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30,376 Article Results

Fraud detection using TabNet* classifier: a machine learning approach

10.11591/ijeecs.v41.i2.pp601-613
G. Anish Mary , S. Sudha
Detecting fraudulent transactions is a big challenge in the digital financial world. Transaction volumes are growing quickly, and new attack methods often outstrip traditional detection systems. Current fraud-detection models usually lack clarity and do not perform reliably on unbalanced real-world datasets. This highlights the urgent need for clear and explainable deep-learning methods for tabular financial data. This paper presents an interpretable deep learning framework built on the TabNet classifier. It uses attention-driven feature selection, sparse representation learning, and sequential decision reasoning to model complex interactions among transactional, demographic, and geographical factors. The model was tested on a real-world credit card transaction dataset with 23 features. It achieved 99.69% accuracy, a 0.975 F1-score, and a 0.956 ROC-AUC. This performance outperforms benchmark models such as random forest, XGBoost, LightGBM, and logistic regression. In addition to outstanding predictive results. Furthermore, interpretability is enhanced by TabNet's attention-based feature attribution. This facilitates the clear understanding of model decisions, supporting its use in regulated financial environments where precision and responsibility are crucial.
Volume: 41
Issue: 2
Page: 601-613
Publish at: 2026-02-01

Hybrid AES-LEA encryption: a performance and security analysis

10.11591/ijeecs.v41.i2.pp532-545
Hala Shaker Mehdy , Mohd Ezanee Rusli , Haider Kadhim Hoomod
The advanced encryption standard-lightweight encryption algorithm (AESLEA) hybrid algorithm (ALESA) addresses a critical gap in cryptographic systems by solving the inherent trade-off between high security and computational efficiency. While the AES offers robust security, its complex operations result in high latency and energy costs, making it less suitable for resource-constrained environments. Conversely, lightweight alternatives like the LEA provide high speed but potentially weaker diffusion properties. This paper proposes a novel hybrid encryption model that strategically integrates AES and LEA by replacing AES’s computationally intensive MixColumns transformation with a streamlined LEA-based operation. This solution delivers the best of both paradigms: the security strength of AES and the operational efficiency of LEA, while also demonstrating superior statistical security by passing all NIST tests with higher p-values and maintaining near-optimal entropy. The hybrid ALESA algorithm thus presents an ideal, balanced solution for applications requiring both strong security guarantees and high performance, particularly in IoT and large-scale data encryption scenarios.
Volume: 41
Issue: 2
Page: 532-545
Publish at: 2026-02-01

Hybrid SVM–ANN system for automated MRI diagnosis of anterior cruciate ligament injuries

10.11591/ijeecs.v41.i2.pp773-781
Sazwan Syafiq Mazlan , Azizi Miskon , Sharizal Ahmad Sobri
Anterior cruciate ligament (ACL) tears are a frequent cause of knee instability, yet magnetic resonance imaging (MRI) interpretation remains time-consuming and observer-dependent. This paper presents an automated MRI framework for ACL injury screening and severity grading using a hybrid support vector machine–artificial neural network (SVM–ANN) model. A balanced dataset of 600 sagittal knee MRI images from Hospital Taiping (normal, partial tear, complete tear) was standardized via resizing, region-of-interest cropping, contrast enhancement, noise filtering, and segmentation. Morphological and texture features were extracted and reduced using principal component analysis (PCA). The SVM performs the initial screening (injured vs. non-injured) and samples predicted as injured are passed to the artificial neural network (ANN) to classify severity. Using confusion-matrix and receiver operating characteristic (ROC) evaluation, the proposed system achieved 86.2% overall accuracy and 81.7% sensitivity, with the ANN reaching approximately 95% accuracy on injured cases forwarded for grading. A clinician usability survey indicated high acceptance (~95%), supporting the feasibility of deployment as a lightweight decision-support tool. Limitations include reliance on single sagittal slices and single-sequence data; future work will incorporate multi-slice/3D and multi-sequence MRI to improve sensitivity and generalizability.
Volume: 41
Issue: 2
Page: 773-781
Publish at: 2026-02-01

An automatic stock price movement prediction using circularly dilated convolutions with orthogonal gated recurrent unit

10.11591/ijeecs.v41.i2.pp823-832
Durga Meena Rajendran , Maharajan Kalianandi , Bhuvanesh Ananthan
Recently, stock trend analysis has played an integral role in gaining knowledge about trading policy and determining stock intrinsic patterns. Several conventional studies reported stock trend prediction analysis but failed to obtain better performance due to poor generalization capability and high gradient vanishing problems. In light of the need to forecast stock price trends using both textual and empirical price data, this research proposed a novel hybridized deep learning (DL) model. Preprocessing, feature extraction, and prediction are the three effective stages that the created research goes through in order to properly estimate the stock movements. Data cleaning, which helps improve data quality, is calculated in the preprocessing step. Next, we use the created CDConv-OGRU technique-hybridized circularly dilated convolutions with orthogonal gated recurrent units-to extract features and make predictions. Python serves as the platform for processing and analyzing the created approach. This research uses a publicly accessible StockNet database for testing and compares results using a number of performance metrics, including accuracy, recall, precision, Mathew’s correlation coefficient (MCC), and f-score. In the experimental part, the created approach obtains a total of 95.16% accuracy, 94.8% precision, 94.89% recall, 95% confidence interval, and 0.9 MCC, in that order.
Volume: 41
Issue: 2
Page: 823-832
Publish at: 2026-02-01

Stable and accurate customer churn prediction: comparative analysis of eight classification algorithms

10.11591/ijeecs.v41.i2.pp655-665
Vincent Alexander Haris , Muhammad Ilyas Arsyad , Nathanael Septhian Adi Nugraha , Yasi Dani , Maria Artanta Ginting
Predicting customer churn is a challenging problem in many subscription-based industries, though it is considered more cost-effective than acquiring new customers. In this research, customer churn is predicted using a public dataset from an internet service provider, with 72,274 instances and 55% churn rate. The main contribution is to provide a comprehensive comparison of the stability and performance of eight classification algorithms in customer churn prediction using a large-scale public dataset. The research process includes data collection, data preprocessing, feature engineering, and model evaluation. The metrics evaluation presents test accuracy, accuracy gap, precision, recall, F1-Score, and ROC AUC, with stratified K-Fold cross-validation. Since the proportion of churn and non-churn in the dataset is relatively balanced, the F1-score is considered as the primary evaluation metric, as it provides a balanced assessment of precision and recall for both classes. The results show that CatBoost and XGBoost are the most effective models that achieve high F1-scores of 94.97% and 94.92%, respectively.
Volume: 41
Issue: 2
Page: 655-665
Publish at: 2026-02-01

Dengue case forecasting using multi-step deep learning models with attention layers

10.11591/ijeecs.v41.i2.pp546-554
Anibal Flores , Hugo Tito Chura , Victor Yana Mamani , Charles Rosado Chavez
Dengue is a viral infection that is transmitted from mosquitoes to people. It is more common in regions with tropical and subtropical climates. Accurate dengue forecasting is important to make the right decisions on time. In this sense, in this study, deep learning models with attention mechanisms such as long short-term memory (LSTM), bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), and bidirectional GRU (BiGRU) were implemented, and to improve the accuracy of model results they were linearly interpolated. According to the results, in most cases, linear interpolation improved the implemented deep learning models with attention mechanisms in terms of mean squared error (RMSE), mean absolute percentage error (MAPE) and R2. For one-step predictions, improvements occurred between 0.08% and 0.13%, for two-step predictions between 8.55% and 22.81%, for three-step predictions between 0.26% and 23.88%, for four-steps between 0.15% and 4.79%, and between 0.11% and 0.19% for five-step predictions. Based on the obtained results, it is possible to experiment with other types of interpolations such as polynomial, spline, and inverse distance weighting (IDW).
Volume: 41
Issue: 2
Page: 546-554
Publish at: 2026-02-01

Development of an educational SCADA training kit for electric railway system monitoring and control

10.11591/ijeecs.v41.i2.pp740-752
Krommavut Nongnuch , Saowalak Leelawongsarote , Tawan Khunarsa , Anucha Zahoh
The increasing dependence on supervisory control and data acquisition (SCADA) technology in electric railway systems underscores the need for practical and low-cost training platforms that reflect real supervisory control environments. Conventional educational tools often rely on software-only simulations or high-cost industrial equipment, resulting in a persistent gap between academic instruction and operational practice. This study presents an educational SCADA training kit designed specifically for railway power monitoring and control. The system replicates essential SCADA functions including real-time data acquisition, breaker operation, environmental monitoring, fault handling, and operator interface visualization through a modular hardware software architecture suitable for academic laboratories. Performance evaluation was conducted across multiple operational scenarios, including normal operation, induced faults, temperature variations, and emergency commands. Key performance indicators such as responsiveness, sensing accuracy, alarm reliability, and stability were measured over 50 repeated trials. Results show 98.7% responsiveness within a 200 ms threshold, sensor accuracy above 97.5%, and 100% alarm reliability across 25 fault events. Continuous testing confirmed stable operation without communication or actuation failures. These findings demonstrate that the proposed kit offers a reliable, scalable, and pedagogically valuable platform for teaching SCADA concepts in railway automation, while also supporting research and prototyping in supervisory control applications.
Volume: 41
Issue: 2
Page: 740-752
Publish at: 2026-02-01

RAC: a reusable adaptive convolution for CNN layer

10.11591/ijeecs.v41.i2.pp753-763
Nguyen Viet Hung , Phi Dinh Huynh , Pham Hong Thinh , Phuc Hau Nguyen , Trong-Minh Hoang
This paper proposes reusable adaptive convolution (RAC), an efficient alternative to standard 3×3 convolutions for convolutional neural networks (CNNs). The main advantage of RAC lies in its simplicity and parameter efficiency, achieved by sharing horizontal and vertical 1×k/k×1 filter banks across blocks within a stage and recombining them through a lightweight 1×1 mixing layer. By operating at the operator design level, RAC avoids post-training compression steps and preserves the conventional Conv–BN–activation structure, enabling seamless integration into existing CNN backbones. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on CIFAR-10 using several architectures, including ResNet-18/50/101, DenseNet, WideResNet, and EfficientNet. Experimental results demonstrate that RAC significantly reduces parameters and memory usage while maintaining competitive accuracy. These results indicate that RAC offers a reasonable balance between accuracy and compression, and is suitable for deploying CNN networks on resource-constrained platforms.
Volume: 41
Issue: 2
Page: 753-763
Publish at: 2026-02-01

The role of digital technologies in the transformation of ethical norms in the educational process

10.11591/ijere.v15i1.32497
ZuoYuan Liu , Alena Gura , Olga Pavlovskaya , Nataliya Antonova
In contemporary education, which increasingly incorporates digital technologies, the issue of adhering to ethical norms by both educators and students has gained particular relevance. This study aims to examine the impact of digital technologies on the transformation of ethical standards within the educational process. A survey was conducted among 45 educators and 345 students from three universities before and after the transition to remote learning, to assess changes in the adherence to ethical standards. The results revealed that after the implementation of remote learning, there was a significant increase in the level of adherence to ethical norms among educators (up to 98%) and students (up to 91%). Additionally, there was an improvement in academic performance, with 46% of students achieving a high level of success following the transition to remote learning. The evaluation of the impact of digital technologies on ethical transformation was found to be moderate but positive. Thus, digital technologies can serve as an effective tool for enhancing ethical standards and improving educational outcomes, particularly in the context of remote learning. These findings underscore the importance of integrating digital technologies into the educational process as a means of supporting ethical culture.
Volume: 15
Issue: 1
Page: 943-954
Publish at: 2026-02-01

Understanding student motivation towards achieving goals among college students: an exploratory research

10.11591/ijere.v15i1.33551
Nilda Wines Balsicas , Eddie Rima Cabrera , Elgien Candelaria Padohinog , Freddie Bulauan
Motivation could be the greatest currency to succeed in a student’s academic life. This study analyzed academic motivation after students were affected by the pandemic or after their two-year hiatus from active academic face-to-face activities. Moreover, this research examined whether students have influenced academic motivation in terms of gender and degree of program. Using a descriptive-sequential research design, 652 college students at St. Dominic College of Asia, Cavite, Philippines, took part in this study. A survey questionnaire adapted from the academic motivation scale (AMS-C 28) college version was used to determine the level of academic motivation of students. Open-ended questions were provided to the students relating to what motivates them to study and to which students are motivated through techniques during online learning. Findings revealed that the degree of program has a positive effect on student motivation, whereas gender does not significantly associate with motivation. Students showed appreciation for a greater convenience to study because of the technology; however, lack of interaction makes it more challenging for some. Helping students as teachers to keep track of their tasks can make them become great learners and succeed with confidence and determination through their personal and scholarly lives.
Volume: 15
Issue: 1
Page: 448-456
Publish at: 2026-02-01

Depth estimation in handheld augmented reality: a review

10.11591/ijeecs.v41.i2.pp589-600
Muhammad Anwar Ahmad , Norhaida Mohd Suaib , Ajune Wanis Ismail
Depth estimation involves capturing the depth information of a scene in the form of depth data. This depth information can be applied in computer vision tasks to enhance perception and comprehension. In handheld augmented reality (AR), depth estimation refers to the capability of a handheld device to estimate the depth or distance of objects in the real world based on input from its camera feed. Currently, there is a lack of work that reviews on this topic. Thus, this paper reviews and discusses the technologies regarding depth estimation on handheld devices and their applications in relation to AR. We employ partially the systematic review procedure to allow more specific focus for our, broken into three main focuses. First, we discuss the methods to obtain depth data on handheld devices. Next, we discuss on the existing frameworks that enable depth estimation for handheld AR. Then, we compile and discuss the applications of depth estimation for handheld AR based on the reviewed papers. Finally, we discuss the novelties and limitations of the current research to determine the gaps in this field of research.
Volume: 41
Issue: 2
Page: 589-600
Publish at: 2026-02-01

Assessing the impact of a business-oriented educational course on the development of entrepreneurial thinking in pre-service primary school teacher

10.11591/ijere.v15i1.36257
Nurzhaugan Balginbayeva , Aktoty Akzholova , Zhuldyzai Baimaganbetova , Abay Duisenbayev , Saule Yerkebayeva , Alua Smanova , Elmira Aitenova
This study aimed to assess the impact of a business-oriented educational course on the development of key components of entrepreneurial thinking among pre-service primary school teachers. The research involved 220 students from M. Dulati Taraz University. A pre-test/post-test design was used with an author-developed questionnaire. Entrepreneurial thinking was assessed both before and immediately after the course. Statistical analysis revealed a significant increase in the overall level of entrepreneurial thinking and its key components, including initiative, creativity, risk-taking, result orientation, and persistence. The course featured innovative teaching methods such as project-based learning, case studies, and business games, and was offered as an elective module on an experimental educational platform. The findings are consistent with international research, highlighting the importance of integrating entrepreneurial thinking into teacher training to enhance professional preparedness. These findings can help shape modern educational programs in Kazakhstan and the countries of the Commonwealth of Independent States, in line with global trends and the challenges of the 21st century.
Volume: 15
Issue: 1
Page: 511-523
Publish at: 2026-02-01

Empowering educators and students through contextualized global citizenship for sustainable development

10.11591/ijere.v15i1.35810
Erwin B. Berry , El Dixon G. Plazo , Ofelia L. Correos
This study explores how educators and students in Philippine secondary schools conceptualize global citizenship education (GCE) and understand their roles in advancing the sustainable development goals (SDGs). Despite its prominence in global education agendas, GCE remains inconsistently understood across local contexts. Using a qualitative research design, in-depth interviews were conducted with 21 teachers and students in Surigao del Sur. Thematic analysis revealed seven interconnected themes: i) holistic education: framing global citizenship beyond academics; ii) cultural sensitivity and respect for diversity; iii) active engagement and global awareness; iv) education as a channel for sustainable development; v) becoming a global citizen as a personal journey; vi) technology and global connectivity; and vii) teaching values for global responsibility. Findings indicate that while both groups support GCE, their interpretations are shaped by lived experiences, institutional conditions, and cultural environments. Teachers highlighted intentional instruction and moral formation, whereas students emphasized identity development, participation, and global awareness. However, gaps remain in critical reflection and structural understanding. In response, this study introduces the contextualized empowerment framework, a strategic model that integrates civic action, values, identity, and digital literacy to guide localized and ethical implementation of GCE. The framework offers actionable insights for curriculum development, teacher training, and educational policy reforms.
Volume: 15
Issue: 1
Page: 16-27
Publish at: 2026-02-01

Tolerance on campus: the impact of religious commitment and respect among university students

10.11591/ijere.v15i1.32607
Mohammad Jaber Thalgi , Nader Al-Refai , Kadir Gömbeyaz , Hanan Bdoor , Ayse Zisan Furat
Religious commitment, particularly within Islamic contexts, is often viewed as a guiding framework for promoting values such as tolerance, respect, and social harmony; however, differing interpretations and personal expressions of religiosity can sometimes challenge these ideals, necessitating deeper exploration of how religiosity influences social interactions. The study investigates the relationship between religious commitment and respect for others regarding the levels of tolerance behavior among university students. The study employed a descriptive quantitative cross-sectional survey from June 16 to August 16, 2023, with a sample of 334 enrolled in the College of Sharia at Yarmouk University in Jordan. The survey consists of three main scales: religious commitment, respect for others, and tolerance. Students’ demographic data, including gender, nationality, age group, academic department, and the year of study, were also collected via the questionnaire. The findings highlight significant gender differences in religious commitment, with males demonstrating higher levels than females. While no significant age differences were observed in religious commitment, tolerance varied notably, particularly among the 24-26 age group. The study participants represented a diverse range of countries of origin. A country-wise analysis revealed that students from Thailand have the highest religious commitment, underscoring the influence of cultural contexts. Departmental comparisons showed no significant differences, although the findings highlight that respect for others impacts tolerance, religious commitment and demography have almost no effect as predicted. The findings emphasize the primary role of respect in fostering social harmony, suggesting that future interventions should focus on promoting respect as a fundamental value in Islamic culture to enhance tolerance.
Volume: 15
Issue: 1
Page: 181-194
Publish at: 2026-02-01

Integration in reading literacy: a systematic review of pedagogical, professional, and engagement approaches

10.11591/ijere.v15i1.34044
Md Zahril Nizam Md Yusoff , Wan Nur Aida Sakinah Wan Jusoh , Norfaizah Abdul Jobar , Noor Zuhidayah Muhd Zulkipli , Muhamad Fadzllah Zaini , Tajul Shuhaizam Said
This systematic literature review (SLR) following preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 who explores integrated approaches to reading literacy across pedagogical integration, teacher development, and student engagement. From 1,108 retrieved articles, 59 article high-quality studies were selected for in-depth synthesis. Based on all studies published between 2020 and 2024, the findings reveal that multimodal instruction, inclusive pedagogy, and proposes a cohesive framework linking instructional innovation, teacher agency, and learner experience, offering actionable insights for educators and policymakers. The analysis identified three key thematic drivers of literacy improvement. First, integrated pedagogical practices such as multimodal, bilingual, and play-based instruction enhance literacy by making learning more inclusive, interactive, and context-responsive. Second, teacher professional development through targeted training in visual literacy, inclusive pedagogy, and digital tools strengthens instructional quality and prepares educators for evolving literacy demands. These approaches reflect current trends in instructional innovation and professional capacity building. Third, student engagement is significantly elevated through identity-driven learning, project-based tasks, and emotionally supportive environments that foster confidence, motivation, and deeper literacy outcomes. Together, these themes forms a new integration model that links teaching practices, teacher agency, and student experience into a cohesive literacy framework. The findings offer practical, evidence-based insights for policymakers, curriculum designers, and practitioners aiming to improve reading literacy across diverse settings.
Volume: 15
Issue: 1
Page: 771-783
Publish at: 2026-02-01
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