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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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Ultraviolet-C lamp control system designed to estimate deactivation of the coronavirus disease Rinanda Saputri, Fahmy; Radithya, Linus Gregorius
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp199-205

Abstract

To prevent the transmission of the coronavirus disease (COVID-19), one approach involves the application of disinfectants containing specific chemical compounds. Nonetheless, an overabundance of chemicals may yield adverse effects on both humans and the environment. Therefore, alternative methods are needed to prevent the spread of the virus without endangering humans and the environment. One method that minimizes the use of chemicals is ultraviolet-C (UV-C) light. The method used in this study is to make a UV-C lamp control system based on the internet of things (IoT). Then conduct experiments on the spread of UV-C radiation using a system that has already been built. Based on the research that has been done, a disinfectant system has been successfully designed using two Philips 30 W UV-C lamps with Wemos D1 mini microcontroller and Blynk application. The results of data collection show that the highest ultraviolet-C radiation irradiation on the intended object is 0.017 mW/cm2 with a distance between the two is 1.5 m.
Object-based image retrieval and detection for surveillance video Jagtap, Swati; Chopade, Nilkanth B.
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4343-4351

Abstract

With technological advancement worldwide, the video surveillance market is growing drastically in a versatile field. Monitoring, browsing, and retrieving a specific object in a long video becomes difficult due to the enormous amount of data produced by the surveillance camera. With limitations on human resources and browsing time, there is a need for a new video analytics model to handle more complex tasks, such as object detection and query retrieval. The current approach involves techniques like unsupervised segmentation, multiscale segmentation, and feature-based descriptions. However, these methods often encounter extensive space and time consumption challenges. A solution has been developed for retrieving targeted objects from surveillance videos via user queries, employing a graphical interface for input. Extracting relevant frames based on user-entered text queries is enabled through using YOLOv8 for object detection. Users interact through a graphical user interface deployed on a Jetson Xavier Development board. The system's outcome is a time-efficient and highly accurate automated model for object detection and query retrieval, eliminating human errors associated with manually locating objects in videos upon user queries.
Efficient rectifier with wide input power range for 5G applications Yamna, Mounira Ben; Dakhli, Nabil; Sakli, Hedi; Aoun, Mohamed
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp3809-3819

Abstract

This article presents three efficient rectifiers for radio frequency energy harvest-ing (RFEH) systems operating at the fifth generation (5G) band (3.5 GHz). Eachrectifier operates at various input power levels (high, low, and across a widepower range). The high and low-power rectifiers feature a single serial topologyusing HSMS-2860 and SMS-7630 Schottky diodes, respectively, along with mi-crostrip lines to implement the input and output filters and the impedance match-ing network. At an radio frequency (RF) power level of 15 dBm, the high-powerrectifier harvests 67.4% to direct current (DC) power with a 300Ωload resistorand an output voltage of 2.5V. The low-power rectifier achieves its maximumpower conversion efficiency (PCE) at -2 dBm, reaching 45% efficiency with a1200Ωload. The rectifier with a extended input power range comprises twobranches of subrectifiers functioning at both high and low power levels. De-pending on the power level, the considered subrectifier harvests radio frequencypower into DC power, while the other subrectifier is deactivated. Across a powerspan of 32.5 dB (ranging from -13 to 19.5 dBm), the rectifier maintains an effi-ciency above 30%. The proposed rectifiers are efficient and suitable for imple-mentation in 5G-enabled RFEH systems.
Braille code classifications tool based on computer vision for visual impaired Sadak, Hany M.; Khalaf, Ashraf A. M.; Hussein, Aziza I.; Salama, Gerges Mansour
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 6: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i6.pp6992-7000

Abstract

Blind and visually impaired people (VIP) face many challenges in writing as they usually use traditional tools such as Slate and Stylus or expensive typewriters as Perkins Brailler, often causing accessibility and affordability issues. This article introduces a novel portable, cost-effective device that helps VIP how to write by utilizing a deep-learning model to detect a Braille cell. Using deep learning instead of electrical circuits can reduce costs and enable a mobile app to act as a virtual teacher for blind users. The app could suggest sentences for the user to write and check their work, providing an independent learning platform. This feature is difficult to implement when using electronic circuits. A portable device generates Braille character cells using light- emitting diode (LED) arrays instead of Braille holes. A smartphone camera captures the image, which is then processed by a deep learning model to detect the Braille and convert it to English text. This article provides a new dataset for custom-Braille character cells. Moreover, applying a transfer learning technique on the mobile network version 2 (MobileNetv2) model offers a basis for the development of a comprehensive mobile application. The accuracy based on the model reached 97%.
Predictive modeling for breast cancer based on machine learning algorithms and features selection methods Al Tawil, Arar; Almazaydeh, Laiali; Alqudah, Bilal; Zaid Abualkishik, Abedallah; A. Alwan, Ali
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1937-1947

Abstract

Breast cancer is one of the leading causes of death among women worldwide. However, early prediction of breast cancer plays a crucial role. Therefore, strong needs exist for automatic accurate early prediction of breast cancer. In this paper, machine learning (ML) classifiers combined with features selection methods are used to build an intelligent tool for breast cancer prediction. The Wisconsin diagnostic breast cancer (WDBC) dataset is used to train and test the model. Classification algorithms, including support vector machine (SVM), light gradient boosting machine (LightGBM), random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and naïve Bayes, were employed. Performance measures for each of them were obtained, namely: accuracy, precision, recall, F-score, Kappa, Matthews correlation coefficient (MCC), and time. The results indicate that without feature selection, LightGBM achieves the highest accuracy at 95%. With minimum redundancy maximum relevance (mRMR) feature selection (15 features), LightGBM outperforms other classifiers, achieving an accuracy of 98%. For Pearson correlation coefficient feature selection (15 features), LightGBM also excels with a 95% accuracy rate. Lasso feature selection (5 features) produces varied results across classifiers, with logistic regression achieving the highest accuracy at 96%. These findings underscore the importance of feature selection in refining model performance and in improving detection for breast cancer.
Neural network control of a nonlinear dynamic plant with a predictive model Siddikov, Isamidin; Khalmatov, Davronbek; Khushnazarova, Dilnoza; Khujanazarov, Ulugbek
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5131-5138

Abstract

The paper considered the possibilities of applications of neural network technologies to control a dynamic plant with nonlinear properties. To give the control system the desired dynamic property, the use of a neural network predictive controller is proposed. The model of the control plant is in the form of a multilayer forward-directional neural network, which allows us to construct a controller using generalized equation methods with prediction. A neural network control algorithm with prediction based on minimizing the quadratic quality functional is proposed. The algorithm makes it possible to minimize the root mean square error of regulation and the control signal rate of change. To determine the sequences of optimal control impacts, the application of the Newton-Raphson method is proposed. To reduce computational costs when receiving control signals, the decomposition of the original matrix, represented as a Hess matrix, is carried out. To predict the behavior of a control plant, a formula is proposed for calculating the gradient of a neural network, discrepant by the possibility of its use in the real-time mode of the control. The proposed algorithm of the neural network control with predictive allows higher quality control of complex nonlinear dynamic plants in the real-time mode.
Pedestrian level of service for sidewalks in Tangier City Benhadou, Marwane; El Gonnouni, Amina; Lyhyaoui, Abdelouahid
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1048-1057

Abstract

The pedestrian level of service (PLOS) is a measure that quantifies walkway comfort levels. PLOS defined into six categories (A, B, C, D, E, and F) each level defines the range of values, for example, a good level (best traffic condition) is defined with the letter A until reaching the worst level, F (high congestion). This article aims to define the PLOS on sidewalks considering walking conditions in Tangier City (Morocco). Sidewalks are analyzed using video recording in the urban center of Tangier City. The collected data are pedestrian flow and effective sidewalk width. Each level contains a range of values that corresponds to the pedestrian flow that defines the level of service. Clustering techniques are used to identify the threshold of each level using a self-organizing map (SOM). The results are different from those obtained with other methods because pedestrian traffic differs from country to country.
Insights of machine learning-based threat identification schemes in advanced network system Narasimhamurthy, Thanuja; Hosahalli Swamy, Gunavathi
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4664-4674

Abstract

An advanced network system (ANS) is characterized by extensive communication features that can support a sophisticated collaborative network structure. This is essential to hosting various forms of upcoming modernized and innovative applications. Security is one of the rising concerns associated with ANS deployment. It is also noted that machine learning is one of the preferred cost-effective ways to optimize the security strength and address various ongoing security problems in ANS; however, it is still unknown about its overall effectivity scale. Hence, this paper contributes to a systematic review of existing variants of machine learning approaches to deal with threat identification in ANS. As ANS is a generalized form, this discussion considers the impact of existing machine learning approaches on its practical use cases. The paper also contributes towards critical gap analysis and highlights the study's potential learning outcome.
Design and analysis of a metamaterial based biosensor to determine blood glucose concentration Aminuzzaman, Mir Md.; Hossam-E-Haider, Md
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i4.pp4088-4096

Abstract

In this paper, a biosensor utilizing metamaterials is designed and simulated to detect blood glucose concentration. The proposed sensor comprised of a microstrip patch antenna designed on a Rogers RT5880 substrate. A circular-shaped complementary split ring resonator (CSRR) cell is integrated onto the patch of the antenna which acts as the sensing region. The sensor is analyzed in order to ascertain the blood glucose concentration ranging from 50-300 mg/dL in a human finger model. The sensing parameter is amplitude of reflection coefficient, which exhibits variation in response to alterations in the dielectric characteristics of the sample being tested. The Cole-Cole relaxation model is employed to predict the dielectric properties of different finger tissues. An analysis of the characteristics of the CSRR was conducted to illustrate its significance in the realm of glucose detection. The glucose level is determined through the utilization of a linear regression model that describes the relationship between the reflection coefficient of the sensor and glucose level. The sensor demonstrates an impressive sensitivity of 1.792 dB per (mgdL-1) and has the ability of determining glucose levels with a good accuracy, as verified by the application of Clarke error grid. This sensor exhibits enhanced performance compared to some other recent glucose sensors.
Financial revolution: a systemic analysis of artificial intelligence and machine learning in the banking sector Jáuregui-Velarde, Raúl; Andrade-Arenas, Laberiano; Molina-Velarde, Pedro; Yactayo-Arias, Cesar
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 1: February 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i1.pp1079-1090

Abstract

This paper reviews the advances, challenges, and approaches of artificial intelligence (AI) and machine learning (ML) in the banking sector. The use of these technologies is accelerating in various industries, including banking. However, the literature on banking is scattered, making a global understanding difficult. This study reviewed the main approaches in terms of applications and algorithmic models, as well as the benefits and challenges associated with their implementation in banking, in addition to a bibliometric analysis of variables related to the distribution of publications and the most productive countries, as well as an analysis of the co-occurrence and dynamics of keywords. Following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework, forty articles were selected for review. The results indicate that these technologies are used in the banking sector for customer segmentation, credit risk analysis, recommendation, and fraud detection. It should be noted that credit analysis and fraud detection are the most implemented areas, using algorithms such as random forests (RF), decision trees (DT), support vector machines (SVM), and logistic regression (LR), among others. In addition, their use brings significant benefits for decision-making and optimizing banking operations. However, the handling of substantial amounts of data with these technologies poses ethical challenges.

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