Online-Journals.org (International Association of Online Engineering)
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Engineering Students' Acceptance of Augmented Reality Technology Integrated with E-Worksheet in The Laboratory Learning
Get PDFThe use of augmented reality (AR) technology in the field of education has emerged as a rapidly growing trend. However, there is an urgent need for more comprehensive research to determine the reactions of engineering students and their acceptance of this technology in laboratory learning. This study investigates the acceptance of integrated augmented reality with e-worksheet (IARE-W) among engineering students in the laboratory learning (IARE-W) among engineering students the electrical machines course (EMC). This research empirically uncovers the factors that influence it based on the technology acceptance model (TAM), specifically perceived ease of use (PEU) and perceived usefulness (PU). Acceptance is indicated by students’ attitudes toward the use. A survey-based quantitative research study using questionnaires was conducted to collect data, involving 102 students in the field of industrial electrical engineering. The partial least squares structural equation modeling (PLS-SEM) analysis was used to analyze the research data. The results demonstrated that engineering students had a highly positive attitude toward the use of the IARE-W in the EMC. Additionally, both PEU and PU had a positive and significant direct effect on engineering students’ attitudes toward using IARE-W. Furthermore, PEU also had a significant and positive indirect effect through PU as a mediating variable. These findings have significant implications for the development of engineering education and the integration of AR technology in laboratory learning contexts. The results of this study underscore the importance of taking into account PEU and PU in the design, development, and implementation of the IARE-W
Educational Data Mining: Employing Machine Learning Techniques and Hyperparameter Optimization to Improve Students’ Academic Performance
Get PDFEducational data mining (EDM) is a specialized field within data mining that focuses on extracting valuable insights from academic data across high school and university levels. A common practice in EDM involves predicting students’ grades to identify at-risk individuals and improve the efficiency of academic tasks. This knowledge benefits students, parents, and institutions equally. Early detection enables interventions that improve student performance. The literature presents various prediction strategies, each with its own unique advantages and disadvantages. This study aims to comprehensively evaluate the methods, tools, and applications of machine learning (ML) and data mining (DM) in education. The main goal is to improve the accuracy of predicting academic achievements by employing eight widely recognized ML algorithms: naïve bayes (NB), k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), logistic regression (LR), extreme gradient boost (XGBOOST), and ensemble voting classifier (EVC). The focus is on improving data quality by eliminating instances of noise. Performance evaluation involves assessing parameters such as accuracy, precision, F-measure, and recall. Incorporating cross-validation and hyperparameter tuning improves classification accuracy. The ML models outperform other ensemble approaches, providing a valuable tool for predicting student performance and assisting educators in making proactive decisions through timely alerts
CNN-Based Approach for Non-Invasive Estimation of Breast Tumor Size and Location Using Thermographic Images
Get PDFThe characterization of tumors is crucial for guiding appropriate treatment strategies and enhancing patient survival rates. Surface thermography shows promise in the non-invasive detection of thermal patterns associated with the existence of breast tumors. Nevertheless, the precise prediction of both tumor size and location using temperature characteristics presents a critical challenge. This is due to the limited availability of thermal images labeled with the corresponding tumor size and location. This work proposes a deep learning approach based on convolutional neural networks (CNN) in combination with thermographic images for estimating breast tumor size and location. Successive COMSOL-based simulations are conducted, including a 3D breast model with various tumor scenarios. Thus, different noise levels were included in the development of the thermographic image dataset. Every image was accordingly labeled with the corresponding tumor location and size to train the CNN model. Mean absolute error (MAE) and the coefficient of determination (R²) were considered as evaluation metrics. The results show that the proposed CNN model achieved a reasonable prediction performance with MAE–R² values of 0.872–98.6% for tumor size, 1.161–96.8% for x location, 1.086–97.1% for y location, and 0.954–96.7% for z location. This study indicates that the combination of surface thermography and deep learning is a convenient tool for predicting breast tumor parameters
Analyzing Emerging Trends in Wireless Implantable Medical Devices (IMDs): A Bibliometric Study
Get PDFIn the present era of advanced medical technology, there has been a significant increase in the utilization of wireless-based implantable medical devices (IMDs) in various biomedical applications, including health diagnosis, monitoring, recording, treatment, and other clinical concerns. It is essential to closely examine the research trends in wireless IMD topics to understand their growth trajectory. This can be achieved through the utilization of bibliometric reviews. While there are numerous experimental studies and survey papers on the topic, there is a dearth of review papers that utilize a bibliometric approach. Therefore, this study aims to conduct a bibliometric analysis to identify emerging research trends in wireless IMDs for biomedical applications for the first time. An analysis was conducted on 1,650 articles retrieved from the Scopus database from its inception until September 2023. The study used tools such as VOS Viewer to measure performance and science map parameters, and Tableau Cloud to visually enhance the analysis process. This research endeavor allowed us to analyze historical trends, influential papers, top journals, institutions, countries, and authors within this field. The top three influential journals in this field are IEEE Transactions on Biomedical Circuits and Systems, IEEE Transactions on Antenna and Propagation, and IEEE access. The National University of Singapore is the most productive institution in terms of overall publications. The United States, China, and India make significant contributions to overall publications. Finally, we also examined the most significant keywords and current research areas within this field
Identification of Medical Ecosystems in the Field of Mental Health and Cardiovascular Diseases at the Cologne Site
Get PDFAs part of the Europe-wide smart health innovation hub implemented in the context of the Horizon Europe SHIFT-HUB project, our work concerns the identification of specific medical research ecosystems in the two fields, namely cardiovascular diseases and mental illness, with Cologne as the central location. To achieve this aim, the websites of involved organizations were used for data research purposes, and the members of each respective ecosystem or network were identified by acquiring information about their cooperation partners. A variety of selection criteria have been applied to filter out whether these partners were suitable to be considered as a further starting point for the research. The results indicate the existence of ecosystems in the two fields, with Cologne as the central location, in which various stakeholders, including healthcare institutions, healthcare providers, foundations, NGOs, and the business community, work closely together. Larger institutions are usually networked at an international level, while smaller institutions increasingly depend on and foster regional partnerships. This promotes cooperation and the exchange of knowledge at the regional level and facilitates direct contact with the people affected, i.e., patients’ groups. Research institutions in both fields often receive financial support from commercial organizations, which highlights the importance of the business community’s involvement in exploiting research results and promoting the quality of healthcare. The article highlights the complexity and interdisciplinarity of the particular ecosystems, with all the different categories of institutions comprising an indispensable position. The interaction amongst stakeholders at international, regional, and local levels can significantly help to deploy resources more effectively and improve the quality of life of people suffering from any of the two conditions
Characterization and Identification of Dependence in EMG Signals from Action Potentials and Random Firing Patterns
Get PDFElectromyographic (EMG) signals are biomedical signals that represent neuromuscular activities. The EMG signal is neither stationary nor periodic and exhibits complex interference patterns of several single motor unit action potentials (SMUAPs). This study aims to characterize EMG signals concerning firing patterns and other characteristics and to identify whether these MUAP firing patterns present short-range dependencies (SRD) or long-range dependencies (LRD). To do so, we characterized 208 EMG signals in terms of the number of phases, turns and combinations of phases. Then, we performed a statistical comparison of the (more efficient) Variance-time plot against the (less bias) Log-scale diagram for the estimation of the Hurst parameter and detection of LRD. Using these estimators, we managed to detect LRD in a sample taken with needle electrodes. In contrast, the tools used for the dependence identification on signals achieved with surface electrodes did not yield conclusive results on such dependence
Design of an EMG Signal Generator Based on Random Firing Patterns
Get PDFElectromyographic (EMG) signals exhibit complex interference patterns that comprise several single motor unit action potentials (SMUAPs). Evidence of a model that can generate EMG signals and considers intrinsic characteristics, such as long-range dependence (LRD) or shortrange dependence (SRD), or that supports the study of pathology-related signals is lacking. Therefore, the present study aimed to develop an EMG signal generator based on SRD or LRD derived from firing patterns. We used a dynamic model to parameterize up to 15 SMUAP waveforms of real EMG signals extracted from a database. Then, we used relative appearance rates for some signals based on the number of SMUAPs to generate the latter randomly. Furthermore, we complemented our model by generating a random firing pattern. The synthetic reconstruction of the signals indicated a displacement compared with their respective firing patterns, with the highest error rate being 4.1%. The model of the EMG signal generator in its current state could be useful for a specialist who intends to study the behavior of the signals, starting with the exploration of synthetic signals and then proceeding to the real signals
RETRACTED: Prediction of Depression Severity and Personalised Risk Factors Using Machine Learning on Multimodal Data
Get PDFOn March 14, 2025, this paper has been retracted by the journal due to an unclear situation of authorship. It is impossible for the journal editors to decide whether or not the authors had the right to publish this paper
Mobile Application Based on Convolutional Neural Networks for Pterygium Detection in Anterior Segment Eye Images at Ophthalmological Medical Centers
Get PDFThis article introduces an innovative mobile solution for Pterygium detection, an eye disease, using a classification model based on the convolutional neural network (CNN) architecture ResNext50 in images of the anterior segment of the eye. Four models (ResNext50, ResNet50, MobileNet v2, and DenseNet201) were used for the analysis, with ResNext50 standing out for its high accuracy and diagnostic efficiency. The research, focused on applications for ophthalmological medical centers in Lima, Peru, explains the process of development and integration of the ResNext50 model into a mobile application. The results indicate the high effectiveness of the system, highlighting its high precision, recall, and specificity, which exceed 85%, thus showing its potential as an advanced diagnostic tool in ophthalmology. This system represents a significant tool in ophthalmology, especially for areas with limited access to specialists, offering a rapid and reliable diagnosis of Pterygium. The study also addresses the technical challenges and clinical implications of implementing this technology in a real-world context
Towards Efficient Lung Cancer Detection: V-Net-based Segmentation of Pulmonary Nodules
Get PDFThe novel approach uses the V-Net architecture to segment pulmonary nodules from computed tomography (CT) scans, enhancing lung cancer detection’s efficiency. Addressing lung cancer, a major global mortality cause, underscores the urgency for improved diagnostic methods. The aim of this research is to refine segmentation, a critical step for early cancer detection. The study leverages V-Net, a three-dimensional (3D) convolutional neural network (CNN) tailored for medical image segmentation, applied to lung nodule identification. It utilizes the LUNA16 dataset, containing 888 annotated CT images, for model training and evaluation. This dataset’s variety of pulmonary conditions allows for a comprehensive method of assessment. The tailored V-Net architecture is optimized for lung nodule segmentation, with a focus on data preprocessing to elevate input image quality. Outcomes reveal significant progress in segmentation precision, achieving a loss score of 0.001 and a mIOU of 98%, setting new standards in the domain. Visuals of segmented lung nodules illustrate the method’s effectiveness, indicating a promising avenue for early lung cancer detection and potentially better patient prognoses. The study contributes significantly to enhancing lung cancer diagnostic methodologies through advanced image analysis. An improved segmentation method based on V-Net architecture surpasses current techniques and encourages further deep learning exploration in medical diagnostics