The environmental factors of landslide susceptibility are generally uncorrelated or non-linearly correlated, resulting in the limited prediction performances of conventional machine learning methods for landslide susceptibility prediction (LSP). Deep learning methods can exploit low-level features and high-level representations of information from environmental factors. In this paper, a novel deep learning–based algorithm, the fully connected spare autoencoder (FC-SAE), is proposed for LSP. The FC-SAE consists of four steps: raw feature dropout in input layers, a sparse feature encoder in hidden layers, sparse feature extraction in output layers, and classification and prediction. The Sinan County of Guizhou Province in China, with a total of 23,195 landslide grid cells (306 recorded landslides) and 23,195 randomly selected non-landslide grid cells, was used as study case. The frequency ratio values of 27 environmental factors were taken as the input variables of FC-SAE. All 46,390 landslide and non-landslide grid cells were randomly divided into a training dataset (70%) and a test dataset (30%). By analyzing real landslide/non-landslide data, the performances of the FC-SAE and two other conventional machine learning methods, support vector machine (SVM) and back-propagation neural network (BPNN), were compared. The results show that the prediction rate and total accuracies of the FC-SAE are 0.854 and 85.2% which are higher than those of the SVM-only (0.827 and 81.56%) and BPNN (0.819 and 80.86%), respectively. In conclusion, the asymmetric and unsupervised FC-SAE can extract optimal non-linear features from environmental factors successfully, outperforms some conventional machine learning methods, and is promising for LSP.

A deep learning algorithm using a fully connected sparse autoencoder neural network for landslide susceptibility prediction

Background: Virtual reality (VR) is the use of computer technology to create an interactive three-dimensional (3D) world, which gives users a sense of spatial presence. In nursing education, VR has been used to help optimize teaching and learning processes.
Objective: The purpose of this study was to evaluate the effectiveness of VR in nursing education in the areas of knowledge, skills, satisfaction, confidence, and performance time.
Methods: We conducted a meta-analysis of the effectiveness of VR in nursing education based on the Cochrane methodology. An electronic literature search using the Cochrane Library, Web of Science, PubMed, Embase, and CINAHL (Cumulative Index to Nursing and Allied Health Literature), up to December 2019 was conducted to identify studies that reported the effectiveness of VR on knowledge, skills, satisfaction, confidence, and performance time. The study selection and data extraction were carried out by two independent reviewers. The methodological quality of the selected studies was determined using the Cochrane criteria for risk-of-bias assessment.
Results: A total of 12 studies, including 821 participants, were selected for the final analysis. We found that VR was more effective than the control conditions in improving knowledge (standard mean difference [SMD]=0.58, 95% CI 0.41-0.75, P<.001, I2=47%). However, there was no difference between VR and the control conditions in skills (SMD=0.01, 95% CI –0.24 to 0.26, P=.93, I2=37%), satisfaction (SMD=0.01, 95% CI –0.79 to 0.80, P=.99, I2=86%), confidence (SMD=0.00, 95% CI –0.28 to 0.27, P=.99, I2=0%), and performance time (SMD=–0.55, 95% CI –2.04 to 0.94, P=.47, I2=97%).
Conclusions: The results of this study suggest that VR can effectively improve knowledge in nursing education, but it was not more effective than other education methods in areas of skills, satisfaction, confidence, and performance time. Further rigorous studies with a larger sample size are warranted to confirm these results.

/pdf/effectiveness-of-virtual-reality-in-nursing-education-meta-4hetwbrb6a.pdf

Effectiveness of Virtual Reality in Nursing Education: Meta-Analysis.

A multi-sensor data fusion enabled ensemble approach for medical data from body sensor networks

The use of virtual reality simulation among nursing students and registered nurses: A systematic review.

The constantly growing offering of online learning materials to students is making it more difficult to locate specific information from data pools. Personalization systems attempt to reduce this complexity through adaptive e-learning and recommendation systems. The latter are, generally, based on machine learning techniques and algorithms and there has been progress. However, challenges remain in the form of data-scarcity, cold-start, scalability, time consumption and accuracy. In this article, we provide an overview of recommendation systems in the e-learning context following four strands: Content-Based, Collaborative Filtering, Knowledge-Based and Hybrid Systems. We developed a taxonomy that accounts for components required to develop an effective recommendation system. It was found that machine learning techniques, algorithms, datasets, evaluation, valuation and output are necessary components. This paper makes a significant contribution to the field by providing a much-needed overview of the current state of research and remaining challenges.

A systematic review: machine learning based recommendation systems for e-learning

Evaluation of practical exercises using an intravenous simulator incorporating virtual reality and haptics device technologies.

This paper proposes the Fuzzy Rule-based Adaptive Coronary Heart Disease Prediction Support Model (FbACHD_PSM), which gives content recommendation to coronary heart disease patients. The proposed model uses a mining technique validated by medical experts to provide recommendations. FbACHD_PSM consists of three parts for heart disease risk prediction. First, a fuzzy membership function is constructed using medical guidelines and statistical methods. Then, a decision-tree rule induction technique creates mining-based rules that are subjected to validation by medical experts. As the rules may not be medically suitable, the experts add rules that have been verified and delete inappropriate rules. Thirdly, using fuzzy inference based on Mamdani's method, the model predicts the risk of heart disease. Based on this, final recommendations are provided to patients regarding normal living, nutrition control, exercise, and drugs. To implement our proposed model and evaluate its performance, we use a dataset from a single tertiary hospital.

Adaptive mining prediction model for content recommendation to coronary heart disease patients

The Ubiquitous Health, or u-Health, service is an IT health care service using the ubiquitous computing environment. U-Health provides customized medical services. As it is a service that has developed from the current hospital visiting medical system, the u-Health service provides a patient with healthcare anywhere and anytime. In this paper, we propose a home health gateway based healthcare services through the u-Health platform. Using home health gateway, u-Health can provide health monitoring, diet, and exercise services using the healthcare decision support module in the ubiquitous environment. This approach would offer specialized services using an external content provider of DB. In addition, a doctor can provide advice to patients using the monitoring service. The proposed u-Health platform provides effective services using home health gateway in ubiquitous environments to customers, which will improve the health of chronic patients.

Home Health Gateway Based Healthcare Services Through U-Health Platform

U-health services have been progressed as treatment and management for specific diseases and prevention services for providing the behavior management to customers according to the increase in chronic patients. The conventional U-health services provide required services and bio-information monitoring only through remote diagnoses and counsels and that represent limitations in preventing and managing metabolic syndrome patients like chronic patients. Thus, in this study a multi platform based U-health service model for managing the health of chronic patients is proposed. The multi-platform based U-health service model can provide continuous health information, diet, and exercise services regardless of the location of customers through PCs and smart phones. In addition, it is able to provide prescription services to doctors and nurses using a CDS (Clinical Decision Support) module based on clinical information. Doctors can identify the life pattern of patients through a behavior modification program and provide customized services to patients. The U-health service model provides effective services in multi-platform environments to customers and that will improve the health of chronic patients.

http://koreascience.or.kr:80/article/JAKO201126235931324.pdf

U-health Service Model for Managing Health of Chronic Patients in Multi-platform Environment

Wireless network based u-Health services enable people's health care anytime, anywhere. Recently technology advance in the area of sensing, networking, processing is developed rapidly. This advance of information technology is raising the change of traditional healthcare system. By using wireless and mobile networks, patients could be monitored and managed outside hospitals and also examined with safety and efficiency in hospitals. Even though there are lots of services for u-Health including commercial services, to envision and make u-Health become more active in the future, safety and usefulness of u-Health in the perspective of medicine should be needed.

Trend of wireless u-Health

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