Online-Journals.org (International Association of Online Engineering)
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Development and Evaluation of the WepMEt Platform for Enhancing a Medical Equipment Management in Hospitals
Get PDFMedical equipment management by using computerized systems is a key success of medical equipment maintenance and repair in hospitals. This system, which is installed in the biomedical engineering department, mostly provides an important management function for equipment registration, repair maintenance, and spare part administration. This study presents the development and evaluation of the WepMEt second version platform for managing medical equipment in hospitals across Thailand. The study focused on an improvement and expanding functionality in five key modules: equipment registration, preventive maintenance, repair, spare parts management, and equipment pooling modules, utilizing a rapid prototyping development method. Feedback information from 15 hospitals and primary criticism from five experts in medical equipment management were intently incorporated to improve an original version. Then, the post-development system was assessed in an intensive hands-on training workshop by 23 users from 16 hospitals, both nurses and biomedical engineers, and also the expert, in order to evaluate a satisfaction comment. The results show that our new WepMEt platform was highly accepted, with an average score of 4.73 and 4.75 for the expert team and all users, respectively. Most summarized comments clearly demonstrate effectiveness in comprehensively meeting the user needs, improving management efficiency significantly, and also reducing workload in hospitals. Our future work will be focused on AI technology to facilitate a modernized hospital by integrating administrative decision-making models for equipment maintenance management, enhancing the smart user interface, and incorporating an IoT and cloud computing technology to support real-time equipment tracking
Advanced Machine Learning Techniques for Enhancing Network Intrusion Detection and Classification Using DarkNet CIC2020
Get PDFThe increase of illegal endeavors and malware traffic inside the Darknet presents a detracting challenge to cybersecurity. This study deals with the problems by applying leading machine learning (ML) techniques to reduce the use of the Darknet misuse while still preserving the aura of mystery, anonymity, and privacy. Leveraging the Darknet CIC2020 dataset, the research performs binary and multiclass classification, which are based on modern algorithms, with autoencoder being one of them. Convolutional neural networks (CNN), long short-term memory (LSTM), and XGBoost were used to discriminate the complicated systems. Results indicate XGBoost performed better in cases of both binary and multi-class classifications showing tremendous reliability, accuracy, recall, and F1-score. Furthermore, the study extends its scope by introducing ensemble techniques such as voting classifier and stacking classifier, aiming to enhance predictive accuracy by joining diverse base estimators. Combining autoencoder and XGBoost, alongside investigating the CNN+LSTM architecture, enhances the model’s effectiveness. These hybrid approaches are implemented so that they can affect the components of various algorithms, creating more finely grained acting metrics on a per-order basis. This approach is to analyze Darknet traffic patterns that enable us to understand the importance of Internet security measures
An Opening-Snap Heart Sound-Aware DGMC-L1,2-Based Multi-Heart Disease Prediction Using ECG and PCG
Get PDFRecently, multi-heart disease diagnosis has become a popular research domain. Yet the traditional systems were ineffective due to the limited signal-processing approaches. Therefore, a deep generalized extreme value mish convolutional neural network with L1,2 regularization (DGMC-L1,2)-based multi-heart disease classification is implemented in this paper using electrocardiogram (ECG) and phonocardiogram (PCG). Primarily, the ECG and PCG are gathered and then preprocessed. After preprocessing, the peak waves are identified using the discrete cross-wavelet transform (DCWT). In the same way, the Osborn wave (OW) and opening snap (OS) are predicted from the ECG and PCG, respectively, using fuzzy exponential-decay-riseton inference system (FEDRIS), followed by wave interval segmentation. Furthermore, the dendrogram with a scatter plot is generated, and then the features are extracted. In addition, the peak localized ECG and PCG are fused together. Subsequently, the subsequent derivatives are estimated, and then a bivariate correlation matrix is created. Then, the feature extraction is done, followed by dimensionality reduction. Here, the dimensionalities of the features are reduced using log-linear scaling2—Principal Component Analysis (LS2-PCA) and then fed into the proposed DGMC-L1,2, which effectively predicts multi-heart diseases. Thus, the experimental results proved that the proposed work had high supremacy with an accuracy of 98.99%
The Impact of Socio-Demographic Factors on the Use of Digital Learning Platforms and Forms of Learning by Generation Z Engineering Students
Get PDFThe demands on higher education are increasing with the rise of generation Z. The emphasis is put on dynamism, a fast pace of teaching, dividing teaching into smaller blocks, alternating methods, digital tools, and using the possibilities of modern technologies. Educators must continually innovate and build improved pedagogical strategies in engineering to support student learning. Therefore, it is necessary to constantly innovate and build more sophisticated educational strategies with an impact on direct teaching and to support student learning. The aim of this study is to investigate the influence of socio-demographic factors (gender, field of study, and form of study) on the use of digital platforms and preferred forms of learning among Czech generation Z university students. The study emphasizes the preferences of generation Z students in engineering degree programs compared to students in economics and humanities degree programs. This comparison results from differences in teaching delivery and learning styles or methods. Hypotheses are tested using the Chi-square test and Cramer’s V. Engineering students prefer videos with a deeper explanation of the problem, and they mainly use the MS Teams platform and platforms allowing formative assessment and expression of their attitude. The practical implication of the study for education is mainly the identification of the basic characteristics of an appropriate educational strategy reflecting the needs of generation Z representatives. The article discusses the use of experimental and laboratory teaching. Similarly, as in the Czech Republic, also in the international environment, the project method, e-learning, gamification, Massive Open Online Courses (MOOCs), or videos in microlearning format are used in working with generation Z in engineering education. The study highlights the need to explore the influence of social factors and gamification elements in learning
Enhancing Teachers’ Professional Identity in a Reflective Learning MOOC
Get PDFSocial interaction in online teacher training can be challenging. Accordingly, we aim to explore how a massive open online course (MOOC) for teacher training affects the professional identity (PI) and development of teachers. This course was developed based on the reflective learning framework (RLF) and utilises modelling as a teaching method. For that, we enrolled in a mixed-method research study. Data were collected from 54 teachers and three discussion boards. Next, it was analysed through content analysis using grounded theory, the application of the narrative reflection assessment rubric (NARRA), and statistical tests. As a result, we observed a significant increase in teachers’ evaluations regarding reflective thinking (from 24% to 67%) and sociocultural learning (from 13% to 39%) after completing the course. Pearson’s r coefficient showed a positive relationship between the fourth NARRA element, transformation, and its predecessors. This indicates that each element plays a role in a continuous development process. The evidence was sufficient to conclude that the training promoted teachers’ PI development, except in the domain of job satisfaction
FOTA-MAC: A Novel Traffic Adaptive MAC Protocol for Wireless Body Area Networks
Get PDFRecently, Wireless Body Area Networks (WBANs) have emerged as a significant breakthrough in the healthcare sector due to their immense potential to revolutionize health outcomes. This type of network is able to support a diverse range of applications with traffic rates ranging from several bits per hour up to 10 megabits per second. The challenge lies in accommodating this wide range of applications and meeting their distinct requirements using a single suitably flexible medium access control protocol. In response to this challenge, our paper proposes a novel Traffic Adaptive MAC protocol designed specifically for the regular traffic of WBANs. This innovative protocol dynamically adapts its operation based on the observed traffic, leading to significant efficiency gains compared to the standardized MAC protocols IEEE 802.15.4 and IEEE 802.15.6. Our contribution aims to address the critical need for a tailored and adaptive MAC protocol that can seamlessly handle varying traffic loads within WBANs. By incorporating our protocol, we envision enhanced communication efficiency and improved performance, unlocking new possibilities for healthcare applications and ultimately revolutionizing the healthcare industry
Proposed Model for Real-Time Anomaly Detection in Big IoT Sensor Data for Smart City
Get PDFA smart city represents an advanced urban environment that utilizes digital technologies to improve the well-being of residents, efficiently manage urban operations, and prioritize long-term sustainability. These technologically advanced cities collect significant data through various Internet of Things (IoT) sensors, highlighting the crucial importance of detecting anomalies to ensure both efficient operation and security. However, real-time identification of anomalies presents challenges due to the sheer volume, rapidity, and diversity of the data streams. This manuscript introduces an innovative framework designed for the immediate detection of anomalies within extensive IoT sensor data in the context of a smart city. Our proposed approach integrates a combination of unsupervised machine learning techniques, statistical analysis, and expert feature engineering to achieve real-time anomaly detection. Through an empirical assessment of a practical dataset obtained from a smart city environment, we demonstrate that our model outperforms established techniques for anomaly detection
Mobile Recommendation System to Provide Emotional Support and Promote Active Aging for Older Adults in the Republic of Panama
Get PDFAging brings with it physical and cognitive changes that can lead to health problems such as chronic disease and cognitive impairment. Technology is a fundamental ally in improving the quality of life of older adults by enabling accurate and early diagnosis. In this context, we present a mobile application designed to provide emotional support and guidance, thus contributing to the well-being of this demographic group. Our study was based on quantitative research methods, using an experimental approach on a sample of users aged between 60 and 80 years. The results showed that 93.3% of users found the app to be a useful resource for adopting a healthier lifestyle. The app provides specific recommendations, such as breathing exercises to reduce anxiety, recreational activities, exercises tailored to physical ability, and meditation practices. These specific features have been shown to improve the well-being of older adults by providing a personalized approach to the challenges of aging
Application of Computer Vision and Mobile Systems in Education: A Systematic Review
Get PDFThe computer vision industry has experienced a significant surge in growth, resulting in numerous promising breakthroughs in computer intelligence. The present review paper outlines the advantages and potential future implications of utilizing this technology in education. A total of 84 research publications have been thoroughly scrutinized and analyzed. The study revealed that computer vision technology integrated with a mobile application is exceptionally useful in monitoring students’ perceptions and mitigating academic dishonesty. Additionally, it facilitates the digitization of handwritten scripts for plagiarism detection and automates attendance tracking to optimize valuable classroom time. Furthermore, several potential applications of computer vision technology for educational institutions have been proposed to enhance students’ learning processes in various faculties, such as engineering, medical science, and others. Moreover, the technology can also aid in creating a safer campus environment by automatically detecting abnormal activities such as ragging, bullying, and harassment
Exploring Interactive Learning Environments Based on Augmented Reality Technology
Get PDFWith the rapid advancement of digital technology, the application of augmented reality (AR) in the field of education has emerged as a focal point of research aimed at enhancing learning efficiency and experience through innovative interactive learning environments. This study focuses on the application of AR technology in education. The goal is to address key technical issues present in existing AR interactive learning environments to promote their widespread adoption in educational settings. An analysis of the current application of AR technology in education is conducted, focusing on its potential to enhance learners’ motivation and comprehension capabilities. However, a common shortfall in existing research methodologies has been identified, namely inadequate control over node consistency and deficient design of collaborative visualization components. These limitations restrict interaction and collaborative efficiency within AR learning environments. In response, a novel predictive algorithm for node consistency control is proposed, significantly enhancing real-time interaction and coherence among multiple learners through optimized information synchronization mechanisms. Furthermore, a set of collaborative visualization components is designed to be personalized according to learners’ behavioral and cognitive characteristics, thereby supporting more effective team-based learning. The outcomes of this study not only provide new theoretical and technical support for the design and implementation of AR interactive learning environments but also offer a fresh perspective on the future direction of educational technology