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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Tidke, Bharat
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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.
Co-Authors Harshitha, Ganadamoole Madhava Jyothi, Rudraraju Leela Murugan, Subbiah Parvathy, Palaniraj Rajidurai Rathinam, Anantha Raman Sasirekha, Venkatesan Seeni, Senthil Kumar Sekar, Satheeshkumar Shanmugathai, Madappa Sujatha, Venugopal Venkatara, Nagaiyanallur Lakshminarayanan