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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 for infectious disease surveillance integrating internet of things for rapid response Sumithra, Subramanian; Radhika, Moorthy; Venkatesh, Gandavadi; Lakshmi, Babu Seetha; Jancee, Balraj Victoria; Mohankumar, Nagarajan; Murugan, Subbiah
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1175-1186

Abstract

Particularly in the case of emerging infectious diseases and worldwide pandemics, infectious disease monitoring is essential for quick identification and efficient response to epidemics. Improving surveillance systems for quick reaction might be possible with the help of new deep learning and internet of things (IoT) technologies. This paper introduces an infectious disease monitoring architecture based on deep learning coupled with IoT devices to facilitate early diagnosis and proactive intervention measures. This approach uses recurrent neural networks (RNNs) to identify temporal patterns suggestive of infectious disease outbreaks by analyzing sequential data retrieved from IoT devices like smart thermometers and wearable sensors. To identify small changes in health markers and forecast the development of diseases, RNN architectures with long short-term memory (LSTM) networks are used to capture long-range relationships in the data. Spatial analysis permits the integration of geographic data from IoT devices, allowing for the identification of infection hotspots and the tracking of afflicted persons' movements. Quick action steps like focused testing, contact tracing, and medical resource deployment are prompted by abnormalities detected early by real-time monitoring and analysis. Preventing or lessening the severity of infectious disease outbreaks is the goal of the planned monitoring system, which would enhance public health readiness and response capacities.
Method of undetermined coefficients for circuits and filters using Legendre functions Manbetova, Zhanat; Dunayev, Pavel; Yerzhan, Assel; Imankul, Manat; Zhazykbayeva, Zhazira; Seitova, Zhadra; Dzhanuzakova, Raushan; Karnakova, Gayni
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp846-854

Abstract

This article presents a new way to implement matching networks and filters using the method of undetermined coefficients. A method is proposed for approximating the transmission coefficient of the synthesized filter, taking into account the required amplitude-frequency characteristics. To synthesize the filter, an approximating function (AF) was used using orthogonal Legendre polynomials, which is a mathematical description using a system of equations. Filter properties whose implementation is based on modified Legendre approximating functions usually depend on the interval on which they are defined and have the property that they are orthogonal on this interval. An example of seventh order filter synthesis using modified Legendre approximating functions is given. The filter circuit is implemented, the elements of the filter circuit are calculated based on the selected approximating modified function. The criteria used were minimization of the unevenness of the group delay time (GDT) and minimization of the complex approximation error for given values of the AF parameters. As a result, the number of filter elements, the group delay value and the complex approximation error are significantly reduced.
Hierarchical Bayesian optimization based convolutional neural network for chest X-ray disease classification Gowru, Bharath Kumar; Giduturi, Appa Rao; Sesetti, Anuradha
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp569-579

Abstract

Pneumonia is an infection that affects the lungs, caused by bacteria or viruses inhaled through the air, leading to respiratory problems. The previous researches on this subject have limitations of high dimensional feature subspace and overfitting which minimize the classifier performance. In this research, hierarchical Bayesian optimization based convolutional neural network (HBO-CNN) method is proposed to effectively classify chest X-ray diseases. The proposed HBO algorithm optimizes hyperparameters of CNN which minimizes the overfitting issue and enhances the performance of classification. The hybrid Mexican axolotl optimization (MAO) and tuna swarm optimization (TSO) based feature selection method is used for selecting relevant features for classification that minimizes the high dimensional features. The ResNet 50 method is used for feature extraction to extract hierarchical features from the pre-processed images to differentiate the classes. The proposed HBO-CNN technique is estimated with performance metrics of accuracy, precision, recall, and F1-score. The proposed method attains the highest accuracy 97.95%, precision 92.00%, recall 89.00% and F1-score 92.00%, as opposed to the conventional methods, deep convolutional neural network (DCNN).
Federated public key infrastructure management for secure internet of things interoperability Abdulkader, Omar Ahmed; Ikram, Muhammad Jawad
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp792-802

Abstract

Proliferating the internet of things (IoT) across all industry fields offers numerous possibilities for invention. It also multiplied the issues of ensuring uniform interoperability among a wide range of devices and platforms. The focus of this paper is to propose an approach for enhancing the security of IoT networks. This study investigated the possible efficacy of employing a federal public key infrastructure (PKI) structure system to serve IoT-based ecosystems. To achieve this goal, we have developed an elaborate experimental framework incorporating different trust models, security protocols, privacy enhancements, and performance metrics that demonstrate the practical benefits of this kind of federated system. One of the contributions of this study is an experimental model that mimics a real IoT ecosystem. It entails many IoT devices, installing vital PKI elements, and the development of safe information transmission channels. Measurements such as latency pointed out the feasibility of various IoT concepts, including such short response time as 2.8 ms for vehicular IoT (V2X). Measures for interoperability ranged with V2X having a 96.4% success, indicating the strength of the standards within that segment. This study reveals the benefits of a federated PKI management system for solving issues of IoT interconnectedness.
Fortifying industrial cybersecurity: a novel industrial internet of things architecture enhanced by honeypot integration Kouari, Oumaima El; Lazaar, Saiida; Achoughi, Tarik
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1089-1098

Abstract

The industrial internet of things (IIoT) has significantly transformed the industrial sectors by connecting devices, machines, and systems to enhance automation, efficiency, and decision-making. However, the increased interconnectivity also poses significant security challenges because IIoT devices control critical infrastructures and processes. Our work presents an implementation of a robust industrial cybersecurity strategy with a segmented network architecture, collaborative efforts between information technology (IT) and operational technology (OT) teams for enhanced resilience and effectiveness, and vertical honeypots across all Industry 4.0 levels integrated with Wazuh for log transmission and proactive threat response, alongside Snort intrusion detection system (IDS) monitoring network traffic. Additionally, we reinforce our architecture by Wazuh with Elasticsearch and Kibana as a security information and event management solution, facilitating data analysis and compliance enforcement through custom rulesets and cybersecurity threat intelligence (CTI) integration, with automatic updates for continuous adaptation against emerging threats.
ReRNet: recursive neural network for enhanced image correction in print-cam watermarking Boujerfaoui, Said; Douzi, Hassan; Harba, Rachid; Gourrame, Khadija
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp356-364

Abstract

Robust image watermarking that can resist camera shooting has gained considerable attention in recent years due to the need to protect sensitive printed information from being captured and reproduced without authorization. Indeed, the evolution of smartphones has made identity watermarking a feasible and convenient process. However, this process also introduces challenges like perspective distortions, which can significantly impair the effectiveness of watermark detection on freehandedly digitized images. To meet this challenge, ResNet50-based ensemble of randomized neural networks (ReRNet), a recursive convolutional neural network-based correction method, is presented for the print-cam process, specifically applied to identity images. Therefore, this paper proposes an improved Fourier watermarking method based on ReRNet to rectify perspective distortions. Experimental results validate the robustness of the enhanced scheme and demonstrate its superiority over existing methods, especially in handling perspective distortions encountered in the print-cam process.
Optimal cleaning robot on solar panels with time-sequence input based on internet of things Fitriyanah, Dwi Nur; Saputra, Rivaldi Dwi Pramana; Abadi, Imam; Musyafa, Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp280-291

Abstract

Solar panels are the main component of solar power generation systems, and they function by converting solar energy into electrical energy. Indonesia has great potential for solar energy. Solar panels will work optimally at temperatures of 25 °C to 28 °C. The greater the temperature of the solar panel, the more power generated by the panel. The influence of solar radiation intensity can be caused by dust and animal droppings attached to the surface of the solar panel module. If the surface of a solar panel is covered with dust or dirt, which can block the entry of solar radiation, the resulting power output is not optimal. The aim of this research is to design and implement an automatic cleaning system for solar power plants. The system used is using ESP32 based on the Blynk application and adding internet of things (IoT) devices with a cleaning method using pumped water spraying, then assisted with wipers which have silicon rubber material to clean dust and dirt. Based on the cleaning optimization simulation calculations, we found that the optimal or efficient cleaning condition was once a month, with an efficiency of 75.17%.
Integrated U-Net segmentation and gated recurrent unit classification for accurate brain tumor diagnosis from magnetic resonance imaging images Sajjanar, Ravikumar; Dixit, Umesh D.
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp1051-1064

Abstract

Early diagnosis and proper grouping of tumors in the brain are critical for successful therapy and positive outcomes for patients. This work proposes a complete technique for identifying brain tumors that employ sophisticated artificial intelligence methodologies and achieve an accuracy rate of 97.18%. The work makes use of the brain tumor magnetic resonance imaging (MRI) collection in Kaggle, which has 723 MRI scans classified as glioma, meningioma, pituitary tumor, and no tumor. These images are initially preprocessed, which includes scaling to a homogeneous size normalizing, and removal of noise to ensure uniformity and clarity. To improve the information set, generative adversarial networks (GANs) are used to perform data augmentation, producing artificial pictures that improve the database variety and resilience. To achieve exact cancer localization, the U-Net construction, recognized for its encoder-decoder design and skip links, is used to divide up tumor areas across images generated by MRI. The image segments are then input into gated recurrent units (GRUs), to analyze a collection of features to capture periods and differences between segments. The last classification is accomplished using an entirely linked layer and then a softmax stimulation, which provides the tumors classes. This method helps for medical experiments and clinical methods.
Flooding distributed denial of service detection in software-defined networking using k-means and naïve Bayes Yzzogh, Hicham; Benaboud, Hafssa
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp817-826

Abstract

Software-defined networking (SDN) is a network architecture that enables the separation of the control plane and data plane, facilitating centralized management of the network. While centralized control offers numerous benefits, it also comes with certain drawbacks. Flooding distributed denial of service (DDoS) attacks pose a significant threat in SDN environments. These attacks involve overwhelming a target system with a large volume of packets, aiming to disrupt its functionality. In this paper, we propose a new approach for detecting DDoS attacks based on multiple k-means models and the naive Bayes algorithm. Our methodology involves training multiple k-means models to cluster each data point within every column of the dataset, where each column represents a feature. This process results in a new dataset with the same shape, containing only clusters, except the column containing the target variable (labels). These clusters are then used as input by naïve Bayes to perform binary classification. We assessed our approach using the InSDN and CIC-DDoS2017 datasets. The results underscore the impressive accuracy of our model, achieving 99.9839% on the InSDN dataset and 99.7030% on the CIC-DDoS2017 dataset. This performance was achieved by optimizing the desired number of clusters.
An efficient Radix-4 butterfly structure based on the complex binary number system and distributed arithmetic Bowlyn, Kevin; Hounsinou, Sena; Tewell, Jordan
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp174-185

Abstract

Complex number arithmetic is pivotal in various applications, requiring the selection of an efficient multiplier for high-performance computations. Fast Fourier transform (FFT)-based multipliers are widely employed for computing complex number products, but their reliance on using dedicated multipliers and treating the real and imaginary parts as two entities significantly add to the cost and complexity of the system. Distributed arithmetic (DA) is a technique that replaces complex multiplications with a bit-level shift and addition mechanism. The complex binary number system (CBNS) utilizes binary arithmetic, which treats the real and imaginary parts as a single entity, which can simplify complex number arithmetic and computations. This paper introduces an approach integrating the CBNS with DA in a Radix-4 decimation in time FFT 8-bit and 16-bit butterfly structure. The proposed design significantly reduces arithmetic computations and eliminates dedicated multipliers, demonstrating a reduction in power consumption, area size, and lookup tables, as well as increasing overall clock performance compared to the conventional FFT architecture on Artix-7, Kintex-7, and Virtex-7 field-programmable gate array chips.

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