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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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Nonlinear regression analysis to predict mandibular landmarks on panoramic radiographs Nafiiyah, Nur; Hanifah, Ayu Ismi; Susanto, Edy; Astuti, Eha Renwi; Fatichah, Chastine; Putra, Ramadhan Hardani; Akbar, Agus Subhan
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.pp2098-2108

Abstract

An automatic system for determining mandibular landmark points on panoramic radiography can reduce errors due to differences in expert professionalism and save time. Previous research has shown that the linear regression method is ineffective at predicting condyle and gonion landmark points in panoramic radiography. So, this research proposes an analysis of nonlinear regression methods (support vector machine (SVM) kernel=‘polynomial’, polynomial regression, ensemble regression) for predicting condyle and gonion landmark points. There are four predicted landmark points, namely the right condyle, left condyle, right gonion, and left gonion. The nonlinear regression methods used are SVM, polynomial regression, and ensemble regression. The Dental and Oral Hospital, within the Faculty of Dentistry at Universitas Airlangga, provides the research data. The research encompasses 119 patients between the ages of 19 and 70, dividing 103 into training and 16 into testing. The research results show that the SVM method is only good at predicting the right condyle point with a mean radial error (MRE) of 4,724 pixels. Meanwhile, to predict the left condyle, right gonion, and left gonion points, it is better to use the polynomial regression method and ensemble regression with an order of success detection rate (SDR) of 37.5%, 18.75%, and 12.5%, respectively.
A convex hull based geofencing system to eradicate COVID Arora, Parul; Deswal, Suman
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.pp1817-1825

Abstract

The World Health Organization (WHO) has identified coronavirus disease (COVID-19), as a global pandemic due to its quick global spread to more than 183 countries. Many countries have used movement control orders (MCO) and high alert levels to halt the spread. The primary goal of this research is to provide a geofencing architecture that is specially tailored to the MCO's standard requirement for monitoring an individual's whereabouts during a lockdown. Whenever an individual tests Corona positive, Geofencing uses technology to notify an anticipated network of people who may be affected and to enable traceability for potential patients. Computational techniques such as Delaunay triangulation (inpolygon) and triangle weight characterization (inside polygon) are applied to analyze the geographical boundary in which the patient is isolated. Convex hull, on the other hand, is a better technique than computational algorithms. It is considered the best mathematical technique because it takes the least amount of time (0.014985 sec) to detect the patient within the geofence layer and has the lowest standard deviation when compared with the other computational techniques.
Enhancing spatiotemporal weather forecasting accuracy with 3D convolutional kernel through the sequence to sequence model Fredyan, Renaldy; Setiawan, Karli Eka; Minor, Kelvin Asclepius
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.pp2022-2030

Abstract

Accurate weather forecasting is important when dealing with various sectors, such as retail, agriculture, and aviation, especially during extreme weather events like heat waves, droughts, and storms to prevent disaster impact. Traditional methods rely on complex, physics-based models to predict the Earth's stochastic systems. However, some technological advancements and the availability of extensive satellite data from beyond Earth have enhanced meteorological predictions and sent them to Earth's antennae. Deep learning models using this historical data show promise in improving forecast accuracy to enhance how models learn the data pattern. This study introduces a novel architecture, convolutional sequence to sequence (ConvSeq2Seq) network, which employs 3D convolutional neural networks (CNN) to address the challenges of spatiotemporal forecasting. Unlike recurrent neural network (RNN)--based models, which are time-consuming due to sequential processing, 3D CNNs capture spatial context more efficiently. ConvSeq2Seq overcomes the limitations of traditional CNN models by ensuring causal constraints and generating flexible length output sequences. Our experimental results demonstrate that ConvSeq2Seq outperforms traditional and modern RNN-based architectures in both prediction accuracy and time efficiency, leveraging historical meteorological data to provide a robust solution for weather forecasting applications. The proposed architecture outperforms the previous method, giving new insight when dealing with spatiotemporal with high density.
Revolutionizing nonrigid demons registration with the whale optimization algorithm Roy, Abhisek; Roy, Pranab Kanti; Mitra, Anirban; Daw, Swarnali; Basu, Dipannita; Chakraborty, Sayan
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.pp2372-2380

Abstract

Image registration is one of the popular image transformation models in satellite and medical imaging currently. Image registration refers to image mapping with two or more than images. The ground-breaking fusion of the whale optimization algorithm and nonrigid demons registration (WOA-NDR) is applied in the current work to improve image registration's precision and effectiveness. NDR is an effective method for aligning images that have pliable structures. Nevertheless, it frequently runs into issues with local minima and massive deformations. To address these issues, WOA-which draws inspiration from whale hunting behavior-is integrated into the NDR architecture. The WOA-NDR approach intelligently explores the solution space, enhancing convergence and avoiding premature convergence. With the innovative WOA and NDR integration, the nonrigid image registration process is revolutionized and yields superior outcomes in terms of robustness and accuracy. The efficiency of the suggested strategy is demonstrated by experimental findings on a dataset of monomodal images. The obtained results are also compared with particle swarm optimization (PSO) based framework.
Tilted fiber Bragg grating based optical sensor for simultaneous measurement of vital signs: a novel approach Arumugam, Ramya; Kumar, Ramamoorthy; Dhanalakshmi, Samiappan
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.pp1434-1445

Abstract

Vital signal monitoring acts as a highly significant diagnostic method. Continuous monitoring of vital signs like temperature, heart rate, and blood pressure aids in easy and fast disease diagnosis. Existing methods that enable continuous monitoring, such as smart watches, are not accurate enough to be used as a benchmark for diagnosing diseases. In this paper, we propose simultaneous temperature and heart rate measurement using tilted fiber Bragg grating (TFBG) which enables concurrent measurement of measurands due to its intrinsic optical property. Temperature and heart rate were considered for measurement. The proposed TFBG-based optical sensor has a sensitivity of 11.81 pm/°C and 1.73 pm/µε towards variation in temperature and strain, respectively. The sensitivity was determined by a shift in the wavelength of core mode resonance for temperature and a differential change in the wavelength of cladding mode resonance for heart rate. The average response time of the proposed TFBG sensor was found to be 3 seconds. Accuracy of 99.68% and 98.42% were achieved in temperature and heart rate measurements by the proposed TFBG sensor. The acceptability of the proposed TFBG sensor was analyzed using the Bland- Altman plot.
Enhancing data security using a multi-layer encryption system Qasim, Osama Abd; Golshannavaz, Sajjad
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.pp1961-1967

Abstract

This study highlights the interesting potential of a new multilayer cryptography scheme for reliable data protection in the field of cybersecurity. To do so, an intensive examination of a multi-layer encryption mechanism is proposed to reinforce the defenses in opposition to online threats to touchy data. The strategy is multilevel, with a superior digital dictionary serving as the foundation for the primary layer. The laborious procedures that went into making this dictionary, including rotation differences, ASCII conversion, and chaotic matrix era, upload to its encoding trouble. A modified model of the advanced encryption standard (AES) algorithm with a brand-new key generation technique, which is based on the chaos idea is furnished through layer 2. A parameter is encrypted using the Rivest-Shamir-Adleman (RSA) method, and further precautions are taken to assure the security of the encryption key. When it comes to encryption time, the first layer significantly outperforms the AES method. In addition to exhibiting instantaneous efficiency in data protection, the first layer outperformed the AES algorithm in terms of encryption time which took more than 3 seconds, and the first layer took less than 0.01 seconds, while both approaches functioned identically in terms of information decryption. In-depth talks are given to customize the suggested method's performance. The results demonstrate the effectiveness of the suggested multi-stage encryption and decryption system and demonstrate its efficacy in protecting text documents.
Automated tomato leaf disease recognition using deep convolutional networks Sohel, Amir; Rahman, Md Mizanur; Hasan, Md Umaid; Islam, MD Kafiul; Rukhsara, Lamia; Rabeya, Tapasy
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.pp1850-1860

Abstract

Agriculture is essential for the entire global population. An advanced, robust, and empirically sound agriculture sector is essential for nourishing the global population. Various leaf diseases cause financial hardships for farmers and related businesses. Early identification of foliar diseases in crops would greatly help farmers, leading to a substantial increase in agricultural productivity. The tomato is a widely recognized and nourishing food that is easily accessible and highly favored by farmers. Early diagnosis of tomato leaf diseases is crucial to maximize tomato crop production. This study aims to utilize a deep learning approach to accurately detect and classify damaged leaves and disease patterns in tomato leaf images. By employing a substantial quantity of deep convolutional network models, we achieved a high level of precision in diagnosing the condition. The dataset used in our study work is a self-contained dataset obtained by direct observation of tomato fields in rural areas of Bangladesh. It consists of four classes: healthy, black mold, grey mold, and powdery mildew. In this study work, we utilized various image pre-processing techniques and applied VGG16, InceptionV3, DenseNet121, and AlexNet models. Our results showed that the DenseNet121 model attained the higher accuracy of 97%. This discovery guarantees accurate detection of tomato diseases in a rapid manner, ushering in a new agricultural revolution.
Efficiency improvement of 50 Hz wireless photovoltaic power transfer using magnetic relay Irwanto, Muhammad; Baharudin, Nor Hanisah; Nugraha, Yoga Tri; Nisja, Indra
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.pp1322-1331

Abstract

A photovoltaic direct current (DC) power can be changed into AC power using an inverter. The inverter must be connected to an alternating current (AC) load using wire, which has drawbacks in terms of air space, wire cost, and an unattractive view of the sky. It is appropriate to suggest a wireless power transfer (WPT) system concept to replace the use of wires in the transfer of electrical power. The WPT system has been conducted by the previous researchers, but it is still in the frequency system of hundred, kilo, mega or gigahertz, thus it can only be applied on DC load after rectifying it, but cannot be applied for a normal AC load, also its distance is relative near. The modelling of wireless photovoltaic power transfer (WPVPT) with a 50 Hz system frequency is presented in this work. The four modelling components that create the WPVPT system are models of the PV module, the transmitter circuit, the magnetic relay, and the receiver circuit. The findings indicate that the efficiency of the proposed WPVPT system is 71.27% without a magnetic relay and 72.82% with a magnetic relay. It suggests that the use of magnetic relay can improve the WPVPT system's efficiency.
Development of twig dryness sensor for internet of things-based peatland fire early detection system Muid, Abdul; Aminah, Nina Siti; Budiman, Maman; Djamal, Mitra
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.pp1532-1543

Abstract

Peatland fires are a severe threat to the global environment. Existing peatland fire early detection systems commonly detect parameters such as air temperature, humidity, gas, smoke, and fire. This paper proposes a new peatland fire early detection method using the twig moisture content parameter. This method utilizes the most significant parameter approach for fire vulnerability compared to current peatland fire early detection systems. In particular, we developed an internet of things (IoT)-based twig dryness sensor to realize a field-applicable system. We propose a twig dryness sensor using the resistive sensing method, which employs a needle electrode to measure twig moisture content. Using the twig dryness sensor, three classifications of flammability were obtained, namely very difficult (moisture above 30%), difficult (moisture between 5%-30%) and easy (moisture less than 5%). This device utilizes readily available compact and portable materials. This instrumentation is digitally controlled with a low- power consumption microcontroller and long range (LoRa) transmitter, providing a long-life battery and long-range data transmission. Sensor data visualization is presented as twig dryness values and categorized according to fire vulnerability levels. The proposed system provides real-time and sustainable measurement.
Design and implementation of an internet of things enabled stress level detection system using fuzzy logic method for enhanced accuracy and real-time monitoring Nurhayati, Nurhayati; Hidayah, Nur; Ahyar, Muh.; Asriyadi, Asriyadi; Yuniarti, Yuniarti; Faraby, Muhira Dzar; Mustika, Mustika; Akhriana, Asmah; Mukhlisin, Mukhlisin
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.pp2499-2512

Abstract

Stress can affect individuals of all ages, from the young to the elderly, leading to a compromised immune system and increased susceptibility to illness. This study addresses this issue by developing an internet of things (IoT) based stress level detection system utilizing fuzzy logic methods. The device measures multiple physiological parameters and processes the data using an ESP32 microcontroller. This allows individuals to monitor and understand their stress levels efficiently and automatically through a liquid crystal display (LCD) display and Android devices. The system integrates various sensors to capture vital signs such as heart rate (HR), respiration rate, and body temperature. These readings are then analyzed using its algorithms to determine the stress level, which is displayed on both the onboard LCD and the connected Android device via an IoT interface. This real-time feedback mechanism empowers users to take proactive measures in stress management. Testing and validation of the device were conducted by comparing its readings with the depression anxiety stress scales (DASS-42) test results. The comparison showed an 80% correlation, demonstrating the device’s accuracy and reliability in detecting stress levels. This innovative approach leverages the advantages of IoT and fuzzy logic to provide a practical and effective solution for stress monitoring and management.

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