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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 6,301 Documents
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Design and performance analysis of an NSFET-based biosensor for the early detection of dengue Patnala, Tulasi Radhika; Tatineni, Madhavi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5183-5192

Abstract

Healthcare industry is changing due to technological breakthroughs that spur creative methods for diagnosing and treating illnesses. This study examines the development of nanowire-based stacked field-effect transistor (NSFET) biosensors for the early detection of dengue virus. Dengue fever is severe threat to public health and a flavivirus spread by mosquitoes. About half of the global population is at risk due to an endemic illness in tropical and subtropical regions, which affects approximately 100 million individuals annually in 130 countries. The virus has four antigenically distinct serotypes, and there may be a fifth. These serotypes induce variety of clinical symptoms. This can include benign infections that go away on their own or extremely serious, potentially fatal consequences like organ failure, plasma leakage, and bleeding. While many techniques are now used to diagnose dengue fever in the laboratory, no single technique satisfies the optimum standards for speed, economy, sensitivity, specificity. To close this gap in dengue diagnosis, newer detection technologies are desperately needed. This ultrasensitive label-free electrical device can detect the dengue virus (DENV) early on and prevent severe additional harm to humans. To detect various DENV concentrations in human blood and demonstrate potential for eventual point-of-care (POC) detection, NSFET constructed and simulated in this work.
Intrusion detection based on image transformations and data augmentation Abood, Nada Ali; Sardroud, Asghar A. Asgharian
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5594-5603

Abstract

The increasing growth of users and communication networks in different platforms has led to the emergence of various types of network attacks. intrusion detection systems (IDS) are one of the important solutions to cope with these problems. An IDS determines whether incoming traffic is intrusive or normal. IDSs often achieve high efficiency with methods based on deep neural networks. However, one of the shortcomings of these methods is the lack of sufficient attention to the spatial features in the data. This research presents an intrusion detection method based on image transformations and data augmentation is presented. In the proposed method, the intrusion detection process is performed by transforming the traffic vector into an image using a convolutional neural network (CNN). Also, we use data augmentation and dimension reduction techniques to increase accuracy and reduce complexity in the proposed method. Simulation results on network security laboratory - knowledge discovery and data mining (NSL-KDD) show that the proposed IDS can classify intrusion traffic with an accuracy of 97.58%.
Data transmission technologies for the development of a drilling rig control and diagnostic system Rastvorova, Irina; Trufanov, Sergei
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5506-5514

Abstract

This article examines telecommunication technologies used in automatic control and diagnostics systems and discusses key aspects of using telecommunication solutions for monitoring and controlling the operation processes of the electrical complex of a drilling rig, including remote access, data transmission and real-time information analysis. It provides a comprehensive overview of such communication technologies as Bluetooth, Wi-Fi, ZigBee, global system for mobile communication (GSM), RS-232, RS-422, RS-485, universal serial bus (USB), Ethernet, narrowband internet of things (NB-IoT), long range wide area network (LoRaWAN), and power line communication (PLC). Technologies that will be most effective for use in control and diagnostics systems of a drilling rig complex are proposed. The possibility of using machine learning to process a large amount of data obtained during the drilling process to optimize the controlled drilling parameters is investigated.
Optimizing radial basis function networks for harmful algal bloom prediction: a hybrid machine learning approach Kamal, Nik Nor Muhammad Saifudin Nik Mohd; Zainuddin, Ahmad Anwar; Hussin, Amir ‘Aatieff Amir; Annas, Ammar Haziq; Mohammad-Noor, Normawaty; Razali, Roziawati Mohd
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5647-5654

Abstract

The deployment of artificial intelligence in environmental monitoring demands models balancing efficiency, interpretability, and computational cost. This study proposes a hybrid radial basis function network (RBFN) framework integrated with fuzzy c-means (FCM) clustering for predicting harmful algal blooms (HABs) using water quality parameters. Unlike conventional approaches, our model leverages localized activation functions to capture non-linear relationships while maintaining computational efficiency. Experimental results demonstrate that the RBFN-FCM hybrid achieved high accuracy (F1-score: 1.00) on test data and identified Chlorophyll-a as the strongest predictor (r = 0.94). However, real-world validation revealed critical limitations: the model failed to generalize datasets with incomplete features or distribution shifts, predicting zero HAB outbreaks in an unlabeled 11,701-record dataset. Comparative analysis with Random Forests confirmed the RBFN-FCM's advantages in training speed and interpretability but highlighted its sensitivity to input completeness. This work underscores the potential of RBFNs as lightweight, explainable tools for environmental forecasting while emphasizing the need for robustness against data variability. The framework offers a foundation for real-time decision support in ecological conservation, pending further refinement for field deployment.
Impact of outlier detection techniques on time-series forecasting accuracy for multi-country energy demand prediction Karnick, Shreyas; Lakshminarayanan, Sanjay; Palati, Madhu; R, Prakash
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5067-5079

Abstract

Accurate energy demand prediction is crucial for efficient grid management and resource optimization, particularly across multiple countries with varying consumption patterns. However, real-world energy demand data often contains outliers that can distort forecasting accuracy. This study evaluates the impact of five outlier detection techniques—Z-Score, density- based spatial clustering of applications with noise (DBSCAN), isolation forest (IF), local outlier factor (LOF), and one-class support vector machine (SVM)—on the performance of three time-series forecasting models: long short-term memory (LSTM) networks, convolutional neural network (CNN) Autoencoders, and LSTM with attention mechanisms. The models are tested using energy demand data from four European countries— Germany, France, Spain, and Italy—derived from real-time consumption records. A comparative analysis based on root mean squared error (RMSE) demonstrates that incorporating outlier detection significantly enhances model robustness, reducing forecasting errors caused by anomalous data. The findings emphasize the importance of selecting appropriate outlier detection strategies to improve the accuracy and reliability of energy demand forecasting. This research provides valuable insights into the trade-offs involved in outlier removal, with implications for policy and operational practices in energy management.
SGcoSim: a co-simulation framework to explore smart grid applications Awad, Abdalkarim; Abu-Issa, Abdallatif; Bazan, Peter; German, Reinhard
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5106-5118

Abstract

Under the smart grid concept, new novel applications are emerging. These applications make use of information and communication technology (ICT) to help the electrical grid run more smoothly. This paper introduces SGcoSim, a co-simulation framework that integrates power system modeling and data communication to enhance smart grid applications. The framework utilizes OpenDSS for simulating power distribution components and OMNeT++ for communication modeling, enabling real-time peer-to-peer interactions via wireless sensor network (WSN) techniques. Virtual cord protocol (VCP) is deployed for efficient routing and data management within the field area network. SGcoSim’s functionality is demonstrated through two case studies: a phasor measurement unit (PMU)-based wide-area monitoring system and an integrated volt/VAR optimization with demand response (IVVO-DR) application. Results indicate significant reductions in energy consumption and power losses, highlighting the capabilities of SGcoSim.
A telemedicine platform empowered by 5G mobile networks for Tunisian rural places Monia, Ibrahim; Bechir, Dadi Mohamed; Chibani, Rhaimi Belgacem
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5433-5442

Abstract

A telemedicine platform needed to be developed to address the various challenges faced by patients in rural areas, such as the lack of specialist doctors, the distance to healthcare and the time spent accessing it, which can present a risk to their lives, especially for those with chronic illnesses. For its realization, we used Laravel 11, a framework that offers powerful features for building modern, high-performance applications. To enable seamless real-time communication, we integrated Laravel reverb, a robust package supporting live interactions, updates, and notifications. The database uses MySQL 8 in combination with PHP 8.2, ensuring performance, scalability, and reliability. The strengths of our systems compared with existing Tunisian platforms are real-time interaction between patient and doctor thanks to 5G, ensuring the transfer of data and access at the same time, real- time communications such as video and audio calls, live notifications and instant messaging.
Flow-guided long short-term memory with adaptive directional learning for robust distributed denial of service attack detection in software-defined networking Ibadi, Huda Mohammed; Sardroud, Asghar A. Asgharian
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5484-5496

Abstract

A software-defined networking (SDN) architecture is designed to improve network agility by decoupling the control and data planes, but while much more flexible, also makes networks more vulnerable to threats, such as distributed denial of service (DDoS) attacks. In this study we present a novel detection model, the flow-guided long short-term memory (LSTM) network with adaptive directional learning (ADL), for the mitigation of DDoS attacks in software defined networking (SDN) environments. While the methodology is based on a flow direction algorithm (FDA), which analyzes traffic patterns and detects anomalies from directional flow behavior. The proposed method integrates FDA in LSTM-based threat detection frameworks within internet of things (IoT) networks, thereby yielding enhanced detection accuracy, as well as a real-time security threat response. The experimental evaluation on two benchmark datasets, namely the InSDN dataset and a real-time dataset utilizing a Mininet and POX controller setup, shows that a detection rate of 99.85% and 99.72%, respectively, thereby showcasing the proposed model’s ability to differentiate between legitimate and malicious network traffic.
Low-power and reduced delay in inverter and universal logic gates using Hvt-FinFET technology Chikkagoudar, Veerappa; Indumathi, G.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5193-5204

Abstract

The rapid scaling of conventional complementary metal–oxide– semiconductor (CMOS) metal–oxide–semiconductor field-effect transistors (MOSFETs) led to significantly increasing power dissipation, delay, and short channel effects (SCEs). Fin field-effect transistor (FinFET) technology is a better alternative to MOSFETs with superior electrostatic control, low power, and reduced leakage current. FinFETs have been chosen for their efficiency in overcoming these issues. This work focuses on the design of high-threshold voltage fin field-effect transistor (Hvt-FinFET) 18 nm technology-based inverter with optimized parameters and implementing universal gates NAND and NOR in Cadence Virtuoso tool. These three gates are basic building blocks for any complex digital system design. The results demonstrate significant improvement in power and reduced propagation delay in comparison with conventional CMOS technology. The Hvt-FinFET inverter obtained power dissipation and delay reduction of 13.63% and 33.33%, respectively. Power and delay optimization of 29.10% and 11.8% have been obtained in the NAND gate and 31.28% and 29.08% in the NOR gate when compared to conventional CMOS circuits. The results demonstrate significant improvements in power savings, reduced propagation delay, and superior energy efficiency, validating the effectiveness of Hvt-FinFET technology for next-generation very large scale integration (VLSI) applications.
Geometrical determination of the focal point of parabolic solar concentrators Maxmudov, Bekzod; Korabayev, Sherzod A.; Sharibaev, Nosir Yu.; Abdulkhaev, Abror; Mahmudova, Xulkarxon; Mahsudov, Sh A.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5055-5066

Abstract

Parabolic solar concentrators play a crucial role in harnessing solar energy by focusing sunlight onto a single focal point, enhancing efficiency in solar thermal applications. However, accurately determining the focal point remains a significant challenge, affecting energy efficiency, stability, and operational costs. This study presents a novel approach to determining the focal point of parabolic solar concentrators using two distinct geometric and mathematical methods. The first method applies standard parabolic equations to derive the focal point, while the second method introduces a geometric approach based on the properties of straight-line tangents and angular measurements. Experimental validation was conducted by comparing the proposed method against laser-based focal point determination. The results demonstrate that the proposed method enhances heat collection efficiency and stability, leading to improved energy output. The findings of this study contribute to optimizing solar concentrator designs, reducing energy losses, and promoting sustainable energy applications.

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