Article Per Year (5 Year)
p-Index From 2021 - 2026
2.278
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Murugan, Subbiah
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

Published : 16 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 16 Documents
Search

 1   2 
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%.
DoS attack detection and hill climbing based optimal forwarder selection Radhakrishnan, Palamalai; Seeni, Senthil Kumar; Devi, Dhamotharan Rukmani; Kanthimathi, Tumuluri; Neels Ponkumar, Devadhas David; Sankaran, Vikram Nattamai; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp882-891

Abstract

Wireless networks are becoming a more and more common form of networking and communication, with several uses in many industries. However, the rising popularity has also increased security risks, such as Denial of Service (DoS) attacks. To solve these issues, Denial of Service Attack Detection and Hill Climbing (DDHC) based optimal forwarder selection in Wireless Network. The suggested method seeks to efficiently identify DoS attacks and enhance network performance by preventing the communication hiccups brought on by such attacks. Fuzzy learning method is suggested to analyze trends and find DoS threats. The node bandwidth, connectivity, packet received rate, utilized energy and response time parameters to detect the node abnormality. This abnormality decides the node's future state and detects the DoS attacker. A fuzzy learning algorithm is proposed to detect DoS attacks, which increases attack detection accuracy and lowers false alarm rates. Using the Hill Climbing (HC) procedure, the proposed system transmits data from sender to receiver. Simulation results illustrate the DDHC mechanism increases the DoS attacker detection ratio and minimizes the false positive ratio. Furthermore, it raises the network throughput and reduces the Delay in the network
Interlined dynamic voltage restorer using time-domain methodologies with Z-source inverter/voltage source inverter Anuradha, Chandrasekar; Raman, Rathinam Anantha; Arunsankar, Ganesan; Joel, Maharajan Robinson; Sathyanathan, Pitchamuthu; Kantari, Hanumaji; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 1: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i1.pp15-25

Abstract

Electronic devices and loads are very sensitive to the voltage disturbances like voltage sag and voltage swell. Significant financial losses and safety issues may emerge from voltage sags and interruptions, which can be caused by variables such as system breakdowns and load changes. In order to protect against voltage fluctuations and keep vital loads running, dynamic voltage restorers (DVRs) have become more popular. To mitigate the voltage disturbances, an interlined DVR (IDVR) using a Z-source inverter (ZSI) is developed to protect the sensitive devices and loads. Back-to-back DVR connects the distributed feeders with a common direct current (DC) link. The IDVR compensates for the sag voltage and supplies the energy to control the power flow. In addition, proposed a modified synchronous reference frame (MSRF)/direct quadrature theory, hysteresis controller, and proportional integral (PI) controller, which provides the required amount of control signals for a ZSI and voltage source inverter (VSI). MATLAB/Simulink validated the simulation results. The experimental findings show that the suggested system can be implemented successfully and is effective at reducing voltage dips and interruptions, allowing crucial loads to keep operating consistently and without interruption in residential as well as commercial environments.
A low-cost localization method in autonomous vehicle by applying light detection and ranging technology Kannan, Raju Jagadeesh; Amru, Malothu; Muthumarilakshmi, Surulivelu; Jeyapriya, Jeyaprakash; Aghalya, Stalin; Muthukumaran, Dhakshnamoorthy; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1739-1749

Abstract

The autonomous platform uses global positioning system (GPS) to localize the vehicle. In addition, light detection and ranging (LIDAR) and the high precision camera help to identify the turns in the road. The proposed system can help to determine the road turns with higher accuracy without utilizing LIDAR and high-precision camera technology. This research aims to implement a cost-effective simultaneous localization system that can reduce the cost by half for any autonomous vehicle. The existing system is more complex due to the inclusion of LIDAR technology. In contrast, the proposed approach uses beacon communication between vehicles and infrastructure and long-range (LoRa) for vehicle-to-vehicle (V2V) and vehicle to infrastructure (V2I) communication. The simulation result illustrates that the proposed approach provides better performance.
Auto digitization of aerial images to map generation from UAV feed Kannan, Raju Jagadeesh; Yadav, Karunesh Pratap; Sreedevi, Balasubramanian; Chelliah, Jehan; Muthumarilakshmi, Surulivelu; Jeyapriya, Jeyaprakash; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 2: November 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i2.pp1338-1346

Abstract

Nowadays the rapid growth of unmanned aerial vehicles (UAVs) bridges the space between worldly and airborne photogrammetry as well as allow flexible acquisition of great solution images. In the case of natural disasters such as floods, tsunamis, earthquakes, and cyclones, their effects are most often felt in the micro-spaces and urban environments. Therefore, rescuers have to go around to get to the victims. This paper presents an auto digitization of aerial images to map generation from UAV feed at night time. In case of a power outage and an absence of alternative light sources, rescue operations are also slowed due to the darkness caused by the lack of electricity and the inability to light additional sources. In other words, to save lives, we need to know about all essential large-scale feature spaces in the dark so that we can use this information in times of disaster. The research proposed a soft framework for crisis mapping to aid in mapping the state of the aerial landscape in disaster-stricken areas, allowing strategic rescue operations to be more effectively planned.
Random forest algorithm with hill climbing algorithm to improve intrusion detection at endpoint and network Sekar, Satheesh Kumar; Parvathy, Palaniraj Rajidurai; Pinjarkar, Latika; Latha, Raman; Sathish, Mani; Reddy, Munnangi Koti; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 1: January 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i1.pp134-142

Abstract

Cloud computing is a framework that enables end users to connect highly effective services and applications over the internet effortlessly. In the world of cloud computing, it is a critical problem to deliver services that are both safe and dependable. The best way to lessen the damage caused by entry into this environment is one of the primary security concerns. The fundamental advantage of a cooperative approach to intrusion detection system (IDS) is a superior vision of an action of network attack. This paper proposes a random forest (RF) algorithm with a hill-climbing algorithm (RFHC) to improve intrusion detection at the endpoint and network. Initially, it is used for feature selection, and the next process is to separate the intrusions detection. The feature selection is maintained by the hill climbing (HC) algorithm that chooses the best features. Then, we utilize the RF algorithm to separate the intrusion efficiently. The experimental results depict that the RFHC mechanism reached more acceptable results regarding recall, precision, and accuracy than a baseline mechanism. Moreover, it minimizes the miss detection ratio and enhances the intrusion detection ratio.
Advancing chronic pain relief cloud-based remote management with machine learning in healthcare Mohankumar, Nagarajan; Reddy Narani, Sandeep; Asha, Soundararajan; Arivazhagan, Selvam; Rajanarayanan, Subramanian; Padmanaban, Kuppan; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1042-1052

Abstract

Healthcare providers face a significant challenge in the treatment of chronic pain, requiring creative responses to enhance patient outcomes and streamline healthcare delivery. It suggests using cloud-based remote management with machine learning (ML) to alleviate chronic pain. Wearable device data, electronic health record (EHR) data, and patient-reported outcomes are all inputs into the suggested system’s data analysis pipeline, which combines support vector machines (SVM) with recurrent neural networks (RNN). SVM’s powerful classification skills make it possible to classify patients’ risks and predict how they will react to therapy. RNNs are very good at processing sequential data, which means they may identify trends in patient symptoms and drug adherence over time. By integrating these algorithms, healthcare professionals may create individualized treatment programs that consider each patient’s preferences and specific requirements. Early intervention and proactive treatment of pain symptoms are made possible by the system’s ability to monitor patients in real-time remotely. The system is further improved by using predictive analytics to identify patients who could benefit from extra support services and to forecast when they will have acute pain episodes. The proposed approach can change the game regarding managing chronic pain. It provides data-driven, individualized treatment that improves patient outcomes while cutting healthcare expenses.
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.
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