Saroj Kumar Panigrahy

@vitap.ac.in

Associate Professor and School of Computer Science and Engineering
VIT-AP University

Saroj Kumar Panigrahy


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https://researchid.co/skpanigrahy

EDUCATION

Ph.D - NIT Rourkela, April 2019
M.Tech - NIT Rourkela, May 2008

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Networks and Communications, Artificial Intelligence, Computer Science, Computer Science Applications
35

Scopus Publications

921

Scholar Citations

14

Scholar h-index

18

Scholar i10-index

Scopus Publications

  • A Comparative Study of Deep Learning Techniques for Breast Cancer Detection Using Digital Breast Tomosynthesis on 3D DICOM Images
    Bhargav Hirani, Pragya Singh, Dipanshi Solanki, Priya Kumari, Saroj Kumar Panigrahy
    Lecture Notes in Electrical Engineering, 2026
  • Quantum-Driven Chaos-Informed Deep Learning Framework for Efficient Feature Selection and Intrusion Detection in IoT Networks
    Padmasri Turaka, Saroj Kumar Panigrahy
    Technologies, 2025
    The rapid development of the Internet of Things (IoT) poses significant problems in securing heterogeneous, massive, and high-volume network traffic against cyber threats. Traditional intrusion detection systems (IDSs) are often found to be poorly scalable, or are ineffective computationally, because of the presence of redundant or irrelevant features, and they suffer from high false positive rates. Addressing these limitations, this study proposes a hybrid intelligent model that combines quantum computing, chaos theory, and deep learning to achieve efficient feature selection and effective intrusion classification. The proposed system offers four novel modules for feature optimization: chaotic swarm intelligence, quantum diffusion modeling, transformer-guided ranking, and multi-agent reinforcement learning, all of which work with a graph-based classifier enhanced with quantum attention mechanisms. This architecture allows as much as 75% feature reduction, while achieving 4% better classification accuracy and reducing computational overhead by 40% compared to the best-performing models. When evaluated on benchmark datasets (NSL-KDD, CICIDS2017, and UNSW-NB15), it shows superior performance in intrusion detection tasks, thereby marking it as a viable candidate for scalable and real-time IoT security analytics.
  • Design of an Efficient Model for Psychological Disease Analysis and Prediction Using Machine Learning and Genomic Data Samples
    Alparthi Kumuda, Saroj Kumar Panigrahy
    Big Data and Cognitive Computing, 2025
    There is a rapid growth in mental disorders, thus leading to a pressing demand for more sophisticated diagnosis techniques. Clinical assessments and symptomatic analyses for traditional diagnostics suffer from subjectivity, delayed diagnosis, and specificity deficiencies. Therefore, this study developed the Psychological Disorders Machine Learning Genomic (PDMLG) model as an amalgamation of genetic algorithms and machine learning techniques in a predictive analysis model using genomic data samples. The two central components of the PDMLG model include the Genomic Fusion Model, which uses ensemble learning techniques like Random Forest, Gradient Boosting, and Neural Networks, and Deep Learning Model of Convolutional and Recurrent Neural Networks in processing genomic sequence data samples. The model enhanced the disease classification and early detection where the model achieved improvement in precision, recall, and specificity by 3.5% to 9.4% compared to the baseline methods Near Neighbor-Boundary Enlargement (NNBE), Collaborative Mmatrix Factorization based on Correntropy (LDCMFC), and Microsatellite Instability (MSI). The area under the curve of this model is up to 94.95%, which reflects the model’s robust performance on a variety of diseases like Schizophrenia, Bipolar Disorders, and Alzheimer’s. In addition, the PDMLG model can indicate important genetic markers, and this is vital for understanding the genetic basis of psychological conditions that may be diagnosed early and treatment plans prepared in advance for this process. This is a step forward in personalized medicine, which could revolutionize clinical practice in mental disorders diagnostics. This would not be substituted for the established psychological or doctor evaluations. However, it was considered a complementary tool auxiliary for the professional know-how and gives data-related insights that the professional should corroborate for this.
  • A ROBUST EXPLAINABLE RECURRENT DEEP Q LEARNING FOR DETECTING MULTICLASS INTRUSIONS IN IOT
    Journal of Theoretical and Applied Information Technology, 2025
  • Chaotic Adaptive Particle Swarm Optimization and Quantum-Inspired Genetic Algorithm for Robust Feature Selection in IoT Intrusion Detection
    Padmasri Turaka, Saroj Kumar Panigrahy
    1st International Conference on Sustainable Energy Technologies and Computational Intelligence Towards Sustainable Energy Transition Setcom 2025, 2025
    The exponential growth of IoT data necessitates efficient feature selection for managing high-dimensional datasets while ensuring optimal classification performance. Traditional algorithms face premature convergence, local optima stagnation, and computational inefficiency. To address these, we used the following three advanced techniques. Chaotic Adaptive Particle Swarm Optimization (CAPSO) which integrates chaotic maps to improve exploration and diversity, avoiding premature convergence. It achieves 40%-60% feature reduction, 97%-98% accuracy, and 2%-3% higher accuracy scores than standard PSO. Hybrid Quantum Genetic Algorithm (HQGA) employs quantum principles to escape local optima, achieving 50%-65% feature reduction, 2% higher accuracy than CAPSO, and 10%-15% lower computational cost. Deep Learning-Assisted Chaotic Sparrow Search Algorithm (DLCSSA) combines chaotic maps with SSA and deep learning (e.g., transformer-based models) to guide the fitness function. It achieves 45%-55% feature reduction, 98%-99% accuracy, and 20% computational savings compared to standard SSA. By integrating chaos theory, quantum mechanics, and deep learning, these state-of-the-art methods significantly enhance performance, reduce computational costs, and improve robustness in IoT data analytics.
  • Dynamic Attack Detection in IoT Networks: An Ensemble Learning Approach With Q-Learning and Explainable AI
    Padmasri Turaka, Saroj Kumar Panigrahy
    IEEE Access, 2024
    Due to the exponential increase in work-from-home adoption, Internet of Things (IoT) networks are under threat of constant attacks from internal and external adversaries. Thus, intrusion detection (ID) has become a vital component while designing interconnected networks. Existing ID Models for IoT either work on static attacks or incorporate high-complexity models for the detection of dynamic network attacks. Moreover, most of these models are unable to scale under hybrid attack scenarios. The work suggests creating an effective Ensemble Learning-based attack detection System for the classification of Dynamic attacks to address these problems. Initially, the suggested methodology uses network logs to gather several data samples for various breaches. These samples are represented as multidomain feature sets including Gabor, entropy, wavelet, frequency, and correlation components among other components. A moth flame optimizer (MFO) is used to choose the extracted components and helps identify Feature variance sets with high interclass variances. The selected features are categorized into different attack classes via an ensemble of k nearest neighbors (kNN), support vector machine (SVM), logistic regression (LR), Naïve Bayes (NB), and multilayer perceptron (MLP) algorithms. The results obtained from these classifiers are further tuned via the use of a Q-Learning based dynamic attack identification process. This process identifies micro-attack signatures via explainable artificial intelligence (XAI) to re-train the feature extraction and selection layer, thereby assisting in the classification of hybrid dynamic attacks. The XAI layer is built using a combination of XceptionNet and Transfer learning, which assists in continuous enhancements in attack mitigation even during dynamic attacks. These procedures allow the suggested model can improve attack classification accuracy by 8.5%, precision by 4.9%, and recall by 6.4%, while reducing the complexity by 5.9% when compared with existing attack categorization techniques.
  • Design of an Efficient Model for Detecting Diseases Using Genomics with Graph Relationship Networks
    Kumuda Alparthi, Saroj Kumar Panigrahy
    2024 4th International Conference on Artificial Intelligence and Signal Processing Aisp 2024, 2024
    This paper introduces novel approaches utilizing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Deep Autoencoders, and Graph Neural Networks (GNNs) in genomics. As the existing methods, while valuable, have limitations that hinder their effectiveness and often struggle with precision, accuracy, and speed in genome classification, leading to suboptimal results. Moreover, their inability to efficiently analyze multiple omics data and non-coding DNA limits our understanding of complex disease mechanisms. In contrast, our proposed models harness the power of CNNs for identifying disease-related genetic markers, achieving automatic feature detection, reducing bias, and enhancing accuracy. Additionally, the utilization of RNNs, specifically Long Short-Term Memory (LSTM) networks, enables the precise annotation of genomic variants by understanding long-term dependencies in genetic sequences. Deep Autoencoders facilitate comprehensive disease analysis and Graph Neural Networks (GNNs) delve into non-coding DNA, revealing intricate regulatory mechanisms and functions previously hidden. Our models exhibit a remarkable 9.5% improvement in genome classification precision, 8.5% higher accuracy, 6.5% enhanced recall, 8.3% increased speed, and a 10.5% superior Area Under the Curve (AUC) when compared to existing methods. This paper represents a significant step forward in genomics research, paving the way for more precise diagnostics and treatments for genetic diseases.
  • Aspect-Based Sentiment Analysis by Leveraging Machine Learning Techniques
    Yadavalli Uday Shankar, Somya Ranjan Sahoo, Saroj Kumar Panigrahy, Medeswara Rao Kondamudi
    Proceedings 2024 Oits International Conference on Information Technology Ocit 2024, 2024
    Based Sentiment Analysis (ABSA) is a task in Natural Language Processing that seeks to identify and extract the sentiment associated to specific parts or aspects of a product or service. ABSA often entails a sequential procedure that commences with the identification of the specific components or characteristics of the product or service that are being addressed in the text. Next, sentiment analysis is conducted to give a sentiment polarity to each aspect, taking into account the context of the sentence or document. Ultimately, the outcomes are combined to generate a comprehensive emotion for each individual feature. The technique entails training machine learning models to categorize the sentiment of text as either positive, negative, or neutral. Initially, we convert textual data using the Term Frequency-Inverse Document Frequency (TF-IDF) technique, which assigns weights to words depending on their significance within a collection of documents. This highlights the use of useful terminology. Subsequently, the TF-IDF features are inputted into the machine learning models. SVM determine an optimal hyperplane to effectively distinguish sentiment classes, whereas Logistic Regression computes the likelihood of a text being assigned to a certain sentiment class. Random Forest, on the other hand, constructs multiple decision trees and aggregates their results to enhance the accuracy and robustness of sentiment analysis. A series of comprehensive experiments were carried out on covid vaccinations dataset. The results indicate that the Logistic Regression model demonstrates exceptional performance in both aspect extraction and sentiment classification. The sentiment expressed on Twitter can exhibit an imbalance, with a prevalence of either positive or negative tweets contingent upon the subject matter. This can have an impact on the training process. Utilizing techniques such as oversampling or undersampling may be required for the minority class. This study examines the efficacy of machine learning algorithms in a particular classification challenge. The performance of Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest was examined. The results demonstrate that Logistic Regression outperformed the SVM and Random Forest in terms of accuracy, achieving a rate of 92.87% compared to 91% and 87%, respectively. This suggests that Logistic Regression is a more appropriate choice for this classification task, given its superior accuracy and overall nerformance.
  • Leveraging Generative and Explainable AI for Electric Vehicle Energy Toward Sustainable, Consumer-Centric Transportation
    Prasant Kumar Mohanty, K. Hemant Kumar Reddy, Saroj Kumar Panigrahy, Diptendu Sinha Roy
    IEEE Access, 2024
  • A Machine Learning Model to Classify Indian Taxi System in Tourism Industry
    Krutibash Nayak, Saroj Kumar Panigrahy
    2023 3rd International Conference on Artificial Intelligence and Signal Processing Aisp 2023, 2023
    India is now becoming a tourism hotspot for tourist. To support the growing tourism industry, the taxi services play a major role and also it plays an important role in urban transportation. In view of the popularity of Taxi services, we have analyzed the sentiment of the taxi industry by taking the reviews of the customer on different taxi service providers. In this research, we addressed text sentiment analysis of taxi reviews, posted by customers on online review sites. All the reviews are based on Indian review sites only. We have compared many machine learning techniques with the dataset. To determine the sentiments of text reviews, machine learning techniques are used, which explore the feeling of a customer and also give the in-hand idea of the taxi services and its amenities. The study presents that among all the common machine learning techniques, Support Vector Machine (SVM) performs better than other algorithms. Considering different evaluation parameters like Accuracy, F1Score, and Recall value, SVM gives the best result with 89%, 82%, and 86% respectively.
  • A Survey and Tutorial on Network Optimization for Intelligent Transport System Using the Internet of Vehicles
    Saroj Kumar Panigrahy, Harika Emany
    Sensors, 2023
  • A Smart Mobile Application for Stock Market Analysis, Prediction, and Alerting Users
    Rutvi Boda, Saroj Kumar Panigrahy, Somya Ranjan Sahoo
    Lecture Notes on Data Engineering and Communications Technologies, 2022
  • A Mobile Application for Sales Representatives: A Case Study of a Liquor Brand
    Sharon Xavier, Saroj Kumar Panigrahy, Asish Kumar Dalai
    Lecture Notes on Data Engineering and Communications Technologies, 2022
  • Performance Analysis of Device-to-Device Communication in Rural Areas
    Udit Narayana Kar, Asish Kumar Dalai, Sambi Reddy Gottam, Saroj Kumar Panigrahy
    International Conference on Artificial Intelligence and Data Engineering Aide 2022, 2022
  • Multistage pipelined architectures of Piccolo cipher for high-speed IoT applications
    Zeesha Mishra, Bibhudendra Acharya, Saroj Kumar Panigrahy, Shubham Mishra
    International Journal of High Performance Systems Architecture, 2022
  • Image-Based Spammer Analysis Using Suitable Features Selection by Genetic Algorithm in OSNs
    Somya Ranjan Sahoo, Asish Kumar Dalai, Sanjit Ningthoujam, Saroj Kumar Panigrahy
    Lecture Notes on Data Engineering and Communications Technologies, 2022
  • Comparative study of ecg-based key agreement schemes in wireless body sensor networks
    Saroj Kumar Panigrahy, Bibhu Prasad Dash, Sathya Babu Korra, Ashok Kumar Turuk, Sanjay Kumar Jena
    Advances in Intelligent Systems and Computing, 2019
  • A biometric based anonymous user authentication technique in wireless body area networks
    Gitanjali Behera, Saroj Kumar Panigrahy, Ashok Kumar Turuk
    Proceedings of the 2018 International Conference on Communication Computing and Internet of Things Ic3iot 2018, 2018
  • Secure key exchange using enhanced Diffie-Hellman protocol based on string comparison
    Ankit Taparia, Saroj Kumar Panigrahy, Sanjay Kumar Jena
    Proceedings of the 2017 International Conference on Wireless Communications Signal Processing and Networking Wispnet 2017, 2017
  • An ANN based approach for wireless device fingerprinting
    Kaushal Kumar, Asish Kumar Dalai, Saroj Kumar Panigrahy, Sanjay Kumar Jena
    Rteict 2017 2nd IEEE International Conference on Recent Trends in Electronics Information and Communication Technology Proceedings, 2017
  • A novel approach for message authentication to prevent parameter tampering attack in web applications
    Asish Kumar Dalai, Saroj Kumar Panigrahy, Sanjay Kumar Jena
    Procedia Engineering, 2012
  • Man-in-the-middle attack and its countermeasure in bluetooth secure simple pairing
    Thrinatha R. Mutchukota, Saroj Kumar Panigrahy, Sanjay Kumar Jena
    Communications in Computer and Information Science, 2011
  • A secure and efficient message authentication protocol for VANETs with privacy preservation
    Bharati Mishra, Saroj Kumar Panigrahy, Tarini Charan Tripathy, Debasish Jena, Sanjay Kumar Jena
    Proceedings of the 2011 World Congress on Information and Communication Technologies Wict 2011, 2011
  • Security in Bluetooth, RFID and wireless sensor networks
    Saroj Kumar Panigrahy, Sanjay Kumar Jena, Ashok Kumar Turuk
    ACM International Conference Proceeding Series, 2011
  • Security in Bluetooth, RFID and Wireless Sensor Networks
    Saroj Kumar Panigrahy, Sanjay Kumar Jena, Ashok Kumar Turuk
    Proceedings of the International Conference on Communication Computing and Security Icccs 2011, 2011
  • Web usage mining: An implementation view
    Sathya Babu Korra, Saroj Kumar Panigrahy, Sanjay Kumar Jena
    Communications in Computer and Information Science, 2011
  • Anomaly detection in ethernet networks using self organizing maps
    Saroj Kumar Panigrahy, Jyoti Ranjan Mahapatra, Jignyanshu Mohanty, Sanjay Kumar Jena
    Communications in Computer and Information Science, 2011
  • A secure e-shopping using voice ordering
    Saroj Kumar Panigrahy, Debasish Jena, Sanjay Kumar Jena
    Communications in Computer and Information Science, 2010
  • A novel and efficient cryptosystem for long message encryption
    Debasish Jena, Saroj Kumar Panigrahy, Sanjay Kumar Jena
    Iciis 2009 4th International Conference on Industrial and Information Systems 2009 Conference Proceedings, 2009
  • A modified remote user authentication scheme using smart card based on ECDLP
    Debasish Jena, Saroj Kumar Panigrahy, Sanjay Kumar Jena, Subhendu Kumar Pani
    Iciis 2009 4th International Conference on Industrial and Information Systems 2009 Conference Proceedings, 2009
  • H-S-X cryptosystem and its application to image encryption
    Bibhudendra Acharya, Sambit Kumar Shukla, Saroj Kumar Panigrahy, Sarat Kumar Patra, Ganapati Panda
    Act 2009 International Conference on Advances in Computing Control and Telecommunication Technologies, 2009
  • On the privacy protection of biometric traits: Palmprint, face, and signature
    Saroj Kumar Panigrahy, Debasish Jena, Sathya Babu Korra, Sanjay Kumar Jena
    Communications in Computer and Information Science, 2009
  • A novel remote user authentication scheme using smart card based on ECDLP
    Debasish Jena, Sanjay Kumar Jena, Debashisa Mohanty, Saroj Kumar Panigrahy
    Proceedings International Conference on Advanced Computer Control Icacc 2009, 2009
  • A novel protocol for smart card using ECDLP
    Debasish Jena, Saroj Kumar Panigrahy, Pradip Kumar Biswal, Sanjay Kumar Jena
    Proceedings 1st International Conference on Emerging Trends in Engineering and Technology Icetet 2008, 2008
  • Improved offline signature verification scheme using feature point extraction method
    Debasish Jena, Banshidhar Majhi, Saroj Kumar Panigrahy, Sanjay Kumar Jena
    Proceedings of the 7th IEEE International Conference on Cognitive Informatics Icci 2008, 2008

RECENT SCHOLAR PUBLICATIONS

  • A Comparative Study of Deep Learning Techniques for Breast Cancer Detection Using Digital Breast Tomosynthesis
    B Hirani, P Singh, D Solanki, P Kumari, SK Panigrahy
    Synergies in Data Analytics and Cyber Security: Proceedings of the … , 2026
    2026
  • A ROBUST EXPLAINABLE RECURRENT DEEP Q LEARNING FOR DETECTING MULTICLASS INTRUSIONS IN IOT
    P TURAKA, SK PANIGRAHY
    Journal of Theoretical and Applied Information Technology 103 (21) , 2025
    2025
  • YOLO-BASED FEATURE LEARNING WITH RF–XGB ENSEMBLES FOR ROBUST CITRUS LEAF DISEASE DETECTION
    KV AJAY, MSKB PADMAJA PULICHERLA, DVD P THIRUMOORTHY, ...
    Journal of Theoretical and Applied Information Technology 103 (21) , 2025
    2025
  • Quantum-Driven Chaos-Informed Deep Learning Framework for Efficient Feature Selection and Intrusion Detection in IoT Networks
    P Turaka, SK Panigrahy
    Technologies 13 (10), 470 , 2025
    2025
    Citations: 3
  • A Network Intrusion Detection System Using Machine Learning Techniques
    S Maji, K Rani, GNS Srinivas, SK Panigrahy
    2025 International Conference on Artificial Intelligence and Machine Vision … , 2025
    2025
    Citations: 1
  • A Network Intrusion Detection System Using Machine Learning Algorithms
    A Barua, P Turaka, SK Panigrahy
    Integrating Advanced Technologies for Enhanced Security and Efficiency, 19-31 , 2025
    2025
  • A Chatbot Using Artificial Neural
    A Sahoo, SK Panigrahy, S Sethi
    Artificial Intelligence and Applications: Proceedings of ICAIA 2024, 347 , 2025
    2025
  • Chaotic adaptive particle swarm optimization and quantum-inspired genetic algorithm for robust feature selection in iot intrusion detection
    P Turaka, SK Panigrahy
    2025 International Conference on Sustainable Energy Technologies and … , 2025
    2025
    Citations: 6
  • Design of an Efficient Model for Psychological Disease Analysis and Prediction Using Machine Learning and Genomic Data Samples
    A Kumuda, SK Panigrahy
    Big Data and Cognitive Computing 9 (3), 49 , 2025
    2025
    Citations: 2
  • A Comparative Study of Deep Learning Techniques for Breast Cancer Detection Using Digital Breast Tomosynthesis on 3D DICOM Images
    B Hirani, P Singh, D Solanki, P Kumari, SK Panigrahy
    International Conference on Data Analytics and Cyber Security, 375-384 , 2024
    2024
  • Aspect-based sentiment analysis by leveraging machine learning techniques
    YU Shankar, SR Sahoo, SK Panigrahy, MR Kondamudi
    2024 OITS International Conference on Information Technology (OCIT), 414-420 , 2024
    2024
    Citations: 2
  • Design of an Efficient Model for Detecting Diseases Using Genomics with Graph Relationship Networks
    K Alparthi, SK Panigrahy
    2024 4th International Conference on Artificial Intelligence and Signal … , 2024
    2024
  • Dynamic attack detection in IoT networks: an ensemble learning approach with Q-learning and explainable AI
    P Turaka, SK Panigrahy
    IEEE Access 12, 161925-161940 , 2024
    2024
    Citations: 8
  • Leveraging generative and explainable AI for electric vehicle energy toward sustainable, consumer-centric transportation
    PK Mohanty, KHK Reddy, SK Panigrahy, DS Roy
    IEEE Access 12, 143721-143732 , 2024
    2024
    Citations: 23
  • A Chatbot Using Artificial Neural Networks for Educational Institutions Related FAQs
    A Sahoo, SK Panigrahy, S Sethi
    International Conference on Artificial Intelligence and its Application, 347-357 , 2024
    2024
    Citations: 1
  • A Machine Learning Model to Classify Indian Taxi System in Tourism Industry
    K Nayak, SK Panigrahy
    2023 3rd International conference on Artificial Intelligence and Signal … , 2023
    2023
    Citations: 1
  • A survey and tutorial on network optimization for intelligent transport system using the internet of vehicles
    SK Panigrahy, H Emany
    Sensors 23 (1), 555 , 2023
    2023
    Citations: 82
  • Performance analysis of device-to-device communication in rural areas
    UN Kar, AK Dalai, SR Gottam, SK Panigrahy
    2022 International Conference on Artificial Intelligence and Data … , 2022
    2022
    Citations: 9
  • A mobile application for sales representatives: a case study of a liquor brand
    S Xavier, SK Panigrahy, AK Dalai
    Machine Intelligence and Data Science Applications: Proceedings of MIDAS … , 2022
    2022
    Citations: 1
  • A Smart Mobile Application for Stock Market Analysis, Prediction, and Alerting Users
    R Boda, SK Panigrahy, SR Sahoo
    Advances in Data Computing, Communication and Security: Proceedings of … , 2022
    2022
    Citations: 2

MOST CITED SCHOLAR PUBLICATIONS

  • Image encryption using advanced hill cipher algorithm
    B Acharya, SK Panigrahy, SK Patra, G Panda
    International Journal of Recent Trends in Engineering 1 (1), 663-667 , 2009
    2009
    Citations: 208
  • Novel methods of generating self-invertible matrix for hill cipher algorithm
    B Acharya, GS Rath, SK Patra, SK Panigrahy
    2007
    Citations: 150
  • A survey and tutorial on network optimization for intelligent transport system using the internet of vehicles
    SK Panigrahy, H Emany
    Sensors 23 (1), 555 , 2023
    2023
    Citations: 82
  • Image encryption using self-invertible key matrix of hill cipher algorithm
    SK Panigrahy, B Acharya, D Jena
    2008
    Citations: 75
  • Improved offline signature verification scheme using feature point extraction method
    D Jena, B Majhi, SK Panigrahy, SK Jena
    2008 7th IEEE International Conference on Cognitive Informatics, 475-480 , 2008
    2008
    Citations: 54
  • A secure and efficient message authentication protocol for VANETs with privacy preservation
    B Mishra, SK Panigrahy, TC Tripathy, D Jena, SK Jena
    2011 World Congress on Information and Communication Technologies, 880-885 , 2011
    2011
    Citations: 38
  • Secure key exchange using enhanced Diffie-Hellman protocol based on string comparison
    A Taparia, SK Panigrahy, SK Jena
    2017 International conference on wireless communications, signal processing … , 2017
    2017
    Citations: 29
  • Study and analysis of human stress detection using galvanic skin response (gsr) sensor inwired and wireless environments
    SK Panigrahy, SK Jena, AK Turuk
    Research Journal of Pharmacy and Technology 10 (2), 545-550 , 2017
    2017
    Citations: 27
  • Man-in-the-middle attack and its countermeasure in bluetooth secure simple pairing
    TR Mutchukota, SK Panigrahy, SK Jena
    International conference on information processing, 367-376 , 2011
    2011
    Citations: 24
  • Security in Bluetooth, RFID and wireless sensor networks
    SK Panigrahy, SK Jena, AK Turuk
    Proceedings of the 2011 International Conference on Communication, Computing … , 2011
    2011
    Citations: 24
  • Leveraging generative and explainable AI for electric vehicle energy toward sustainable, consumer-centric transportation
    PK Mohanty, KHK Reddy, SK Panigrahy, DS Roy
    IEEE Access 12, 143721-143732 , 2024
    2024
    Citations: 23
  • HSX cryptosystem and its application to image encryption
    B Acharya, SK Shukla, SK Panigrahy, SK Patra, G Panda
    2009 International Conference on Advances in Computing, Control, and … , 2009
    2009
    Citations: 16
  • On the privacy protection of biometric traits: palmprint, face, and signature
    SK Panigrahy, D Jena, SB Korra, SK Jena
    International Conference on Contemporary Computing, 182-193 , 2009
    2009
    Citations: 15
  • A secure RSU-aided aggregation and batch-verification scheme for vehicular networks
    S Mohanty, D Jena, SK Panigrahy
    Intemational Conference on Soft Computing and its Applications (ICSCA2012 … , 2012
    2012
    Citations: 14
  • A novel and efficient cryptosystem for long message encryption
    D Jena, SK Panigrahy, SK Jena
    2009 International Conference on Industrial and Information Systems (ICIIS), 7-9 , 2009
    2009
    Citations: 13
  • A Rotational-and Translational-Invariant Palmprint Recognition System
    SK Panigrahy, D Jena, SK Jena
    2008
    Citations: 12
  • An ANN based approach for wireless device fingerprinting
    K Kumar, AK Dalai, SK Panigrahy, SK Jena
    2017 2nd IEEE International Conference on Recent Trends in Electronics … , 2017
    2017
    Citations: 10
  • A novel approach for message authentication to prevent parameter tampering attack in web applications
    AK Dalai, SK Panigrahy, SK Jena
    Procedia engineering 38, 1495-1500 , 2012
    2012
    Citations: 10
  • Performance analysis of device-to-device communication in rural areas
    UN Kar, AK Dalai, SR Gottam, SK Panigrahy
    2022 International Conference on Artificial Intelligence and Data … , 2022
    2022
    Citations: 9
  • A novel remote user authentication scheme using smart card based on ECDLP
    D Jena, SK Jena, D Mohanty, SK Panigrahy
    2009 International Conference on Advanced Computer Control, 246-249 , 2009
    2009
    Citations: 9