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29,325 Article Results

Image classification using two neural networks and activation functions: a case study on fish species

10.11591/ijece.v16i1.pp383-394
Oppir Hutapea , Ford Lumban Gaol , Tokuro Matsuo
Lake Toba is utilized for aquaculture fishing as a clear example of how this technology can be applied. One of the species presents is the red devil fish (Amphilophus labiatus), which is known to have started appearing in the last 10 years. This species is known to be very aggressive and damage the ecosystem. When their populations go unchecked, red-devils can cause a decline in local fish populations, potentially destroying the balance of the food chain in those waters. This study used artificial neural network (ANN) and convolutional neural network (CNN) algorithms to successfully create two classification models for fish species from Lake Toba: red devil fish (Amphilophus labiatus), mujahir fish (Oreochromis mossambicus), sepat fish (Trichogaster trichopterus). The purpose of this model is to automatically identify fish species by using image-based classification techniques. According to the study's findings, both models performed exceptionally well and had a high degree of accuracy. This study addresses the lack of effective automated fish classification systems for ecosystems like Lake Toba, Indonesia, which are threatened by invasive species such as the red devil fish. By comparing CNN and ANN models with different activation functions and optimizers, we found that CNN with rectified linear unit (ReLU) activation and Adam optimizer provides the most accurate and stable results. The findings offer practical implications for fisheries management and biodiversity conservation.
Volume: 16
Issue: 1
Page: 383-394
Publish at: 2026-02-01

Experimental comparison of air, oil, and liquid nitrogen cooling media on the efficiency of a single-phase transformer

10.11591/ijece.v16i1.pp25-35
Heri Nugraha , Agung Imaduddin , Eka Rakhman Priandana , Asep Dadan Hermawan , Nono Darsono , Andika Widya Pramono , Adi Noer Syahid , Sudirman Palaloi , Satrio Herbirowo , Hendrik Hendrik
Transformers are critical component in electric power system, where minimizing energy losses is essential for efficiency and reliability. While ideal transformers operate with zero losses, practical transformers dissipate energy through winding and core losses caused by resistive heating. This study investigates the impact of three cooling media with ambient air, mineral oil, and liquid nitrogen on the efficiency and thermal performance of a 1 kVA single phase copper wound transformer. The experiment applied a resistive load under each cooling condition, recording input and output parameters using a HIOKI power meter model PW3360. Thermal behavior was monitored using infrared thermography and thermocouples. Copper winding resistivity was evaluated using a four-point probe within a cryogenic magnet system. The results show that liquid nitrogen cooling significantly reduced copper resistivity due to low-temperature conditions, achieving a transformer efficiency of 89.9%. Oil cooling improved efficiency to 86.0%, compared to 80.7% with air cooling. Although liquid nitrogen provided the greatest efficiency enhancement, its practical use is limited due to handling complexity and cost. In contrast, oil cooling offers a more feasible and effective solution for improving transformer performance in real world applications. These finding provide valuable insight for optimizing transformer thermal management strategies in power systems.
Volume: 16
Issue: 1
Page: 25-35
Publish at: 2026-02-01

A systematic review of software fault prediction techniques: models, classifiers, and data processing approaches

10.11591/ijece.v16i1.pp545-554
R. Kanesaraj Ramasamy , Venushini Rajendran , Parameswaran Subramanian
Software fault prediction (SFP) plays a critical role in improving software reliability by enabling early detection and correction of defects. This paper presents a comprehensive review of 25 recent and significant studies on SFP techniques, focusing on data preprocessing strategies, classification algorithms, and their effectiveness across various datasets. The review categorizes the approaches into traditional statistical models, machine learning methods, deep learning architectures, and hybrid techniques. Notably, wrapper-based feature selection, neural network classifiers, and support vector machines (SVM) are identified as the most effective in achieving high accuracy, particularly when dealing with imbalanced or noisy datasets. The paper also highlights advanced approaches such as variational autoencoders (VAE), Bayesian classifiers, and fuzzy clustering for fault prediction. Comparative analysis is provided to assess performance metrics such as accuracy, F-measure, and area under the curve (AUC). The findings suggest that no single method fits all scenarios, but a combination of appropriate preprocessing and robust classification yields optimal results. This review provides valuable insights for researchers and practitioners aiming to enhance software quality through predictive analytics. Future work should explore ensemble learning and real-time SFP systems for broader applicability.
Volume: 16
Issue: 1
Page: 545-554
Publish at: 2026-02-01

Organizing students’ research activities based on STEM elements in the study process

10.11591/ijere.v15i1.35075
Shakhislam Laiskhanov , Seminar Yerkegul
Although science, technology, engineering, and mathematics (STEM) integration in higher education is advancing globally, its adoption in geography programs in Kazakhstan remains limited. This study examines the effectiveness of embedding selected STEM elements into the course “Geography of Aktobe Oblаst” as а means of strengthening students’ research competencies. A mixed-method design was employed, combining analysis of satellite-derived indicаtоrs, wоrk with geоspаtiаl plаtfоrms (аrcGIS Prо, EоSDа Crоp Mоnitоring, and Eо Brоwser), prаcticаl climаte-bаsed cаlculаtiоns, clаssrооm оbservаtiоns аnd cоmpаrаtive аssessment аnаlysis. The 24 students pаrticipаted in the interventiоn, cоmpleting а series оf inquiry-driven tаsks invоlving the Normalized Difference Vegetation Index (NDVI) interpretаtiоn, spectrаl reflectаnce аnаlysis аnd climаtоlоgicаl cоrrelаtiоns. Survey dаtа indicаted thаt mоre thаn 90% оf pаrticipаnts repоrted imprоved understаnding оf envirоnmentаl prоcesses, while mаny nоted gаins in аnаlyticаl reаsоning аnd dаtа-driven interpretаtiоn. Midterm perfоrmаnce results shоwed а mоdest but cоnsistent imprоvement fоllоwing the implementаtiоn оf STEM-оriented аssignments. The findings suggest thаt structured integrаtiоn оf geоspаtiаl аnd аnаlyticаl STEM tооls cаn meаningfully suppоrt the develоpment оf reseаrch skills in university geоgrаphy cоurses. By enаbling students tо wоrk with аuthentic envirоnmentаl dаtаsets, the аpprоаch cultivаtes higher-оrder reаsоning, interdisciplinаry thinking аnd sustained learner engаgement. The results highlight the pоtentiаl fоr brоаder аpplicаtiоn оf STEM-bаsed instructiоnаl mоdels in Kаzаkhstаni higher educаtiоn аnd underscоre the need fоr further lоngitudinаl аnd cоmpаrаtive studies tо evаluаte lоng-term impаcts.
Volume: 15
Issue: 1
Page: 705-713
Publish at: 2026-02-01

Deep learning architecture for detection of fetal heart anomalies

10.11591/ijece.v16i1.pp414-422
Nusrat Jawed Iqbal Ansari , Maniroja M. Edinburgh , Nikita Nikita
Research has demonstrated that artificial intelligence (AI) techniques have shown tremendous potential over the past decade for analyzing and detecting anomalies in the fetal heart during ultrasound tests. Despite their potential, the adoption of these algorithms remains limited due to concerns over patient privacy, the scarcity of large well-annotated datasets and challenges in achieving high accuracy. This research aims to overcome these limitations by proposing an optimal solution. Two methods such as deterministic image augmentation techniques and Wasserstein generative adversarial network with gradient penalty (WGAN-GP) showcase the framework's capacity to seamlessly and effectively expand original datasets to 14 times and 17 times respectively, thereby effectively tackling the problem of data scarcity. It uses an annotation tool to precisely categorize anomalies identified in the echocardiogram dataset. Segmentation of the annotated data is done to highlight region of interest. Nine distinct fetal heart anomalies are identified with respect to the fewer covered in existing research. This study also investigates the state-of-the-art architectures and optimization techniques used in deep learning models. The results clearly indicate that the ResNet-101 model demonstrated superior precision accuracy of 99.15%. To ensure the reliability of the proposed model, its performance underwent thorough evaluation and validation by certified gynecologists and fetal medicine specialists.
Volume: 16
Issue: 1
Page: 414-422
Publish at: 2026-02-01

Autonomous mobile robot implementation for final assembly material delivery system

10.11591/ijece.v16i1.pp158-173
Ahmad Riyad Firdaus , Imam Sholihuddin , Fania Putri Hutasoit , Agus Naba , Ika Karlina Laila Nur Suciningtyas
This study presents the development and implementation of an autonomous mobile robot (AMR) system for material delivery in a final assembly environment. The AMR replaces conventional transport methods by autonomously moving trolleys between the warehouse, production stations, and recycling areas, thereby reducing human intervention in repetitive logistics tasks. The proposed system integrates a laser-SLAM navigation approach, customized trolley design, RoboShop programming, and robot dispatch system coordination, enabling real-time route planning, obstacle detection, and material scheduling. Experimental validation demonstrated high accuracy in path following, with root mean square error values ranging between 0.001 to 0.020 meters. The AMR achieved an average travel distance of 118.81 meters and a cycle time of 566.90 seconds across three final assembly stations. The overall efficiency reached 57%, primarily due to reduced idle time and optimized material replenishment. These results confirm the feasibility of AMR deployment as a scalable and flexible intralogistics solution, supporting the transition toward Industry 4.0 smart manufacturing systems.
Volume: 16
Issue: 1
Page: 158-173
Publish at: 2026-02-01

Credit card fraud data analysis using proposed sampling algorithm and deep ensemble learning

10.11591/ijece.v16i1.pp311-320
Aye Aye Khine , Zin Thu Thu Myint
Credit card fraud detection is challenging due to the severe imbalance between legitimate and fraudulent transactions, which hinders accurate fraud identification. To address this, we propose a deep learning-based ensemble model integrated with a proposed sampling algorithm based on random oversampling. Unlike traditional methods, the proposed sampling algorithm addresses the oversight of parameter selection and manages class imbalance without eliminating any legitimate samples. The ensemble framework combines the strengths of convolutional neural networks (CNN) for spatial feature extraction, long short-term memory (LSTM) networks for capturing sequential patterns, and multilayer perceptrons (MLP) for efficient classification. Three ensemble strategies—Weighted average, unweighted average, and unweighted majority voting—are employed to aggregate predictions. Experimental results show that all ensemble methods achieve perfect scores (1.00) in precision, recall, and F1-score for both fraud and non-fraud classes. This study demonstrates the effectiveness of ensemble model with optimized sampling approach for robust and accurate fraud detection.
Volume: 16
Issue: 1
Page: 311-320
Publish at: 2026-02-01

Meta-skills-oriented academic management in higher education: evidence from Chinese HEIs

10.11591/ijere.v15i1.37685
Chi Che , Sukanya Chaemchoy , Pruet Siribanpitak
Higher education institutions (HEIs) increasingly face pressure to develop graduates’ meta-skills, yet meta-skills are typically treated as learner-level outcomes rather than an institutional management orientation that can reshape academic management systems. This study proposes and tests a meta-skills-oriented academic management framework that links meta-skills orientation (metacognitive capacity, emotional intelligence, and motivational competence) to academic management frames (structural, human resource, political, and symbolic) and institutional performance. Using a cross-sectional survey of 2,406 academic administrators and faculty from 60 Chinese HEIs (national key, provincial, and regular colleges) selected through stratified sampling, the study employed validated questionnaire measures and analyzed data via structural equation modeling (SEM) (AMOS 26.0). The model demonstrated acceptable fit (CFI=0.968; RMSEA=0.042). Meta-skills orientation positively predicted structural (β=0.685), human resource (β=0.573), political (β=0.412), and symbolic (β=0.524) frames (all p<0.001), while structural (β=0.486) and human resource (β=0.445) frames significantly predicted institutional performance (all p<0.001). Multi-group analysis indicated stronger meta-skills-to-structural pathways in national key universities than other HEI types. The findings position meta-skills orientation as an actionable institutional logic and provide frame-specific levers for evidence-based academic management reform.
Volume: 15
Issue: 1
Page: 327-341
Publish at: 2026-02-01

Comprehension competence: personal attitudes’ effect on the intentions to read critically in a foreign language

10.11591/ijere.v15i1.35983
Migena Alimehmeti , Irena Shehu
This study explores the relationship between personal attitudes and the intention to engage in critical reading among students learning a foreign language. Grounded in theories of motivation, cognition, and critical pedagogy, the research aims to determine whether favorable attitudes influence students’ willingness to read critically. A quantitative approach was employed, using a structured questionnaire administered to a purposive sample of 200 undergraduate students at the Faculty of Foreign Languages, University of Tirana. The instrument measured two key constructs: personal attitudes and intentions to read critically, through Likert-scale items. Reliability and validity were confirmed via Cronbach’s alpha (.855 and .920) and factor analysis (Kaiser-Meyer-Olkin (KMO)=.885). Data were analyzed using SPSS, including descriptive statistics and Pearson correlation. Results revealed a statistically significant and moderately strong positive correlation between personal attitudes and critical reading intentions (r=.645, p<.01), suggesting that students who hold more favorable views toward critical reading are more likely to intend to engage in it. Despite overall positive attitudes, the intentions to pursue critical reading beyond academic settings were only moderate, indicating potential barriers or a lack of long-term motivation. The study highlights the need for pedagogical strategies that enhance students’ appreciation for critical reading, as fostering positive attitudes may lead to greater engagement and skill development in this essential area. The study concludes with pedagogical recommendations for fostering positive attitudes to enhance engagement in critical reading.
Volume: 15
Issue: 1
Page: 795-804
Publish at: 2026-02-01

Efficiency enhancement of off-grid solar system

10.11591/ijece.v16i1.pp111-120
Satish Kumar , Asif Jamil Ansari , Anil Kumar Singh , Deepak Gangwar
This paper presents the design and implementation of a sensor-enabled off-grid solar charge controller aimed at maximizing the utilization of renewable energy. The proposed system integrates solar and load power sensors to minimize solar energy wastage. A microcontroller is employed to efficiently monitor and regulate battery voltage, solar power generation, and load demand. This system is designed to optimize solar energy usage, reduce dependency on the electrical grid, and lower electricity bills. Additionally, a main supply controller board with a display is introduced, along with a smart scheduler for appliance management. Prior to deployment, total solar power wastage was recorded at 93.1 watts per day. After implementing the proposed solution, wastage was reduced to 13.1 watts per day—reflecting an 85.92% reduction. These results confirm the system’s effectiveness in reducing energy loss, increasing self-consumption, and promoting energy sustainability in off-grid environments. It is important to note that this value may vary based on factors such as temperature, cloud cover, fog, and irradiation levels.
Volume: 16
Issue: 1
Page: 111-120
Publish at: 2026-02-01

Accessibility in e-government portals: a systematic mapping study

10.11591/ijece.v16i1.pp357-372
Mohammed Rida Ouaziz , Laila Cheikhi , Ali Idri , Alain Abran
In recent years, several researchers have investigated the challenges of accessibility in e-government portals and have contributed to many proposals for improvements. However, no comprehensive review has been conducted on this topic. This study aimed to survey and synthesize the published work on the accessibility of e-government portals for people with disabilities. We carried out a review using a systematic mapping study (SMS) to compile previous findings and provide comprehensive state-of-the-art. The SMS collected studies published between January 2000 and March 2025 were identified using an automated search in five known databases. In total, 112 primary studies were selected. The results showed a notable increase in interest and research activities related to accessibility in e-government portals. Journals are the most widely used publication channel; studies have mainly focused on evaluation research and show a commitment to inclusivity. “AChecker” and “Wave validator” are the most used accessibility evaluation tools. The findings also identified various accessibility guidelines, with the most frequently referenced being the web content accessibility guidelines (WCAG). Based on this study, several key implications emerge for researchers, and addressing them would be beneficial for researchers to advance e-government website accessibility in a meaningful way.
Volume: 16
Issue: 1
Page: 357-372
Publish at: 2026-02-01

Perceptions and attitudes of postgraduate students toward the flipped classroom model: case study at Vinh University, Vietnam

10.11591/ijere.v15i1.31880
Huong Thi Nguyen , Le Van Vinh , Thai Thi Hong Lam , Nhi Thi Nguyen
Teaching using the flipped classroom model (FCM) is one of the methods of organizing teaching that combines (blended learning) electronic learning methods (e-learning) with traditional teaching and learning methods. The research aims to understand the perceptions and attitudes of postgraduate students at Vinh University towards the flipped classroom and analyze the benefits and difficulties that students encounter. When teaching according to FCM, thereby proposing solutions to improve efficiency when teaching according to this model. The research was conducted using a combination of qualitative and quantitative methods. Quantitative data were obtained from the participation of 121 postgraduate students in teacher training. Qualitative data was taken from the interview results of seven students who experienced the flipped classroom. Results from the survey show that the majority of students have a positive attitude toward learning in the FCM. Research also shows that the FCM positively impacts students’ attitudes toward the course through the benefits it brings. Students participating in the survey also shared the difficulties they encountered when studying in the FCM and gave some suggestions to improve the effectiveness of applying this model in the future.
Volume: 15
Issue: 1
Page: 616-625
Publish at: 2026-02-01

Facial emotion recognition under face mask occlusion using vision transformers

10.11591/ijece.v16i1.pp395-403
Ashraf Yunis Maghari , Ameer M. Telbani
Facial emotion recognition (FER) systems face significant challenges when individuals wear face masks, as critical facial regions are occluded. This paper addresses this limitation by employing vision transformers (ViT), which offer a promising alternative with reduced computational complexity compared to traditional deep learning methods. We propose a ViT-based FER framework that fine-tunes a pre-trained ViT architecture to enhance emotion recognition under mask-induced occlusion. The model is fine-tuned and evaluated on the AffectNet dataset, which originally represents eight emotion categories. These categories are restructured into five broader classes to mitigate the impact of occluded features. The model’s performance is assessed using standard metrics, including accuracy, precision, recall, and F1 score. Experimental results demonstrate that the proposed framework achieves an accuracy of 81%, outperforming several state-of-the-art approaches. These findings highlight the potential of vision transformers in recognizing emotions under masked conditions and support the development of more robust FER systems for real-world applications in healthcare, surveillance, and human–computer interaction. This work introduces a scalable and effective approach that integrates self-attention, synthetic mask augmentation, and emotion class restructuring to improve emotion recognition under facial occlusion.
Volume: 16
Issue: 1
Page: 395-403
Publish at: 2026-02-01

Artificial intelligence of things solution for Spirulina cultivation control

10.11591/ijece.v16i1.pp488-504
Abdelkarim Elbaati , Mariem Kobbi , Jihene Afli , Abdelrahim Chiha , Riadh Haj Amor , Bilel Neji , Taha Beyrouthy , Youssef Krichen , Adel M. Alimi
In the evolving field of Spirulina cultivation, the integration of the internet of things (IoT) has facilitated the optimization of spirulina growth and significantly enhanced biomass yield in the culture medium. This study outlines a control open-pond system for Spirulina cultivation that employs generative artificial intelligence (AI) and edge computing within an IoT framework. This transformative approach maintains optimal conditions and automates tasks traditionally managed through labor-intensive manual processes. The system is designed to detect, acquire, and monitor basin data via electronic devices, which is then analyzed by a large language model (LLM) to generate precise, context-aware recommendations based on domain-specific knowledge. The final output comprises SMS notifications sent to the farm manager, containing the generated recommendations, which keep them informed and enable timely intervention when necessary. To ensure continued autonomous operation in case of connectivity loss, pre-trained TinyML models were integrated into the Raspberry Pi. These models display alarm signals to alert the farm owner to any irregularities, thereby maintaining system stability and performance. This system has substantially improved the growth rate, biomass yield, and nutrient content of Spirulina. The results highlight the potential of this system to transform Spirulina cultivation by offering an adaptable, autonomous solution.
Volume: 16
Issue: 1
Page: 488-504
Publish at: 2026-02-01

Application of deep learning and machine learning techniques for the detection of misleading health reports

10.11591/ijece.v16i1.pp373-382
Ravindra Babu Jaladanki , Garapati Satyanarayana Murthy , Venu Gopal Gaddam , Chippada Nagamani , Janjhyam Venkata Naga Ramesh , Ramesh Eluri
In the current era of vast information availability, the dissemination of misleading health information poses a considerable obstacle, jeopardizing public health and overall well-being. To tackle this challenge, experts have utilized artificial intelligence methods, especially machine learning (ML) and deep learning (DL), to create automated systems that can identify misleading health-related information. This study thoroughly investigates ML and DL techniques for detecting fraudulent health news. The analysis delves into distinct methodologies, exploring their unique approaches, metrics, and challenges. This study explores various techniques utilized in feature engineering, model architecture, and evaluation metrics within the realms of machine learning and deep learning methodologies. Additionally, we analyze the consequences of our results on enhancing the efficacy of systems designed to detect counterfeit health news and propose possible avenues for future investigation in this vital area.
Volume: 16
Issue: 1
Page: 373-382
Publish at: 2026-02-01
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