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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 113 Documents
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Search results for , issue "Vol 13, No 3: June 2023" : 113 Documents clear
Compressive speech enhancement using semi-soft thresholding and improved threshold estimation Smriti Sahu; Neela Rayavarapu
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2788-2800

Abstract

Compressive speech enhancement is based on the compressive sensing (CS) sampling theory and utilizes the sparsity of the signal for its enhancement. To improve the performance of the discrete wavelet transform (DWT) basis-function based compressive speech enhancement algorithm, this study presents a semi-soft thresholding approach suggesting improved threshold estimation and threshold rescaling parameters. The semi-soft thresholding approach utilizes two thresholds, one threshold value is an improved universal threshold and the other is calculated based on the initial-silence-region of the signal. This study suggests that thresholding should be applied to both detail coefficients and approximation coefficients to remove noise effectively. The performances of the hard, soft, garrote and semi-soft thresholding approaches are compared based on objective quality and speech intelligibility measures. The normalized covariance measure is introduced as an effective intelligibility measure as it has a strong correlation with the intelligibility of the speech signal. A visual inspection of the output signal is used to verify the results. Experiments were conducted on the noisy speech corpus (NOIZEUS) speech database. The experimental results indicate that the proposed method of semi-soft thresholding using improved threshold estimation provides better enhancement compared to the other thresholding approaches.
An intelligent system to detect slow denial of service attacks in software-defined networks Prathima Mabel John; Rama Mohan Babu Kasturi Nagappasetty
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3099-3110

Abstract

Slow denial of service attack (DoS) is a tricky issue in software-defined network (SDN) as it uses less bandwidth to attack a server. In this paper, a slow-rate DoS attack called Slowloris is detected and mitigated on Apache2 and Nginx servers using a methodology called an intelligent system for slow DoS detection using machine learning (ISSDM) in SDN. Data generation module of ISSDM generates dataset with response time, the number of connections, timeout, and pattern match as features. Data are generated in a real environment using Apache2, Nginx server, Zodiac FX OpenFlow switch and Ryu controller. Monte Carlo simulation is used to estimate threshold values for attack classification. Further, ISSDM performs header inspection using regular expressions to mark flows as legitimate or attacked during data generation. The proposed feature selection module of ISSDM, called blended statistical and information gain (BSIG), selects those features that contribute best to classification. These features are used for classification by various machine learning and deep learning models. Results are compared with feature selection methods like Chi-square, T-test, and information gain.
Detecting COVID-19 in chest X-ray images Worapan Kusakunniran; Punyanuch Borwarnginn; Thanongchai Siriapisith; Sarattha Karnjanapreechakorn; Krittanat Sutassananon; Trongtum Tongdee; Pairash Saiviroonporn
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3290-3298

Abstract

One reliable way of detecting coronavirus disease 2019 (COVID-19) is using a chest x-ray image due to its complications in the lung parenchyma. This paper proposes a solution for COVID-19 detection in chest x-ray images based on a convolutional neural network (CNN). This CNN-based solution is developed using a modified InceptionV3 as a backbone architecture. Self-attention layers are inserted to modify the backbone such that the number of trainable parameters is reduced and meaningful areas of COVID-19 in chest x-ray images are focused on a training process. The proposed CNN architecture is then learned to construct a model to classify COVID-19 cases from non-COVID-19 cases. It achieves sensitivity, specificity, and accuracy values of 93%, 96%, and 96%, respectively. The model is also further validated on the so-called other normal and abnormal, which are non-COVID-19 cases. Cases of other normal contain chest x-ray images of elderly patients with minimal fibrosis and spondylosis of the spine, whereas other abnormal cases contain chest x-ray images of tuberculosis, pneumonia, and pulmonary edema. The proposed solution could correctly classify them as non-COVID-19 with 92% accuracy. This is a practical scenario where non-COVID-19 cases could cover more than just a normal condition.
A novel hybrid deep learning model for price prediction Walid Abdullah; Ahmad Salah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3420-3431

Abstract

Price prediction has become a major task due to the explosive increase in the number of investors. The price prediction task has various types such as shares, stocks, foreign exchange instruments, and cryptocurrency. The literature includes several models for price prediction that can be classified based on the utilized methods into three main classes, namely, deep learning, machine learning, and statistical. In this context, we proposed several models’ architectures for price prediction. Among them, we proposed a hybrid one that incorporates long short-term memory (LSTM) and Convolution neural network (CNN) architectures, we called it CNN-LSTM. The proposed CNN-LSTM model makes use of the characteristics of the convolution layers for extracting useful features embedded in the time series data and the ability of LSTM architecture to learn long-term dependencies. The proposed architectures are thoroughly evaluated and compared against state-of-the-art methods on three different types of financial product datasets for stocks, foreign exchange instruments, and cryptocurrency. The obtained results show that the proposed CNN-LSTM has the best performance on average for the utilized evaluation metrics. Moreover, the proposed deep learning models were dominant in comparison to the state-of-the-art methods, machine learning models, and statistical models.
Evaluation of the strength and performance of a new hashing algorithm based on a block cipher Kunbolat Algazy; Kairat Sakan; Nursulu Kapalova
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3124-3130

Abstract

The article evaluates the reliability of the new HBC-256 hashing algorithm. To study the cryptographic properties, the algorithm was implemented in software using Python and C programming languages. Also, for the algebraic analysis of the HBC-256 algorithm, a system of Boolean equations was built for one round using the Transalg tool. The program code that implements the hashing algorithm was converted into a software program for generating equations. As a result, one round of the compression function was described as conjunctive normal form (CNF) using 82,533 equations and 16,609 variables. To search for a collision, the satisfiability (SAT) problem solver Lingeling was used, including a version with the possibility of parallel computing. It is shown that each new round doubles the number of equations and variables, and the time to find the solution will grow exponentially. Therefore, it is not possible to find solutions for the full HBC256 hash function.
Low-cost real-time internet of things-based monitoring system for power grid transformers Kaoutar Talbi; Abdelghani El Ougli; Belkassem Tidhaf; Hafida Zrouri
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2579-2588

Abstract

One of the most common causes of blackouts is unexpected failures at power system transformer levels. The purpose of this project is to create a low-cost Internet of things (IoT)-based monitoring system for power grid transformers in order to investigate their working status in real-time. Our monitoring system’s key functions are the gathering and display of many metrics measured at the transformer level (temperature, humidity, oil level, voltage, vibration, and pressure). The data will be collected using various sensors connected to a microcontroller with an embedded Wi-Fi module (DOIT Esp32 DevKit v1), and then supplied to a cloud environment interface with a full display of all the ongoing changes. This technology will provide the power grid maintenance center with a clear image of the transformers’ health, allowing them to intervene at the right time to prevent system breakdown. The method described above would considerably improve the efficiency of a power transformer in a smart grid system by detecting abnormalities before they become critical.
A novel triangular wave quadrature oscillator without passive components for sinusoidal pulse width modulation DC-AC power conversion Dodi Garinto; Theodora Valerie; Harki Apri Yanto; Tole Sutikno; Joni Welman Simatupang
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3572-3584

Abstract

In this study, a low-cost quadrature triangle oscillator using a voltage-controlled closed-loop dual operational amplifier (Op-Amp) architecture is proposed. Unlike other typical designs, this oscillator does not require any passive components. The use of an Op-Amp-based circuit is attractive for a triangle oscillator because it is more cost-effective than a microcontroller-based solution. This is especially true for sinusoidal pulse width modulation (SPWM) DC-AC power conversion applications. The slew-rate restriction of an Op-Amp is a useful characteristic for producing a triangle waveform when seen from the perspective of wave shaping techniques. The MC4558 and the JRC4558D are two examples of dual Op-Amps that are evaluated, contrasted, and described in this article. At supply voltages of +7 V and -7 V, the suggested quadrature triangle oscillator that uses Op-Amps MC4558 and JRC4558D has the same oscillation frequency, which is 63 kHz, as demonstrated by simulation and experimental data. The frequency stability is estimated to be around 0.23%. In addition, the findings from the experiment demonstrate that the proposed oscillator is a practical solution for the SPWM DC-AC power conversion application.
Initial location selection of electric vehicles charging infrastructure in urban city through clustering algorithm Handrea Bernando Tambunan; Ruly Bayu Sitanggang; Muhammad Muslih Mafruddin; Oksa Prasetyawan; Kensianesi Kensianesi; Istiqomah Istiqomah; Nur Cahyo; Fefria Tanbar
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3266-3280

Abstract

Transportation is one of the critical sectors worldwide, mainly based on fossil fuels, especially internal combustion engines. In a developing country, heightened dependence on fossil fuels affected energy sustainability issues, greenhouse gas emissions, and increasing state budget allocation towards fuel subsidies. Moreover, shifting to electric vehicles (EVs) with alternative energy, primely renewable energy sources, is considered a promising alternative to decreasing dependence on fossil fuel consumption. The availability of a sufficient EV charging station infrastructure is determined as an appropriate strategy and rudimentary requirement to optimize the growth of EV users, especially in urban cities. This study aims to utilize the k-mean algorithm’s clustering method to group and select a potential EV charging station location in Jakarta an urban city in Indonesia. This study proposed a method for advancing the layout location’s comprehensive suitability. An iterative procedure determines the most suitable value for K as centroids. The K value is evaluated by cluster silhouette coefficient scores to acquire the optimized numeral of clusters. The results show that 95 potential locations are divided into 19 different groups. The suggested initial EV charging station location was selected and validated by silhouette coefficient scores. This research also presents the maps of the initially selected locations and clustering.
Slum image detection and localization using transfer learning: a case study in Northern Morocco Tarik El Moudden; Rachid Dahmani; Mohamed Amnai; Abderrahmane Aït Fora
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3299-3310

Abstract

Developing countries are faced with social and economic challenges, including the emergence and proliferation of slums. Slum detection and localization methods typically rely on regular topographic surveys or on visual identification of high-resolution spatial satellite images, as well as socio-environmental surveys from land surveys and general population censuses. Yet, they consume so much time and effort. To overcome these problems, this paper exploits well-known seven pretrained models using transfer learning approaches such as MobileNets, InceptionV3, NASNetMobile, Xception, VGG16, EfficientNet, and ResNet50, consecutively, on a smaller dataset of medium-resolution satellite imagery. The accuracies obtained from these experiments, respectively, demonstrate that the top three pretrained models achieve 98.78%, 97.9%, and 97.56%. Besides, MobileNets have the smallest memory sizes of 9.1 Mo and the shortest latency of 17.01 s, which can be implemented as needed. The results show the good performance of the top three pretrained models to be used for detecting and localizing slum housing in northern Morocco.
Research trends on microgrid systems: a bibliometric network analysis Handrea Bernando Tambunan; Nur Widi Priambodo; Joko Hartono; Indra Ardhanayudha Aditya; Meiri Triani; Rasgianti Rasgianti
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp2529-2545

Abstract

The numeral of academic publications in the microgrid system field has rapidly grown. A microgrid system is a group of interconnected distributed generation, loads, and energy storage operating as a single controllable entity. Many published articles recently focused on distributed generation, system control, system stability, power quality, architectures, and broader focus areas. This work analyzes microgrid: alternating current (AC), direct current (DC), and hybrid AC/DC microgrid systems with bibliometric network analysis through descriptive analysis, authors analysis, sources analysis, words analysis, and evolutionary path based on the Scopus database between 2010 and 2021. The finding helps find out the top authors and most impact sources, most relevant and frequently used in the research title, abstract, and keyword, graphically mapping the research evolved and identifying trend topic.

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