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28,296 Article Results

Influence of the graph density on approximate algorithms for the graph vertex coloring problem

10.11591/ijece.v15i5.pp4714-4722
Velin Kralev , Radoslava Kraleva
This research explores two heuristic algorithms designed to efficiently solve the graph coloring problem. The implementation codes for both algorithms are provided for better understanding and practical application. The experimental methodology is thoroughly discussed to ensure clarity and reproducibility. The execution times of the algorithms were measured by running the test applications six times for each analyzed graph. The results indicate that the first algorithm generally produced better solutions than the second. In only two instances did the first algorithm produce solutions comparable to those of the second. The results reveal another trend: as the graph density exceeds 85%, the number of required colors increases significantly for both algorithms. However, even at a density of 95%, the number of colors required to color the graph's vertices does not exceed half the total number of vertices. As the graph density increases from 95% to 100%, the number of colors required to color the graph rises significantly. However, when the graph density exceeds 97%, both algorithms produce identical solutions.
Volume: 15
Issue: 5
Page: 4714-4722
Publish at: 2025-10-01

Enhancing software fault prediction using wrapper-based metaheuristic feature selection methods

10.11591/ijece.v15i5.pp4803-4812
Ha Thi Minh Phuong , Dang Thi Kim Ngan , Dao Khanh Duy , Nguyen Thanh Binh
The application of software fault prediction (SFP) to predict faulty components at the early stage has been investigated in various studies. Reducing feature redundancy is key to enhancing the predictive accuracy of SFP models. Feature selection methods are utilized to select and retain the features that contribute the most information while eliminating irrelevant or redundant features from software fault datasets. However, feature selection (FS) in the field of SFP remains a broad and continuously evolving field, encompassing a diverse range of techniques and methodologies. In this work, we study and perform empirical evaluation of ten wrapper FS methods, namely artificial butterfly optimization (ABO), atom search optimization (ASO), equilibrium optimizer (EO), Henry gas solubility optimization (HGSO), poor and rich optimization (PRO), generalized normal distribution optimization (GNDO), slime mold algorithm, Harris hawk’s optimization, pathfinder algorithm (PFA) and manta ray foraging optimization for resolving the data redundancy issue in SFP datasets. Experimental results on nine fault datasets from the PROMISE and AEEEM repositories show that the EO achieves the best performance, with PRO and HGSO ranking next. The comparative analysis revealed that ten wrapper-based FS methods demonstrated a substantial improvement in handling data redundancy issues for SFP.
Volume: 15
Issue: 5
Page: 4803-4812
Publish at: 2025-10-01

New approximations for the numerical radius of an n×n operator matrix

10.11591/ijece.v15i5.pp4732-4739
Amer Hasan Darweesh , Adel Almalki , Kamel Al-Khaled
Many mathematicians have been interested in establishing more stringent bounds on the numerical radius of operators on a Hilbert space. Studying the numerical radii of operator matrices has provided valuable insights using operator matrices. In this paper, we present new, sharper bounds for the numerical radius 1/4 ‖|A|^2+|A^* |^2 ‖≤w^2 (A)≤1/2 ‖|A|^2+|A^* |^2 ‖, that found by Kittaneh. Specifically, we develop a new bound for the numerical radius w(T) of block operators. Moreover, we show that these bounds not only improve upon but also generalize some of the current lower and upper bounds. The concept of finding and understanding these bounds in matrices and linear operators is revisited throughout this research. Furthermore, the study emphasizes the importance of these bounds in mathematics and their potential applications in various mathematical fields.
Volume: 15
Issue: 5
Page: 4732-4739
Publish at: 2025-10-01

Development of a smart portable cupping suction device with multi-mode control using PID regulation

10.11591/ijece.v15i5.pp5003-5018
Mohd Riduwan Ghazali , Mohd Ashraf Ahmad , Luqman Hakim Akmalmas
Cupping therapy is a well-established traditional treatment with various health benefits. However, existing electric cupping devices lack precise pressure control and portability which limit their usability across different skin types. This paper presents the development of a smart and portable cupping suction device with multi-mode functionality for dry, wet, and massage cuppings. Designed using an ESP32C3 XIAO microcontroller, a differential pressure sensor (MPX5100DP), and a motor driver (L293D) to enable real-time pressure regulation, the system incorporates a proportional-integral derivative (PID) to maintain a consistent suction performance at the negative pressures of -25, -35, and -45 kPa. The device was tested on different skin conditions of clean, less hairy, and slightly hairy surfaces. A real-time monitoring interface was additionally integrated using a web server to track the variation in pressure. Experimental results demonstrate effectiveness of the PID control system in achieving stable pressure with minimal fluctuations with enhanced user safety and comfort. It advances the medical devices for therapeutic automation by offering a portable, precise, and user-friendly cupping solution.
Volume: 15
Issue: 5
Page: 5003-5018
Publish at: 2025-10-01

Development of a fuzzy logic-based greenhouse system for optimizing bio-fertigation

10.11591/ijece.v15i5.pp4555-4568
Achouak Touhami , Amina Bourouis , Amel Mahammedi , Sana Mechraoui , Sana Touhami
Modern agriculture faces growing challenges in meeting food and resource demands, particularly with increasing pressure on water and fertilizer usage. This study proposes a fuzzy logic-based algorithm to optimize bio-fertigation by managing key greenhouse parameters—temperature, humidity, soil pH, and soil moisture. Implemented in MATLAB, the system automates the control of actuators (fan, heater, irrigation, fertilization and fertigation pumps) based on sensor data and fuzzy rules. Results show a 27.58% reduction in water use, 58.82% decrease in fertilizer consumption, and a 47.5% increase in tomato yield. Additionally, statistical error metrics mean absolute error (MAE), mean squared error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) were reduced to zero, confirming the system’s high precision and effectiveness in promoting sustainable agricultural practices.
Volume: 15
Issue: 5
Page: 4555-4568
Publish at: 2025-10-01

Remote sensing applied to cocoa crop identification, a thematic review

10.11591/ijece.v15i5.pp4848-4855
Luisa Fernanda Cuellar-Escobar , Vladimir Henao-Céspedes
This article presents a thematic review of 25 publications related to the use of remote sensing techniques for the identification of cocoa crops from 2000 to 2023. Although the use of remote sensing techniques is widely used for mapping different covers because it is very useful in discriminating them, the generation of maps of cocoa crops presents challenges due to their spectral behavior similar to that of forests. This is because cocoa cultivation, being an agroforestry system that is developed in association with timber trees, causes the classification algorithms used to fail to differentiate between forest cover and cocoa crops. For this reason, this study seeks to investigate the different remote sensing techniques used in the mapping of cocoa crops, as well as an analysis of the structure of the publications highlighting the connections between countries and the factors that motivated the authors to research this crop.
Volume: 15
Issue: 5
Page: 4848-4855
Publish at: 2025-10-01

Real time object detection for advanced driver assistance systems using deep learning techniques

10.11591/ijece.v15i5.pp4942-4953
Sudarshan Sivakumar , Shikha Tripathi
Object detection plays a critical role in advanced driver assistance systems (ADAS), where timely and accurate detection of objects on road is essential for vehicular safety. In this study, we propose and evaluate deep learning-based object detection techniques—specifically, convolutional neural networks (CNN) and dense neural networks for real-time object detection. The proposed model is trained on a publicly available image dataset demonstrating its potential to enhance the reliability of ADAS systems without the use of an image preprocessing block. Here the system automatically stops without any human intervention. Our results highlight the strengths and limitations of using CIFAR-10, CIFAR-100 and YOLO datasets for transfer learning, pre-training and algorithm classification. Improvements in model optimization and hardware integration have been achieved using hardware in loop (HIL) set up. The models are evaluated on CIFAR-10, CIFAR-100 and YOLO datasets, with a focus on the impact of image pre-processing on detection accuracy and speed. Experimental results show that the proposed algorithm outperforms the previous methods, by achieving a better accuracy, contributing to safer and robust system without an additional image preprocessing block.
Volume: 15
Issue: 5
Page: 4942-4953
Publish at: 2025-10-01

Low complexity human fall detection using body location and posture geometry

10.11591/ijece.v15i5.pp4620-4629
Pipat Sakarin , Suchada Sitjongsataporn
This paper presents the human fall detection using body location (HFBL) and posture geometry. The main contribution of the proposed HFBL system is to reduce the computational complexity of fall detection system while maintaining accuracy, as most fall detection techniques rely on computationally complex algorithms from machine learning or deep learning. This approach examines the human posture by applying the image segmentation and ratio by posture geometry. Then, the distance transform is used to calculate the high brightness points on the human body. These points are the maximum values compared with the edge values. Afterward, one of these points is selected as a center point. A line is formed by this center point aligned horizontally to separate the upper area and lower area, then an intersection line is drawn through this center point vertically that can separate the four quadrants of body location. With the help of posture geometry, the angles are employed for prediction “Fall” or “NotFall” actions at each frame of video sequence. Referring to the dynamic balance, the ratio between the distance vectors from the center point to the right and left legs is calculated to confirm fall and non-fall activities, utilizing the Pythagorean trigonometric identity. For experiments, 2,542 images from the UR fall detection dataset, with dimensions of 640×480×3 were prepared through image segmentation to find the human body shape for analysis using the proposed HFBL system. Results demonstrate that the low computational HFBL approach can provide 91.23% accuracy, the precision value is 99.14%, the recall value is 84.48%, and the F1-score value is 91.22%.
Volume: 15
Issue: 5
Page: 4620-4629
Publish at: 2025-10-01

Experimental validation of a dual-band printed antenna array operating at 2.45/5.8 GHz with a high efficiency for wireless power transmission applications

10.11591/ijece.v15i5.pp4662-4670
Walid En-Naghma , Mohamed Latrach , Hanan Halaq , Abdelghani El Ougli
This paper presents a simple dual-band antenna element with a rectangular patch designed for the industrial, scientific, and medical bands at 2.45/5.8 GHz. The antenna achieves satisfactory simulated performance at both resonant frequencies, including a reflection coefficient below -10 dB, a voltage standing wave ratio (VSWR) not exceeding 1.2, radiation efficiencies of approximately 90% at 2.45 GHz and 95.55% at 5.8 GHz, and bandwidths of 125.80 MHz around 2.45 GHz and 308.60 MHz around 5.8 GHz. Building on this single-element design, an antenna array configuration comprising two elements etched on a Taconic TLY-5 substrate is developed. The two rectangular patches are connected via a T-junction to a 50 Ω excitation port. The proposed array's effectiveness is validated through simulations using three electromagnetic solvers and experimental measurements. The fabricated antenna array demonstrates improved performance, including a measured return loss of -16.78 dB at 2.45 GHz and -20.61 dB at 5.8 GHz, a VSWR not exceeding 1.5 (1.34 and 1.22 at 2.45 and 5.8 GHz, respectively), input impedance close to 50 Ω, high gain exceeding 8 dBi, bandwidths of 179.50 MHz at 2.45 GHz and 462.90 MHz at 5.8 GHz, and high radiation efficiencies of 96.54% at 2.45 GHz and 98.65% at 5.8 GHz. With only two patches, the proposed antenna array offers a compact, efficient, and practical solution for wireless power transmission applications, particularly for small wireless devices like rectenna systems, due to its simplicity, compact design, and excellent radiation efficiency.
Volume: 15
Issue: 5
Page: 4662-4670
Publish at: 2025-10-01

Determinants of undergraduate recycling behavior: an extended model of the theory of planned behavior

10.11591/ijere.v14i5.32391
Tan Owee Kowang , Lim Kim Yew , Ong Choon Hee , Goh Chin Fei
Promoting positive recycling behavior should be part of the higher educational objectives. The main purpose of this research is to identify the determinants of recycling behavior among undergraduates, to assess the differences in undergraduates’ recycling behavior based on their demographic, and to explore the relationship between the determinants and behavior of recycling. The research extended the theory of planned behaviors (TPB) by adding environmental awareness as a determinant of recycling behavior in addition to attitude toward recycling, subjective norm of recycling, and perceived behavioral control over recycling. The research population comprises management programs undergraduates from a public university in Malaysia. A total of 259 responses were collected via structured questionnaire. Descriptive and Pearson correlation analysis results suggested that respondents strongly agreed that undergraduates’ attitudes and environmental awareness are the most important determinants of recycling behavior, and both determinants are strongly correlated with recycle behavior. The analysis of variance (ANOVA) analysis result also reveals that there is significant difference on recycle behaviors among undergraduates based on year of study, with year 4 undergraduates exhibiting the highest recycling behavior. This finding suggests that the green campus initiatives taken by the university are effective. Additionally, extending the TPB model by adding awareness implies a theoretical contribution of this research.
Volume: 14
Issue: 5
Page: 3727-3734
Publish at: 2025-10-01

Maintenance management of physical infrastructure in educational institutions: a systematic review

10.11591/ijere.v14i5.33130
Julisa del Rosario Quispe Vilca , Dennys Geovanni Calderón Paniagua , Grisely Rosalie Quispe Vilca , Isabel Evelyna Choque Siguairo , Alexander Nicolás Vilcanqui Alarcón
The physical infrastructure of education in Latin America (LATAM) requires actions to ensure its conservation and maintenance in the different systems and levels. This is due to the absence of a maintenance programmed proposed by the State and the lack of trained personnel to implement it. The objective of this study was to analyze the importance of maintenance management of physical infrastructure in educational institutions. A systematic review was conducted following the guidelines of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. The search process was carried out in the Scopus, ERIC, and Web of Science (WoS) databases, and eligibility criteria were established. The review covered the time interval between 2015 and 2023, and 16 English-language papers were selected. The results indicate that the lack of adequate and sustained investment, together with the lack of scheduled maintenance of educational infrastructure and the absence of structured maintenance plans, have a negative impact on student achievement. It is necessary for national and local governments to develop public policies focused on the conservation and improvement of educational infrastructure, incorporating modern management tools to facilitate this process.
Volume: 14
Issue: 5
Page: 3490-3501
Publish at: 2025-10-01

An efficient direction oriented block-based video inpainting using morphological operations and adaptively dimensioned search region with direction-oriented block-based inpainting

10.11591/ijece.v15i5.pp4705-4713
Shyni Shajahan , Y. Jacob Vetha Raj
Video inpainting is a technique in computer vision used to remove unwanted objects from video sequences while preserving visual consistency, so that modifications remain unnoticeable to the human eye. This paper presents an accurate video inpainting model based on the adaptively dimensioned search region with direction-oriented block-based inpainting (ADSR-DOBI) algorithm. The model operates in five main phases: preprocessing, background separation, morphological operations, object removal, and video inpainting. Initially, the input video is converted into frames, followed by preprocessing steps such as deionizing and resizing. These frames are then processed using a background subtraction module, where object localization and foreground detection are performed using the binomially distributed foreground segmentation network (BDFgSegNet) and morphological techniques. This results in segmented foreground objects tracked across frames. The object removal phase eliminates the identified foreground objects and defines the missing regions (holes) to be filled. The ADSR-DOBI algorithm is then applied to inpaint these regions seamlessly. Experimental results demonstrate that this approach outperforms existing state-of-the-art methods in both accuracy and efficiency.
Volume: 15
Issue: 5
Page: 4705-4713
Publish at: 2025-10-01

Synthesis of nonlinear multilinked control systems of thermal power plants

10.11591/ijece.v15i5.pp4500-4507
Oksana Porubay , Isamiddin Siddikov
The paper addresses the synthesis of nonlinear control laws for the technological parameters of drum boiler steam generators in thermal power plants, based on a synergetic control approach. The controlled system is considered to be multidimensional and highly interconnected. The inherent nonlinearity and interdependence of the technological parameters in thermal power plants necessitate the use of nonlinear control laws to achieve effective regulation. This approach enables the expansion of the range of permissible variations in regulator parameters, thereby ensuring the desired dynamic behavior of the controlled variables. An analytical method for synthesizing nonlinear vector control laws for steam generators is proposed. A methodology is developed for designing dynamic regulators capable of compensating for uncertain disturbances while accounting for control constraints. A Lyapunov function is constructed to describe the internal state dynamics of the control object. The proposed method for constructing the dynamic regulator ensures the asymptotic stability of the control system and stabilization of the controlled parameters over a wide range of load variations.
Volume: 15
Issue: 5
Page: 4500-4507
Publish at: 2025-10-01

Tomographic image reconstruction enhancement through median filtering and K-means clustering

10.11591/ijece.v15i5.pp4395-4408
Nguyen Quang Huy , Nguyen Truong Thang
Ultrasound tomography is a powerful and widely utilized imaging technique in the field of medical diagnostics. Its non-invasive nature and high sensitivity in detecting small objects make it an invaluable tool for healthcare professionals. However, a significant challenge associated with ultrasound tomography is that the reconstructed images often contain noise. This noise can severely compromise the accuracy and interpretability of the diagnostic information derived from these images. In this paper, we propose and rigorously evaluate the application of a median filter to address and mitigate noise artifacts in the reconstructed images obtained through the distorted born iterative method (DBIM). The primary aim is to enhance the quality of these images and thereby improve diagnostic reliability. The effectiveness of our proposed noise reduction approach is quantitatively assessed using the normalized error evaluation metric, which provides a precise measure of improvement in image quality. Furthermore, to enhance the interpretability and utility of the reconstructed images, we incorporate a basic machine learning technique known as K-means clustering. This method is employed to automatically segment the reconstructed images into distinct regions that represent objects, background, and noise. Hence, it facilitates a clearer delineation of different components within the images. Our results demonstrate that K-means clustering, when applied to images processed with the proposed median filter method, effectively delineates these regions with a significant reduction of noise. This combination not only enhances image clarity but also ensures that critical diagnostic details are preserved and more easily interpreted by medical professionals. The substantial reduction in noise achieved through our approach underscores its potential for improving the accuracy and reliability of ultrasound tomography in medical diagnostics.
Volume: 15
Issue: 5
Page: 4395-4408
Publish at: 2025-10-01

Enhancing source currents and ensuring load voltage stability in railway electrification system via unified power quality conditions implementation

10.11591/ijece.v15i5.pp4430-4444
Kittaya Somsai , Jeerapong Srivichai , Veera Thanyaphirak
In recent years, interest in electric railway system as a transportation solution for large urban areas has grown significantly. This increased attention stems from several key advantages, including environmental friendliness, high performance, reduced maintenance costs, and lower energy expenses. Railway electrification system rely on supplying power to trains through single-phase transformers. However, these transformers can cause issues such as current imbalances and harmonics at the system connection point, which may impact critical loads. Additionally, fluctuations in source voltage can influence the system's performance. This study examines the causes of unbalanced loading in railway electrification system and introduces an innovative unified power quality conditioner (UPQC) specifically designed for integration into low-voltage railway electrification system. The proposed UPQC aims to restore current balance, minimize harmonics, and enhance overall power quality. Furthermore, it addresses the mitigation of voltage sags in the power distribution network. The simulation results generated through MATLAB programming demonstrate the UPQC's effectiveness in enhancing system performance. The findings reveal that the UPQC reduces source current imbalance to less than 1.6% and total harmonic distortion (THD) to below 4.89% across all test scenarios. Additionally, the UPQC successfully maintains a load bus voltage of 25 kV during single-phase-to-ground and unbalanced three-phase-to-ground fault conditions.
Volume: 15
Issue: 5
Page: 4430-4444
Publish at: 2025-10-01
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