Dr. Bhavanishankar K

@rnsit.ac.in

Professor, Dept. of Computer Science & Engineering
RNS Institute of Technology

Dr. Bhavanishankar K


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https://researchid.co/bhavanishankar

EDUCATION

B.E. in Computer Science and Engineering - Bengalore University
M.Tech. in Computer Science and Engineering - NITK Surathkal
PHD is Computer Science and Engineering - VTU

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Science, Computer Science Applications, Computer Vision and Pattern Recognition, Software

FUTURE PROJECTS

Am Alert

A system that alerts the drivers about the approaching of ambulance in their way


Applications Invited
12

Scopus Publications

67

Scholar Citations

4

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • Volumetric ResNet for Enhanced Lung Nodule Classification: A High-Accuracy Approach using Deep Learning
    V Asha, K Bhavanishankar
    3rd International Conference on Intelligent Data Communication Technologies and Internet of Things Idciot 2025, 2025
    Lung cancer remains a leading cause of mortality, affecting both men and women, has posed significant challenges for accurate identification for decades. Recent advancements in Deep Learning (DL) have revolutionized the field of Computer-Aided Diagnosis (CAD) for detection and classification of lung cancer. This study introduces a novel approach leveraging Volumetric ResNet architecture for accurate classification of candidate Lung Nodules into Nodule and Non-Nodule categories. The pipeline begins with histogram equalization for enhanced Computed Tomography (CT) scan contrast, followed by Volumetric ResNet for classification with false positive reduction. Evaluation on the LUNA16 dataset demonstrates the superiority of the proposed model in terms of accuracy (99.49%), specificity (99.74%), sensitivity (100%), precision (99.75%) with augmentation and of accuracy (99.26%), specificity (98.89%), sensitivity (99.63%), precision (98.90%), False Positive Rate (FPR) 1.11% and False Negative Rate (FNR) 0.37% without augmentation, it surpasses the existing state-of-the-art methods.
  • Multi-scale Attention Feature Map based USegNet for Lung Nodule Segmentation and DCFL-CNN for Classification Using Computed Tomography Images
    International Journal of Intelligent Engineering and Systems, 2025
  • Learning Engagement and Adaptation Platform (LEAP): An LSTM-Based Approach Leveraging VARK Learning Preferences
    Harsha S, Sreevidya Rampura Chandrappa, Bhavanishankar K, Priyanga P
    Tale 2025 2025 IEEE International Conference on Teaching Assessment and Learning for Engineering Proceedings, 2025
  • Towards Efficient Lung Cancer Detection: V-Net-basSegmentation of Pulmonary Nodules
    Asha V, Bhavanishankar K
    International Journal of Online and Biomedical Engineering, 2024
    The novel approach uses the V-Net architecture to segment pulmonary nodules from computed tomography (CT) scans, enhancing lung cancer detection’s efficiency. Addressing lung cancer, a major global mortality cause, underscores the urgency for improved diagnostic methods. The aim of this research is to refine segmentation, a critical step for early cancer detection. The study leverages V-Net, a three-dimensional (3D) convolutional neural network (CNN) tailored for medical image segmentation, applied to lung nodule identification. It utilizes the LUNA16 dataset, containing 888 annotated CT images, for model training and evaluation. This dataset’s variety of pulmonary conditions allows for a comprehensive method of assessment. The tailored V-Net architecture is optimized for lung nodule segmentation, with a focus on data preprocessing to elevate input image quality. Outcomes reveal significant progress in segmentation precision, achieving a loss score of 0.001 and a mIOU of 98%, setting new standards in the domain. Visuals of segmented lung nodules illustrate the method’s effectiveness, indicating a promising avenue for early lung cancer detection and potentially better patient prognoses. The study contributes significantly to enhancing lung cancer diagnostic methodologies through advanced image analysis. An improved segmentation method based on V-Net architecture surpasses current techniques and encourages further deep learning exploration in medical diagnostics.
  • Strategic Teaching Enhancement through Predictive Analysis for Individuals (STEP.AI)
    Harsha S, Sreevidya Rampura Chandrappa, Priyanga P, Bhavanishankar K
    2024 IEEE International Conference on Teaching Assessment and Learning for Engineering Tale 2024 Proceedings, 2024
    In the evolving landscape of educational technology, predictive assessment using learning level classification has emerged as a pivotal tool for enhancing personalized learning experiences. This research paper delves into the methodologies and efficacy of predictive assessment models that classify learners' proficiency levels to forecast their future academic performance. By leveraging machine learning algorithms and extensive educational data, our study develops a robust framework capable of dynamically assessing student capabilities and predicting their learning trajectories. The proposed regression-based model integrates a variety of features including prior academic records, engagement metrics, and cognitive skills assessments to create a comprehensive learning profile for each student. The research findings demonstrate that predictive assessment models can significantly improve the accuracy of proficiency level classification, thus enabling educators to tailor instructional strategies to individual student needs. The implementation of these models in real-world classroom settings shows a marked improvement in student outcomes, as the predictions allow for timely interventions and support. Moreover, this research highlights the potential of predictive assessments to identify at-risk students early, providing a proactive approach to educational support. In conclusion, the integration of predictive assessment and learning level classification represents a transformative approach in education, promising enhanced educational experiences and outcomes through data-driven insights. Future work will focus on refining these models to accommodate diverse learning environments and further validating their effectiveness across different educational contexts.
  • Hand Gesture based Computer Screen Control
    A N Ramya Shree, R C Sreevidya, K Bhavanishankar, Aryan Prasad, G Aishwarya, et al.
    2024 International Conference on Knowledge Engineering and Communication Systems Ickecs 2024, 2024
    The hand gesture-based Computer Screen Control approach has had a lot of popularity in these years. This article examines recent advancements in hand gesture recognition technology and its applications across diverse fields such as gaming, robotics, virtual reality, and sign language recognition. The challenges encountered by researchers in developing precise and dependable gesture recognition systems by considering variability in hand shapes, varying lighting conditions, and occlusion, are explored. The document offers an overview of different methodologies and techniques employed in gesture recognition and computer screen control. This search also supports real-time gesture recognition, multi-modal interaction, and integrating language processing with gestures as further enhancements.
  • Lung Cancer Detection using CT Scans: Image Processing through Deep Learning - A Review
    Asha V, Bhavanishankar K
    Proceedings of the 8th International Conference on Communication and Electronics Systems Icces 2023, 2023
    Lung cancer being one of the catastrophic diseases is haunting mankind from past seven decades. Unfortunately, early detection of lung cancer is unlikely, hence leading to highest mortality rates. However, various imaging modalities including Computed Tomography (CT) helps in detecting the lung cancer possible at the earliest. Processing such huge data of CT scans is highly time demanding and Computed Aided Diagnosis system (CAD)does a great job from image acquisition till the detection/classification of lung nodules through series of processing stages. This research study covers all the processing stages and major contributions in those stages. This study also summarizes various methods used in basic image processing through deep learning algorithms. A tabulation of various datasets and metrics descriptions is also discussed.
  • Model Design to Analyse Coronary Artery Disease Using Machine Learning Techniques (MDACADMLT)
    R. C. Sreevidya, K. Bhavanishankar, G. Jalaja
    Lecture Notes in Networks and Systems, 2023
  • Tailor-Made Teller: A Hybrid Screen Reader
    Anagha Naga Krishna, Bhamini N Kashyap, Jahnavi T A, Pooja K Bhat, Bhavanishankar K
    2021 International Conference on Circuits Controls and Communications Ccube 2021, 2021
    The Tailor-Made Teller (TMT) serves as a file screen reader, which enables the user to upload a file or text, to have it read aloud along with text highlights; which is relevant for students, people with learning disabilities and the visually impaired. Existing screen readers enable users to experience the Text-to-Speech functionality as an accessibility tool for web pages and on-screen text. However, the currently available systems pertain to specific operating systems and do not support various file formats for a free cost. The Tailor-made teller extracts text from various file programs and leverages the Google Text-to-Speech API to obtain an audio file, which contains the converted speech. The text from the uploaded file gets displayed on the user's screen, where the audio and text highlights run in synchrony. TMT has been tested on Image, PDF, Text and docx files of various sizes and possesses an average accuracy of 98.9%.
  • Classification of lung nodules into benign or malignant and development of a CBIR system for lung CT scans
    K. Bhavanishankar, M. V. Sudhamani
    Advances in Intelligent Systems and Computing, 2020
  • Novel techniques for classification of lung nodules using deep learning approach
    K. Bhavanishankar, M. V. Sudhamani
    Open Biomedical Engineering Journal, 2019
  • Filter based approach for automated detection of candidate lung nodules in 3D computed tomography images
    K. Bhavanishankar, M. V. Sudhamani
    Communications in Computer and Information Science, 2018

RECENT SCHOLAR PUBLICATIONS

  • Learning Engagement and Adaptation Platform (LEAP): An LSTM-Based Approach Leveraging VARK Learning Preferences
    S Harsha, SR Chandrappa, K Bhavanishankar, P Priyanga
    2025 IEEE International Conference on Teaching, Assessment, and Learning for … , 2025
    2025
  • Volumetric ResNet for Enhanced Lung Nodule Classification: A High-Accuracy Approach using Deep Learning
    V Asha, K Bhavanishankar
    2025 3rd International Conference on Intelligent Data Communication … , 2025
    2025
  • Advanced Lung Nodule Segmentation for Early Detection of Lung Cancer Using SAM and Transfer Learning
    A Venugopal, B Kundapura
    International Conference on Computer Vision and Image Processing, 63-77 , 2024
    2024
    Citations: 1
  • Strategic Teaching Enhancement through Predictive Analysis for Individuals (STEP. AI)
    S Harsha, SR Chandrappa, P Priyanga, K Bhavanishankar
    2024 IEEE International Conference on Teaching, Assessment and Learning for … , 2024
    2024
    Citations: 4
  • Towards Efficient Lung Cancer Detection: V-Net-based Segmentation of Pulmonary Nodules
    BK Asha V
    International Journal of Online and Biomedical Engineering (iJOE) 20 (11), 31-45 , 2024
    2024
    Citations: 2
  • Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning
    V Asha, K Bhavanishankar
    preprint, 1-25 , 2024
    2024
    Citations: 2
  • Hand Gesture based Computer Screen Control
    KBK A. N. R. Shree, R. C. Sreevidya
    IEEE International Conference on Knowledge Engineering and Communication … , 2024
    2024
  • Lung Cancer Detection using CT Scans: Image Processing through Deep Learning-A Review
    V Asha, K Bhavanishankar
    2023 8th International Conference on Communication and Electronics Systems … , 2023
    2023
    Citations: 1
  • Model Design to Analyse Coronary Artery Disease Using Machine Learning Techniques (MDACADMLT)
    RC Sreevidya, K Bhavanishankar, G Jalaja
    International Conference on Information and Communication Technology for … , 2023
    2023
  • Detection of Zebra Crossing Violation by Automotive using IoT automation
    AG Bhavanishankar L
    International Research Journal of Engineering and Technology (IRJET) 9 (6 … , 2022
    2022
  • Tailor-Made Teller: A Hybrid Screen Reader
    AN Krishna, BN Kashyap, PK Bhat
    2021 International Conference on Circuits, Controls and Communications … , 2021
    2021
    Citations: 1
  • Application of Machine Learning Techniques for Extraction of Soil Features for Pattern Classification
    SRBSK Srigowri M V, Dr. Bhavanishankar K
    International Journal of Advances in Engineering and Management 2 (1), 1052-1058 , 2020
    2020
  • Novel techniques for classification of lung nodules using deep learning approach
    K Bhavanishankar, MV Sudhamani
    The Open Biomedical Engineering Journal 13 (1) , 2019
    2019
    Citations: 3
  • Classification of Lung Nodules into Benign or Malignant and Development of a CBIR System for Lung CT Scans
    K Bhavanishankar, MV Sudhamani
    International Conference On Computational Vision and Bio Inspired Computing … , 2019
    2019
    Citations: 4
  • Vibration Guided Automatic Vision for Enhanced Security
    BK Ipsita Sanyal, K. R. Dhavana,Kailash T. V., Kruthika R.
    International Journal of Innovative Technology and Exploring Engineering … , 2019
    2019
  • Shuddhi -A Cleaning Agent
    BK Shashank R., Shreyas B., S. Shashank , Yashwanth Venkat Chandolu
    International Journal of Innovative Technology and Exploring Engineering … , 2019
    2019
  • Smartphone enabled Counterfeit Note Detection using Siamese Network
    BK Dhanush C., Adith Kumar B. A., Ajay Umakanth, Ajay Deshpande
    International Journal of Innovative Technology and Exploring Engineering … , 2019
    2019
    Citations: 3
  • Techniques for Lung Cancer detection from CT Image
    B SUGANDHA SAXENA, S. N. PRASAD
    International Journal of Innovative Technology and Exploring Engineering … , 2019
    2019
  • Smart grid and smart transformer powered by IoT
    BLBK Suresha
    International Journal of Advance Research, Ideas and Innovations in … , 2018
    2018
  • Filter based approach for automated detection of candidate lung nodules in 3D computed tomography images
    K Bhavanishankar, MV Sudhamani
    International Conference on Cognitive Computing and Information Processing … , 2017
    2017
    Citations: 4

MOST CITED SCHOLAR PUBLICATIONS

  • Techniques for Detection of Solitary Pulmonary Nodules in Human Lung and their Classifications- A Survey
    Bhavanishankar K, Sudhamani M V
    International Journal on Cybernetics and Informatics (IJCI) 4, 27-40 , 2015
    2015
    Citations: 39
  • Strategic Teaching Enhancement through Predictive Analysis for Individuals (STEP. AI)
    S Harsha, SR Chandrappa, P Priyanga, K Bhavanishankar
    2024 IEEE International Conference on Teaching, Assessment and Learning for … , 2024
    2024
    Citations: 4
  • Classification of Lung Nodules into Benign or Malignant and Development of a CBIR System for Lung CT Scans
    K Bhavanishankar, MV Sudhamani
    International Conference On Computational Vision and Bio Inspired Computing … , 2019
    2019
    Citations: 4
  • Filter based approach for automated detection of candidate lung nodules in 3D computed tomography images
    K Bhavanishankar, MV Sudhamani
    International Conference on Cognitive Computing and Information Processing … , 2017
    2017
    Citations: 4
  • Novel techniques for classification of lung nodules using deep learning approach
    K Bhavanishankar, MV Sudhamani
    The Open Biomedical Engineering Journal 13 (1) , 2019
    2019
    Citations: 3
  • Smartphone enabled Counterfeit Note Detection using Siamese Network
    BK Dhanush C., Adith Kumar B. A., Ajay Umakanth, Ajay Deshpande
    International Journal of Innovative Technology and Exploring Engineering … , 2019
    2019
    Citations: 3
  • 3-D Segmentation of Lung Parenchyma in Computed Tomography Scans
    DMVS Bhavanishankar K
    International Journal of Applied Engineering Research (IJAER) 10 , 2015
    2015
    Citations: 3
  • Towards Efficient Lung Cancer Detection: V-Net-based Segmentation of Pulmonary Nodules
    BK Asha V
    International Journal of Online and Biomedical Engineering (iJOE) 20 (11), 31-45 , 2024
    2024
    Citations: 2
  • Advanced Lung Nodule Segmentation and Classification for Early Detection of Lung Cancer using SAM and Transfer Learning
    V Asha, K Bhavanishankar
    preprint, 1-25 , 2024
    2024
    Citations: 2
  • Advanced Lung Nodule Segmentation for Early Detection of Lung Cancer Using SAM and Transfer Learning
    A Venugopal, B Kundapura
    International Conference on Computer Vision and Image Processing, 63-77 , 2024
    2024
    Citations: 1
  • Lung Cancer Detection using CT Scans: Image Processing through Deep Learning-A Review
    V Asha, K Bhavanishankar
    2023 8th International Conference on Communication and Electronics Systems … , 2023
    2023
    Citations: 1
  • Tailor-Made Teller: A Hybrid Screen Reader
    AN Krishna, BN Kashyap, PK Bhat
    2021 International Conference on Circuits, Controls and Communications … , 2021
    2021
    Citations: 1
  • Learning Engagement and Adaptation Platform (LEAP): An LSTM-Based Approach Leveraging VARK Learning Preferences
    S Harsha, SR Chandrappa, K Bhavanishankar, P Priyanga
    2025 IEEE International Conference on Teaching, Assessment, and Learning for … , 2025
    2025
  • Volumetric ResNet for Enhanced Lung Nodule Classification: A High-Accuracy Approach using Deep Learning
    V Asha, K Bhavanishankar
    2025 3rd International Conference on Intelligent Data Communication … , 2025
    2025
  • Hand Gesture based Computer Screen Control
    KBK A. N. R. Shree, R. C. Sreevidya
    IEEE International Conference on Knowledge Engineering and Communication … , 2024
    2024
  • Model Design to Analyse Coronary Artery Disease Using Machine Learning Techniques (MDACADMLT)
    RC Sreevidya, K Bhavanishankar, G Jalaja
    International Conference on Information and Communication Technology for … , 2023
    2023
  • Detection of Zebra Crossing Violation by Automotive using IoT automation
    AG Bhavanishankar L
    International Research Journal of Engineering and Technology (IRJET) 9 (6 … , 2022
    2022
  • Application of Machine Learning Techniques for Extraction of Soil Features for Pattern Classification
    SRBSK Srigowri M V, Dr. Bhavanishankar K
    International Journal of Advances in Engineering and Management 2 (1), 1052-1058 , 2020
    2020
  • Vibration Guided Automatic Vision for Enhanced Security
    BK Ipsita Sanyal, K. R. Dhavana,Kailash T. V., Kruthika R.
    International Journal of Innovative Technology and Exploring Engineering … , 2019
    2019
  • Shuddhi -A Cleaning Agent
    BK Shashank R., Shreyas B., S. Shashank , Yashwanth Venkat Chandolu
    International Journal of Innovative Technology and Exploring Engineering … , 2019
    2019

Publications

V, Asha, and Bhavanishankar K. 2024. Towards Efficient Lung Cancer Detection: V-Net-Based Segmentation of Pulmonary Nodules. International Journal of Online and Biomedical Engineering (iJOE) 20 (11):pp. 31-45. Q2

Prasanna Kumar M, Kiran P, Bhavani Shankar K, Dhanraj, Defining a Standard Classification in Activity Model Confirmation, Approval and Adjustment, International Journal of Intelligent Systems and applications in Engineering 12, 21s (Jul. 2024), 4591

A. N. R. Shree, R. C. Sreevidya, K. Bhavanishankar, A. Prasad, G. Aishwarya and A. Devappa, Hand Gesture based Computer Screen Control, 2024 International Conference on Knowledge Engineering and Communication Systems (ICKECS) IEEE, Chikkaballapur, India, 2024, pp. 1-6,

Prasanna Kumar M, Dhanraj S, Bhavanishankar K, Malicious URL Detection Using Machine Learning and Deep Learning, International Journal of Innovative Research in Technology (IJIRT), Volume-9, Issue-12, pp. 768- 774, 2023.

Sreevidya R C, Bhavanishankar K, Jalaja G, Model Design to Analyze Coronary Artery Disease using Machine Learning Techniques, 7th International Conference on Information and Communication Technology for Intelligent Systems (ICTIS -2023), Springer, 2023.

Asha V, Bhavanishankar K, Lung Cancer Detection using CT scans: Image Processing through Deep Learning - a review, 8th International Conference on Communication and Electronics Systems (ICCES 2023) IEEE. pp. 1201-1211, 2023.

Prasanna Kumar