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

30,938 Article Results

A new hybrid model based on machine learning and fuzzy logic for QoS enhancing in IoT

10.11591/ijeecs.v41.i2.pp624-632
Oussama Lagnfdi , Marouane Myyara , Anouar Darif
The fast expansion of internet of things (IoT) devices presents a more complicated scenario for maintaining a stable quality of service (QoS), which would guarantee the network’s dependable operation. The emergence of increasingly complex applications that call for additional devices makes this even more crucial. Adaptive intelligence solutions that guarantee optimal network behavior are therefore required. This paper presents a hybrid optimized solution for a three-layer IoT network that models the application, network, and perception layers of an IoT network using machine learning and fuzzy logic (FL). This method guarantees optimal QoS prediction with improved network adaptability by using fuzzy membership parameters. When the number of devices increases from 100 to 1,500, FLGA maintains an average QoS of 95% to 87%, while FL maintains 84% and RANDOM maintains 79%. At the application level, genetic algorithm (GA) continues to outperform RANDOM by 15.57% and FL by 6.32%. The goal of this paper is to provide a solid network solution that could enhance the consistency of QoS performance in order to combat the increasingly complex scenario of an IoT network.
Volume: 41
Issue: 2
Page: 624-632
Publish at: 2026-02-01

Intelligent cybersecurity framework for real-time threat detection and data protection

10.11591/ijeecs.v41.i2.pp504-514
Gunti Viswanath , Kurapati Srinivasa Rao
Organizations operating across cloud, mobile, and enterprise environments are increasingly exposed to sophisticated cyberattacks that traditional rule-based security systems struggle to detect in real time. These legacy approaches lack adaptability, making it difficult to continuously monitor distributed networks, identify anomalies, and prevent zero-day threats before sensitive data is compromised. To address these challenges, this paper proposes an intelligent cybersecurity framework that integrates real-time network monitoring with AI/ML-based anomaly detection models. The framework utilizes structured preprocessing, feature engineering, and supervised learning on the UNSW-NB15 dataset (version 2015, Cyber Range Lab) to enhance detection accuracy and reduce response time. The experimental setup evaluates multiple ML classifiers using stratified train- test splitting and 5-fold cross-validation, ensuring robust performance validation. Experimental results show that the random forest (RF) model achieves 94.28% accuracy, a 2.93% false-positive rate, and an average detection time of 0.41 seconds, outperforming other baseline models. In addition to the detection layer, the framework incorporates mobile device management (MDM) controls and cloud-storage policy enforcement to strengthen organizational security posture. The main contributions of this work include: i) a unified AI/ML-driven anomaly detection model, ii) integration of MDM and cloud policy enforcement for end-to-end protection, and iii) improved empirical performance validated using a benchmark cybersecurity dataset. This combined architecture significantly enhances real-time threat identification and reduces alert latency, supporting a more security-aware and resilient enterprise environment.
Volume: 41
Issue: 2
Page: 504-514
Publish at: 2026-02-01

Control of multi-level NPC inverters in PV/grid systems using ADRC and MADRC

10.11591/ijeecs.v41.i2.pp456-469
Gherici Dinar , Ahmed Tahour
Grid-connected photovoltaic (PV) systems consist of solar panels that convert sunlight into electrical energy, interconnected directly with the utility grid. These systems comprise several key components: PV, multilevel, controllers, and grid interface equipment. In this context, fivelevel inverters are increasingly favoured over three-level inverters due to their ability to reduce total harmonic distortion (THD), improve efficiency, and ensure better power quality in grid-connected applications. This research presents a three-level enhanced control scheme aimed at optimizing the performance of a grid-connected photovoltaic system with a five-level inverter. A fractional-order proportional-integral (FOPI) controller is utilized for maximum power point tracking (MPPT) to ensure precise tracking under variable irradiance conditions. At the grid-interface stage, a modified active disturbance rejection controller (MADRC) is developed for grid-interface, featuring an inner loop for DC-link voltage regulation based on Lyapunov theory, leading to improved dynamic performance with lower THD of the grid current and enhanced efficiency. Simulation results highlight the effectiveness of the proposed system. Compared with the FOPI-ADRC, a three-level configuration (0.38% THD), the proposed FOPI-MADRC with a five-level inverter achieves superior performance, with only (0.22% THD). These results confirm the advantages of combining advanced control strategies with multilevel inverter technology in improving both power quality and system efficiency.
Volume: 41
Issue: 2
Page: 456-469
Publish at: 2026-02-01

Enhancing industrial cybersecurity via IoT device-trusted remote attestation framework with zero trust architecture in brewery operations

10.11591/ijeecs.v41.i2.pp720-730
Muhammad Salman , Alan Budiyanto
The rapid expansion of industrial internet of things (IIoT) adoption in Industry 4.0 has improved automation and real-time control yet simultaneously increased security risks in operational technology (OT) environments, where device integrity and system reliability are critical. Existing attestation approaches such as SAFEHIVE, SEDA, CRA, and ERASMUS provide scalable verification capabilities but still lack continuous hardware-rooted validation and adaptive access control required for real-time industrial systems. To address this gap, this study proposes a hybrid cybersecurity framework that integrates IoT device-trusted remote attestation (ID-TRA) based on trusted platform module (TPM) with zero trust architecture (ZTA) to ensure continuous device trustworthiness in brewery operations. The framework was implemented on an industrial testbed with programmable logic controllers (PLCs), edge devices, and industrial switches, and it was evaluated through measurements of attestation latency, false positive rate, communication overhead, and TPM resource utilization. Experimental results show that the framework achieves an average attestation latency of 250 ms, a false positive rate below 2%, and a communication overhead of only 1.1%, while TPM resource usage remains within acceptable bounds (62% CPU and 48 MB RAM). These outcomes demonstrate that the proposed solution can reliably detect unauthorized firmware modifications, prevent compromised devices from accessing critical network zones, and maintain compatibility with real-time control processes. Overall, the integration of ID-TRA and ZTA enhances device-level assurance and strengthens industrial cybersecurity resilience against firmware tampering, replay attacks, and unauthorized lateral movement.
Volume: 41
Issue: 2
Page: 720-730
Publish at: 2026-02-01

Stress, self-care, and wellbeing: a study on the experiences of educational leaders in NEMSU

10.11591/ijere.v15i1.36269
Nemesio G. Loayon , Erwin B. Berry , El Dixon G. Plazo
The wellbeing of educational leaders is a critical concern, particularly in resource-constrained higher education institutions. This study aimed to explore the experiences of educational leaders at North Eastern Mindanao State University (NEMSU) by identifying their primary sources of stress, examining the perceived impacts on wellbeing and job performance, and assessing their self-care practices, as well as determining the relationships among these variables. Using a descriptive correlational design, data were collected from 117 educational leaders through a validated survey instrument with a reliability coefficient of 0.828. Descriptive statistics and Spearman rank-order correlation were used to analyze relationships among stress sources, impacts, and self-care. Results showed that administrative tasks, role overload, and limited institutional resources are the main stressors, significantly affecting health, cognitive functioning, job performance, and family life. Self-care practices such as social support, spiritual routines, and work boundaries were moderately practiced but showed no significant relationship with stress reduction or its impacts. These findings emphasize the limits of individual coping strategies and the need for institutional support. The study suggests refining the job demands-resources (JD-R) model and conservation of resources (COR) theory to account for organizational and cultural factors in developing country contexts, with implications for leadership development, stress management, and institutional policy reforms.
Volume: 15
Issue: 1
Page: 408-423
Publish at: 2026-02-01

School stress among Malaysian secondary school students: prevalence and demographic correlates

10.11591/ijere.v15i1.35568
Tie-Seng Te , Hutkemri Zulnaidi , Norfaezah Md Khalid
Understanding school stress is essential, given the significant time students spend in school and its impact on well-being. Acknowledging its importance, this study aimed to assess the level of school stress among Malaysian secondary school students. The respondents in this study consisted of 485 Malaysian secondary school students, selected through multistage sampling techniques to ensure national representativeness. Data were collected using a school-related stress questionnaire adapted from the shortened version of the adolescent stress questionnaire (ASQ-S). Data were analyzed using descriptive statistics, Rasch Wright mapping, independent samples t-tests for gender differences, and one-way ANOVA for grade and regional comparisons. The results indicated that, overall, Malaysian secondary school students experience low levels of school stress. Descriptively, female students reported higher levels of school stress than their male counterparts. Among different forms, Form 4 students reported the highest level of school stress, followed by Form 1 students. Regionally, students from the central and southern zones exhibited the highest levels of school stress. This study contributes to a better understanding of school stress among Malaysian secondary school students, providing insights for developing strategies to reduce stress in schools.
Volume: 15
Issue: 1
Page: 102-111
Publish at: 2026-02-01

Understanding student errors in hydronym perception: a cognitive–pedagogical perspective

10.11591/ijere.v15i1.36570
Akmaral Dautkulova , Mereke Atabayeva , Madiyeva Gulmira , Maxot Rakhmetov
Despite the cultural and linguistic significance of hydronyms, little empirical research has examined how university students cognitively conceptualize them and why systematic misinterpretations arise. This study aims to identify and classify the dominant cognitive mechanisms underlying student errors in hydronym perception. A descriptive quantitative design was employed, analyzing written responses from 120 university students using a seven-category error typology grounded in frame semantics, prototype theory, and psycholinguistic models of proper-name processing, with high inter-rater reliability (κ=0.87). The results show that cognitive errors occur significantly more frequently than inattentive or interpretative errors, indicating that students’ difficulties stem primarily from incomplete activation of geographical, cultural, and historical conceptual frames rather than from surface-level inattention. Frequent reliance on phonological similarity further suggests that when conceptual knowledge is weakly integrated, learners’ default to form-based processing strategies instead of semantic interpretation. These findings indicate the need for pedagogical approaches that explicitly connect hydronyms to broader cultural and conceptual frameworks, supporting more cognitively grounded instruction in linguistics and onomastics.
Volume: 15
Issue: 1
Page: 696-704
Publish at: 2026-02-01

Validating a critical thinking scale: an essential skill for vocational education students in the era of artificial intelligence

10.11591/ijere.v15i1.33693
Eva María Olmedo-Moreno , José Javier Romero-Díaz de la Guardia , Jorge Expósito-López , Ramón Chacón-Cuberos , Noelia Parejo-Jiménez
Critical thinking is a fundamental competence in education and professional training, particularly in a technology-driven era increasingly shaped by generative artificial intelligence (GenAI). To address the need for contextually relevant assessment tools, this study aimed to evaluate the reliability and validity of the critical thinking disposition scale (CTDS) among vocational training (VT) students. A descriptive, exploratory, and cross-sectional design was applied using quantitative methods. Data was collected from 879 students enrolled in VT programs. Exploratory and confirmatory factor analyses were conducted to examine the scale’s factorial structure, reliability, and construct validity. Results supported an 11-item structure comprising two dimensions—executive function and reflective thinking—with satisfactory psychometric properties (KMO=0.817; CFI=0.883; TLI=0.851; RMSEA=0.0672; SRMR=0.0428). The findings confirm that the CTDS is a reliable and valid instrument for assessing critical thinking dispositions in vocational education. This tool provides educators and researchers with a robust means to evaluate critical thinking, a key competence for learning, personal development, and professional preparedness in the age of GenAI. This is the first validation of the CTDS with VT students in Spain.
Volume: 15
Issue: 1
Page: 303-314
Publish at: 2026-02-01

Development of a learning management model for science teachers

10.11591/ijere.v15i1.33745
Saksri Suebsing , Channarong Wisetsat
The goal of this study was to create a learning management model for primary science teachers that considers the factors affecting the learning management strategies of science instructors in the Province of Roi Et. The sample consisted of 300 basic science teachers in the Province of Roi Et and was created via a straightforward random selection of 20 schools. Among the study tools are science learning management manuals, assessments, and surveys. The following statistics were used in the data analysis: mean and standard deviation. The results showed that the science learning management model consists of five steps: i) involvement, ii) survey, iii) justification, iv) detailed description, and v) evaluation. Therefore, it is clear that the elements of learning management and innovation creation are significant factors in this context. The inspiration of having a public mind has a direct impact on the learning management strategies of primary science instructors at the 0.01 level, with an influence value of 0.58.
Volume: 15
Issue: 1
Page: 195-204
Publish at: 2026-02-01

Blockchain integration into the university education process: a systematic review of application, justification, and impact

10.11591/ijere.v15i1.36591
Irma Aybar-Bellido , Maritza Arones , José Antonio Arévalo-Tuesta , Willy Adauto-Medina , Hernán Ochoa-Carbajal
As universities seek to integrate disruptive technologies to optimize their academic and administrative processes, a gap persists in the adoption of Blockchain as a strategic tool. Many higher education institutions still lack its implementation, limiting its potential to improve traceability, transparency, security, and the automation of different processes. Given this scenario, this study aims to identify the areas of application, the justifications for its use, and the impact of Blockchain when integrated into the university educational process. To this end, a systematic literature review was conducted with a mixed approach and an exploratory-descriptive scope, following the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guideline, in the Scopus, ERIC, and SAGE databases. Of 3,469 manuscripts identified, 42 met the inclusion and exclusion criteria. The results show a predominance of applications focused on the validation and monitoring of academic achievements, with limited integration into pedagogical approaches such as active methodologies, adaptive learning, or competency-based learning. Based on a comparative analysis of trends, application areas, and thematic gaps, it is concluded that the expansion of Blockchain into educational models requires progressive implementation strategies, curricular integration, and teacher training, thereby generating more personalized, efficient, and transparent learning environments.
Volume: 15
Issue: 1
Page: 360-375
Publish at: 2026-02-01

Classroom learning environment as a determinant of psychological well-being in adolescents

10.11591/ijere.v15i1.35642
Yicen Meng , Nik Rosila Nik Yaacob , Yasmin Othman Mydin
Psychological well-being (PWB) is critical in adolescent development as well as academic success. In terms of self-determination theory, satisfaction of autonomy, competence, and relatedness psychological needs leads to PWB. In schooling contexts, these psychological needs can be satisfied in a supportive classroom learning environment (CLE), which in turn will promote adolescents’ overall well-being. The study examines the predictive impact of CLE on Chinese adolescent PWB as well as which specific dimensions of CLE are effective predictors to PWB. A sample consisting of 918 14- to 19-year-old adolescents participated. What Is Happening In this Class (WIHIC) questionnaire and the Brief Chinese Version of the psychological well-being scale (PWBS) were applied for data collection. Pearson correlation showed a positive correlation between CLE’s seven dimensions and PWB. Meanwhile, structural equation modelling (SEM) analysis identified student cohesiveness, teacher support, involvement, and task orientation, dimensions of CLE, significantly predict PWB. The result indicated CLE plays a crucial role in predicting PWB. The findings inform recommendations for improving adolescents’ PWB and discusses further implications.
Volume: 15
Issue: 1
Page: 53-65
Publish at: 2026-02-01

Teacher preparedness for competency-based curriculum in Kenyan schools: training and perceptions prior to implementation

10.11591/ijere.v15i1.35742
Nathan Maina Mwangi , Simon Karuku , Elizabeth Obura
Pre-implementation training and teachers’ perceptions are critical factors influencing successful implementation of a new curriculum. This paper reports on some of the findings from a study that assessed the preparedness of primary school educators to implement the newly introduced competency-based curriculum (CBC) in Kenya. The study participants were primary school teachers and school heads drawn from 37 public elementary schools in Embu, Kenya. Using a mixed-methods approach, both qualitative and quantitative data were gathered through surveys, interviews, and observations. Qualitative data were analyzed using thematic analysis, whereas quantitative data were examined using descriptive and inferential statistics. The study revealed that 95% of teachers had been trained to implement CBC, and 65% of the teachers held negative perceptions toward the new curriculum. The study also established a weak but significant correlation (Spearman’s rho=0.268, p<0.05) between pre-implementation training and teachers’ perceptions on the CBC implementation. The findings suggest that continuous, structured in-service training is critical for CBC success, particularly in building competencies and improving teachers’ perceptions.
Volume: 15
Issue: 1
Page: 714-724
Publish at: 2026-02-01

Neuroeducation and teaching perception: a systematic review from the qualitative approach

10.11591/ijere.v15i1.29451
Belén Valdés-Villalobos , Mariana Lazzaro-Salazar
Neuroeducation is a discipline that considers aspects contributed by the natural sciences, including cognitive abilities, brain functioning, and the emotional system, among others, as topics derived from knowledge in fields such as neuroscience, cognitive science and psychology, and which are articulated in the social domain. The aim of the present review was to learn about teachers’ perceptions of neuroeducation and to determine which qualitative methods are most commonly used in this field of research. The review followed the guidelines of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) method, searching the databases with the highest impact between 2015-2023. The selection process yielded nine eligible studies and they were analyzed in terms of the type of knowledge studied and the methods used in neuroeducational research. The results discuss the most frequently developed qualitative methodologies in the neuroeducational discipline, offering recommendations from the methodological cluster to strengthen future research in the discipline. Therefore, this study promotes neuroeducational research from a qualitative approach will improve the resonance between neuroeducation experts and teachers. Furthermore, it highlights the importance of proposing situated research, using descriptive methodologies in the field, which communicate in a language appropriate to educators and their context.
Volume: 15
Issue: 1
Page: 28-39
Publish at: 2026-02-01

Interpretable artificial intelligence system for personalized cognitive stimulation

10.11591/ijai.v15.i1.pp164-176
Rubén Baena-Navarro , Yulieth Carriazo-Regino , Mario Macea-Anaya
The growing need to preserve cognitive health in aging populations has intensified interest in adaptive digital interventions that provide personalized and interpretable support. This study presents a web-based cognitive stimulation system for older adults integrating a multilayer perceptron (MLP) classifier, expert-derived symbolic rules, and explainable artificial intelligence (XAI) techniques, including Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME). The platform was evaluated through a 24-week intervention involving 150 participants aged 65 years and older, combining baseline cognitive profiling, rule-guided recommendation logic, and neural prediction to support individualized task allocation. Compared with a control group, participants in the intervention arm showed statistically significant improvements in cognitive outcomes (p <0.05), with measurable gains in memory- and attention-related tasks. The explainability component enabled examination of model behavior at the level of individual features through feature attribution analysis and symbolic consistency checks, supporting interpretation beyond aggregate performance metrics. Unlike approaches dependent on high-end extended reality (XR) infrastructures or game centered interaction, the system was implemented to operate under low connectivity conditions and was tested with participants from diverse educational backgrounds. This hybrid configuration provides an interpretable basis for cognitive support initiatives adaptable to community settings contexts.
Volume: 15
Issue: 1
Page: 164-176
Publish at: 2026-02-01

Deep feature-based multi-class Alzheimer’s disease classification with statistical performance evaluation

10.11591/ijai.v15.i1.pp695-706
Maysaloon Abed Qasim , Marwa Mawfaq Mohamedsheet Al-Hatab , Lubab H. Albak
This study evaluated the performance of multiple machine learning classifiers for the classification of Alzheimer’s disease (AD) stages using deep features extracted from a pre-trained SqueezeNet model. Magnetic resonance imaging (MRI) scans were processed through SqueezeNet to generate high-dimensional feature vectors, which were then used as achieved an accuracy of 94.78% input to six classifiers: k-nearest neighbors (KNN), decision tree (DT), support vector machine (SVM), neural network (NN), naive Bayes (NB), and logistic regression (LR). Models were assessed using a 70/30% training-testing split and 5-, 10-, and 20-fold stratified cross validation. Principal component analysis (PCA) was applied to retain 99% of variance. On the original dataset consisting of 6,400 images, KNN has achieved 97.48% accuracy and 0.998 area under the curve (AUC), and when a larger dataset of 44,000 images was used it achieved an accuracy and of 94.78% and an AUC of 0.987, demonstrating the system’s robustness across scales. Statistical tests, including paired t-tests and Wilcoxon signed-rank tests, confirmed that KNN has significantly leveraged from PCA. These outcomes demonstrate that combining deep feature extraction with PCA improved the reliability and efficiency of the classifier for AD stage prediction.
Volume: 15
Issue: 1
Page: 695-706
Publish at: 2026-02-01
1 2 3
...
2062 2063
Show 113 of 2063

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