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31,456 Article Results

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

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

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

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

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

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

Classifying the suitability of educational videos for attention deficit hyperactivity disorder students with deep neural networks

10.11591/ijece.v15i5.pp4889-4898
Alshefaa Emam , Eman Karam Elsyed , Mai Kamel Galab
This paper presents a comprehensive deep learning-based system to evaluate the educational videos' suitability for students with attention deficit hyperactivity disorder (ADHD). Current methods frequently ignore important instructional elements that are necessary for improving learning experiences for students with ADHD, such as instructor hand movements, video length, object variety, and audio-visual quality. We emphasize two key issues for how to address these difficulties, first, we present the ADHD online instructor (AOI) dataset, a particular benchmark for assessing instructional hand movement in video suitability to solve the absence of a reference dataset for classifying educational videos relevant to ADHD. Second, the system includes creating an enhanced multitask deep learning model that increases classification accuracy by using task-specific enhancements and optimized architectures. This solves the requirement for a strong model that can distinguish between suitable and unsuitable instructional content. Comprehensive tests using pretrained convolutional neural network (CNN) models indicate that the enhanced VGG16 model outperforms baseline methods by achieving a highest accuracy of 97.84%. The results highlight the value of integrating deep learning methods with structured benchmark datasets, exposing up the path to more resilient and flexible instructional materials designed for students with ADHD.
Volume: 15
Issue: 5
Page: 4889-4898
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

Language model optimization for mental health question answering application

10.11591/ijece.v15i5.pp4829-4836
Fardan Zamakhsyari , Agung Fatwanto
Question answering (QA) is a task in natural language processing (NLP) where the bidirectional encoder representations from transformers (BERT) language model has shown remarkable results. This research focuses on optimizing the IndoBERT and MBERT models for the QA task in the mental health domain, using a translated version of the Amod/mental_health_counseling_conversations dataset on Hugging Face. The optimization process involves fine-tuning IndoBERT and MBERT to enhance their performance, evaluated using BERTScore components: F1, recall, and precision. The results indicate that fine-tuning significantly boosts IndoBERT’s performance, achieving an F1-BERTScore of 91.8%, a recall of 89.9%, and precision of 93.9%, marking a 28% improvement. For the model, M-BERT’s fine-tuning results include an F1-BERTScore of 79.2%, recall of 73.4%, and precision of 86.2%, with only a 5% improvement. These findings underscore the importance of fine-tuning and using language-specific models like IndoBERT for specialized NLP tasks, demonstrating the potential to create more accurate and contextually relevant question-answering systems in the mental health domain.
Volume: 15
Issue: 5
Page: 4829-4836
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

Designing cyber safety and security literacy programs to enhance cyber security competency of prospective teachers

10.11591/ijere.v14i5.32359
Santhosh Thangan , Thiyagu Kaliappan , Vrinda Vijayan , Venukanti Sai Abhinav , Ashalatha Shanthipalla
This study examines the design, development, and evaluation of a cyber safety and security literacy program (CSLP) aimed at enhancing cyber security competency (CSC) among prospective teachers. Utilizing a research and development (R&D) method, the program was structured using the analysis, design, development, implementation, evaluation (ADDIE) model to create instructional modules covering key cyber safety topics. The validity of the program was ensured through expert evaluations, and its effectiveness was tested via a pre-experimental research design with a single group of 50 prospective teachers from training colleges in Kerala, India. Pre- and post-test assessments were conducted using a standardized CSC scale. Statistical analysis, including t-tests and Cohen’s d, revealed a significant improvement in participants’ CSC, with a large effect size (Cohen’s d=4.32), indicating the program’s substantial impact. This study emphasizes the importance of incorporating structured cyber safety education into teacher training programs to bridge the gap between digital engagement and online safety. The findings also highlight the broader need for integrating cyber security literacy in professional contexts to foster responsible digital behavior.
Volume: 14
Issue: 5
Page: 4088-4098
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

Field-programmable gate array-based voltage-feedback-driven battery charging with DC-DC buck converter

10.11591/ijece.v15i5.pp4993-5002
Afarulrazi Abu Bakar , Suhaimi Saiman , Tharnisha Sithananthan , Muhammad Nafis Ismail , Saidina Hamzah Che Harun
This paper presents the design and development of a reference-driven field-programmable gate array (FPGA)-based controllable battery charging system featuring a buck converter. The controller tracks and adjusts the system's duty cycle based on output voltage feedback. The primary goal was to introduce a digital pulse-width modulation generator program using a Hardware Description Language within a feedback loop. To enhance the buck converter's accuracy, the system's switching frequency was set to 20 kHz with an 8-bit counter, achieving a resolution of 0.390625% per clock cycle. An 8-bit parallel analog-to-digital converter provided feedback by measuring the output voltage and comparing it with the reference setpoint. The simulation model was developed using MATLAB/Simulink, while the Quartus II software was employed for controller programming. The resultant data was meticulously analyzed to assess the circuit's performance across various voltage and control parameters. To validate the proposed controller's effectiveness, a 400 W system prototype comprising a step-down transformer, rectifier, and buck converter was constructed and tested for voltage ranging from 24 to 72 V. Through FPGA-based digital control, this system demonstrated a voltage regulation accuracy of ±0.39 per clock cycle and the capability to continuously track and regulate the duty cycle with each clock trigger, ensuring precise control over the charging process.
Volume: 15
Issue: 5
Page: 4993-5002
Publish at: 2025-10-01
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