Dr Lakshmana Rao Battarusetty

@gist.edu.in

Professor, Department of CSE
GEETHANJALI INSTITUTE OF SCIENCE & tECHNOLOGY

Dr Lakshmana Rao Battarusetty


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https://researchid.co/lakshmannlr

RESEARCH, TEACHING, or OTHER INTERESTS

Multidisciplinary, Artificial Intelligence, Computer Engineering, Computer Science
5

Scopus Publications

20

Scholar Citations

3

Scholar h-index

1

Scholar i10-index

Scopus Publications

  • An Automated Oil Spill Classification Framework Using VGG-19 Deep Learning Architecture
    Venkata KondaReddy Gajjala, Lakshmana Rao Battarusetty, Venkateswarlu Avula, Chandrakala P, Chiranjeevi S V
    Conference Proceedngs Wccst 2026 World Conference on Computational Science and Technology, 2026
    Oil spills are one of the serious environmental effects and this needs automated, accurate, and real-time monitoring systems. In this paper, the classicalification model based on the VGG-19 architecture with the deep learning concept is suggested to distinguish between cases of oil spills and non-spills utilizing image data sets. A dataset of 600 images (500 training and 100 test images) was used to train and test the model. The proposed VGG-19 model achieved a level of accuracy of 86.66%, precision of 86.38%, recall of 86.87%, and F1 score of 86.53%. They were compared to the current machine learning models. Random Forest achieved 72.58% accuracy, 72.58% precision, 72.59% recall, and 72.56% F1 score. The SVM model achieved 83.87% accuracy, 83.38% precision, 83.35% recall, and 83.35% F1 score. The Deep Neural Network (DNN) model achieved 85.48% accuracy, 85.58% precision, 85.59% recall, and 85.58% F1 score. The comparative study is well articulated to indicate that VGG-19 model is better than the conventional RF, SVM, and DNN algorithms in all the key performance indicators. The findings validate the fact that the deep learning models can be applicable in real-life environmental monitoring.
  • Two-Stage Dual Boosting for Alzheimer's Disease Prediction Optimizing Accuracy with Feature Selection
    Manaswini Nagabansa, M Padmapriya, V T Ram Pavan Kumar M, Damodar Ganji, Kiran Kumar Reddy Penubaka, Lakshmana Rao Battarusetty
    2nd International Conference on Machine Learning and Autonomous Systems Icmlas 2025 Proceedings, 2025
    Alzheimer's disease (AD) is a difficult task to manage since it is chronic and difficult to diagnose in the initial stages. Forecasting of this diseases progress is very important in terms of treatment and management at the right time. This work presents the 2DB model, a stacked ensemble of AdaBoost and XGBoost classifiers, to improve the accuracy of the AD diagnosis. The dataset adopted includes extensive health, lifestyle, and cognitive factors collected from Kaggle. The work includes an extensive data pre-processing step where issues such as missing values, and normalization, were also tackled alongside SMOTE to deal with the class imbalances. Feature selection is done through Analysis of Variance (ANOVA) to select the relevant predictors. Two-Stage Dual Boosting (2DB) combines the weight distribution of AdaBoost that reassigns weights to all the misclassified instances with XGBoost's gradient boosting method for classification, where logistic regression is the base classifier for meta-classification. The proposed model was assessed with quantitative measurements such as accuracy, precision, recall, and F1-score to its maximum accuracy of 94.6%. The performance shows that the proposed Two-Stage Dual Boosting (2DB) model's generalization significantly outperforms traditional classifiers while providing accurate predictions of Alzheimer's disease with better robustness and interpretability. The results of this work show the possibility of increasing the diagnostic accuracy of the junction hybrid machine learning models and have important potential for clinical usage in early diagnosis.
  • OncoScan Web Based Deep Learning System for Multi Class Skin Lesion Diagnosis
    Yash M Dalal, R Deepak, Shaik Thasleem Bhanu, H Anwar Basha, Lakshmana Rao Battarusetty, K. Thanuja
    2025 IEEE International Conference on Intelligent Signal Processing and Effective Communication Technologies Inspect 2025, 2025
    Skin cancer is a life-threatening disease that requires early detection for effective treatment. Traditional diagnostic methods, which rely on visual examination by dermatologists, can be subjective and prone to error. Recent advancements in deep learning have shown promise in automating skin cancer detection through skin image analysis. This study presents a Convolutional Neural Network (CNN)-based system specifically designed for multi-class skin lesion classification. A curated dataset comprising cancerous and non-cancerous skin images was used, with cancerous lesions categorized into seven types: Actinic Keratosis, Basal Cell Carcinoma, Dermatofibroma, Melanoma, Nevus, Pigmented Benign Keratosis, and Seborrheic Keratosis, and non-cancerous lesions classified into five types: Skin Warts, Scabies, Infectious Erythema, Impetigo, and Chickenpox. All images underwent preprocessing, including resizing, normalization, and augmentation, to enhance feature extraction and address class imbalance. The proposed CNN architecture, with optimized convolutional and pooling layers, achieved high classification performance, demonstrating its effectiveness in automated skin lesion diagnosis. Furthermore, we outline a Hybrid Attention-based Deep Learning framework combining ResNet-50 with Channel and Spatial Attention Modules as a future direction to further improve feature representation and robustness. This automated approach has the potential to support dermatologists in early diagnosis, increase reliability, and contribute to better patient outcomes.
  • Modeling Automated Image Watermarking Using Meta-heuristic-based Deep Learning with Wavelet Approach
    Lakshman Rao Battarusetty, G. Rosline Nesa Kumari, R. Tamilkodi, B. Sunil Kumar
    Sensing and Imaging, 2023
  • Impeccable watermarking for digital images
    Journal of Advanced Research in Dynamical and Control Systems, 2019

RECENT SCHOLAR PUBLICATIONS

  • A Vision-Based Sign Language Recognition & Voice Conversion System for Inclusive Communication
    VKR Gajjala, TJ Nagalakshmi, LR Battarusetty, V Avula
    2025.0
  • Two-Stage Dual Boosting for Alzheimer's Disease Prediction Optimizing Accuracy with Feature Selection
    M Nagabansa, M Padmapriya, D Ganji, KKR Penubaka, LR Battarusetty
    2025 International Conference on Machine Learning and Autonomous Systems … , 2025
    2025.0
  • A resilient Digital Watermarking Approach for Digital Images using Probabilistic Neural System
    LR Battarusetty, S Rayalu
    2024.0
  • Modeling Automated Image Watermarking Using Meta-heuristic-based Deep Learning with Wavelet Approach
    RTBSK Lakshman Rao Battarusetty, G. Rosline Nesa Kumari
    Sensing and Imaging , 2023
    2023.0
    Citations: 10
  • A Robust Wavelet based Image Watermarking for Copyright Protection
    BL Rao
    Saveetha Institute of Medical and Technical Science , 2023
    2023.0
  • A Perception Based Image Watermarking Using DWT-SVD
    BL Rao, GRN Kumari, R Tamilkodi, SM Perumal
    Design Engineering, 5485-5496 , 2021
    2021.0
  • Impeccable Watermarking for Digital Images
    DSM B.Lakshmana Rao, Dr. G. Roseline Nesa Kumari
    Jour of Adv Research in Dynamical & Control Systems 11 (8), 54-59 , 2019
    2019.0
  • Efficient web based geospatial information system application using bing maps
    K Penchalaiah, V Sai Charan, B LaNshmana Rao
    Intern. J. Advanced Res. Computer Communic. Engin 2 (10), 3916-3921 , 2013
    2013.0
    Citations: 5
  • Jamming attacks prevention in wireless sensor networks using secure packet hiding method
    GJ Lakshmi, S Babu, BL Rao, P Mohan, BS Kumar
    International Journal of Advanced Research in Computer and Communication … , 2013
    2013.0
    Citations: 5
  • African Journal of Biological Sciences
    LR Battarusetty, K Kiran, T Ramakrishna, VB Kumar, VS Latha, ...
  • Privacy Preserving in Knowledge Discovery and Data Publishing
    BL Rao, GVK Reddy, G Yedukondalu
  • Geo Spatial Image Retrieval Using Content-Based Image Retrieval Technique
    BS Kumar, P Neelakantan, J Velmurugan, BL Rao
  • TARF with MAC Addresses: A Trust-Aware Routing Framework for WSNs with MAC Addresses
    BL Rao, V Manikanta, B sunil Kumar

MOST CITED SCHOLAR PUBLICATIONS

  • Modeling Automated Image Watermarking Using Meta-heuristic-based Deep Learning with Wavelet Approach
    RTBSK Lakshman Rao Battarusetty, G. Rosline Nesa Kumari
    Sensing and Imaging , 2023
    2023.0
    Citations: 10
  • Efficient web based geospatial information system application using bing maps
    K Penchalaiah, V Sai Charan, B LaNshmana Rao
    Intern. J. Advanced Res. Computer Communic. Engin 2 (10), 3916-3921 , 2013
    2013.0
    Citations: 5
  • Jamming attacks prevention in wireless sensor networks using secure packet hiding method
    GJ Lakshmi, S Babu, BL Rao, P Mohan, BS Kumar
    International Journal of Advanced Research in Computer and Communication … , 2013
    2013.0
    Citations: 5
  • A Vision-Based Sign Language Recognition & Voice Conversion System for Inclusive Communication
    VKR Gajjala, TJ Nagalakshmi, LR Battarusetty, V Avula
    2025.0
  • Two-Stage Dual Boosting for Alzheimer's Disease Prediction Optimizing Accuracy with Feature Selection
    M Nagabansa, M Padmapriya, D Ganji, KKR Penubaka, LR Battarusetty
    2025 International Conference on Machine Learning and Autonomous Systems … , 2025
    2025.0
  • A resilient Digital Watermarking Approach for Digital Images using Probabilistic Neural System
    LR Battarusetty, S Rayalu
    2024.0
  • A Robust Wavelet based Image Watermarking for Copyright Protection
    BL Rao
    Saveetha Institute of Medical and Technical Science , 2023
    2023.0
  • A Perception Based Image Watermarking Using DWT-SVD
    BL Rao, GRN Kumari, R Tamilkodi, SM Perumal
    Design Engineering, 5485-5496 , 2021
    2021.0
  • Impeccable Watermarking for Digital Images
    DSM B.Lakshmana Rao, Dr. G. Roseline Nesa Kumari
    Jour of Adv Research in Dynamical & Control Systems 11 (8), 54-59 , 2019
    2019.0
  • African Journal of Biological Sciences
    LR Battarusetty, K Kiran, T Ramakrishna, VB Kumar, VS Latha, ...
  • Privacy Preserving in Knowledge Discovery and Data Publishing
    BL Rao, GVK Reddy, G Yedukondalu
  • Geo Spatial Image Retrieval Using Content-Based Image Retrieval Technique
    BS Kumar, P Neelakantan, J Velmurugan, BL Rao
  • TARF with MAC Addresses: A Trust-Aware Routing Framework for WSNs with MAC Addresses
    BL Rao, V Manikanta, B sunil Kumar