Online-Journals.org (International Association of Online Engineering)
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Deontic Knowledge Representation and Reasoning in Industrial Accident Prevention Training by Means of Time Travel Prevention Games
Get PDFIndustrial accident prevention is an issue of societal relevance to avoid loss of human lives, injuries, damage of installations, and financial losses. The authors deploy game-based training in virtual environments where trainees experience challenges of safe operation and disastrous self-induced accidents. Nothing is more affective and, thus, effective than a trainee’s own experience. Time travel prevention games are a game category particularly tailored to the needs of human players who look for opportunities to make good for a damage. Time travel pre-vention games for purposes such as accident prevention in the industries are ad-vantageous due to their conservation of resources including human health and lives. They are affective by allowing for unprecedented learner/player/trainee ex-periences and they are effective due to the fascination of application-oriented game play including opportunities to influence the fate, the latter being less close to reality, but the more attractive and worth telling. For optimal guidance to human trainees, the digital game system needs to learn about the trainees’ strength and weaknesses, about needs and desires. In terms of behavioral sciences, the system observing a human’s behavior hypothesizes theories of mind. In training games, modalities of events/actions are decisive. There are modalities of events/actions such as possibility, unavoidability, and the like as well as obliga-tions and oughts. Training aims at the emergence of cognitive states that are use-ful in practice. The system’s reasoning is deontic
Certainty-Based Self-Assessment in Higher Education: A Strategy for All?
Get PDFThis study explores students’ behavior in response to a study-learning system during one semester at university, based on the certainty-based marking strategy. The learning support system pursues to encourage diagnostic formative self-assessment. In total, 258 students complete the full study program and respond to an additional sociodemographic questionnaire. An exploratory study was performed. Two individual variables were considered for basic statistical analysis: gender and prior academic experience. Results show progression of students’ confident responses, especially by the third trial. No differences were found with respect to gender. In contrast, strong differences were found related to prior academic experience. These latter results must be taken carefully, in expectance of further research to look for connections with other individual variables
Research Work for Students: Its Peculiarities at Ukrainian Higher Education Institutions in the Conditions of Martial Law
No full textThe paper presents analysis of two ways for organizing the research work for students at the Department of Foreign Languages in Lviv National Environmental University: a) the use of modern technologies, robotization of production processes abroad and in Ukraine; b) educational material researched by students from massive open online courses platforms, other electronic resources for students as additional educational materials to the main ones proposed by teachers for the taught disciplines. There are also considered some electronic educational materials implemented for students and discussed by professors of different Ukrainian higher educational institutions at teachers’ conferences as well as the ways of spreading the results of students research work and exchanging their experience in usage of the reviewed materials are proposed. Under martial law, the research work for students of higher education institutions of Ukraine should be brought to a new level, especially in the military sphere and for helping our people to quickly restore all spheres of the national economy destroyed during the Russian-Ukrainian war. The results of the students research can be used by the teaching staff for planning and improving the educational process at our and other universities
Assessing Subjective Visual Vertical Reliability: A Comparison of the “Bucket Test,” a Mobile App, and a Virtual System
Get PDFThe subjective visual vertical (SVV) is a potential indicator of vestibular dysfunction as it assesses an individual’s perception of a vertical line. Despite this, and as a result of specific logistical impediments, SVV has not entered standard clinical practice. Dizziness is the third most common clinical complaint by patients (20%) in outpatient offices. It adversely affects the patient’s life and is often accompanied by intensive healthcare. This study aims to determine whether the bucket test and mobile phone app are as reliable as the Virtual SVV system in assessing the SVV. This study involves four types of investigation to determine the relationship or difference among three tests, including their performance comparison, descriptive analysis, one-way ANOVA test, receiver operating characteristic (ROC) curve, and correlation analysis. After organizing the raw data from 207 healthy volunteer participants for 8 trials, it was found that 59% were female and 41% were male. The data was analyzed utilizing the SPSS program. The test performance is measured using the ROC curve, and the results indicate that the bucket with the highest ROC coefficient is 0.72
Evaluation of an Indoor Location System Using Edge Computing and Machine Learning Algorithms
Get PDFThe paper aims to evaluate precise location techniques with indoor devices using edge computing technologies, which are important for services such as smart homes and health. Despite their growing importance, indoor locations lack precise and standard methods, especially in complex environments. Solving this is being attempted through technologies such as reconfigurable surfaces and deep learning models, with attention to overcoming the challenges of indoor placement. The main objective of the study is to design a low-cost indoor location system using the ESP32 module and RSSI signals, integrated with embedded machine learning algorithms. The system to be developed will allow determining the location of objects or people with a location device through SSID signals from access points. The main objective is to evaluate the performance of three machine learning algorithms—random forest (RF), decision tree (DT) and support vector machine (SVM)—in the detection of four different locations (bathroom, kitchen, bedroom, and living room), involving the definition of system characteristics, data acquisition, the development of classifiers, and their integration in the ESP32 module to transmit location data wirelessly through the MQTT protocol. As a result of the evaluation, the DT model stands out for its efficiency under limited resource conditions during real-time implementation, but it may face challenges related to overfitting and resources at the implementation stage
Detection of Breast Cancer through the Analysis of Radiographic Images Using Machine Learning: A Systematic Review
Get PDFBreast cancer is an illness that affects many women and can cause even death; this is a case of not being detected on time, which could be due to a human error during the analysis of radiographic images or not going on time in a health center. For this, using machine learning (ML) to analyze radiographic images is proposed as a support tool for radiologists aiming to reduce false diagnostic rates. While researching information, it was detected that this technology has many benefits in the health area; however, it also has limitations or disadvantages. The importance of this paper is to demonstrate that there are not enough clinical tests nor details about the methodologies that were used; there should be more to assert that ML is defined at the moment of making a diagnosis, which generates no conclusive results regarding effectiveness and therefore creates mistrust in doctors, and some people might rather use deep learning (DL) for its application in the detection of breast cancer because DL has more practical tests and fewer limitations than machine learning
Static, Dynamic, and High Cycle Fatigue Analysis of Crossed Spherical Gearing for Robotic Arm Ball Joint: A Finite Element Analysis Approach
Get PDFCrossed spherical gearing is used in the joints of robotic arm prostheses and allows mobility in 3 degrees of freedom. This paper aims to evaluate the design of a cross-spherical gear with three different materials, PEEK, AISI 304L, and Ti-6Al-4V, for a robotic arm prosthesis by finite element analysis. ANSYS mechanical software (version 2021 R1) was used to perform the static analysis and evaluate the deformations and stresses, modal analysis of natural frequencies and vibration modes, and high cycle fatigue analysis to determine fatigue resistance. The results obtained in the static analysis show that the maximum stresses are in the same zones for the three materials and have similar values. However, the Ti-6Al-4V and ASI 304L materials have a higher safety factor than PEEK, with a value of 5.17. In conclusion, the crossed spherical gearing is numerically validated using the finite element analysis so that the prototype can be later manufactured at an experimental level, and the values obtained for the crossed spherical gearing of the robotic arm prosthesis can be verified
Diabetes Prediction: Optimization of Machine Learning through Feature Selection and Dimensionality Reduction
Get PDFDiabetes, a pervasive global health concern, presents diagnostic challenges due to its nuanced onset and far-reaching implications. Traditional diagnostic approaches, reliant on time-consuming assessments, necessitate a paradigm shift towards more efficient methodologies. In response, this study introduces a diagnostic support system leveraging the power of optimized machine learning algorithms. Addressing class imbalance within a dataset comprising 768 records, our methodology intricately weaves together feature selection, dimensionality reduction techniques, and grid search optimization. Specifically, the Extra Trees model, fine-tuned via grid search, emerges as the most potent, showcasing remarkable performance metrics: an accuracy score of 92.5%, an F1-score of 93.7%, and an AUC-ROC of 92.47%. These findings underscore the pivotal role of machine learning in reshaping diabetes diagnosis, offering transformative possibilities for global healthcare enhancement
Analysis and Measurement of Tuberculin Skin Test Induration Using Deep Neural Network
Get PDFThe World Health Organization (WHO) posited that tuberculosis (TB) is among the world’s ten greatest causes of mortality. Early case identification and timely treatment could minimize TB morbidity and death rates. This study adopts the UNets model for automatically detecting TB in subjects by using a deep neural network to assess the size of induration after tuberculin was injected into their hands. In order to do this, two neural network models were fine-tuned utilizing pre-learned weights from the 2012 ILSVRC ImageNet. Algorithms were developed to perform semantic segmentation of induration and compare it to that of a reference object of a known dimension. This was used to classify the status of the subject as either positive or negative. A series of experiments performed demonstrated that the optimal selection of neural network hyperparameters may provide a satisfactorily high F1 score of up to 0.977
Revolutionizing Healthcare: Convergence of IoT and Open-Source ERP Systems in Health Information Management
Get PDFOver the last decade, health information systems (HIS) have undergone significant changes, particularly in embracing flexible frameworks for ongoing development. This evolution underscores the necessity for information technology (IT) infrastructures that rapidly align with clinical processes. The paper investigates the transition of hospital information systems to comprehensive strategies, examining the benefits and challenges involved. It also examines the increasing demands on health data management systems (HDMS) for patient care and biomedical research. The focus is on how the integration of the Internet of Things (IoT) and open-source enterprise resource planning (ERP) systems, such as Odoo, impacts health information management. The study evaluates the effectiveness and implications of combining these technologies. It provides examples of these integrated systems in action, particularly in resource-limited settings, and evaluates their potential to improve care. The document provides a comprehensive review of the current status and evolution of HIS and HDMS, highlighting the importance of integrating IoT and other cutting-edge technologies in healthcare. This is a crucial aspect for developing countries, where these advancements can significantly improve healthcare outcomes