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All Journal International Journal of Electrical and Computer Engineering Indonesian Journal of Electrical Engineering and Computer Science
Murugan, Subbiah
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Computer Science & IT Electrical & Electronics Engineering

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Journal : Indonesian Journal of Electrical Engineering and Computer Science

 1 
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.
Enhancing mobility with customized prosthetic designs driven by genetic algorithms Seeni, Senthil Kumar; Harshitha, Ganadamoole Madhava; Rathinam, Anantha Raman; Venkatara, Nagaiyanallur Lakshminarayanan; Sasirekha, Venkatesan; Tidke, Bharat; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp876-886

Abstract

Using genetic algorithms, this research intends to usher in a new era of prosthetic design that is redefining mobility. Through repeated evolutionary processes influenced by natural selection, the goal is to optimize prosthetic design parameters including material composition, structure, and control systems. The objective is to create prosthetic limbs that are more personalized to each user's requirements, improving their efficiency, comfort, and functioning via the application of genetic algorithms. The goal of this study is to show that the suggested strategy may improve mobility and user happiness more than standard ways by simulating and testing prosthetic devices in real-world settings. The end goal is to create conditions for a new age of prosthetic technology, where amputees' quality of life is greatly enhanced by devices that are individually designed to meet their biomechanical needs. The impact of prosthetic design and individual patient factors patient dataset derived from a random 5-sample with the following characteristics: ages 32–68, weight 65–90, height 155–180, crossover rate 0.6–0.9, mutation rate 0.05–0.2, population size 70–120, generations 30–60.
Intrusion detection in clustering wireless network by applying extreme learning machine with deep neural network algorithm Parvathy, Palaniraj Rajidurai; Sekar, Satheeshkumar; Tidke, Bharat; Jyothi, Rudraraju Leela; Sujatha, Venugopal; Shanmugathai, Madappa; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 2: May 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i2.pp887-896

Abstract

Nowadays, intrusion detection systems (IDSs) have growingly come to be considered as an important method owing to their possible to expand into a key factor, which is crucial for the security of wireless networks. In wireless network, when there is a thousand times more traffic, the effectiveness of normal IDS to identify hostile network intrusions is decreased by an average factor. This is because of the exponential growth in network traffic. This is due to the decreased number of possibilities to discover the intrusions. This is because there are fewer opportunities to see possible risks. We intend an extreme learning machine with deep neural network (DNN) algorithm-based intrusion detection in clustering (EIDC) wireless network. The main objective of this article is to detect the intrusion efficiently and minimize the false alarm rate. This mechanism utilizes the extreme learning machine (ELM) with a deep neural network algorithm for optimizing the weights of input and hidden node biases to deduce the network output weights. Simulation outcomes illustrate that the EIDC mechanism not only assures a better accuracy for detection, considerably minimizes an intrusion detection time, and shortens the false alarm rate.
Enhanced performance and efficiency of robotic autonomous procedures through path planning algorithm Latha, Raman; Sriram, Saravanan; Bharathi, Balu; Fernandes, John Bennilo; Raju, Ayalapogu Ratna; Boopathy, Kannan; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp214-224

Abstract

To optimize surgical routes for better patient outcomes and more efficient operations, we want to test how well these algorithms work. Finding the best algorithms for different types of surgeries and seeing how they affect things like time spent in surgery, precision, and patient safety is the goal of this exhaustive study. By shedding light on the effectiveness of route planning algorithms, this work aspires to aid in the development of autonomous robotic surgery. To find out how well various algorithms work in actual surgical settings; this study compares them. The results of this work have the potential to enhance robotic surgery efficiency and improve surgical outcomes by informing the creation of more efficient route planning algorithms. The overarching goal of this study is to provide evidence that autonomous robotic surgery can benefit from using sophisticated route planning algorithms, which might lead to more accurate, faster, and safer procedures. The surgical patient dataset exhibits a wide variety of medical variables, including ages 38–62, weight 65–85 kg, height 160–180 cm, blood pressure 110–140/90 mm Hg, heart rate 70–85 bpm, hemoglobin 12–14 g/DL, and body mass index (BMI) 25.4–29.4.
An efficient segmentation using adaptive radial basis function neural network for tomato and mango plant leaf images Smitha, Jolakula Asoka; Shadaksharappa, Bichagal; Parvathy, Sheela; Veena, Kilingar; Jenifer, Albert; Nirmala, Baddala Vijaya; Murugan, Subbiah
Indonesian Journal of Electrical Engineering and Computer Science Vol 39, No 1: July 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v39.i1.pp202-213

Abstract

Agriculture has become simply to feed ever-growing populations. The tomato is arguably the most well-known vegetable in agricultural areas and plays a significant role in the growth of vegetables in our daily lives. However, because this tomato has multiple diseases, image segmentation of the diseased leaf shows a key role in classifying the disease by the leaf's symptoms. Therefore, in this paper, an efficient plant disease segmentation using an adaptive radial basis function neural network (ARBFNN) classifier. The proposed radial basis function (RBF) neural network is enhanced by using the flower pollination algorithm (FPA). Firstly, the noise is detached by an adaptive median filter and histogram equalization. Then, from every leaf image, different kind of color features is extracted. After the extraction of features, those are fed to the segmentation phase to section the disease serving from the input image. The efficiency of the suggested method is analyzed based on various metrics and our technique attained a better accuracy of 97.58%.
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