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29,325 Article Results

Systematic review of artificial intelligence applications in predicting solar photovoltaic power production efficiency

10.11591/ijece.v16i1.pp463-476
M. Rizki Ikhsan , Muhammad Modi Lakulu , Ismail Yusuf Pannesai , Muhammad Rizali , Bayu Nugraha , Liliana Swastina
The global energy crisis and climate change demand more accurate and efficient renewable energy forecasting methods. Solar photovoltaic (PV) systems offer abundant clean energy but their efficiency is highly affected by weather variability, requiring advanced predictive models. This systematic review of 69 studies published between 2020 and 2024 evaluates artificial intelligence (AI) and machine learning (ML) applications in PV forecasting, with a focus on hybrid algorithms such as convolutional neural network-long short-term memory (CNN-LSTM). Results demonstrate that hybrid models consistently outperform traditional statistical methods and standalone AI approaches by capturing spatiotemporal patterns more effectively, achieving significant error reductions and improving reliability. A notable gap identified is the limited integration of consumer behavior into forecasting models, despite evidence that incorporating demand-side patterns enhances accuracy. Challenges also remain in data availability, scalability across diverse climates, and computational requirements. This review contributes by synthesizing recent advances and emphasizing consumer integration as an underexplored but critical dimension for future research. The findings provide a foundation for developing more precise, resilient, and scalable PV forecasting models, supporting optimized energy management and accelerating the transition toward sustainable energy systems.
Volume: 16
Issue: 1
Page: 463-476
Publish at: 2026-02-01

Detection of islanding using empirical mode decomposition and support vector machine

10.11591/ijece.v16i1.pp10-24
Balwant Patil , Diwakar Joshi , Sagar Santaji , Sudhakar C. J.
Accurate detection of islanding remains to be a challenge for grid connected microgrid system. An effective method to identify the islanding of microgrid has been presented which uses only the voltage at point of common coupling (PCC). Accurate islanding detection is necessary to impose appropriate control for the microgrid operation. Following the islanding of microgrid the intrinsic mode functions (IMF’s) of voltage at PCC obtained by empirical mode decomposition (EMD) will be analyzed by support vector machine (SVM) model which identifies the islanding of the microgrid. SVM model learns through the training data set. As many as 150 simulated cases have been used to train the SVM. A practical microgrid system has been simulated for various operating conditions and the data generation has been carried out by series of simulations for various islanding and non-islanding events using MATLAB Simulink. The proposed method gives optimistic results with high accuracy, zero non detection zone (NDZ) and detection time as low as 63.11 ms. Accurate islanding detection leads to smooth transition of microgrid control essential for operators.
Volume: 16
Issue: 1
Page: 10-24
Publish at: 2026-02-01

Improving international students’ acculturation and Chinese vocabulary through problem-based learning with augmented reality

10.11591/ijere.v15i1.34040
Yirong Jiang , Thoriq Tri Prabowo , Thanin Ratana-Olarn
The integration of problem-based learning (PBL) and augmented reality (AR) has emerged as a promising approach to improve language learning and acculturation. This study aimed to develop and evaluate a problem-based learning and augmented reality (PBLAR) instructional model to enhance international students’ acculturation and Chinese vocabulary. The study used a quasi-experimental design with 70 first-year international students randomly assigned to an experimental group (EG) (PBLAR, n=35) or a control group (CG) (traditional teaching method, n=35). The intervention lasted two weeks, and both groups completed a pre-test to ensure baseline comparability. The EG achieved significantly higher post-test scores in Chinese vocabulary (F=296.65, p<0.001) and acculturation (F=840.62, p<0.001). In this context, PBLAR demonstrated stronger short-term outcomes in Chinese vocabulary and acculturation; it should be viewed as a complementary pedagogy that highlights the value of integrating PBL and AR. Findings highlight PBLAR’s potential for immersive, problem-driven language instruction that bridges technology and pedagogy in multicultural contexts.
Volume: 15
Issue: 1
Page: 555-564
Publish at: 2026-02-01

Advances in AI, IoT, and smart systems for emerging electrical and computer engineering applications

10.11591/ijece.v16i1.pp555-558
Tole Sutikno
The current issue of the International Journal of Electrical and Computer Engineering (IJECE) showcases a diverse array of research at the intersection of artificial intelligence (AI), Internet of Things (IoT), machine learning (ML), and advanced engineering systems. Highlighted studies explore the application of autonomous mobile robots for logistics and material handling, sensorless control and acceleration profiling of electric drives, hybrid control strategies for high-performance electric vehicles, and deep learning methods for image recognition, emotion detection, and software fault prediction. Further contributions address practical implementations of IoT in heatstroke prevention, hydroponics, Spirulina cultivation, and energy-efficient greenhouse management, demonstrating how intelligent systems can optimize resource use, safety, and productivity. The issue also emphasizes AI-empowered modeling in accelerator design, solar photovoltaic power forecasting, and GIS automation, while exploring cybersecurity through intrusion detection frameworks and fraud detection in financial systems. Cutting-edge deep learning models such as convolutional neural networks (CNN), vision transformers, and TinyML are leveraged for healthcare, nuclear monitoring, and prenatal diagnostics. Collectively, these contributions underline the transformative role of AI, IoT, and hybrid intelligent systems in electrical and computer engineering, bridging theoretical advances with practical, real-world applications. This issue aims to inspire continued research and development toward efficient, secure, and adaptive technologies that advance smart engineering solutions worldwide.
Volume: 16
Issue: 1
Page: 555-558
Publish at: 2026-02-01

AI SWLM: artificial intelligence-based system for wildlife monitoring

10.11591/ijece.v16i1.pp216-229
Arun Govindan Krishnan , Jayaraman Bhuvana , Mirnalinee Thanga Nadar Thanga Thai , Bharathkumar Azhagiya Manavala Ramanujam
Detection and recognition of wild animals are essential for animal surveillance, behavior monitoring and species counting. Intrusion of animals and the disaster to be caused can be averted by the timely recognition of intruding animals. An artificial intelligence-based system for wildlife monitoring (AI SWLM) is designed and implemented on the camera trap images. The challenges such as detecting and recognizing animals of different sizes, shape, angles and scale, recognizing the animals of same and different species, detecting them under various illumination conditions, with pose variants and occlusion are addressed by identifying the optimal weights of the deep learning architecture, AI SWLM. Models were trained using Gold Standard Snapshot Serengeti dataset with random weights and the best weights of model were used as initial weights for training the augmented data. This has doubled the performance in terms of mean average precision, which can be interpreted.
Volume: 16
Issue: 1
Page: 216-229
Publish at: 2026-02-01

Cumulative aging effects of five-year intermittent exposure on flexible amorphous solar cells

10.11591/ijece.v16i1.pp65-75
Djerroud Salima , Boudghene Stambouli Amine , Benabadji Noureddine , Lakhdari Abdelghani
Amorphous silicon (a-Si) is rarely used for large scale photovoltaic energy production, it remains relevant in flexible electronic applications, where mechanical flexibility and lightweight design are prioritized, where exposure to sunlight is typically limited or irregular. This study conducts an experimental analysis of the long-term aging effects on the proprieties of an amorphous solar cells, under five years of intermittent outdoor climate conditions. Unlike conventional aging studies that focus on degradation over time, this research highlights the cumulative effects of environmental exposure, considering the discontinuous nature of exposure cycles and the non-linearity of degradation phenomena because of the abrupt transitions between outdoor exposure phases and indoor laboratory rest periods. The results show that nearly 50% of the panel’s performances is reduced, with the losses observed as follows: a substantial decline in the fill factor from 55.3% to 30%, a decrease in energy conversion efficiency from 11.36% to 5.5%. This accelerated deterioration mainly attributed to harsh environmental transitions caused by intermittent exposure, which amplify aging mechanism compared to continuous exposure. Beyond the experimental findings, the approach presented here, constitutes a meaningful scientific contribution. By introducing a realistic and underexplored aging scenario, it lays the groundwork for a new line of research.
Volume: 16
Issue: 1
Page: 65-75
Publish at: 2026-02-01

Machine learning-based predictive maintenance framework for seismometers: is it possible?

10.11591/ijece.v16i1.pp187-205
Arifrahman Yustika Putra , Titik Lestari , Adhi Harmoko Saputro
Seismometers are crucial in earthquake and tsunami early warning systems, since they record ground vibrations due to significant seismic events. The health condition of a seismometer is strongly related to the measurement of seismic data quality, making seismometer health condition maintenance critical. Predictive maintenance is the most advanced control or measurement system maintenance method, since it informs about the faults that have occurred in the system and the remaining lifetime of the system. However, no research has proposed a seismometer predictive maintenance framework. Thus, this article reviews general predictive maintenance methods and seismic data quality analysis methods to find the feasibility of developing a predictive maintenance framework for seismometers in seismic stations. Based on the review, it is found that such a framework can be built under particular challenges and requirements. Finally, machine learning is the best approach to build the classification and regression models in the predictive maintenance framework due to its robustness and high prediction accuracy.
Volume: 16
Issue: 1
Page: 187-205
Publish at: 2026-02-01

An enhanced improved adaptive backstepping–second-order sliding mode hybrid control strategy for high-performance electric vehicle drives

10.11591/ijece.v16i1.pp121-134
Huu Dat Tran , Ngoc Thuy Pham
This paper proposes an enhanced hybrid speed control strategy, termed improved adaptive backstepping–second-order sliding mode (IABSSOSM), for six-phase induction motor (SPIM) drives in electric vehicle (EV) propulsion systems. The proposed method combines the systematic design framework of Backstepping in the outer speed and flux loops with a second-order sliding mode controller in the inner current loop. An innovation of the approach is the integration of a variable-gain super-twisting algorithm (VGSTA), which dynamically adjusts the control effort based on disturbance levels, thereby minimizing chattering and enhancing robustness against system uncertainties. To further improve disturbance rejection, a predictive torque estimator is incorporated using a forward Euler discretization, enabling accurate torque prediction and proactive compensation. This hybrid structure significantly improves convergence speed, enhances reference speed tracking accuracy, and ensures fast and precise torque response, and its strong resilience to external load disturbances, system parameter variations enable stable and reliable operation under challenging conditions. The effectiveness of the proposed approach is validated through comprehensive simulations using the MATLAB/Simulink.
Volume: 16
Issue: 1
Page: 121-134
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

Evaluating plant growth performance in a greenhouse hydroponic salad system using the internet of things

10.11591/ijece.v16i1.pp505-517
Chonthisa Rattanachu , Wiyuda Phetjirachotkul , Isara Chaopisit , Kronsirinut Rothjanawan
Hydroponic salad cultivation is becoming increasingly popular. However, a common challenge is the lack of time to maintain hydroponic vegetables due to other responsibilities. This study presents a hydroponic system based on the internet of things (IoT) technique, designed to save time by enabling remote control through a mobile application connected to a NodeMCU microcontroller. Various sensors are integrated with the NodeMCU for real-time monitoring and automation. The study also explores the use of RGB LEDs, which significantly accelerated plant growth and reduced cultivation time. A comparative experimental design was employed to evaluate the growth rate of green oak salad vegetables under two different greenhouse systems. The primary factor compared was the greenhouse system type, with plant growth rate as the outcome variable. Each treatment was replicated 10 times. F-tests were used to statistically determine significant differences in growth rates between the two systems across measured intervals. Results showed that the automated greenhouse system produced the highest leaf width and plant weight values. The use of RGB LEDs reduced the cultivation period from 45 days to 30 days, enabling more planting cycles and ultimately increasing overall yield.
Volume: 16
Issue: 1
Page: 505-517
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

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

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

Students performance clustering for future personalized in learning virtual reality

10.11591/ijece.v16i1.pp297-310
Ghalia Mdaghri Alaoui , Abdelhamid Zouhair , Ilhame Khabbachi
This study investigates five clustering algorithms—K-Means, Gaussian mixture model (GMM), hierarchical clustering (HC), k-medoids, and spectral clustering—applied to student performance in mathematics, reading, and writing to support the development of virtual reality (VR)-based adaptive learning systems. Cluster quality was assessed using Davies-Bouldin and Calinski-Harabasz indices. Spectral clustering achieved the best results (DBI = 0.75, CHI = 1322), followed by K-Means (DBI = 0.79, CHI = 1398), while HC demonstrated superior robustness to outliers. Three distinct student profiles—beginner, intermediate, and advanced—emerged, enabling targeted adaptive interventions. Supervised classifiers trained on these clusters reached up to 99% accuracy (logistic regression) and 97.5% (support vector machine (SVM)), validating the discovered groupings. This work introduces a novel, data-driven methodology integrating unsupervised clustering with supervised prediction, providing a practical framework for designing immersive VR learning environments.
Volume: 16
Issue: 1
Page: 297-310
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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