Articles

Smart water distribution for smart cities based on Internet of Things

10.11591/ijece.v16i3.pp1655-1668

Amal Douli , Khelifa Benahmed , Belkacem Draoui

Against an unprecedented water crisis in our country, balancing water supply and demand is necessary for a secure and sustainable water supply. This challenge requires systems capable of delivering the necessary quantities while conserving resources. Numerous research initiatives focus on addressing water distribution challenges with the help of smart water systems to optimize network operations and minimize water demand. Based on these advancements, this paper proposes a new smart water distribution system for southwest of Algeria. The system integrates the Internet of Things (IoT), information and communication technologies, and smart technologies to address critical attributes for enhancing efficiency. To achieve the efficient management of two-way flows (both water and data) based on water demand and its availability, two innovative architectures have been proposed, using various measurements of water quantity and quality parameters. Algorithms to automate and optimize water distribution are also proposed. According to obtained results, performance has improved, with an accuracy rate of over 98%. These results establish the suggested system as a strong option for intelligent and sustainable water resource management by demonstrating its efficacy and durability.

Volume: 16

Issue: 3

Page: 1655-1668

Publish at: 2026-06-01

Tuning feature selection to enhance machine learning predictions of bandgap and efficiency in chalcogenide perovskites

10.11591/ijece.v16i3.pp1508-1517

Osphanie Mentari Primadianti , Ryan Nur Iman , Muhammad Zimamul Adli , Agung Muhamad Toha , Agung Surya Wibowo

Solar cell technology has advanced rapidly in efficiency and material innovation. As a renewable energy source, solar cells help mitigate the global energy crisis. Perovskite-based solar cells have recently achieved efficiencies above 25%, surpassing conventional silicon cells. Among emerging materials, chalcogenide perovskites show great promise due to their superior stability compared to halide perovskites. However, they remain in the exploration stage, making accurate predictions of their electrical properties, especially bandgap, essential for assessing potential in solar cell applications. This study predicts bandgap values using computational methods, emphasizing efficiency and cost reduction compared to experimental approaches. Key features derived from collected data include oxidation state, electronegativity, coordination number, ionic radius, and density. Several machine learning (ML) algorithms: AdaBoost Regressor, gradient boosting regressor, support vector regressor, CatBoost Regressor, and k-neighbor regressor, were implemented using Python. The research process involved data collection, preprocessing (feature scaling, fusion, reduction, and selection), model training and testing with 5-fold cross-validation, and hyperparameter optimization to achieve optimal results. Among the tested models, CatBoost Regressor yielded the best performance, achieving a coefficient of determination (R2) of 69.34%, a mean absolute error (MAE) of 23.1%, and root-mean-square error (RMSE) of 29.49%, demonstrating its effectiveness in predicting chalcogenide perovskite bandgaps.

Volume: 16

Issue: 3

Page: 1508-1517

Publish at: 2026-06-01

Analyzing learners' perceptions of engagement and learning interaction in gamified massive open online courses for TVET using SEM-PLS

10.11591/ijece.v16i3.pp1319-1328

Azizul Mohd Yusoff , Sazilah Salam , Siti Nurul Mahfuzah Mohamad , Rujianto Eko Saputro

The introduction of gamified massive open online courses (G-MOOCs) represents a novel advancement in technical and vocational education and training (TVET). The use of gamification in education has been shown to increase engagement and motivation, which are crucial for effective learning. However, there is limited research on the specific impacts of G-MOOCs on learner outcomes in TVET. A key feature of G-MOOCs is the integration of gamification elements to enhance learner engagement and interest. This research employs structural equation modelling with partial least squares (SEM-PLS) to examine learners' perceptions of their participation and learning experiences in G-MOOCs for TVET. Specifically, the study aims to identify how gamification approaches such as fun, engagement, and learner interaction influence knowledge acquisition, skills development, satisfaction, and overall learning outcomes. The analysis reveals that G-MOOCs have a strong positive correlation (0.505) with learning engagement. Additionally, learning engagement significantly moderates learning outcomes (p=0.002). Interaction also has a significant impact (p=0.381) on learning outcomes. Overall, the findings indicate a significant positive relationship between learners' activities and their performance in G-MOOCs.

Volume: 16

Issue: 3

Page: 1319-1328

Publish at: 2026-06-01

Using the technology theory to adoption virtual reality among university students

10.11591/ijece.v16i3.pp1485-1492

Ghaliya AlFarsi , Raghad M. Tawafak , Roy Mathew , Sohail Iqbal Malik , Abir AlSideiri

Virtual reality is a technology field that has become an integral part in most areas of life. Before the 20th century, virtual reality consisted primarily of artificial illusions. Students encounter early obstacles in learning and the current virtual reality (VR) learning mechanism. The research is based on previous studies by filling in the blank by observing the problems that students were facing. The second main point of this research was unified theory using model of technology acceptance and use. This paper focuses on the adoption of a virtual reality learning model in order to improve student academic performance. The results of this paper prove that hypotheses have a positive impact on the factors to use the proposed model.

Volume: 16

Issue: 3

Page: 1485-1492

Publish at: 2026-06-01

AI-enabled energy-aware routing approach for future-wireless sensor networks

10.11591/ijece.v16i3.pp1543-1561

Shamsher Singh , Mandeep Kumar

Next-generation wireless sensor networks (WSNs) demand intelligent, energy-aware communication mechanisms capable of sustaining long-term operation in environments with varying conditions and strict resource limitations. Traditional routing protocols often fail to optimize energy consumption under varying network densities, heterogeneous traffic patterns, and environmental uncertainties. This research proposes an AI-enabled energy-efficient routing protocol (AI-EERP) designed to enhance network lifetime, stability, and data delivery performance in next-generation WSNs. The protocol integrates machine learning–based node selection, adaptive clustering, and predictive residual-energy estimation to make optimized routing decisions in real time. Using AI-driven models, AI-EERP dynamically adjusts routing paths based on energy patterns, link quality, and network topology changes. The simulation outcomes clearly indicate that the proposed approach achieves notable gains in energy efficiency, packet delivery reliability, and network lifetime when compared with traditional routing protocols, including LEACH, PEGASIS, and HEED. The proposed approach establishes a robust and scalable framework for future intelligent WSN deployments across applications including smart cities, precision agriculture, environment-focused applications and automated industrial operations.

Volume: 16

Issue: 3

Page: 1543-1561

Publish at: 2026-06-01

Instructional and learning needs in evolution education: a multi-stakeholder needs assessment

10.11591/ijere.v15i3.38972

Vanjoreeh A. Madale , Monera A. Salic-Hairulla

Evolution is a foundational concept in biology, yet it remains challenging for learners and teachers alike. This study conducted a descriptive convergent mixed-methods needs assessment to examine instructional and learning needs in evolution education from the perspectives of students, teachers, and administrators. A total of 50 participants (35 students, 10 teachers, and 5 administrators) from a public senior high school completed researcher-developed questionnaires containing Likert-scale and open-ended items. Quantitative data were analyzed using descriptive statistics, while qualitative responses underwent thematic analysis, with integration occurring at interpretation. Findings indicate that students possess moderate conceptual understanding but struggle with abstract processes such as natural selection, long-term species change, and evidence interpretation, alongside gaps in science process skills. Teachers report difficulties addressing misconceptions, integrating inquiry-based practices, and accessing contextualized instructional materials. Administrators emphasize the need for laboratory resources, curriculum-aligned materials, and sustained professional development. Overall, results highlight that strengthening evolution education requires coordinated strategies that integrate structured science process skills, targeted teacher training, and institutional support systems. The study provides a multi-stakeholder framework to guide evidence-based instructional and systemic improvement in evolution education.

Volume: 15

Issue: 3

Page: 2062-2072

Publish at: 2026-06-01

Beyond diagnosis: using PNImodified and composite priority indices to orchestrate meta-skills-driven academic management innovation

10.11591/ijere.v15i3.38478

Chi Che , Sukanya Chaemchoy , Pruet Siribanpitak

This study translates an academic management–meta-skills integration framework into a data-driven innovation roadmap for private higher education institutions (HEIs) in Sichuan, China. Using an explanatory sequential mixed-methods design, Phase 1 surveyed 400 undergraduates who provided dual ratings of current performance (degree of success, D) and desired priority (importance, I), enabling computation of the modified priority needs index (PNImodified=(I−D)/D) across meta-skills domains and academic management subcomponents. In Phase 2, institutional leaders and senior academics rated the feasibility and impact of aligned innovations; these ratings were integrated with PNImodified to calculate a composite priority index (CPI) and propose phased implementation sequencing. Results indicated the largest perceived meta-skills development needs in adaptive expertise (PNImodified=0.48) and relational dynamics (0.34). At the academic management level, curriculum development (0.56) and evaluation and assessment (0.45) emerged as the most critical domains. Curriculum structuring (0.65), instructional design (0.53), meta-skills evaluation modules (0.59), and learning engagement (0.59) consistently ranked as top subcomponent priorities and were positioned as Phase 1 actions in CPI-based sequencing. Experts rated the overall innovation as highly suitable (M=4.55) and feasible (M=4.53). The combined indices provide a practical decision tool for sequencing meta-skills-oriented academic management innovations in Sichuan private HEIs.

Volume: 15

Issue: 3

Page: 1862-1875

Publish at: 2026-06-01

Optimization of transfer learning for facial emotion classification on the FER-2013 dataset

10.11591/ijece.v16i3.pp1213-1226

Nida Muhliya Barkah , Shofwatul ‘Uyun

Facial expressions play a key role in non-verbal communication by naturally reflecting human emotions. Facial emotion recognition (FER) using computer vision has gained attention with advances in deep learning. However, deep learning models require large datasets to perform well, posing a challenge for FER tasks with limited data. Transfer learning is a promising approach to address this issue, but a standardized method for FER is yet to be established. This study optimizes three transfer learning models ResNet-50, Inception V3, and Xception on the FER-2013 dataset. Experiments include testing input image sizes, hyperparameter tuning, data augmentation, layer addition, and training methods. Results show each model requires different input sizes for best accuracy. Hyperparameter tuning improves accuracy by 6.35%, 4.69%, and 1.04% for ResNet-50, Inception V3, and Xception, respectively. Augmenting only the disgust class yields better accuracy than augmenting all classes. The freeze fine-tuning method is less effective than fine-tuning alone on datasets with thousands of samples but outperforms the freeze layer method. The best accuracies achieved are 64.89% (ResNet-50), 65.83% (Xception), and 66.40% (Inception V3). These findings provide insights into freeze fine-tuning limitations and guidance for optimizing transfer learning in FER with limited data.

Volume: 16

Issue: 3

Page: 1213-1226

Publish at: 2026-06-01

Prostate magnetic resonance imaging/transrectal ultrasound registration using vision transformer and convolutional neural network

10.11591/ijece.v16i3.pp1188-1198

Hanae Mahmoudi , Hiba Ramadan , Jamal Riffi , Hamid Tairi

Multimodal registration of 3D medical images (3D-MReg) plays a key role in several medical applications and remains a very challenging task as it deals with multimodal images and volumetric objects at the same time. Recently, convolutional neural networks (CNNs) based approaches have been proposed to solve 3D-MReg. However, these techniques cannot preserve the global spatial context required for accurate affine registration since they rely on convolution and regional clustering operations. To solve these problems, we propose a supervised approach that combines both CNN and the vision transformer (ViT) to predict a dense displacement field (DDF). In a first step, our method investigates the power of ViT to capture global voxels dependencies for initial rigid alignment. Then we exploit the force of CNNs to focus on local details within pre-aligned concatenated input 3D moving and fixed images and estimate DDF, which is then applied to the moving labels. Our method has been validated in a prostate magnetic resonance imaging/transrectal ultrasound (MRI/TRUS) dataset and achieved promising results compared to previous work based on only CNNs.

Volume: 16

Issue: 3

Page: 1188-1198

Publish at: 2026-06-01

Exploring the relationship of learning engagement, learning interaction, and learning outcomes in gamified massive open online courses

10.11591/ijece.v16i3.pp1329-1338

Azizul Mohd Yusoff , Sazilah Salam , Siti Nurul Mahfuzah Mohamad , Bambang Pudjoatmodjo

This study investigates the interplay between learning engagement, interaction, and outcomes within the context of gamified massive open online courses (G-MOOCs). By synthesizing literature on MOOCs, gamification, and user engagement, the research identifies significant correlations among these variables. Utilizing a structural equation model partial least squares (SEM-PLS) approach, the study analyzes data from a survey of Bachelor of Computer Science students at a technical and vocational education and training (TVET) public university. Results indicate that both learning engagement and interaction significantly influence learning outcomes, with optimal results achieved when both factors are high. These findings highlight the potential of gamification to enhance educational experiences and suggest directions for future research in gamified learning environments.

Volume: 16

Issue: 3

Page: 1329-1338

Publish at: 2026-06-01

Expert validation of a causal model of 21st-century academic leadership in northeastern Thailand

10.11591/ijere.v15i3.39095

Dusadee Butburee , Nawee Udorn , Paitoon Puangyod

This study aimed to validate and refine a causal model of factors influencing 21st-century academic leadership among secondary school administrators in northeastern Thailand. The study addresses the growing need for an integrative leadership framework that reflects digital transformation, instructional demands, and contextual constraints in contemporary educational reform. A qualitative expert validation design was employed. A total 10 experts in educational leadership, curriculum administration, and organizational development were purposively selected to participate in semi-structured in-depth interviews. Data were analyzed using thematic content analysis and cross-expert validation to ensure conceptual clarity and contextual relevance. The findings confirmed four interrelated causal domains: leadership personality and identity; contextual and organizational support systems; proactive instructional and curriculum leadership; and innovation-oriented professional learning culture. Instructional leadership emerged as a central mediating mechanism linking internal leadership capacity and external organizational conditions to academic leadership outcomes. A refined causal model with validated indicators was synthesized, providing a theoretically grounded foundation for future instrument development and structural equation modeling (SEM). The findings offer practical and policy implications for leadership development and sustainable school improvement in rapidly changing educational environments.

Volume: 15

Issue: 3

Page: 2292-2304

Publish at: 2026-06-01

Hybrid convolutional neural network–transformer models for liver tumor segmentation: a comprehensive review

10.11591/ijece.v16i3.pp1382-1398

Ibrahim Mohamed Attiya , Mostafa Thabet , Mostafa R. Kaseb

Liver cancer is a major cause of cancer deaths worldwide, and early and accurate segmentation of liver tumors is a critical step in cancer diagnosis and treatment. However, existing image segmentation techniques have difficulty handling the variability of liver tumors on different image modalities. The emergence of deep learning (DL) and the development of convolutional neural networks (CNNs) have revolutionized image segmentation techniques. However, CNNs have limitations in handling long-range dependencies, which is a critical requirement for tumor segmentation. To overcome these limitations, researchers have proposed hybrid deep learning architectures, which combine CNNs and attention mechanisms or transformers, to integrate local and global information for image segmentation. In this paper, we provide a comprehensive and analytical review of over 50 state-of-the-art deep learning architectures for liver and tumor segmentation. In addition, we provide an extensive evaluation of 38 hybrid and advanced architectures for liver tumor segmentation and a comprehensive discussion of hybrid CNN-transformer architectures. We propose a novel multi-dimensional taxonomy and evaluate the state-of-the-art architectures on various dimensions, including architectural innovation, segmentation accuracy, computational efficiency, and clinical applicability using benchmark datasets such as LiTS and 3DIRCADb. In our critical evaluation of the state-of-the-art architectures, we identify some of the limitations and challenges of existing research and propose a unified evaluation framework and future research directions on self-supervised learning, explainable artificial intelligence (XAI), federated learning, and lightweight architectures.

Volume: 16

Issue: 3

Page: 1382-1398

Publish at: 2026-06-01

Critical literacy and curriculum reform in the digital age: a pedagogical framework for artificial intelligence-integrated education

10.11591/ijere.v15i3.36952

Pablo Agustin Artero Abellan , Maria Abellan

Artificial intelligence (AI) is reshaping education and challenging traditional curriculum models, prompting new attention to critical literacy. However, most current approaches emphasize technical proficiency while neglecting ethical, epistemological, and civic dimensions. This article addresses this gap by proposing a conceptual framework for integrating AI into curriculum design through critical pedagogy and advanced learning theory. Using a theory-driven literature review, the study synthesizes global policy frameworks and educational innovations to develop a four-part model for AI-informed critical literacy. The guiding principles include: i) interrogation of AI outputs and logics; ii) development of multiliteracies and digital semiotics; iii) promotion of democratic dialogue and participatory ethics; and iv) design of adaptive, inquiry-based learning environments. Grounded in constructivist, connectivist, and Freirean theories, the framework positions AI as a context for critical inquiry and educational transformation. The article concludes with strategies for educators and policymakers to foster equity, agency, and ethical reflection in AI-integrated learning environments, and proposes directions for future empirical research.

Volume: 15

Issue: 3

Page: 2597-2607

Publish at: 2026-06-01

Pakistan English language policy alignment with IDLE-informed policy model

10.11591/ijere.v15i3.38739

Waqas Ahmad , Muhammad Taufiq Al Makmun

English controls academic and professional access in Pakistan, yet the National Education Policy Development Framework (NEPDF) 2024 completely ignores informal digital learning of English (IDLE), which refers to self-directed learning through digital tools: WhatsApp, YouTube, and chatbots. No prior study has examined this policy-practice gap within Pakistan’s post-2024 framework, particularly across urban and rural communities in Khyber Pakhtunkhwa, Punjab, and Sindh. This qualitative case study gathered perspectives from 20 undergraduate students, 10 teachers, and 5 policymakers through semi-structured interviews, focus groups, and content analysis of NEPDF 2024 and provincial policy texts, analyzed using NVivo-facilitated STAP thematic analysis. Findings show that students and teachers actively use IDLE tools while policymakers remain largely unaware. The IDLE-informed policy model (IIPM), grounded in connectivism, sociocultural theory (SCT), and learner autonomy, is proposed as a practical policy framework that incorporates low-bandwidth tools like WhatsApp to expand access for under-resourced learners. This study contributes to educational evaluation by assessing the alignment between Pakistan’s national language policy and grassroots IDLE practices, producing a transferable policy evaluation model for global south English as a foreign language (EFL) context.

Volume: 15

Issue: 3

Page: 2194-2204

Publish at: 2026-06-01

AI-assisted speaking instruction and English as a foreign language learners’ oral accuracy and fluency: a quasi-experimental study

10.11591/ijere.v15i3.38907

Xiaolin Wang , Malini Ganapathy

This study investigated the effects of artificial intelligence (AI)-assisted speaking instruction using a generative AI (GenAI) chatbot on Chinese vocational college English as a foreign language (EFL) learners’ oral accuracy and fluency. A quasi-experimental pretest-posttest control group design was adopted with 80 students. The experimental group engaged in AI-mediated speaking activities, while the control group received conventional instruction. Oral performance was assessed using analytic rubrics adapted from international English language testing system (IELTS) criteria. Results showed significant improvement in oral fluency for the experimental group, while gains in accuracy were not statistically significant. These findings suggest that GenAI chatbots provide interaction-rich environments that enhance fluency development but require complementary form-focused support to improve accuracy. Implications are discussed for integrating AI tools into vocational EFL speaking instruction.

Volume: 15

Issue: 3

Page: 2700-2707

Publish at: 2026-06-01

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30,938 Article Results

Smart water distribution for smart cities based on Internet of Things

Tuning feature selection to enhance machine learning predictions of bandgap and efficiency in chalcogenide perovskites

Analyzing learners' perceptions of engagement and learning interaction in gamified massive open online courses for TVET using SEM-PLS

Using the technology theory to adoption virtual reality among university students

AI-enabled energy-aware routing approach for future-wireless sensor networks

Instructional and learning needs in evolution education: a multi-stakeholder needs assessment

Beyond diagnosis: using PNImodified and composite priority indices to orchestrate meta-skills-driven academic management innovation

Optimization of transfer learning for facial emotion classification on the FER-2013 dataset

Prostate magnetic resonance imaging/transrectal ultrasound registration using vision transformer and convolutional neural network

Exploring the relationship of learning engagement, learning interaction, and learning outcomes in gamified massive open online courses

Expert validation of a causal model of 21st-century academic leadership in northeastern Thailand

Hybrid convolutional neural network–transformer models for liver tumor segmentation: a comprehensive review

Critical literacy and curriculum reform in the digital age: a pedagogical framework for artificial intelligence-integrated education

Pakistan English language policy alignment with IDLE-informed policy model

AI-assisted speaking instruction and English as a foreign language learners’ oral accuracy and fluency: a quasi-experimental study

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