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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 111 Documents
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Search results for , issue "Vol 15, No 2: April 2025" : 111 Documents clear
IC-CGAN: Imbalanced class-conditional generative adversarial network with weighted loss function Ravi, Chaitra; Gaddadevara Matt, Siddesh
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1632-1646

Abstract

This research proposes an advanced deep learning model that deals with the over-distribution of plant leaf disease classes by using an imbalanced class-conditional generative adversarial network (IC-CGAN) that is coupled with a weighted loss function. IC-CGAN model provides a solution to class imbalance through the synthesis of tomato leaf disease images and adding them to the dataset which as a consequence, improves the accuracy of disease detection. The weighted loss function essentially does a crucial job of solving the problem of imbalance in class during the training stage. Mixing of these models leads to the generation of realistic leaf disease synthetic images and balancing class distribution in the dataset, hence improving of tomato disease detection model’s accuracy. This study is another step toward the development of effective disease detection systems for agricultural purposes by addressing the concern of class imbalance with IC-CGAN through the vector-weighted loss function. The proposed IC-CGAN has a high chance of enhancing the disease detection at its early stage with a much higher level of accuracy (99.95%), precision (99.98%), recall (99.98%) and F1-score (99.98%) in tomato plant leaf disease detection.
Efficient power optimized very-large-scale integration architecture of proportionate least mean square adaptive filter Lakshmaiah, Gangadharaiah Soralamavu; Krishnappa, Narayanappa Chikkajala; Ramappa, Poornima Golluchinnappanahalli; Narasimhaiah, Divya Muddenahally; Radder, Umesharaddy; Chandrasekhar, Chakali
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2513-2522

Abstract

The focus on power optimization in embedded systems is especially important for embedded applications since it has brought in many methods and factors that are necessary for developing systems that are both power- and area-efficient. In contrast to the current delayed wavelet μ-law proportionate least mean square (DWMPLMS) and delayed least mean square (DLMS) algorithms, this work offers the development of adaptive filters based on the least mean square (LMS) method, which improves power and timing performance. In order to improve area and time efficiency, the proportionate least mean square (PLMS) algorithm's architecture has been modified to remove delay, add a proportionate gain block, design for a fixed length, include an approximate multiplier block, and swap out standard blocks for floating-point adder and divider blocks. According to a power and temporal comparison with the DWMPLMS and DLMS algorithms, field-programmable gate array (FPGA) synthesis reduces power usage by 95% for a 32-bit filter length in PLMS when compared to the above methods.
Low complexity rate control for versatile video coding with hybrid Lagrange multiplier Bukit, Alexander Victor; Suwadi, Suwadi; Wirawan, Wirawan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1686-1695

Abstract

Versatile video coding (VVC) has a notable increase in encoding efficiency over high efficiency video coding (HEVC) but bandwidth and storage are limited in real-world video applications, so rate control is crucial. Even if VVC shows a notable increase in encoding capacity over HEVC, the rate control may create fluctuations in video quality and computational complexity. This fluctuation can have a significant impact on the watching experience, particularly in low-bitrate settings. The rate control variables of the consistent resolution and the decreased resolution are proposed in order to make the rate control method hybrid to consistent resolutions and reduce computational complexity. To achieve optimal coding parameters, the frames with varying resolutions Lagrange multiplier (λ), skip coding tree unit (CTU) and quantization parameter (QP) are combined. After evaluation, the average encoding time savings (????????????????????????????) were found to be 19.49%, with a Bjontegaard delta bit rate (BDBR) of -0.44% indicating insignificant quality loss.
A lightweight machine learning approach for denial-of-service attacks detection in wireless sensor networks Loughmari, Mohamed; El Affar, Anass
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2089-2097

Abstract

Wireless sensor networks (WSNs) are increasingly prevalent in the Internet of Things ecosystem and have been used in several fields such as environmental monitoring, military, and healthcare. However, their limited resources and distributed architecture remain two main challenges: energy and security. Furthermore, denial of service (DoS) attacks are one of the principal cyber threats to WSNs. This research proposes a lightweight machine learning (ML) approach based on the extreme gradient boosting (XGBoost) model to detect these attacks in WSNs. Through an extensive investigation, we evaluate four prominent ML algorithms: random forest (RF), k-nearest neighbor (KNN), stochastic gradient descent (SGD), and XGBoost, using the WSN-DS dataset. In addition, we implement and investigate several feature selection techniques in order to have an improved version of the original dataset. Moreover, we evaluate the performance using various performance metrics, which include accuracy, precision, recall, F1-score, and processing time. The latter is a crucial consideration in WSN environments. For validation, we have employed 5-fold cross-validation to ensure robust and reliable results. The proposed model has achieved good performance in all metrics, with a maximum accuracy of up to 99.73%, and a 68% lower processing time compared to the other investigated classifiers.
Computer vision-based sun tracking control for optimizing photovoltaic power generation Uchaipichat, Nopadol; Wibunsin, Chotiwat; Chokjulanon, Kewalin; Tanthanuch, Nutthaphong
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1251-1261

Abstract

As global energy consumption rises and fossil fuel reserves dwindle, the transition to renewable energy sources becomes imperative. Solar photovoltaic (PV) technology, crucial in this shift, faces challenges in efficiency and cost. This study explores a motorized sun-tracking system employing image processing techniques to optimize solar panel orientation and maximize energy capture. Using an Arduino Mega 2560 microcontroller, L298N motor driver, Raspberry Pi 3 Model B, and webcam integration, the system dynamically adjusts solar panels based on real-time sun position detection. Experiments compare the performance of fixed and sun-tracking solar panels, revealing that sun-tracking panels consistently outperform fixed ones, particularly during low sun angles, resulting in up to 84.9% higher power output. These findings underscore the potential of sun-tracking technology to significantly enhance solar energy efficiency and support sustainable energy goals. Future research should focus on refining tracking algorithms and optimizing system design to further boost energy capture and reliability.
Topographic and flow direction model: a case study of Khuan Kreng peat swamp forest, Southern Thailand Musik, Panjit; Limchoowong, Nunticha; Sricharoen, Phitchan; Nateewattana, Jintapat; Amnuaywattanakul, Tanutta; Chansuvarn, Woravith; Wanthong, Uraiwun
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1978-1989

Abstract

Floods and droughts are contrasting natural phenomena. The risk of forest fires tends to be increased by the dry and hot conditions of the dry season. A topographic and flow direction model is aimed to be created using Mathematica and ArcGIS programs. The purpose of this model is to assist in water management to prevent forest fires in the Khuan Kreng peat swamp forest located in Nakhon Si Thammarat Province, Southern Thailand. Digital elevation models obtained from the Department of Land Development, representing altitude data of the terrain at a scale of 1:4,000, are utilized in this work. Using cellular automata principles with eight sub-cell flow pathways with a precision of 5×5 meters, identification was carried out. The Universal Transverse Mercator (UTM) coordinate system can store horizontal (X, Y) and vertical (Z) data in one cell, providing information about 2D and 3D topography. Our findings regarding flow direction are comparable to reference values for summer under dry conditions, where water mass is limited. The topographic model data was found to be compatible with data obtained from ArcGIS, Google Maps, and surveys. The ArcGIS flow modeling results are found to be suitable for flood simulation. The proposed method is applicable for regulating water use during droughts and preventing forest fires.
Identification of Android APK malware through local and global feature extraction using meta classifier Herawan, Yoga; Sitanggang, Imas Sukaesih; Neyman, Shelvie Nidya
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1834-1849

Abstract

Android, the most widely used mobile operating system, is also the most vulnerable to malware due to its high popularity. This has significantly focused on Android malware detection in mobile security. While extensive research has been conducted using various methods, new malware’s emergence underscores this field’s dynamic nature and the need for continuous research. The motivation that drives malware developers to create Android malware constantly is the potential to access Android devices, thereby gaining access to sensitive user information. This study, which is a complex and in-depth exploration, aims to detect Android malware using a meta-classifier that combines the single-classifier light gradient boosting machine, support vector machine, and random forest. The process involves converting disassembled malware codes into grey images for global and local feature extraction. The classification accuracy is 97% at best on a malware dataset of 3,963 samples. The main contribution of this paper is to produce an Android APK malware detector model that works by combining multiple machine learning algorithms trained using the dataset resulting from local and global feature extraction algorithms.
Design of a novel three phase hybrid converter for microgrids application using renewable energy sources Devulal, Bhukya; Siva, Manickam; Kumar, Dasari Ravi
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1473-1486

Abstract

A multi-level inverter (MLI) plays a vital role in recent days with an increasing trend of usage of microgrid and distributed generator. MLIs are popular in high voltage and high-power applications. MLIs operates with dominant switching frequency pulse width-modulation (PWM) techniques. These MLIs not only generate the output voltage with fewer Harmonic but also reduces the dV/dt stress on switches. The induction machine connected to these MLIs, adds greater advantages in real time applications. This paper presents a novel 13 and 21-level hybrid H-bridge inverter (HHBI) connected to induction drive by using a photovoltaic module for microgrid applications using maximum power point tracking (MPPT) through a PV array. Hybrid H-bridge inverters combine elements from different inverter topologies to optimize appearance in terms of efficiency, harmonics and system complexity. The main aim is to reduce harmonics using high level of inverter and by controlling motor characteristics. Here a novel PWM control method is used for making the exchanging sequences for the corresponding switches. From the MATLAB results presented, it can be noticed that with the proposed methodology the THD is reduced to 4.66 and number of switches to 39, which reduces the complexity of the system. It also minimized the switching losses and increases efficiency.
Homomorphic encryption, privacy-preserving feature extraction, and decentralized architecture for enhancing privacy in voice authentication Murugesan, Kathiresh; Subbarayalu Ramamurthy, Lavanya; Palanisamy, Boopathi; Chandrasekar, Yamini; Shanmugam, Kavitha Masagoundanpudhur; Nithya, Balluru Thammaiahshetty Adishankar; Thiyagaraja, Velumani; Muniappan, Ramaraj
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp2150-2160

Abstract

This paper introduces a novel framework designed to bolster privacy protections within automated voice authentication systems, addressing mounting concerns as voice-based authentication grows in prominence. The widespread adoption of these systems has underscored apprehensions regarding the storage and processing of sensitive voice biometric data without adequate safeguards. To mitigate these risks, a modified framework is proposed, aiming to enhance privacy without compromising authentication accuracy and efficiency. Three key techniques are implemented to address these challenges. Firstly, advanced encryption methods are employed for secure voice data storage and transmission, through the homomorphic encryption to enable authentication processing on encrypted data. Secondly, a privacy-preserving feature extraction method is introduced, transforming raw voice inputs into irreversible representations to shield original biometric information. Additionally, the framework incorporates differential privacy mechanisms, adding controlled noise to aggregated voice data to prevent individual identification within large datasets. A user-centric consent and control model is proposed, empowering individuals to manage their voice profiles and authentication settings. Experimental findings demonstrate that the framework achieves enhanced authentication accuracy while markedly reducing privacy risks compared to conventional systems. This contribution addresses the ongoing challenge of balancing security and privacy in biometric authentication technologies.
Optimized control and simulation of a grid-integrated photovoltaic and fuel cell hybrid power system Satif, Amal; Hlou, Laamari; Elgouri, Rachid
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 2: April 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i2.pp1411-1423

Abstract

In this study, we propose an advanced power control strategy tailored for a grid-connected photovoltaic-hydrogen hybrid system aimed at ensuring reliable energy supply to consumers. This hybrid system integrates photovoltaic (PV) panels as the primary energy source supplemented by hydrogen fuel production through electrolysis of surplus PV-generated electricity. To optimize system flexibility and efficiency, we employ several advanced control techniques. A novel maximum power point tracking (MPPT) method, enhanced with a proportional-integral (PI) controller, surpasses conventional perturb and observe (P&O) techniques to maximize PV power output. For precise control of the three-phase photovoltaic inverter, we utilize a space vector pulse width modulation (SVPWM) algorithm. Synchronization with the utility grid is ensured by a phase-locked loop (PLL), maintaining phase coherence between the inverter output and grid supply. The primary goal is to develop a comprehensive and efficient control strategy for grid-connected hybrid systems, addressing both energy generation and storage challenges. MATLAB simulations validate the system's performance, demonstrating high energy conversion efficiency and robust control across varying conditions. This study underscores the potential of hybrid PV-hydrogen systems to provide sustainable and resilient energy solutions for future grid integration.

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