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31,456 Article Results

Four quadrant operation of bidirectional DC-DC converter for light electric vehicles

10.11591/ijeecs.v37.i2.pp740-748
Caroline Ann Sam , Varghese Jegathesan
This paper discusses the closed-loop control of a bidirectional full bridge DC-DC converter which aids in the four-quadrant operation of an electric vehicle (EV). Several topologies of bidirectional converters have been recently investigated for optimizing vehicle performance. The bidirectional converters with buck and boost modes of operation aid the four-quadrant operation of drives. The proposed bidirectional converter aids buck and boost modes of operation in both forward and reverse directions of the drive. The buck/boost operation in the forward direction is suitable to operate the traction drive in motoring mode. Also, the buck/boost operation in the reverse direction aids the drive to operate in charging mode. The performance analysis of the bi-directional converter-fed EV drive is done using MATLAB/Simulink software. The different modes of operation of the converter which is utilized for the four-quadrant operation of the drive are validated using a 12-60V hardware prototype. DSP TMS2837D controller is used to control the bi-directional converter and the code generation for the controller is done in MATLAB-DSP integrated platform. The hardware results validate theoretical analysis and simulation studies.
Volume: 37
Issue: 2
Page: 740-748
Publish at: 2025-02-01

Multi-class chronic lung disease classification based on guided backpropagation convolutional neural network using chest X-ray images

10.11591/ijeecs.v37.i2.pp1328-1338
Rakesh Selva Raj , Pavan Kumar Madalu Palakshamurthy , Bidarakere Eswarappa Rangaswamy
Clinical diagnosis is crucial as chronic lung disease is a leading cause of mortality worldwide. Chest X-ray imaging is essential for the early and accurate diagnosis of lung diseases. However, due to the complexity of pathological abnormalities and detailed annotation, the computer-aided diagnosis of lung diseases is challenging. To overcome this challenge, this research proposes the guided backpropagation convolutional neural network (GBPCNN) for the classification of chronic lung disease into 14 classes, by adjusting the network’s weights in CNN layers. The GBP technique enhances result accuracy by pinpointing the regions in an input image. Initially, the chest X-ray radiography (CXR) dataset is collected for estimating the effectiveness of the classifier. After collecting the dataset, the pre-processing is performed by utilizing image denoising using gaussian filter and normalization techniques. Then, the pre-processed data is fed to the feature extraction process, and it is done by using EfficientNetB2. Finally, extracted features are provided to the classification process to categorize chronic lung disease into 14 classes. The experimental results show that the proposed GBPCNN method attains better results and it achieves the accuracy of 97.94% as compared to the existing approaches like MobileLungNetV2 and CX-Ultranet. These results highlight the potential of our approach for clinical applications.
Volume: 37
Issue: 2
Page: 1328-1338
Publish at: 2025-02-01

Improved Bi-GRU for parkinson’s disease severity analysis

10.11591/ijeecs.v37.i2.pp1140-1149
Malathi Arunachalam , Ramalakshmi Ramar , Vaibhav Gandhi , Bhuvanesh Ananthan
Parkinson’s disease (PD) is a common neuro-degenerative issue, evaluated via the continuous deterioration of motor functions over time. This condition leads to a gradual decline in movement capabilities. For diagnosing clinical set of PDs, medical experts utilize medical observations. These observations are highly based on the expert’s experience and can vary among clinicians due to its subjective nature, leading to differences in evaluation. The gait patterns of individuals with PD typically exhibit distinctions from those of adults. Evaluating these gait malformations not only aids in diagnosing PD but can also enable the categorization of severity stages with respect to symptoms of motor movement. Therefore, this paper introduces a classification of gait model based on the optimized deep learning (DL) model bidirectional gated recurrent unit-artificial hummingbird optimizer (BI-GRU-AHO). The training and testing involved the sequential segmentation of the right and left instances from the signals of vertical ground reaction force (VGRF) based on the identified gait cycle. The outcomes of the proposed BI-GRU-AHO exhibits reliable and accurate assessment of PD and achieved better accuracy of 98.7 %. The proposed model is trained and tested satisfactorily; hence it can be implemented in a real-time environment by integrating the model into a software application or system capable of receiving real-time data from PD patients.
Volume: 37
Issue: 2
Page: 1140-1149
Publish at: 2025-02-01

Machine learning-based emotions recognition model using peripheral signals

10.11591/ijeecs.v37.i2.pp976-984
Tarun Kumar , Rajendra Kumar , Ram Chandra Singh
This work proposes a system for emotion recognition using four peripheral signals electromyography, galvanic skin response, blood volume pulse, and respiration. Peripheral signals cannot be modified, unlike other expression like voice and facial expression. The proposed method is applied to the DEAP datasets to verify the accuracy of emotion recognition. The proposed model focuses on accuracy and F1-score. DEAP dataset has more signals but only thirty-seven features from four peripheral signals were extracted for each trail and each video. On the DEAP datasets, the implementation found that the classification accuracy for arousal, valence, liking, and dominance was, respectively, 80%, 75%, 71%, and 78%. For two classes of problems, the corresponding F1-scores for arousal, valence, liking, and dominance are 0.50, 0.49, 0.47, and 0.47. The proposed model was implemented in MATLAB R2017a.
Volume: 37
Issue: 2
Page: 976-984
Publish at: 2025-02-01

iFoodAR: augmented reality for high school food design technology

10.11591/ijere.v14i1.29702
Nur Ain Safura Azizoon , Wan Nurlisa Wan Ahmad , Qistina Ahmad Fizal , Tang Jing Rui , Mohd Yusof Kamaruzaman
Technology is advancing with the times. Augmented reality (AR) received great attention in education because it focuses on technology that connects the real and virtual worlds in real-time. The blending of AR technology and educational content aims to generate innovative thinking that can improve the effectiveness of teaching and learning for students in real-life scenarios. This study aimed to develop an AR module and application (apps.) for high school students to learn about food technology. Five phases of the analysis, design, development, implementation, and evaluation (ADDIE) model were used to develop food technology applications in the classroom based on AR technology. An interview was conducted to obtain the usability of the AR module from the selected expert. The results showed that iFoodAR fulfilled the requirement in line with the standard curriculum and assessment document (DSKP) of the Malaysian Ministry of Education and gained positive feedback on using AR in the classroom from the expert teachers. It is concluded that iFoodAR apps have the potential to cater the diversity learning styles, leading to a shift in teaching methods incorporating more interactive and visually stimulating learning experiences.
Volume: 14
Issue: 1
Page: 406-414
Publish at: 2025-02-01

Influence of high school grade point average on university performance: a case study from Albania

10.11591/ijere.v14i1.29142
Emiljan Karma , Mitat Dautaj
This study aimed to investigate the impact of high school grade point average (GPA), as an admission criterion, on university performance using administrative data from a non-profit private university in Albania. This paper also examines additional variables that may influence the final degree outcome at Catholic University Our Lady of Good Counsel (Tirana). An ordinal regression analysis was conducted to forecast university performance based on selected university characteristics across various degree courses for 1,485 graduates. The study indicates that the high school GPA, which serves as an entrance requirement for Albanian universities, has a partially insignificant impact on the final university degree. Other factors, such as nationality, gender, university grade point average, and time required for graduation, directly and significantly affect the degree outcome. This result has implications for the efficiency of high school GPA as a mandatory university access criterion. At the same time, the negative impact of high school GPA as a mere barrier to university access is highlighted.
Volume: 14
Issue: 1
Page: 463-471
Publish at: 2025-02-01

Compressor performance prediction: gradient boosting regression model and sensitivity analysis

10.11591/ijeecs.v37.i2.pp1201-1208
Kuo-Chien Liao , Hom-Yu Wu , Hung-Ta Wen , Jui-Tang Sung , Muhamad Hidayat , Will Wei-Juen Wang
This study introduces the use of gradient boosting regression (GBR) models to estimate the compressor performance of aero-engines. The model exhibits a mean absolute error (MAE) of 0.078, showcasing superior performance compared to previous studies. Through sensitivity analysis, optimal values for three key parameters were determined: 280 estimators, a max depth of 9, and a learning rate of 0.085. Furthermore, a comparison with a prior study revealed an impressive MAE value lower than 0.002, highlighting the GBR model’s success in accurately predicting compressor performance. This demonstrates the model’s effectiveness and predictive accuracy, making it a valuable tool for aero-engine compressor performance estimation.
Volume: 37
Issue: 2
Page: 1201-1208
Publish at: 2025-02-01

Perceive social support, academic self-efficacy, and learning engagement among high school students in China

10.11591/ijere.v14i1.31029
Liu Yang , Lim Hooi Lian
The aim of this study was to analyze the mediating role of academic self-efficacy in the relationship between perceive social support and learning engagement among Chinese senior high school students. A quantitative research method was adopted, and 572 Chinese senior high school students from Inner Mongolia Autonomous Region in China completed three self-report questionnaires. Correlation analysis revealed that senior high school students perceive social support, academic self-efficacy, and learning engagement were significantly correlated with each other. In addition, structural equation modeling analysis showed that perceive social support exerted its indirect effect on learning engagement through the mediation of academic self-efficacy. These findings have practical implication for government policymakers, education administrators, teachers, students, and parents, informing evidence-based policies, interventions, and strategies to enhance learning engagement and academic success.
Volume: 14
Issue: 1
Page: 629-635
Publish at: 2025-02-01

Comparative study of pothole detection using deep learning on smartphone

10.11591/ijeecs.v37.i2.pp995-1004
Achyar Ulul Amri , Gede Putra Kusuma
Potholes present a significant problem in many countries, leading to vehicle damage and traffic accidents. These road imperfections pose safety risks and impose economic burdens. Despite existing detection methods using sensors and computer vision deep learning processed on PCs, a gap remains in deploying cost-effective, widely accessible solutions. This study aims to bridge this gap by developing deep learning models optimized for smartphones, reducing costs and enhancing deployment feasibility. We developed multiple models for pothole detection, utilizing transfer learning and Bayesian hyperparameter tuning to optimize detection accuracy and resource efficiency. Our evaluations focused on computationally light models such as YOLOv8 small, YOLOv8-nano, YOLOv7 tiny, and faster R-CNN MobileNetV3. In terms of detection accuracy, YOLOv8 small and YOLOv8 nano stood out, achieving average precisions (AP) of 83.5% and 82.5%, respectively. YOLOv8 nano proved the most efficient, offering high detection accuracy, a file size three times smaller than YOLOv8 small in TFLite format, and the fastest inference time of 0.72 seconds per image. This study highlights the potential of smartphones in urban pothole detection, contributing to improved road maintenance and urban policy.
Volume: 37
Issue: 2
Page: 995-1004
Publish at: 2025-02-01

Integrating blockchain, internet of things, and cloud for secure healthcare

10.11591/ijeecs.v37.i2.pp928-936
K Senthur Kumaran , Ganesh Khekare , Thanu Athitya M , Aakash Arulmozhivarman , Arvind Pranav M , Hiritish Chidambaram N
This research paper shows a decentralized healthcare architecture using the integration of internet of things (IoT), blockchain, and cloud to improve speed up tuple broken security as well as scalability. Real time health information (e.g., pulse rate, sugar level) from patients is captured by IoT devices and preprocessed at the fog computing layer to securely send them to a cloud platform. Immutability and transparency Patient health records recorded by blockchain solutions are highly irreversible due to the underlying technology, while smart contracts take care of data integrity and privacy. The cloud layer delivers storage that scales and works, also including real-time analytics to access patient data from anywhere for healthcare providers while the core helps manage long-term information architecture. It does so by automating healthcare workflows and taking some of the manual interventional processes out such that care delivery becomes even more efficient. Together, these technologies provide a secure, efficient, patient-centered healthcare system whose architecture can easily support future needs in remote patient monitoring and inter-institutional collaboration, responding to emerging demands from modern healthcare systems.
Volume: 37
Issue: 2
Page: 928-936
Publish at: 2025-02-01

Strategies for students’ well-being development: the task-oriented classroom approach

10.11591/ijere.v14i1.30158
Esa Nur Wahyuni , Ali Maksum , Rahmat Aziz , Retno Mangestuti
Promoting students’ well-being is a vital aspect of education. Creating a conducive classroom learning environment is essential, and task orientation plays a significant role. This paper aims to explore three research objectives related to the influence of the learning environment on student well-being by focusing on creating a task-oriented learning environment. The study employs a quantitative approach with a cross-sectional study design. The study analyzed 1,698 students (676 male and 994 female students) from nine cities in East Java, Indonesia. The results of the simultaneous regression analysis showed that R=.578, R2=.334, and p<.10. The learning environment was found to predict student well-being by 33.4% significantly. At the same time, task orientation was identified as the most dominant factor affecting student well-being. The study’s findings suggest that task orientation could be a solution to enhance student well-being in classroom learning practices. This study suggests the need for teacher development to improve teachers’ professional ability to facilitate learning in the classroom. Future research should consider using central variables, including moderating and intervening variables, to explore the relationship between the learning environment and student well-being.
Volume: 14
Issue: 1
Page: 535-543
Publish at: 2025-02-01

Imitation of the human upper limb by convolutional neural networks

10.11591/ijai.v14.i1.pp193-203
Paula Useche Murillo , Robinson Jimenez Moreno , Javier Eduardo Martinez Baquero
The paper outlines the development of an algorithm focused on imitating movements of a human arm and replicating strokes generated by the user's hand within a working environment. The algorithm was crafted to discern the position of either the user's left or right arm, tracking each section (fingers, wrist, elbow, and shoulder) through a detection and tracking system. These movements are then replicated onto a virtual arm, simulating the actions of a cutting tool, generating strokes as it moves. Convolutional neural networks (CNNs) were employed to detect and classify each arm section, while geometric analysis determined the rotation angles of each joint, facilitating the virtual robot's motion. The stroke replication program achieved an 84.2% accuracy in stroke execution, gauged by the closure of the polygon, distance between initial and final drawing points, and generated noise, which was under 10%, with a 99% probability of drawing a closed polygon. A Fast region-based convolutional neural network (Fast R-CNN) network detected each arm section with 60.2% accuracy, producing detection boxes with precision ranging from 17% to 59%. Any recognition shortcomings were addressed through mathematical estimation of missing points and noise filters, resulting in a 90.4% imitation rate of human upper limb movement.
Volume: 14
Issue: 1
Page: 193-203
Publish at: 2025-02-01

High-gain circularly polarized metasurface antenna for NR257 band millimeter-wave 5G communication

10.11591/ijeecs.v37.i2.pp867-877
Praveen Haandi Lakshman , Yerriswamy Thatti , Puneeth Kumar Tharehalli Rajanna , Shambulinga Mudukavvanavar
A metasurface inspired circularly polarized (CP) compact patch antenna with high gain for fifth-generation communication systems is designed and implemented in this article. The proposed structure features a corner truncated patch antenna and a metasurface of 3×3 double sided identical circular metallic patches. The attribute that puts this design distinct is that it minimizes the impact of scattering and edge diffraction at millimeter wave frequencies. The metasurface above a patch with an air gap is designed using the similar substrate material with the same thickness, resulting in a simplified antenna design with high gain and low cost. The antenna’s overall dimension is , with a peak gain of 11.5 dBic and a 3-dB axial ratio bandwidth of 28.45 - 28.88 GHz. The simulated and experimental results show that the metasurface-inspired antenna has better impedance matching and radiation efficiency between 28.23 - 30.01 GHz. Additionally, the experimental results of the proposed antenna exhibits stable right-hand circular polarization in the desired frequency range and a flat gain response with a little variation. The proposed antenna design could be well suited for millimeter-wave communication systems, in scenarios requiring robust long-range performance and high data throughput.
Volume: 37
Issue: 2
Page: 867-877
Publish at: 2025-02-01

Modified-LSTM and feed forward neural network enabled resource allocation for 6G wireless networks

10.11591/ijeecs.v37.i2.pp811-818
Pradnya Kamble , Alam N. Shaikh
The 6G wireless networks utilize terahertz (THz) frequency and intended to tremendously dynamic and diverse applications with deep learning enabled network, harvested significant attention and able to solve complex problems. Efficient resource allocation is a key requirement of next generation wireless networks. This research focuses on the resource allocation optimization challenge which includes storage, computing power, bandwidth and memory in the milieu of 6G wireless networks with device-to-device (D2D) communication enabled. The proposed model uses modified long short-term memory (mLSTM) and feed forward neural network to allocate resources to various tasks as per requirement such as information access, audio/video streaming, information access and productivity activity applications. The proposed work focuses on network parameters like channel noise, signal to noise ratio (SNR), distance from base station and includes D2D communication decisions to improve network performance. This research gives a novelty learning based solution for resource allocation for 6G wireless networks which contributes to the enhancement of next generation wireless communication networks. The lowest computing power utilized is 1%, Bandwidth utilized is 3% of total bandwidth and 2% storage.
Volume: 37
Issue: 2
Page: 811-818
Publish at: 2025-02-01

Challenges of implementing protection systems in smart grids: a review

10.11591/ijeecs.v37.i2.pp715-729
Sabat Anwari , Dini Fauziah , Lita Lidyawati
Based on the emergence of increasingly advanced technology, the conventional power grid can be upgraded to a smart grid by adding bidirectional communication, computer algorithms, and equipment that uses artificial intelligence (AI). A smart grid is a revolution in the current electricity network that can control the two-way generation and transmission process by utilizing an intelligent system so that the distribution of electric power can be handled optimally and in real time. The challenge of the smart grid is that there are distributed generators and microgrids that must be controlled in real time with rapidly changing loads. To meet these criteria, several points are proposed, i.e., finding an effective procedure to construct self-healing capability; developing a protection system based on AI; and proposing a systematic procedure to realize self-healing and protection systems with the help of a multi-agent system (MAS). Multi-agent systems are one of the AI approaches. Each agent can work independently and can also communicate with one another and with other devices on the network. Agents used as models can be classified into several categories, such as grid component agents, distributed resource agents, end-user agents, failure control agents, data analysis agents, and graphical visualization agents.
Volume: 37
Issue: 2
Page: 715-729
Publish at: 2025-02-01
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