Lingamallu Naga Srinivasu

@diet.ac.in

Assistant Professor in the Department of ECE
Dhanekula Institute of Engineering and Technology

Lingamallu Naga Srinivasu


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https://researchid.co/lingamallusrinivas

RESEARCH, TEACHING, or OTHER INTERESTS

Signal Processing, Artificial Intelligence, Computer Vision and Pattern Recognition, Computer Science Applications
15

Scopus Publications

69

Scholar Citations

4

Scholar h-index

3

Scholar i10-index

Scopus Publications

  • Multiple kidney stones prediction with efficient RT-DETR model
    Ponduri Vasanthi, Lingamallu Naga Srinivasu, Ventrapragada Teju, Kambhampati Venkata Sowmya, Anca Stan, Valentin Sita, Liviu Miclea, Ovidiu Stan
    Computers in Biology and Medicine, 2025
  • PREDICTION OF DIABETES, HEART DISEASE AND PARKINSON'S DISEASE USING ML
    Ventrapragada Teju, Golla Bhavana, Burada Rohit, Jajam Sai Punitha, Krishna Prakash, et al.
    Iet Conference Proceedings, 2025
    Healthcare has witnessed significant evolution with the integration of machine learning (ML), enabling early disease detection and accurate predictions. This project presents a Multiple Disease Prediction System that utilizes RF, DT, and SVM to diagnose Diabetes, Heart Disease, and Parkinson’s Disease using Kaggle datasets. The system employs a data-driven approach, leveraging feature selection, preprocessing, and model optimization to enhance predictive performance. Among the algorithms evaluated, Random Forest achieved the highest accuracy across all three diseases, demonstrating its robustness in handling large datasets with complex patterns. The Decision Tree classifier was incorporated for its interpretability, providing insights into feature importance, while SVM was effective for high-dimensional medical data classification. The system follows a structured ML pipeline including data cleaning, normalization, hyperparameter tuning, and model evaluation to ensure optimal results.
  • SMART WEB-BASED HOSPITAL MANAGEMENT SYSTEM WITH MACHINE LEARNING INTEGRATION
    Ventrapragada Teju, Jinka Poojitha, Bobbili Sri Sagarika, Ginnela Naga Amrutha Valli, Gowrisetti Jagadeesh Nagendra, et al.
    Iet Conference Proceedings, 2025
    Web-Based HMS is an all-embracing web-based system that targets the automation of the operations in the hospital, avoiding long inefficiencies of paper work and saving the environment. PHP, MySQL, HTML5, CSS3, JavaScript, Python, and Flask are the technologies on which it is deployed. It facilitates efficient management of patient data, timetables, and administrative processes. It eradicates time-worn slow processes with a centralized electronic system for data accessing and processing in real-time. Administrators at hospitals can store, retrieve, and modify patient data without the use of paper records. Patients can easily register and book appointments through an easy-to-use interface. An English, Telugu, and Hindi appointment assistant facilitates scheduling, making it more accessible. A symptom checker offers initial guidance to minimize unwanted hospital visits. Through secure, scalable database management, the solution reduces administrative tasks, maximizes utilization of resources, improves patient satisfaction, and promotes environmental responsibility by minimizing paper use.
  • HYBRID MODEL FOR DETECTION OF SEVERITY IN CAR ACCIDENTS
    V. Teju, P. Sravani, S. Anitha, S. Sai Siddhartha, T. Silviya, Krishna Prakash, K.V. Sowmya, Lingamallu Naga Srinivasu
    Iet Conference Proceedings, 2025
    This research proposes a hybrid deep learning model for predicting car accident severity (minor, moderate, severe) using image categorization. The suggested model extracts features using ResNext50 and improves accuracy with Random Forest. It also evaluates ResNet50 and VGG19 as baseline models. Preprocessing methods such as normalization and augmentation enhance generalization. Adam optimizer was utilized to train the models, and they were assessed using accuracy, precision, recall, and F1-score. The proposed hybrid model performs at 98% accuracy, outperforming existing methods. Confusion matrices are utilized to validate effectiveness of categorization. The method gives automatic severity prediction, leading to faster emergency response. The research puts focus on how deep learning helps to decide on the severity of an accident, and the research indicates that it has promise for use in road safety.
  • ENHANCED BLOOD CELL CLASSIFICATION USING NEURAL NETWORKS
    V. Teju, Naga Bhavani Devarasetti, Swathi Boga, Kireeti Dumpala, Lakshmi Priya Challa, et al.
    Iet Conference Proceedings, 2025
    The automatic classification of blood cells is important for early-stage diagnosis and treatment of blood diseases, it minimizes the need for human sorting and classification of blood cells. Deep learning and Convolutional Neural Networks (CNNs) in particular, have improved classification accuracy, due to their strength in identifying unique characteristics within microscopic images. This paper is about developing a CNN-based model that helps predicting the type of blood cell from an input blood cell image. The paper also analyzes model's effectiveness through a comparison with conventional machine learning approaches. It will additionally explore the effects of preprocessing, optimization and data augmentation techniques to address issues like class imbalance and data variability. A graphical user interface (GUI) will also be implemented for user-friendly interaction with the model, enabling users to submit image for instant blood cell type detection. In summary, this work contributes to the development of an effective and accurate deep learning-based classification model to the field of automated haematology.
  • Classification of Skin Cancer using Deep Learning
    V. Teju, Redrowthu Neeraja Sivathmika, Vundavalli Sanjosh Chowdary, Shaik Salman Raza, Yarra Naga Venkata Praveen, et al.
    Iet Conference Proceedings, 2025
    The scarcity of medical resources is making skin cancer one of the most prevalent diseases on the globe. Prevention of skin cancer relies on accurate detection. Prompt detection of skin cancer is a challenge for dermatologists, and deep learning has found applications in both supervised and unsupervised learning. For object recognition and classification, Convolutional Neural Networks (CNN) are superior to all other types of networks. The MNIST: HAM10000 dataset contains seven types of skin lesions and one hundred and fifteen samples for the experiment. Preprocessing steps include autoencoder/decoder segmentation, dull razor sampling, and data cleaning. The model is trained using transfer learning methods like Resnet 50 and Dense Net 169 and obtained accuracy 88.4 and 64.9%, respectively. Whereas, enhance classification accuracy, with Xception approx. 96%. Using under sampling, DenseNet169 achieved respectable accuracy and f1-measure, in contrast to Resnet50's oversampling. The goal of expanding this study is to increase the accuracy of forecasts by adjusting certain parameters.
  • REAL-TIME IMPLEMENTATION OF LWT BASED NOVEL IR AND VI FUSION ALGORITHM USING RASPBERRY PI PLATFORM
    Lingamallu Naga Srinivasu, Sumanth Kumar Panguluri, Srinivasa Rao Kandula, Ponduri Vasanthi
    Telecommunications and Radio Engineering English Translation of Elektrosvyaz and Radiotekhnika, 2024
    One of the most important and active areas of image processing research is visible and thermallight image fusion. Moreover, real-time visible and thermal-light image fusion has been utilized in multiple kinds of applications such as surveillance devices, military applications, medical diagnosis, remote sensing, etc. Hence, this paper suggests a real-time application of visible and infrared (V-I) image fusion using lift wavelet transform (LWT) and implemented on the Raspberry Pi. The proposed morphological operations-based unsharp masking enhancement technique overcomes the low contrast issue of the fused image. Next, the LWT provides the good feature (smooth and detail) layers of visible image (VI) and infrared image (IRI) when compared to other transformations. Due to this, we have overcome the problem of spatial distortions in the fused image. Moreover, the proposed novel fusion techniques such as, mean-weighted fusion rule based on filters and max-weighted fusion rule based on filters generate the fused image with improved texture information and overcome the problem of edge information loss. The real-time application of the V-I image fusion is implemented through the portable hardware device Raspberry Pi. Finally, the experimental results show that the suggested framework has produced a fused image with improved visual quality, rich edge information, high contrast, low spatial distortions, and more texture information, when compared to the results of existing methodologies.
  • Steganography using wavelet transform for secured data transmission
    Lingamallu Naga Srinivasu, Vijayaraghavan Veeramani
    Journal of Ambient Intelligence and Humanized Computing, 2023
  • Covert and Secure Communication Using Multiple Invisible Stego Images by Split-Logical Algorithm
    Lingamallu Naga Srinivasu, Vijayaraghavan Veeramani
    IETE Journal of Research, 2023
    The use of the web is developing for surreptitious and non-surreptitious communication with the expansion of brilliant innovations. In this circumstance, there is a need to improve the security level of the covert information. Our motive is to propose a unique algorithm by using steganography along with cryptography which provides a better solution for secret communication and improves security with more levels. In this paper, the two-level stationary wavelet transform technique is applied to cover image to acquire HH2 sub-band alongside the remaining sub-bands. The data are implanted into the HH2 sub-band of the cover image by utilizing the random pixel embedded strategy to accomplish the stego image. To improve the security of the stego image, the surreptitious text data are changed into cipher form with a novel cryptography algorithm that incorporates unique data conversion and gray-code techniques. The resultant stego image undergoes to split-logical algorithm to create numerous imperceptible stego images. The main purpose of imperceptible stego images is to provide better security to covert information and also it increases the payload capacity. The concept of generating imperceptible stego images is, each of them will be treated as a stego image for hackers. But original stego image is reconstructed by combining all generated imperceptible stego images. These imperceptible stego images are ready to oppose the diverse steganalysis assaults like visual, noise, rotational assaults, and so forth.
  • ENHANCEMENT OF SECURED DATA TRANSMISSION USING n∗3 INCOMPREHENSIBLE STEGO FILES BASED ON LS, SL, AND LC ALGORITHMS
    Lingamallu Naga Srinivasu, Vijayaraghavan Veeramani
    Telecommunications and Radio Engineering English Translation of Elektrosvyaz and Radiotekhnika, 2023
    In this paper, security of covert communication is increased by using <i>n</i><sup>*</sup>3 incomprehensible stego files based on the novel algorithms, which are logical and statistical (LS), split logical (SL), and logical concealing (LC). Generally, cryptography and steganography techniques are used to achieve a secured data transmission. Cryptography is used to generate the "ciphertext" and steganography is used to produce the understandable stego image. The results of these two techniques alert the intruder to the fact that secret information is being exchanged. To overcome this drawback, this paper generates n*3 incomprehensible stego files with four levels of security using covert data and a container (cover) image. The main novelty of the proposed framework is that it provides multilevel security for data transmission by converting understandable stego images into non-understandable (incomprehensible) stego files. The LS encryption algorithm generates the lesser length of ciphertext with the input of covert data. The steganography generates a good visual quality of an understandable stego image by using discrete framelet transform (DFT) and random pixel embedding (RPE) techniques. Finally, the SL and LC algorithms produce <i>n</i><sup>*</sup>3 incomprehensible stego files using the understandable stego image. These stego files are incomprehensible, meaningless, and invisible in nature. The experimental results have shown that the incomprehensible stego files improve the payload capacity, quality metrics (peak signal-to-noise ratio, correlation, and number of pixels change in rate), and also four levels of security to the secret data. It is also able to face the various steganalysis attacks, such as files deletion, data deletion, and data modification.
  • CNN based “Text in Image” Steganography using Slice Encryption Algorithm and LWT
    Lingamallu Naga Srinivasu, Vijayaraghavan Veeramani
    Optik, 2022
  • Secure 'text in image' steganography scheme based on Alexnet and contour-let transform
    Lingamallu Naga Srinivasu, Vijayaraghavan Veeramani
    2022 2nd International Conference on Artificial Intelligence and Signal Processing Aisp 2022, 2022
  • Secure and covert communication using steganography by Wavelet Transform
    Naga Srinivasu Lingamallu, Vijayaraghavan Veeramani
    Optik, 2021
  • Video steganography using two-level SWT and SVD
    Lingamallu Naga Srinivasu, Kolakaluri Srinivasa Rao
    Handbook of Research on Pattern Engineering System Development for Big Data Analytics, 2018
  • Aadhaar card voting system
    Lingamallu Naga Srinivasu, Kolakaluri Srinivasa Rao
    Lecture Notes on Data Engineering and Communications Technologies, 2018

RECENT SCHOLAR PUBLICATIONS

  • A Novel Ensemble Method for Predicting Heart Disease Using Quine McCluskey Binary Classifiers (QMBC)
    LN Srinivasu, TLN Sai, KN Jyothi, AR Kumar, S Praveen, V Teju
    Research Digest on Engineering Management and Social Innovations 2 (1), 1-16 , 2026
    2026
  • Re: Multiple Kidney Stones Prediction With Efficient RT-DETR Model Editorial Comment
    P Vasanthi, LN Srinivasu, V Teju
    JOURNAL OF UROLOGY 214 (5), 546-546 , 2025
    2025
  • Prediction of diabetes, heart disease and Parkinson's disease using ML
    V Teju, G Bhavana, B Rohit, JS Punitha, K Prakash, KV Sowmya, ...
    Parul University International Conference on Engineering and Technology 2025 … , 2025
    2025
  • Smart web-based hospital management system with machine learning integration
    V Teju, J Poojitha, BS Sagarika, GNA Valli, GJ Nagendra, K Prakash, ...
    Parul University International Conference on Engineering and Technology 2025 … , 2025
    2025
  • Enhanced blood cell classification using neural networks
    V Teju, NB Devarasetti, S Boga, K Dumpala, LP Challa, K Prakash, ...
    Parul University International Conference on Engineering and Technology 2025 … , 2025
    2025
  • Classification of skin cancer using deep learning
    V Teju, RN Sivathmika, VS Chowdary, SS Raza, YNV Praveen, K Prakash, ...
    IET Conference Proceedings CP920 2025 (7), 1505-1511 , 2025
    2025
  • Hybrid model for detection of severity in car accidents
    V Teju, P Sravani, S Anitha, SS Siddhartha, T Silviya, K Prakash, ...
    IET Conference Proceedings CP920 2025 (7), 849-855 , 2025
    2025
  • Multiple kidney stones prediction with efficient RT-DETR model
    P Vasanthi, LN Srinivasu, V Teju, KV Sowmya, A Stan, V Sita, L Miclea, ...
    Computers in Biology and Medicine 190, 110023 , 2025
    2025
    Citations: 12
  • REAL-TIME IMPLEMENTATION OF LWT BASED NOVEL IR AND VI FUSION ALGORITHM USING RASPBERRY PI PLATFORM
    LN Srinivasu, SK Panguluri, SR Kandula, P Vasanthi
    Telecommunications and Radio Engineering 83 (4) , 2024
    2024
    Citations: 1
  • Steganography using wavelet transform for secured data transmission
    LN Srinivasu, V Veeramani
    Journal of Ambient Intelligence and Humanized Computing 14 (7), 9509-9527 , 2023
    2023
    Citations: 8
  • ENHANCEMENT OF SECURED DATA TRANSMISSION USING n *3 INCOMPREHENSIBLE STEGO FILES BASED ON LS, SL, AND LC ALGORITHMS
    LN Srinivasu, V Veeramani
    Telecommunications and Radio Engineering 82 (1) , 2023
    2023
    Citations: 1
  • CNN based “Text in Image” Steganography using Slice Encryption Algorithm and LWT
    LN Srinivasu, V Veeramani
    Optik 265, 169398 , 2022
    2022
    Citations: 13
  • Proceedings of International Conference on Computational Intelligence and Data Engineering: ICCIDE 2021
    N Chaki, N Devarakonda, A Cortesi, H Seetha
    Springer Nature , 2022
    2022
    Citations: 2
  • Secure ‘text in image’steganography scheme based on alexnet and contour-let transform
    LN Srinivasu, V Veeramani
    2022 2nd International Conference on Artificial Intelligence and Signal … , 2022
    2022
    Citations: 2
  • Curve-Let Transform Based'Text-In-Image Steganography'By Using Huffman Coding
    LN SRINIVASU, V VEERAMANI
    International Journal of communication and computer Technologies 9 (1), 10-14 , 2021
    2021
    Citations: 4
  • Secure and covert communication using steganography by Wavelet Transform
    NS Lingamallu, V Veeramani
    Optik 242, 167167 , 2021
    2021
    Citations: 18
  • Covert and Secure Communication Using Multiple Invisible Stego Images by Split-Logical Algorithm
    LN Srinivasu, V Veeramani
    IETE Journal of Research , 2021
    2021
  • CASH NOTE WITH HIGH PERFORMANCE SECURITY
    LN Srinivasu, KS Rao
    International Journal of Applied Research in Bioinformatics 9 (1), 20-35 , 2019
    2019
    Citations: 2
  • Video Steganography using two-level SWT and SVD
    LN Srinivasu, KS Rao
    Handbook of Research on Pattern Engineering System Development for Big Data … , 2018
    2018
    Citations: 2
  • Aadhaar card voting system
    LN Srinivasu, KS Rao
    Proceedings of International Conference on Computational Intelligence and … , 2017
    2017
    Citations: 4

MOST CITED SCHOLAR PUBLICATIONS

  • Secure and covert communication using steganography by Wavelet Transform
    NS Lingamallu, V Veeramani
    Optik 242, 167167 , 2021
    2021
    Citations: 18
  • CNN based “Text in Image” Steganography using Slice Encryption Algorithm and LWT
    LN Srinivasu, V Veeramani
    Optik 265, 169398 , 2022
    2022
    Citations: 13
  • Multiple kidney stones prediction with efficient RT-DETR model
    P Vasanthi, LN Srinivasu, V Teju, KV Sowmya, A Stan, V Sita, L Miclea, ...
    Computers in Biology and Medicine 190, 110023 , 2025
    2025
    Citations: 12
  • Steganography using wavelet transform for secured data transmission
    LN Srinivasu, V Veeramani
    Journal of Ambient Intelligence and Humanized Computing 14 (7), 9509-9527 , 2023
    2023
    Citations: 8
  • Curve-Let Transform Based'Text-In-Image Steganography'By Using Huffman Coding
    LN SRINIVASU, V VEERAMANI
    International Journal of communication and computer Technologies 9 (1), 10-14 , 2021
    2021
    Citations: 4
  • Aadhaar card voting system
    LN Srinivasu, KS Rao
    Proceedings of International Conference on Computational Intelligence and … , 2017
    2017
    Citations: 4
  • Proceedings of International Conference on Computational Intelligence and Data Engineering: ICCIDE 2021
    N Chaki, N Devarakonda, A Cortesi, H Seetha
    Springer Nature , 2022
    2022
    Citations: 2
  • Secure ‘text in image’steganography scheme based on alexnet and contour-let transform
    LN Srinivasu, V Veeramani
    2022 2nd International Conference on Artificial Intelligence and Signal … , 2022
    2022
    Citations: 2
  • CASH NOTE WITH HIGH PERFORMANCE SECURITY
    LN Srinivasu, KS Rao
    International Journal of Applied Research in Bioinformatics 9 (1), 20-35 , 2019
    2019
    Citations: 2
  • Video Steganography using two-level SWT and SVD
    LN Srinivasu, KS Rao
    Handbook of Research on Pattern Engineering System Development for Big Data … , 2018
    2018
    Citations: 2
  • REAL-TIME IMPLEMENTATION OF LWT BASED NOVEL IR AND VI FUSION ALGORITHM USING RASPBERRY PI PLATFORM
    LN Srinivasu, SK Panguluri, SR Kandula, P Vasanthi
    Telecommunications and Radio Engineering 83 (4) , 2024
    2024
    Citations: 1
  • ENHANCEMENT OF SECURED DATA TRANSMISSION USING n *3 INCOMPREHENSIBLE STEGO FILES BASED ON LS, SL, AND LC ALGORITHMS
    LN Srinivasu, V Veeramani
    Telecommunications and Radio Engineering 82 (1) , 2023
    2023
    Citations: 1
  • A Novel Ensemble Method for Predicting Heart Disease Using Quine McCluskey Binary Classifiers (QMBC)
    LN Srinivasu, TLN Sai, KN Jyothi, AR Kumar, S Praveen, V Teju
    Research Digest on Engineering Management and Social Innovations 2 (1), 1-16 , 2026
    2026
  • Re: Multiple Kidney Stones Prediction With Efficient RT-DETR Model Editorial Comment
    P Vasanthi, LN Srinivasu, V Teju
    JOURNAL OF UROLOGY 214 (5), 546-546 , 2025
    2025
  • Prediction of diabetes, heart disease and Parkinson's disease using ML
    V Teju, G Bhavana, B Rohit, JS Punitha, K Prakash, KV Sowmya, ...
    Parul University International Conference on Engineering and Technology 2025 … , 2025
    2025
  • Smart web-based hospital management system with machine learning integration
    V Teju, J Poojitha, BS Sagarika, GNA Valli, GJ Nagendra, K Prakash, ...
    Parul University International Conference on Engineering and Technology 2025 … , 2025
    2025
  • Enhanced blood cell classification using neural networks
    V Teju, NB Devarasetti, S Boga, K Dumpala, LP Challa, K Prakash, ...
    Parul University International Conference on Engineering and Technology 2025 … , 2025
    2025
  • Classification of skin cancer using deep learning
    V Teju, RN Sivathmika, VS Chowdary, SS Raza, YNV Praveen, K Prakash, ...
    IET Conference Proceedings CP920 2025 (7), 1505-1511 , 2025
    2025
  • Hybrid model for detection of severity in car accidents
    V Teju, P Sravani, S Anitha, SS Siddhartha, T Silviya, K Prakash, ...
    IET Conference Proceedings CP920 2025 (7), 849-855 , 2025
    2025
  • Covert and Secure Communication Using Multiple Invisible Stego Images by Split-Logical Algorithm
    LN Srinivasu, V Veeramani
    IETE Journal of Research , 2021
    2021