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Remaining useful life estimation of turbofan engine: a sliding time window approach using deep learning

10.11591/ijeecs.v41.i1.pp283-299
Alawi Alqushaibi , Mohd Hilmi Hasan , Said Jadid Abdulkadir , Shakirah Mohd Taib , Safwan Mahmood Al-Selwi , Ebrahim Hamid Sumiea , Mohammed Gamal Ragab
System degradation is a common and unavoidable process that frequently oc curs in aerospace sector. Thus, prognostics is employed to avoid unforeseen breakdowns in intricate industrial systems. In prognostics, the system health status, and its remaining useful life (RUL) are evaluated using numerous sen sors. Numerous researchers have utilized deep-learning techniques to estimate RUL based on sensor data. Most of the studies proposed solving this problem with a single deep neural network (DNN) model. This paper developed a novel turbofan engine RUL predictor based on several DNN models. The method includes a time window technique for sample preparation, enhancing DNN’s ability to extract features and learn the pattern of turbofan engine degradation. Furthermore, the effectiveness of the proposed approach was confirmed using well-known model evaluation metrics. The experimental results demonstrated that among four different DNNs, the long short-term memory (LSTM)-based predictor achieved the better scores on an independent testing dataset with a root mean-square error of 15.30, mean absolute error score of 2.03, and R-squared score of 0.4354, which outperformed the previously reported results of turbofan RULestimation methods.
Volume: 41
Issue: 1
Page: 283-299
Publish at: 2026-01-01

Cyber hygiene awareness among Malaysian youth

10.11591/ijeecs.v41.i1.pp210-219
Amily Fikry , Azreen Joanna Abdul , Khairul Nazlin Kamaruzaman , Asnawati Asnawati
The study examined cyber hygiene awareness among Malaysian youth by analyzing the roles played by individual knowledge, awareness, attitudes, gender differences, and educational level. An online survey was conducted with 414 respondents in Peninsular Malaysia. The results showed no significant differences in cyber hygiene awareness based on gender and educational level. This suggests equal access to cybersecurity information and training across genders and education levels in Malaysia. This study also found significant relationships between individual characteristics (knowledge, rationality, and attitude) and cyber hygiene awareness. These findings indicate that individuals who are more knowledgeable, have positive attitudes, and make rational decisions tend to have higher cyber hygiene awareness. The results highlight the importance of fostering rationality and consistency in approaches to cybersecurity practices. The study contributes to the thoughtfully reflective decision-making (TRDM) theory, providing insights for developing targeted cybersecurity training programs and policies. Future research could explore additional factors influencing cyber hygiene awareness and examine how these findings translate to actual cybersecurity behaviors in professional settings.
Volume: 41
Issue: 1
Page: 210-219
Publish at: 2026-01-01

Advancing intelligent, sustainable, and secure engineering systems for future technologies

10.11591/ijeecs.v41.i1.pp1-2
Tole Sutikno
This editorial introduces Volume 41, Number 1, January 2026, of the Indonesian Journal of Electrical Engineering and Computer Science (IJEECS), highlighting pivotal research trajectories expected to influence future progress in electrical engineering and computer science. Instead of covering all aspects of the field, this issue is structured around three strategic macroclusters: intelligent and sustainable engineering systems, AI-driven healthcare and human-centered technologies, and secure, comprehensible, and interconnected intelligent infrastructure. These themes show how artificial intelligence, sustainability, and security are coming together more and more in modern engineering applications. The editorial talks about how important intelligent energy systems, advanced control and hardware solutions, data-driven healthcare innovations, and reliable digital infrastructures are for solving global technological problems. This issue's contributions demonstrate IJEECS's dedication to publishing significant, cross-disciplinary research that bridges theory and practice. This issue of the journal makes it clear that it is a progressive platform that wants to promote smart, long-lasting, and safe technologies for the engineering systems of the future.
Volume: 41
Issue: 1
Page: 1-2
Publish at: 2026-01-01

Deep-fuzzy personalisation framework for robot-assisted learning for children with autism

10.11591/ijeecs.v41.i1.pp320-330
Rose-Mary Owusuaa Mensah Gyening , James Ben Hayfron-Acquah , Michael Asante , Kate Takyi , Peter Appiahene
Research exploring the efficacy of robots in autism therapy has predominantly relied on the Wizard-of-Oz method, where robots execute predetermined behaviours. However, this approach is constrained by its heavy reliance on human intervention. To address this limitation, we introduce a novel deep-fuzzy personalization framework for social robots to enhance adaptability in interactions with autistic children. This framework incorporates a deep learning model called singleshot emotion detector (SED) with a mean average precision of 93% and a fuzzy-based engagement prediction engine, utilizing factors such as scores, IQ levels, and task complexity to estimate the engagement of autistic children during robot interactions. Implemented on the humanoid robot RoCA, our study assesses the impact of this personalization approach on learning outcomes in interactions with Ghanaian autistic children. Statistical analysis, specifically Mann Whitney tests (U=3.0, P=0.012), demonstrates the significant improvement in learning gains associated with RoCA's adoption of the deep fuzzy approach.
Volume: 41
Issue: 1
Page: 320-330
Publish at: 2026-01-01

Enhancing cybersecurity in 5G networks systems through optical wireless communications

10.11591/ijeecs.v41.i1.pp250-257
Iyas Abdullah Alodat , Shadi Al-Khateeb
In this paper we will discuss with the recent global deployment of 5G networks, it has become imperative to ensure secure and reliable communications in addi tion to basic responsibility. Given that standard radio frequency (RF) communi cations have security flaws such as eavesdropping, signal jamming, and cyber attacks, wireless optical communications (WOC) offers a viable alternative. Us ing technologies such as visible light communications (VLC) and the free space optics (FSO) technologies, 5G networks can enhance the speed and efficiency of data transmission, while simultaneously enhancing cyber security. In addition to discussing the advantages of wireless on-chip communication technology com pared to RF solutions and the challenges that need to be addressed, this paper examines how WOC technology can enhance cyber security in 5G networks.
Volume: 41
Issue: 1
Page: 250-257
Publish at: 2026-01-01

Lossy ECG signal compression based on RR intervals detection with wavelet transform and optimized run-length encoding

10.11591/ijeecs.v41.i1.pp109-118
Nabil Boukhennoufa , Messaoud Garah
It is expensive to transmit or store significant amounts of electrocardiogram (ECG) records, particularly when using telecommunications channels that charge according to the volume of transferred data. The advancement of telemedicine renders compressing ECG signals even more necessary. Compression aims to reduce the size of data while maintaining the features of ECG signals. This paper presents a novel strategy for compressing ECG signals based on 3D format conversion. After identifying the RR intervals, we divide the signal into cardiac cycles and proceed with the cut and align process. A 3D discrete wavelet transform (DWT) is employed to minimize the correlation existing between two adjacent voxels. Moreover, an optimized run-length encoding (RLE), a novel lossless compression technique, has been proposed to increase the compression ratio (CR). The proposed strategy is applied to different types of ECG records of the Arryyhmia database. This algorithm demonstrates improved performance in terms of CR and percentage root-mean-square difference (PRD) compared to several recently published works.
Volume: 41
Issue: 1
Page: 109-118
Publish at: 2026-01-01

YOLOv8m enhancement using α-scaled gradient-normalized sigmoid activation for intelligent vehicle classification

10.11591/ijeecs.v41.i1.pp153-167
Renz Raniel V. Serrano , Jen Aldwayne B. Delmo , Cristina Amor M. Rosales
Vehicle classification plays a vital part in the development of intelligent transportation systems (ITS) and modern traffic management, where the ability to detect and identify vehicles accurately in real time is essential for maintaining road efficiency and safety. This paper presents an enhancement to the YOLOv8m model by refining its activation function to achieve higher accuracy and faster response in diverse traffic and environmental situations. In this study, two alternative activation functions—Mish and Swish—were integrated into the YOLOv8m structure and tested against the model’s default sigmoid linear unit (SiLU). Training and evaluation were carried out using a comprehensive dataset of vehicles captured under different lighting and weather conditions. The experimental findings show that the modified activation design leads to better model convergence, improved generalization, and a noticeable boost in detection performance, recording up to 5.4% higher accuracy and 6.6% better mAP scores than the standard YOLOv8m. Overall, the results confirm that fine-tuning activation behavior can make deep learning models more adaptive and reliable for vehicle classification tasks in real-world intelligent transportation environments.
Volume: 41
Issue: 1
Page: 153-167
Publish at: 2026-01-01

Intelligent dust monitoring and cleaning optimization on photovoltaic panels

10.11591/ijeecs.v41.i1.pp409-418
Ali Kourtiche , Souaad Belhia , Santiago Felici-Castell , Mohammed El Amine Said , Rania Bouanani
Dust deposition on photovoltaic (PV) panels is a significant operational issue, often leading to power losses exceeding 15–30% in regions with high airborne particle concentrations. Although numerous studies have investigated either visual detection of dust or analytical estimation of performance loss, most approaches focus on a single task and provide limited practical insight for real-time maintenance. This work introduces a dual-task deep learning framework that simultaneously classifies dust severity and predicts the corresponding power loss from panel images. Five recent architectures vision transformer (ViT), swin transformer, GhostNet, DenseNet, and MobileNetV2 are employed as backbone feature extractors, with extracted embeddings processed by a multi-head multi-layer perceptron (MLP) combining shared representation learning with separate classification and regression outputs. The system is trained and evaluated on a real-world dataset of PV panels, and performance is assessed using accuracy and mean absolute error. DenseNet achieves the highest accuracy (94%) and lowest prediction error, while lightweight convolutional neural network (CNN) backbones demonstrate the best balance between precision and computational efficiency. By integrating hybrid processing and dual predictive capability, the proposed method offers a more comprehensive and deployable solution for automated PV monitoring compared to existing single-output approaches.
Volume: 41
Issue: 1
Page: 409-418
Publish at: 2026-01-01

Optimization of a hybrid forward chaining and certainty factor model for malaria diagnosis based on clinical and laboratory data

10.11591/ijeecs.v41.i1.pp419-429
Patmawati Hasan , Rahmat H. Kiswanto , Susi Lestari
Malaria remains a serious public health problem in Indonesia, particularly in Papua Province, which accounts for 89% of national malaria cases. The similarity of malaria symptoms with other infectious diseases and limited laboratory facilities often lead to delays and inaccuracies in diagnosis. The study proposes an optimized hybrid model that combines forward chaining and certainty factor (CF) by integrating clinical and laboratory data to improve the accuracy of malaria diagnosis. The research design includes acquiring knowledge from medical experts, developing a rule-based system using forward chaining, and applying CFs to overcome uncertainty in symptom interpretation. The system is implemented using Python with support from libraries such as NumPy and PyKnow. The test results showed that the integration of laboratory data significantly improved diagnostic performance, with accuracy increasing from 81% malaria-positive using clinical data alone to 98% malaria-positive after combining with laboratory data. Expert testing to validate the accuracy of clinical and laboratory data results compared to expert validation results in an accuracy score of 98%. These findings show that the optimization of the hybrid forward chaining model and CF for malaria diagnosis based on clinical and laboratory data as a recommendation tool for early diagnosis of malaria in endemic areas.
Volume: 41
Issue: 1
Page: 419-429
Publish at: 2026-01-01

Neural-network based representation framework for adversary identification in internet of things

10.11591/ijece.v15i6.pp%p
Thanuja Narasimhamurthy , Gunavathi Hosahalli Swamy
Machine learning is one of the potential solutions towards optimizing the security strength towards identifying complex forms of threats in internet of things (IoT). However, a review of existing machine learning-based approaches showcases their sub-optimal performance when exposed to dynamic forms of unseen threats without any a priori information during the training stage. Hence, this manuscript presents a novel machine-learning framework towards potential threat detection capable of identifying the underlying patterns of rapidly evolving threats. The proposed system uses a neural network-based learning model emphasizing representation learning where an explicit masked indexing mechanism is presented for high-level security against unknown and dynamic adversaries. The benchmarked outcome of the study shows to accomplish 11% maximized threat detection accuracy and 33% minimized algorithm processing time.
Volume: 15
Issue: 6
Page: 6043-6052
Publish at: 2025-12-18

Challenges in radar-based non-supercell tornado detection using machine learning approaches

10.12928/telkomnika.v24i1.27451
Kiki; IPB University Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG) Kiki , Yonny; IPB University Koesmaryono , Rahmat; IPB University Hidayat , Donaldi; Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG) Sukma Permana , Perdinan; IPB University Perdinan , Abdullah; Indonesian Agency for Meteorology, Climatology and Geophysics (BMKG) Ali
Tornado detection in Indonesia remains challenging as most areas are monitored by single-polarization weather radar, while dual-polarization systems offer superior detection capabilities. This study presents a novel approach by applying random forest (RF) and XGBoost machine learning algorithms to detect tornadoes using single-polarization radar data, addressing a critical gap in tropical tornado monitoring where dual-pol infrastructure is limited. Four tornado cases in Surabaya during 2024 were analyzed. Radar features including reflectivity, radial velocity, vorticity, and angular momentum were extracted through a multi-elevation sliding window technique. Spatial labels were assigned based on reports from the Indonesian National Meteorological Services (BMKG) with a 7.5 km radius from the event center. The dataset was balanced using synthetic minority over sampling technique (SMOTE). Evaluation was performed using the leave one-case-out (LOCO) scheme. Within-case evaluation showed strong performance with area under the curve (AUC) >0.94 for both models. XGBoost achieved higher probability of detection (POD 0.67-0.72) but with elevated false alarm rates (FAR up to 70%). RF demonstrated more balanced performance (POD 0.61-0.65, FAR 0.34-0.35). LOCO evaluation revealed significant POD reduction and FAR increase when tested on new cases. This indicates generalization challenges due to variability in tornado characteristics. This study demonstrates the potential of machine learning for tropical tornado early detection using readily available single-polarization radar.
Volume: 24
Issue: 1
Page: 162-174
Publish at: 2025-12-08

Backstepping control in speed loop combined with load torque observer-ESO for IPMSM in electric vehicle

10.11591/ijpeds.v16.i4.pp2271-2279
An Thi Hoai Thu Anh , Tran Hung Cuong , Nguyen Van Hoa
Electric vehicles are gaining popularity due to their environmental friendliness and the need to conserve dwindling fossil fuel resources. In this field, interior permanent magnet (IPM) motors are considered the top choice for propulsion systems due to their high efficiency, high torque-to-current ratio, durability, and low noise. To optimize the speed control performance of IPM motors in the presence of disturbances, a nonlinear speed control algorithm for IPM systems using the backstepping method is developed in this paper. Additionally, a load torque observer using the extended state observer (ESO) method is implemented to enable the system to respond quickly and accurately to load changes while minimizing the effects of disturbances, thereby enhancing the operation and reliability of electric vehicles. The simulation results, conducted in MATLAB/Simulink, demonstrate that the combination of backstepping control and ESO offers good stability for the motor system, while mitigating the impact of disturbances and load variations. This is an important step in optimizing the control system of electric vehicles, contributing to the improvement of performance and reliability in electric vehicle applications.
Volume: 16
Issue: 4
Page: 2271-2279
Publish at: 2025-12-01

Implementation of adaptive PID control for maintaining temperature stability during steady-state conditions in stirred heating tank

10.11591/ijpeds.v16.i4.pp2389-2399
Pricylia Valentina , Hendro Tjahjono , Agus Sunjarianto Pamitran , Iwan Roswandi , Putut Hery Setiawan , Arif Adtyas Budiman , Dedy Haryanto , Sanda Sanda , Kukuh Prayogo , Mulya Juarsa
Temperature stability is a crucial factor in industries such as chemicals, pharmaceuticals, and food processing, where fluctuations can damage product quality and increase energy consumption. This study aims to optimize heater power control using an adaptive proportional integral derivative (PID) control system to maintain temperature stability under steady-state conditions. The method involves applying adaptive PID control to a stirred heating tank using LabVIEW software with a national instruments controller module and a single-phase SCR to regulate heater power and adjust control parameters in real time. The results indicate that the system operates more effectively under stable conditions, with faster response times and a lower overshoot of less than 0.12%. However, under disturbed conditions, such as water drainage and replacement, the system requires more time to adjust the temperature and experiences increased energy consumption and heat loss. Despite this, the system still achieves an energy efficiency improvement, with efficiency values ranging from 77.66% to 80.03%. The implementation of adaptive PID control demonstrates significant potential in enhancing system accuracy and response to temperature changes, contributing to the development of more efficient industrial control technologies.
Volume: 16
Issue: 4
Page: 2389-2399
Publish at: 2025-12-01

Speed control of 3-phase induction motor with modified DTC using HTAF-ANN

10.11591/ijpeds.v16.i4.pp2197-2211
Arpita Banik , Raja Gandhi , Chandan Kumar , Achyuta Nand Mishra , Rakesh Roy
In this research paper, an artificial neural network (ANN) algorithm is implemented with modifications to enhance the performance of a direct torque controlled (DTC) induction motor drive. Since the main challenge in the conventional DTC technique is to tune the PI controller appropriately therefore in this work, an ANN technique is incorporated in place of the conventional PI controller. Sudden changes in speed and loading in induction motor drives lead to sharp fluctuations and disturb the motor performance. In order to overcome these issues, a trained ANN controller is initially used here to enhance motor drive performance. Subsequently, the performance is further improved by modifying the activation function in the ANN controller. Here, motor parameters at rated and variable speed with various loading conditions have been analyzed and compared for the DTC with a conventional PI controller with ANN, and a proposed ANN controller. Simulation of the complete model with the conventional and proposed controllers is done using MATLAB/Simulink platform to observe the various speed responses for different conditions, and the experimental setup is used to demonstrate the effectiveness and performance of the proposed system.
Volume: 16
Issue: 4
Page: 2197-2211
Publish at: 2025-12-01

Bidirectional AC/DC converter connecting AC and DC microgrids for smart grids

10.11591/ijpeds.v16.i4.pp2549-2561
Nguyen Van Dung , Nguyen The Vinh
This paper proposes a converter connecting two independent AC and DC microgrids in a flexible microgrid and smart grid system. With this converter, basic DC/DC converter types such as Flyback are used to develop the power circuit and controller for the converter that is capable of integrating the operating functions for the operation between microgrids. The converter uses bidirectional switching locking technology to simplify the control algorithm. The energy is converted in two directions, AC/DC and DC/AC, with different working principles of increasing and decreasing voltage according to the standards of the distribution grid and DC microgrid. The TDH value is significantly limited when using the recovery circuit solution. The converter is designed, simulated based on OrCAD software, and tested with a capacity in the range of 2-10 kW. The DC microgrid output voltage is 400 VDC, voltage is 220 VAC.
Volume: 16
Issue: 4
Page: 2549-2561
Publish at: 2025-12-01
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