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25,002 Article Results

An improved WOA of PI control for three phase PWM rectifier

10.11591/ijeecs.v38.i1.pp39-49
Shwetha G , Guruswamy K P
In the empire of electric vehicle (EV) propulsion systems, efficient energy conversion is paramount for extending driving range and enhancing overall performance. Rectifiers play a crucial role in converting AC from the grid into DC for battery charging and motor operation. However, the performance of rectifiers is heavily influenced by the control algorithms employed. This work presents an optimized proportional-integral (PI) controller design for rectifiers in EV applications. The proposed controller aims to achieve high efficiency, fast response, and robustness to variations in load and input voltage. The optimization process incorporated in this work utilized whale optimization topology for tuning the PI controller parameters. The objective is to minimize cost function that represents deviation of rectifier output from desired characteristics under various operating conditions. The outcomes of simulation demonstrate that suggested controller works to provide greater accuracy than traditional control techniques. Moreover, experimental validation verifies the proposed controller's reliability and efficiency in practical EV applications. The optimized PI controller contributes to maximizing energy efficiency, extending battery life, and enhancing the overall reliability of electric vehicle propulsion systems.
Volume: 38
Issue: 1
Page: 39-49
Publish at: 2025-04-01

Simulation of ray behavior in biconvex converging lenses using machine learning algorithms

10.11591/ijeecs.v38.i1.pp357-366
Juan Deyby Carlos-Chullo , Marielena Vilca-Quispe , Whinders Joel Fernandez-Granda , Eveling Castro-Gutierrez
This study used machine learning (ML) algorithms to investigate the simulation of light ray behavior in biconvex converging lenses. While earlier studies have focused on lens image formation and ray tracing, they have not applied reinforcement learning (RL) algorithms like proximal policy optimization (PPO) and soft actor-critic (SAC), to model light refraction through 3D lens models. This study addresses that gap by assessing and contrasting the performance of these two algorithms in an optical simulation context. The findings of this study suggest that the PPO algorithm achieves superior ray convergence, surpassing SAC in terms of stability and accuracy in optical simulation. Consequently, PPO offers a promising avenue for optimizing optical ray simulators. It allows for a representation that closely aligns with the behavior in biconvex converging lenses, which holds significant potential for application in more complex optical scenarios.
Volume: 38
Issue: 1
Page: 357-366
Publish at: 2025-04-01

A novel multi-objective economic load dispatch solution using bee colony optimization method

10.11591/ijece.v15i2.pp1385-1395
Wanchai Khamsen , Chiraphon Takeang , Thawinan Janthawang , Apinan Aurasopon
This article presents a novel multi-objective economic load dispatch solution with the bee colony optimization method. The purposes of this research are to find the lowest total power generation cost and the lowest total power loss at the transmission line. A swarm optimization method was used to consider the non-smooth fuel cost function characteristics of the generator. The constraints of economic load dispatch include the cost function, the limitations of generator operation, power losses, and load demand. The suggested approach evaluates an IEEE 5, 26, and 118 bus system with 3, 6, and 15 generating units at 300, 1,263, and 2,630 megawatt (MW) and uses a simulation running on the MATLAB software to confirm its effectiveness. The outcomes of the simulation are compared with those of the exchange market algorithm, the cuckoo search algorithm, the bat algorithm, the hybrid bee colony optimization, the multi-bee colony optimization, the decentralized approach, the differential evolution, the social spider optimization, and the grey wolf optimization. It demonstrates that the suggested approach may provide a better-quality result faster than the traditional approach.
Volume: 15
Issue: 2
Page: 1385-1395
Publish at: 2025-04-01

Efficiency of channel codes for different fading models in 5G enhanced mobile broadband scenario

10.11591/ijece.v15i2.pp1754-1773
Mikhail Khmelevsky , Gennady Kazakov
In urban environments, 5th generation (5G) signals are subject to interference, multiple propagation and thermal noise, resulting in a significant amount of errors. In this regard, channel coding is applied, which allows to increase the reliability of the transmitted message. This work focuses on comparing the performance of low-density parity check (LDPC) and polar codes standardized by the 3rd generation partnership project (3GPP) for application in 5G networks in physical downlink shared channel (PDSCH) under multipath propagation conditions in enhanced mobile broadband (eMBB) scenario. The performance of the codes under study was investigated considering all signal processing operations implemented in hardware in 5G channels. We used clustered delay line (CDL) and tapped delay line (TDL) models as propagation channel models. Channel configuration and selection of signal parameters were based on the analysis of commercially launched 5G networks. One of the simulations results we observed was the high signal-to-noise ratio (SNR) required to transmit the signal while ensuring a given block error rate (BLER). Polar codes demonstrated both a gain in coding over LDPC codes and a loss in decoding delay of the received signal due to a more complex decoding algorithm.
Volume: 15
Issue: 2
Page: 1754-1773
Publish at: 2025-04-01

Trust factor validation for distributed denial of service attack detection using machine learning

10.11591/ijece.v15i2.pp1455-1462
Manju Jayakumar Raghvin , Manjula R. Bharamagoudra , Ritesh Dash
Distributed denial of service (DDoS) attacks, predicted to be 100 Gbps and greater, are expected to begin in the first quarter of 2019, with 77% of all attacks concentrated on at least two vectors. According to a Neustar Research Agency assessment, DDoS attacks are becoming more powerful and common. Among many other issues, distributed denial of service is a notable security issue. A large number of research projects have been conducted to address this issue, but their methodologies are either inaccurate or computationally expensive, making developing an effective DDoS assault detection method a critical demand of current research. A DDoS attack employs a huge number of agents or resources to carry out the attack, resulting in a large-scale attack power. The attackers use their intelligence to discover the weak system, which is then coordinated and managed remotely. The suggested detection framework uses a frequent time interval balancing module with node trust factor validation (FTIBM-NTFV) that is used to identify the DDoS attacks in the system for improving the security levels of the network. The proposed model is compared with the traditional methods and the results are analyzed that represents the proposed model is achieving better outcomes.
Volume: 15
Issue: 2
Page: 1455-1462
Publish at: 2025-04-01

Revisiting self-supervised contrastive learning for imbalanced classification

10.11591/ijece.v15i2.pp1949-1960
Xiaoling Gao , Muhammad Izzad Ramli , Marshima Mohd Rosli , Nursuriati Jamil , Syed Mohd Zahid Syed Zainal Ariffin
Class imbalance remains a formidable challenge in machine learning, particularly affecting fields that depend on accurate classification across skewed datasets, such as medical imaging and software defect prediction. Traditional approaches often fail to adequately address the underrepresentation of minority classes, leading to models that exhibit high performance on majority classes but have poor performance on critical minority classes. Self-supervised contrastive learning has become an extremely encouraging method for this issue, enabling the utilization of unlabeled data to generate robust and generalizable models. This paper reviews the advancements in self-supervised contrastive learning for imbalanced classification, focusing on methodologies that enhance model performance through innovative contrastive loss functions and data augmentation strategies. By pulling similar instances closer and pushing dissimilar ones apart, these techniques help mitigate the biases inherent in imbalanced datasets. We critically analyze the effectiveness of these methods in diverse scenarios and propose future research directions aimed at refining these approaches for broader application in real-world settings. This review serves as a guide for researchers exploring the potential of contrastive learning to address class imbalances, highlighting recent successes and identifying crucial gaps that need addressing.
Volume: 15
Issue: 2
Page: 1949-1960
Publish at: 2025-04-01

Integrating deep learning and optimization algorithms to forecast real-time stock prices for intraday traders

10.11591/ijece.v15i2.pp2254-2263
Nilesh B. Korade , Mahendra B. Salunke , Amol Bhosle , Dhanashri Joshi , Kavita Patil , Sunil M. Sangve , Rushali A. Deshmukh , Aparna S. Patil
The number of stock investors is steadily increasing due to factors such as the availability of high-speed internet, smart trading platforms, lower trading commissions, and the perception that trading is an effective way of earning extra income to enhance financial stability. Accurate forecasting is crucial to earning profits in the stock market, as it allows traders to anticipate price changes and make strategic investments. The traders must skillfully negotiate short-term market changes to maximize gains and minimize losses, as intraday profit mostly depends on the timing of buy and sell decisions. In the presented work, we provide minute-by-minute forecasts that assist intraday traders in making the best decisions on when to buy and sell, consequently maximizing profits on each trade they make. We have implemented a one-dimensional convolutional neural network and bidirectional long-short-term memory (1DCNN-BiLSTM) optimized with particle swarm optimizer (PSO) to forecast the value of stocks for each minute using real-time data extracted from Yahoo Finance. The proposed method is evaluated against state-of-the-art technology, and the results demonstrate its strong potential to accurately forecast the opening price, stock movement, and price for the next timeframe. This provides valuable insights for intraday traders to make informed buy or sell decisions.
Volume: 15
Issue: 2
Page: 2254-2263
Publish at: 2025-04-01

High school students’ 21st-century learning skills in organic chemistry group learning

10.11591/ijere.v14i2.30607
Maneerat Sa-ngiemjit , Ángel Vázquez-Alonso , María-Antonia Manassero-Mas
Children with 21st-century learning abilities thrive in today’s globalized world, underscoring the importance of early skill development by both schools and parents. This study aims to evaluate high school students’ grasp of organic chemistry in relation to critical thinking, creativity, communication, and collaboration skills. Conducted with 15 groups of grade 12 students in science-mathematics, the research employed a mixed-methods approach, combining written surveys and observations. Statistical tools such as averages, standard deviations, and correlation coefficients were utilized for data analysis, followed by specific statistical tests to ensure validity and significance. Correlation analysis was used to examine the relationships between each 21st-century learning skill. Findings indicate that in organic chemistry, students hone critical thinking through data evaluation, and problem-solving, while fostering creativity in molecule synthesis and solution-finding. Effective communication fosters collaboration and teamwork, essential for achieving common goals. Average scores from writing surveys for critical thinking, creativity, and communication were 1.62, 1.65, and 1.68, respectively, with collaboration evaluated through observation scoring 2.03. Notably, a significant positive correlation was found between each skill, indicating that enhancing one skill often leads to improvements in the others. This highlights the importance of a holistic approach to developing 21st-century educational abilities.
Volume: 14
Issue: 2
Page: 1417-1426
Publish at: 2025-04-01

Adult education in Greek municipalities during COVID-19 pandemic

10.11591/ijere.v14i2.29678
Athanasia Ntafou , Noelia N. Jiménez-Fanjul , David Gutiérrez-Rubio
State and local governments’ primary purpose is to provide services to their populations by shaping their social and economic life. Adult education is a good example of a public service that extends deeply into people’s everyday lives. It helps adults increase their social roles as employees, parents, and retiree, gain more fulfilment in their personal lives, and solve personal and community problems. The main research purpose of this paper is to investigate and describe adult education in Greek Municipalities during the COVID-19 pandemic, specifically in the Municipality of Piraeus. For this purpose, the research was conducted using the focus group research method. Two groups participated, the first consisting of four employees of the Center for Lifelong Learning of the Municipality of Piraeus and the second group of four adult learners representing the four courses held. The results of the survey show that the unprecedented situation of COVID-19, particularly affected adult education which had stopped for a while and then many courses were discontinued. The imperative to strengthen digital skills, the uncertainty of continuing education and the change of education as a result of the changing world were also identified.
Volume: 14
Issue: 2
Page: 1302-1309
Publish at: 2025-04-01

Improving ESP Vietnamese learners’ EFL speaking fluency and vocabulary with DMGA scaffolding: a modular approach

10.11591/ijere.v14i2.30468
Nguyen Tran Uyen Nhi , Asmaa AlSaqqaf
Teaching English as a foreign language (EFL) speaking in an English for specific purposes (ESP) classroom can be challenging as many Vietnamese students find it difficult to master this language skill. To address this issue, scaffolding is believed to be beneficial in language learning programs. This paper aims to investigate the effectiveness of the Diagnosing, Modeling, Sharing, Guiding, and Applying (DMGA) scaffolding-based module on improving the speaking skills of ESP Vietnamese learners at a public university in Vietnam in terms of fluency and vocabulary use. The study employed a quantitative method with an experimental design. The participants were 25 ESP undergraduates. The English-speaking performance test (ESPT), which served as a pretest and posttest, revealed that most posttest indicators improved from pretest values, though significance and size effects varied. Students performed significantly better in both breakdown fluency and speech rate, but there was no progress in repair fluency. While there was no statistically significant improvement in all vocabulary metrics (type-token ratios (TTR), voice-to-text ratio (VOCD), English vocabulary profile (EVP)), the students did achieve higher mean scores on the measures of vocabulary used in the post-test. Based on the findings, the DMGA scaffolding model should be applied to teach speaking skills in ESP settings within an EFL context to benefit both teachers and learners.
Volume: 14
Issue: 2
Page: 1528-1536
Publish at: 2025-04-01

Kafka-machine learning based storage benchmark kit for estimation of large file storage performance

10.11591/ijece.v15i2.pp1990-1999
Sanjay Kumar Naazre Vittal Rao , Anitha Chikkanayakanahalli Lokesh Kumar , Subhash Kamble
Efficient storage and maintenance of big data is important with respect to assuring accessibility and cost-friendliness to improve risk management and achieve an effective comprehension of the user requirements. Managing the extensive data volumes and optimizing storage performance poses a significant challenge. To address this challenge, this research proposes the Kafka-machine learning (ML) based storage benchmark kit (SBK) designed to evaluate the performance of the file storage system. The proposed method employs Kafka-ML and a drill-down feature to optimize storage performance and enhance throughput. Kafka-ML-based SBK has the capability to optimize storage efficiency and system performance through space requirements and enhance data handling. The drill-down search feature precisely contributes through reducing disk space usage, enabling faster data retrieval and more efficient real-time processing within the Kafka-ML framework. The SBK aims to provide transparency and ease of utilization for benchmarking purposes. The proposed method attains maximum throughput and minimum latency of 20 MBs and 70 ms, respectively on the number of data bytes is 10, as opposed to the existing method SBK Kafka.
Volume: 15
Issue: 2
Page: 1990-1999
Publish at: 2025-04-01

Anxiety and self-belongingness of inclusive learners: the stance, facet overcoming

10.11591/ijere.v14i2.29993
Panneerselvam G. , Bella Wiselet S.
It was inevitable that young learners at this time would have to pay special attention to both prosaic life and their schoolwork. It is also acknowledged that the majority of learners live in a multi varied situations. This study focuses on and analyses academic anxiety and self-belongingness in learners in an inclusive setup. Academic anxiety is related to distinct characteristic of learner, including gender, venue, instructional media, and family structure. The study focused on these four personal characteristics, and an experimental design technique was used to perform the analysis. This was accomplished using a convenience sampling procedure, which yielded 284 samples. The descriptive analysis is assisted to examine the collected data. However, there is a significant variance regarding their locality of residency. As a result, there is an essential geographical divide between them. The study outcomes also revealed a correlation between learners’ academic concerns and sense of belonging. Significant differences in personnel parameters, such as the residential location of learners, were observed.
Volume: 14
Issue: 2
Page: 1286-1294
Publish at: 2025-04-01

Enhancing mathematics learning in phase E: assessing Wordwall effectiveness

10.11591/ijere.v14i2.30051
Sri Rezeki , Sindi Amelia
The use of technology, classroom atmosphere, facilities, and learning resources can support quality learning outcomes in students. Wordwall, as a gamification tool, has been proven to be effective for elementary and junior high school students in mathematics. However, the effectiveness of Wordwall in enhancing senior high school students’ cognitive abilities in mathematics learning has not been investigated. Previous studies have only shown its effectiveness in improving affective abilities. Therefore, this study endeavors to evaluate the effects of using Wordwall on the mathematics learning outcomes of senior high school students in phase E. Through quasi-experimental research with pre- and post-test group design, 38 experimental class students and 37 control class students were selected as samples in this study. The study found a statistically significant difference (sig. 0.000<0.05) in the mean learning outcomes of students who used Wordwall compared to those who did not. Descriptively, the experimental group displayed superior average mathematics learning outcomes compared to the control group, demonstrating a moderate level of effectiveness (ES=0.57). The strong effect of Wordwall can be realized if it is used not only as an exercise tool within the classroom but also as an instrument for knowledge transformation, incorporating consideration of students’ learning styles.
Volume: 14
Issue: 2
Page: 1246-1252
Publish at: 2025-04-01

Principal instructional leadership practice and its relationship with teacher job performance

10.11591/ijere.v14i2.31127
Xiang Yuanyuan , Bity Salwana Alias
Excellent principal leadership actions will be guidance for teachers’ high-quality teaching performance. However, the role of instructional principal is not fully utilized, and principals lack the highly effective leadership qualities necessary to promote teachers in improving teacher job performance. This study was conducted in Shenzhen, China to investigate the effect of principal instructional leadership practices on teachers’ job performance. This study employed a quantitative approach. The sample of the study was all private secondary school teachers in Shenzhen China, consisting of 297 teachers selected from 1,300 populations. To figure out the research questions, SPSS software was used with the help of descriptive and inferential analysis. The findings of this study show that both principal instructional leadership practice and teachers’ job performance are at a high level. Moreover, there was a very strong positive relationship between two variables (r=0.974). In conclusion, the findings underscore the significance of principals’ instructional leadership attributes on teacher job performance and provide implications for the government and the school principals regarding the implementation of principal training system or the improvement of educational management policy in private schools throughout Shenzhen, China.
Volume: 14
Issue: 2
Page: 1097-1104
Publish at: 2025-04-01

Accounting education from 1960 to 2023: a bibliometric review

10.11591/ijere.v14i2.30486
Guojing Hu , Haslinah Muhamad , Hasri Mustafa , Mushtaq Yousif Alhasnawi
Since 1960, publications in accounting education have demonstrated a high level of productivity and influence. However, few studies have attempted to use bibliometric methods to map and visualize accounting education research. This study aimed to identify global issues in accounting education through bibliometric analysis, covering the period from 1960 to 2023, by exploring publication trends, top topics, and major contributors in the field. Tools such as Microsoft Excel, VOSviewer, the bibliometric R-package, and WordSift were utilized to comprehensively analyze 717 Scopus-indexed documents. The findings underscore a continuous and recently accelerated historical evolution of accounting education publications, with primary research areas clustering around “business, management, and accounting.” Noteworthy trends include a geographic concentration in the United States, the United Kingdom, and Australia, with RMIT University emerging as a key contributor. Hassell, from the United States, is recognized as the most influential author, and “accounting education” from Taylor & Francis stands out as the most productive journal. Works by Bui and Porter in 2020 emerge as the most frequently cited documents. The research results indicate that future research directions should focus on students, higher education, and the profession, emphasizing themes such as curriculum, learning, and skills.
Volume: 14
Issue: 2
Page: 849-861
Publish at: 2025-04-01
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