BIJAYA KUMAR SETHI

@vardhaman.org

Assistant Professor, Computer Science & Engineering (Data Science)
vardhaman college of engineering

BIJAYA KUMAR SETHI


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https://researchid.co/vce1540

RESEARCH, TEACHING, or OTHER INTERESTS

Artificial Intelligence, Artificial Intelligence, Computer Science Applications
13

Scopus Publications

128

Scholar Citations

5

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • An advanced CNN-based method for prostate cancer detection using YOLOv9
    Bijaya Kumar Sethi, Debabrata Singh, Saroja Kumar Rout, Kottu Santosh Kumar
    Multidisciplinary Science Journal, 2026
    Prostate cancer (PCa), one of the most common tumours in men, has a high death rate and is occasionally brought on by an inaccurate or delayed diagnosis. The urgent need for new accurate and dependable imaging-based diagnostic tools is highlighted by the low sensitivity and specificity of traditional screening methods such digital rectal examination and prostate-specific antigen (PSA) testing. In this paper, we provide a novel deep learning architecture for prostate cancer diagnosis based on the speed and accuracy of You Only Look Once, version 9 (YOLOv9). Our method minimizes the need for intensive post-processing by precisely localizing and classifying malignant lesions in multiparametric MRI scans via domain-specific data augmentation, adaptive anchor box adjustment, and fine-grained feature fusion. A carefully chosen and annotated collection of prostate pictures was divided into training, validation, and test sets using a patient-wise split to prevent data leakage. On the independent test cohort, the improved YOLOv9 model obtained a precision-recall value of 98%, an F1-score of 96%, a precision-recall of 91%, and a recall of 100%. These findings show a notable improvement in performance over the state-of-the-art techniques currently in use, especially when it comes to reducing false negatives, which is a crucial factor in clinical decision-making. Qualitative heat map analysis further showed that the model consistently focused on clinically important areas that were strongly aligned to expert radiologist observations. This attests to the model's interpretability and appropriateness for clinical real-time applications. In order to improve diagnostic confidence and facilitate early intervention, the suggested YOLOv9-based framework provides a quick, precise, and understandable method for PCa detection. Future studies will focus on merging multimodal imaging data and validating the model across bigger, multi-center surveys to ensure generalisability and therapeutic use.
  • Graceful labeling of paths, cycles and caterpillars via inverse transformation
    Bijayani Pattanaik, Amiya Kumar Behera, Chapala Bohidar, Debi Prasad Bhatta, Bijaya Kumar Sethi
    Journal of Discrete Mathematical Sciences and Cryptography, 2026
    Out of the enormous branches of mathematics, graph theory plays a vital role in the fields of applied mathematics and scientific computing. One of the major fields of graph theory is the study of graph labeling problems. The present work involves the graceful labeling of a cycle of length three by joining isomorphic copies of paths to all of its vertices. The present work also analyzes the design of graceful graphs, which are created by joining caterpillars with either one or two end vertices of a cycle. Further, it establishes graceful labeling by joining two hairy cycle graphs with a common edge. To find all the results in this paper, the inverse transformation technique is used.
  • A Precision Tessellated Fundus Detection: Leveraging Color and Texture Features with SVM Classification
    Kachi Anvesh, Bharati M. Reshmi, Saroj Kumar Rout, Bijaya Kumar Sethi, Satya Sobhan Panigrahi
    Lecture Notes in Networks and Systems, 2025
  • Mitigating Threats in PHY-Layer Authentication: A Proactive Defense Against Membership Inference Attacks in Wireless Signal Classifiers
    D. Madhuri, V. Nikitha Reddy, M. Keerthi Reddy, V. N. L. N. Murthy, Saroja Kumar Rout, Bijaya Kumar Sethi
    Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering Lnicst, 2025
  • Employee Attrition Prediction Using Machine Learning
    Gunna Akanksha, Sheikh Sajida Anjum Fatima, Korani Avinash Kumar, S. Satheeshkumar, Saroja Kumar Rout, Bijaya Kumar Sethi
    Smart Innovation Systems and Technologies, 2025
  • Image Dehazing for Real Time Images Using Deep Learning
    M. Shriyanshu, M. Geetha Chawan, A. Pavithra, Ch. Dhanalaxmi, Saroja Kumar Rout, Bijaya Kumar Sethi
    Smart Innovation Systems and Technologies, 2025
  • Prostate Cancer Prediction Using Convolutional Neural Networks
    Bijaya Kumar Sethi, Debabrata Singh, Saroja Kumar Rout
    Proceedings of Nkcon 2024 3rd Edition of IEEE Nkss S Flagship International Conference Digital Transformation Unleashing the Power of Information, 2024
    Cancer-related mortality in men is highest among men who suffer from prostate cancer. The lack of clarity and consistency of early symptoms often makes diagnosis a challenge in the later stages (stages III and IV). Existing diagnostic techniques face challenges such as subjectivity, variability between observers, and lengthy testing processes involving biomarkers, biopsies, and imaging tests. This paper introduces a novel convolutional neural network (CNN) algorithm for prostate cancer diagnosis and prediction to overcome these drawbacks. An additional dataset of histopathology images was used to train and validate the system before it was put through its learning phase. In the study, 95.12% of cancerously derived cells were identified correctly and 93.02% were identified correctly, a remarkable accuracy of 98.07%. As a result of this study, Various challenges associated with expert evaluations by humans were successfully addressed, including higher misclassification rates, interdependencies between observers, and lengthy analysis periods. Prostate cancer diagnosis and prognosis have been made much simpler and faster by this research. Moving forward, to optimize the effectiveness of our proposed method, future investigations should explore the latest developments and innovations in this field.
  • Long Short-Term Memory-Deep Belief Network-Based Gene Expression Data Analysis for Prostate Cancer Detection and Classification
    Bijaya Kumar Sethi, Debabrata Singh, Saroja Kumar Rout, Sandeep Kumar Panda
    IEEE Access, 2024
    Prostate cancer (PRC) is the major reason of mortality globally. Early recognition and classification of PRC become essential to enhance the quality of healthcare services. A newly established deep learning (DL) and machine learning (ML) approach with different optimization tools can be employed to classify accurately of PRC accurately using microarray gene expression data (GED). Though the microarray data structures are important to diagnosing different kinds of diseases, the optimum hyperparameter tuning of the DL models poses a major challenge to achieving maximum classification performance. To resolve these issues, this study develops a new Gene Expression Data Analysis using Artificial Intelligence for Prostate Cancer Diagnoses (GEDAAI-PCD) technique. The proposed GEDAAI-PCD technique examines the GED for the identification of PRC. To accomplish this, the GEDAAI-PCD technique initially normalizes the GED into a uniform format. In addition, the long short-term memory-deep belief network (LSTM-DBN) model was applied for PRC classification purposes. The wild horse optimization (EWHO) system was utilized as a hyperparameter tuning strategy to optimize the performance of the LSTM-DBN model. The experimental assessment of the GEDAAI-PCD system occurs on open open-accessed gene expression database. The experimental outcomes emphasized the supremacy of the GEDAAI-PCD method on PRC classification.
  • Diabetes Prediction using Hybrid Machine Learning Techniques
    Bhukya Devendar Naik, Anumula Spoorthy, Sakshi Baherji, Saroja Kumar Rout, Bijaya Kumar Sethi
    2024 Opju International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4 0 Otcon 2024, 2024
    The rise in blood glucose levels is the primary factor contributing to the development of diabetes. Given the significance of preventing diabetes or delaying its onset, despite numerous efforts utilizing machine learning for medical diagnostics, there remains a notable gap in research concerning long-term disease prediction, especially for type 2 diabetes. However, the traditional method of diagnosing diabetes involves patients undergoing blood glucose tests administered by doctors, which can be limited by clinical resources. Many patients consequently encounter delays in getting a diagnosis. To create a predictive model for diabetes diagnosis, this study used six traditional machine learning models: boosting, neural networks, decision trees, random forests, logistic regression, and support vector machines. The study employed machine learning (ML) algorithms to predict diabetes using an authentic dataset from Safety Pressure Primary Health Care. With a validation accuracy of 84%, the study offers important new information on who is most likely to develop type 2 diabetes. By precisely predicting the type of diabetes and examining the importance of each indication in the prediction process, the goal of this study is to improve the accuracy of diabetes prediction.
  • Breast Cancer Detection Using Convolutional Neural Network
    Sai Sudharshan Saniganti, Shriyans Reddy Gaddam, Srinivas Reddy Eppa, Saroja Kumar Rout, Bijaya Kumar Sethi, Bhaskerreddy Kethireddy
    2024 2nd International Conference on Disruptive Technologies Icdt 2024, 2024
    This research endeavors by combining transfer learning techniques with Convolutional Neural Networks (CNNs), this study aims to improve the categorization of breast cancer. The primary objective is to improve diagnostic precision in detecting malignancies from mammographic images, ultimately impacting clinical decision-making and patient care positively. The study employs a robust methodology utilizing a diverse dataset for training and validation. Transfer learning optimizes CNNs' efficiency, fine-tuning the architecture to adapt to breast cancer detection nuances. Rigorous training-validation cycles refine the model, ensuring generalizability across diverse datasets. The automated system minimizes subjective variability, contributing to a more objective diagnostic process. Scalability is achieved by designing the model to handle large volumes of mammographic images, a critical feature for widespread implementation. The integration of CNNs and transfer learning yields promising results, demonstrating a substantial improvement in accuracy compared to existing methods. Automation significantly reduces diagnosis time, while introduced objectivity minimizes result variability. The property of scalability shows promise for broad use since it works well in managing massive amounts of images. These outcomes highlight the viability and effectiveness of the suggested strategy in improving the diagnosis of breast cancer. In conclusion, the developed model, combining CNNs and transfer learning, represents a significant advancement with the potential to revolutionize clinical decision-making and patient care, offering a more accurate, efficient, and widely applicable approach to breast cancer diagnosis.
  • Automated Interview Evaluation System Using RoBERTa Technology
    G. Sri Harsh, Y. Sai Sri Vivek, Maneesha. P, Saroja Kumar Rout, S. Ranjith Reddy, Bijaya Kumar Sethi
    2024 1st International Conference on Cognitive Green and Ubiquitous Computing IC Cgu 2024, 2024
  • Medical Insurance Fraud Detection Based on Block Chain and Deep Learning Approach
    Bijaya Kumar Sethi, Debabrata Singh, Prakash Kumar Sarangi
    Proceedings of 2nd IEEE International Conference on Disruptive Technologies for Multi Disciplinary Research and Applications Centcon 2022, 2022
  • Medical Insurance Fraud Detection Based on Block Chain and Machine Learning Approach
    Bijaya Kumar Sethi, Prakash Kumar Sarangi, Adepu Sai Aashrith
    4th International Conference on Emerging Research in Electronics Computer Science and Technology Icerect 2022, 2022

RECENT SCHOLAR PUBLICATIONS

  • An advanced CNN-based method for prostate cancer detection using YOLOv9
    BK Sethi, D Singh, SK Rout, KS Kumar
    Multidisciplinary Science Journal 8 (3) , 2025
    2025.0
  • Prostate Cancer Prediction Using Convolutional Neural Networks
    BK Sethi, D Singh, SK Rout
    2024 IEEE North Karnataka Subsection Flagship International Conference … , 2024
    2024.0
  • Image Dehazing for real time images using deep learning
    M Shriyanshu, M Geetha Chawan, A Pavithra, C Dhanalaxmi, SK Rout, ...
    International Conference on Frontiers of Intelligent Computing: Theory and … , 2024
    2024.0
    Citations: 1
  • Employee Attrition Prediction Using Machine Learning
    G Akanksha, SSA Fatima, KA Kumar, S Satheeshkumar, SK Rout, ...
    International Conference on Frontiers of Intelligent Computing: Theory and … , 2024
    2024.0
    Citations: 1
  • Diabetes Prediction using Hybrid Machine Learning Techniques
    BD Naik, A Spoorthy, S Baherji, SK Rout, BK Sethi
    2024 OPJU International Technology Conference (OTCON) on Smart Computing for … , 2024
    2024.0
    Citations: 1
  • Breast cancer detection using convolutional neural network
    SS Saniganti, SR Gaddam, SR Eppa, SK Rout, BK Sethi, B Kethireddy
    2024 2nd International Conference on Disruptive Technologies (ICDT), 1409-1414 , 2024
    2024.0
    Citations: 5
  • A Precision Tessellated Fundus Detection: Leveraging Color and Texture Features with SVM Classification
    K Anvesh, BM Reshmi, SK Rout, BK Sethi, SS Panigrahi
    International Conference on Machine Learning, IoT and Big Data, 77-89 , 2024
    2024.0
    Citations: 1
  • Automated interview evaluation system using RoBERTa technology
    GS Harsh, YSS Vivek, SK Rout, SR Reddy, BK Sethi
    2024 1st International conference on cognitive, green and ubiquitous … , 2024
    2024.0
    Citations: 6
  • Mitigating Threats in PHY-Layer Authentication: A Proactive Defense Against Membership Inference Attacks in Wireless Signal Classifiers
    D Madhuri, V Nikitha Reddy, MK Reddy, V Murthy, SK Rout, BK Sethi
    International Conference on Broadband Communications, Networks and Systems … , 2024
    2024.0
    Citations: 1
  • Long short-term memory-deep belief network-based gene expression data analysis for prostate cancer detection and classification
    BK Sethi, D Singh, SK Rout, SK Panda
    IEEE Access 12, 1508-1524 , 2023
    2023.0
    Citations: 87
  • Medical insurance fraud detection based on block chain and machine learning approach
    BK Sethi, PK Sarangi, AS Aashrith
    2022 Fourth International Conference on Emerging Research in Electronics … , 2022
    2022.0
    Citations: 11
  • Medical insurance fraud detection based on block chain and deep learning approach
    BK Sethi, D Singh, PK Sarangi
    2022 International Conference on Disruptive Technologies for Multi … , 2022
    2022.0
    Citations: 14
  • Graceful labeling of paths, cycles and caterpillars via inverse transformation
    B Pattanaik, AK Behera, C Bohidar, DP Bhatta, BK Sethi
  • Optimizing Node Localization in Wireless Sensor Networks Using an Enhanced Cuckoo Search Algorithm
    G Omkar, N Gangothri, R Vishal, SK Rout, KS Kumar, BK Sethi

MOST CITED SCHOLAR PUBLICATIONS

  • Long short-term memory-deep belief network-based gene expression data analysis for prostate cancer detection and classification
    BK Sethi, D Singh, SK Rout, SK Panda
    IEEE Access 12, 1508-1524 , 2023
    2023.0
    Citations: 87
  • Medical insurance fraud detection based on block chain and deep learning approach
    BK Sethi, D Singh, PK Sarangi
    2022 International Conference on Disruptive Technologies for Multi … , 2022
    2022.0
    Citations: 14
  • Medical insurance fraud detection based on block chain and machine learning approach
    BK Sethi, PK Sarangi, AS Aashrith
    2022 Fourth International Conference on Emerging Research in Electronics … , 2022
    2022.0
    Citations: 11
  • Automated interview evaluation system using RoBERTa technology
    GS Harsh, YSS Vivek, SK Rout, SR Reddy, BK Sethi
    2024 1st International conference on cognitive, green and ubiquitous … , 2024
    2024.0
    Citations: 6
  • Breast cancer detection using convolutional neural network
    SS Saniganti, SR Gaddam, SR Eppa, SK Rout, BK Sethi, B Kethireddy
    2024 2nd International Conference on Disruptive Technologies (ICDT), 1409-1414 , 2024
    2024.0
    Citations: 5
  • Image Dehazing for real time images using deep learning
    M Shriyanshu, M Geetha Chawan, A Pavithra, C Dhanalaxmi, SK Rout, ...
    International Conference on Frontiers of Intelligent Computing: Theory and … , 2024
    2024.0
    Citations: 1
  • Employee Attrition Prediction Using Machine Learning
    G Akanksha, SSA Fatima, KA Kumar, S Satheeshkumar, SK Rout, ...
    International Conference on Frontiers of Intelligent Computing: Theory and … , 2024
    2024.0
    Citations: 1
  • Diabetes Prediction using Hybrid Machine Learning Techniques
    BD Naik, A Spoorthy, S Baherji, SK Rout, BK Sethi
    2024 OPJU International Technology Conference (OTCON) on Smart Computing for … , 2024
    2024.0
    Citations: 1
  • A Precision Tessellated Fundus Detection: Leveraging Color and Texture Features with SVM Classification
    K Anvesh, BM Reshmi, SK Rout, BK Sethi, SS Panigrahi
    International Conference on Machine Learning, IoT and Big Data, 77-89 , 2024
    2024.0
    Citations: 1
  • Mitigating Threats in PHY-Layer Authentication: A Proactive Defense Against Membership Inference Attacks in Wireless Signal Classifiers
    D Madhuri, V Nikitha Reddy, MK Reddy, V Murthy, SK Rout, BK Sethi
    International Conference on Broadband Communications, Networks and Systems … , 2024
    2024.0
    Citations: 1
  • An advanced CNN-based method for prostate cancer detection using YOLOv9
    BK Sethi, D Singh, SK Rout, KS Kumar
    Multidisciplinary Science Journal 8 (3) , 2025
    2025.0
  • Prostate Cancer Prediction Using Convolutional Neural Networks
    BK Sethi, D Singh, SK Rout
    2024 IEEE North Karnataka Subsection Flagship International Conference … , 2024
    2024.0
  • Graceful labeling of paths, cycles and caterpillars via inverse transformation
    B Pattanaik, AK Behera, C Bohidar, DP Bhatta, BK Sethi
  • Optimizing Node Localization in Wireless Sensor Networks Using an Enhanced Cuckoo Search Algorithm
    G Omkar, N Gangothri, R Vishal, SK Rout, KS Kumar, BK Sethi