
    RR Papalkar, J Jadhav, T Pattewar, V Thorat, P Morey, M Deshmukh, ...
    Neural Processing Letters 57 (2), 31 , 2025
    2025
    Citations: 18
  • Neuro-guard: Reinforcing web security with convolutional neural networks against cross-site scripting attacks
    RR Papalkar, J Jadhav, V Thorat, P Morey, M Pal, S Ali
    Intelligent Computing and Communication Techniques, 762-769 , 2025
    2025
    Citations: 13
  • Forecasting the future of healthcare expenses: The role of machine learning in insurance cost estimation
    P Morey, M Pal, J Jadhav, R Papalkar
    Intelligent Computing and Communication Techniques, 682-688 , 2025
    2025
  • DEFENCE-AGAINST RANSOMWARE: SMART TECHNIQUE TO DETECT AND MITIGATE ATTACKS
    R Papalkar, AS Alvi, J Jadhav, M Pal, P Morey, V Thorat
    2025
    Citations: 6
  • Navigating the path: Deep neural networks for accurate pothole and road quality detection
    J Jadhav, R Papalkar, P Morey, S Dash, M Pal, R Agnihotri, V Thorat, ...
    Artificial Intelligence and Information Technologies, 508-515 , 2024
    2024
    Citations: 11

MOST CITED SCHOLAR PUBLICATIONS

  • Analysis of defense techniques for DDos attacks in IoT–A review
    RR Papalkar, AS Alvi
    Electrochemical Society Transactions 107 (1), 3061-3068 , 2022
    2022.0
    Citations: 36
  • Securing the internet of things: Investigating common attacks and defense strategies for a resilient ecosystem
    R Papalkar, AS Alvi, J Jadhav, R Agnihotri, S Ali, V Thorat
    Artificial Intelligence and Information Technologies, 516-523 , 2024
    2024.0
    Citations: 25
  • An optimized feature selection guided light-weight machine learning models for DDoS attacks detection in cloud computing
    RR Papalkar, AS Alvi, S Ali, M Awasthy, R Kanse
    Artificial Intelligence, Blockchain, Computing and Security Volume 1, 975-982 , 2023
    2023.0
    Citations: 20
  • WeSafe: A safety app for all
    R Kanse, S Ajagekar, T Patil, H Motekar, V Rathod, R Papalkar, S Ali
    Artificial Intelligence, Blockchain, Computing and Security Volume 1, 441-446 , 2023
    2023.0
    Citations: 19
  • WACSO: Wolf crow search optimizer for convolutional neural network hyperparameter optimization
    RR Papalkar, J Jadhav, T Pattewar, V Thorat, P Morey, M Deshmukh, ...
    Neural Processing Letters 57 (2), 31 , 2025
    2025.0
    Citations: 18
  • A speech emotion recognition system using machine learning
    R Kanse, S Ajagekar, T Patil, H Motekar, V Rathod, R Papalkar, S Ali
    Artificial Intelligence, Blockchain, Computing and Security Volume 1, 749-754 , 2023
    2023.0
    Citations: 18
  • Enhanced-honey bee based load balancing algorithm for cloud environment
    S Singhal, S Ali, DK Shukla, A Dagur, R Papalkar, V Rathod, M Awasthy
    Artificial Intelligence, Blockchain, Computing and Security Volume 1, 951-956 , 2023
    2023.0
    Citations: 17
  • Neuro-guard: Reinforcing web security with convolutional neural networks against cross-site scripting attacks
    RR Papalkar, J Jadhav, V Thorat, P Morey, M Pal, S Ali
    Intelligent Computing and Communication Techniques, 762-769 , 2025
    2025.0
    Citations: 13
  • Navigating the path: Deep neural networks for accurate pothole and road quality detection
    J Jadhav, R Papalkar, P Morey, S Dash, M Pal, R Agnihotri, V Thorat, ...
    Artificial Intelligence and Information Technologies, 508-515 , 2024
    2024.0
    Citations: 11
  • A hybrid CNN approach for unknown attack detection in edge-based IoT networks
    RR Papalkar, AS Alvi
    Eai Endorsed Transactions on Scalable Information Systems 11 (6) , 2024
    2024.0
    Citations: 10
  • Issues of concern in storage system of IoT based big data
    RR Papalkar, PR Nerkar, CA Dhote
    2017 International Conference on Information, Communication, Instrumentation … , 2017
    2017.0
    Citations: 10
  • Review of unknown attack detection with deep learning techniques
    RR Papalkar, AS Alvi
    Artificial Intelligence, Blockchain, Computing and Security Volume 1, 989-997 , 2023
    2023.0
    Citations: 7
  • DEFENCE-AGAINST RANSOMWARE: SMART TECHNIQUE TO DETECT AND MITIGATE ATTACKS
    R Papalkar, AS Alvi, J Jadhav, M Pal, P Morey, V Thorat
    2025.0
    Citations: 6
  • Fuzzy clustering in web text mining and its application in ieee abstract classification
    RR Papalkar, G Chandel
    International Journal of Computer Sciences and Management Research 2 (2 … , 2013
    2013.0
    Citations: 5
  • (2023). A speech emotion recognition system using machine learning
    R Kanse, S Ajagekar, T Patil, H Motekar, V Rathod, R Papalkar, S Ali
    In Artificial Intelligence, Blockchain, Computing and Security 1, 749-754 , 0
    Citations: 5
  • Enhancing IoT security: a creative swagger optimization algorithm for DDoS defence
    RR Papalkar, AS Alvi
    Network: Computation in Neural Systems 37 (2), 273-311 , 2026
    2026.0
    Citations: 3
  • Technical aspects of robust multi-frame super-resolution image reconstruction across diverse scenes
    P Morey, M Pal, J Jadhav, R Papalkar, S Dash, R Agnihotri, V Thorat, ...
    Artificial Intelligence and Information Technologies, 535-539 , 2024
    2024.0
    Citations: 3
  • An algorithm to study the mechanisms for exploring ChatGPT's effectiveness
    M Pal, P Morey, J Jadhav, R Papalkar, R Agnihotri, V Thorat
    Artificial Intelligence and Information Technologies, 540-546 , 2024
    2024.0
    Citations: 2
  • Crow Way: An Optimization Technique for generating the Weight and Bias in Deep CNN
    P Rahul Papalkar, Dr. Abrar Alvi, Solavande, D Deshmukh
    International Journal 10 (2), 1732-1750 , 2023
    2023.0
    Citations: 2
  • AI-Driven Dermatology: A Study on Skin Disease Identification Through Advanced Medical Imaging
    MP Deshmukh, R Jagdale, R Papalkar
    International Conference on AI Systems and Sustainable Technologies, 325-337 , 2025
    2025.0
    Citations: 1