Saroj Kumar Chandra

@opju.ac.in

Assistant Professor Computer Science and Engineering
OP Jindal University Raigarh

Saroj Kumar Chandra


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https://researchid.co/saroj.chandra
I am Saroj Kumar Chandra, I have completed my Ph.D. (Computer Science and Engineering) from Indian Institute of Information Technology, Design and
Manufacturing, Jabalpur (M.P.), India in the year 2020. I have completed my M.Tech. (Computer Science and Engineering) degree from National Institute of Technology, Durgapur (W.B.), India in the year 2010 and My B.E. (Information Technology and Engineering) degree from Institute of Technology, Guru Ghasidas Central University, Bilaspur (C.G.), India in the year 2007. I have five- and half-year teaching experience as Assistant Professor. Also, I have four-year research experience as a Ph.D. scholar

EDUCATION

Computer Science and Engineering, Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, Madhya-Pradesh, India, 2015- 2020

M.Tech.: Computer Science and Engineering, National Institute of Technology, Durgapur, West-Bengal, India, 2008-2010.

B.E.: Information Technology and Engineering, Institute of Technology, Guru Ghasidas Central University, Bilaspur, Chhattisgarh, India, 2002-2007.

RESEARCH INTERESTS

Image Processing
Machine Learning
Deep Learrning
Data Science
Block Chain
32

Scopus Publications

326

Scholar Citations

12

Scholar h-index

12

Scholar i10-index

Scopus Publications

  • Enhancement in Reliability of IEEE 802.15.4 WBAN Using Greedy Spider Monkey Algorithm
    Umashankar Pandey, Saroj Kumar Chandra, Narendra Kumar Dewangan
    International Journal of Networked and Distributed Computing, 2025
    Wireless Body Area Networks (WBANs) hold immense potential in healthcare monitoring, but ensuring reliable data transmission is crucial. While the IEEE 802.15.4 standard offers low-power operation and basic security, its Contention-Based Access (CBA) mechanism can lead to packet collisions and reduced reliability, especially in congested scenarios. This paper proposes a novel approach to enhance reliability in 802.15.4 WBANs by incorporating a Greedy Spider Monkey Optimization Algorithm (GSMA). The GSMA mimics the intelligent foraging behaviour of spider monkeys, enabling dynamic channel selection and optimal transmission scheduling. Our approach aims to minimize packet collisions by selecting channels with lower traffic based on historical data and real-time network conditions; the GSMA reduces the likelihood of collisions and data loss. Secondly, the algorithm prioritizes critical data packets based on their urgency and channel availability, ensuring the timely delivery of essential medical information. Finally, by reducing collisions and optimizing scheduling, the GSMA helps maintain efficient data flow within the WBAN. This paper presents the design and implementation of the GSMA-based approach within the 802.15.4 framework. Simulation results are presented to evaluate the effectiveness of the proposed method in improving reliability, reducing packet loss, and enhancing overall network performance in WBANs. The findings demonstrate the potential of the GSMA to address the limitations of 802.15.4 and contribute to developing more reliable and efficient WBAN solutions for healthcare applications.
  • Improving AI Accuracy: Identifying and Fixing Hallucinations in Large Language Models
    Nisha Dadoriya, Saroj Kumar Chandra
    1st IEEE International Conference on Data Science and Intelligent Network Computing Icdsinc 2025, 2025
    The text generation skill of AI models sometimes faces a fundamental limitation because, along with relatable and accurate outputs, it has also been found to generate false information or misleading textual content, affecting their suitability in healthcare, law, or finance. Existing mitigation strategies fall short in specific ways. In our research, we are improving the shortcomings of previous work by performing the AI response by combining three similarity metrics, such as cosine similarity, Jaccard similarity, or BLEU score, as an assessment tool to find hallucination levels. In addition, implementing the SHAP XAI technique works to refine the model by analyzing its responses. We first evaluated the hallucination rate in our dataset using a hybrid simi-larity approach and then employed Shapley Additive Explanation (SHAP) to investigate and reduce it. The outcomes of our analysis demonstrate that SHAP improves the reliability of the model by producing a remarkable reduction in hallucination rates that occur similarly in the score. Quantitative inspection shows that the SHAP input explains the variance, thereby increasing the interpretability and accuracy of the AI model.
  • DeepFake Image Detection and Classification using EfficientNet Model
    Sunny Singh, P Sarala, Saroj Kumar Chandra, Mikkili Dileep Kumar
    2024 Opju International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4 0 Otcon 2024, 2024
    Deep learning models such as Vision Transformer (VIT) and EfficientNet, have brought significant advancements to computer vision tasks like image classification, object detection, and image generation. In this paper, comparative analysis of VIT and EfficientNet models has been done with more attention of on their architectural disparities, training procedures, and performance characteristics. Deep learning models like Vision Transformer (VIT) and EfficientNet have revolutionized computer vision. VIT uses self-attention techniques instead of convolutional layers to capture global relationships but with higher computational. EfficientNet models, with compound scaling offer a trade-off between accuracy and efficiency. EfficientNet models are computationally fast with competitive accuracy and is suitable for resource-limited contexts. The paper suggests choosing the best model based on specific use cases and resource limitations. Quantitative analysis of the present work has been done using confusion matrix. It has been observed that EfficientNet models are providing higher performance ratio.
  • Efficient Machine Learning and Factional Calculus Based Mathematical Model for Early COVID Prediction
    Saroj Kumar Chandra, Manish Kumar Bajpai
    Human Centric Intelligent Systems, 2023
    Diseases are increasing with exponential rate worldwide. Its detection is challenging task due to unavailability of the experts. Machine learning models provide automated mechanism to detect diseases once trained. It has been used to predict and detect many diseases such as cancer, heart attack, liver infections, kidney infections. The new coronavirus has become one of the deadliest diseases. Its case escalated in unexpected ways. In the literature, many machine learning models such as Extreme Gradient Boosting (XGBoosting), Support Vector Machine (SVM), regression, and Logistic regression have been used. It has been observed that these models can predict COVID cases early but are unable to find the peak point and deadline of the disease. Hence, mathematical models have been designed to early predict and find peak point and dead-line in disease prediction. These mathematical models use integral calculus-based Ordinary Differential Equations (ODEs) to predict COVID cases. Governments are dependent on these models’ pre- diction for early preparation of hospitalization, medicines, and many more. Hence, higher prediction accuracy is required. It has been found in the literature that fractional calculus-based models are more accurate in disease prediction and detection. Fractional models provides to choose order of derivative with fractional value due to which information processing capability increases. In the present work, mathematical model using fractional calculus has been devised for prediction of COVID cases. In the model, quarantine, symptomatic and asymptomatic cases have been incorporated for accurate prediction. It is found that the proposed fractional model not only predicts COVID cases more accurately but also gives peak point and dead-line of the disease.
  • Impact of Fake News on Society with Detection and Classification Techniques
    Saroj Kumar Chandra
    Applied Computer Vision and Soft Computing with Interpretable AI, 2023
    The trend of fake news spreading has gained much attention. It is used to defame people or misguide the reader. It has a great impact in many fields, including justice, democracy, politics, public news, the ecosystem, and academic communities. It easy to spread fake news nowadays. Blogs, social media, and online newspapers are a popular source of it. It can manipulate public opinion and views, such as during an election. Hence, there is a need to detect fake news spread through social platforms. This chapter deals with detecting fake news using the text, title, and the combination of both on two real-world datasets. A count vectorizer and the TfIdf vectorizer are used with machine learning models to detect and classify the news.
  • Heart Disease Prediction and Classification Using Machine Learning Models
    Sourabh Kumar, Saroj Kumar Chandra
    Lecture Notes in Electrical Engineering, 2023
  • Sentiment Analysis for Depression Detection and Suicide Prevention Using Machine Learning Models
    Sunny Singh, Saroj Kumar Chandra
    Lecture Notes in Networks and Systems, 2023
  • Efficient Machine Learning Model For Covid-19 Spread Prediction
    Sunny Singh, Saroj Kumar Chandra
    2022 Opju International Technology Conference on Emerging Technologies for Sustainable Development Otcon 2022, 2023
    Machine learning models have shown great performance in prediction and detection of many diseases such as cancer, heart attack, liver infection, and kidney infection. COVID-19 emerged as one of the deadly disease. Its cases grownin unpredictable manner. Regression is the mathematical technique in machine learning that can used to find relation between outcome variable with independent variable. In the present manuscript, regression has been used to predict COVID-19 growth. It has been found that the model is highly accurate in the COVID case prediction.
  • Heart Disease Detection and Classification using Machine Learning Models
    Saroj Kumar Chandra, Ram Narayan Shukla, Ashok Bhansali
    Lecture Notes in Electrical Engineering, 2023
  • Hybrid Image Captioning Model
    Lipismita Panigrahi, Raghab Ranjan Panigrahi, Saroj Kumar Chandra
    2022 Opju International Technology Conference on Emerging Technologies for Sustainable Development Otcon 2022, 2023
    Image captioning is implemented using Deep learning and NLP (Natural Language Processing) resulting in producing a description of an image. The proposed model generates a caption for an image using a Convolutional Neural Network (CNN) together with a Recurrent Neural Network (RNN) and area of attention. Previously, the image names were used as keys to map the images with descriptions. In order to achieve high performance, in the proposed model the image caption is based on the relationship between the areas of a picture (attention model), the words used in the caption, and the state of an RNN language model. The approach of progressive loading is employed for the loading of the image dataset. Further, for encoding the image dataset into a feature vector, VGG16 a pre-trained CNN is used. The extracted feature vector is given as input to the RNN model. These image encodings are output to a specific type of RNN model known as Long Short-Term Memory (LSTM) networks. Subsequently, the LSTM works on decoding the feature vector and predicts the sequence of words, resulting in the generation of descriptions or captions. The training performance is measured using one of the model’s quantitative analysis metrics known as BLEU.
  • DeepFake Videos Detection and Classification Using Resnext and LSTM Neural Network
    Suman Patel, Saroj Kumar Chandra, Amit Jain
    2023 3rd International Conference on Smart Generation Computing Communication and Networking Smart Gencon 2023, 2023
  • The Impact of Alteration of Superframe Duration on the Consumption of Energy in the IEEE 802.15.4 MAC
    Uma Shankar Pandey, Gulshan Soni, Saroj Kumar Chandra
    Proceedings 5th International Conference on Smart Systems and Inventive Technology Icssit 2023, 2023
  • Dynamic duty cycle based MAC protocols-A Comprehensive Survey
    Uma Shankar Pandey, Gulshan Soni, Saroj Kumar Chandra
    2022 Opju International Technology Conference on Emerging Technologies for Sustainable Development Otcon 2022, 2023
  • Industry 4.0 based Machine Learning Models for Anomalous Product Detection and Classification
    Sourabh Kumar, Saroj Kumar Chandra, Ram Narayan Shukla, Lipismita Panigrahi
    2022 Opju International Technology Conference on Emerging Technologies for Sustainable Development Otcon 2022, 2023
  • Neural Network Prediction of Slurry Erosion Wear of Ni-WC Coated Stainless Steel 420
    Sourabh Kumar, Saroj Kumar Chandra, Saurav Dixit, Kaushal Kumar, Shivam Kumar, Gunasekaran Murali, Nikolay Ivanovich Vatin, Mohanad Muayad Sabri Sabri
    Metals, 2022
  • Fractional Model with Social Distancing Parameter for Early Estimation of COVID-19 Spread
    Saroj Kumar Chandra, Manish Kumar Bajpai
    Arabian Journal for Science and Engineering, 2022
  • CNN Based Architecture for Automatically Detecting People without Face Mask
    Saroj Kumar Chandra, Ashok Bhansali
    2021 IEEE International Conference on Emerging Trends in Industry 4 0 Eti 4 0 2021, 2021
  • Mathematical Model with Social Distancing Parameter for Early Estimation of COVID-19 Spread
    Saroj Kumar Chandra, Avaneesh Singh, Manish Kumar Bajpai
    Lecture Notes in Electrical Engineering, 2021
  • Three-Dimensional Fractional Operator for Benign Tumor Region Detection
    Saroj Kumar Chandra, Abhishesk Shrivastava, Manish Kumar Bajpai
    Lecture Notes in Electrical Engineering, 2021
  • Study of non-pharmacological interventions on covid-19 spread
    Avaneesh Singh, Saroj Kumar Chandra, Manish Kumar Bajpai
    CMES Computer Modeling in Engineering and Sciences, 2020
  • Fractional Crank-Nicolson finite difference method for benign brain tumor detection and segmentation
    Saroj Kumar Chandra, Manish Kumar Bajpai
    Biomedical Signal Processing and Control, 2020
  • Efficient three-dimensional super-diffusive model for benign brain tumor segmentation
    Saroj Kumar Chandra, Manish Kumar Bajpai
    European Physical Journal Plus, 2020
  • Fractional mesh-free linear diffusion method for image enhancement and segmentation for automatic tumor classification
    Saroj Kumar Chandra, Manish Kumar Bajpai
    Biomedical Signal Processing and Control, 2020
  • Brain tumor detection and segmentation using mesh-free super-diffusive model
    Saroj Kumar Chandra, Manish Kumar Bajpai
    Multimedia Tools and Applications, 2020
  • Image Reconstruction Using Deep Convolutional Neural Network
    Muthineni Shireesha, Gargi Yadav, Saroj Kumar Chandra, Manish Kumar Bajpai
    2020 International Conference on Artificial Intelligence and Signal Processing Aisp 2020, 2020
  • Mesh free alternate directional implicit method based three dimensional super-diffusive model for benign brain tumor segmentation
    Saroj Kumar Chandra, Manish Kumar Bajpai
    Computers and Mathematics with Applications, 2019
  • Two-Sided Implicit Euler Based Superdiffusive Model for Benign Tumor Segmentation
    Saroj Kumar Chandra, Manish Kumar Bajpai
    Proceedings of 2019 IEEE Region 10 Symposium Tensymp 2019, 2019
  • Fractional anisotropic diffusion model for image smoothing
    Saroj Kumar Chandra, Manish Kumar Bajpai
    2019 8th International Conference on Modeling Simulation and Applied Optimization Icmsao 2019, 2019
  • Finite difference method based super-diffusive model for benign brain tumor segmentation
    Saroj Kumar Chandra, Manish Kumar Bajpai
    2019 8th International Conference on Modeling Simulation and Applied Optimization Icmsao 2019, 2019
  • Image enhancement using fractional partial differential equation
    Dharmendra Sharma, Saroj Kumar Chandra, Manish Kumar Bajpai
    2019 2nd International Conference on Advanced Computational and Communication Paradigms Icaccp 2019, 2019
  • Fractional Anisotropic Diffusion for Image Denoising
    Saroj Kumar Chandra, Manish Kumar Bajpai
    Proceedings of the 8th International Advance Computing Conference Iacc 2018, 2018
  • Effective algorithm for benign brain tumor detection using fractional calculus
    Saroj Kumar Chandra, Manish Kumar Bajpai
    IEEE Region 10 Annual International Conference Proceedings TENCON, 2018

RECENT SCHOLAR PUBLICATIONS

  • Improving AI Accuracy: Identifying and Fixing Hallucinations in Large Language Models
    N Dadoriya, SK Chandra
    2025 1st International Conference on Data Science and Intelligent Network … , 2025
    2025
  • Enhancement in reliability of IEEE 802.15. 4 WBAN using greedy spider monkey algorithm
    U Pandey, SK Chandra, NK Dewangan
    International Journal of Networked and Distributed Computing 13 (1), 9 , 2025
    2025
    Citations: 5
  • DeepFake Image Detection and Classification using EfficientNet Model
    S Singh, P Sarala, SK Chandra, MD Kumar
    2024 OPJU International Technology Conference (OTCON) on Smart Computing for … , 2024
    2024
    Citations: 5
  • Efficient machine learning and factional calculus based mathematical model for early COVID prediction
    SK Chandra, MK Bajpai
    Human-Centric Intelligent Systems 3 (4), 508-520 , 2023
    2023
    Citations: 3
  • 18 Impact of Fake News on
    SK Chandra
    Applied Computer Vision and Soft Computing with Interpretable AI, 273 , 2023
    2023
  • Impact of Fake News on Society with Detection and Classification Techniques
    SK Chandra
    Applied Computer Vision and Soft Computing with Interpretable AI, 273-278 , 2023
    2023
  • Classification using Machine Learning
    SK Chandra, RN Shukla, A Bhansali
    Machine Learning and Computational Intelligence Techniques for Data … , 2023
    2023
  • Heart Disease Prediction and Classification Using Machine Learning Models
    S Kumar, SK Chandra
    Machine Vision and Augmented Intelligence: Select Proceedings of MAI 2022 … , 2023
    2023
  • Hybrid image captioning model
    L Panigrahi, RR Panigrahi, SK Chandra
    2022 OPJU International Technology Conference on Emerging Technologies for … , 2023
    2023
    Citations: 7
  • Dynamic duty cycle based MAC protocols-A Comprehensive Survey
    US Pandey, G Soni, SK Chandra
    2022 OPJU international technology conference on emerging technologies for … , 2023
    2023
    Citations: 1
  • Efficient Machine Learning Model For Covid-19 Spread Prediction
    S Singh, SK Chandra
    2022 OPJU International Technology Conference on Emerging Technologies for … , 2023
    2023
  • Industry 4.0 based machine learning models for anomalous product detection and classification
    S Kumar, SK Chandra, RN Shukla, L Panigrahi
    2022 OPJU International Technology Conference on Emerging Technologies for … , 2023
    2023
    Citations: 16
  • The impact of alteration of superframe duration on the consumption of energy in the IEEE 802.15. 4 MAC
    US Pandey, G Soni, SK Chandra
    2023 5th International Conference on Smart Systems and Inventive Technology … , 2023
    2023
    Citations: 1
  • Sentiment analysis for depression detection and suicide prevention using machine learning models
    S Singh, SK Chandra
    International Conference on Information Systems and Management Science, 452-460 , 2022
    2022
    Citations: 3
  • Neural network prediction of slurry erosion wear of Ni-WC coated stainless steel 420
    S Kumar, SK Chandra, S Dixit, K Kumar, S Kumar, G Murali, NI Vatin, ...
    Metals 12 (5), 706 , 2022
    2022
    Citations: 16
  • Heart Disease Detection and Classification using Machine Learning Models
    SK Chandra, RN Shukla, A Bhansali
    International Conference on Machine Intelligence and Signal Processing, 403-412 , 2022
    2022
    Citations: 7
  • Fractional model with social distancing parameter for early estimation of COVID-19 spread
    SK Chandra, MK Bajpai
    Arabian Journal for Science and Engineering 47 (1), 209-218 , 2022
    2022
    Citations: 15
  • Three-Dimensional Fractional Operator for Benign Tumor Region Detection
    SK Chandra, A Shrivastava, MK Bajpai
    Machine Vision and Augmented Intelligence—Theory and Applications: Select … , 2021
    2021
  • Mathematical model with social distancing parameter for early estimation of COVID-19 spread
    SK Chandra, A Singh, MK Bajpai
    Machine Vision and Augmented Intelligence—Theory and Applications: Select … , 2021
    2021
    Citations: 12
  • CNN Based Architecture for Automatically Detecting People without Face Mask
    SK Chandra, A Bhansali
    2021 Emerging Trends in Industry 4.0 (ETI 4.0), 1-6 , 2021
    2021

MOST CITED SCHOLAR PUBLICATIONS

  • Effective algorithm for benign brain tumor detection using fractional calculus
    SK Chandra, MK Bajpai
    TENCON 2018-2018 IEEE Region 10 Conference, 2408-2413 , 2018
    2018
    Citations: 48
  • Fractional mesh-free linear diffusion method for image enhancement and segmentation for automatic tumor classification
    SK Chandra, MK Bajpai
    Biomedical Signal Processing and Control 58, 101841 , 2020
    2020
    Citations: 39
  • Estimation of critical gap using intersection occupancy time
    S Chandra, M Mohan, TJ Gates
    Nineteenth International Conference of Hong Kong Society for Transportation … , 2014
    2014
    Citations: 24
  • Study of non-pharmacological interventions on COVID-19 spread
    A Singh, SK Chandra, MK Bajpai
    Computer Modeling in Engineering & Sciences 125 (3), 966-989 , 2020
    2020
    Citations: 23
  • Mesh free alternate directional implicit method based three dimensional super-diffusive model for benign brain tumor segmentation
    SK Chandra, MK Bajpai
    Computers & Mathematics with Applications 77 (12), 3212-3223 , 2019
    2019
    Citations: 23
  • Brain tumor detection and segmentation using mesh-free super-diffusive model
    SK Chandra, MK Bajpai
    Multimedia Tools and Applications 79 (3), 2653-2670 , 2020
    2020
    Citations: 17
  • Industry 4.0 based machine learning models for anomalous product detection and classification
    S Kumar, SK Chandra, RN Shukla, L Panigrahi
    2022 OPJU International Technology Conference on Emerging Technologies for … , 2023
    2023
    Citations: 16
  • Neural network prediction of slurry erosion wear of Ni-WC coated stainless steel 420
    S Kumar, SK Chandra, S Dixit, K Kumar, S Kumar, G Murali, NI Vatin, ...
    Metals 12 (5), 706 , 2022
    2022
    Citations: 16
  • Fractional model with social distancing parameter for early estimation of COVID-19 spread
    SK Chandra, MK Bajpai
    Arabian Journal for Science and Engineering 47 (1), 209-218 , 2022
    2022
    Citations: 15
  • Fractional Crank-Nicolson finite difference method for benign brain tumor detection and segmentation
    SK Chandra, MK Bajpai
    Biomedical Signal Processing and Control 60, 102002 , 2020
    2020
    Citations: 14
  • Mathematical model with social distancing parameter for early estimation of COVID-19 spread
    SK Chandra, A Singh, MK Bajpai
    Machine Vision and Augmented Intelligence—Theory and Applications: Select … , 2021
    2021
    Citations: 12
  • Image enhancement using fractional partial differential equation
    D Sharma, SK Chandra, MK Bajpai
    2019 Second International Conference on Advanced Computational and … , 2019
    2019
    Citations: 12
  • Fractional anisotropic diffusion for image denoising
    SK Chandra, MK Bajpai
    2018 IEEE 8th International Advance Computing Conference (IACC), 344-348 , 2018
    2018
    Citations: 8
  • Hybrid image captioning model
    L Panigrahi, RR Panigrahi, SK Chandra
    2022 OPJU International Technology Conference on Emerging Technologies for … , 2023
    2023
    Citations: 7
  • Heart Disease Detection and Classification using Machine Learning Models
    SK Chandra, RN Shukla, A Bhansali
    International Conference on Machine Intelligence and Signal Processing, 403-412 , 2022
    2022
    Citations: 7
  • Image reconstruction using deep convolutional neural network
    M Shireesha, G Yadav, SK Chandra, MK Bajpai
    2020 International Conference on Artificial Intelligence and Signal … , 2020
    2020
    Citations: 6
  • Enhancement in reliability of IEEE 802.15. 4 WBAN using greedy spider monkey algorithm
    U Pandey, SK Chandra, NK Dewangan
    International Journal of Networked and Distributed Computing 13 (1), 9 , 2025
    2025
    Citations: 5
  • DeepFake Image Detection and Classification using EfficientNet Model
    S Singh, P Sarala, SK Chandra, MD Kumar
    2024 OPJU International Technology Conference (OTCON) on Smart Computing for … , 2024
    2024
    Citations: 5
  • Exploring quantum dot cellular automata based reversible circuit
    SK Chandra, DK Netam
    International Journal of Advanced Computer Research 2 (1), 70 , 2012
    2012
    Citations: 4
  • Efficient machine learning and factional calculus based mathematical model for early COVID prediction
    SK Chandra, MK Bajpai
    Human-Centric Intelligent Systems 3 (4), 508-520 , 2023
    2023
    Citations: 3