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Well-being and engagement: its implications for university policy on administrative employee’s wellness program

10.11591/ijere.v14i5.34387
John Michael D. Aquino , Jayson L. de Vera
The well-being and engagement of administrative employees are critical to creating a productive and sustainable work environment. This study investigates causes of university administrative staff well-being and professional involvement. This study examines: i) employee engagement and well-being; ii) administrative employees’ biggest workplace challenges; and iii) how wellness programs promote personal and professional progress. This study used a concurrent triangulation mixed-method research approach. Gallup’s employee engagement survey found that 124 employees have overall favorable attitudes, with a composite mean score of 4.36 demonstrating moderate to high levels of engagement across key workplace indicators. The inconsistent recognition may have an impact on involvement, with the lowest mean of 3.80 and the biggest variability of 1.09. Meanwhile, semi-structured interviews were conducted with 12 administrative employees from a university in region 4A. The findings highlight factors influencing well-being, such as effective communication, work-life balance, positive office environments, and opportunities for promotion. Stress, heavy workloads, and insufficient recognition were seen to be significant challenges, whereas coping strategies including task prioritization, emotional regulation, and peer support were regarded as critical. The results show that well-being boosts commitment and productivity, whereas engagement improves mental health and job happiness. Universities must offer stress management, professional development, and recognition to improve results and staff engagement.
Volume: 14
Issue: 5
Page: 3515-3525
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

Enhancing internet of things network efficiency with clustering and random forest fusion techniques

10.11591/ijece.v15i5.pp4954-4964
Ahmed Gamal Soliman Soliman Deabes , Hani Attar , Jafar Ababneh , Hala Abd El-kader Mansour , Michael Nasief , Esraa M. Eid
The internet of things (IoT) is a key element of the future internet, enabling the acquisition and transfer of data to improve efficiency. One challenge in IoT networks is managing the energy consumption of nodes. IoT innovation constantly evolves dynamically, contributing significantly to sustainable cities and economies. Clustering techniques can help conserve energy and extend the operational lifespan of network nodes. Cluster heads (CH) manage all cluster member (CM) nodes within their group, establishing intra-cluster and inter-cluster connections. Enhancing the CH selection process can further prolong the network lifespan. Various algorithms aim to extend the active duration of IoT nodes and the overall network lifespan. A comparison of the five algorithms shows that one algorithm is better than the others in some cases. This paper discusses how fusion techniques using the random forest (RF) algorithm can enhance energy efficiency in IoT networks. Five algorithms are compared using RF, a robust machine-learning algorithm renowned for its ensemble learning capabilities. It selects the best one based on active nodes per round, residual energy for each round, and the average end-to-end delay.
Volume: 15
Issue: 5
Page: 4954-4964
Publish at: 2025-10-01

Wind farm integration with the objective of transmission expansion power in South Africa

10.11591/ijeecs.v40.i1.pp34-46
Nomihla Wandile Ndlela , Katleho Moloi , Musasa Kabeya
Growing renewable energy (RE) use mitigates climate change. The integration of large-scale intermittent renewable energy resources (RER) like wind energy into electrical networks has increased during the past decade. However, careful planning is needed to accommodate the long-term energy demand increase. Transmission network expansion planning (TNEP) is the methodical and profitable process of increasing power infrastructure to meet predicted electricity demand while preserving reliability. This article is for those interested in integrating renewable energy sources (RES) into HVTL to increase power availability and decrease losses. The Eros-VuyaniNeptune 400 kV transmission powerline connecting KwaZulu-Natal to the Eastern Cape is used in this study. It was implemented during the transfer of affected residents in the Ingquza Hill Local Municipality, which includes Lusikisiki and Flagstaff villages. This study connects the existing Metro wind farm to the Vuyani substation, which is connected to the Eros substation through a 400 kV transmission line. This research enhanced transmission line power while preserving grid stability with a 27 MW wind farm, and also increased external grid reserve capacity for future usage or unexpected power demand. This paper outlines TNEP’s significant advances using classic (mathematical) and advanced (heuristic and meta-heuristic) optimization approaches.
Volume: 40
Issue: 1
Page: 34-46
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

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

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

A solar-powered autonomous power system for aquaculture: optimizing dual-battery management for remote operation

10.11591/ijece.v15i5.pp4376-4386
Thomas Yuven Handaka Laksi , Levin Halim , Ali Sadiyoko
In Indonesia, growing fish consumption demands necessitate expanded, yet sustainable, fish production without sacrificing quality. The process of feeding and the quality of the surrounding water are important factors influencing fish quality. To address this, Parahyangan Catholic University's Fishery 4.0 project pioneers a unique technology that integrates water quality monitoring with a fish feeding feature. The design and implementation of an independent, reliable power module, which is fundamental to the functionality of this system, is at the focus of this research. This study shows that a designed power module adapted to the specific needs of Fishery 4.0 is feasible. The system powers all modules with a 12 V battery and is recharged with a solar panel. The battery can be charged to 95% capacity, yielding 8550 mAh from a 9000 mAh capacity. A UC-3906 charger IC controls the charging process, deliberately managing the parameters required for optimal battery charging. Particularly, when exposed to ideal solar radiation, the charger recharges a 9 Ah battery from 30% to full capacity in about 10 hours and 10 minutes. This study proposes a novel to battery management, which is critical for the operation of aquaculture equipment at isolated locations.
Volume: 15
Issue: 5
Page: 4376-4386
Publish at: 2025-10-01

Comparative analysis of convolutional neural network architecture for post forest fire area classification based on vegetation image

10.11591/ijece.v15i5.pp4723-4731
Ahmad Bintang Arif , Imas Sukaesih Sitanggang , Hari Agung Adrianto , Lailan Syaufina
This study presents a comparative analysis of 7 Convolutional Neural Network (CNN) architectures—MobileNetV2, VGG16, VGG19, LeNet5, AlexNet, ResNet50, and InceptionV3—for classifying post-forest fire areas using field-based vegetation imagery. A total of 56 models were evaluated through combinations of batch size, input size, and optimizer. The results show that MobileNetV2, VGG16, and VGG19 outperformed other models, with validation accuracies exceeding 88%. MobileNetV2 emerged as the most balanced model, achieving 96% accuracy with the lowest model size and training time, making it ideal for resource-constrained applications. This study highlights the potential of CNN-based classification using mobile field imagery, offering an efficient alternative to costly and condition-dependent satellite or drone data. The findings support real-time, localized identification of burned areas after forest fires, providing actionable insights for prioritizing recovery areas and guiding ecological restoration and land rehabilitation strategies.
Volume: 15
Issue: 5
Page: 4723-4731
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

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

Decomposition and multi-scale analysis of surface electromyographic signal for finger movements

10.11591/ijece.v15i5.pp4593-4604
Afroza Sultana , Md. Tawhid Islam Opu , Md. Shafiul Alam , Farruk Ahmed
Decomposition of the surface electromyography (sEMG) signal is vital for separating the composite, complex, noisy signals recorded from muscles into their integral motor unit action potentials (MUAPs). By precisely identifying each motor unit’s activity, this method offers greater insights into the functioning of the neuromuscular system, which helps isolate each motor unit's contribution, making it essential for understanding muscle coordination and diagnosing neuromuscular disorders. In this study, we employ the maximal overlapping discrete wavelet transform (MODWT), which is well-suited for analyzing signals in the time-frequency domain. The study decomposed the sEMG signal into six levels to identify the neural activity of finger movements and analyzed the motor unit action potential (MUAP). In the frequency range of 30.2 and 64.6 Hz, the signal exhibits the highest MUAP which is independent of movement. Using inverse MODWT, it was rebuilt from the decomposed levels. With 95.8% accuracy, the similarity between the reassembled signal and the original signal was determined using correlation analysis to assess the efficacy of the method.
Volume: 15
Issue: 5
Page: 4593-4604
Publish at: 2025-10-01

On design of a small-sized arrays for direction-of-arrival-estimation taking into account antenna gains

10.11591/ijece.v15i5.pp4642-4652
Ilia Peshkov , Natalia Fortunova , Irina Zaitseva
In the paper a technique for designing antenna arrays composed of directional elements for direction-of-arrival (DOA) estimation is proposed. Especially this approach is applied for developing hybrid antenna arrays with increased accuracy which features digital spatial spectral estimation after preliminary analog beamforming. The earlier obtained explicit formula for calculating the Cramér–Rao lower bound (CRLB) which determines the relationship between the variance of the DOA-estimation and antenna elements' radiation patterns, array geometry, has been used. Main idea of the proposed technique is that it takes into account spatial pattern and gain of the antenna elements. The high gain unlike the number of the antenna elements or interelement distance is the most important factor which allows reducing the value of the DOA-estimation errors. A couple of the examples of calculating radiation patterns of antenna elements improving accuracy of DOA-estimation with super-resolution are provided in the paper. Proposed antenna arrays are modeled according to the method of moments (MoM). The values of the root mean square error after the DOA-estimation are obtained. It is shown that the resulting hybrid systems can reduce the error value in DOA-estimation with super-resolution.
Volume: 15
Issue: 5
Page: 4642-4652
Publish at: 2025-10-01
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