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29,196 Article Results

Augmented reality for ancient attractions

10.11591/ijece.v15i6.pp5717-5727
Numtip Trakulmaykee , Katchaphon Janpetch , Patchanee Ladawong , Atitaya Khamouam
The study focuses on augmented reality (AR) understanding, development and evaluation. For evaluation, this paper assesses the role of multimedia types in perceived enjoyment, and investing in how perceived usefulness, ease-of-use, and enjoyment affect the adoption of AR by tourists. A quantitative approach was employed to collect data from 115 participants who experienced an AR application designed for 14 ancient attractions in Songkhla, Thailand. The multimedia content included 3D models, historical videos, drone videos, billboard navigations, and text animations. Structural equation modeling (SEM) was used to test the proposed relationships. The findings revealed that perceived ease-of-use and enjoyment significantly influence behavioral intention (BI) as significant factors at 0.01, while perceived usefulness did not affect BI in the context of ancient attractions. Moreover, the multimedia types directly impacted the perceived enjoyment at a significant level of 0.05, and indirectly impacted BI. This study contributes to the theoretical understanding of AR adoption in tourism by integrating multimedia types with tourist perceptions and BI. Practically, it provides insights for designing AR applications that enhance visitor engagement and satisfaction in heritage tourism.
Volume: 15
Issue: 6
Page: 5717-5727
Publish at: 2025-12-01

Evaluating clustering algorithms with integrated electric vehicle chargers for demand-side management

10.11591/ijece.v15i6.pp5837-5846
Ayoub Abida , Redouane Majdoul , Mourad Zegrari
The integration of electric vehicles (EVs) and their effects on power grids pose several challenges for distribution operators. These challenges are due to uncertain and difficult-to-predict loads. Every electric vehicle charger (EVC) has its specific pattern. This challenge can be addressed by clustering methods to determine EVC energy consumption clusters. Demand side management (DSM) is an effective solution to manage the incoming load of EVs and the large number of EVCs. Considering the challenges of peak consumptions and valleys, the adoption of vehicle-to-grid (V2G) technology requires mastering load clusters to develop energy management systems for distributors. This work used clustering algorithms (K-means, DBSCAN, C-means, BIRCH, Mean-Shift, OPTICS) to identify load curve patterns, and for performance evaluation of algorithms, it worked on metrics like the Silhouette coefficient, Calinski-Harabasz index (CHI), and Davies-Bouldin index (DBI) to evaluate results. C-means achieves the best overall clustering performance, evidenced by the highest Silhouette coefficient (0.30) and a strong Calinski-Harabasz score (543). Mean-Shift excels in the Davies-Bouldin Index (1.13) but underperforms on other metrics. BIRCH provides a balanced approach, delivering moderate results across evaluated metrics.
Volume: 15
Issue: 6
Page: 5837-5846
Publish at: 2025-12-01

Enhanced ankle physiotherapy robot with electromyography - triggered ankle velocity control

10.11591/ijece.v15i6.pp5314-5326
Dimas Adiputra , Radithya Anjar Nismara , Muhammad Rafli Ramadhan Lubis , Nur Aliffah Rizkianingtyas , Kensora Bintang Panji Satrio , Rangga Roospratama Arif , Annisa Salsabila
Previous ankle physiotherapy robots, called picobot rely on predefined trajectories continuous passive movement without considering patient intent, limiting the encouragement of user-intent motion. This study then integrates electromyography (EMG) signals as triggers into picobot with an ankle velocity-based control system. The upgraded robot activates movement in specific gait phases based on muscle activity, synchronizing therapy with the patient’s intent. Functionality test on 7 young male healthy subjects investigates leg muscles, such as Tibialis Anterior, Soleus, and Gastrocnemius muscles for the most significantly contribute to ankle movements. Then, the muscle is tested to trigger picobot movements. Functionality tests revealed the Tibialis muscle significantly contributes to gait phases 2, the Soleus is prominent in phases 3 and 4, and gastrocnemius is active on phase 1. The robot successfully performs plantarflexion when EMG signals exceed a 1.58 V threshold, reaching a target position of -0.11 rad at a constant velocity of -0.62 rad/s. These findings establish a foundation for future trials since patient testing has not yet been conducted. By promoting active participation, this innovation has the potential to enhance rehabilitation outcomes. Incorporating user-intent triggers may accelerate recovery and improve healthcare accessibility in Indonesia, offering a significant advancement in physiotherapy technologies.
Volume: 15
Issue: 6
Page: 5314-5326
Publish at: 2025-12-01

Platforma: a modular and agile framework for simplified platformer game development

10.11591/ijece.v15i6.pp5535-5542
Rickman Roedavan , Abdullah Pirus Leman , Bambang Pudjoatmodjo
Research on game development frameworks has been extensively conducted; however, most frameworks are still too general. Conventional game frameworks are challenging for students who are new to game development, especially with their limited information and skills. Beginner game developers should ideally be guided by a practical and specific framework to help them better understand the structure of game development in a more directed manner. This paper proposes platformer modular and agile framework (Platforma) that specifically designed for platformer game development. The framework is built based on the atomic design model, breaking down each minor feature of a platformer game element and grouping these features into more specific modules. The framework was tested on three teams of students. Each team was tasked with developing a platformer game with a minimum of 15 levels of the reach game goals typology. Testing results involving 100 respondents using the game experience questionnaire (GEQ) indicated that the games developed had a positive aspect score of 3.48 and a negative aspect score of 2.65. Overall, these results suggest that the Platforma can serve as an effective guide for beginners in developing platformer games.
Volume: 15
Issue: 6
Page: 5535-5542
Publish at: 2025-12-01

Optimization of water resource management in crops using satellite technology and artificial intelligence techniques

10.11591/ijece.v15i6.pp5847-5853
Erick Salvador Reyes-Galván , Fredy Alexander Bolivar-Gomez , Yeison Alberto Garcés-Gómez
This study aims to optimize water consumption in avocado crops through the application of satellite technology, machine learning algorithms, and precise climate data from the climate hazards group infrared precipitation with stations (CHIRPS) system. Crop classification in satellite images is conducted using the random forest algorithm, enabling detailed categorization of cultivated areas, urban land, soil, and vegetation, with a specific focus on avocados due to their high-water demand. Given its economic importance and status as one of the most water-intensive crops, avocado cultivation presents a critical challenge for agricultural sustainability. To validate predictive models and ensure classification accuracy, advanced evaluation methodologies such as the confusion matrix and Cohen's kappa index are utilized, quantifying the precision and reliability of the results. This estimation of water consumption under deficit and surplus conditions offers key insights for efficient water management in avocado cultivation. The results generated can enhance agricultural efficiency by aligning water use with the crop’s actual requirements, thereby contributing to the reduction of its water footprint.
Volume: 15
Issue: 6
Page: 5847-5853
Publish at: 2025-12-01

Machine learning model for accurate prediction of coronary artery disease by incorporating error reduction methodologies

10.11591/ijece.v15i6.pp5655-5666
Santhosh Gupta Dogiparthi , Jayanthi K. , Ajith Ananthakrishna Pillai , K. Nakkeeran
Coronary artery disease (CAD) remains a leading cause of mortality worldwide, with an especially high burden in developing countries such as India. In light of increasing patient loads and limited medical resources, there is an urgent need for accurate and reliable diagnostic support systems. This study introduces a machine learning (ML) framework that aims to enhance CAD prediction accuracy by specifically addressing the reduction of false negatives (FN), which are critical in medical diagnostics. Utilizing a stacked ensemble model comprising five base classifiers and a meta-classifier, the framework integrates cost-sensitive learning, classification threshold tuning, engineered features, and manual weighting strategies. The model was developed using a clinically acquired dataset from the Jawaharlal Institute of postgraduate medical education and research (JIPMER), consisting of 428 patient records with 36 original features. Evaluation metrics show that the proposed model achieved an accuracy of 92.19%, sensitivity of 98%, and an F1-score of 95.15%. These improvements are significant in a clinical context, potentially reducing missed diagnoses and improving patient outcomes. The model is intended for deployment in cardiology outpatient settings and demonstrates a scalable, adaptable approach to medical diagnostics.
Volume: 15
Issue: 6
Page: 5655-5666
Publish at: 2025-12-01

Hardware efficient multiplier design for deep learning processing unit

10.11591/ijece.v15i6.pp5205-5214
Jean Shilpa V. , Anitha R. , Anusooya S. , Jawahar P. K. , Nithesh E. , Sairamsiva S. , Syed Rahaman K.
Deep learning models increasing computational requirements have increased the demand for specialized hardware architectures that can provide high performance while using less energy. Because of their high-power consumption, low throughput, and incapacity to handle real-time processing demands, general-purpose processors frequently fall short. In order to overcome these obstacles, this work introduces a hardware-efficient multiplier design for deep learning processing unit (DPU). To improve performance and energy efficiency, the suggested architecture combines low-power arithmetic circuits, parallel processing units, and optimized dataflow mechanisms. Neural network core operations, such as matrix computations and activation functions, are performed by dedicated hardware blocks. By minimizing data movement, an effective on-chip memory hierarchy lowers latency and power consumption. According to simulation results using industry-standard very large-scale integration (VLSI) tools, compared to traditional processors, there is a 25% decrease in latency, a 40% increase in computational throughput, and a 30% reduction in power consumption. Architecture’s scalability and modularity guarantee compatibility with a variety of deep learning applications, such as edge computing, autonomous systems, and internet of things devices.
Volume: 15
Issue: 6
Page: 5205-5214
Publish at: 2025-12-01

Image-based assessment of cattle manure-induced soil erosion in grazing systems

10.11591/ijece.v15i6.pp5360-5370
Cristian Gómez-Guzmán , Yeison Alberto Garcés-Gómez
Extensive livestock farming significantly impacts soil erosion, necessitating accurate monitoring and assessment to mitigate environmental damage and enhance sustainable pasture management. This study employs unsupervised classification of high-resolution drone imagery to detect and quantify soil erosion associated with cattle manure in pastures, focusing on evaluating classification algorithms, identifying relevant spectral and textural features, and quantifying the extent and severity of erosion. The results demonstrate the effectiveness of unsupervised classification in identifying erosion zones and their impact on soil health and water quality. Field validation confirms the accuracy of the analysis, emphasizing the need for sustainable management practices such as controlled manure redistribution and soil conservation to mitigate erosion and protect natural resources. This approach offers practical tools for mitigating the environmental impacts of semi-extensive livestock farming and promoting more sustainable management. The findings provide practical recommendations for sustainable pasture management, contributing to environmental conservation and the long-term health of live-stock systems.
Volume: 15
Issue: 6
Page: 5360-5370
Publish at: 2025-12-01

Hybrid artificial intelligence approach to counterfeit currency detection

10.11591/ijece.v15i6.pp5804-5814
Monther Tarawneh
The use of physical money continues, posing ongoing challenges in the form of counterfeit money. This problem not only poses a threat to economic stability but also undermines confidence in the financial systems in use. Traditional methods such as manual inspections and testing of security features have become ineffective in detecting advanced counterfeiting techniques on an ongoing basis. This study proposes a hybrid model that harnesses the power of artificial intelligence, combining convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and support vector machines (SVMs) for counterfeit detection. The proposed model leverages the diverse strengths of a number of artificial intelligence techniques, combining the ability to detect counterfeiting, analyse visual aspects, and sequences of banknotes. The proposed model was tested using real Jordanian currency sets of different denominations and datasets generated using generative adversarial networks (GANs). The results showed that the model was able to detect counterfeiting with high accuracy of 98.6%. and minimal errors compared to other methods. This outstanding performance demonstrates the benefits of integrating artificial intelligence (AI) technologies and that there is room for development and solutions that can keep up with advanced counterfeiting strategies. The study demonstrates the importance of integrating AI in maintaining the integrity of physical currency transactions.
Volume: 15
Issue: 6
Page: 5804-5814
Publish at: 2025-12-01

Designing, developing and analyzing of a rectangular-shaped patch antenna at 3.5 GHz for 5G applications at S band

10.11591/ijece.v15i6.pp5422-5432
Sukanto Halder , Md. Sohel Rana , Md Abdul Ahad , Md. Shehab Uddin Shahriar , Md. Abdulla Al Mamun , Md. Mominur Rahaman , Omer Faruk , Md. Eftiar Ahmed
This research study focuses on the design and analysis of two distinct patch antennas for 5G applications at 3.5 GHz. Rogers RT5880 served as the foundational material for antenna designs I and II. A 50 Ω feed line is utilized to supply both antennas. According to the calculations, Design I exhibits a reflection coefficient (S11) of -32.98 dB, a voltage standing wave ratio of 1.045, a gain of 7.81 dBi, an efficiency of 89.2%, and a surface current of 66.82 A/V. Design II has a reflection coefficient (S11) of 34.98 dB, voltage standing wave ratio (VSWR) of 1.036, gain of 8.78 dBi, efficiency of 89.87%, and surface current of 62.7 A/V. Among the two antenna designs, design II outperformed design I, and the results indicate that the antenna fulfilled the designated purpose. The novelties of the proposed paper are to design two different patch antennas using same materials and highlight the performance of the design parameters. Design II is proficient in supporting 5G services owing to its advantageous performance. In addition, S11 of the antenna is reduced to bring the VSWR value is close to 1. Also, improve gain, directivity and efficiency by bringing the antenna impedance matching close to 50 Ω.
Volume: 15
Issue: 6
Page: 5422-5432
Publish at: 2025-12-01

Adaptive tilt acceleration derivative filter control based artificial pancreas for robust glucose regulation in type-I diabetes mellitus patient

10.11591/ijece.v15i6.pp5297-5313
Smitta Ranjan Dutta , Akshaya Kumar Patra , Alok Kumar Mishra , Ramachandra Agrawal , Dillip Kumar Subudhi , Lalit Mohan Satapathy , Sanjeeb Kumar Kar
This study proposes an Aquila optimization–based tilt acceleration derivative filter (AO-TADF) controller for robust regulation of blood glucose (BG) levels in patients with type-I diabetes mellitus (TIDM) using an artificial pancreas (AP). The primary objective is to develop a controller that ensures normo-glycemia (70–120 mg/dl) while enhancing stability, accuracy, and robustness under physiological uncertainties and external disturbances. The AO algorithm tunes the control gains of the TADF controller to minimize the integral time absolute error (ITAE), ensuring optimal insulin infusion in real time. The AO-TADF controller introduces a filtered structure to improve the dynamic response and noise rejection capability, effectively handling the nonlinear nature of glucose-insulin dynamics. Simulation results demonstrate that the proposed approach achieves a faster settling time (230 minutes), lower peak overshoot (3.9 mg/dl), and reduced noise (1%) compared to conventional proportional integral derivative (PID), fuzzy, sliding mode (SM), linear quadratic gaussian (LQG), and H∞ controllers. The closed-loop system achieves a stable glucose level of 81 mg/dl under varying meal and exercise disturbances, validating the superior performance and robustness of the AO-TADF approach.
Volume: 15
Issue: 6
Page: 5297-5313
Publish at: 2025-12-01

Smart wearable glove for enhanced human-robot interaction using multi-sensor fusion and machine learning

10.11591/ijece.v15i6.pp5162-5172
Nourdine Herbaz , Hassan El Idrissi , Hamza Sabir , Abdelmajid Badri
Hand gesture recognition (HGR) using flexible sensors (flex-sensor) and the MPU6050 sensor has proved to be a key area of research in human-machine interaction, with major applications in biasing, rehabilitation, and assisted robotics. This paper proposes a wearable intelligent glove designed to operate a robotics arm in real time, relying on multi-sensor fusion and machine learning methods to enhance the system's responsiveness and precision. The proposed system enables the intuitive reproduction of hand movements and precise control of the robotic arm. In the context of Industry 4.0 and internet of things (IoT), the classification of gestures is necessary for maintaining operational efficiency. To guarantee gesture recognition, data signals from the smart glove are collected and trained by a recurrent neural network (RNN), which achieves 98.67% accuracy for real-time classification of seven gestures. Beyond industrial applications, the wearable smart glove can be exploited in a recognized circuit of all systems, including rehabilitation exercises that involve recording the progression of muscular activity for the assessment of motor functions and serve as a tool for patient recovery.
Volume: 15
Issue: 6
Page: 5162-5172
Publish at: 2025-12-01

A new algorithm for quality-of-service improvement in mobile ad hoc networks

10.11591/ijece.v15i6.pp5466-5483
Hanafy M. Ali , Adel F. El-Kabbany , Yahia B. Hassan
The quality of service (QoS) in mobile ad hoc networks (MANETs) plays a crucial role in optimizing overall network resource utilization. MANET routing protocols, fundamental to QoS, demand adaptive and swift solutions for efficient path searching. In this context, our paper introduces a novel algorithm based on MANETs, employing a hybrid approach that combines ant colony optimization (ACO) with hybrid multipath quality of service ant (HMQAnt) routing protocols. Our algorithm emphasizes bandwidth optimization as a pivotal factor for providing effective paths. By incorporating bandwidth as a significant parameter in the MANETs algorithm, we aim to enhance its overall properties. The proposed routing protocol, focusing on bandwidth optimization, is anticipated to improve the delivery of total network traffic. Evaluation of the algorithm's performance is conducted through QoS metrics, which are overhead, end-to-end delay, and jitter, throughputs, utilizing a MATLAB simulator. Simulation results indicate that our proposed routing protocol holds a distinct advantage compared to ad hoc on-demand distance vector (AODV), destination- sequenced distance (DSDV), dynamic source routing (DSR), and hybrid ant colony optimization-based (ACO) routing protocol called (ANTMANET) algorithms.
Volume: 15
Issue: 6
Page: 5466-5483
Publish at: 2025-12-01

Design and development of home-grown biometric fingerprint device and software for attendance and access control

10.11591/ijece.v15i6.pp5616-5632
Jumoke Soyemi , Ogunyinka Olawale Ige , Olugbenga Babajide Soyemi , Ajibodu Franklin Ademola , Adaramola Ojo Jayeoba , Afolayan Andrew Olumide , Habeeb O. Amode , Mukail Aremu Akinde
This study details the design, development, and deployment of an Android-based Biometric Fingerprint system tailored for institutional access control, attendance tracking, exam monitoring, and staff management. Developed collaboratively by the Innovation Centre and departments across engineering and information and communication technology (ICT), the system integrates custom hardware and software. Hardware includes fingerprint sensors connected to an ATMEGA8 microcontroller and Android interfaces for portability. The software uses modular architecture, comprising a Kotlin-based mobile app with Jetpack Compose, a Laravel-powered web admin panel, and a secure backend API hosted on a virtual private server (VPS). Fingerprint data is safely stored using base64 encoding, enabling accurate user authentication and real-time tracking. A functional prototype was built, tested, and refined, with 95 units deployed in a pilot phase. The system supports multiple fingerprint profiles, secure data handling, and integration with existing institutional platforms. Emphasizing customization, modularity, and adherence to ICT policies, the research also serves as a training tool for staff and students, enhancing operational efficiency and supporting local technology development. Performance evaluation showed a FAR of 0.5%, FRR of 1.2%, and an average authentication time of 2.3 seconds. Post-deployment, student attendance increased by 15%, fee compliance by 10%, and 89% of users rated the system as easy to use. This work demonstrates effective hardware-software co-design for scalable biometric authentication in educational settings.
Volume: 15
Issue: 6
Page: 5616-5632
Publish at: 2025-12-01

Nonlinear backstepping and model predictive control for grid-connected permanent magnet synchronous generator wind turbines

10.11591/ijece.v15i6.pp5091-5105
Adil El Kassoumi , Mohamed Lamhamdi , Ahmed Mouhsen , Mohammed Fdaili , Imad Aboudrar , Azeddine Mouhsen
This research investigates and compares two nonlinear current-control strategies, backstepping control (BSC) and finite control set model predictive control (FCS-MPC) for machine-side and grid-side converters in grid-connected direct-drive permanent magnet synchronous generator (DD-PMSG) wind turbines. Addressing the control challenges in wind energy systems with varying speeds, the study aims to determine which strategy offers superior performance under identical operating conditions. The nonlinear BSC regulates stator and grid currents using Lyapunov-based techniques, while FCS-MPC leverages model predictions to select optimal switching states based on a cost function. A comprehensive simulation using MATLAB/Simulink is conducted, analyzing each controller’s transient behavior, steady-state response, torque ripple, and power quality total harmonic distortion (THD). Results show that FCS-MPC achieves faster convergence, lower overshoot, and superior power quality compared to BSC, though it requires higher computational resources. Statistical validation supports the robustness of FCS-MPC under parameter uncertainties. This work contributes a structured comparison of advanced nonlinear strategies for PMSG-based wind turbines and provides a foundation for future implementations in real-time embedded control systems. Future directions include experimental validation and hybrid model predictive controller- artificial intelligence (MPC-AI) control frameworks.
Volume: 15
Issue: 6
Page: 5091-5105
Publish at: 2025-12-01
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