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Development of internet of things-based exoskeleton for monitoring elbow rehabilitation therapy

10.11591/ijres.v15.i2.pp553-562
Geevanthran A/L Vegurgama , Mohd Razali Mohamad Sapiee , Khalil Azha Mohd Annuar
The elbow joint is a complex articulation comprising the humeroulnar and humeroradial joints, facilitating flexion-extension movements essential for daily activities. Leveraging advancements in connected systems and paradigms such as the internet of things (IoT), this study proposes an affordable, effective, and IoT-enabled one-degree-of-freedom (1DOF) elbow exoskeleton for home-based rehabilitation. The exoskeleton is designed to provide a natural range of elbow movements (flexion and extension) while enabling real-time monitoring of rehabilitation progress through mobile applications and web servers. The system collects qualitative data on elbow movements, which are critical in rehabilitation therapy, and enables patients to save their rehabilitation status for future reference. This data can be accessed by doctors remotely, ensuring continuity of care. For patients unable to lift their arm independently, a servomotor provides mechanical assistance, enabling them to achieve desired angles for rehabilitation. The IoT platform generates real-time graphs, offering detailed insights into the recovery process through data analysis. This project is a significant advancement in clinical and healthcare settings, as it reduces dependency on human support or physiotherapists. By integrating IoT technology, the proposed exoskeleton ensures effective, autonomous, and data-driven rehabilitation for elbow joint recovery.
Volume: 15
Issue: 2
Page: 553-562
Publish at: 2026-07-01

Predicting student academic outcomes from e-learning interaction data using hybrid machine learning models

10.11591/ijres.v15.i2.pp259-268
Sajithunisa Hussain , Jayachandran Jeyachidra
The rapid growth of digital learning platforms has generated large volumes of student interaction data, providing opportunities for intelligent prediction of academic outcomes. Beyond educational analytics, such prediction tasks are relevant for reconfigurable systems, embedded platforms, very large scale integration (VLSI) accelerators, and internet of things (IoT)-enabled edge devices in smart learning environments. This study proposes a hybrid machine learning framework for predicting student performance using the e-learning student reactions dataset, which captures engagement patterns, behavioral responses, and interaction dynamics. Eight classifiers— eXtreme gradient boosting (XGBoost), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), radial basis function (RBF), and deep neural network (DNN)—are evaluated using both an 80–20 train–test split and K-fold cross-validation to assess accuracy and generalization. Results show the RBF model achieves the highest accuracy of 1.00, demonstrating its ability to capture complex, nonlinear behavior. From a systems perspective, the framework can be mapped onto field programmable gate arrays (FPGAs) or embedded devices, leveraging parallel computation for low-latency inference, and integrated with IoT-enabled smart classrooms for real-time edge analytics. These findings confirm that hybrid machine learning models not only improve student performance prediction but also serve as practical workloads for reconfigurable, embedded, and VLSI-based intelligent systems in digital education.
Volume: 15
Issue: 2
Page: 259-268
Publish at: 2026-07-01

Tracking a person and determining the location by using convolutional neural network technology

10.11591/csit.v7i2.p203-213
Zinah Shiker Makki , Ahmet Zengin
Tracking individuals in real-world environments requires robust, non-intrusive methods that overcome the limitations of device-based systems. This study proposes a convolutional neural network (CNN)-driven person-tracking framework that identifies targeted individuals directly from camera feeds, eliminating the need for wearable or global positioning system (GPS) devices and addressing a major drawback of traditional tracking technologies. The system utilizes a TensorFlow-trained CNN model that can detect, recognize, and locate persons of interest in real-time, even under varying illumination conditions. Unlike conventional approaches, our method integrates facial feature extraction with encrypted identity management, enabling secure multi-person detection and rapid location reporting. Experimental results demonstrate a 92% accuracy in low-light settings and 100% accuracy under normal lighting, confirming the system’s effectiveness for security-oriented applications. The findings highlight the novelty of combining lightweight CNN architecture, real-time facial recognition, and hash-based identity protection within a unified tracking pipeline.
Volume: 7
Issue: 2
Page: 203-213
Publish at: 2026-07-01

An IoT-enabled vision-aid for the blind integrating ultrasonic obstacle detection and GPS-based location tracking

10.11591/ijres.v15.i2.pp386-395
Varuna Kumara , Akshatha Naik , Ashwini Ashwini , Navilgone Krishna Vaishnavi , Ruchitha Kamath Subhashchandra , Trapthi Trapthi
Visual impairment significantly affects independent mobility and personal safety, creating a need for affordable and reliable assistive navigation technologies. This paper presents the design and implementation of a low-cost wearable Vision-Aid system to support visually impaired individuals during outdoor navigation. The primary objective of the study is to enhance obstacle awareness, location tracking, and emergency communication using accessible embedded technologies. The proposed system integrates ultrasonic sensors for real-time obstacle detection, an Arduino microcontroller for data processing, a global positioning system (GPS) module for location tracking, and a global system for mobile communication (GSM) module for emergency alert transmission. Audio feedback is provided through a voice module to guide the user safely. Experimental evaluations were conducted under various environmental conditions to assess obstacle detection accuracy, response time, and location reliability. The results demonstrate accurate obstacle detection, timely audio alerts, and reliable real-time location sharing with caregivers. The proposed system improves user confidence, mobility, and safety while maintaining low implementation cost. This work highlights the potential of embedded and internet of things (IoT)–based assistive devices to enhance autonomy for visually impaired individuals and provides a foundation for future integration of artificial intelligence (AI)-based object recognition.
Volume: 15
Issue: 2
Page: 386-395
Publish at: 2026-07-01

Implementation and design of GPS tracker monitoring system on car rental vehicles based on internet of things using Nodemcu ESP-32

10.11591/csit.v7i2.p214-223
Indah Purnama Sari , Al-Khowarizmi Al-Khowarizmi , Asrar Aspia Manurung
Internet of things (IoT) based vehicle tracking system is an effective solution to overcome various problems in the vehicle rental industry, such as asset loss, route misuse, and late returns. This study aims to design and implement a real-time vehicle position monitoring system using the NodeMCU ESP-32 module integrated with the NEO-6M GPS module and Wi-Fi connectivity to send data to a cloud-based server. This system is designed to display the vehicle position directly through a web-based digital map interface, which can be accessed by vehicle owners anytime and anywhere. The methodology used includes hardware and software design, location accuracy testing, and data integration with a web-based visualization platform using a map API. The test results show that the system is capable of sending vehicle location data with a position accuracy level of up to ±5 meters and data updates every 10 seconds under stable network conditions. In addition, the system has good power efficiency, with an average current consumption of 80–100 mA when active. All data was successfully stored and visualized in real-time using the Google Maps API, and the system was able to operate stably for 24 hours of non-stop testing. Based on these results, the IoT-based GPS tracker system with NodeMCU ESP-32 can be effectively implemented on rental vehicles as a modern monitoring solution that is cost-effective, flexible, and easily accessible. This system provides added value in fleet monitoring and supports faster and data-based decision making.
Volume: 7
Issue: 2
Page: 214-223
Publish at: 2026-07-01

Performance evaluation of the deep learning system for weed recognization

10.11591/csit.v7i2.p167-178
Abd Abrahim Mosslah , Reyadh Hazim Mahdi , Hassan Kassim Albahadily
Numerous approaches based on machine learning have emerged in recent years to enhance crop protection efficiency. One example is the utilization of deep neural networks (DNNs) to differentiate between various weed types in actual events scenarios. Nevertheless, these methods often need substantial input from experts who work iteratively to design the robust deep learning system. To simplify such process and conserve resources, researchers have explored a fresh method known as automated deep learning our technology’s recognization of weeds through the use of machine learning was evaluated using plant seedlings and weed collections from plants dataset to address a issue of weed recognization. The study compared various configurations, including plant segmentation, using a collection of classifiers in place of Softmax, and training with datasets that contain noise. The findings indicated ensuring performance, with F1-scores of 93.1% and 90.2% based on the dataset utilised. These results align together with automated machine learning (AutoML-linked) studies, while fall short of manually fine-tuned deep-learning-based systems created through human specialists. To conclude, exploring the potential of combining manual expert work and automated deep learning could be a promising direction for enhancing efficiency in plant defence.
Volume: 7
Issue: 2
Page: 167-178
Publish at: 2026-07-01

Fuzzy logic–based consensus protocol for educational blockchain networks

10.11591/csit.v7i2.p131-140
Igor Ivanov , Svetlana Zhdanova
This paper addresses the growing challenge of ensuring trust, authenticity, and transparency in the management and verification of educational credentials within modern, digitally oriented learning ecosystems. Rapid expansion of e-learning, lifelong learning, and global mobility has intensified document fraud, revealing the limitations of traditional verification mechanisms. To respond to these systemic risks, the study proposes a socially oriented block-validation protocol integrated into a distributed blockchain environment designed specifically for educational data security. The protocol forms the core of the EduBLOCK system, developed by the authors, and introduces an innovative consensus mechanism that incorporates human-centered reputation assessments rather than computational or financial power. The approach employs fuzzy-set theory to evaluate user activity, institutional credibility, and delegate reputation, enabling a more nuanced and context-sensitive model of trust. Delegates responsible for validating blocks are selected through a dynamic, reputation-driven procedure that excludes financial contributions and subjective parameter tuning. The proposed algorithm combines cryptographic guarantees, peer-to-peer (P2P) communication, and soft-computing methods to ensure fairness, prevent manipulation, and maintain stable system functioning. Block validity is determined through open voting, requiring approval by more than two-thirds of elected delegates.
Volume: 7
Issue: 2
Page: 131-140
Publish at: 2026-07-01

Development of an IoT-based waste monitoring and notification system for smart environmental management

10.11591/ijeecs.v42.i3.pp729-741
Enggar Utari , Ika Rifqiawati , Wahyuni Martiningsih , Izzal Ihasani , Aditya Rahman , Bagus Dwicahyono
Rapid urban population growth has intensified solid waste generation, while many existing waste management systems still rely on manual inspection and single-parameter monitoring, resulting in delayed responses and inefficient handling. Previous studies have primarily focused on isolated sensing or offline monitoring, highlighting the need for integrated, real-time, and user-oriented waste monitoring solutions. This study used RnD method, proposes a smart garbage level and information hub (SIGALIH), an IoT based waste monitoring and notification system designed to address these limitations. SIGALIH combines multi-parameter sensing, including waste level, temperature–humidity, gas concentration, and ambient light, with an ESP32 microcontroller, a cloud-based data platform, and a real-time notification service using a messaging bot. System evaluation involved sensor accuracy testing, communication latency analysis, and functional verification. Experimental results indicate an average sensor accuracy of 96.8%, with an average data transmission latency of 1.84 seconds and a notification delay of 2.14 seconds, indicating reliable real-time performance under varying network conditions. Functional testing confirmed stable operation of all system modules. The system was also integrated into an Environmental Education learning module to support environmental literacy and awareness through contextual learning on sustainable waste management. SIGALIH is designed for small- to medium-scale urban and community-based applications. However, performance depends on wireless network availability, which may reduce reliability in low-connectivity areas. Overall, SIGALIH provides a low-cost, scalable, integrated solution supporting smart environmental management and sustainable urban waste initiatives.
Volume: 42
Issue: 3
Page: 729-741
Publish at: 2026-06-10

Evaluation of aerodynamic and structural design to enhance solar energy absorption for e-Cars

10.11591/ijeecs.v42.i3.pp649-665
Mohamed Abubakr Mahgoub Hassan , Belal Ahmed Hamida , El Sayed Soliman , Muhammed Zaharadeen Ahmed
The integration of photovoltaic (PV) systems into electric vehicles (EVs) offers a promising solution for extending driving range and reducing dependence on grid-based charging. However, vehicle-integrated PV systems are limited by aerodynamic drag, structural integration challenges, thermal losses, and inefficient energy management. This study presents a multidisciplinary simulation framework to evaluate aerodynamic and structural optimization strategies for enhancing solar energy absorption in EVs. Computational fluid dynamics (CFD) was used to analyze airflow and reduce aerodynamic drag, while finite element analysis (FEA) assessed structural integrity and weight optimization after PV integration. PV energy flow and thermal models were also developed to evaluate power generation, battery charging behavior, and temperature-dependent efficiency losses. The optimized design reduced the drag coefficient from 0.310 to 0.236, a 23.7% improvement, while maintaining a structural safety factor above 1.75 through lightweight composite materials. The optimized PV configuration increased solar conversion efficiency from 17.6% to 22.3% and daily energy generation from 2.91 kWh/day to 3.75 kWh/day, corresponding to a 28.9% increase in harvested energy. Thermal management strategies lowered average PV operating temperature by about 12 °C, improving efficiency by an additional 5%–7%. Unlike existing studies that examine aerodynamic, structural, or PV performance separately, this work provides a unified framework that evaluates their combined impact on solar energy harvesting in EVs. The proposed integrated design approach demonstrates that coordinated aerodynamic, structural, thermal, and energy-management optimization can substantially improve the practicality and energy contribution of solar-assisted EVs in high-irradiance environments.
Volume: 42
Issue: 3
Page: 649-665
Publish at: 2026-06-10

Velocity hemodynamic patterns in aortic valve stenosis: a study of inlet velocity during systole phase

10.11591/ijeecs.v42.i3.pp884-891
Nur’Afifah Yousri , Nabilah Ibrahim , Ishkrizat Taib
This work highlighted a close-up version of the aortic valve that provide detail parameter and clearer graphics compared to the 3D version. Therefore, four simplified models are designed and simulated by using computational fluid dynamics (CFD) which are one healthy valve model (100%) and three stenotic models with varying valve opening (70%, 50%, and 30%). The model dimensions and setup parameters are determined by comparing the healthy aortic valve with the previous data. The analysis focused on two different views, which are the view on a targeting line velocity along the x-axis, and the view at the y-axis around the aortic valve. Results on the evaluation graph at the x-axis and y-axis show significant differences in flow patterns between healthy and aortic valve stenosis. The healthy model of 100% valve opening depicted a lower velocity (m/s) at 1.5m/s compared to the stenotic model of 70%, 50%, and 30% valve opening that showed higher velocities of 3.24 m/s, 6.09 m/s, and 14.57 m/s, respectively, due to the narrowing of the valve opening. Thus, the smallest orifice of the valve produced a higher velocity. This finding highlights the importance of hemodynamic assessment in aortic valve stenosis by providing valuable insight for clinicians in pre-surgical evaluation.
Volume: 42
Issue: 3
Page: 884-891
Publish at: 2026-06-10

Harmonization of regulations and innovation: VR/AR teacher readiness model in Indonesian education under public policy based on Pancasila

10.11591/ijeecs.v42.i3.pp767-773
Helga Charolina Antonia Silubun , Dadan Rosana , Samsul Hadi
The adoption of immersive technologies such as virtual reality (VR) and augmented reality (AR) faces the challenge of significant infrastructure disparities between developed countries (HIC) and developing countries (LMIC). In Indonesia, this implementation is hampered by an acute digital divide and the absence of an adaptive regulatory framework. This research proposes Pancasila-driven VR/AR educational architecture (PD-VAREA), a multidimensional framework that integrates principles of social justice into technical optimization through edge computing and adaptive rendering. The novelty of this research lies in the formalization of the teacher readiness index (Tready) using an integral calculus approach to predict systemic readiness. Numerical simulation results show that the PD-VAREA model produces a Tready value of 2.4 (High Readiness), far exceeding the conventional market-driven model, which reaches 0.6. These findings prove that the integration of cost-effective technology (frugal technology) with public policies based on the Fifth Principle of Pancasila is able to emphasize network latency below 20 ms while ensuring equitable access. This article provides a contribution in the form of a predictive model and strategic recommendations for policymakers in LMIC to mitigate the risks of digital inequality in the global education transformation.
Volume: 42
Issue: 3
Page: 767-773
Publish at: 2026-06-10

Sensor-based prediction of ALS progression: exploring PHI and feature engineering

10.11591/ijeecs.v42.i3.pp835-845
Chibuzor Chukwuemeka Okere , Edwin Thuma , Gontlafetse Mosweunyane
Amyotrophic lateral sclerosis (ALS) is a serious disease that affects nerve and muscle function, with no known cure. Early and accurate monitoring is essen tial to help physicians provide better care. Although machine learning has been applied to predict the progression of ALS, many models struggle with issues such as poor data quality and missing information, which affect accuracy. In this paper, our aim is to improve existing models by introducing better features to enhance prediction performance. A key contribution is the development of a new feature called the physical health index (PHI), which combines four im portant patient attributes: body mass index (BMI), weight, forced vital capacity (FVC), and basal calories. This feature provides a clearer view of the physical health of the patient, enabling the model to learn more effectively. We used the IDPP CLEF 2024 BTO dataset and performed three experiments: using 50 raw features, 29 engineered features, and 25 further engineered features including PHI. The results showed that the R-squared of the XGBoost model improved from 0.9573 to 0.9663 and finally 0.9828, while RMSE decreased from 0.2317 to 0.1801 and then 0.1182 with PHI. This study highlights how targeted feature engineering can improve the prediction of ALS using machine learning.
Volume: 42
Issue: 3
Page: 835-845
Publish at: 2026-06-10

On exploring text mining approaches to sentiment analysis based on the combination of word-based and ontology-based approaches

10.11591/ijeecs.v42.i3.pp827-834
Suthira Plansangket , Supaporn Kansomkeat , Supasit Kajkamhaeng
Currently, sentiment analysis plays an important role in business. Entrepreneurs try to understand customer needs for products and services. If they know about the needs, they can create the marketing plans or strategy plans in their business that help improve products and services. Therefore, this study explores two novel approaches to improve the classification accuracy of sentiment analysis data using a combination of a word-based approach (TF-IDF or CSDF) and an ontology-based approach (ontoSen) to provide two new methods, called ontoTF IDF and ontoCSDF. The experimental results show that CSDF method had the best classification accuracy among all the methods in this study: ontoCSDF did not improve further the classification accuracy of sentiment analysis data. Furthermore, ontoTFIDF method improved the classification by IBk algorithm significantly (p
Volume: 42
Issue: 3
Page: 827-834
Publish at: 2026-06-10

Adaptive fractional-order PID-controlled DVR optimized by zebra algorithm for harmonic suppression

10.11591/ijeecs.v42.i3.pp786-797
Milind Paraye , Rajendra G. Sutar
Dynamic voltage restorers (DVRs) are widely employed to mitigate power quality disturbances in modern power grids. Existing DVR control strategies frequently struggle to adequately suppress harmonic distortions and voltage sags due to nonlinear grid behaviour, rapidly varying disturbances, and limited tuning flexibility. We suggest a grid-connected DVR with an adaptive fractional order proportional integral derivative (FOPID) controller whose parameters are improved using an improved zebra algorithm (IZA) in order to close this gap. The IZA algorithm is used to improve the FOPID controller parameters, ensuring rapid convergence and superior accuracy. The effectiveness of the proposed system is assessed under two different operating conditions. In case 1, the harmonic compensation is analyzed, in which the DVR reduces systemic harmonic disturbances. The results reveal that the proposed controller reduces the total harmonic distortion (THD) from 1.36% to 0.01% while maintaining a constant voltage amplitude of around 0.9986 V, demonstrating strong harmonic suppression capability. Voltage sag mitigation is assessed in Case 2. The load voltage is effectively restored from 0.722 V to 0.9986 V by the DVR, which also reduces THD from 32.97% to 1.6% by injecting the required compensatory current. Overall, the results confirm that the adaptive FOPID–IZA controlled DVR significantly improves power quality and voltage stability in grid-connected systems by effectively mitigating both harmonic distortion and voltage sags.
Volume: 42
Issue: 3
Page: 786-797
Publish at: 2026-06-10

Voice portraits: building faces through voice analysis

10.11591/ijeecs.v42.i3.pp902-912
Anandhu T. G. , John K. Joseph , Navneeth Krishnan J. , Richu Shibu , Elizabeth Isaac
Generation of a person’s appearance from their voice alone is an intriguing challenge. The proposed framework centers on recreating a person’s facial image based solely on a short audio recording of that person speaking. Using a deep neural network trained on millions of YouTube recordings where faces and voices appear together, the system learns voice-face relationships, enabling it to generate images that capture physical traits such as age, gender, and ethnicity. Operating in a self-supervised manner, this method takes advantage of the pairing of faces and voices in online videos, eliminating the need for explicit property modeling. The model achieved a classification accuracy of (95%) for gender, (83%) for age, and (65%) for race prediction from voice inputs, demonstrating an exceptional performance in demographic trait identification. The generated images are evaluated against real photographs of the speakers, assessing how closely these reconstructions resemble actual appearance. This framework has practical applications in forensic analysis, security systems, and privacy-conscious biometric identification, offering a non-invasive alternative to traditional facial recognition methods.
Volume: 42
Issue: 3
Page: 902-912
Publish at: 2026-06-10
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