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Systematic review of teaching methods in language education: trends and innovation

10.11591/ijere.v14i3.31774
Nur Atikah Mohd Noor , Zamri Mahamod , Nurfaradilla Mohamad Nasri
This systematic literature review (SLR) examines the developing pedagogical methods in language instruction, emphasizing modern practices, technological advancements, and cultural diversity. The research seeks to fill significant gaps in the literature by examining effective pedagogical strategies that improve language acquisition results. In accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, a systematic search of the Scopus and Web of Science (WoS) databases was performed, focusing on publications published from 2022 to 2024. 30 primary studies were examined through topic synthesis and integrative analysis. The findings are categorized into three themes: i) contextualized and adaptive teaching methods in language education: balancing traditional approach and innovation; ii) impact of innovative teaching on language learning: technology and student engagement; and iii) cultural diversity’s impact on language education and engagement. The results underscore the imperative of modifying teaching strategies according to different situations and incorporating technology to enhance engagement and results. Furthermore, culturally sensitive techniques were demonstrated to improve inclusivity in multilingual classrooms. These insights are pertinent to academic and professional settings, indicating widespread significance for enhancing communication and cross-cultural skills. Future research should investigate the long-term effects of these initiatives and ensure equal access to educational resources and teacher training.
Volume: 14
Issue: 3
Page: 2031-2041
Publish at: 2025-06-01

Comparative analysis of machine learning models for fake news detection in social media

10.11591/ijai.v14.i3.pp1951-1959
Bahaa Eddine Elbaghazaoui , Mohamed Amnai , Youssef Fakhri , Ali Choukri , Noreddine Gherabi
The rapid rise of information sharing on social media has amplified the spread of fake news, making its detection increasingly critical. As fake news continues to proliferate, the need for efficient detection mechanisms has become more urgent to protect users from misinformation and disinformation. This paper presents a comparative analysis of multiple machine learning models for detecting text based fake news on social media platforms. Using models such as gradient boosting, XGBoost, and linear support vector classifier (SVC) on the Infor mation Security and Object Technology (ISOT) fake news dataset, the study demonstrates that gradient boosting achieves the highest accuracy of 99.61%, while XGBoost provides a strong balance with 99.59% accuracy and a signifi cantly lower execution time, making it more suitable for real-time applications. These results offer valuable insights into the trade-offs between accuracy and computational efficiency, contributing to the development of more practical de tection systems and future research in the field.
Volume: 14
Issue: 3
Page: 1951-1959
Publish at: 2025-06-01

Navigating complexities in on-the-job training at vocational institutions: a systematic literature review

10.11591/ijere.v14i3.31977
Selvi Rajamanickam , Ridzwan Che Rus , Mohd Nazri Abdul Raji
This study aims to systematically review and analyze the integration of fourth industrial revolution (IR 4.0) technologies into technical and vocational education and training (TVET) through on-the-job training (OJT), focusing on key themes such as skills development in the digital age, workforce productivity, relevance of IR 4.0 technologies, and the role of OJT in TVET. Additionally, it seeks to identify the challenges and best practices associated with this integration, offering actionable insights for policymakers, educators, and industry stakeholders to enhance skills development and workforce adaptability in the context of the IR 4.0. A systematic literature review was conducted to understand the multifaceted challenges and opportunities surrounding OJT programs within TVET institutions. Given TVET’s vital role in equipping individuals with workforce-relevant skills, optimizing OJT programs is crucial for meeting modern industry demands. The PRISMA framework guided the review, using advanced search techniques on databases such as Scopus, ERIC, and IEEE, leading to the analysis of 35 primary sources. The review addressed areas including the adaptation of training to modern technologies, labor market outcomes, innovative practices for competency development, and ensuring equity and access in vocational training. It identified best practices, highlighted knowledge gaps, and provided recommendations to optimize OJT in TVET. Key findings emphasized aligning OJT with emerging technologies, enhancing employment outcomes, promoting innovative training methods, and ensuring inclusive and effective vocational training. The study concludes by offering recommendations to improve the quality and outcomes of OJT in TVET, ensuring alignment with evolving workforce and industry needs.
Volume: 14
Issue: 3
Page: 1856-1869
Publish at: 2025-06-01

Generative artificial intelligence as an evaluator and feedback tool in distance learning: a case study on law implementation

10.11591/ijai.v14.i3.pp2490-2505
Dian Nurdiana , Muhamad Riyan Maulana , Siti Hadijah Hasanah , Madiha Dzakiyyah Chairunnisa , Avelyn Pingkan Komuna , Muhammad Rif'an
The development of generative artificial intelligence (GAI) has impacted various fields, including higher education. This research examines the use of GAI as an evaluator and feedback provider in distance legal education. This study tested five GAI models: ChatGPT, Perplexity, Gemini, Bing, and You, using a sample of 20 students and evaluations from legal experts. Descriptive statistical analysis and non-parametric tests, including Wilcoxon, intraclass correlation coefficient (ICC), Kappa, and Kendall's W, were used to assess accuracy, feedback quality, and usability. The results showed that ChatGPT was the most effective GAI, with the highest mean scores of 4.22 from experts and 4.12 from students, followed by Gemini with scores of 4.15 and 4.07. In terms of binary judgement accuracy, Gemini scored 80%, ChatGPT 60%, while Perplexity, Bing, and You had lower scores. Statistical analysis showed moderate agreement (ICC=0.439) and low alignment (Kappa=-0.058) between the GAIs and expert evaluations, with a Kendall's W value of 0.576 indicating moderate consistency in judgements. These findings emphasize the importance of selecting effective GAIs such as ChatGPT and Gemini to improve academic evaluation and learning in legal education, and pave the way for further innovations in the use of AI.
Volume: 14
Issue: 3
Page: 2490-2505
Publish at: 2025-06-01

A single-stage constant-power and optimal-efficiency double-sided LCC wireless battery charger

10.11591/ijpeds.v16.i2.pp1409-1416
Jiabo Yan , Mohd Junaidi Bin Abdul Aziz , Nik Rumzi Nik Idris , Tole Sutikno
This article proposes a novel single-stage double-sided LCC (DS-LCC) constant power (CP) wireless battery charger. The proposed CP charger uses a closedloop control in the secondary side with the active rectifier to make the DS-LCC charger achieve CP charging and optimal efficiency. Compared to previous work, the proposed CP wireless power transfer system does not involve any switch-controlled capacitor (SCC), does not require wireless communication, and can achieve optimal efficiency throughout the charging process. The proposed charger reduces cost and system complexity while improving efficiency. The proposed wireless charger is validated by simulation, and the efficiency remains between 94.44% and 94.52%, surpassing the previous work.
Volume: 16
Issue: 2
Page: 1409-1416
Publish at: 2025-06-01

Plant leaf disease detection and classification using artificial intelligence techniques: a review

10.11591/ijeecs.v38.i2.pp1308-1323
Kusuma R , R. Rajkumar
Agriculture is a cornerstone of human civilization, providing both food and economic stability. While not necessarily fatal, leaf diseases are a crucial threat to plant health. Accurate detection and classification of diseases in early stages are essential to minimize damage. Manual identification can be challenging, and delays in detection can lead to crop devastation. Fortunately, computer-aided image processing offers a solution. Researchers have explored several techniques for disease detection and classification by usage of affected leaf images, making significant progress over time. However, there's always room for improvement. Machine learning (ML), Deep learning (DL) techniques have shown hopeful results. ML, DL approaches act as black-box; eXplainable AI (XAI) provides clear explanations on decisions made by these black-boxes. This study aims to present a comprehensive review on plant leaf disease detection and classification by means of ML, DL and XAI methods with an overview of the outcomes of existing techniques, summarizes their performance, evaluation metrics, and analyses the challenges in existing systems, and offers the study's inferences.
Volume: 38
Issue: 2
Page: 1308-1323
Publish at: 2025-05-01

Calibration of phased array antenna with the minimum point finding method of the array factor

10.11591/ijeecs.v38.i2.pp854-864
Nguyen Xuan Luong , Nguyen Trong Nhan , Tran Van Thanh , Dang Thi Thanh Thuy
The problem of phased array antenna calibration with the minimum point finding method of the array factor is investigated. A mathematical model of the minimum point finding method is presented. Then, the proposed method is applied to the phased array antenna and compared with the traditional rotating-element electric-field vectors method. Experimental verification of the mathematical model of the proposed method showed the following: the minimum point finding method determines the phase shift more accurately than the maximum point finding method of the array factor; the proposed method showed a better detection range per phase change corresponding to a 35 dB higher resolution. The error ranges of the minimum and the maximum point finding methods were 50 and 700 , respectively. The peak of the combined beam when using the minimum point finding method is higher than the maximum point finding method which is 3.7 ... 4.1 dB. One can use the research results in large-scale phased array antenna calibration systems during the production phase.
Volume: 38
Issue: 2
Page: 854-864
Publish at: 2025-05-01

Enhancing accessibility: deep learning-based image description for individuals with visual impairments

10.11591/ijeecs.v38.i2.pp1051-1060
Nidhi B. Shah , Amit P. Ganatra
Technological developments in artificial intelligence, namely in the area of deep learning, have created new avenues for enhancing accessibility for those with visual impairments. In order to improve the capacity of people who are blind or visually impaired to understand and interact with visual material, this research investigates the creation and use of deep learning-based image description systems. We provide a comprehensive method that uses recurrent neural networks (RNNs) to generate natural language descriptions and convolutional neural networks (CNNs) and Autoencoders for extracting picture features. Our technology automatically creates comprehensive, context-aware descriptions of photographs by incorporating these models, giving users a better knowledge of their surroundings. We show the accuracy and reliability of the system on a wide range of photos through comprehensive testing. According to our research, deep learning-based picture description systems and converting the description in audio and making a promise to empower people who are visually impaired and foster diversity in the digital sphere.
Volume: 38
Issue: 2
Page: 1051-1060
Publish at: 2025-05-01

Acute lymphoblastic leukemia diagnosis and subtype segmentation in blood smears using CNN and U-Net

10.11591/ijeecs.v38.i2.pp950-959
Hamim Reza , Nazrul Islam Tareq , M M Fazle Rabbi , Sharia Arfin Tanim , Rifat Al Mamun Rudro , Kamruddin Nur
Acute lymphoblastic leukaemia (ALL) is a severe disease requiring invasive, expensive, and time-consuming diagnostic tests for definitive diagnosis. Initial diagnosis using blood smear pictures (BSP) is crucial but challenging due to the similar indications and symptoms of ALL, often leading to misdiagnoses. This study presents a custom approach using Convolutional Neural Networks (CNNs) to detect all cases and categorize subtypes. Utilizing publicly available databases, the study includes 3562 blood smear images from 89 patients. The innovative combination of U-Net for segmentation and various CNN architectures (U-Net, MobileNetV2, InceptionV3, ResNet50, NASNet) for feature extraction, with DenseNet201 being the most effective, forms the core of this method. The U-Net model achieved a segmentation accuracy of 98% by recognizing patterns within blood smear images. Following segmentation, CNN architectures extracted high-level features, with DenseNet201 proving the most effective in diagnostic and classification tasks. Our proposed custom CNN model achieved a test accuracy of 98%, with a training accuracy of 99.31% and validation accuracy of 97.09%. This approach enables an accurate distinction between ALL and non-pathologic cases.
Volume: 38
Issue: 2
Page: 950-959
Publish at: 2025-05-01

Enhance big data security based on HDFS using the hybrid approach

10.11591/ijeecs.v38.i2.pp1256-1264
Fayçal Zine-Dine , Sara Alcabnani , Ahmed Azouaoui , Jamal El Kafi
Hadoop has emerged as a prominent open-source framework for the storage, management, and processing of extensive big data through its distributed file system, known as Hadoop distributed file system (HDFS). This widespread adoption can be attributed to its capacity to provide reliable, scalable, and cost-effective solutions for managing large datasets across diverse sectors, including finance, healthcare, and social media. Nevertheless, as the significance and scale of big data applications continue to expand, the challenge of ensuring the security and safeguarding of sensitive data within Hadoop has become increasingly critical. In this study, the authors introduce a novel strategy aimed at bolstering data security within the Hadoop storage framework. This approach specifically employs a hybrid encryption technique that leverages the advantages of both advanced encryption standard (AES) and data encryption standard (DES) algorithms, whereby files are encrypted in HDFS and subsequently decrypted during the map task. To assess the efficacy of this method, the authors performed experiments with various file sizes, benchmarking the outcomes against other established security measures.
Volume: 38
Issue: 2
Page: 1256-1264
Publish at: 2025-05-01

A recurrent network technique for energy optimization in 6G networks with dynamic device-to-device communication

10.11591/ijeecs.v38.i2.pp897-903
Sonia Aneesh , Alam N. Shaikh
Energy efficiency has become a paramount concern in the design and deployment of 6G networks, driven by the exponential growth of connected devices and increasing traffic demands. For domain experts grappling with dynamic device-to-device (D2D) communication scenarios, optimizing energy consumption while maintaining reliable connectivity poses a significant challenge. To address this issue, we propose a novel recurrent network technique that dynamically configures D2D communication patterns, adaptively allocating temporary base stations among network nodes to enable efficient data transmission while minimizing energy expenditure. Our simulations demonstrate substantial energy savings, extended node lifetimes, and reliable performance, with a 37% reduction in overall network energy consumption and a 65% increase in average node lifetime compared to traditional cellular communication scenarios. In conclusion, this innovative approach paves the way for sustainable and energy efficient 6G communication systems, benefiting society by reducing operational costs, minimizing environmental impact, and prolonging the usability of mobile devices.
Volume: 38
Issue: 2
Page: 897-903
Publish at: 2025-05-01

Spatial-temporal data imputation for predictive modeling in intelligent transportation systems

10.11591/ijeecs.v38.i2.pp794-807
Yohanes Pracoyo Widi Prasetyo , Linawati Linawati , Dewa Made Wiharta , Nyoman Putra Sastra
Data imputation is necessary to overcome data loss in intelligent transportation systems (ITS) due to the many sensors used to monitor traffic conditions. Sensor malfunction, hardware limitations, and technical glitches can lead to incomplete data, potentially leading to errors in traffic data analysis. This analysis investigated spatial-temporal data imputation approaches applied for predictive modeling in ITS. Each approach's strengths, weaknesses, and applicability in the context of ITS are evaluated. We analyzed various imputation approaches involving statistical, machine learning, and combined methods. Statistical methods are more straightforward but could effectively handle modern traffic's complexity. On the other hand, machine learning and combined approaches, such as hybrid convolutional neural network (CNN)- long short-term memory (LSTM), offer more robust capabilities in capturing non-linear patterns present in spatio-temporal data. This research aims to investigate the effectiveness of each approach in overcoming data incompleteness and the accuracy of predicting future traffic conditions with the widespread adoption of IoT, electric vehicles, and autonomous vehicles. The results of this investigation provide an understanding of the most suitable approaches to address the challenges of spatio-temporal data imputation and provide practical guidance for predictive modeling in ITS.
Volume: 38
Issue: 2
Page: 794-807
Publish at: 2025-05-01

Credit card fraud detection using CNN and LSTM

10.11591/ijeecs.v38.i2.pp1402-1410
Nishant Upadhyay , Nidhi Bansal , Divya Rastogi , Rekha Chaturvedi , Mohammad Asim , Suraj Malik , Khel Prakash Jayant , Abhay Kumar Vajpayee
Credit card fraud is an evolving problem with the fraudsters developing new technologies to perform fraud. Fraudsters have found diverse ways to make a fraud transaction to the card holder. Thus, detecting suspicious behavior of a card is critical for preventing fraudulent transactions to happen. Artificial intelligence techniques, in particular deep learning algorithms can tackle these credit card fraud attacks by identifying patterns that predict transactions as fraud or legitimate. One-dimensional convolutional neural network (1D CNN) and long short-term memory (LSTM) both performs well on the sequential data especially on transactions data, yet there are not many studies done on combining these two algorithms to make an effective fraud detection approach. However, the dataset is highly imbalanced containing only 492 fraud transaction out of two lacs transactions. In this experimental study, firstly datasets will get prepared by using different sampling techniques along with their hybrid techniques secondly, observing the performance of individual CNN and LSTM on the datasets, finally on those datasets in which CNN and LSTM are performing well, by implementing ensemble on those data. The performance of the ensembles is observed using the performance metrics namely accuracy, F1-score, precision and recall. In the proposed experimental study, getting the F1-score of 99.96% and 99.89% in ensemble: early fusion and ensemble: late fusion respectively.
Volume: 38
Issue: 2
Page: 1402-1410
Publish at: 2025-05-01

Enhancing uncollateralized loan risk assessment accuracy through feature selection and advanced machine learning techniques

10.11591/ijeecs.v38.i2.pp1149-1161
Shahrul Nizam Salahudin , Yosza Dasril , Yosy Arisandy
Accuracy in evaluating the risk of credit applications is crucial for lenders, particularly when dealing with unsecured loans. Accuracy can be enhanced by selecting suitable features for a machine learning model. To better identify high-risk borrowers, this study applies an elaborate feature selection technique. This study uses the light gradient boosting machine (LGBM) Classifier model with boosting type gradient boosting decision tree (GBDT) algorithm and n_estimator value 100 for feature selection process. This work uses advanced machine learning techniques namely stacking to improve accuracy model perform. The dataset consists of 307,506 applicants from European lenders who have applied for loans in Southeast Asia. Each applicant is described by 126 different features. Using GDBT algorithm GBDT, 30 best features were selected based on their maximum accuracy compared to another feature. By employing a stacking technique that combines the LGBM, gradient boosting (GB), and random forest (RF) models, and utilizing logistic regression (LR) as the final estimator, an accuracy of 0.99637 was reached. This study demonstrates an improved the accuracy compared to previous research. This discovery indicates that utilizing feature selection and stacking method can provide one of the most precise choices for modelling the binary class classification among the current models.
Volume: 38
Issue: 2
Page: 1149-1161
Publish at: 2025-05-01

Artificial intelligence approaches for cardiovascular disease prediction: a systematic review

10.11591/ijeecs.v38.i2.pp1208-1218
Jasim Faraj Hammadi , Aliza Binti Abdul Latif , Zaihisma Binti Che Cob
Cardiovascular disease (CVD) remains a top global cause of mortality, highlighting the critical need for precise prediction models to improve patient outcomes and optimize healthcare resource allocation. Accurate prediction of CVD is paramount for early diagnosis and reducing mortality rates. Achieving efficient CVD detection and prediction requires a deep understanding of health history and the underlying causes of heart disease. Harnessing the power of data analytics proves advantageous in leveraging vast datasets to make informed predictions, aiding healthcare clinics in disease prognosis. By consistently maintaining comprehensive patient-related data, healthcare providers can anticipate the emergence of potential diseases. Our study conducts a meticulous comparative analysis of CVD prediction methods, focusing on various artificial intelligence (AI) algorithms, particularly classification and predictive algorithms. Scrutinizing approximately sixty papers on cardiovascular disease through the prism of AI techniques, this study carefully assesses the selected literature, uncovering gaps in existing research. The outcomes of this study are expected to empower medical practitioners in proactively predicting potential heart threats and facilitating the implementation of preventive measures.
Volume: 38
Issue: 2
Page: 1208-1218
Publish at: 2025-05-01
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