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Exposure to nature through an urban natural monument

10.11591/ijere.v15i2.33911
Isabel María Muñoz-García , Jorge Alcántara-Manzanares , Jerónimo Torres-Porras
Society is experiencing a decrease in opportunities to connect with nature, a problem that is particularly acute during childhood. Numerous studies indicate that increasing the frequency of participation in nature-related activities in urban environments, through elements such as interpretive trails and sensory trails, improves important variables such as connection with nature (NC) and biodiversity awareness. Therefore, the objective of this study is to determine whether it is possible to foster NC and improve biodiversity awareness in children through a sensory trail in a natural urban environment. This study is part of a project carried out by an educational association that operates in three schools in the city in collaboration with the University of Córdoba, Spain. Therefore, the student population was determined by the association itself, with a total of 111 students aged 10 to 12 (48% female, 52% male). The study consisted of pre-post analyses, and the instruments used were the Cheng and Monroe NC scale and questions to determine children’s knowledge of environmental biodiversity. Data analysis included descriptive statistics to determine correctly identified biodiversity, correlation analysis between variables, and nonparametric tests to determine significant differences. The results reveal a relationship, before completing the route, between NC and nature awareness, and that the intervention had a positive impact on all variables. It is concluded that sensory routes in urban green spaces are an excellent educational resource for fostering NC in children, and their knowledge about biodiversity.
Volume: 15
Issue: 2
Page: 1227-1236
Publish at: 2026-04-23

Physiotherapy education and game-based learning: developing the SPINE framework

10.11591/ijere.v15i2.37549
Diana Filipa Salvador Bernardo , Manuel Joaquim da Silva P. G. Paquete , Marlene Cristina Neves Rosa
Innovative pedagogical approaches are increasingly essential in physiotherapy education to foster engagement and competency development. This multicenter cross-sectional study explored physiotherapy students’ perceptions of game-based learning (GBL) across six Portuguese higher education institutions. A total of 208 students completed a structured questionnaire assessing attitudes toward GBL in teaching–learning contexts and competency development. Results indicated generally positive perceptions of GBL, emphasizing its value for motivation, engagement, and integration of theoretical and practical learning. Transversal competencies—such as teamwork, communication, and empathy—were consistently recognized across academic years, while perceived benefits for technical skills increased with clinical exposure. Female students and those with prior health-related experience reported more favorable attitudes. However, students also highlighted limited curricular implementation and the need for clearer alignment between game activities and learning objectives, suggesting barriers at the institutional and pedagogical levels. These insights point to a gap between students’ enthusiasm and current educational practices, underlining the importance of structured guidance for integrating GBL effectively. Building on these findings, the student-perceived integration for novel education (SPINE) framework is proposed as a decision-making model to guide the pedagogically grounded GBL in physiotherapy curricula, emphasizing evidence-informed, context-sensitive, and sustainable implementation.
Volume: 15
Issue: 2
Page: 1388-1397
Publish at: 2026-04-23

Justification for a self-regulated learning training program for higher education students in massive open online courses

10.11591/ijere.v15i2.30648
Cao-Tuong Dinh , Hoang-Yen Phuong
Self-regulated learning (SRL) has been well documented in the literature for its benefits for students’ learning success. However, there is still a dearth of imperial intervention that helps promote students’ self-regulation, and a theoretical justification for such a program is essential. To date, literature has shown three prominent theories: social cognitive, social-cultural, and cognitive constructivist. The goal is to explore the conceptualization of SRL, which, despite it is long history, lacks a universally accepted definition. We analyze these theories and their models to determine which best supports the design of a SRL strategy intervention for university students in massive open online course (MOOC) environments. Based on this analysis, we propose a working definition of SRL that fits the unique demands of MOOCs. The results suggest that the social-cognitive theory offers the most suitable framework, as it integrates cognitive, metacognitive, motivational, and social aspects of learning. Additionally, it provides a practical model of strategies that can be implemented in MOOC-based learning environments.
Volume: 15
Issue: 2
Page: 1607-1617
Publish at: 2026-04-23

Power-aware design-for-test: a survey of DFT techniques and scan chain reordering approaches

10.11591/ijeecs.v42.i1.pp30-39
V. Rajitha Rani , Mamatha Samson
The rapid scaling of semiconductor technologies has significantly increased the integration density and introduced new categories of manufacturing defects, thereby increasing the test complexity and time. Scan-based design for-test (DFT) architectures remain the most widely adopted method for digital IC testing, where test vectors are shifted serially into and out of scan chains. Because shift operations dominate the overall test time, reducing power during scan shifting is essential to prevent IR-drop, thermal issues, reliability degradation, and potential yield loss, and to enable higher shift frequencies. A higher shift frequency directly reduces the test application time and, consequently, the overall test cost. Excessive switching during scan shift remains a significant challenge, particularly in today’s low-power devices, prompting extensive research on low-power DFT. This paper presents a structured survey of recent advancements in shift-power reduction, covering automatic test pattern generation (ATPG)-based low power test pattern generation, built-in self-test (BIST)-based low-transition pattern generation, and modern scan-chain optimization and reordering strategies. The survey highlights that among various solutions, scan chain reordering stands out as one of the most effective and scalable power-aware DFT techniques, due to its minimal implementation overhead, seamless integration with existing ATPG/BIST flows, and significant ability to reduce 20–50% scan-shift power without requiring pattern regeneration.
Volume: 42
Issue: 1
Page: 30-39
Publish at: 2026-04-01

Enhanced image compression through hybrid staggered downsampling and DCT

10.11591/ijeecs.v42.i1.pp62-70
Benlabbes Haouari , Khair Younes , Beladgham Mohammed , El Hendi Hichem
Image compression is crucial for multimedia applications with the aim of reduc ing storage and/or transmission costs, while preserving reliable visual quality. In this research, we propose a novel hybrid image compression technique based on staggering downsampling combined with discrete cosine transform (DCT). The proposed approach not only overlaps downsample images to reduce data re dundancy but also utilizes the energy compaction properties of DCT for efficient compression. The proposed method performance on benchmark grayscale im ages such as Lena, House, and other refernce images were evaluated by means of image quality assessment metrics, namely, peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), visual information fidelity (VIF); and com pression efficiency metrics: bitrate and compression ratio. The results clearly show that the proposed algorithm outperforms JPEG and Set partitioning in hi erarchical trees (SPIHT) + discrete wavelet transform (DWT) method, with the following results: PSNR of 45.02, MSSIM of 0.9856, VIF of 0.8271, Bitrate of 0.12 bpp and a Compression Ratio of 64.00 (i.e. a reduction of 64 times). The suggested hybrid image compression method optimizes bug multimedia stor age and transmission by minimizing storage space and bandwidth usage while maintaining image quality. It, therefore, achieves a balance between percep tual quality and compression efficiency, making it the best option for resource constrained applications such as remote sensing, embedded systems, video com pression, and medical image archival.
Volume: 42
Issue: 1
Page: 62-70
Publish at: 2026-04-01

Characteristics dipole antenna for partial discharge in gas insulated switchgear

10.11591/ijeecs.v42.i1.pp13-22
Rian Nurdiansyah , Farradita Nugraha , Nadya Glaudira , Linda Faridah
The insulation condition of high-voltage equipment can be determined by measuring partial discharge (PD), which is an important indicator in insulation degradation. One of the PD detection methods that can be used is to use antennas as sensors in detecting electromagnetic waves generated from PD activities, especially in gas insulated switchgear (GIS) systems. This study focuses on designing and testing dipole antennas in the ultra-high frequency (UHF) frequency range of 300 Mhz-3 GHz to detect PD signals in GIS. Previous studies on dipole antennas with dimensions of 66×15 mm have reported a bandwidth of 336 MHz and a return loss of -22.4 dB at 1.3 GHz. The antenna was fabricated using an FR4-epoxy substrate with a thickness of 1.6 mm, a substrate radius of 102 mm, and a gap distance of 2 mm. Optimization of the antenna length and width significantly improved performance characteristics. Simulation results show that a dipole antenna with dimensions of 35×40 mm antenna produced a wider bandwidth of 989 MHz with a return loss of −28.47 dB at 1.4 GHz. Experimental validation using vector network analyzer (VNA) and PD testing on GIS confirmed that the optimized dipole antenna effectively detected PD activity at a voltage level of 16 kV.
Volume: 42
Issue: 1
Page: 13-22
Publish at: 2026-04-01

Language models and deep neural networks for Arabic named entity recognition

10.11591/ijeecs.v42.i1.pp142-148
Somia Khedimi , Abdelghani Bouziane
Token type identification lies at the core of named entity recognition, allowing models to distinguish named entities from non-entity tokens and thereby better capture sentence meaning. This paper presents a deep learning approach for the Arabic named entity recognition task, leveraging deep neural networks and pretrained language models. The proposed model is a combination of the AraELECTRA language model with the bidirectional long short-term memory (BiLSTM) neural network. We utilize the WojoodNER dataset, which provides fine-grained annotations of Arabic text across 21 entity types. The results of this approach are encouraging, with an accuracy of 98.29% and an F1-score of 87%.
Volume: 42
Issue: 1
Page: 142-148
Publish at: 2026-04-01

Complexity aware cascade architecture for improving user satisfaction in conversational AI

10.11591/ijeecs.v42.i1.pp205-214
Constantinus Satrio , Devi Fitrianah
Conventional task-oriented chatbots frequently suffer from task incompletions and low user satisfaction when handling complex queries. This research intro duces the complexity aware cascade, an adaptive architecture that improves user service quality by dynamically matching query complexity with the appropri ate computational response. The system uses confidence and relevance scores to intelligently route requests through a sequence of a natural language under standing (NLU) model, a retrieval-augmented generation (RAG) pipeline, or a large language model (LLM). The tiered architecture was evaluated via a ran domized controlled trial (RCT) with 150 participants, measuring task success and user satisfaction. The full cascade achieved a 90% journey completion rate, representing a 92.3% improvement over baseline system and substantial gains in SERVQUAL-based service quality scores. The experiment was conducted in a domain-specific knowledge base (essential oils) with a convenience sam ple that does not represent the global population, and no real-time deployment or long-term cost analysis was performed. Accordingly, the findings should be interpreted as evidence of effectiveness in a limited setting rather than as directly scalable to all domains. Even with these limitations, this study provides arigorously tested blueprint for developing more robust and user-centric conversational AI systems.
Volume: 42
Issue: 1
Page: 205-214
Publish at: 2026-04-01

Dilated residual U-Net for vegetation detection from high resolution drone aerial imagery

10.11591/ijeecs.v42.i1.pp115-122
Mgs. M. Luthfi Ramadhan , Rizal Maulana , Lalu Syamsul Khalid
Vegetation plays a vital role in regulating air quality and mitigating climate change by converting carbon dioxide into oxygen. However, ongoing human activity continues to degrade vegetation ecosystems, necessitating scalable and accurate monitoring methods. Traditional field-based statistical approaches are often costly and inefficient. This study proposes a deep learning model, dilated residual U-Net, for semantic segmentation of vegetation from drone-acquired aerial imagery. The model incorporates residual connections to reduce infor mation loss and dilated convolutions to enhance receptive field coverage with out increasing computational cost. Experiments conducted on the DroneDeploy Segmentation dataset demonstrate that the proposed model achieves a Dice co efficient of 0.4451 with an inference speed of 0.0675 seconds per image, outper forming baseline U-Net and Residual U-Net models. These results highlight the potential of lightweight, CNN-based architectures for environmental monitoring in resource-constrained settings.
Volume: 42
Issue: 1
Page: 115-122
Publish at: 2026-04-01

Integrating blind source separation and self-supervised learning for Algerian Arabic connected-digit recognition

10.11591/ijeecs.v42.i1.pp71-80
Mourad Reggab , Mohammed Belkhiri
This paper proposes an improvement in Arabic automatic speech recognition (ASR) by combining blind source separation (BSS) with self-supervised acous tic modeling. The study concentrates on the Algerian Arabic connected-digit recognition task and reexamines the classical degenerate unmixing estimation technique (DUET) as a front-end approach for suppressing noise and inter ference. The output of the BSS stage is fed into a Hidden Markov model (HMM) recognizer developed using the HTK toolkit. To contextualize DUET’s performance, it is compared with modern neural separation techniques (Conv TasNet, SepFormer) paired with both traditional and self-supervised ASR back ends (Wav2Vec 2.0 and Whisper). A new corpus of 11,230 utterances from 37 speakers, representing dialectal and gender diversity, was collected. Experimen tal outcomes indicate that DUET enhances word accuracy under stereo mixing conditions; however, neural separation combined with self-supervised ASR re sults in considerably lower word-error rates and stronger robustness in noisy or overlapping-speech scenarios. The study emphasizes practical trade-offs be tween computational cost and accuracy for deploying low-resource Arabic ASR systems.
Volume: 42
Issue: 1
Page: 71-80
Publish at: 2026-04-01

Fraud detection in financial transactions: state of the art

10.11591/ijeecs.v42.i1.pp272-282
Hamza Badri , Youssef Balouki , Fatima Guerouate
The surge in digital financial transactions, fueled by the proliferation of online banking, ecommerce, and emerging technologies, has brought significant oppor- tunities and equally critical vulnerabilities. Fraudulent activities have evolved in parallel, leveraging the complexity and global reach of digital systems to exploit weaknesses. This paper investigates the multifaceted nature of fraud in financial transactions, focusing on key types such as credit card fraud, money laundering, insurance fraud, and emerging threats in cryptocurrency systems. In this paper, we establish a state-of-the art overview of fraud detection method- ologies, analyzing their strengths and limitations. Traditional rule-based ap- proaches are contrasted with modern machine learning (ML) models, hybrid frame- works, and the application of advanced technologies. The study highlights the critical role of systems capable of identifying complex fraud patterns while ad- dressing persistent challenges. By synthesizing findings from existing research and evaluating innovative methods, this paper provides actionable insights into enhancing the effectiveness and resilience of fraud detection systems.
Volume: 42
Issue: 1
Page: 272-282
Publish at: 2026-04-01

A decentralized call recording in voice over IP based on blockchain using smart contracts

10.11591/ijeecs.v42.i1.pp164-173
Abdelhadi Rachad , Lotfi Gaiz , Khalid Bouragba , Mohammed Ouzzif
Although voice over IP (VoIP) has established itself as the new paradigm for universal telecommunications, its massive deployment within businesses and government agencies has paradoxically increased the attack surface for cyber threats: stream injection fraud, identity theft, and, more recently, the emergence of voice deepfakes, rendering traditional security architectures obsolete. At the same time, conventional centralized recording systems raise trust issues, as they are vulnerable to data manipulation, unauthorized access, and single points of failure. This article presents a new architecture that decentralizes the recording and securing of VoIP calls by combining three key technologies: blockchain for immutability; smart contracts to automate communications governance and ensure the transition from a centralized to an algorithmic trust model; and artificial intelligence (AI) agents that analyze audio streams in real time. This approach transforms VoIP recording from a simple passive file into a secure, auditable, and confidential digital asset. By removing centralized control and strengthening identity verification, this architecture provides a concrete response to security requirements.
Volume: 42
Issue: 1
Page: 164-173
Publish at: 2026-04-01

Experimental investigation of soil pH Engineering with eco enzyme to improve grounding performance

10.11591/ijeecs.v42.i1.pp23-29
I Wayan Jondra , Zulkurnain Abdul-Malek , I Nengah Sunaya , Made Sudana , I Made Purbhawa
The reliability of electric power distribution, in mitigating fault and disturbances, is strongly influenced by the effectiveness of grounding systems. A key factor in achieving low grounding resistance an essential requirement per construction and safety standards is soil condition. High grounding resistance is frequently observed in field implementations and is closely linked to soil resistivity, type, stratification, moisture content, and acidity (pH). This quantitative applied research addresses the persistent challenge of high grounding resistance by experimenting with investigating six grounding system models subjected to varying soil acidity levels. The study introduces the use of eco enzyme as a natural additive to modify soil pH and examines its effect on grounding resistance. Findings reveal that eco enzyme application successfully lowers soil pH, with an optimal reduction in grounding resistance observed at pH 3.8 achieving a drop from 40 ohms to 9 ohms. However, further lowering the pH below 3.8 results in a rise in resistance, indicating a threshold where acidic conditions become counterproductive. This research opens opportunities for broader applications of eco enzyme-treated soil in non-rod electrode systems and across diverse soil types, suggesting promising pathways for enhancing grounding systems in various environmental conditions.
Volume: 42
Issue: 1
Page: 23-29
Publish at: 2026-04-01

Agraph neural network framework for vascular streak dieback recognition

10.11591/ijeecs.v42.i1.pp194-204
Slamin Slamin , Rizky Alfanio Atmoko , Antonius Cahya Prihandoko , Muhammad Ariful Furqon , Qurrota A’yuni Ar Ruhimat , Annisa Fitri Maghiroh Harvyanti , Bayu Taruna Widjaja Putra , Roslan Hasni
Vascular streak dieback (VSD) is one of the most destructive diseases affecting cocoa production in Southeast Asia, including Indonesia, where early visual symptoms are often subtle and spatially distributed across the leaf sur face. Conventional image-based disease recognition approaches, particularly those relying solely on convolutional neural networks (CNNs), are effective in extracting local visual features but remain limited in modeling long-range structural relationships such as venation disruption and lesion spread. To ad dress this limitation, this study investigates a hybrid CNN-graph neural network (CNN-GNN) framework for automated VSD recognition from cocoa leaf im ages. A primary dataset consisting of 1,000 RGB images collected directly from cocoa plantations in Jember Regency was used to reflect realistic field condi tions. In the proposed approach, CNNs are employedfor local feature extraction, while graph-based representations enable GNNs to capture global relational pat terns through message passing. Experimental results demonstrate stable learning behavior and strong classification performance, achieving a maximum validation accuracy of 95.2% and an area under the curve (AUC) of approximately 0.94. Further analysis shows balanced precision and recall across classes, indicating reliable discrimination between Sehat and VSD-infected leaves. These findings suggest that hybrid CNN-GNN modeling provides an effective strategy for cap turing both local and distributed structural characteristics of VSD symptoms and highlights the potential of graph-based reasoning to complement convolutional feature learning in plant disease diagnostics.
Volume: 42
Issue: 1
Page: 194-204
Publish at: 2026-04-01

ViHateT5 with LoRA: efficient vietnamese toxic news classification on social media

10.11591/ijeecs.v42.i1.pp123-130
Tran Duc Duong , Hai Hoan Do
We propose an efficient transformer-based approach to detect toxic or misleading news in Vietnamese social media. Motivated by the societal harm of viral misinformation in Vietnam, we fine-tune a Vietnamese T5 model (ViHateT5) on a new dataset of 2,962 social-media news snippets labeled as toxic vs. non-toxic. We use low-rank adaptation (LoRA) to inject trainable layers into ViHateT5, allowing high accuracy with minimal additional parameters. Our model achieves 97.5% macro-F1 on a held-out test set, significantly higher than a PhoBERT baseline by 2.7 points. By focusing on Vietnamese data and a parameter-efficient method, we demonstrate a practical pipeline for low-resource fake-news detection. These results suggest that transformer pretraining on social-media text can effectively capture the subtle cues of deceptive or defamatory news. Limitations: the current model is trained on a specific labeled dataset and may not generalize to all domains; future work should evaluate its fairness and biases in deployment.
Volume: 42
Issue: 1
Page: 123-130
Publish at: 2026-04-01
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