Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

30,895 Article Results

Artificial intelligence technologies in teaching Russian as a foreign language

10.11591/ijere.v15i4.38077
Larissa Krymova , Navruz Khasanov , Zhamiila Arstanbekova , Ariya Azamatova , Nuraisha Bekeyeva
The present study aims to investigate the impact of integrating artificial intelligence (AI) technologies into teaching Russian as a foreign language (RFL) from the perspective of educators. Employing a mixed-methods research design, the study utilized several methodologies, including a teacher survey, an analytical-descriptive approach to data interpretation, and the development and evaluation of AI-based interventions. The study sample comprised 120 RFL instructors from three public universities in Kazakhstan. Preliminary findings revealed a considerable awareness among teachers regarding various AI technologies, such as chatbots, voice assistants, the ChatGPT neural network, educational platforms, gaming applications, and task design tools. Nonetheless, the practical utilization of these technologies varied significantly, with only a subset of teachers incorporating them into their regular teaching practices. The study culminated in the development of a conceptual framework for AI-driven educational interventions, incorporating platforms such as Coursera, Moodle, Open EdX, and eFront; game-based applications including Duolingo, Talk2Russia, and Russian Verbs Pro; and task creation tools such as Kahoot! and Quizlet. Following the integration of these interventions into the curriculum, post-implementation evaluations indicated that teachers generally perceived the tools as effective, with the average effectiveness rating surpassing 4.0 out of 5.0 across all assessed categories. The findings of this study have practical applicability; they can be used to enhance professional development programs for teachers of the Russian language and to formulate strategies for the integration of AI technologies into language education within the Central Asian region.
Volume: 15
Issue: 4
Page: 3422-3438
Publish at: 2026-08-01

Teachers’ perspectives on pedagogical challenges of AI integration in English language teaching

10.11591/ijere.v15i4.39226
Parthiban Ganesan , Karthikeyan Padmanathan , Thiyagu Kaliappan , Raja Kumar Subburaj , Durgaprasad Sahoo
Artificial intelligence (AI) tools are rapidly being integrated into English language teaching (ELT). However, limited empirical evidence exists regarding the pedagogical challenges teachers encounter during classroom implementation. This study investigates whether demographic variables influence ELT teachers’ perceptions of AI effectiveness and related pedagogical challenges. A quantitative survey design was employed, involving 200 ELT teachers with prior AI exposure. Data were collected using a validated Likert-scale instrument (α=0.81) and analyzed using independent samples t-tests, one-way ANOVA with Tukey post hoc analysis, and Chi-square (χ²) tests. Results revealed significant gender differences in perceived effectiveness (t=2.504, p=0.044) and pedagogical challenges (t=2.627, p=0.018), with female teachers reporting higher mean scores. Locality differences were significant only for perceived effectiveness (F=4.610, p0.05) or teaching experience (χ²=4.266, p>0.05). Educational level taught was significantly associated with perceived effectiveness (χ²=13.816, p
Volume: 15
Issue: 4
Page: 3439-3450
Publish at: 2026-08-01

Development of ergonomic skills of future teachers of preschool organizations based on klax pedagogy

10.11591/ijere.v15i4.39441
Feruza Abdrimova , Sholpan Kolumbayeva , Aliya Kosshygulova , Gulnur Amirzhanova , Saltanat Khassanova
Training future preschool teachers requires the systematic development of ergonomic skills due to the high physical and emotional demands of their professional activity. However, existing teacher education programs often emphasize theoretical knowledge while providing limited opportunities for the development of practical ergonomic competencies. This study addresses this gap by investigating the effectiveness of klax pedagogy, a movement-oriented and experiential approach, in dev eloping ergonomic competence among future preschool teachers. A quasi-experimental pre-test/post-test control group design was employed with 84 students (experimental group: n=42; control group: n=42). The experimental group participated in klax-based activities focused on posture control, movement coordination, spatial organization, and ergonomic awareness, while the control group received traditional instruction. The results revealed a statistically significant improvement in ergonomic skills in the experimental group (t=9.84, p
Volume: 15
Issue: 4
Page: 3489-3496
Publish at: 2026-08-01

Psychosocial factors of cyberbullying experienced by Malaysian public university undergraduates

10.11591/ijere.v15i4.39582
Siti Nurfathin Idris , Mastura Mahfar , Faizah Mohd Fakhruddin , Azlina Mohd Kosnin , Aslan Amat Senin , Halimah Mohd Yusof , Nur Azmina Paslan
Cyberbullying has emerged as a critical issue among university students in Malaysia, driven by the widespread use of digital technologies and associated with serious psychological consequences. Despite its increasing prevalence, limited research has explored how psychosocial factors shape cyberbullying victimization within the Malaysian higher education context. This study addresses this gap by exploring the psychosocial factors influencing cyberbullying victimization among university students in Malaysia. A qualitative case study approach was employed, involving five university students who had experienced cyberbullying. Data was collected through semi-structured, in-depth interviews and analyzed using thematic analysis. The findings suggest that cyberbullying victimization is influenced by an interplay of psychological and social factors. Psychological factors include passive behavior, low self-esteem, irrational beliefs and personality traits, while social factors encompass peer relationships, parenting styles, social media engagement, and online gaming environments. This study suggests that cyberbullying is a multifaceted phenomenon shaped by both individual vulnerabilities and environmental influences. These findings highlight the need for comprehensive, psychosocial-based interventions to support students’ wellbeing and promote safer digital environments in higher education institutions.
Volume: 15
Issue: 4
Page: 2795-2805
Publish at: 2026-08-01

Competency-based evaluation of scientific-pedagogical master’s programs

10.11591/ijere.v15i4.37191
Nurzada Kozhamberdiyeva , Aliya Kudaibergenova , Bakytgul Imanbekova , Sarash Konyrbayeva , Zhanar Bekturganova , Yücel Gelişli
The goal of this study is to conduct a comprehensive evaluation of the master’s degree programs at the Faculty of Philosophy and Political Science of Al-Farabi Kazakh National University from the perspective of the competency-based approach. The study involves a targeted sample of 120 participants directly involved in the implementation and evaluation of scientific and pedagogical master’s programs: 70 master’s students, 20 teachers, 20 graduates, and 10 employers. The study employs a mixed-method approach, including surveys of master’s degree students, faculty members, graduates, and employers, as well as qualitative analysis of interviews with participants. Course relevance received the highest rating. Faculty members positively evaluate the alignment of the programs with the competency-based approach. Graduates emphasize the applicability of their knowledge, while employers express satisfaction with the graduates’ level of preparation. Kruskal–Wallis test analysis does not reveal statistically significant differences between groups for most indicators, confirming a consistent perception of educational quality. The interviews reveal a generally positive attitude toward the programs but highlighted several shortcomings, such as uneven competency development, insufficient practical focus, and limited engagement with the professional environment. The novelty of the work lies in the comprehensive empirical evaluation of master’s programs based on a competency-based approach from the perspectives of students, teachers, graduates, and employers. The practical significance of the findings lies in justifying the need for stronger integration of theory and practice, greater transparency in competency assessment, and expanded collaboration with employers to enhance the quality of master’s degree training.
Volume: 15
Issue: 4
Page: 3375-3391
Publish at: 2026-08-01

AI literacy and academic engagement in higher education: the mediating role of foreign language anxiety

10.11591/ijere.v15i4.38082
Ashraf Ragab Ibrahim , Mohamed Megahed Nasreldeen , Hesham Hussein Yakout , Hanan Mohamed Elsied , Mohammed Saad Eltawela , Mohamed Ali Nemt-allah
The rapid integration of artificial intelligence (AI) in higher education has raised questions about how technological competencies influence student outcomes, particularly in foreign language learning where anxiety significantly affects performance. This study investigated whether foreign language anxiety (FLA) mediates the relationship between AI literacy (AIL) and academic engagement (AE) among 1,052 English as a foreign language (EFL) learners enrolled in foreign language programs at Al-Azhar University, Egypt. Participants completed three validated instruments: the artificial intelligence literacy scale (AILS), the short-form foreign language classroom anxiety scale (S-FLCAS), and the academic engagement scale (AES). Mediation analysis was conducted using PROCESS Model 4 with 5,000 bootstrap iterations to generate bias-corrected confidence intervals (CI). Correlation analyses revealed that AIL positively correlated with AE (r=.31, p
Volume: 15
Issue: 4
Page: 3613-3622
Publish at: 2026-08-01

Deep learning-based prognostic modeling of brain tumors

10.11591/ijai.v15.i4.pp3792-3804
Laiali Almazaydeh , Arar Al Tawil
Headline accuracy is not enough to move a deep-learning brain tumor magnetic resonance imaging (MRI) classifier into the clinic. A model also needs repeated-split validation, trustworthy probabilities, a statistical sanity check, and a small enough footprint for hospital hardware. This study cover all four in one framework. Five convolutional neural network (CNN) backbones (visual geometry group (VGG)-16, residual network (ResNet)-50V2, MobileNetV2, EfficientNetB0, and dense convolutional network (DenseNet)-121) are trained on 4,600 public brain MRI scans (2,513 tumor, 2,087 healthy) under a shared two-phase transfer-learning recipe. The usual single split is replaced by stratified 5-fold cross-validation (CV), with paired McNemar and DeLong tests and 1,000 bootstrap resamples. Calibration is judged by expected calibration error (ECE), Brier, reliability diagrams, and temperature scaling. VGG16 wins mean accuracy (98.72 ± 0.42%); ResNet50V2 has the tightest area under the curve (AUC) (0.9986 ± 0.0006); the two are statistically tied. MobileNetV2 is the best-calibrated model out of the box (ECE = 0.0021) with only 2.59 M parameters, making it the most deployable. A four-level confidence-based risk score turns calibrated probabilities into triage tags. This study also flag a measured 3.23% format-duplicate leakage in the public dataset and presents the framework as a radiologist co-pilot, not a prognostic model.
Volume: 15
Issue: 4
Page: 3792-3804
Publish at: 2026-08-01

EEB7-UNet: a deep learning framework for automated segmentation of fractured C-spine vertebrae

10.11591/ijai.v15.i4.pp3770-3781
Abhishek Kumar Pandey , Pateel G. P. , Kedarnath Senapati
Accurate identification of vertebral fracture (VF) regions in computed tomography (CT) images is crucial for surgeons prior to treatment planning, but remains challenging due to irregular vertebral boundaries, low contrast, noise, and image unevenness. Recent advancements in deep learning have shown promising results compared to conventional manual diagnosis methods in detecting anomalies and segmenting regions of interest in medical imaging. In this study, a deep learning model, enhanced EfficientNetB7 U-Net (EEB7-UNet), is proposed to segment the fractured cervical vertebrae. It includes custom data augmentation to increase the data size and a hybrid learning rate scheduler strategy technique for faster convergence, which increases the generalizability and robustness of the model. The proposed model achieved an improved dice score index of 95.53% and Jaccard coefficient index of 93.85% on the test dataset. Furthermore, the EEB7-UNet has emerged as a moderate size with 98.6 MB. The approach yields superior performance in terms of dice score index and Jaccard coefficient index compared to the other state of the art convolutional neural network (CNN) used as an encoder in the U-Net. This research also compared the performance of the proposed model with three other studies in similar contexts, reported in the literature.
Volume: 15
Issue: 4
Page: 3770-3781
Publish at: 2026-08-01

Unsupervised voice activity detection based on the envelope's fractal dimension

10.11591/ijai.v15.i4.pp3805-3817
Nesrine Abajaddi , Youssef Elfahm , Laila Elmaazouzi , Ilham Mounir , Badia Mounir , Abdelmajid Farchi
Currently, voice activity detection (VAD) is utilized in many fields, including forensics, healthcare, and medicine, to detect vocal anomalies, as well as in telecommunications and mobile telephony. Due to its importance and the difficulty of distinguishing between speech and nonspeech segments, especially in noisy environments (low signal-to-noise ratio (SNR)), this area remains under continuous development. Most existing VAD algorithms require predefined thresholds or training data, which reduces their compatibility. This study proposes an unsupervised VAD system that utilizes the Katz algorithm to calculate the fractal dimension of envelopes obtained through a single frequency filtering (SFF) approach. This method allows for high temporal and frequency resolution. The proposed VAD algorithm does not require any training data and is suitable for various types of noise and SNRs. To evaluate the effectiveness of the proposed method, two different databases are used: the Texas Instruments Massachusetts Institute of Technology (TIMIT) database and the King Saud University (KSU) Arabic speech database. The experimental results reveal an average detection accuracy of 96.86%, demonstrating its considerable value in various applications.
Volume: 15
Issue: 4
Page: 3805-3817
Publish at: 2026-08-01

Strategic optimization of artificial intelligence digital learning in informatics engineering

10.11591/ijai.v15.i4.pp2999-3008
Aan Ansori , Birru Muqdamien , Ahmad Tabrani , Reza Syafrizal , Sutanto Sutanto , Eko Wahyu Wibowo , Syifa Amara Dhestiyani
This study examines the strategic optimization of artificial intelligence (AI)-based digital learning in the Department of Informatics Engineering by analyzing the interaction between internal and external factors that influence successful implementation. Employing a qualitative approach based on strengths, weaknesses, opportunities, and threats (SWOT) analysis, this research utilizes the internal factor analysis summary (IFAS) and external factor analysis summary (EFAS) to assess institutional readiness, constraints, and strategic opportunities for AI integration in higher education. The results indicate that external factors exert a stronger strategic influence than internal factors, as reflected by a higher EFAS score (1.50) compared to the IFAS score (1.25). Key external opportunities include the increasing demand for AI competencies, supportive national policies, and opportunities for industry collaboration, while the main internal limitations are limited faculty expertise in AI and insufficient AI-specific learning resources. Based on these findings, this study formulates a SWOT-based strategic framework that converts analytical outcomes into practical recommendations for curriculum development, faculty capacity building, and the adoption of adaptive AI learning technologies. This research contributes a context-specific and empirically grounded strategic model that advances AI integration beyond descriptive analysis, supporting more effective, personalized, and sustainable digital learning, particularly within Informatics Engineering programs in Indonesian Islamic higher education institutions.
Volume: 15
Issue: 4
Page: 2999-3008
Publish at: 2026-08-01

Leveraging artificial intelligence in education a comprehensive review of the Arab literature

10.11591/ijai.v15.i4.pp2985-2998
Mohamed Abdelraouf Elsayed , Taleb Saleh Alattas
This descriptive analytical study relied on the systematic literature review (SLR) addressing the role of artificial intelligence (AI) in improving education management and delivery, empowering teaching and teachers, learner learning, and learning assessment. The SLR sample was determined in 125 articles from Almandumah and Scopus databases. The analysis results revealed: 39.2% of articles were related to the Saudi Arabia context, 34.4% were published in 2024, and 68.8% were quantitative. The most prominent AI applications used were: ChatGPT, Alexa, Grammarly, Duolingo, Chatbot, and Coursera. The results also showed that the most areas of the educational process in which AI applications can be implemented were: empowering teaching and teachers (62.4%), and improving learner learning (48.8%). The SLR also came up with some practices that can be applied to enhance the AI use in improving the educational process. An opinion poll was applied on 37 academic experts to identify their agreement on the potential of implementing these developed practices in the areas of the educational process. The results showed that the experts' agreement on the potential of implementing these practices in the areas of the educational process was very high. The study recommended some useful mechanisms for implementing these practices.
Volume: 15
Issue: 4
Page: 2985-2998
Publish at: 2026-08-01

Intelligent land use and land cover classification using Sentinel-2 multispectral imagery

10.11591/ijeecs.v43.i2.pp586-594
Neha Vyas , Koushik Sundar , Narayan Vyas
Accurate land use and land cover (LULC) classification is essential for environmental monitoring, agricultural planning, and sustainable resource management. This study investigates the effectiveness of Sentinel-2 multispectral satellite imagery for LULC classification by comparing the performance of three supervised classification algorithms: maximum likelihood classifier (MLC), minimum distance classifier (MDC), and neural networks (NN). Before classification, Sentinel-2 imagery underwent comprehensive preprocessing, including atmospheric correction, radiometric calibration, and cloud masking using ERDAS software to improve image quality and classification reliability. The Villupuram district of Tamil Nadu, India, was selected as the study area due to its diverse land cover characteristics. Classification performance was evaluated using overall accuracy (OA), producer’s accuracy (PA), user’s accuracy (UA), and the Kappa coefficient. Experimental results demonstrate that the MLC achieved the highest OA of 94.81% with a Kappa coefficient of 0.9308, outperforming MDC (90.65%, 0.8753) and NN (84.38%, 0.7917). These findings confirm that supervised classification of Sentinel-2 multispectral imagery provides reliable and accurate LULC mapping, offering valuable geospatial information to support precision agriculture, environmental monitoring, land resource management, and sustainable regional planning.
Volume: 43
Issue: 2
Page: 586-594
Publish at: 2026-08-01

OCTModNet: a deep learning-based framework for optical coherence tomography image multi-class classification

10.11591/ijeecs.v43.i2.pp495-506
Saja Ataallah Muhammed , Abbas M. Ali , Dler Salih Hasan
Sight is one of the most significant senses in human beings. Losing sight can change a human’s life dramatically. Early diagnosis and detection of retinal diseases will prevent sight loss. Optical coherence tomography (OCT) imaging is helpful in ocular imaging with its high resolution and non invasive imaging for diagnosing retinal diseases. This study proposes OCTModNet, a novel multi-class hybrid classification model that is capable of classifying seven different retinal diseases using a combination of publicly available datasets, Kaggle OCT-C8, and OCTDL datasets. For effective extraction of the significant features, the collected data is resized and normalized. Augmentation techniques are used to improve the training and learning process. For model development, we applied transfer learning and fine-tuned several state-of-the-art deep learning models, ResNet50, InceptionV3, DenseNet121, VGG16, and EfficientNetV2S. Two models that performed best, VGG16 and EfficientNetV2S, were selected as the model backbones with integration of a squeeze and excitation block. The results showed that the OCTModNet achieved an outstanding performance with 99% test accuracy, 99% precision, 99% recall, 99% F1-score, and an area under the curve (AUC) as 99% across the unseen data. These results point out the robustness and reliability of the proposed model to classify OCT images and have the potential to enhance clinical decision-making and assist ophthalmologists in the early detection of diseases.
Volume: 43
Issue: 2
Page: 495-506
Publish at: 2026-08-01

An artificial neural network-based decision support model for early prediction of mathematics learning challenges using the CRISP DM framework

10.11591/ijeecs.v43.i2.pp618-627
Harry Dhika , Surajiyo Surajiyo , Lasia Agustina , Abdul Muchlis
Identifying mathematics learning difficulties remains a challenge for educators due to the subjectivity and inefficiency of conventional methods in capturing psychological factors. To address this limitation, this study proposes an artificial neural network (ANN)-based decision support model developed within the cross-industry standard process for data mining (CRISP-DM) framework. The model integrates 16 academic indicators (quizzes, exams, remedial frequency, online activity) and psychological factors (motivation, anxiety, self-confidence, interest) from 163 student records at SMA Muhammadiyah 16 Jakarta. Synthetic minority over-sampling technique (SMOTE) and focal loss are applied to handle class imbalance and improve reliability. The proposed model achieves 98% accuracy and a 0.97 F1-score in classifying students into three difficulty levels: Easy, moderate, and difficult. These findings demonstrate the model’s effectiveness in capturing complex relationships between cognitive and affective features. Unlike prior studies that rely solely on academic performance, this work contributes a robust, comprehensive data-driven framework that enhances multiclass classification for early educational intervention within the Indonesian context.
Volume: 43
Issue: 2
Page: 618-627
Publish at: 2026-08-01

Confidence-driven adaptive operating-point optimization for photovoltaic systems under partial shading conditions

10.11591/ijeecs.v43.i2.pp672-682
Sarah Kawther Sedjar , Mourad Benmessaoud
Partial shading conditions (PSC) generate highly nonlinear multi-peak photo voltaic (PV) characteristics, complicating reliable global maximum power point tracking (GMPP). Although numerous intelligent optimization techniques exist, most rely on extensive exploration mechanisms that limit their applicability in embedded real-time controllers. This paper introduces a confidence-driven adaptive maximum power point tracking (MPPT) framework in which the optimization search space is dynamically regulated according to the reliability of a predictive operating-region estimator. Unlike standard artificial neural network (ANN)-assisted MPPT strategies that offer only power prediction, the proposed approach exploits a confidence index to continuously contract or expand the exploration domain, minimizing search effort while preserving global tracking capability. The framework was developed using real measurements from the PV DAQ database and validated through a nonlinear two-diode thermal-electrical PV model incorporating irradiance mismatch and temperature-dependent effects. Static and dynamic PSC scenarios were investigated to evaluate convergence behavior and computational performance. Experimental results demonstrate that the proposed confidence-governed strategy achieves an average tracking efficiency of 90.89%, reduces convergence effort through adaptive search-space contraction, and matches real measurements with an R2 value of 0.8664, offering a low-complexity solution for real-time embedded PV energy management.
Volume: 43
Issue: 2
Page: 672-682
Publish at: 2026-08-01
1 2 3
...
2059 2060
Show 19 of 2060

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration