manjunathan nanjundan

@veltech.edu.in

Assistant Professor and CSE
vel Tech Rangarajan Dr.Sagunthala R & D Institute of Science and Technology

manjunathan nanjundan


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https://researchid.co/nmanjunathan25051985
22

Scopus Publications

Scopus Publications

  • Designing a deep learning approach to categorize rupee notes and assist the visually impaired members to recognize different denominations
    K. Suganya, G. Prabaharan, A. Prathik, N. Manjunathan, R. Vijayabharathi
    Aip Conference Proceedings, 2026
  • Security and flexible framework key plan for dynamic gaming approach
    N. Manjunathan, G. Prabaharan, A. Prathik, S. Ravikumar, N. K. Manikandan, S. Durai
    Aip Conference Proceedings, 2026
  • AI and Big Data Analytics in Healthcare: Current Applications and Future Trends
    N. Manjunathan, N. Prakash, S. Muthulingam, R. Sivakumar, P. Girija, R. Siva Subramanian
    Communications in Computer and Information Science, 2026
  • Smart water management systems for sustainable urban and agricultural applications
    G. Sekar, N. Manjunathan, S. Agalya, K. Sudha, T. Nithya, P. Rashmi, K. Vijayakumar
    Leveraging Urban Computing for Sustainable Urban Development, 2025
    Smart Water Management Systems have gradually become important enablers to solve global water shortage, urbanization, and sustainable resource management. This survey is focused on understanding how IoT, data analytics, and AI can advance the typical approaches to water management. Some of the main uses of SWMS are explained such as Real time monitoring of water usage, Detection of water leakage, Monitoring of water quality, Automated Irrigation and Flood control systems. These technologies allow cities and the industrial sector to efficiently allocate water in terms of supply, use and control and adapt to changing circumstances. However, the implementation of SWMS suffers from some issues such as data security, interoperability, scalability and high initial cost for infrastructure. In the light of such growing integration of smart technologies in cities, SWMS can thus be seen as an instrumental facet of global water sustainability and efficiency.
  • A comparative analysis of the health monitoring process using deep learning methods for brain tumour
    N. Manjunathan, N. Gomathi
    Measurement Sensors, 2025
    The use of Internet of Things (IoT) devices has been growing rapidly recently. As technology improves, products for older people are developed in the health industry. Applications for virtual and remote interactions with patients are somewhat too simple to use. If IoT technology is used well, it may be possible to treat physically erratic individuals without having to see a doctor often. As a result of this research, a prototype of an Internet of Things–based remote health monitoring system for senior patients has been developed. The suggested technique enables the care to better manage and keep an eye on the well-being of older patients. The system will design and implement efficient contact with the patient's families. This model has a number of sensors, including sensors for arthritis, body temperature, skin response, and pulse. Each sensor is paired with a system of proposals for analysis and validation. The data feasibility of the data obtained from the IoT sensors of the proposed system efficacy is being explored. The information obtained from the sensors and the extracted data is sent to cloud storage via distributed storage. In the performance studies, the efficacy of the proposed system is evaluated based on the data retrieved and used against certain health metrics like heartbeat and temperature sensors . IoT combined with wellness wearables may eliminate the need to visit a doctor for urgent health conditions. To ensure data accuracy & system scaling, Internet of Things devices are employed in the proposed system, & the power consumption and battery life are analysed.
  • Nephrolithcipher: Decoding Nephrolithiasis Through Deep Neural Networks
    N Manjunathan, Tummalapally Ramya
    Proceedings 3rd International Conference on Self Sustainable Artificial Intelligence Systems Icssas 2025, 2025
    Kidney stone disease (nephrolithiasis) is a growing global health concern, often diagnosed using medical imaging modalities such as computed tomography (CT). Manual interpretation of CT images can be subjective and error-prone. This study presents a comparative analysis of two established convolutional neural network (CNN) architectures—VGG16 and MobileNetV2—for the classification of kidney-related abnormalities, including stones, cysts, and tumors, using CT scan images. The models were evaluated based on classification accuracy, processing time, and suitability for medical imaging tasks. Preprocessing steps, including resizing and segmentation, were applied to enhance model performance. Experimental results showed that VGG16 achieved an accuracy of 96%, while MobileNetV2 reached 94%, indicating the effectiveness of both models, with VGG16 slightly outperforming in accuracy and MobileNetV2 in computational efficiency. This analysis offers insight into the strengths and limitations of each model, aiding future research in selecting appropriate CNN architectures for kidney stone detection.
  • A FUSION MODEL FOR STOCK MARKET PREDICTION USING PROPHET AND LONG SHORT-TERM MEMORY NEURAL NETWORKS
    Shruti Mishra, N Manjunathan, Parangat Singh, Sandeep Kumar Satapathy, Sung-Bae Cho, Sachi Nandan Mohanty
    Proceedings on Engineering Sciences, 2025
    Predicting the stock market can be difficult because of its inherent volatility and complexity. Machine learning approaches have demonstrated potential in identifying patterns and trends in financial data, enabling precise prediction-making in recent times. In this work, we combine the advantages of Prophet regression and Long Short-Term Memory (LSTM) neural networks to propose a fusion model for stock market prediction. Because the LSTM model is so good at capturing temporal relationships in sequential data, it is a great choice for studying historical trends in stock prices. Conversely, Facebook's Prophet is a powerful time-series forecasting tool that makes accurate predictions by taking into account patterns, seasonality, and holidays. Our fusion strategy takes advantage of the complimentary capabilities of both techniques by merging Prophet and LSTM models. The Prophet component takes seasonal trends and outside influences into consideration to further improve forecasts, while the LSTM component analyzes past stock market data to identify intricate patterns. We validate our fusion model's efficacy through tests on real-world stock market datasets. We compare our forecasts' accuracy to that of individual LSTM and Prophet models as well as conventional forecasting techniques. Our findings show that the fusion model performs better than stand-alone methods, resulting in increased reliability and prediction accuracy. Predicting the stock market can be difficult because of its inherent volatility and complexity. Machine learning approaches have demonstrated potential in identifying patterns and trends in financial data, enabling precise prediction-making in recent times. In this work, we combine the advantages of Prophet regression and Long Short-Term Memory (LSTM) neural networks to propose a fusion model for stock market prediction. Because the LSTM model is so good at capturing temporal relationships in sequential data, it is a great choice for studying historical trends in stock prices. Conversely, Facebook's Prophet is a powerful time-series forecasting tool that makes accurate predictions by taking into account patterns, seasonality, and holidays. Our fusion strategy takes advantage of the complimentary capabilities of both techniques by merging Prophet and LSTM models. The Prophet component takes seasonal trends and outside influences into consideration to further improve forecasts, while the LSTM component analyzes past stock market data to identify intricate patterns. We validate our fusion model's efficacy through tests on real-world stock market datasets. We compare our forecasts' accuracy to that of individual LSTM and Prophet models as well as conventional forecasting techniques. Our findings show that the fusion model performs better than stand-alone methods, resulting in increased reliability and prediction accuracy.
  • Exploring Machine Learning and Advanced Modeling Techniques in Financial Data Science
    N Manjunathan, Udhaya Shankar S, R. Nithyanandhan, S. Swetha, Maheswari B, Nalini M
    Proceedings of 8th International Conference on Trends in Electronics and Informatics Icoei 2025, 2025
    This paper aims at discussing how financial data science revolutionized the financial industry with specific emphasis on supervised, unsupervised, and reinforcement learning. It focuses on new modeling techniques, such as time series analysis and deep learning used in algorithmic, trading, risk assessment, and fraud identification. This paper also discusses several important issues like data quality problem, model interpretation issue, and computational cost issue. Apart from that, it explores about the recent advancements including blockchain and quantum computing with emphasis on legal aspects of privacy concerns. Finally, the survey emphasizes the need for developing new approaches to financial data science accompanied by the challenges arising with its application.
  • Sustainable Cloud Computing: Ecofriendly Strategies and Innovations for Green Data Centers
    N. Manjunathan, T. Venkata Ramana, A. Rajasekar, D. Vijayakumar, V. Sameswari, S. M. Nandha Gopal, R. Siva Subramanian
    Energy Efficient Algorithms and Green Data Centers for Sustainable Computing, 2025
    Cloud computing has experienced great growth in the recent past and has been attributed to bringing huge economic value but at the same time has been associated with energy usage and environmental consequences. The problem of how to make cloud computing environmentally friendly and create green data centers is one of the major concerns of the cloud computing industry at present. This survey focuses on the following four dimensions of sustainability in the context of cloud environments: energy efficiency, resource optimization, renewable energy, and sustainable hardware. Furthermore, the authors also explore the limitations associated with cost, size, and security requirements in terms of environmental goals. The chapter gives a classification of sustainable cloud techniques, discusses present industry activities, and gives potential future technologies such as AI-driven energy management and edge computing as potential solutions. The goal of this chapter is to provide a literature review of sustainable practices and encourage more investigations into ecoconscious cloud computing.
  • Advances in data processing, machine learning, and data security
    N. Manjunathan, K. Aravindaraj, J. Anitha, V. K. Ramya Bharathi, V. Sathya, R. Senthil, R. Siva Subramanian
    Strategic Innovations of AI and ml for E Commerce Data Security, 2024
    This study examines how data processing, sophisticated machine learning (ML), and data security are crucial to data-driven decision-making. It covers accurate data collecting, cleaning, and pre-processing methods, which are the foundation for trustworthy ML models. Exploratory data analysis and feature engineering provide difficult dataset insights. Data quality depends on how missing data and outliers are handled. Predictive modelling uses ML approaches such supervised, unsupervised, semi-supervised, ensemble, and deep learning. Reducing dimensionality and selecting features improve model efficiency and interpretation. The model creation, training, and assessment methods ensure performance quality. The study also emphasises data security, ML ethics, privacy, and justice. New technologies in eCommerce data security are revolutionising protection methods, and AI-based solutions are moving cybersecurity towards transparency and confidentiality. However, future study will examine ethical problems and explainability to improve data-driven applications.
  • K-Nearest Neighbors in Machine Learning: Theory, Practice, and Advances
    N Manjunathan, R. Vijaya Prabhu, K. Sudha, D. Lekha, M. Ezhilvendan, Gayathri S
    2024 International Conference on Smart Technologies for Sustainable Development Goals Icstsdg 2024, 2024
  • Ensemble Learning: Importance, Applications, Challenges, and Future Work
    N. Manjunathan, Shanmugapriya D, P. Girija, Vasudevan M, Murugesan S, Siva Subramanian R
    2024 International Conference on Integration of Emerging Technologies for the Digital World Icietdw 2024, 2024
  • Analyzing and Forecasting Financial Services using Packet Transform through Wavelet Transitions
    N. Manjunathan, G. Prabaharan, A. Prathik, D. Jaganathan, N. K. Manikandan, S. Muthulingam
    2nd International Conference on Self Sustainable Artificial Intelligence Systems Icssas 2024 Proceedings, 2024
  • Assessing the health ability based on health monitoring using fuzzy for monitoring geriatrics care and amputees
    N. Gomathi, N. Manjunathan
    Soft Computing, 2023
  • Effective image segmentation through MRI with fuzzy-based cell detection using deep learning
    N. Manjunathan, N. Gomathi
    Soft Computing, 2023
  • Early Detection of Breast Cancer using Machine Learning
    N. Manjunathan, N. Gomathi, S. Muthulingam
    International Conference on Sustainable Computing and Smart Systems Icscss 2023 Proceedings, 2023
  • IoT based Gas Leakage Detection System
    N. Manjunathan, S. Muthulingam, D. Jaganathan
    2nd International Conference on Sustainable Computing and Data Communication Systems Icscds 2023 Proceedings, 2023
  • DCTCADM: Data-Centric Cognitive Agent Decision Making Based an Effective Handoff for Heterogeneous Wireless Communication
    S. Muthulingam, V. Pandiyaraju, K Malar, U. Samson Ebenezar, N Manjunathan, Goutham Elumalai
    Proceedings of the 2023 6th International Conference on Recent Trends in Advance Computing Icrtac 2023, 2023
  • Cardiovascular Disease Prediction using Enhanced Support Vector Machine Algorithm
    N. Manjunathan, S. Girirajan, D. Jaganathan
    Proceedings 6th International Conference on Computing Methodologies and Communication Iccmc 2022, 2022
  • Morphogenesis Method to Detect Brain Tumor using Machine Learning Technique and Noise Filtering
    D. Jaganathan, N. Manjunathan, S. Girirajan
    International Conference on Sustainable Computing and Data Communication Systems Icscds 2022 Proceedings, 2022
  • Unmanned dam monitoring system using wireless sensor networks
    International Journal of Innovative Technology and Exploring Engineering, 2019
  • Drunk and drive detection using iot
    International Journal of Innovative Technology and Exploring Engineering, 2019