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30,938 Article Results

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

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

Cascaded speech enhancement system using deep learning method

10.11591/ijece.v16i2.pp806-817
Kavitha A , Mahesh Chandra , Vijay Kumar Gupta
Here, a two-stage cascaded noise minimization from noisy speech is proposed for noise cancellation from highly corrupted speech signals. In the first stage, corrupted speech is passed through speech enhancement system based on wavelet domain adaptive filter using least mean square algorithm (WDAF-LMS) and performance is evaluated for noisy signal corrupted by babble noise, car noise and machine gun noises. Then this output is given to second stage for further improvement. This is fully connected deep neural network using stochastic gradient descent with momentum optimizer (FCDNN-SGDM) used to improve the quality of speech signal. The system is tested for highly corrupted noisy speech signals where noise signal power level is equal to or more than clean signal power. Input signal-to-noise ratio (SNR) level is taken as 0 dB and -5 to -13 dB. The proposed system improved the quality and intelligibility of speech at all SNR levels for all three noises.
Volume: 16
Issue: 2
Page: 806-817
Publish at: 2026-04-01

Assistive tool of energy metering system for power utility companies

10.11591/ijece.v16i2.pp577-586
Keh-Kim Kee , Ramli Rashidi , Huong-Yong Ting , Lo Tzu Hsiung , Owen Kwong-Hong Kee , Yeo Hong Zheng , Michelle Anak Ini
The growing demand for electricity and the complexity of power quality management highlight the need for advanced energy monitoring systems. Existing solutions often could not provide the real-time, detailed data necessary for smart grids, smart cities, and Industrial 4.0. They also fail to monitor power quality effectively, avoid equipment damage and ensure safety. To address this, we developed an internet of things (IoT)-based tool that leverages standard energy meters. The system monitors and analyzes electrical energy consumption and its power quality in real-time. The system adopts a multi-layered IoT architecture, where fog computing handles immediate data processing and the cloud computing supports machine learning for power quality detection. In this work, measurement accuracy is validated against a commercial power multimeter, achieving mean absolute percentage error (MAPE) values below 1.0% across different appliances. A companion web portal allows for real-time data visualization, time-series analysis, remote control of appliances and power quality detection that comply with IEC and IEEE standards. The proposed system is scalable and user-friendly, offering a practical smart metering solution for modern energy management. It aligns with the needs of smart grids and smart cities, contributing to efficient and intelligent energy consumption in the context of Industry 4.0.
Volume: 16
Issue: 2
Page: 577-586
Publish at: 2026-04-01

Influence of doping concentration on the performances of multi-junction solar cell InGaP/InGaAs/Ge

10.11591/ijece.v16i2.pp619-628
Khadidja Djeriouat , Salim Kerai , Kheireddine Ghaffour
Recently, because of the high costs of experimentation, researchers have turned to simulation. This type of simulation makes it possible to determine, at any point in the volume of a component, the densities of carriers, electrons and holes, the energies, the recombination rates, the electric fields and other parameters that can be deduced from it, such as currents and voltages. Our paper presents the simulation results of the heterojunction solar cell made of GaInP/GaInAs/Ge materials using Silvaco's Atlas software to optimize its electrical efficiency by acting on the doping of photoactive layers. We have chosen a tandem structure when the top cell is constructed by Ga0.4In0.6P, in the middle cell, we used Ga0.1In0.9As and the bottom cell is formed by germanium (Ge). The simulation is performed under the following conditions: 1-sun (0.1 w/cm2), AM1.5G illumination and at temperature 300 K. We obtained an efficiency of 24.65%.
Volume: 16
Issue: 2
Page: 619-628
Publish at: 2026-04-01

Fractional-order chaos modelization and sliding mode control in a biological enzyme system

10.11591/ijece.v16i2.pp729-738
Sakina Benrabah , Bachir Bourouba , Samir Ladaci
This paper proposes two main contributions to fractional-order modeling and control of biological systems that may exhibit chaotic behavior. First, a fractional-order chaotic model is designed to represent a biological enzyme using bifurcation diagrams and fractional orders tuning inspired by the available integer order model. This new approach improves the biological model by introducing physical properties specific to fractional order systems such as the memory effect, fractal properties, tissue heterogeneity and non-local behavior. Furthermore, this makes the use of a more effective, robust and powerful fractional-order control easier and more natural. The second main contribution is to propose a fractional-order sliding mode surface in order to derive a sliding mode control (SMC) controller that is able to stabilize this fractional-order biological system asymptotically. We successfully performed the stability analysis using the Lyapunov theory. Numerical simulations using MATLAB are given to demonstrate the efficiency of the proposed fractional-order controller with a drastic improvement in convergence time comparatively to the integer-order counterpart.
Volume: 16
Issue: 2
Page: 729-738
Publish at: 2026-04-01

Internet of things and YOLOv11 for orangutan intestinal nematode parasite detection

10.11591/ijece.v16i2.pp981-990
Rony Teguh , Nahumi Nugrahaningsih , Adventus Panda
The health of Bornean orangutans is increasingly threatened by intestinal nematode parasites, which cause significant morbidity and mortality. Traditional microscopic diagnosis is accurate but slow, labor-intensive, and impractical in remote conservation areas. This paper presents a proof-of-concept smart diagnostic automated system that integrates internet of things (IoT) enabled mobile microscopy with a deep learning model based on you only look once version 11 (YOLOv11). A publicly available dataset of 4,000 annotated parasite egg images, derived from human fecal samples and used as a proxy for orangutan infections, was employed for model training and evaluation. The proposed system achieved a mean average precision (mAP) of 0.9957 and a mean intersection over union (IoU) of 0.9098 across four target classes. Compared with prior works using YOLOv4, YOLOv5, and lightweight models, our approach provides higher segmentation fidelity and is embedded in an IoT-based framework suitable for field deployment. Importantly, a pilot test conducted in the field using real orangutan fecal samples confirmed the system feasibility, with near real-time inference (~300 ms per image) and usability by non-specialist users under low-resource conditions. While broader validation with larger orangutan specific datasets remains necessary, this study demonstrates how IoT and computer vision can be combined into a scalable diagnostic tool for wildlife health monitoring and conservation applications.
Volume: 16
Issue: 2
Page: 981-990
Publish at: 2026-04-01

Data analytics and prediction of cardiovascular disease with machine learning models: a systematic literature review

10.11591/ijece.v16i2.pp914-923
Ravipa Sonthana , Sakchai Tangprasert , Yuenyong Nilsiam , Nalinpat Bhumpenpein , Siranee Nuchitprasitchai
Cardiovascular disease (CVD) remains one of the leading causes of death globally, underscoring the need for effective early risk prediction. This systematic literature review analyzes research published between 2013 and 2023 on the application of machine learning (ML) in CVD risk prediction. Key areas examined include feature selection, data preprocessing, algorithm choice, and model evaluation. Studies were selected from ACM Digital Library, IEEE Xplore, ScienceDirect, and Scopus based on predefined research questions. Common challenges include limited or low-quality datasets, inconsistent preprocessing methods, and the need for clinically interpretable models. Widely used algorithms include random forest (RF), support vector machine (SVM), decision tree (DT), logistic regression (LR), naïve Bayes (NB), k-nearest neighbor (K-NN), and extreme gradient boosting (XGBoost). The review highlights that robust preprocessing, optimal feature selection, and thorough model validation significantly improve predictive accuracy. It also emphasizes the importance of balancing performance with interpretability for clinical adoption. Finally, the study proposes a structured framework to guide future research and practical implementation, including the integration of genetic and behavioral data to support more personalized and effective cardiovascular care.
Volume: 16
Issue: 2
Page: 914-923
Publish at: 2026-04-01

An extensive review of islanding detection approaches in microgrids for distribution generations

10.11591/ijece.v16i2.pp608-618
Resna S. R. , Devi Vighneshwari B.
Microgrids integrated with distributed systems provide several benefits to the power grid, including faster detection times, superior power quality, and energy savings. Microgrids are managed using various methodologies in both grid-connected and island states. Microgrids must detect inadvertent islanding to protect individuals and prevent device damage. Monitoring and identifying magnitude anomalies are the foundation of the majority of islanding detection approaches (IDAs). This study summarizes the IDAs used in microgrids. An islanding fault is a microgrid that inadvertently disconnects from itself owing to a problem in the utility grid. A through categorization of IDAs is provided, with a focus on both local and remote approaches. Local IDAs can be further classified using passive, active, and hybrid methods. Furthermore, the power-quality effect, nondetection zone (NDZ), detection time (DT), and error detection rate (EDR) statistical comparison of the IDAs is examined. The benefits, drawbacks, and research gaps in the current work are evaluated. Lastly, challenges and recommendations for future research are highlighted.
Volume: 16
Issue: 2
Page: 608-618
Publish at: 2026-04-01

An energy-optimized A* algorithm for path planning of autonomous underwater vehicles in dynamic flow fields

10.11591/ijece.v16i2.pp753-765
Do Khac Tiep , Nguyen Van Tien , Cao Duc Thanh
This paper presents the development and implementation of an energy-optimized A* algorithm for autonomous underwater vehicle (AUV) path planning in these complex environments. The core of the approach is the integration of a computationally efficient flow field model and a detailed AUV energy consumption model directly into the A* search heuristic. The energy model considers factors such as drag forces, relative velocity between the AUV and the flow, and AUV maneuvering. The A* cost function is modified to prioritize paths that minimize the predicted total energy expenditure, while simultaneously ensuring obstacle avoidance and path feasibility. The algorithm was implemented and validated using a simulated environment with varying flow conditions. Results demonstrate that the proposed energy-optimized A* algorithm achieves a significant reduction in energy consumption – up to 50% in tested scenarios – compared to a standard A* implementation, while successfully generating collision-free and dynamically feasible paths. This work contributes a practical and effective solution for energy-aware AUV navigation in dynamic underwater environments, enabling longer mission durations and improved operational efficiency.
Volume: 16
Issue: 2
Page: 753-765
Publish at: 2026-04-01

Dynamic analysis of a human-transporting robot climbing stairs

10.11591/ijece.v16i2.pp638-650
Duong Tan Dat , Le Hong Ky , Tran Duc Thuan
Robots used for transporting people on stairs face several limitations regarding tipping and safety hazards. Changes in the robot's center of gravity during stair climbing can generate tipping moments, leading to instability, tipping, and increased danger to users. This paper presents the modeling and analysis results of a tracked robot for transporting people on stairs, equipped with an anti-tipping mechanism based on center of gravity balance, combined with a vibration-damping mechanism mounted at the rear of the robot to enhance stability during stair climbing. Based on Newton-Euler's formulas, robot dynamics equations are established to describe the motion and analyze the robot's stability characteristics. Simulation and experimental results investigating the changes in center of gravity, velocity, tipping moment, and balancing moment of the robot during uphill and downhill movement were performed using MATLAB Simulink software. Simulation results indicate that the robot's center of gravity is adjusted and stabilized throughout both uphill and downhill movements. Practical experiments conducted on a fabricated robot model, capable of carrying a 100 kg load and moving up and down stairs with a 35-degree incline, demonstrated the feasibility and effectiveness of the proposed mechanical design. The results showed good agreement in kinematic trends between experimental and simulated data during the stair climbing, stair-on, and stair-step transition phases. This agreement between experimental and simulation results proved the correctness of the robot system and the constructed dynamic model. The research results provide a basis for developing control algorithms for robots that efficiently transport people up and down stairs in buildings.
Volume: 16
Issue: 2
Page: 638-650
Publish at: 2026-04-01

Cloud internet of things-based cyber-physical system for microalgae integrated-aquaculture recirculating system in Sarawak

10.11591/ijece.v16i2.pp1030-1038
Keh-Kim Kee , John Sie Yon Lau , Alan Huong Ting Yong
The escalating demand for high-quality protein has driven commercial aquaculture's growth, and microalgal biomass shows potential to support this sector and contribute to global food security. Digitalizing integrated microalgal-aquaculture systems can significantly enhance sustainable protein production. Enabling technologies like the internet of things (IoT) and cyber-physical systems (CPS) are crucial for creating resilient aquaculture systems that ensure profitability, ecosystem health, and climate adaptability. However, applying cloud IoT and CPS solutions in the microalgae industry, especially the integrated microalgae and prawn farms remain underexplored. This work aims to develop a smart system for real-time monitoring and analysis of integrated microalgae and prawn farms in Sarawak, supported by an intelligent decision-support system. Utilizing a hybrid cloud-fog architecture, the system ensures efficient data acquisition, storage, and analysis and provides real-time monitoring through various user interfaces. Deployed in the plant site for over three months, the proposed system has proven effective in enhancing process efficiency and functionality, offering valuable reference in sustainable aquaculture for future enhancements such as multi-sensor and multi-site deployment in other farming systems to promote holistic environment sustainability and digital transformation.
Volume: 16
Issue: 2
Page: 1030-1038
Publish at: 2026-04-01

Analyzing normalized beta wave power in EEG signals: a comparative study between C4-A1 and EMG1-EMG2 channels for RBD sleep disorder detection

10.11591/ijece.v16i2.pp818-826
Mohd. Maroof Siddiqui , Prajoona Valsalan
Sleep disorders are medical conditions affecting the sleep patterns of individuals or living beings, with some being severe enough to disrupt normal physical, mental, and emotional functioning. This research article discusses the analysis of the attributes and waveforms of electroencephalogram (EEG) signals in humans. The major objective is to present the findings through signal spectrum analysis, highlighting changes through various sleep stages. The objective of this research is to assess the potential effectiveness of EEG patterns in diagnosing sleep disorders, particularly those associated with rapid eye movement behavior disorder. These conditions frequently lead to detectable alterations in the electrical and chemical processes within the brain, which can be analyzed by examining brain signals and images. This research paper utilizes the short time-frequency analysis of power spectrum density (STFAPSD) method on EEG signals to diagnose various types of sleep disorders. Calculated values are normalized and the average power of the spectral signal spectra, relating to EEG wave components (delta: 1-4 Hz; theta: 4-8 Hz; alpha: 8-13 Hz; beta 13--25~30 Hz). These indices are used as diagnoses to discriminate among different types of sleep disturbances. The results comparison performs accurate power spectral density (PSD) estimations for several sleep disorders, which makes this technique highly efficient to analyze a large database in a short time. Importantly, we achieve significantly results when analyzing the normalized beta power of both C4-A1 and EMG1-EMG2 channels during the rapid eye movement (REM) stage in the EEG signal. This observation demonstrates a strong difference in PSD values (beta normalized) between normals and REM sleep behavior disorders (RBDs).
Volume: 16
Issue: 2
Page: 818-826
Publish at: 2026-04-01

Parametric analysis to optimize a tradeoff between the efficiency and demagnetization of line-start permanent magnet synchronous motors

10.11591/ijece.v16i2.pp563-576
Le Anh Tuan , Trinh Bien Thuy , Do Nhu Y.
The line-start permanent magnet synchronous motors (LSPMSMs) have many advantages, such as high efficiency and power factor, high energy density, and the ability to line-start. Therefore, the LSPMSMs are being studied to partially replace the induction motors (IMs) currently in use. However, LSPMSMs have disadvantages, including poor starting capability, and the permanent magnets may experience irreversible demagnetization during operation. Thus, this paper uses parametric analysis method to analyze the size of the permanent magnets to optimize the efficiency of the motor while ensuring that the permanent magnets do not undergo irreversible demagnetization. A 15 kW, 2-pole LSPMSM was used for experimentation, and the results show that the motor achieves the highest efficiency of ηmax = 95.5% at wM = 35 mm. However, when the motor thickness wM is greater than or equal to 34 mm, the motor experiences significant demagnetization. Thus, selecting permanent magnets (PM) size and material type that balance motor efficiency and avoid irreversible demagnetization needs careful consideration. Additionally, the experimental and simulation results are consistent, confirming the accuracy between the two methods.
Volume: 16
Issue: 2
Page: 563-576
Publish at: 2026-04-01

Design and prototyping of the planar inverted-F antenna for V2X communications

10.11591/ijece.v16i2.pp842-849
Loubna Berrich , Adnane Addaim
In the context of intelligent transportation systems (ITS) development, vehicle-to-everything (V2X) communication plays a central role by enabling information exchange between vehicles (V2V), infrastructure (V2I), pedestrians (V2P), and the network (V2N). The effectiveness of these systems relies heavily on the performance of the antennas employed, which must meet strict requirements in terms of compactness, bandwidth, gain, and electromagnetic compatibility. One of the main challenges lies in designing antennas suitable for the embedded vehicular environment, where space is limited and the propagation conditions are complex. In this context, the present study aims to design, simulate, fabricate, and experimentally evaluate a planar inverted-F antenna (PIFA) dedicated to V2X communication in the 5.8 GHz band. The primary objective is to develop an antenna that is both compact and high-performing, tailored to the specific constraints of V2X applications. The adopted methodology involves a comprehensive parametric study, focusing on several key design parameters that influence the antenna’s performance, such as substrate selection, feeding point location, and the addition of a slot in the structure. These factors are analyzed to optimize the radiation characteristics, resonant frequency, and impedance matching of the antenna. The results demonstrate the feasibility of a PIFA antenna that offers an excellent trade-off between miniaturization and performance, making it well suited for V2X communication applications at 5.8 GHz.
Volume: 16
Issue: 2
Page: 842-849
Publish at: 2026-04-01

An interpretable deep learning framework for early detection of depression using hybrid architectures

10.11591/ijece.v16i2.pp895-904
Chaithra Indavara Venkateshagowda , Roopashree Hejjajji Ranganathasharma , Yogeesh Ambalagere Chandrashekaraiah
Current techniques for detecting depression are labor-intensive and subjective, depending on clinical interviews or self-reports. There is a growing adoption of machine learning (ML) and natural language processing (NLP) to automatically identify depression in textual data. The lack of interpretability, which is essential for healthcare applications, is still a major obstacle, though. By combining convolution neural network (CNN) for feature extraction, bidirectional long short-term memory (BiLSTM) for capturing sequential dependencies, and transformer-based pre-trained language model (PTLM) for contextual understanding, this study offers an interpretable framework for early depression identification. Additionally, the system uses a novel interpretability method to guarantee transparent decision-making. The outcome of the proposed system is found to achieve 96.2% accuracy, 94.5% precision, 95.1% recall, and 94.8% F1-score, which is a significant improvement over current method. This framework acts as a useful tool for early mental health intervention.
Volume: 16
Issue: 2
Page: 895-904
Publish at: 2026-04-01
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