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International Journal of Electrical and Computer Engineering
Published by Institute of Advanced Engineering and Science
ISSN : 20888708     EISSN : 27222578     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Electrical & Electronics Engineering
International Journal of Electrical and Computer Engineering (IJECE, ISSN: 2088-8708, a SCOPUS indexed Journal, SNIP: 1.001; SJR: 0.296; CiteScore: 0.99; SJR & CiteScore Q2 on both of the Electrical & Electronics Engineering, and Computer Science) is the official publication of the Institute of Advanced Engineering and Science (IAES). The journal is open to submission from scholars and experts in the wide areas of electrical, electronics, instrumentation, control, telecommunication and computer engineering from the global world.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 83 Documents
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Search results for , issue "Vol 15, No 3: June 2025" : 83 Documents clear
Electroencephalography classification technique based on statistical denoising and modified k-nearest neighbor algorithm with bipolar sigmoid rectified linear unit’s function Mahalingegowda, Thejaswini Bekkalale; George, Glan Devadhas; Yoga, Satheesha Tumakur; Ezhilarasan, Kaliyamoorthy
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2786-2795

Abstract

Accurate classification of electroencephalography (EEG) data is much needed for early identification of diseases to treat various disorders. In this paper, we propose EEG classification technique based on statistical denoising & modified k-nearest neighbor (k-NN) algorithm with bipolar sigmoid rectified linear units (ReLU) function. The EEG data is subjected to statistical methods to remove the artifacts and then applied to modified k-NN algorithm to categorize the appropriate features giving preference to neighbors closer to one another considering the weighted votes of the k-nearest neighbors before selecting the class label based on the highest weighted vote. A customized activation function that combines these two functions called as hybrid function that uses various portions of each function in particular ranges is used in our work i.e., use of bipolar sigmoid for negative values and the ReLU function for positive values which helps to limit the signal in a particular range. The proposed algorithm's detection accuracy is tested for the confusion matrix of true positive (TP), false positive (FP), false negative (FN)and true negative (TN) and compared to the detection accuracy of other existing algorithms, demonstrating the algorithm's efficiency with a classification accuracy of almost 85 percent and sensitivity of 91% for standard Kaggle Dataset.
An integrated framework for data breach on the dark web in brand monitoring data hunting Ahmad, Siti Arpah; Khairuddin, Muhammad Al’Imran Mohd; Bashah, Nor Shahniza Kamal; Raman, Nurul Aishah Ab
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3162-3170

Abstract

In today's digital landscape, data breaches pose a substantial threat, with the dark web serving as a prevalent platform for malevolent actors to perpetrate such incidents. Currently, security analysts use various tools to solve the problem, which is very time-consuming. This paper introduces a novel framework that integrates data breach monitoring within the dark web, focusing on brand monitoring and data hunting. The framework starts from the scraping process and continues with the utilisation of the Splunk dashboard. The dashboard provides an exhaustive overview of data breaches related to brands for both manual inquiries and rule-based detection mechanisms. The framework comprises five phases: data sourcing, data collection, integration, monitoring, and visualisation. The visualisation phase encompasses alert generation, notification mechanisms, and reporting functionalities. Moreover, the monitoring phase provides real-time surveillance, advanced search capabilities, brand monitoring, and threat intelligence integration. The integration phase involves security information and event management (SIEM) systems and security orchestration, automation, and response (SOAR) systems. This paper's result contributes to enhancing the National Institute of Standards and Technology (NIST) cybersecurity framework, offering a comprehensive solution to the data breaches challenge within the dark web and the frontiers of knowledge and security practices.
Optimizing switching states using a current predictive control algorithm for multilevel cascaded H-bridge converters in solar photovoltaic integration into power grids Anh, An Thi Hoai Thu; Cuong, Tran Hung
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2726-2734

Abstract

Solar power is the best solution for renewable energy sources. Nowadays, solar power plants are invested and developed strongly in many places. Converting direct current (DC) energy from photovoltaic (PV) systems to the alternating current (AC) grid is critical to widely use this power source at high voltage levels. This paper presents an algorithm to optimize the valve-switching process for a cascading H-bridge multilevel converter (CHB) to convert energy from a PV system connected to the grid. This is done by a model predictive control algorithm (MPC) before a valve switching cycle, its process will be carried out in future forecast cycles and applied in the present time. From there, choose the best switching state for a working cycle. This will ensure the best quality of current and voltage with a low total harmonic distortion (THD) index to connect to the power grid. This method's advantages are reducing volume calculation for the controller, Selecting the most suitable valve switching state to achieve low valve switching frequency, reducing losses, and improving conversion efficiency. The implementation results are proven by simulation and evaluation of results on MATLAB-Simulink software.
Remote medical care monitoring system Alsaraira, Amer; Alabed, Samer; Saraereh, Omar
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2888-2899

Abstract

Neglecting one's health is a major contributor to the decline in overall well-being, often resulting in the onset of various diseases and health issues. The avoidance of such complications becomes feasible with the introduction of a device capable of monitoring heart pulses at regular intervals, ideally every 60 seconds. The main goal of this article is to design a healthcare system that ensures continuous monitoring of heart activity and temperature, functioning as a proactive tool to keep individuals informed about their physiological parameters. This involves the incorporation of a heart rate sensor and temperature sensor in a wearable device, essentially serving as a first aid tool. The heart rate is measured by detecting pulses and calculating beats per minute, utilizing an appropriate heart monitoring sensor tailored to the specific needs of the individual. The main concept revolves around designing a wearable device that harnesses the capabilities of the digital age, making use of features such as wireless sensors and rapid data transfer through the internet of things, accessible on various smart devices. The device focuses on detecting and monitoring heart rate and temperature, with the sent data being relayed to the healthcare provider. The doctor can then monitor the patient's status through the displayed data on thing-speak.
A survey on enhancements of routing protocol for low power and lossy networks: focusing on objective functions Vyas, Ditixa; Patel, Ritesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3458-3476

Abstract

People live in the age of smart devices. The concept of the internet of things (IoT) needs to be brought up whenever smart gadgets are shown. Furthermore, every gadget is gradually turning into a mobile node. These devices are utilized in low power and lossy networks because of their characteristics. Numerous obstacles exist in this field, motivating academics to focus on routing, connections, data transfer, and communications between nodes. In relation to this, the internet engineering task force (IETF) group already created a routing protocol for low power and lossy network (RPL), which was suggested for static networks and has since undergone numerous improvements. This article introduces the low power wireless network (LPWN) with a detailed model of the RPL protocol. It has also been considered how the destination-oriented directed acyclic graph (DODAG) is formed, and control messages are used to communicate between nodes in the RPL. The objective function (OF) is the center of the RPL. The principal objective functions objective function zero (OF0) and minimum rank with hysteresis objective function (MRHOF), which IETF group suggested, cannot function in the existing mobile network due to node disconnection and intermittent connectivity. The authors have enumerated and briefly discussed numerous RPL enhancements with new OFs. Numerous problems that the RPL routing protocol faced with mobility have been resolved.
Hybrid optimization tuned deep neural network-based wind power generation system for permanent magnet synchronous generator control Chinamalli, Prashant Kumar S. S; Sasikala, Mungamuri
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2599-2615

Abstract

Wind energy, a cost-effective renewable source, has seen substantial growth. permanent magnet synchronous generator (PMSG) equipped wind turbines demonstrate superior performance in variable-speed applications. However, there remains a notable research gap in optimizing the overall system efficiency for such wind energy systems. Therefore, this research presents to develop a deep learning-based optimization technique that improves the efficiency of PMSG-based wind energy systems by minimizing overall system losses and maximizing energy output. Core loss and rotor speed data were fed into a deep neural network for various operating conditions ranging from 50 to 1000 rpm, to determine optimal system parameters. This work introduces a hybrid lyrebird-based coati optimization algorithm (LB-COA) to optimize the deep neural networks (DNN) classifier, combining two advanced optimization techniques to improve model performance. Simulation results validate that the proposed optimization strategy efficiently boosts the system's dynamic performance and overall power efficiency.
Comparative assessment of an improved asymmetrical fuzzy logic control-based maximum power point tracking for photovoltaic systems under partially shaded conditions Ariffin, Athirah Batrisyia Kamal; Zakaria, Muhammad Iqbal; Munim, Wan Noraishah Wan Abdul; Kamarudin, Muhammad Nizam; El Fezazi, Nabil
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2642-2654

Abstract

This paper presents an enhanced asymmetrical fuzzy logic control (AFLC) based maximum power point tracking (MPPT) algorithm designed for photovoltaic (PV) systems under partial shading conditions (PSCs). With the increasing global energy demand and growing environmental concerns, maximizing solar energy efficiency has become more essential than ever. The proposed AFLC-MPPT algorithm tackles the challenges of accurately tracking the global maximum power point (GMPP) in PSCs, where conventional methods frequently underperform. By utilizing asymmetrical membership functions and optimized rule sets, the algorithm significantly improves sensitivity and precision in detecting and responding to variations in shading. Simulations conducted in MATLAB/Simulink compare the performance of the proposed AFLC-based MPPT with the conventional perturb and observe (P&O) method across multiple shading scenarios. The results demonstrate that the AFLC approach outperforms the conventional method in terms of tracking speed, stability, and overall efficiency, particularly in dynamically changing environmental conditions. Furthermore, the AFLC algorithm provides substantial improvements in voltage regulation, reduces settling time, and minimizes steady-state oscillations, contributing to the more efficient and reliable operation of PV systems under partial shading conditions.
Q-learning based active monitoring with weighted least connection round robin load balancing principle for serverless computing Balan, Yashwanth; Paul Rajan, Arokia
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3171-3179

Abstract

Serverless computing is considered one of the most promising technologies for real-time applications, with function as a service (FaaS) managing service requests in serverless computing. Load balancing played a vital role in assigning tasks in serverless computing for customers; user requests were controlled by load balancing algorithms and managed using machine learning techniques to deliver results and performance metrics within specified time limits. All serverless computing applications aimed to achieve optimal performance based on the most effective load balancing techniques, which directed requests to the appropriate servers in a timely manner. This research focused on developing a novel Q-learning based active monitoring with least connection round robin load balancing principle (Q-LAMWLR LB) for serverless computing to address the aforementioned challenge. Also, aimed to intelligently assign requests to serverless computing based on the number of requests arriving at the load balancer and how intelligently they could be directed to the appropriate server. This work utilized standard techniques to calculate the average response time for each scheduling algorithm and develop a novel intelligent load-balancing technique in serverless computing. Required experiment were conducted and the results are giving the improvement as compared to other load balancing principles. The further research in this area also identified and presented.
An intelligent approach to design big data on e-commerce in cloud computing environment Syed, salma; Sundari, Nadimpalli Usha Deepa; Dogiparti, Satish Babu; Rani, Duggimpudi Mary Sharmila; Yenireddy, Ankireddy; Kumar, Narayana Srinivas; Sunkara, Rajeev; Narasimha Raju, Buddaraju Naga Venkata
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp3439-3448

Abstract

Web resources extract useful knowledge by the process of web mining. Web server maintains the log files for analyzing them from behavior of customer and improves business as the challenging task for E-commerce companies. The processing and computing of big data was increased day by day by the demand of computer system’s ability. The emphasis on data was increased gradually by the rapid development of information technology. Various businesses are exploring effective data analysis methods, and this system proposes an intelligent approach to designing big data for e-commerce in a cloud computing environment. This paper aims to develop and implement the relevancy vector (RV) algorithm, an innovative page ranking algorithm based on Hadoop distributed file system (HDFS) map reduce. The research provides customers with a robust meta search tool that makes it easy for them to understand personalized search requirements and make purchases based on their preferences. The intelligent meta search system adverse events (IMSS-AE) tool and the RV page ranking algorithm were shown to be efficient and effective by a thorough experimental evaluation in terms of reduced response time, enhanced page freshness, high personalized relevance, and high hit rates.
Development of watershed algorithm for identification of diabetic retinopathy based on fundus images Surmayanti, Surmayanti; Sumijan, Sumijan; Bukhori, Saiful
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i3.pp2845-2856

Abstract

Diabetic retinopathy (DR) is a serious complication of diabetes that can lead to blindness if not detected early. This research presents a novel method for the identification of DR using fundus images, employing the Watershed Algorithm for accurate image segmentation and the gray level co-occurrence matrix (GLCM) for texture feature extraction. The image processing pipeline involves several stages, including grayscale conversion, noise reduction through Gaussian and median filters, and Otsu's thresholding to isolate key features such as retinal lesions. The watershed algorithm is applied to delineate the boundaries of abnormal regions, while the GLCM method extracts texture features like contrast, correlation, energy, and homogeneity, which are essential for diagnosing retinal abnormalities. The proposed approach demonstrates a high accuracy rate of 92%, successfully identifying abnormalities in 46 out of 50 fundus images. The method shows significant potential for enhancing early detection of DR, providing accurate segmentation and texture analysis, making it a valuable tool for medical professionals in diagnosing retinal diseases.

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