Online-Journals.org (International Association of Online Engineering)
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Advancing Non-Cuff Hypertension Detection: Leveraging 1D Convolutional Neural Network and Time Domain Physiological Signals
Get PDFTimely identification of hypertension (HT) is crucial for effectively managing and reducing the potential health consequences, including cardiovascular events such as heart attacks and strokes, as well as the development of kidney disease. Traditional cuff-based devices often discourage regular monitoring because they cause discomfort. Furthermore, the lack of symptoms in HT complicates the early detection of this condition. To address these challenges, our study employs a non-cuff methodology that utilizes unprocessed electrocardiogram (ECG) and photoplethysmogram (PPG) signals. We utilize a customized approach to enhance the features of a one-dimensional convolutional neural network (CNN) specifically tailored to optimize timeseries data. In contrast to previous research, our methodology avoids the need for complex signal extraction or transformation techniques. The main goal is to identify the optimal input signals and fine-tune the critical hyperparameters of CNNs. The clinical data underwent analysis, which revealed that the use of an integrated ECG and PPG approach resulted in the highest level of accuracy for detection. Notably, the F1 score achieved an impressive value of 98.88%. When evaluated separately, ECG outperformed PPG. Our study contributes to the advancement of the field by introducing a new approach that combines comfort and high accuracy in the early detection of HT. This method is practical and ensures a patient-friendly experience
An Intelligent Mathematics Problem-Solving Tutoring System Framework: A Conceptual of Merging of Fuzzy Neural Networks and Neuroscience Mechanistic
Get PDFThis study proposed a novel framework for redesigning problem-solving activities in an intelligent tutoring system (ITS) called the intelligent neural-mechanistic mathematics problem- solving tutoring system (IN-MP-STS). This concept paper presents a new approach to ITS by incorporating elements of neuroscience mechanisms as a learning strategy that focuses on optimizing the brain’s ability through neural mechanisms. It also introduces fuzzy neural networks (FNNs) as a tool for modulating assessment and analyzing outcomes. This framework offers an alternative perspective on delivery methods and learning approaches in the ITS module. By effectively integrating neuroscience mechanistic elements such as motivation, activation, regulation, execution, memorization, and interactivities, deep learning can be achieved, leading to improved student competence. This framework also proposes an adaptive assessment component based on FNNs, which will enhance the measurement and feedback modules in the system. It is necessary to modify the way that ITS and soft computing methods, such as the study of neural networks (NNs), are combined to make learning measurement and assessment more transparent. This innovation has not been fully disclosed, so researchers are encouraged to further test the concepts presented to assess their alignment with the existing system and ethical considerations. This framework enhances the conceptual research findings of FNNs and incorporates neuroscience-based strategies into architecture and autonomous problem-solving skills within an ITS model. It also offers references for the development of problem-solving learning. IN-MP-STS has the potential to significantly enhance students’ competencies and abilities, thereby fostering the development of more comprehensive, holistic, and sustainable ITS. This approach also has the potential to enrich the existing literature on the sustainability of neural networks
Examining the Relation of Transformational Leadership in Clinical Engineering on the Performance of Medical Equipment: A Neural Network Approach
Get PDFIn the realm of healthcare administration, heightened expectations can lead to stress, therefore impacting working conditions. Specifically, studies on leadership styles have provided a valuable understanding of the factors that hinder performance, particularly in relation to the implementation of transactional leadership. Despite its crucial significance, there is a dearth of research on the management styles employed in clinical engineering. This study examines the impact of leadership styles on the functioning of medical equipment. We assess leadership styles and the performance of medical equipment from the perspective of end-users using a cross-sectional survey and questionnaires that consider many significant criteria. A neural network model is employed to classify the leadership styles exhibited by the Clinical Engineering Department (CED) and to analyze the correlation between these styles and the equipment’s performance. The results suggest a significant correlation between the leadership styles of those in charge of CED and the functioning of medical equipment. A strong and favorable correlation exists between transformative leadership and equipment performance (r = 0.856**, P = 0.000). The data suggests that transformative leadership is highly significant, with a mean score of 3.07 ± 0.817
A Design and Construction of the Vibration Perception Threshold Measurement Device for Diabetes
Get PDFThe purpose of this study was to design and build a mechanical vibration assessment tool for diabetic patients’ peripheral nervous systems. There is a substantial correlation between peripheral sensory neuropathy and vibration perception threshold (VPT). Peripheral sensory neuropathy has been identified by VPT determination utilizing the VPT measurement instrument built for that purpose. The designed device can assist in determining the threshold and tracking any progressive changes or trends. This designed device consists of two main components: hardware and software. The hardware part includes a DC power supply circuit, an Arduino NANO, a display, an isolation MOSFET driver for the electrical isolation circuit, a transducer driver, and a transducer head. The software part uses C programming on the Arduino to generate signals and display the transducer supply voltage. The testing results consist of 1) the voltage settings results (5–30 volts) comparing the voltage values on the display between the designed device and a digital multimeter, which has an average error of 0.75%, 2) According to the transducer head pressing test results, the Vibrotest Digital Biothesiometer and the proposed device had different pressing weights of ±0.02 g, and 3) the electrical safety testing results of the designed device is in the standard of IEC60601-1. (IEC: International Electrotechnical Commission)
An Optimized Effective Authentication Process for E-Health Application
Get PDFBecause of the availability of more than an actor and a wireless component in an e-health application, providing more security and safety to users of this type of applications is expected. Moreover, ensuring protection of data user available or shared within different services from any security attack becomes an important requirement. In this paper, we are interested essentially in the authentication process, and we propose an improved Landmarkbased algorithm as a tool to extract, firstly, key features from analysed faces, and hence to accelerate the authentication operation. The suggested approach beats other state-of-the-art works in terms of accuracy and speed-up attaining time execution constraint, according to experimental evaluations
The Role of 3D Printing in Science, Technology, Engineering, and Mathematics (S.T.E.M.) Education in General and Special Schools
Get PDFThis paper explores the significant role of 3D printing and digital applications in enhancing science, technology, engineering, and mathematics (STEM) education in both general and special schools. With the advent of the Fourth Industrial Revolution, the integration of these technologies into educational systems has become increasingly critical. This study reviews various study works that demonstrate how 3D printing technology enriches learning by fostering critical thinking and problem-solving skills and offering a more interactive and engaging educational experience. Special attention is given to its impact in special education, where it facilitates inclusive learning and aids in creating tailored educational tools. A case study from a vocational high school in Greece is presented, showcasing the successful implementation of a 3D printing and educational robotics club. This club highlights the practicality and effectiveness of 3D printing in enhancing student engagement and learning outcomes. The paper concludes by emphasizing the transformative potential of 3D printing in preparing students for future challenges and advocating for its wider adoption in STEM education
Classification of Alzheimer’s Disease Based on Deep Learning Using Medical Images
Get PDFNeurodegenerative disorders, notably Alzheimer’s, pose an escalating global health challenge. Marked by the degeneration of brain neurons, these conditions lead to a gradual decline in nerve cells. Worldwide, over 55 million people grapple with dementia, with Alzheimer’s prominently impacting the aging demographic. The primary hurdle to early Alzheimer’s detection is the widespread lack of awareness. The main goal is to design and implement an artificial intelligence system using deep learning (DL) to detect Alzheimer’s disease (AD) through medical images and classify them into various stages, such as non-demented, moderate dementia, mild dementia, and very mild dementia. The dataset contains 6400 magnetic resonance images in .jpg format, with standardized dimensions of 176 × 208 pixels. To demonstrate the advantages of data augmentation and transformation techniques, four scenarios were created: two without these techniques, utilizing the Adam and SGD optimizers, and two with these techniques, also employing the Adam and SGD optimizers, respectively. The main results revealed that scenarios utilizing these techniques exhibited more stable performance when validated with a new dataset. Scenario 3, using the Adam optimizer, achieved a weighted average accuracy of 91.83%, whereas scenario 4, employing the SGD optimizer, reached 87.58% accuracy. In contrast, scenarios 1 and 2, which omitted these techniques, obtained low accuracies below 55%. It is concluded that classifying AD with a DL model exceeding 90% accuracy is feasible. This is the importance of utilizing data augmentation and transformation techniques to improve generalizability to input image variations, which is a consistent factor in the healthcare sector
Effectiveness of Microlearning as an Additional Teaching Instrument in Orthopaedics and Traumatology University Course
Get PDFOrthopedics and traumatology are clinical specialties that require continuous learning and skill enhancement. Traditional teaching methods may not always be sufficient to meet the needs of contemporary learners. This study aims to compare the effectiveness of microlearning as an additional tool in orthopedics and traumatology university courses alongside traditional teaching methods. The study concluded that microlearning significantly improved students’ knowledge retention, practical skills, and overall performance compared to traditional teaching methods alone. The findings suggest that integrating microlearning into orthopedics and traumatology curricula can improve student learning outcomes and better prepare them for real-world practice
Intelligent Interconnected Healthcare System: Integrating IoT and Big Data for Personalized Patient Care
Get PDFThis paper introduces the intelligent interconnected healthcare system (IIHS), an innovative fusion of the Internet of Things (IoT) and big data analytics technologies designed to revolutionize proactive and personalized healthcare. IIHS facilitates the integration of real-time data from various devices, ambient sensors, and hospital equipment, creating a continuous stream of comprehensive healthcare data. Leveraging advanced data analysis, IIHS offers actionable insights for ongoing patient health monitoring, trend prediction through machine learning, and rapid information access via a user-friendly interface. The system architecture features a combination of centralized cloud storage and edge storage at healthcare facilities, enhancing both efficiency and security in data management. The effectiveness of IIHS has been demonstrated in two healthcare facilities, which reported significant reductions in patient length of stay and readmission rates. This indicates the system’s potential to improve patient care while seamlessly integrating with existing healthcare infrastructures. IIHS represents the future of digital and personalized medicine, offering a scalable, patient-centric solution that supports the ongoing transformation towards data-driven healthcare
Attitudes of Secondary Technical School Students towards Their Choice of Educational Path
Get PDFThis paper addresses the challenges regarding the choice of educational pathway for an adolescent from primary to secondary education. The aim is to highlight comparative differences in student decision-making when choosing a technically oriented study program. One sub objective is to emphasise the evolving preferences and roles of students in the decisionmaking process. The introduction provides the reader with a definition of decision-making processes and the choice of an educational pathway in the context of the Czech Republic. The following chapters present the main findings of the study. In Chapter 4, the risks associated with choosing a study path are presented, and the transition to the research part of the article is smooth. Three research hypotheses were defined to conduct the research. The study was conducted using a specifically designed non-standardised questionnaire. Details of the questionnaire design and the sample for the new research are provided. Subsections 4.3–4.6 present an overview of the results from the confirmatory and exploratory analyses. The discussion and conclusion summarise the results within a broader professional context. The primary findings indicate that students seem to be more independent in selecting technical schools. Students most often consider three criteria when making their choice: 1. the prospect of a career as a technician or a professional; 2. professional advantages in the job market; and 3. interest in the field and its study. We also present one of the possible correlation models of students’ personal preferences when choosing a secondary technical school