Chunka Chunka

@nitap.ac.in

Assistant Professor
National Institute of Technology Arunachal Pradesh

Chunka Chunka


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https://researchid.co/chukhu

RESEARCH INTERESTS

Cryptography and Network Security
23

Scopus Publications

211

Scholar Citations

8

Scholar h-index

7

Scholar i10-index

Scopus Publications

  • Synergizing linguistic features and transformer networks for detecting AI-generated text
    Yadagiri Annepaka, Pratik Kumar, Yashraj Poddar, Partha Pakray, Chukhu Chunka
    Knowledge and Information Systems, 2026
  • Design and Implementation of a Secure Web Based Chat Application Using Elliptic Curve Diffie-Hellman
    Arunangshu Roy, Nikita Roy, Pronob Bhattacharjee, Chukhu Chunka
    Smart Innovation Systems and Technologies, 2026
  • Stress Segmentation of Potato Plantation from Aerial Images using Deep Learning
    Asim Sana, Chukhu Chunka, Sounak Majumdar
    Indiscon 2025 IEEE 6th India Council International Subsections Conference Proceedings, 2025
    In the agricultural field, one of the most important crops is potato, which is a staple food for numerous countries around the world. However, potato cultivation faces challenges due to climate change and pests, leading to decreased yields and potential food scarcity. Research and innovation in potato farming practices are crucial to ensure sustainable production and food security for future generations. In order to accurately monitor crop health and identify early signs of stress or disease, deep learning (DL) algorithms have been used in this work to evaluate aerial images of potato cultivation. Most of the existing research focuses on object detection models to accurately identify stressed and healthy areas, but the problem can be solved more efficiently by segmenting images into smaller regions and analyzing them individually. This approach can provide more detailed information on specific areas of interest, leading to more accurate results in identifying stress levels in crops. This study proposes a U-Net variant model, named AgriSegNet, a lightweight DL architecture combining U-Net segmentation with channel-wise concatenation, optimized for real-time aerial image analysis. The proposed model AgriSegNet, achieves 0.83 average Dice Score Coefficient (DSC), outperforming state-of-the-art models in both dice score and computational efficiency. This work proposes an efficient method for early stress detection and targeted crop management, addressing the need for accurate and faster analysis of aerial images in agriculture.
  • An Optimized Obstructive Sleep Apnea Detection Model Using Particle Swarm Optimization and Machine Learning
    Atiya Khan, Saroj K. Biswas, Chukhu Chunka
    Proceedings of the 2025 10th International Conference on Integrated Circuits Design and Verification Icdv 2025, 2025
    Obstructive Sleep Apnea (OSA) is a severe health issue all over the world, characterized by repeated interruption in breathing during sleep. Traditional modes of diagnosis, like polysomnography, require huge costs and time for evaluation, due to which there is a need for efficient and automated diagnostic systems. This paper presents an enhanced model named Optimized Intelligent System for Obstructive Sleep Apnea (OISOSA) for efficient OSA detection using single-lead EEG data. The proposed model utilizes the discrete wavelet transform with db8 for the decomposition of the EEG signal into the sub-bands and extraction of features from each of the sub-bands. Further, the model incorporates Gaussian filter for feature smoothing, Isolation Forest algorithm for the removal of outliers, and Particle Swarm Optimization (PSO) algorithm for feature optimization. Finally, the classification of apnea and non-apnea is done using the extra tree classifier. Performance evaluation using metrics such as accuracy, precision, recall and F1-score have been used to demonstrate the effectiveness of the proposed system. OISOSA achieved the highest accuracy of 86% with 10-fold cross-validation and 85% with holdout validation, outperforming the benchmark models. This study highlights the potential of PSO-enhanced ensemble analytics for reliable and accessible OSA detection.
  • Intelligent Sleep Apnea Detection by Advanced ML Using Single-Lead EEG Signal Data
    Atiya Khan, Saroj Kr. Biswas, Chukhu Chunka, Barnana Baruah
    IEEE Sensors Journal, 2025
    Obstructive sleep apnea (OSA) is a common sleep disorder that causes repeated disruptions in breathing during sleep. The traditional method to diagnose OSA is polysomnography, which is complex and time-consuming and requires an overnight stay in a sleep lab. However, electroencephalography (EEG) sensor-based methods offer the possibility of simpler, in-home testing, improving accessibility and patient comfort. This article proposes an intelligent expert system for the OSA detection (IESOSAD) model. The proposed model aims to efficiently detect apnea utilizing single-lead EEG data and ensemble learning algorithms. The IESOSAD model begins by analyzing the C4-A1 channel of the EEG signal and uses discrete wavelet transform (DWT) with a Daubechies-8 wavelet (db8) to decompose it into subbands. Statistical features (Sfs) are then extracted from these subbands to create a dataset for further analysis. Furthermore, the dataset undergoes preprocessing with a Gaussian filter for feature smoothing and isolation forest (IF) for anomaly detection, leading to enhanced data quality. Subsequently, the artificial bee colony (ABC) feature selection algorithm is applied to eliminate irrelevant features. The final stage of the IESOSAD model involves classification using an Extremely Randomized Trees classifier. The IESOSAD model’s performance is rigorously evaluated under holdout, tenfold, and fivefold cross-validation (CV) using a comprehensive set of metrics, including precision, recall, accuracy, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${F}1$ </tex-math></inline-formula>-score, and ROC AUC curve. The results demonstrate that IESOSAD achieves the highest accuracy of 88.12%, surpassing other state-of-the-art machine and ensemble learning algorithms. Moreover, IESOSAD has outperformed benchmark OSA detection models by a significant margin, facilitating a more streamlined and reliable OSA detection system.
  • Detecting AI-Generated Text Using Machine Learning and Deep Learning Approaches
    Annepaka Yadagiri, Lekkala D M Satya Sai Teja, Partha Pakray, Chukhu Chunka
    Computacion Y Sistemas, 2025
    Recent advances in natural language processing may enable artificial intelligence models to generate writing identical to human written form in the future. This might have profound ethical, legal, and social consequences. This study aims to address this problem by developing an accurate AI detector model that distinguishes between AI-generated and human-written texts. Our approach applies k-fold cross-validation to well-established machine learning and deep learning models, including Logistic Regression, Extra Trees Classifier, CNN, RNN, LSTM, etc. Furthermore, our results demonstrate that CNN outperforms the other models in distinguishing AI-generated from human-generated content. Providing a comprehensive analysis of the current state of AI-generated text identification in our assessment of pertinent studies. Our testing yielded positive findings, showing that our strategy is successful, with CNN emerging as the most probable answer. We analyze the research's societal implications, highlighting the possible advantages for various industries while addressing sustainability issues about morality and the environment. The LSTM and RNN models achieve accuracies of 0.83 each in this study. The Detect-CNN model achieves the highest accuracy in this investigation, achieving an accuracy of 0.85.
  • AI-Generated Text Detection Using DeBERTa with Auxiliary Stylometric Features
    Annepaka Yadagiri, L. D. M. S. Sai Teja, Partha Pakray, Chukhu Chunka
    International Conference Recent Advances in Natural Language Processing Ranlp, 2025
    The global proliferation of Generative Artificial Intelligence (GenAI) has led to the increasing presence of AI-generated text across a wide spectrum of topics, ranging from everyday content to critical and specialized domains.Often, individuals are unaware that the text they interact with was produced by AI systems rather than human authors, leading to instances where AI-generated content is unintentionally combined with human-written material.In response to this growing concern, we propose a novel approach as part of the Multi-Domain AI-Generated Text Detection (M-DAIGT) shared task, which aims to accurately identify AIgenerated content across multiple domains, particularly in news reporting and academic writing.Given the rapid evolution of large language models (LLMs), distinguishing between human-authored and AI-generated text has become increasingly challenging.To address this, our method employs fine-tuning strategies using transformer-based language models for binary text classification.We focus on two specific domains news and scholarly writing and demonstrate that our approach, based on the DeBERTa transformer model, achieves superior performance in identifying AI-generated text.Our team CNLP-NITS-PP achieved 5 th position in Subtask 1 and 3 rd position in Subtask 2.
  • An Ensemble Learning-Assisted Obstructive Sleep Apnea Detection Model Using EEG Physiological Signals and Improved Extra Tree Classifier
    Atiya Khan, Saroj Kr. Biswas, Chukhu Chunka
    IEEE Sensors Letters, 2024
    Obstructive Sleep Apnea (OSA) is a sleep disorder where people experience repeated interruptions in their breathing during sleep. Electroencephalogram (EEG) data can provide valuable insights into the disruptions caused by OSA during sleep. This letter proposes an ensemble expert system for obstructive sleep apnea detection (EESOSAD-I) using a single-lead EEG signal. The EESOSAD-I leverages discrete wavelet transform with db8 for EEG signal decomposition into sub-bands and formulates essential statistical features for information encapsulation. A state-of-the-art data preprocessing and feature fusion pipeline has been designed and developed for feature refinement and underlying pattern enhancement toward OSA detection. With the developed data refinement and incorporated ensemble extra tree classifier, the proposed EESOSAD-I achieved a notable performance accuracy of 84%, outperforming all the incorporated benchmarks with optimal margins. Further, the proposed model has also been compared with multiple state-of-the-art models, and it demonstrated superior performance in various evaluation schemes with the highest performance of 84% and an average performance accuracy of 82%.
  • A Machine Learning Model for Obstructive Sleep Apnea Detection Using Ensemble Learning and Single-Lead EEG Signal Data
    Atiya Khan, Saroj Kr. Biswas, Chukhu Chunka, Akhil Kumar Das
    IEEE Sensors Journal, 2024
    Sleep is crucial for cognitive and physical functions, and sleep disorders like Obstructive Sleep Apnea (OSA) can significantly affect a person’s health. Polysomnography is the gold standard for diagnosing OSA, but despite its effectiveness, it is time-consuming and prone to human errors. To address this issue, this paper proposes an Ensemble Expert System for Obstructive Sleep Apnea Detection - II (EESOSAD-II) that leverages the single channel (C4-A1) Electroencephalography (EEG) signal and an ensemble learning model. The proposed model employs Discrete Wavelet Transform (DWT) with db8 for efficient EEG sub-band separation and statistical feature extraction. To enhance the data quality, the proposed model incorporates a Gaussian filter for feature smoothing and an Isolation Forest for outlier treatment. To further enhance the pre-processing pipeline, Recursive Feature Elimination (RFE) is used for sub-optimal feature set selection, and the Extra Tree classifier is employed for efficient classification of apnea and non-apnea events. The performance of the proposed model is evaluated using multiple evaluation metrics like - Precision, Recall, Accuracy, F1-Score and ROC_AUC curve for detailed analytical and benchmark comparison. The verification result shows that the proposed model achieved an average accuracy of 86% in comparatively optimized computational time than the state-of-the-art feature selection techniques. Furthermore, the EESOSAD-II outperformed the benchmark OSA detection model with optimal performance margin and achieved efficient performance results.
  • Ensembled Obstructive Sleep Apnea Detection Using Extra Tree Ensemble Technique
    Atiya Khan, Saroj Kr. Biswas, Chukhu Chunka, Subhas Barman
    Proceedings 2nd International Conference on Advancement in Computation and Computer Technologies Incacct 2024, 2024
    Obstructive Sleep Apnea (OSA) is a sleep-related disorder with repetitive episodes of complete or partial upper airway obstruction during sleep. These episodes result in disrupted breathing patterns and reduced oxygen levels in the blood. Electroencephalogram is a valuable tool in detecting and diagnosing sleep apnea as it provides insights into the disruptions of sleep caused by repeated apnea events. This paper proposes an efficient model to detect OSA named Ensembled Expert System for Obstructive Sleep Apnea Detection (EESOSAD) using EEG physiological sensors and an Extra Tree ensemble classifier. The proposed model incorporates Discrete Wavelet Transform with Daubechies order-8 for decomposing EEG signal into multiple sub-bands and extracting statistical features like - Maximum, Minimum, Mean, Standard Deviation, Kurtosis, Power, Energy, Root Mean Square, and Variance. A state-of-the-art data preprocessing pipeline is also designed and developed for treating corrupted, missing, duplicates, and outliers, as medical data are prone to noise, anomalies, and inconsistencies that can distort patterns and compromise analysis validity. Finally, for the classification task, the Extra Tree ensemble classifier is used for OSA detection to comprehensively evaluate the performance of the developed data pre-processing and feature fusion pipeline. Multiple evaluation metrics, including Precision, Recall, Accuracy, F1-score, and AUC-ROC curve, have been employed to compare and assess the model’s effectiveness. Furthermore, diverse state-of-the-art ensemble learning models have been incorporated for benchmark comparison and discussion. The proposed EESOSAD model achieved an average accuracy of 59% and outperformed multiple benchmark models with an optimal performance margin.
  • A secure communication using multifactor authentication and key agreement techniques in internet of medical things for COVID-19 patients
    Chukhu Chunka, Subhasish Banerjee, Gupta Sachin Kumar
    Concurrency and Computation Practice and Experience, 2023
  • ESAD: Expert System for Apnea Detection Using Enhanced DWT Feature Extraction and Machine Learning Algorithms
    Atiya Khan, Saroj K. Biswas, Chukhu Chunka
    2023 14th International Conference on Computing Communication and Networking Technologies Icccnt 2023, 2023
  • A Secure Key Agreement Protocol Defiant to Denial-of-Service Attack based on Three Party Authentication
    Chukhu Chunka, Subhasish Banerjee, Soumyajit Nag, Rajat Subhra Goswami
    Journal of the Institution of Engineers India Series B, 2022
  • Twin Support Vector Machines Classifier Based on Intuitionistic Fuzzy Number
    Parashjyoti Borah, Ranjan Phukan, Chukhu Chunka
    Smart Innovation Systems and Technologies, 2022
  • Fittest Secret Key Selection Using Genetic Algorithm in Modern Cryptosystem
    Chukhu Chunka, Avinash Maurya, Parashjyoti Borah
    Smart Innovation Systems and Technologies, 2022
  • An Efficient Mutual Authentication and Symmetric Key Agreement Scheme for Wireless Body Area Network
    Chukhu Chunka, Subhasish Banerjee
    Arabian Journal for Science and Engineering, 2021
  • An Efficient User Authentication and Session Key Agreement in Wireless Sensor Network Using Smart Card
    Chukhu Chunka, Subhasish Banerjee, Rajat Subhra Goswami
    Wireless Personal Communications, 2021
  • A Secure Key Agreement Protocol for Data Communication in Public Network Based on the Diffie-Hellman Key Agreement Protocol
    Chukhu Chunka, Subhasish Banerjee, Soumyajit Nag, Rajat Subhra Goswami
    Lecture Notes in Networks and Systems, 2020
  • A Novel Key Generating Scheme of Automatic Variable Key
    Chukhu Chunka, Subhasish Banerjee, Rajat Subhra Goswami
    International Symposium on Advanced Networks and Telecommunication Systems Ants, 2019
  • Technique to generate the secure symmetric key under variable keys environment using genetic algorithm
    Chukhu Chunka, Subhasish Banerjee, Rajat Subhra Goswami
    International Journal of Engineering and Advanced Technology, 2019
  • An Enhanced and Secure Biometric Based User Authentication Scheme in Wireless Sensor Networks Using Smart Cards
    Subhasish Banerjee, Chukhu Chunka, Srijon Sen, Rajat Subhra Goswami
    Wireless Personal Communications, 2019
  • A novel approach to generate symmetric key in cryptography using genetic algorithm (GA)
    Chukhu Chunka, Rajat Subhra Goswami, Subhasish Banerjee
    Advances in Intelligent Systems and Computing, 2019
  • CODES: A collaborative detection strategy for SSDF attacks in cognitive radio networks
    Amar Taggu, Chukhu Chunka, Ningrinla Marchang
    ACM International Conference Proceeding Series, 2015

RECENT SCHOLAR PUBLICATIONS

  • Synergizing linguistic features and transformer networks for detecting AI-generated text: Y. Annepaka et al.
    Y Annepaka, P Kumar, Y Poddar, P Pakray, C Chunka
    Knowledge and Information Systems 68 (1), 54 , 2026
    2026
  • Detecting AI-Generated Text Using Machine Learning and Deep Learning Approaches
    A Yadagiri, LDM Sai Teja, P Pakray, C Chunka
    Computación y Sistemas 29 (4), 1929 , 2025
    2025
  • Fine-grained detection of ai-generated text using sentence-level segmentation
    LDMSS Teja, A Yadagiri, P Pakray, C Chunka, MS Vardhan
    Proceedings of the 14th International Joint Conference on Natural Language … , 2025
    2025
    Citations: 2
  • Mixture of Detectors: A Compact View of Machine-Generated Text Detection
    ST Lekkala, Y Annepaka, AK Challa, SR Machireddy, P Pakray, ...
    arXiv preprint arXiv:2509.22147 , 2025
    2025
    Citations: 1
  • AI-generated text detection using DeBERTa with auxiliary stylometric features
    A Yadagiri, LDMSS Teja, P Pakray, C Chunka
    Proceedings of the Shared Task on Multi-Domain Detection of AI-Generated … , 2025
    2025
    Citations: 1
  • Mixture of Detectors: A Compact View of Machine-Generated Text Detection
    S Teja Lekkala, Y Annepaka, AK Challa, SR Machireddy, P Pakray, ...
    arXiv e-prints, arXiv: 2509.22147 , 2025
    2025
  • Stress Segmentation of Potato Plantation from Aerial Images using Deep Learning
    A Sana, C Chunka, S Majumdar
    2025 IEEE 6th India Council International Subsections Conference (INDISCON), 1-6 , 2025
    2025
  • An Optimized Obstructive Sleep Apnea Detection Model Using Particle Swarm Optimization and Machine Learning
    A Khan, SK Biswas, C Chunka
    2025 10th IEEE International Conference on Integrated Circuits, Design, and … , 2025
    2025
    Citations: 5
  • Intelligent Sleep Apnea Detection by Advanced ML Using Single-Lead EEG Signal Data
    A Khan, SK Biswas, C Chunka, B Baruah
    IEEE Sensors Journal 25 (2), 3859-3866 , 2024
    2024
    Citations: 2
  • Design and Implementation of a Secure Web Based Chat Application Using Elliptic Curve Diffie-Hellman
    A Roy, N Roy, P Bhattacharjee, C Chunka
    International Conference on Modeling, Simulation and Optimization, 250-268 , 2024
    2024
  • Ensembled obstructive sleep apnea detection using extra tree ensemble technique
    A Khan, SK Biswas, C Chunka, S Barman
    2024 2nd International Conference on Advancement in Computation & Computer … , 2024
    2024
    Citations: 4
  • A Machine Learning Model for Obstructive Sleep Apnea Detection Using Ensemble Learning and Single-Lead EEG Signal Data
    A Khan, SK Biswas, C Chunka, AK Das
    IEEE Sensors Journal , 2024
    2024
    Citations: 13
  • An Ensemble Learning_Assisted Obstructive Sleep Apnea Detection Model Using EEG Physiological Signals and Improved Extra Tree Classifier
    K Atiya, B Saroj, Kr, C Chukhu
    IEEE Sensors Letters 1 (3) , 2024
    2024
    Citations: 4
  • ESAD: Expert system for apnea detection using enhanced DWT feature extraction and machine learning algorithms
    A Khan, SK Biswas, C Chunka
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023
    Citations: 8
  • A secure communication using multifactor authentication and key agreement techniques in internet of medical things for COVID‐19 patients
    C Chunka, S Banerjee, SK Gupta
    Concurrency and Computation: Practice and Experience 35 (7), e7602 , 2023
    2023
    Citations: 35
  • Fittest Secret Key Selection Using Genetic Algorithm in Modern Cryptosystem
    C Chunka, A Maurya, P Borah
    Intelligent and Cloud Computing: Proceedings of ICICC 2021, 227-241 , 2022
    2022
  • Twin Support Vector Machines Classifier Based on Intuitionistic Fuzzy Number
    P Borah, R Phukan, C Chunka
    Intelligent and Cloud Computing: Proceedings of ICICC 2021, 371-384 , 2022
    2022
  • A secure key agreement protocol defiant to denial-of-service attack based on three party authentication
    C Chunka, S Banerjee, S Nag, RS Goswami
    Journal of The Institution of Engineers (India): Series B 103 (2), 329-340 , 2022
    2022
    Citations: 4
  • An efficient mechanism to generate dynamic keys based on genetic algorithm
    C Chunka, RS Goswami, S Banerjee
    Security and Privacy 4 (5), e37 , 2021
    2021
    Citations: 14
  • An Efficient Mutual Authentication and Symmetric Key Agreement Scheme for Wireless Body Area Network
    C Chunka, B Subhasish
    Arabian Journal for Science and Engineering, 1-17 , 2021
    2021
    Citations: 21

MOST CITED SCHOLAR PUBLICATIONS

  • An Enhanced and Secure Biometric Based User Authentication Scheme in Wireless Sensor Networks Using Smart Cards
    S Banerjee, C Chunka, S Sen, RS Goswami
    Wireless Personal Communications 107 (1), 243–270 , 2019
    2019
    Citations: 47
  • A secure communication using multifactor authentication and key agreement techniques in internet of medical things for COVID‐19 patients
    C Chunka, S Banerjee, SK Gupta
    Concurrency and Computation: Practice and Experience 35 (7), e7602 , 2023
    2023
    Citations: 35
  • An efficient user authentication and session key agreement in wireless sensor network using smart card
    C Chunka, S Banerjee, RS Goswami
    Wireless Personal Communications 117 (2), 1361-1385 , 2021
    2021
    Citations: 23
  • An Efficient Mutual Authentication and Symmetric Key Agreement Scheme for Wireless Body Area Network
    C Chunka, B Subhasish
    Arabian Journal for Science and Engineering, 1-17 , 2021
    2021
    Citations: 21
  • An efficient mechanism to generate dynamic keys based on genetic algorithm
    C Chunka, RS Goswami, S Banerjee
    Security and Privacy 4 (5), e37 , 2021
    2021
    Citations: 14
  • A novel approach to generate symmetric key in cryptography using genetic algorithm (ga)
    C Chunka, RS Goswami, S Banerjee
    Emerging Technologies in Data Mining and Information Security: Proceedings … , 2018
    2018
    Citations: 14
  • A Machine Learning Model for Obstructive Sleep Apnea Detection Using Ensemble Learning and Single-Lead EEG Signal Data
    A Khan, SK Biswas, C Chunka, AK Das
    IEEE Sensors Journal , 2024
    2024
    Citations: 13
  • ESAD: Expert system for apnea detection using enhanced DWT feature extraction and machine learning algorithms
    A Khan, SK Biswas, C Chunka
    2023 14th International Conference on Computing Communication and Networking … , 2023
    2023
    Citations: 8
  • Codes: A collaborative detection strategy for ssdf attacks in cognitive radio networks
    A Taggu, C Chunka, N Marchang
    Proceedings of the third international symposium on women in computing and … , 2015
    2015
    Citations: 7
  • An Optimized Obstructive Sleep Apnea Detection Model Using Particle Swarm Optimization and Machine Learning
    A Khan, SK Biswas, C Chunka
    2025 10th IEEE International Conference on Integrated Circuits, Design, and … , 2025
    2025
    Citations: 5
  • Ensembled obstructive sleep apnea detection using extra tree ensemble technique
    A Khan, SK Biswas, C Chunka, S Barman
    2024 2nd International Conference on Advancement in Computation & Computer … , 2024
    2024
    Citations: 4
  • An Ensemble Learning_Assisted Obstructive Sleep Apnea Detection Model Using EEG Physiological Signals and Improved Extra Tree Classifier
    K Atiya, B Saroj, Kr, C Chukhu
    IEEE Sensors Letters 1 (3) , 2024
    2024
    Citations: 4
  • A secure key agreement protocol defiant to denial-of-service attack based on three party authentication
    C Chunka, S Banerjee, S Nag, RS Goswami
    Journal of The Institution of Engineers (India): Series B 103 (2), 329-340 , 2022
    2022
    Citations: 4
  • Fine-grained detection of ai-generated text using sentence-level segmentation
    LDMSS Teja, A Yadagiri, P Pakray, C Chunka, MS Vardhan
    Proceedings of the 14th International Joint Conference on Natural Language … , 2025
    2025
    Citations: 2
  • Intelligent Sleep Apnea Detection by Advanced ML Using Single-Lead EEG Signal Data
    A Khan, SK Biswas, C Chunka, B Baruah
    IEEE Sensors Journal 25 (2), 3859-3866 , 2024
    2024
    Citations: 2
  • A secure key agreement protocol for data communication in public network based on the Diffie-Hellman key agreement protocol
    C Chunka, S Banerjee, S Nag, RS Goswami
    Micro-Electronics and Telecommunication Engineering: Proceedings of 3rd … , 2020
    2020
    Citations: 2
  • An approach to generate variable keys based on vertical horizontal mechanism
    C Chunka, RS Goswami, S Banerjee, CT Bhunia
    International Journal of Security and Its Applications 11 (3), 61-70 , 2017
    2017
    Citations: 2
  • Mixture of Detectors: A Compact View of Machine-Generated Text Detection
    ST Lekkala, Y Annepaka, AK Challa, SR Machireddy, P Pakray, ...
    arXiv preprint arXiv:2509.22147 , 2025
    2025
    Citations: 1
  • AI-generated text detection using DeBERTa with auxiliary stylometric features
    A Yadagiri, LDMSS Teja, P Pakray, C Chunka
    Proceedings of the Shared Task on Multi-Domain Detection of AI-Generated … , 2025
    2025
    Citations: 1
  • A Novel Key Generating Scheme of Automatic Variable Key
    C Chunka, S Banerjee, RS Goswami
    2019 IEEE International Conference on Advanced Networks and … , 2019
    2019
    Citations: 1