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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
 584   585   586   587   588 
Handling class imbalance in education using data-level and deep learning methods Kannan, Rithesh; Ng, Hu; Yap, Timothy Tzen Vun; Wong, Lai Kuan; Chua, Fang Fang; Goh, Vik Tor; Lee, Yee Lien; Wong, Hwee Ling
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.pp741-754

Abstract

In the current field of education, universities must be highly competitive to thrive and grow. Education data mining has helped universities in bringing in new students and retaining old ones. However, there is a major issue in this task, which is the class imbalance between the successful students and at-risk students that causes inaccurate predictions. To address this issue, 12 methods from data-level sampling techniques and 2 methods from deep learning synthesizers were compared against each other and an ideal class balancing method for the dataset was identified. The evaluation was done using the light gradient boosting machine ensemble model, and the metrics included receiver operating characteristic curve, precision, recall and F1 score. The two best methods were Tomek links and neighbourhood cleaning rule from undersampling technique with a F1 score of 0.72 and 0.71 respectively. The results of this paper identified the best class balancing method between the two approaches and identified the limitations of the deep learning approach.
Digital adaptive control with pulse width modulation of signals Siddikov, Isamiddin; Alimova, Gulchekhra; Rustamova, Malika; Usanov, Mustafaqul
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.pp252-259

Abstract

The paper presented research results of a digital control system for a dynamic plant with pulse-width modulation (PWM) of control impacts. As the control PWM signal is taken the pulse duty cycle, is calculated on each current cycle of the sample from the measured values. A control algorithm is proposed based on a hybrid application of the linear-quadratic optimization procedure and the theory of observers of minimal complexity. To ensure execution that the conditions of Astatism are met, the dynamic model of the plant is supplemented with a discrete integrator. The proposed approach makes it possible to reduce hardware costs and increase the robustness of the control system due to the exclusion of operations for digital–analogue transformations of signals. The proposed algorithm for digital control of a dynamic plant with varying duty cycle values of the PWM signal shows that the PWM model turned out to be linear and practically inertia less, which makes it easy to take into account the modulator model, which significantly simplifies the solution of the problem of synthesizing a control system for a dynamic plant. The possibility of receiving a high-quality modulated control signal allows for significant suppression of signal pulsations and high control accuracy.
Field programmable gate array implementation frameworks of a variable-length pseudorandom pattern generator Somanathan, Geethu Remadevi; Bhakthavatchalu, Ramesh
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.pp186-195

Abstract

A pseudorandom pattern generator produces sequences similar to true random sequences. A variable length pseudo-random pattern generator (PRPG), which can be used as a pattern generator for various applications like built-in self-test (BIST) or cryptography, is proposed in this paper. Our work is based on a linear feedback shift register circuit platform. The proposed design can generate patterns corresponding to different characteristic polynomials of any given polynomial degree. These characteristic polynomials are integrated into the linear feedback shift register circuit by providing an option to select the feedback paths of any of these polynomials. This paper implements and evaluates the proposed design for primitive and non-primitive characteristic polynomials of degrees 3 to 15. The circuit generates output patterns of different periods based on user inputs. Compared to other pseudorandom pattern generator circuits, the proposed circuit can generate a large set of patterns and consumes less power. Adequate results from the experiments demonstrate the functionalities and performance of the proposed pattern generator from degrees 3 to 15. The proposed circuit generates pseudorandom patterns that can be used not only for built-in self-test but also for cryptography and wireless communication applications.
An efficient strategy for optimizing a neuro-fuzzy controller for mobile robot navigation Hilali, Brahim; Ramdani, Mohammed; Naji, Abdelwahab
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.pp1065-1078

Abstract

Autonomous navigation is one of the key challenges in robotics. In recent years, several research studies have tried to improve the quality of this task by adopting artificial intelligence approaches. Indeed, the neuro-fuzzy approach stands out as one of the most commonly employed methods for developing autonomous navigation systems. Nevertheless, it may encounter problems of accuracy, complexity, and interpretability due to redundancy in the fuzzy rule base, particularly in the fuzzy sets associated with the system’s variables. In this work, a strategy is proposed to optimize an adaptive-network-based fuzzy inference system (ANFIS) controller for reactive navigation by addressing the problem of complexity and accuracy. It consists in combining a suite of methods, namely, data-driven fuzzy modeling, fuzzy sets merging, fuzzy rule base simplification, and parameter training. This process has produced a fuzzy inference system-based controller with high accuracy and low complexity, enabling smooth and near-optimal navigation. This system receives local information from sensors and predicts the appropriate kinematic behavior that enables the robot to avoid obstacles and reach the target in cluttered and previously unknown environments. The performance of the proposed controller and the efficiency of the followed strategy are demonstrated
Product reviews analysis to extract sentimental insights with class confidence rate using self-organizing map neural network Ahsain, Sara; Elyusufi, Yasyn; Ait Kbir, M'hamed
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.pp980-994

Abstract

Customer data analysis helps companies to understand customer intentions and behaviors better. This study introduces an analysis of product reviews to help managers adopt a more efficient strategy to extract valuable knowledge and help detect segment of customers that need a special attention and products that need improvement or with the most impact. The used dataset is a set of Amazon reviews divided into multiple categories; each review has a target column called ‘overall’ that takes a value between 1 and 5 (customer's satisfaction). Based on the ‘overall’ column, multiple labeling methods have been used and compared to get a binary target variable, positive or negative, that affects a class to a review. This dataset contains more than one million reviews and can give companies great insight into products’ quality and customers’ retention. This work has materialized by using customer segmentation and competitive learning with self-organizing map (SOM) Model and adopting a new approach to explore the generated network/map, it is based on clustering and map nodes labelling using a majority voting process. The results show that the proposed dual approach combining the prior knowledge, related to supervised learning, and the competitive learning abilities enhances the SOM model’s capabilities.
Predicting academic performance: toward a model based on machine learning and learner’s intelligences Rafiq, Jamal Eddine; Abdelali, Zakrani; Amraouy, Mohammed; Nouh, Said; Bennane, Abdellah
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.pp645-653

Abstract

With the rapid evolution of online learning environments, the ability to predict students' academic performance has become crucial for personalizing and enhancing the educational experience. In this article, we present a predictive model based on machine learning techniques, designed to be integrated into online learning platforms using the competency-based approach. This model leverages features from four key dimensions: demographic, social, emotional, and cognitive, to accurately predict learners' academic performance. We detail the methodology for collecting and processing learning traces, distinguishing between explicit traces, such as demographic data, and implicit traces, which capture learners' interactions and behaviors during their learning process. The analysis of these data not only improves the accuracy of performance predictions but also provides valuable insights into skill acquisition and learners' personal development. The results of this study demonstrate the potential of this model to transform online education by making it more adaptive and focused on individual learners' needs.
Harnessing deep learning for medicinal plant research: a comprehensive study Ananda, Vidya Hullekere; Rao, Narasimha Murthy Madiwala Sathyanarayana; Krishnamurthy, Thara Dharmapura
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.pp908-920

Abstract

In today’s world, people are more prone to diseases due to food adulteration and pollution in the environment, and people have found a way of using herbal medicine as an alternative to allopathic medicine, especially since coronavirus disease 2019 (COVID-19). Medicinal plants are the source of herbal medicines that increase the immunity of humans. Medicinal plants are used in many applications, like pharmaceuticals, cosmetics, and drugs. Medicinal plants are of great importance, and hence this work presents a review of the medicinal plants grown in Karnataka State, India. The work also highlights species identification and disease detection of medicinal plants employing machine learning and deep learning approaches. The paper provides information about datasets available for various medicinal plant leaf images. The deep learning models used for species identification and disease detection in medicinal plants have been discussed along with the results.
Airport infrastructure and runway precision aids for forecasting flight arrival delays Alla, Hajar; Balouki, Youssef
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.pp1038-1050

Abstract

Recent research has concentrated on using machine learning approaches to forecast flight delays. The majority of prior prediction algorithms were based on simple and standard attributes collected from the database from which the data were pulled. This article is the first attempt to propose novel features linked to airport capacity and infrastructure. The total runways, the total runway intersections, the longest runway length, the shortest runway length, the runway precision rate, the total terminals, and the total gates were all examined. In this paper, we suggest an optimized multilayer perceptron to predict flight arrival retards implementing data for domestic flights operated in United States airports. We employed data normalization, sampling techniques, and hyper-parameter tuning to strengthen the reliability of the suggested model. The experimental findings demonstrated that data normalization, sampling approaches, and Bayesian optimization produced the most accurate model with 92.49% accuracy. The achievements of the study were compared to other benchmark research from literature. The time complexity for the proposed model was computed and presented at the end of the investigation.
Estimation of harmonic impedance and resonance in power systems Alashaary, Haitham Ali; Al Shaba'an, Ghadeer Nyazi; Abu Shehab, Wael Fawzi; Ali, Shehab Abdulwadood
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.pp67-75

Abstract

Since power systems are designed to work at the fundamental frequency, the presence of other frequencies from various sources may induce series and parallel resonances, leading to damage. The behavior of the power system in the presence of harmonics becomes evident with knowledge of harmonic impedance. Measurement offers the most accurate means of estimating harmonic impedance. However, when precise data of the power system parameters are available, highly satisfactory results can be achieved through calculation methods, particularly regarding loads, which are unknown and always change. This paper presents a study on estimating harmonic impedance using the Electromagnetic Transients Program Alternative Transient Program Draw (EMTP-ATPDraw) program, applied to an authentic network of Petrovice line 67, 22/0.4 kV, located in the Czech Republic. Hypothetically, the network was subjected to harmonic injection from a source (3rd, 5th, 7th, 9th, and 11th harmonics), and the harmonic impedance was calculated for three different variants: individual harmonics, all harmonics, and all except the 9th harmonic. The results show that the presence of the 9th harmonic can lead to a parallel resonance. This study is the first to employ EMTP-ATPDraw for programming this network. It gives the possibility to create a network database for different operating conditions, offering an asset for future project planning.
Enhancing millimeter-wave communication: a tropical perspective on raindrop size distribution and signal attenuation Yusof, Nurul Najwa Md; Din, Jafri; Yin, Lam Hong
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.pp467-479

Abstract

This study tackles rain attenuation in millimeter-wave (mm-wave) communication, a critical concern for the advancement of 5G wireless technology. It examines the variability of raindrop size distribution (DSD) and its impact on specific attenuation, with a focus on tropical environments such as Malaysia. Using the Joss-Waldvogel disdrometer, this study collected and analyzed extensive DSD data over three years, revealing that the highest DSD concentrations do not necessarily result in the greatest specific attenuation. This study adopted a machine learning approach, specifically supervised learning with linear regression, to enhance the accuracy of attenuation prediction models. A new set of coefficients for the power-law model of specific attenuation was derived and benchmarked against the ITU-R P.838-3 standard and similar studies in comparable climates. The findings emphasize the importance of developing region-specific models that consider local meteorological variations, potentially offering significant improvements to the reliability and design of mm-wave communication systems in the future.

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