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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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Deep learning-based semantic segmentation of tomato leaf diseases using U-Net and classification of blight using ResNet Shankaregowda, Asha Mangala; Kadegowda, Yogish Hullukere
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.pp3373-3381

Abstract

Effective disease control requires the early identification and diagnosis of plant diseases, especially those affecting tomato leaves. A crucial stage in this process is segmenting images of diseased leaves, but this can be difficult because of the uneven shapes, varied sizes, vibrant colors, and frequently blurry borders of the affected portions, in addition to the messy backgrounds. We propose a deep learning-based strategy based on the U-Net architecture for addressing these issues, enabling precise segmentation and timely identification of tomato leaf diseases. With a DICE score of 0.93 and an accuracy of 93% in identifying healthy from diseased locations, this technique shows promise in helping farmers carry out focused interventions. Furthermore, the ResNet18 model has good levels of specificity, sensitivity, and accuracy when used to classify early and late blight. These outcomes highlight the way our suggested models perform in actual agricultural environments. Subsequent research endeavors will center on augmenting the model's generalizability in various agricultural settings and investigating multi-modal imaging methodologies. It is also intended for the advancement of mobile applications and edge computing to enable real-time disease monitoring and sustainable farming methods worldwide.
Morphological features for multi-model rice grain classification D., Suma; V. G., Narendra; M., Raviraja Holla; M., Darshan Holla
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.pp3212-3225

Abstract

In the realm of agriculture and food processing, the automated classification of rice grains holds significant importance. The diverse varieties of rice available demand a systematic approach to categorization. This study tackles this challenge by employing diverse machine learning models, including support vector machine (SVM), random forest (RF), logistic regression (LR), decision tree (DT), Gaussian naive Bayes (GNB), and k-nearest neighbors (K-NN). The dataset, sourced from Kaggle, features five distinct rice types: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. After the images undergo preprocessing, a set of 13 distinct morphological features is extracted. These features ensure a comprehensive representation of rice grains for accurate classification. This study aims to create an intelligent system for efficient and precise rice grain classification, contributing to optimizing agricultural and food industry processes. Among the models, K-NN demonstrated the highest classification accuracy at 97.80%, surpassing random forest (97.51%), DT (97.48%), GNB (96.99%), SVM (96.85%), and LR (96.05%). Our proposed K-NN-based classification model achieves an accuracy of 97.8%, demonstrating competitive performance and outclassing several state-of-the-art methods such as artificial neural network (ANN) and modified visual geometry group16 (VGG16) while maintaining simplicity and computational efficiency. This underscores the effectiveness of K-NN and RF in enhancing the precision of rice variety classification.
Comparative analysis of active filters, inductor-capacitor and inductor-capacitor-inductor passive filters in reducing harmonics Siregar, Yulianta; Azhari, Naomi; Mohamed, Nur Nabila
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.pp2567-2582

Abstract

Control equipment at substations requires a rectifier to convert alternative current (AC)-direct current (DC) electric current to provide DC power for relays, motors for disconnector switches and power breaker switches, and telecommunications equipment. Rectifiers have non-linear load characteristics, which can result in a waveform that is not pure sinusoidal due to the interaction of fundamental frequency sinusoidal waves with other waves known as harmonics. Therefore, to not interfere with the equipment's work, a filter is needed to reduce the harmonics produced by the rectifier. In this research, using MATLAB/Simulink, prevention was carried out using active filters, inductor-capacitor (LC), and inductor-capacitor-inductor (LCL) passive filters (Ta, Tc, and Td designs) separately. After the research was carried out, it was found that the amount of harmonics before installing the filter was 49.61%. Then, after installing the active filter, the harmonics were reduced to 0.29%, the installation of the passive LC filter was reduced to 9.29%, and the installation of the LCL filter (Ta, Tc, and Td) became 1.44%, 0.29%, and 1.44%.
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.

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