Predictive Modeling for Cirrhosis Diagnosis: A Machine Learning Exploration Sudeesh Kumar V, B. Natarajan, Murali P, Prabu Selvam, Venkatraman K, N.R. Nagarajan 1st International Conference on Electronics Computing Communication and Control Technology Iceccc 2024, 2024 Cirrhosis manifests as a condition wherein healthy liver tissue is gradually replaced by scar tissue, resulting in a decline in liver function. This ailment frequently correlates with autoimmune disorders, non-alcoholic fatty liver disease, excessive alcohol intake, and persistent liver conditions such as hepatitis B or C. As cirrhosis progresses, it can cause liver failure and various complications such as ascites, portal hypertension, hepatic encephalopathy, and an increased risk of liver cancer. Since the liver plays a crucial role in filtering blood and performing essential functions, individuals with acute liver failure are more prone to infections, particularly those affecting the circulatory, respiratory, and urinary systems. Kidney failure is a common consequence of liver failure, especially in cases of acetaminophen overdose, as this medication can damage both the liver and kidneys. In the realm of healthcare, the integration of machine learning has enabled the early prediction of liver cirrhosis using historical datasets. The aim of this research is to create a machine learning model that utilizes artificial intelligence to forecast the occurrence of liver cirrhosis using supplied data. By employing hyperparameter tuning techniques to enhance accuracy, this research employs machine learning techniques like logistic regression and Gradient Boosted Classifier using XGBoost. Utilizing GridSearch within XGBoost, this research work achieves an impressive accuracy rate of 98%, accompanied by precision, recall, and F1-scores of 96%, 97%, and 96%, respectively. These findings hold promise for the advancement of more sophisticated and accurate liver cirrhosis prediction models, thereby benefiting researchers in the field.
EXPLORE STRATEGIES FOR SUSTAINABLE WATER MANAGEMENT IN RAPIDLY GROWING URBAN SYSTEMS Journal of Environmental Protection and Ecology, 2024
Enhancing Stock Price Predictions Through LSTM-based Recurrent Neural Networks G. V. Krishna Kumar, B. Natarajan, Venkatraman K, S. Gayathri Devi, Prabu Selvam, N. R. Nagarajan 2nd International Conference on Integrated Circuits and Communication Systems Icicacs 2024, 2024 Analyzing finances has become increasingly challenging in today’s investment landscape, where making valuable and informed investment decisions is crucial. The fluctuation of share prices plays a pivotal role in determining investors’ profits or losses. Current forecasting techniques encompass both linear and non-linear algorithms. However, these methods primarily emphasize forecasting changes in the stock index or forecasting prices for individual companies based on their daily concluding rates. The proposed methodology introduces a model-agnostic method. Rather than conforming data to a specific model, this approach aims to identify unseen trends inherent in the data through deep learning models. In this study, three distinct deep learning models—HMM, RNN, and LSTM—are employed to predict prices using the dataset from ICICI Bank. Their performances are compared, revealing that the LSTM model adeptly discerns evolving trends. The LSTM model displays the lowest error percentage at 2.36%, outperforming other models such as HMM (7.32%) and RNN (3.94%).
Reconfigurable Architecture Application Using Machine Learning in Edge Computing for IoT Devices M. Baritha Begum, J. Eindhumathy, J. Sangeetha Priya, M. Padmaa, N. R. Nagarajan, S.J. Muhamed Suhail International Conference on Parallel Distributed and Grid Computing Pdgc, 2024 This paper delves into the utilization of reconfigurable architecture integrated with machine learning (ML) to advance edge computing for Internet of Things (IoT) devices. The proposed framework synergizes Deep Q-Networks (DQN) ML algorithms with reconfigurable hardware to enhance computational efficiency, adaptability, and energy consumption in IoT devices. With the increasing deployment of IoT devices in diverse environments, the need for efficient, adaptable, and energy-conscious computational solutions at the edge has become imperative. By embedding machine learning algorithms into reconfigurable hardware, the framework enables IoT devices to dynamically adjust their processing capabilities based on the current workload and environmental conditions. This adaptability is crucial in managing the limited computational and energy resources typical of IoT devices. The framework's design emphasizes flexibility, allowing IoT devices to optimize their operations on the fly, thereby improving their performance and energy efficiency. Experiments conducted to assess the framework's effectiveness revealed significant improvements in processing speed and resource utilization. These results validate the framework's potential to transform IoT edge computing by offering a scalable and efficient solution to the challenges posed by the burgeoning IoT ecosystem. This approach underscores the promise of combining reconfigurable hardware and DQN ML to push the boundaries of IoT device capabilities, ensuring they can meet future demands while conserving valuable resources.
Intrusion Detection System for Big Data Analytics in IoT Environment M. Anuradha, G. Mani, T. Shanthi, N. R. Nagarajan, P. Suresh, C. Bharatiraja Computer Systems Science and Engineering, 2022 In the digital area, Internet of Things (IoT) and connected objects generate a huge quantity of data traffic which feeds big data analytic models to discover hidden patterns and detect abnormal traffic. Though IoT networks are popular and widely employed in real world applications, security in IoT networks remains a challenging problem. Conventional intrusion detection systems (IDS) cannot be employed in IoT networks owing to the limitations in resources and complexity. Therefore, this paper concentrates on the design of intelligent metaheuristic optimization based feature selection with deep learning (IMFSDL) based classification model, called IMFSDL-IDS for IoT networks. The proposed IMFSDL-IDS model involves data collection as the primary process utilizing the IoT devices and is preprocessed in two stages: data transformation and data normalization. To manage big data, Hadoop ecosystem is employed. Besides, the IMFSDL-IDS model includes a hill climbing with moth flame optimization (HCMFO) for feature subset selection to reduce the complexity and increase the overall detection efficiency. Moreover, the beetle antenna search (BAS) with variational autoencoder (VAE), called BAS-VAE technique is applied for the detection of intrusions in the feature reduced data. The BAS algorithm is integrated into the VAE to properly tune the parameters involved in it and thereby raises the classification performance. To validate the intrusion detection performance of the IMFSDL-IDS system, a set of experimentations were carried out on the standard IDS dataset and the results are investigated under distinct aspects. The resultant experimental values pointed out the betterment of the IMFSDL-IDS model over the compared models with the maximum accuracy 95.25% and 97.39% on the applied NSL-KDD and UNSW-NB15 dataset correspondingly.
A survey on UFMC filter designs for 5G M2M N R Nagarajan, M Maheswari Journal of Physics Conference Series, 2020 As of now, the 5G attempts to address the current OFDM-based LTE issues, for example, Bit Error Rate (BER), Spectral Loss, Signal to Noise Ratio (SNR), Symbol Error Rate (SER) and high peak-to-average power ratio (PAPR). Universal-filtered multi-carrier (UFMC) method can be measured as an up-and-comer waveform for 5G correspondences since it gives advantages by minimizing the Inter Symbol Interference (ISI) and Inter Carrier Interference (ICI) reasonable for low-idleness situations. There are various filters for designing an UFMC which suits for a 5G application. This survey is carried out because of the Industrial 4.0 revolutionary is going on at a higher phase for which Machine to Machine (M2M) communication is widely required for suitability and reliability leading to the 5G requirement for high speed data transfer with appropriate filter design.
An ease UFMC transmitter using IFFT K. Kiruthiga, N.R. Nagarajan 2019 International Conference on Intelligent Computing and Control Systems Iccs 2019, 2019 UFMC is a suitable waveform for 5G requirements. It has the ability to overcome the drawbacks of the UFMC, FBMC, GFDM and OFDM techniques .IDFT and filtering part generates the UFMC waveform. This paper discusses about the IDFT part and filtering part. In IDFT part for reducing the hardware complexity and improves the performance of the system. IDFT part using IFFT radix-2 Decimation in Time technique (DIT) is used for reducing the complexity. The filtering part is dedicated mainly to achieve the flexibility and scalability requirements. It transmits the large input data by using small number of multipliers and registers.
RECENT SCHOLAR PUBLICATIONS
Reconfigurable Architecture Application Using Machine Learning in Edge Computing for IoT Devices MB Begum, J Eindhumathy, JS Priya, M Padmaa, NR Nagarajan, ... 2024 Eighth International Conference on Parallel, Distributed and Grid … , 2024 2024 Citations: 20
Dynamic Network Security Leveraging Efficient CoviNet with Granger Causality-Inspired Graph Neural Networks for Data Compression in Cloud IoT Devices DPRAP Dr. M. Baritha Begum (Assistant Professor) 1 , Dr. Yogeshwaran A ... Knowledge-Based Systems , 2024 2024 Citations: 34
Metasurface Based Surface Plasmon Resonance (SPR) Biosensor for Cervical Cancer Detection with Behaviour Prediction using Machine Learning Optimization Based on Support Vector … JWMSKSMGSMNRNSKNDRTPANZ Rashed9 Plasmonics , 2024 2024 Citations: 47
Predictive Modeling for Cirrhosis Diagnosis: A Machine Learning Exploration S Kumar V, B Natarajan, M P, P Selvam, V K, NR Nagarajan International Conference on Electronics, Computing, Communication and … , 2024 2024 Citations: 6
Recreation of AC/AC Converter Utilizing Single Stage Grid Converter for Wave Energy Converter NRN S. Saravanan, V. S. Arulmurugan, Jagannath Jadhav, P. Sathiyamurthi International Journal of Intelligent Systems and Applications in Engineering … , 2024 2024
Enhancing Stock Price Predictions Through LSTM-based Recurrent Neural Networks GVK Kumar, B Natarajan, K Venkatraman, SG Devi, P Selvam, ... 2024 International Conference on Integrated Circuits and Communication … , 2024 2024 Citations: 6
Optimizing Chemical Sensing Performance: A New Terahertz Waveguide Plasmonic Sensor with Hybrid Refractive Index Integration NR Nagarajan, D Kundu, S Hossain, KV Karthikeyan, N Neelamegam, ... Plasmonics, 1-14 , 2024 2024 Citations: 4
EXPLORE STRATEGIES FOR SUSTAINABLE WATER MANAGEMENT IN RAPIDLY GROWING URBAN SYSTEMS NR GANDHI, MOHD. A., RAMANI, P., PILLI, D., BHONGADE, A. S., PRASAD, S. V. G ... Journal of Environmental Protection and Ecology 25 (6), 2042 - 2052 , 2024 2024
Framework to find expiry of Medicinal Drugs in Pharmacy. NR Nagarajan, S Balaharini, J Balaji, C Binoshlin, PC Prabha Journal of Pharmaceutical Negative Results 13 , 2022 2022 Citations: 1
Intrusion Detection System for Big Data Analytics in IoT Environment CB M. Anuradha, G. Mani, T. Shanthi, N. R. Nagarajan, P. Suresh Computer Systems Science and Engineering 43 (1), 381-396 , 2022 2022 Citations: 7
Securing Medical Data using Extended Role Based Access Control Model and Twofish Algorithms on Cloud Platform SR T.Jayasankar , R.M.Bhavadharini , N.R.Nagarajan , G.Mani European Journal of Molecular & Clinical Medicine 8 (1), 1075-1089 , 2021 2021 Citations: 19
A survey on UFMC filter designs for 5G M2M NR Nagarajan, M Maheswari Journal of Physics: Conference Series 1706 (1), 012158 , 2020 2020 Citations: 2
Programmed Food Deliverance Scheme for Eatery NR NAGARAJAN, R BALAMURUGAN, T MURUGANANTHAM Bioscience Biotechnology Research Communications Special Issue 13 (2), 94-97 , 2020 2020
Industrial Parameters Monitoring Using Embedded System R BALAMURUGAN, T MURUGANANTHAM, NR NAGARAJAN Bioscience Biotechnology Research Communications 13 (2), 34-37 , 2020 2020
Biometric Of Speaker Authentication Using CNN T Muruganantham, NR Nagarajan, R Balamurugan International Journal of Future Generation Communication and Networking 13 … , 2020 2020 Citations: 3
AN EASE UFMC TRANSMITTER USING IFFT K KIRUTHIGA, NR NAGARAJAN International Conference on Intelligent Computing and Control Systems , 2020 2020 Citations: 3
Performance Analysis of UFMC System with Different Prototype Filters for 5G Communication M Maheswari, NR Nagarajan, M Banupriya International Conference on Artificial Intelligence, Smart Grid and Smart … , 2020 2020 Citations: 7
A 16X16 High Speed Vedic Multiplier for Area and Power Reduction M T, N N. R, S HUSAIN S International Journal of Advanced Research in Computer and Communication … , 2019 2019 Citations: 1
A Novel Architecture for Multiplier and Accumulator unit by using Parallel Prefix Adders NR NAGARAJAN, T MURUGANANTHAM, S RAJAPRIYA International Journal of Advanced Research in Computer and Communication … , 2019 2019 Citations: 1
DESIGN AND IMPLEMENTATION OF A PLANAR MIMO ANTENNA FOR LTE-APPLICATIONS DS ANGEL, NR NAGARAJAN, U SURENDAR INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMPUTER AND COMMUNICATION … , 2019 2019
MOST CITED SCHOLAR PUBLICATIONS
Metasurface Based Surface Plasmon Resonance (SPR) Biosensor for Cervical Cancer Detection with Behaviour Prediction using Machine Learning Optimization Based on Support Vector … JWMSKSMGSMNRNSKNDRTPANZ Rashed9 Plasmonics , 2024 2024 Citations: 47
Dynamic Network Security Leveraging Efficient CoviNet with Granger Causality-Inspired Graph Neural Networks for Data Compression in Cloud IoT Devices DPRAP Dr. M. Baritha Begum (Assistant Professor) 1 , Dr. Yogeshwaran A ... Knowledge-Based Systems , 2024 2024 Citations: 34
Reconfigurable Architecture Application Using Machine Learning in Edge Computing for IoT Devices MB Begum, J Eindhumathy, JS Priya, M Padmaa, NR Nagarajan, ... 2024 Eighth International Conference on Parallel, Distributed and Grid … , 2024 2024 Citations: 20
Securing Medical Data using Extended Role Based Access Control Model and Twofish Algorithms on Cloud Platform SR T.Jayasankar , R.M.Bhavadharini , N.R.Nagarajan , G.Mani European Journal of Molecular & Clinical Medicine 8 (1), 1075-1089 , 2021 2021 Citations: 19
Automatic Robotic ARM using Hand Gestures R Balamurugan, NR Nagarajan International Journal of Communication and Computer Technologies 5 (2), 43-45 , 2017 2017 Citations: 9
Intrusion Detection System for Big Data Analytics in IoT Environment CB M. Anuradha, G. Mani, T. Shanthi, N. R. Nagarajan, P. Suresh Computer Systems Science and Engineering 43 (1), 381-396 , 2022 2022 Citations: 7
Performance Analysis of UFMC System with Different Prototype Filters for 5G Communication M Maheswari, NR Nagarajan, M Banupriya International Conference on Artificial Intelligence, Smart Grid and Smart … , 2020 2020 Citations: 7
Predictive Modeling for Cirrhosis Diagnosis: A Machine Learning Exploration S Kumar V, B Natarajan, M P, P Selvam, V K, NR Nagarajan International Conference on Electronics, Computing, Communication and … , 2024 2024 Citations: 6
Enhancing Stock Price Predictions Through LSTM-based Recurrent Neural Networks GVK Kumar, B Natarajan, K Venkatraman, SG Devi, P Selvam, ... 2024 International Conference on Integrated Circuits and Communication … , 2024 2024 Citations: 6
A DFT Tactic Aimed At Testable Q-Flop Rudiments NR NAGARAJAN, R BALAMURUGAN International Journal of Advanced Research in Electrical, Electronics and … , 2019 2019 Citations: 5
Optimizing Chemical Sensing Performance: A New Terahertz Waveguide Plasmonic Sensor with Hybrid Refractive Index Integration NR Nagarajan, D Kundu, S Hossain, KV Karthikeyan, N Neelamegam, ... Plasmonics, 1-14 , 2024 2024 Citations: 4
SMART SHOPPE TROLLEY R BALAMURUGAN, NR NAGARAJAN INTERNATIONAL JOURNAL OF ADVANCED RESEARCH IN COMPUTER AND COMMUNICATION … , 2019 2019 Citations: 4
Smart Method of Vehicular Tactical Manoeuvre System R BALAMURUGAN, NR NAGARAJAN International Journal of Advanced Research in Electrical, Electronics and … , 2019 2019 Citations: 4
Biometric Of Speaker Authentication Using CNN T Muruganantham, NR Nagarajan, R Balamurugan International Journal of Future Generation Communication and Networking 13 … , 2020 2020 Citations: 3
AN EASE UFMC TRANSMITTER USING IFFT K KIRUTHIGA, NR NAGARAJAN International Conference on Intelligent Computing and Control Systems , 2020 2020 Citations: 3
IMPLEMENTATION OF LOW COMPLEX UNIVERSAL FILTERED MULTICARRIER K KIRUTHIGA, NR NAGARAJAN International Journal of Advanced Scientific Research & Development 6 (3), 68-72 , 2019 2019 Citations: 3
A survey on UFMC filter designs for 5G M2M NR Nagarajan, M Maheswari Journal of Physics: Conference Series 1706 (1), 012158 , 2020 2020 Citations: 2
Framework to find expiry of Medicinal Drugs in Pharmacy. NR Nagarajan, S Balaharini, J Balaji, C Binoshlin, PC Prabha Journal of Pharmaceutical Negative Results 13 , 2022 2022 Citations: 1
A 16X16 High Speed Vedic Multiplier for Area and Power Reduction M T, N N. R, S HUSAIN S International Journal of Advanced Research in Computer and Communication … , 2019 2019 Citations: 1
A Novel Architecture for Multiplier and Accumulator unit by using Parallel Prefix Adders NR NAGARAJAN, T MURUGANANTHAM, S RAJAPRIYA International Journal of Advanced Research in Computer and Communication … , 2019 2019 Citations: 1
