Controlling the screen using hand gestures Tirupathi Saimanikanta, K. Lakshmi Nadh, S. Siva Nageswararao, V. Maheshbabu, K. V. Narasimhareddy Aip Conference Proceedings, 2025
Unveiling the Potential of Deep Learning: A Multifaceted Approach to Pulmonary Disease Detection and Clinical Integration K LakshmiNadh, Gurram Siva Anjali, Pandi Jyoshna Devi, Gude Lavanya, Chalicheema Rajani, Dodda Venkata Reddy 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2025, 2025 Pulmonary diseases are major challenges in health care basically because of the complexities of diagnosing and treating them. However, deep learning technology has shown that enhancing disease detection and integrating these technologies within healthcare environments is possible. This project aims to improve the accuracy of pulmonary disease diagnosis focusing on viral pneumonitis, bacterial pneumonitis, COVID-19, and normal lung conditions through deep learning models. Our models leverage sophisticated, specifically developed CNNs that identify subtle patterns and differences indicative of these diseases from a variety of clinical imaging modalities, including chest radiographs and computed tomography scans. In addition, the project explores ways of incorporating such AI-based ways into present-day clinical practice so that we can shift from traditional methods towards those informed by AI. During this research work among different groups of patients, we have conducted rigorous tests on our models against established diagnostic standards. The findings show significant changes in early detection and significantly reduced diagnostic error rates which emphasize the disruptive ability of deep learning to pulmonary disease management. It also discusses ethical and practical challenges in the use of AI in healthcare, particularly in ensuring patient privacy, making AI-driven decisions transparent, and the need for education and training of healthcare professionals. This work emphasizes the potential that deep learning possesses in revolutionizing the detection of pulmonary diseases and paves the way for its wide application in clinical practice.
Advanced Pest Identification: An Efficient Deep Learning Approach Using VGG Networks K. Lakshminadh, Divvela Chandu Venkateswara Guptha, Jujjuri Sai, Kandula Rajesh, Sireesha Moturi, Yaragani Neelima, Dodda Venkata Reddy 2025 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation Iatmsi 2025, 2025 Accurate pest identification is crucial for both effective pest management and crop protection. Pests must be found early in order to minimise damage and guarantee crop security. Conventional techniques typically entail visual examination and professional involvement, which might be time-consuming and susceptible to errors by humans. On the other hand, deep learning-powered high-performance systems can now more accurately identify pests thanks to developments in computer vision. In this work, we employed the Keras-based deep learning models VGG16 and VGG19 to construct a passive pest detection system. We greatly improved the efficacy of these models in identifying pest species by using strategies such data augmentation, model optimization, and modification of validated models. The VGG16 model produced an amazing accuracy rate of 99.8% and VGG19 model produced an accuracy of 96.8 % in our testing.
Ensemble-Based Transfer Learning for Multi-Class Plant Disease Detection Using VGG16, ResNet50, and Xception Models Popuri Mohana Siva Lakshmi, K. LakshmiNadh, K.V. Narasimha Reddy, Dodda Venkata Reddy Proceedings of 5th International Conference on Iot Based Control Networks and Intelligent Systems Icicnis 2024, 2024 In the world, plant diseases pose a serious threat to agricultural productivity and food security. Early, accurate, and rapid identification of plant diseases is important for con-trolling loss of crops. In the following research, transfer learning models VGG16, ResNet50, and Xception are applied to attempt overcoming this challenge of multiclass plant disease detection. To improve classification accuracy, we propose an ensemble model that combines the strengths of these pre-trained networks. Multiple plant species and disease categories were experimented on extensively on publicly available plant disease datasets. The results show that ensemble model achieves better precision, precision and recall than individual models and therefore presents a robust solution for identifying several plant diseases together as a pack. Results from the experiment demonstrate that the proposed method could be deployed in real-time agricultural systems and have potential to provide a scalable and efficient diagnostic tool for farmers and agronomists to detect plant diseases and reduce their impact. This work is among the growing body of work in AI based agricultural solutions and indicates that transfer learning and ensemble techniques are promising in precision farming.
Brain Tumour Detection Using CNN Sri Lekha Jagannadham, K. Lakshmi Nadh, M. Sireesha Proceedings of the 5th International Conference on I Smac Iot in Social Mobile Analytics and Cloud I Smac 2021, 2021
A Combined DQN with Dueling and Noisy Networks: A Unified Framework for Deep Reinforcement K LakshmiNadh, SSN Rao, G Parimala Integration of Artificial Intelligence in IoT: Opportunities and Challenges … , 2025 2025
A Novel Method of Image Colorization Using Convolutional Neural Networks SSN Rao, K LakshmiNadh, G Parimala Integration of Artificial Intelligence in IoT: Opportunities and Challenges … , 2025 2025
Controlling the screen using hand gestures T Saimanikanta, KL Nadh, SS Nageswararao, V Maheshbabu, ... AIP Conference Proceedings 3342 (1), 060004 , 2025 2025
An examination of big data analytics-based high-speed data implementations KL Nadh, SK Khaja Mohiddin Basha, P Prasanthi, BU Rani AIP Conference Proceedings 3342 (1), 060005 , 2025 2025
A Novel Approach for Early Detection of Forest Fire from Images with Deep Learning: A Machine Vision Course Experiment BNV Udaya Lakshmi, K Lakshminadh, K Suresh Babu, ... International Conference on Computing and Communication Systems for … , 2025 2025
Advanced pest identification: An efficient deep learning approach using VGG networks K Lakshminadh, DCV Guptha, J Sai, K Rajesh, S Moturi, Y Neelima, ... 2025 IEEE International Conference on Interdisciplinary Approaches in … , 2025 2025 Citations: 12
Unveiling the Potential of Deep Learning: A Multifaceted Approach to Pulmonary Disease Detection and Clinical Integration K LakshmiNadh, GS Anjali, PJ Devi, G Lavanya, C Rajani, DV Reddy 2025 IEEE International Conference on Interdisciplinary Approaches in … , 2025 2025
Neural Network-Based Named Entity Recognition for Bodo: A Deep Learning Approach K LakshmiNadh, P Nikhitha, S Mahishabi, AR Lakshmi, V Karuna Kumar, ... International Conference on Information Technology and Artificial … , 2025 2025
Ensemble-Based Transfer Learning for Multi-Class Plant Disease Detection Using VGG16, ResNet50, and Xception Models PMS Lakshmi, K LakshmiNadh, KVN Reddy, DV Reddy 2024 International Conference on IoT Based Control Networks and Intelligent … , 2024 2024
A Novel Method of Image Colorization Using Convolutional Neural Networks S Siva Nageswara Rao, K LakshmiNadh, G Parimala International Conference on Internet of Things and Connected Technologies … , 2024 2024
A Combined DQN with Dueling and Noisy Networks: A Unified Framework for Deep Reinforcement Learning K LakshmiNadh, S Siva Nageswara Rao, G Parimala International Conference on Internet of Things and Connected Technologies … , 2024 2024
An Improved and More Effective FSPC-Based Cloud Consumer Legality Process for Protected Data D Priyanka, P Anjaneyulu, KL Nadh, SKKM Basha International Conference on Computing, Communication and Learning, 413-424 , 2024 2024
Enhancing Profanity Detection in Textual Data Using Bidirectional Long Short-Term Memory Networks K Lakshminadh, V Sravanthi, K Koushik, CS Bhaskar 2023 International Conference on Self Sustainable Artificial Intelligence … , 2023 2023
Deep learning model for emotion prediction from speech, facial expression and videos C Rajyalakshmi, K LakshmiNadh, MS Reddy 2023 5th International Conference on Smart Systems and Inventive Technology … , 2023 2023 Citations: 2
A Binary Multi Class and Multi Level Classification with Dual Priority Labelling Model for COVID-19 and Other Thorax Disease Detection K Gumma, L.N. , Thiruvengatanadhan, R. , Lakshmi, P.D. , LakshmiNadh International Information and Engineering Technology Association, 657-664 , 2022 2022 Citations: 2
LUNG DISORDER DETECTION USING CORRELATED PIXEL DENOISING MODEL WITH TAGGED FEATURE SELECTION USING CONVOLUTION NEURAL NETWORKS KLN Lakshmi Narayana Gumma, Ramalingam Thiruvengatanadhan, Pattusamy Dhana ... MATERIAL SCIENCE AND TECHNOLOGY 21, 53-63 , 2022 2022
CUcovid: U-Net incorporated CNN based Deep-learning system of chest X-ray image classification for COVID-19 detection. LN Gumma, R Thiruvengatanadhan, KL Nadh, PD Lakshmi NeuroQuantology 20 (6), 6188-6205 , 2022 2022
A survey on convolutional neural network (deep-learning technique)-based lung cancer detection LN Gumma, R Thiruvengatanadhan, LN Kurakula, T Sivaprakasam SN Computer Science 3 (1), 66 , 2022 2022 Citations: 23
Brain tumour detection using CNN SL Jagannadham, KL Nadh, M Sireesha 2021 fifth international conference on I-SMAC (IoT in social, mobile … , 2021 2021 Citations: 46
AN EFFICIENT SPATIAL TEMPORAL PROVENANCE MECHANISM FOR ADHOC MOBILE USERS KLN K Sai Divya, S.Siva Nageswara Rao International Journal of Innovative Technology and Exploring Engineering … , 2019 2019
MOST CITED SCHOLAR PUBLICATIONS
Brain tumour detection using CNN SL Jagannadham, KL Nadh, M Sireesha 2021 fifth international conference on I-SMAC (IoT in social, mobile … , 2021 2021 Citations: 46
A survey on convolutional neural network (deep-learning technique)-based lung cancer detection LN Gumma, R Thiruvengatanadhan, LN Kurakula, T Sivaprakasam SN Computer Science 3 (1), 66 , 2022 2022 Citations: 23
Advanced pest identification: An efficient deep learning approach using VGG networks K Lakshminadh, DCV Guptha, J Sai, K Rajesh, S Moturi, Y Neelima, ... 2025 IEEE International Conference on Interdisciplinary Approaches in … , 2025 2025 Citations: 12
Markova Scheme for Credit Card Fraud Detection BS Gandhi, RL Naik, SG Krishna, K Lakshminadh International Conference on Advanced Computing, Communication and Networks … , 2011 2011 Citations: 11
Improving TCP performance with delayed acknowledgments over wireless networks: A receiver side solution KL Nadh, YKS Krishna, KN Rao Fifth International Conference on Advances in Recent Technologies in … , 2013 2013 Citations: 6
DDSRC: Algorithm for improving QOS in VANET G Parimala, S Nageswararao, K LakshmiNadh Int. J. Recent Technol. Eng.(IJRTE) 7, 1327-1331 , 2019 2019 Citations: 4
ANALYSIS OF TCP ISSUES IN INTERNET OF THINGS DK Lakshminadh International Journal of Pure and Applied Mathematics 118, 163-166 , 2018 2018 Citations: 4
Deep learning model for emotion prediction from speech, facial expression and videos C Rajyalakshmi, K LakshmiNadh, MS Reddy 2023 5th International Conference on Smart Systems and Inventive Technology … , 2023 2023 Citations: 2
A Binary Multi Class and Multi Level Classification with Dual Priority Labelling Model for COVID-19 and Other Thorax Disease Detection K Gumma, L.N. , Thiruvengatanadhan, R. , Lakshmi, P.D. , LakshmiNadh International Information and Engineering Technology Association, 657-664 , 2022 2022 Citations: 2
A Combined DQN with Dueling and Noisy Networks: A Unified Framework for Deep Reinforcement K LakshmiNadh, SSN Rao, G Parimala Integration of Artificial Intelligence in IoT: Opportunities and Challenges … , 2025 2025
A Novel Method of Image Colorization Using Convolutional Neural Networks SSN Rao, K LakshmiNadh, G Parimala Integration of Artificial Intelligence in IoT: Opportunities and Challenges … , 2025 2025
Controlling the screen using hand gestures T Saimanikanta, KL Nadh, SS Nageswararao, V Maheshbabu, ... AIP Conference Proceedings 3342 (1), 060004 , 2025 2025
An examination of big data analytics-based high-speed data implementations KL Nadh, SK Khaja Mohiddin Basha, P Prasanthi, BU Rani AIP Conference Proceedings 3342 (1), 060005 , 2025 2025
A Novel Approach for Early Detection of Forest Fire from Images with Deep Learning: A Machine Vision Course Experiment BNV Udaya Lakshmi, K Lakshminadh, K Suresh Babu, ... International Conference on Computing and Communication Systems for … , 2025 2025
Unveiling the Potential of Deep Learning: A Multifaceted Approach to Pulmonary Disease Detection and Clinical Integration K LakshmiNadh, GS Anjali, PJ Devi, G Lavanya, C Rajani, DV Reddy 2025 IEEE International Conference on Interdisciplinary Approaches in … , 2025 2025
Neural Network-Based Named Entity Recognition for Bodo: A Deep Learning Approach K LakshmiNadh, P Nikhitha, S Mahishabi, AR Lakshmi, V Karuna Kumar, ... International Conference on Information Technology and Artificial … , 2025 2025
Ensemble-Based Transfer Learning for Multi-Class Plant Disease Detection Using VGG16, ResNet50, and Xception Models PMS Lakshmi, K LakshmiNadh, KVN Reddy, DV Reddy 2024 International Conference on IoT Based Control Networks and Intelligent … , 2024 2024
A Novel Method of Image Colorization Using Convolutional Neural Networks S Siva Nageswara Rao, K LakshmiNadh, G Parimala International Conference on Internet of Things and Connected Technologies … , 2024 2024
A Combined DQN with Dueling and Noisy Networks: A Unified Framework for Deep Reinforcement Learning K LakshmiNadh, S Siva Nageswara Rao, G Parimala International Conference on Internet of Things and Connected Technologies … , 2024 2024
An Improved and More Effective FSPC-Based Cloud Consumer Legality Process for Protected Data D Priyanka, P Anjaneyulu, KL Nadh, SKKM Basha International Conference on Computing, Communication and Learning, 413-424 , 2024 2024
