Pawan Kumar Verma

@sharda.ac.in

Associate Professor, Computer Science & Engineering Department
Sharda University, Greater Noida, Uttar Pradesh, India

Pawan Kumar Verma


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https://researchid.co/abes.pawan
Associate Professor(CSE) |ML Researcher |Mentor@ Worldwide AI Hackathon |Mentor@ upGrad | 1 IEEE Transactions | 6 SCIE | 6 Scopus | 3 ESCI | Wipro Certified Java Trainer | 11.5 Yrs Exp. | Pega CSA Certified

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence
34

Scopus Publications

768

Scholar Citations

10

Scholar h-index

11

Scholar i10-index

Scopus Publications

  • Enhanced detection and mitigation of HELLO flood attacks in IoT networks using modified deep neural networks and optimization algorithms
    Pawan Kumar Verma, Mohit Kumar, Abhishek Gupta, Ashwini Kumar Saini, Ajeet Kumar Sharma, Nitin Rakesh
    Discover Internet of Things, 2026
    Abstract Internet of Things (IoT) is intermittently targeted by Distributed Denial of Service (DDoS) attacks, which occupy computational resources and bandwidth for preventing potential users from accessing services. The attack strategy involves massively flooding of packets. As the use of IoT foundations spreads around the globe, so are the number of attacks and threats that these systems face. The IoT are susceptibility to a range of network attacks due to their low processing capacity and wireless connection. One among these is the HELLO Flood attack, where an attacker who is not a legitimate node in the network floods HELLO requests to every genuine node in the network, compromising Wireless Sensor Network security. In this work we have designed a novel HELLO Flood attack detection and prevention model using the artificial intelligence. Here, the development of Modified Deep Neural Network (MDNN) is introduced for HELLO Flood attack detection, in which the hybrid Coyote Optimization Algorithm (COA) and Elephant Herding Optimization (EHO) called CoYote-Elephant Herding Algorithm (CY-EHA) is used for weight optimization as a training algorithm. The modified DNN works with the input parameters like “RSS, route discovery time, inter-route discovery, distance, data rate, and packet arrival rate”. Once the attack node is identified, it is removed utilizing CY-EHA. Experimental analysis showed that the proposed CY-EHA-MDNN reduced the cost function by 15.22%, 33.90%, 1.68%, and 30% compared to EHO-MDNN, COA-MDNN, GWO-MDNN, and PSO-MDNN, respectively, demonstrating superior performance in HELLO flood attack detection and prevention for IoT.
  • Correction: A Novel Decentralized Blockchain Architecture for the Preservation of Privacy and Data Security against Cyberattacks in Healthcare (Sensors, (2022), 22, 15, (5921), 10.3390/s22155921)
    Ajitesh Kumar, Akhilesh Kumar Singh, Ijaz Ahmad, Pradeep Kumar Singh, Anushree, Pawan Kumar Verma, Khalid A. Alissa, Mohit Bajaj, Ateeq Ur Rehman, Elsayed Tag-Eldin
    Sensors, 2025
    In the published publication [...]
  • Detection of DDoS Attack Using Machine Learning Techniques
    Komal Shakya, Esha Singh, Akshat Gautam, Ajeet Kumar Sharma, Pawan Kumar Verma
    2025 7th International Conference on Information Systems and Computer Networks Iscon 2025, 2025
  • ViTCon: a hybrid CNN-ViT model for improved plant leaf disease detection
    Pawan Kumar Verma, Nidhi Gupta, Ajeet Kumar Sharma, Nitin Rakesh, Monali Gulhane
    Cogent Food and Agriculture, 2025
    Sustainable agriculture relies on timely and efficient detection of leaf diseases to prevent the risk of crop contamination and the dependency on chemical treatments. The Convolutional Neural Network (CNN) has made a significant contribution in improving image-based disease detection. However, traditional CNNs often struggle with complex patterns, require large datasets, high computational costs, and memory consumption. In addition, Vision Transformers (ViT) have been established as a powerful tool because of their ability to capture long-range dependencies and complex patterns. However, it does not capture local and multiscale features of images, which is one of the primary requirements of image classification. To address these issues, this work proposes a novel approach called ViTCon that combines the advantages of CNN and ViT for the classification of leaf disease. The experimental results showed that the ViTCon approach outperforms than other approaches, evaluated on three different publicly available datasets of corn, rice, and wheat. The proposed approach shows the accuracy for corn, wheat and rice plants as 99.19%, 99.46% and 99.24% for binary classification, 99.20%, 99.46% and 99.28% for crop-wise multiclass classification, with overall average accuracy of 99.56% of multiclass classification. The strong performance of the ViTCon model ensures its potential in agricultural environment.
  • Design of AI Algorithms for Predicting Epidemic Outbreaks
    Sharmishtha K. Garud, Monali Gulhane, Kalpana Malpe, Pawan Verma, Shubham Kaldate, Nitin Rakesh
    Proceedings 2025 IEEE 3rd International Symposium on Sustainable Energy Signal Processing and Cybersecurity Isssc 2025, 2025
    The speed with which deadly diseases are spreading around the world has made it even more important to have good models that can identify when epidemics will happen. Most of the time, traditional ways of predicting epidemics are based on data from the past and the knowledge of experts. These methods may not be able to handle the complexity of new diseases well. This essay looks at how to create and use advanced AI systems that can predict when an epidemic will happen. These studies use many types of machine learning methods, like decision trees, support vector machines (SVMs), and recurrent neural networks (RNNs), to look for trends in data about early disease spread or guided learning. We suggest a unified method that uses real-time data from social media sites, public health sources, and weather data to make the model better at making predictions. The study also utilizes reinforcement learning techniques to continuously enhance forecasts based on new data received, and the researchers test those algorithms using a variety of data to see how well they work. This includes information about past outbreaks, maps and health reports from various regions. The results demonstrate that the AI-powered method makes high-quality predictions, is timely and offers a substantial flexibility benefit compared to off-the-shelf methods. Combining AI algorithms and real-time monitoring systems can also be used by health organizations and states to inform resourcing, plan actions and mitigations against the spread of outbreaks. The results highlight the potential that AI could have for shaping global health policy and making disease control more nimble and effective.
  • LPITutor: An LLM based personalized intelligent tutoring system using RAG and prompt engineering
    Zhensheng Liu, Prateek Agrawal, Saurabh Singhal, Vishu Madaan, Mohit Kumar, Pawan Kumar Verma
    Peerj Computer Science, 2025
    Development of large language models (LLMs) has transformed the landscape of personalized education through intelligent tutoring systems (ITS) which responds to diverse learning requirements. This article proposed a model named LLM based Personalized Intelligent Tutoring System (LPITutor) that is based on LLM for personalized ITS that leverages retrieval-augmented generation (RAG) and advanced prompt engineering techniques to generate customized responses aligned with students’ requirements. The aim of LPITutor is to provide customized learning content that adapts to different levels of learners skills and question complexity. The performance of proposed model was evaluated on accuracy, completeness, clarity, difficulty alignment, coherence, and relevance. The finding of LPITutor indicates that it effectively balances the response accuracy and clarity with significant alignment to the difficulty level of student queries. The proposed work also emphasises the broader implications of artificial intelligence (AI)-driven ITS in education and presents future directions for improving the adaptation and optimization of LPITutor.
  • Human-Robot Interaction in Healthcare: Enhancing Safety and Efficiency in Clinical Settings
    Annasaheb B. Gharge, Monali Gulhane, Vibha Vyas, Pawan Verma, Snigdha Sehghal, Nitin Rakesh
    Proceedings 2025 IEEE 3rd International Symposium on Sustainable Energy Signal Processing and Cybersecurity Isssc 2025, 2025
    The field of healthcare is being revolutionized by robotics technology like never before, especially within the areas of hospitals. What was once considered science fiction is now a reality with robots introduced into the arena to aid and support medical personnel across the board from robots supporting surgical procedures to robot aid in patient care and handling routine medical administrative matters. Any desire to lead in this sector must now include a most thorough knowledge of how robots may be integrated most effectively into the healthcare situation. Hospitals nowadays use robots for surgical accuracy, therapeutic insertion and the streamlining of everyday duties. In spite of problems of utilization, enormous problems exist in connection with robotics, with an emphasis on the protection of patients and staff. A resolution to the problems involved in the continuous development of artificial intelligence and sensory equipment is most important if robots are to be learned to see and conform to the ever-changing and unpredictable hospital areas. Attention is called within the literature to the practical utilization of robots, pointing out hospitals where robotic technology has been successfully introduced to improve flows of work, assist the hospital personnel, and eliminate the hazards of human error. All of these procedures, while increasing operational efficiency, contribute to a better safety for the working environment, eliminate costs, etc. But the introduction of robotics into healthcare is not without its problems of ethics in connection with patient confidentiality or the affects of emotional results on the delivery of care, all which must be considered very carefully. In conclusion, this study confirms the necessity for hospital administrators to improve their state of technological literacy.
  • An Ontology Alignment based on Machine learning for Integration of Patient Health Data
    Nidhi Gupta, Pawan Kumar, Sundeep Raj, Anu Shree, Nitin Rakesh, Monali Gulhane
    International Journal of Computing and Digital Systems, 2025
    : The integration of patient data is crucial in healthcare informatics. It involves organizing and integrating heterogeneous health data from various Electronic Health Records (EHRs). Attribute alignment is a fundamental step in data integration. It involves mapping data attributes across di ff erent datasets. Most of the data maintained in EHRs does not follow standard terminologies in healthcare. Therefore, it becomes di ffi cult to integrate patient health data from diverse data sources for generating historic medical records. The research work carried out overcomes this problem by developing a vital sign ontology using OpenEHR health standards. It helps to map the vital signs observations of the patients from its proprietary sources uniformly. The work also leverages the power of supervised learning algorithms to automate the mapping of di ff erent health datasets to the proposed ontology. The approach is evaluated on patient health datasets, considering both standard and non-standard datasets. The research work employs di ff erent machine learning algorithms, such as Support Vector Machine (SVM), Naive Bayes, Logistic Regression, k-nearest neighbor (KNN), AdaBoost, and Neural network, in order to evaluate the best algorithm for the proposed approach. The evaluation results conclude that Naive Bayes exhibits the highest accuracy, with minimum misclassification rate, in both the training and validation phases for automatically mapping the health datasets with the proposed ontology.
  • AI-Based Wearable Systems for Detecting Cardiovascular Anomalies
    Suhas R. Mule, Monali Gulhane, Diksha Gabhane, Nitin Rakesh, Rahul M., Pawan Kumar Verma
    2025 Global Conference on Information Technology and Communication Networks Gitcon 2025, 2025
    Cardiovascular illnesses (CVDs) are still the top cause of death in the world. This highlights the importance of identifying cardiovascular health problems early and monitoring them closely. Traditional ways of finding heart problems often use fixed and invasive methods, which might not be possible for regular checks, especially for people with a high risk. This study suggests an intelligent AI system that can find problems with the heart in real time, offering a noninvasive and effective way to start early evaluation and treatment. The system uses high-tech devices to gather bodily data like blood pressure, heart rate, and an EKG (ECG). It then uses machine learning techniques to look at this data for signs of possible problems, such as seizures, ischemia, and other issues. It uses both controlled and unsupervised learning methods to make sure that the AI model correctly sorts and predicts circulatory events. The portable system constantly checks the wearer's cardiovascular health and sends real-time alerts so that medical help can be given right away if needed. This proactive method not only improves patient health outcomes but also personalizes healthcare by providing information on each person's health trends based on data. The system can work continuously and quietly, making it suitable for both professional and everyday use. It gives patients and healthcare workers an easy-to-use tool for controlling cardiovascular risk. With its unique approach to early diagnosis and avoidance of cardiovascular diseases, the suggested AI-based innovative system is a big step towards better cardiovascular health management.
  • Engineering AI-Driven Predictive Maintenance Systems for Medical Equipment
    Mario Antony, Monali Gulhane, Nisha Wankhade, Nitin Rakesh, Kalpana Malpe, Pawan Kumar Verma
    2025 Global Conference on Information Technology and Communication Networks Gitcon 2025, 2025
    The standard of patient care has gotten a lot better as medical technology has grown so quickly. But the dependability and life of medical tools are very important for keeping these progresses going. Unplanned downtime because of broken equipment can make it harder to care for patients and cost more to maintain. Scheduled checks and fixes done after the fact are common ways of maintaining things the old way, which might not catch problems before they break. This paper shows a new way to use AI-driven systems for predicted repair on hospital tools. Using machine learning algorithms and IoT (Internet of Things) devices, the suggested system checks on the health of equipment all the time, looks at data in real time, and guesses when problems might happen before they do. The AI model is taught by looking at old data from medical devices to find trends of wear and failure. This lets repair plans be made ahead of time. This forecast method increases the usage of equipment, lowers the cost of upkeep, and makes sure that medical gadgets are working at their best, which makes healthcare centres more efficient overall. In addition, the method makes medical operations safer by lowering the chance that tools will break down at crucial times. Case studies from healthcare settings show how well the system works in different kinds of medical technology, like surgery tools, testing machines, and life-support systems. The paper talks about the problems with execution, like combining data and making sure the model is correct, and gives ways to get around these problems. In the end, the suggested AI-driven predictive maintenance system would completely change how medical equipment is handled, leading to better care for patients, lower costs, and flawless operations.
  • Federated query processing for data integration using semantic web technologies: A review
    Nidhi Gupta, Pawan Verma, Monali Gulhane, Nitin Rakesh, Ahmed A. Elngar
    Artificial Intelligence Using Federated Learning Fundamentals Challenges and Applications, 2024
  • Machine learning and internet of things in tourism and hospitality: Opening new frontiers with old challenges
    Integration of Artificial Intelligence and Machine Learning Methods for Smart Internet of Things Systems and Its Applications, 2024
  • An Evaluation and Comparison for Phishing Attack Detection using Machine Learning Approaches
    Ajeet Kumar Sharma, Anushree, Nitin Rakesh, Pawan Kumar Verma
    2024 3rd International Conference on Power Electronics and Iot Applications in Renewable Energy and Its Control Parc 2024, 2024
  • A Comparative Analysis of Alzheimer's Disease Detection using Deep Learning
    Garvit Sharma, Dinki Gupta, Priyanshu Bhardwaj, Ramneet, Pawan Verma
    3rd International Conference on Communication Control and Intelligent Systems Ccis 2024, 2024
  • Front Matter ACI-2023
    Ceur Workshop Proceedings, 2024
  • Online Signature Classification Based on Dynamic Nature of Features Selection Framework
    Akhilesh Kumar Singh, Surabhi Kesarwani, Anushree Anushree, Pawan Kumar Verma, Nitin Rakesh, Monali Gulhane
    International Journal of Computing and Digital Systems, 2024
  • Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework
    Md. Amir Khusru Akhtar, Mohit Kumar, Sahil Verma, Korhan Cengiz, Pawan Kumar Verma, Ruba Abu Khurma, Moutaz Alazab
    Journal of Intelligent Systems, 2024
  • Energy Efficient Load Balancing Algorithm for Cloud Computing Using Rock Hyrax Optimization
    Saurabh Singhal, Ashish Sharma, Anushree, Pawan Kumar Verma, Mohit Kumar, Sahil Verma, Kavita, Maninder Kaur, Joel J. P. C. Rodrigues, Ruba Abu Khurma, Maribel García-Arenas
    IEEE Access, 2024
  • MCred: multi-modal message credibility for fake news detection using BERT and CNN
    Pawan Kumar Verma, Prateek Agrawal, Vishu Madaan, Radu Prodan
    Journal of Ambient Intelligence and Humanized Computing, 2023
  • UCred: fusion of machine learning and deep learning methods for user credibility on social media
    Pawan Kumar Verma, Prateek Agrawal, Vishu Madaan, Charu Gupta
    Social Network Analysis and Mining, 2022
  • A Novel Decentralized Blockchain Architecture for the Preservation of Privacy and Data Security against Cyberattacks in Healthcare
    Ajitesh Kumar, Akhilesh Kumar Singh, Ijaz Ahmad, Pradeep Kumar Singh, Anushree, Pawan Kumar Verma, Khalid A. Alissa, Mohit Bajaj, Ateeq Ur Rehman, Elsayed Tag-Eldin
    Sensors, 2022
  • Quality analysis for reliable complex multiclass neuroscience signal classification via electroencephalography
    Ashutosh Shankhdhar, Pawan Kumar Verma, Prateek Agrawal, Vishu Madaan, Charu Gupta
    International Journal of Quality and Reliability Management, 2022
  • PropFND: Propagation Based Fake News Detection
    Pawan Kumar Verma, Prateek Agrawal
    Lecture Notes in Electrical Engineering, 2022
  • WELFake: Word Embedding over Linguistic Features for Fake News Detection
    Pawan Kumar Verma, Prateek Agrawal, Ivone Amorim, Radu Prodan
    IEEE Transactions on Computational Social Systems, 2021
  • Study and Detection of Fake News: P2C2-Based Machine Learning Approach
    Pawan Kumar Verma, Prateek Agrawal
    Advances in Intelligent Systems and Computing, 2021
  • Exploration of text classification approach to classify news classification
    International Journal of Advanced Science and Technology, 2020
  • Palm print recognition using CEDA
    Shalini Agarwal, Vivek Sharma, Pawan Kumar Verma
    Proceedings of the 3rd International Conference on Computing Methodologies and Communication Iccmc 2019, 2019
  • Auto adaptive differential evolution algorithm
    Vivek Sharma, Shalini Agarwal, Pawan Kumar Verma
    Proceedings of the 3rd International Conference on Computing Methodologies and Communication Iccmc 2019, 2019
  • Credibility investigation for tweets and its users
    Pawan Kumar Verma, Vivek Sharma, Shalini Agarwal
    Proceedings of the 3rd International Conference on Computing Methodologies and Communication Iccmc 2019, 2019
  • Opinion mining considering roman words using Jaccard similarity algorithm based on clustering
    Pawan Kumar Verma, Shalini Agarwal, Mohd Aamir Khan
    8th International Conference on Computing Communications and Networking Technologies Icccnt 2017, 2017
  • An optimized palm print recognition approach using Gabor filter
    Shalini Agarwal, Pawan Kumar Verma, Mohd Aamir Khan
    8th International Conference on Computing Communications and Networking Technologies Icccnt 2017, 2017
  • Enterprise systems development: Impact of Aspect Oriented Software Architecture
    Seke 2010 Proceedings of the 22nd International Conference on Software Engineering and Knowledge Engineering, 2010
  • Using aspect oriented software architecture for enterprise systems development
    Pawan Kumar Verma, Deepak Dahiya, Pooja Jain
    2010 5th International Conference on Digital Information Management Icdim 2010, 2010
  • J2EE framework perspective for security augmentation
    Pawan Kumar Verma, Neetu Singh, Rahul Katarya
    IEEE Region 10 Annual International Conference Proceedings TENCON, 2009

RECENT SCHOLAR PUBLICATIONS

  • ViTCon: a hybrid CNN-ViT model for improved plant leaf disease detection
    PK Verma, N Gupta, AK Sharma, N Rakesh, M Gulhane
    Cogent Food & Agriculture 11 (1), 2562165 , 2025
    2025
    Citations: 4
  • A novel classification of meditation techniques via optimised chi-squared 1D-CNN method based on complexity, continuity and connectivity features
    A Jain, R Raja, M Kumar, PK Verma
    Connection Science 37 (1), 2467387 , 2025
    2025
    Citations: 6
  • LPITutor: an LLM based personalized intelligent tutoring system using RAG and prompt engineering
    Z Liu, P Agrawal, S Singhal, V Madaan, M Kumar, PK Verma
    PeerJ Computer Science 11, e2991 , 2025
    2025
    Citations: 19
  • Federated Query Processing for Data Integration Using Semantic Web Technologies: A Review
    N Gupta, P Verma, M Gulhane, N Rakesh, AA Elngar
    Artificial Intelligence Using Federated Learning, 276-291 , 2024
    2024
    Citations: 1
  • Unifying optimization forces: Harnessing the fine-structure constant in an electromagnetic-gravity optimization framework
    MAK Akhtar, M Kumar, S Verma, K Cengiz, PK Verma, RA Khurma, ...
    Journal of Intelligent Systems 33 (1), 20230306 , 2024
    2024
  • A Comparative Analysis of Alzheimer’s Disease Detection using Deep Learning
    G Sharma, D Gupta, P Bhardwaj, P Verma
    2024 International Conference on Communication, Control, and Intelligent … , 2024
    2024
    Citations: 3
  • An ontology alignment based on machine learning for integration of patient health data
    N Gupta, S Raj, PK Verma, N Rakesh, M Gulhane
    University of Bahrain , 2024
    2024
    Citations: 3
  • Online Signature Classification Based on Dynamic Nature of Features Selection Framework
    A Kumar Singh, S Kesarwani, P Kumar Verma, N Rakesh, M Gulhane
    University of Bahrain , 2024
    2024
  • Energy efficient load balancing algorithm for cloud computing using rock hyrax optimization
    S Singhal, A Sharma, PK Verma, M Kumar, S Verma, M Kaur, ...
    IEEE access 12, 48737-48749 , 2024
    2024
    Citations: 55
  • An evaluation and comparison for phishing attack detection using machine learning approaches
    AK Sharma, N Rakesh, PK Verma
    2024 3rd International conference on Power Electronics and IoT Applications … , 2024
    2024
    Citations: 8
  • Front Matter ACI-2023
    P Agrawal, A Kumar, V Madaan, V Kashansky, PK Verma
    2024
  • MCred: multi-modal message credibility for fake news detection using BERT and CNN
    PK Verma, P Agrawal, V Madaan, R Prodan
    Journal of Ambient Intelligence and Humanized Computing 14 (8), 10617-10629 , 2023
    2023
    Citations: 85
  • UCred: fusion of machine learning and deep learning methods for user credibility on social media
    PK Verma, P Agrawal, V Madaan, C Gupta
    Social Network Analysis and Mining 12 (1), 54 , 2022
    2022
    Citations: 31
  • PropFND: propagation based fake news detection
    PK Verma, P Agrawal
    Applications of Artificial Intelligence and Machine Learning: Select … , 2022
    2022
    Citations: 6
  • A novel decentralized blockchain architecture for the preservation of privacy and data security against cyberattacks in healthcare
    A Kumar, AK Singh, I Ahmad, P Kumar Singh, Anushree, PK Verma, ...
    Sensors 22 (15), 5921 , 2022
    2022
    Citations: 95
  • Quality analysis for reliable complex multiclass neuroscience signal classification via electroencephalography
    A Shankhdhar, PK Verma, P Agrawal, V Madaan, C Gupta
    International Journal of Quality & Reliability Management 39 (7), 1676-1703 , 2022
    2022
    Citations: 9
  • A PLS-SEM Approach for Analysing Job Satisfaction and Human Resource Practices in Indian Banking Sector
    N Gupta, PK Verma
    Proceedings of Second International Conference in Mechanical and Energy … , 2022
    2022
    Citations: 2
  • A PLS-SEM Approach for Analysing Job
    N Gupta, PK Verma
    Proceedings of Second International Conference in Mechanical and Energy … , 2022
    2022
  • REGRESSION ANALYSIS OF MICROBIAL CONCRETE ON CALCINED CLAY BASED CONCRETE
    A SHUKLA, N GUPTA, P VERMA
    2022
  • Regression Analysis for Assessing the Impact of Megaterium Bacterial Solution on Bagasse Ash Concrete
    AK Parashar, A Gupta, N Gupta, P Verma
    2022
    Citations: 1

MOST CITED SCHOLAR PUBLICATIONS

  • WELFake: Word embedding over linguistic features for fake news detection
    PK Verma, P Agrawal, I Amorim, R Prodan
    IEEE Transactions on Computational Social Systems 8 (4), 881-893 , 2021
    2021
    Citations: 360
  • A novel decentralized blockchain architecture for the preservation of privacy and data security against cyberattacks in healthcare
    A Kumar, AK Singh, I Ahmad, P Kumar Singh, Anushree, PK Verma, ...
    Sensors 22 (15), 5921 , 2022
    2022
    Citations: 95
  • MCred: multi-modal message credibility for fake news detection using BERT and CNN
    PK Verma, P Agrawal, V Madaan, R Prodan
    Journal of Ambient Intelligence and Humanized Computing 14 (8), 10617-10629 , 2023
    2023
    Citations: 85
  • Energy efficient load balancing algorithm for cloud computing using rock hyrax optimization
    S Singhal, A Sharma, PK Verma, M Kumar, S Verma, M Kaur, ...
    IEEE access 12, 48737-48749 , 2024
    2024
    Citations: 55
  • UCred: fusion of machine learning and deep learning methods for user credibility on social media
    PK Verma, P Agrawal, V Madaan, C Gupta
    Social Network Analysis and Mining 12 (1), 54 , 2022
    2022
    Citations: 31
  • LPITutor: an LLM based personalized intelligent tutoring system using RAG and prompt engineering
    Z Liu, P Agrawal, S Singhal, V Madaan, M Kumar, PK Verma
    PeerJ Computer Science 11, e2991 , 2025
    2025
    Citations: 19
  • An optimized palm print recognition approach using Gabor filter
    S Agarwal, PK Verma, MA Khan
    2017 8th International Conference on Computing, Communication and Networking … , 2017
    2017
    Citations: 18
  • Performance evaluation of bio concrete by cluster and regression analysis for environment protection
    A Shukla, N Gupta, KR Singh, P Kumar Verma, M Bajaj, AA Khan, ...
    Computational Intelligence and Neuroscience 2022 (1), 4411876 , 2022
    2022
    Citations: 13
  • Study and Detection of Fake News: P 2 C 2 -Based Machine Learning Approach
    PK Verma, P Agrawal
    Data Management, Analytics and Innovation: Proceedings of ICDMAI 2020 … , 2020
    2020
    Citations: 11
  • A mobile ad-hoc routing algorithm with comparative study of earlier proposed algorithms
    PK Verma, T Gupta, N Rakesh, N Nitin
    Jaypee University of Information Technology, Solan, HP , 2010
    2010
    Citations: 11
  • Opinion mining considering roman words using Jaccard similarity algorithm based on clustering
    PK Verma, S Agarwal, MA Khan
    2017 8th International Conference on Computing, Communication and Networking … , 2017
    2017
    Citations: 10
  • Quality analysis for reliable complex multiclass neuroscience signal classification via electroencephalography
    A Shankhdhar, PK Verma, P Agrawal, V Madaan, C Gupta
    International Journal of Quality & Reliability Management 39 (7), 1676-1703 , 2022
    2022
    Citations: 9
  • An evaluation and comparison for phishing attack detection using machine learning approaches
    AK Sharma, N Rakesh, PK Verma
    2024 3rd International conference on Power Electronics and IoT Applications … , 2024
    2024
    Citations: 8
  • A novel classification of meditation techniques via optimised chi-squared 1D-CNN method based on complexity, continuity and connectivity features
    A Jain, R Raja, M Kumar, PK Verma
    Connection Science 37 (1), 2467387 , 2025
    2025
    Citations: 6
  • PropFND: propagation based fake news detection
    PK Verma, P Agrawal
    Applications of Artificial Intelligence and Machine Learning: Select … , 2022
    2022
    Citations: 6
  • Credibility investigation for tweets and its users
    PK Verma, V Sharma, S Agarwal
    2019 3rd International Conference on Computing Methodologies and … , 2019
    2019
    Citations: 6
  • Auto adaptive differential evolution algorithm
    V Sharma, S Agarwal, PK Verma
    2019 3rd International Conference on Computing Methodologies and … , 2019
    2019
    Citations: 5
  • ViTCon: a hybrid CNN-ViT model for improved plant leaf disease detection
    PK Verma, N Gupta, AK Sharma, N Rakesh, M Gulhane
    Cogent Food & Agriculture 11 (1), 2562165 , 2025
    2025
    Citations: 4
  • Using aspect oriented software architecture for enterprise systems development
    PK Verma, D Dahiya, P Jain
    2010 Fifth International Conference on Digital Information Management (ICDIM … , 2010
    2010
    Citations: 4
  • A Comparative Analysis of Alzheimer’s Disease Detection using Deep Learning
    G Sharma, D Gupta, P Bhardwaj, P Verma
    2024 International Conference on Communication, Control, and Intelligent … , 2024
    2024
    Citations: 3