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Murugan, Subbiah
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Computer Science & IT Electrical & Electronics Engineering

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Journal : international journal of electrical and computer engineering

 1 
Cloud based prediction of epileptic seizures using real-time electroencephalograms analysis Thahniyath, Gousia; Yadav, Chelluboina Subbarayudu Gangaiah; Senkamalavalli, Rajagopalan; Priya, Shanmugam Sathiya; Aghalya, Stalin; Reddy, Kuppireddy Narsimha; Murugan, Subbiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp6047-6056

Abstract

This study aims to improve the accuracy of epileptic seizure prediction using cloud-based, real-time electroencephalogram analysis. The goal is to build a strong framework that can quickly process electroencephalogram (EEG) data, extract relevant features, and use advanced machine learning algorithms to predict seizures with high accuracy and low latency by taking advantage of cloud platforms' computing power and scalability. The main objective is to provide patients and their caregivers with timely notifications so that they may control epilepsy episodes proactively. The goal of this project is to improve the lives of people with epilepsy by reducing the impact of seizures and improving treatment results via real-time analysis of EEG data. Cloud computing also allows the suggested seizure prediction system to be more accessible and scalable, meaning more people worldwide could benefit from it. This section discusses the results from five separate datasets of patients with epileptic seizures who underwent EEG analysis with the following details as frontopolar (FP1, FP2), frontal (F3, F4), frontotemporal (F7, F8), central (C3, C4), temporal (T3, T4), parieto-temporal (T5, T6), parietal (P3, P4), occipital (O1, O2), time (HH:MM:SS).
Network intrusion detection system by applying ensemble model for smart home Amru, Malothu; Jagadeesh Kannan, Raju; Narasimhan Ganesh, Enthrakandi; Muthumarilakshmi, Surulivelu; Padmanaban, Kuppan; Jeyapriya, Jeyaprakash; Murugan, Subbiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 3: June 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i3.pp3485-3494

Abstract

The exponential advancements in recent technologies for surveillance become an important part of life. Though the internet of things (IoT) has gained more attention to develop smart infrastructure, it also provides a large attack surface for intruders. Therefore, it requires identifying the attacks as soon as possible to provide a secure environment. In this work, the network intrusion detection system, by applying the ensemble model (NIDSE) for Smart Homes is designed to identify the attacks in the smart home devices. The problem of classifying attacks is considered a classification predictive modeling using eXtreme gradient boosting (XGBoosting). It is an ensemble approach where the models are added sequentially to correct the errors until no further improvements or high performance can be made. The performance of the NIDSE is tested on the IoT network intrusion (IoT-NI) dataset. It has various types of network attacks, including host discovery, synchronized sequence number (SYN), acknowledgment (ACK), and hypertext transfer protocol (HTTP) flooding. Results from the cross-validation approach show that the XGBoosting classifier classifies the nine attacks with micro average precision of 94% and macro average precision of 85%.
Implementing cloud computing in drug discovery and telemedicine for quantitative structure-activity relationship analysis Ramapraba, Palayanoor Seethapathy; Babu, Bellam Ravindra; Paul, Nallathampi Rajamani Rejin; Sharmila, Varadan; Babu, Venkatachalam Ramesh; Ramya, Raman; Murugan, Subbiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1132-1141

Abstract

This work aims to use cutting-edge machine learning methods to improve quantitative structure-activity relationship (QSAR) analysis, which is used in drug development and telemedicine. The major goal is to examine the performance of several predictive modeling approaches, including random forest, deep learning-based QSAR models, and support vector machines (SVM). It investigates the potential of feature selection techniques developed in chemoinformatics for enhancing model accuracy. The innovative aspect is using cloud computing resources to strengthen computational skills, allowing for managing massive amounts of chemical information. This strategy produces accurate and generalizable QSAR models. By using the cloud's scalability and constant availability, remote healthcare apps have a workable answer. The goal is to show how these methods may improve telemedicine and the drug development process. Utilizing cloud computing equips researchers with a flexible set of tools for precise and timely QSAR analysis, speeding up the discovery of bioactive chemicals for therapeutic use. This new method fits well with the dynamic nature of pharmaceutical study and has the potential to transform the way drugs are developed and delivered to patients via telemedicine.
Video conferencing algorithms for enhanced access to mental healthcare services in cloud-powered telepsychiatry Senkamalavalli, Rajagopalan; Prasad, Subramaniyan Nesamony Sheela Evangelin; Shobana, Mahalingam; Sri, Chellaiyan Bharathi; Sandiri, Rajendar; Karthik, Jayavarapu; Murugan, Subbiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1142-1151

Abstract

Exploring the video conferencing algorithms for cloud-powered telepsychiatry to improve mental healthcare access. The goal is to evaluate and optimise these algorithms' latency, bandwidth utilisation, packet loss, and jitter across worldwide locations. To provide a smooth and high-quality virtual consultation between patients and mental health providers. Using performance data to identify areas for development, the effort aims to lower technological hurdles and increase telepsychiatry session dependability. Findings will help create strong, efficient algorithms that can handle different network situations, increasing patient outcomes and extending mental healthcare services. In the 1st instance latent analysis in a sample of 5 cities, the average latency (ms) is 45, the peak latency is 120, the off-peak latency is 30, and the packet loss is 0.5. In another instance, bandwidth utilisation in a sample of 5 sessions ranged from 30 to 120 minutes, with data supplied in MB - 150-600 and received in MB - 160-620, with average bandwidth (Mbps) - 5-15 and maximum bandwidth: 10-20.
Deep learning for infectious disease surveillance integrating internet of things for rapid response Sumithra, Subramanian; Radhika, Moorthy; Venkatesh, Gandavadi; Lakshmi, Babu Seetha; Jancee, Balraj Victoria; Mohankumar, Nagarajan; Murugan, Subbiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1175-1186

Abstract

Particularly in the case of emerging infectious diseases and worldwide pandemics, infectious disease monitoring is essential for quick identification and efficient response to epidemics. Improving surveillance systems for quick reaction might be possible with the help of new deep learning and internet of things (IoT) technologies. This paper introduces an infectious disease monitoring architecture based on deep learning coupled with IoT devices to facilitate early diagnosis and proactive intervention measures. This approach uses recurrent neural networks (RNNs) to identify temporal patterns suggestive of infectious disease outbreaks by analyzing sequential data retrieved from IoT devices like smart thermometers and wearable sensors. To identify small changes in health markers and forecast the development of diseases, RNN architectures with long short-term memory (LSTM) networks are used to capture long-range relationships in the data. Spatial analysis permits the integration of geographic data from IoT devices, allowing for the identification of infection hotspots and the tracking of afflicted persons' movements. Quick action steps like focused testing, contact tracing, and medical resource deployment are prompted by abnormalities detected early by real-time monitoring and analysis. Preventing or lessening the severity of infectious disease outbreaks is the goal of the planned monitoring system, which would enhance public health readiness and response capacities.
Intrusion detection and prevention using Bayesian decision with fuzzy logic system Sekar, Satheeshkumar; Parvathy, Palaniraj Rajidurai; Gupta, Gopal Kumar; Rajagopalan, Thiruvenkadachari; Basavaraddi, Chethan Chandra Subhash Chandra Basappa; Padmanaban, Kuppan; Murugan, Subbiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1200-1208

Abstract

Nowadays, intrusion detection and prevention method has comprehended the notice to decrease the effect of intruders. denial of service (DoS) is an attack that formulates malicious traffic is distributed into an exacting network device. These attackers absorb with a valid network device, the valid device will be compromised to insert malicious traffic. To solve these problems, the Bayesian decision model with a fuzzy logic system based on intrusion detection and prevention (BDFL) is introduced. This mechanism separates the DoS packets based on the type of validation, such as packet and flow validation. The BDFL mechanism uses a fuzzy logic system (FLS) for validating the data packets. Also, the key features of the algorithm are excerpted from data packets and categorized into normal, doubtful, and malicious. Furthermore, the Bayesian decision (BD) decide two queues as malicious and normal. The BDFL mechanism is experimental in a network simulator environment, and the operations are measures regarding DoS attacker detection ratio, delay, traffic load, and throughput.
Co-Authors Aghalya, Stalin Amru, Malothu Anuradha, Chandrasekar Arivazhagan, Selvam Arunsankar, Ganesan Asha, Soundararajan Babu, Bellam Ravindra Babu, Venkatachalam Ramesh Basavaraddi, Chethan Chandra Subhash Chandra Basappa Bharathi, Balu Boopathy, Kannan Chelliah, Jehan Devi, Dhamotharan Rukmani Fernandes, John Bennilo Gupta, Gopal Kumar Harshitha, Ganadamoole Madhava Jagadeesh Kannan, Raju Jancee, Balraj Victoria Jenifer, Albert Jeyapriya, Jeyaprakash Joel, Maharajan Robinson Jyothi, Rudraraju Leela Kannan, Raju Jagadeesh Kantari, Hanumaji Kanthimathi, Tumuluri Karthik, Jayavarapu Lakshmi, Babu Seetha Latha, Raman Mohankumar, Nagarajan Muthukumaran, Dhakshnamoorthy Muthumarilakshmi, Surulivelu Narasimhan Ganesh, Enthrakandi Neels Ponkumar, Devadhas David Nirmala, Baddala Vijaya Padmanaban, Kuppan Parvathy, Palaniraj Rajidurai Parvathy, Sheela Paul, Nallathampi Rajamani Rejin Pinjarkar, Latika Prasad, Subramaniyan Nesamony Sheela Evangelin Priya, Shanmugam Sathiya Radhakrishnan, Palamalai Radhika, Moorthy Rajagopalan, Thiruvenkadachari Rajanarayanan, Subramanian Raju, Ayalapogu Ratna Raman, Rathinam Anantha Ramapraba, Palayanoor Seethapathy Ramya, Raman Rathinam, Anantha Raman Reddy Narani, Sandeep Reddy, Kuppireddy Narsimha Reddy, Munnangi Koti Sandiri, Rajendar Sankaran, Vikram Nattamai Sasirekha, Venkatesan Sathish, Mani Sathyanathan, Pitchamuthu Seeni, Senthil Kumar Sekar, Satheesh Kumar Sekar, Satheeshkumar Senkamalavalli, Rajagopalan Shadaksharappa, Bichagal Shanmugathai, Madappa Sharmila, Varadan Shobana, Mahalingam Smitha, Jolakula Asoka Sreedevi, Balasubramanian Sri, Chellaiyan Bharathi Sriram, Saravanan Sujatha, Venugopal Sumithra, Subramanian Thahniyath, Gousia Tidke, Bharat Veena, Kilingar Venkatara, Nagaiyanallur Lakshminarayanan Venkatesh, Gandavadi Yadav, Chelluboina Subbarayudu Gangaiah Yadav, Karunesh Pratap