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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Selvaperumal, Sathish Kumar
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Electrical & Electronics Engineering

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Blue light therapy device for wound healing Kamal, Minahil; Kamal, Aleena; Abid, Azka; Ahmed, Sarah; Hussain, Syed Muddusir; Ur Rahman, Jawwad Sami; Selvaperumal, Sathish Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1527-1539

Abstract

Cuts, diabetic ulcers, and pressure sores are examples of chronic skin wounds that pose a serious healthcare danger because of their delayed healing rates. This problem emphasizes the necessity of creating noninvasive, economical, and successful wound treatment plans. Conventional treatments, such as skin grafting, negative pressure wound therapy, and hyperbaric oxygen therapy, have demonstrated effectiveness; nevertheless, they are frequently costly, intrusive, and have possible side effects. On the other hand, blue light treatment has become a viable substitute due to its antimicrobial characteristics and capacity to encourage cellular restoration. However, there is a crucial gap in the development of a portable, noninvasive, and cost-effective photobiomodulation device for wound treatment and monitoring, despite its demonstrated potential in wound healing. This work aims to address this gap by creating a novel blue light therapy tool specifically suited for wound healing. The gadget allows for controlled blue light exposure and real-time temperature monitoring to minimize overheating. It has a portable arm housing with integrated blue light strips, a temperature sensor, and an integrated fan. An STM 32 microcontroller powers the system’s pulse width modulation (PWM) technology, which modifies light intensity and therapy duration in response to conditions unique to each wound. This novel strategy seeks to improve the effectiveness of wound healing, lower the likelihood of adverse effects, and offer patients and healthcare providers a workable alternative that is noninvasive, inexpensive, and easy to use.
A comparative study on electricity load forecasting using statistical and deep learning approaches Butt, Tehreem Fatima; Tameer, Sana; Saleem, Muhammad; Ur Rehman, Jawwad Sami; Selvaperumal, Sathish Kumar
Indonesian Journal of Electrical Engineering and Computer Science Vol 38, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v38.i3.pp1540-1552

Abstract

Load forecasting has become reproving aspect of an energy management system (EMS). It gives basic advantage to grid stability, cost effectiveness and battery storage system (BSS). For this purpose, machine learning (ML) is widely adopted to forecast the electricity load. This research paper investigates the performances of various time series estimating models applied to electricity load data for an Irish company. The research mainly adopts the autoregressive integrated moving average (ARIMA) model, long short-term memory (LSTM) networks and transformer neural network (TNN) to forecast the electricity load. A comparison evaluation is conducted encompassing various quantifying measures such as root mean square error (RMSE), mean square error (MSE) and mean absolute error (MAE). The results are then compared to get an understanding whether the TNN using attention-based mechanism is better than the two state of the art models. Hence provides a complete understanding about which of the model needs improvements in its architecture for enhancement of operational efficiency and cost effectiveness in the realm of EMS.
Predictive modelling of osteoporosis and effect of BMI on the risk of fracture in femur bone using COMSOL Multiphysics: a computational modelling approach Kamal, Aleena; Kamal, Minahil; Fatima, Mashal; Hussain, Syed Muddusir; Sami Ur Rahman, Jawwad; Selvaperumal, Sathish Kumar
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.pp89-100

Abstract

This study explores the intricate relationship between osteoporosis, body mass index (BMI), and the risk of femur fractures using computational modeling. Osteoporosis is a silent metabolic disorder that depletes bone density and structure, significantly increasing the risk of fractures, particularly in weight-bearing bones such as the femur. To analyze the impact of mechanical stress on osteoporotic bones, COMSOL Multiphysics was utilized to simulate stress distribution in a femur under varying BMI conditions, providing valuable insights into how BMI influences bone health and fracture risk. A three-dimensional (3D) femur model was designed using computer-aided design (CAD) software, with specific material properties assigned for both healthy and osteoporotic bones. Finite element analysis was conducted by applying different load conditions, representing body weight, on the femur head. The results highlighted stress distribution and deformation patterns, identifying regions most prone to fracture. The findings demonstrate that while higher BMI typically correlates with increased bone density, it also leads to greater deformation in osteoporotic bones under stress, emphasizing the complex interplay between BMI and bone strength. These insights underscore BMI’s critical role in fracture risk management. Future research should incorporate advanced fracture mechanics models and clinical data to enhance predictive accuracy and develop targeted strategies for fracture prevention in osteoporotic patients.
A deep Q-learning approach for adaptive cybersecurity threat detection in dynamic networks Bharathi, P. Shyamala; Selvaperumal, Sathish Kumar; Ramasenderan, Narendran; Thiruchelvam, V.; Annamalai, Deepak Arun; Reddy, M. Jaya Bharatha
Bulletin of Electrical Engineering and Informatics Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i5.9494

Abstract

Cybersecurity faces persistent challenges due to the rapid growth and complexity of network-based threats. Conventional rule-based systems and classical machine learning approaches often lack the adaptability required to detect advanced and dynamic attacks in real time. This study introduces a deep Q-learning framework for autonomous threat detection and mitigation within a simulated network environment that reflects realistic traffic, malicious behaviors, and system conditions. The framework incorporates experience replay and target network stabilization to strengthen learning and policy optimization. Evaluation was performed on a synthesized dataset containing benign traffic and multiple attack categories, including distributed denial of service (DDoS), phishing, advanced persistent threats, and malware. The proposed system achieved 96.7% detection accuracy, an F1-score of 0.94, and reduced detection latency to 50 ms. These results surpassed the performance of rule-based methods and traditional classifiers such as support vector machines, random forests, convolutional neural networks, and recurrent neural networks. A central contribution lies in combining dynamic feature selection with reinforcement learning (RL), allowing the agent to adapt to evolving threats and diverse network conditions. The findings demonstrate the potential of deep reinforcement learning (DRL) as a scalable and efficient solution for real-time cybersecurity defense.
Co-Authors Abid, Azka Ahmed, Sarah Annamalai, Deepak Arun Bharathi, P. Shyamala Butt, Tehreem Fatima Fatima, Mashal Hussain, Syed Muddusir Kamal, Aleena Kamal, Minahil Ramasenderan, Narendran Reddy, M. Jaya Bharatha Saleem, Muhammad Sami Ur Rahman, Jawwad Tameer, Sana Thiruchelvam, V. Ur Rahman, Jawwad Sami Ur Rehman, Jawwad Sami