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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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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.
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