Articles

Access the latest knowledge in applied science, electrical engineering, computer science and information technology, education, and health.

Advanced Search
Filter Icon

Filters article

Years

FAQ Arrow
Single Year
Range Year
0
0

Source Title

FAQ Arrow

Authors

FAQ Arrow

30,376 Article Results

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

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

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

IDPS: A machine learning framework for real-time intrusion detection and protection system for malicious internet activity

10.11591/ijece.v16i1.pp437-449
Raisa Fabiha , Stein Joachim Reberio , Zubayer Farazi , Fernaz Narin Nur , Shaheena Sultana , A. H. M. Saiful Islam
With the increasing frequency and complexity of cyber threats, there is a pressing need for effective real-time solutions to detect and prevent malicious activities. This study introduces a novel machine learning-based architecture for real-time cybersecurity to enhance accurate identification and prevention of malicious cyber activities. The proposed framework combines advanced machine learning algorithms with Wireshark network traffic analysis to effectively detect and classify a wide range of cyberattacks, providing timely and actionable insights to cybersecurity professionals. A core component of this system is a prototype blocker, which is seamlessly integrated with Cisco infrastructure, enabling proactive intervention by blocking suspicious IP addresses in real-time. In addition, a user-friendly web application enhances system operability by offering intuitive data visualization and analytical tools, enabling rapid and informed decision-making. This comprehensive approach not only strengthens network security and protects digital assets but also equips defenders with the capability to respond effectively to the dynamic landscape of cyber threats.
Volume: 16
Issue: 1
Page: 437-449
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

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

Application of the model reference adaptive system method in sensorless control for elevator drive systems using 3-Phase permanent magnet synchronous motors

10.11591/ijece.v16i1.pp149-157
Tran Van Khoi , An Thi Hoai Thu Anh , Tran Trong Hieu
Improving sensorless control performance in elevator drive systems using three-phase permanent magnet synchronous motors (PMSM) has become increasingly popular to reduce costs and enhance system stability. The primary operation of the elevator involves motor mode when the cabin moves upward and shifts to generator mode or braking mode under the influence of gravity when moving downward. This presents significant challenges for sensorless control. To address these issues, the model reference adaptive system (MRAS) based on the mathematical d-q axis model of the PMSM is proposed to estimate rotor speed and position. Combined with field-oriented control (FOC), this method optimizes performance and precisely controls motor torque without requiring physical sensors. Additionally, a low-pass filter is employed to process input signals, such as voltage and current, to improve estimation accuracy and optimize speed response. Simulation results from MATLAB/Simulink demonstrate highly accurate speed responses, particularly under continuous load variations.
Volume: 16
Issue: 1
Page: 149-157
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

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

The evolution of digital competence in pre-service teachers: a global perspective from 2009-2024

10.11591/ijere.v15i1.33434
An Bien Thuy , Ha Van Dung , Nguyen Thi Lan-Ngoc , Pham Thi Hong-Hanh , Nguyen Thi Viet-Nga , Trinh Thi Phuong Thao
In the context of the strong digital transformation occurring in education, digital competence (DC) is one of the essential skills for pre-service teachers. Therefore, equipping students in teacher training programs with DC is a significant public concern. This study analyzes global research output on pre-service teachers’ DC to identify publication trends, key contributors, and predominant research themes. To gain a comprehensive view of pre-service teachers’ DC, the research team conducted a bibliometric analysis of 278 publications from the Scopus database, from 2009 to 2024. The results reveal a substantial increase in publications on DC in pre-service teachers, particularly in the last 3 years. Spain is a leading country in this collection, with the most publications and funding mentions. The most prominent author is Çebi from Turkey. The journal that published the most articles is Education and Information Technologies (EAIT). Through the analysis of co-occurring keywords, four main research trends on this topic have been identified. The findings of this study provide valuable insights into the research topic and contribute to shaping future research directions in this field.
Volume: 15
Issue: 1
Page: 66-81
Publish at: 2026-02-01

Integration in reading literacy: a systematic review of pedagogical, professional, and engagement approaches

10.11591/ijere.v15i1.34044
Md Zahril Nizam Md Yusoff , Wan Nur Aida Sakinah Wan Jusoh , Norfaizah Abdul Jobar , Noor Zuhidayah Muhd Zulkipli , Muhamad Fadzllah Zaini , Tajul Shuhaizam Said
This systematic literature review (SLR) following preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 who explores integrated approaches to reading literacy across pedagogical integration, teacher development, and student engagement. From 1,108 retrieved articles, 59 article high-quality studies were selected for in-depth synthesis. Based on all studies published between 2020 and 2024, the findings reveal that multimodal instruction, inclusive pedagogy, and proposes a cohesive framework linking instructional innovation, teacher agency, and learner experience, offering actionable insights for educators and policymakers. The analysis identified three key thematic drivers of literacy improvement. First, integrated pedagogical practices such as multimodal, bilingual, and play-based instruction enhance literacy by making learning more inclusive, interactive, and context-responsive. Second, teacher professional development through targeted training in visual literacy, inclusive pedagogy, and digital tools strengthens instructional quality and prepares educators for evolving literacy demands. These approaches reflect current trends in instructional innovation and professional capacity building. Third, student engagement is significantly elevated through identity-driven learning, project-based tasks, and emotionally supportive environments that foster confidence, motivation, and deeper literacy outcomes. Together, these themes forms a new integration model that links teaching practices, teacher agency, and student experience into a cohesive literacy framework. The findings offer practical, evidence-based insights for policymakers, curriculum designers, and practitioners aiming to improve reading literacy across diverse settings.
Volume: 15
Issue: 1
Page: 771-783
Publish at: 2026-02-01

Information technology value engineering through partial adjustment valuation theory

10.12928/telkomnika.v24i1.27478
Lukman; Telkom University Abdurrahman , Candiwan; Telkom University Candiwan
The paper proposes a systems management approach that utilizes information technology (IT) treatment as a framework to help firms enhance future performance by optimising key parameters. The method certifies a valuation approach that enables businesses to better manage their IT infrastructure and improve performance. A case study of A case study of PT Telekomunikasi Indonesia (Telkom) and PT XL Axiata (XL) (2004–2018) shows the method’s effectiveness. Once the IT value is identified, specific parameters can be engineered to improve performance without changing other variables. The approach uses a partial adjustment valuation model, enabling performance gains at lower costs. The results show significant improvements in both firms’ performance values and ratios compared to their originals. This supports adopting a cost leadership strategy, making IT based businesses more efficient, cost-effective, and better performing across financial, business, and strategic dimensions.
Volume: 24
Issue: 1
Page: 111-125
Publish at: 2026-02-01

Microbubble size and rise velocity measurement in dissolved air flotation system

10.11591/ijece.v16i1.pp174-186
Jeimmy Adriana Muñoz Alegría , Jesús Emilio Pinto Lopera , Elena Muñoz-España , Juan Fernando Flórez-Marulanda
Water reuse and resource recovery are priority environmental goals under increasing water scarcity and climate stress. Dissolved air flotation (DAF) is widely applied in municipal, industrial, and decentralized treatment trains because fine microbubbles (MB) enhance solids removal efficiency. Accurate, low-cost characterization of MB size and rise velocity is therefore valuable for process monitoring and optimization. This study develops and validates a smartphone-based, computer-vision pipeline for laboratory-scale DAF systems. After camera calibration and lens un-distortion, each video sequence (235 frames per run) is processed through grayscale conversion, median, Gaussian, and local-Laplacian filtering, gamma correction, and Otsu thresholding, followed by morphological refinement. Circular Hough transform then identifies MB candidates, providing their diameters and centroid locations. These detections are then linked frame-to-frame using a distance-gated nearest-neighbor tracker with dynamic memory allocation to accommodate new MBs under turbulent, bubble-clustering conditions. Rise velocity is computed from interframe centroid displacement and frame interval. The system reliably tracked up to 32 microbubbles simultaneously per video. Across four operating pressure/airflow combinations, mean MB diameters ranged 95.47–216.42 µm and mean rise velocities 9.40×10³–2.76×10⁴ µm/s. The approach is low cost, computationally lightweight, and suitable for rapid MB characterization to support DAF monitoring, optimization, and research.
Volume: 16
Issue: 1
Page: 174-186
Publish at: 2026-02-01

An integrated FSM-BABER-SROA framework for secure and energy-efficient internet of things networks using blockchain consensus

10.11591/ijece.v16i1.pp518-534
Achyut Yaragal , Kirankumar Bendigeri
The rapid expansion of the internet of things (IoT) and wireless sensor networks (WSNs) has intensified the demand for energy-efficient, reliable, and secure data transmission. Traditional clustering and static sleep scheduling approaches often fail to ensure long-term sustainability and tamper-resistant communication. This paper presents BABER-SROAChain, a hybrid optimization and security framework that integrates four core modules: i) Fuzzy similarity matrix (FSM)-based clustering for spatial-energy-aware node grouping, ii) Binary Al-Biruni earth radius (BABER) optimization for intelligent cluster head (CH) selection, iii) ship rescue optimization algorithm (SROA) for adaptive sleep scheduling, and iv) a lightweight blockchain protocol with modified practical byzantine fault tolerance (PBFT) consensus for secure inter-cluster communication. The unified objective function incorporates cluster efficiency, redundancy minimization, latency reduction, and packet delivery ratio maximization. Simulation experiments on large-scale WSNs (100–300 nodes) demonstrate that BABER-SROAChain achieves up to 20% improvement in network lifetime, 18% lower energy consumption, and 15% higher packet delivery ratio compared to state-of-the-art models. Additionally, it minimizes blockchain consensus latency while ensuring high data integrity. The proposed framework offers a scalable, secure, and energy-aware solution suitable for real-time IoT applications, including smart cities, healthcare monitoring, and industrial automation, while addressing the dual challenges of performance optimization and blockchain-based security.
Volume: 16
Issue: 1
Page: 518-534
Publish at: 2026-02-01
1 2 3
...
2025 2026
Show 85 of 2026

Discover Our Library

Embark on a journey through our expansive collection of articles and let curiosity lead your path to innovation.

Explore Now
Library 3D Ilustration