E-Learning Readiness Assessment Using Machine Learning Methods

Assessing e-learning readiness is crucial for educational institutions to identify areas in their e-learning systems needing improvement and to develop strategies to enhance students' readiness. This paper presents an effective approach for assessing e-learning readiness by combining the ADKAR model and machine learning-based feature importance identification methods. The motivation behind using machine learning approaches lies in their ability to capture nonlinearity in data and flexibility as data-driven models. This study surveyed faculty members and students in the Economics faculty at Tlemcen University, Algeria, to gather data based on the ADKAR model's five dimensions: awareness, desire, knowledge, ability, and reinforcement. Correlation analysis revealed a significant relationship between all dimensions. Specifically, the pairwise correlation coefficients between readiness and awareness, desire, knowledge, ability, and reinforcement are 0.5233, 0.5983, 0.6374, 0.6645, and 0.3693, respectively. Two machine learning algorithms, random forest (RF) and decision tree (DT), were used to identify the most important ADKAR factors influencing e-learning readiness. In the results, ability and knowledge were consistently identified as the most significant factors, with scores of ability (0.565, 0.514) and knowledge (0.170, 0.251) using RF and DT algorithms, respectively. Additionally, SHapley Additive exPlanations (SHAP) values were used to explore further the impact of each variable on the final prediction, highlighting ability as the most influential factor. These findings suggest that universities should focus on enhancing students' abilities and providing them with the necessary knowledge to increase their readiness for e-learning. This study provides valuable insights into the factors influencing university students' e-learning readiness.

Areas within the United States at the Highest Risk for African Swine Fever, Classical Swine Fever, and Foot-and-Mouth Disease Introduction

Domestic livestock production is a major component of the agricultural sector, contributing to food security and human health and nutrition and serving as the economic livelihood for millions worldwide. The impact of disease on global systems and processes cannot be understated, as illustrated by the effects of the COVID-19 global pandemic through economic and social system shocks and food system disruptions. This study outlines a method to identify the most likely sites of introduction into the United States for three of the most concerning foreign animal diseases: African swine fever (ASF), classical swine fever (CSF), and foot-and-mouth disease (FMD). We first created an index measuring the amount of potentially contaminated meat products entering the regions of interest using the most recently available Agricultural Quarantine Inspection Monitoring (AQIM) air passenger inspection dataset, the AQIM USPS/foreign mail, and the targeted USPS/foreign mail interception datasets. The risk of introduction of a given virus was then estimated using this index, as well as the density of operations of the livestock species and the likelihood of infected material contaminating the local herds. Using the most recently available version of the datasets, the most likely places of introduction for ASF and CSF were identified to be in central Florida, while FMD was estimated to have been most likely introduced to swine in western California and to cattle in northeastern Texas. The method illustrated in this study is important as it may provide insights on risk and can be used to guide surveillance activities and optimize the use of limited resources to combat the establishment of these diseases in the U.S.

Identifying Safety Subgroups at Risk: Assessing the Agreement Between Statistical Alerting and Patient Subgroup Risk: An International Journal of Medical Toxicology and Drug Experience

Introduction Identifying individual characteristics or underlying conditions linked to adverse drug reactions (ADRs) can help optimise the benefit-risk ratio for individuals. A systematic evaluation of statistical methods to identify subgroups potentially at risk using spontaneous ADR report datasets is lacking. Objectives In this study, we aimed to assess concordance between subgroup disproportionality scores and European Medicines Agency Pharmacovigilance Risk Assessment Committee (PRAC) discussions of potential subgroup risk. Methods The subgroup disproportionality method described by Sandberg et al., and variants, were applied to statistically screen for subgroups at potential increased risk of ADRs, using data from the US FDA Adverse Event Reporting System (FAERS) cumulative from 2004 to quarter 2 2021. The reference set used to assess concordance was manually extracted from PRAC minutes from 2015 to 2019. Mentions of subgroups presenting potential differentiated risk and overlapping with the Sandberg method were included. Results Twenty-seven PRAC subgroup examples representing 1719 subgroup drug-event combinations (DECs) in FAERS were included. Using the Sandberg methodology, 2 of the 27 could be detected (one for age and one for sex). No subgroup examples for pregnancy and underlying condition were detected. With a methodological variant, 14 of 27 examples could be detected. Conclusions We observed low concordance between subgroup disproportionality scores and PRAC discussions of potential subgroup risk. Subgroup analyses performed better for age and sex, while for covariates not well-captured in FAERS, such as underlying condition and pregnancy, additional data sources should be considered.

A New Approach to Extracting Tourism Focus Points from Chinese Inbound Tourist Reviews after COVID-19

The number of inbound tourists in Japan has been increasing steadily in recent years. However, due to the COVID-19 pandemic, the number of inbound tourists decreased in 2020. This is particularly worrisome for Japan, as the number of inbound tourists is expected to reach 60 million per year by 2030. In order to help Japan's tourism industry to recover from the pandemic, we propose a method of identifying elements that attract the attention of inbound tourists (focus points) by analyzing reviews on tourist sites. We focus on Hokkaido, a popular area in Japan for tourists from China. Our proposed method extracts high-frequency n-gram patterns from reviews written by Chinese inbound tourists, showing which aspects are mentioned most often. We then use seven types of motivational factors for tourists and principal component analysis to quantify the focus points of each tourist destination. Finally, we estimate the focus points by clustering the n-gram patterns extracted from the tourists' reviews. The results show that our method successfully identifies the features and focus points of each tourist spot.

Framework for biometric iris recognition in video, by deep learning and quality assessment of the iris-pupil region

In the current world scenario the influence of the COVID19 pandemic has reached universal proportions affecting almost all countries. In this sense, the need has arisen to wear gloves or to reduce direct contact with objects (such as sensors for capturing fingerprints or palm prints) as a sanitary measure to protect against the virus. In this new reality, it is necessary to have a biometric identification method that allows safe and rapid recognition of people at borders, or in quarantine controls, or in access to places of high biological risk, among others. In this scenario, iris biometric recognition has reached increasing relevance. This biometric modality avoids all the aforementioned inconveniences with proven high efficiency. However, there are still problems associated with the iris capturing and segmentation in real time that could affect the effectiveness of a System of this nature and that it is necessary to take into account. This work presents a framework for real time iris detection and segmentation in video as part of a biometric recognition system. Our proposal focuses on the stages of image capture, iris detection and segmentation in RGB video frames under controlled conditions (conditions of border and access controls, where people collaborate in the recognition process). The proposed framework is based on the direct detection of the iris-pupil region using the YOLO network, the evaluation of its quality and the semantic segmentation of iris by a Fully Convolutional Network. (FCN). The proposal of an evaluation step of the quality of the iris-pupil region reduce the passage to the system of images with problems of out of focus, blurring, occlusions, light changing and pose of the subject. For the evaluation of image quality, we propose a measure that combines parameters defined in ISO/IEC 19794-6 2005 and others derived from the systematization of the knowledge of the specialized literature. The experiments carried out in four different reference databases and an own video data set demonstrates the feasibility of its application under controlled conditions of border and access controls. The achieved results exceed or equal state-of-the-art methods under these working conditions.

Machine Learning Algorithm for Trend Analysis in Short term Forecasting of COVID-19 using Lung X-ray Images

With the development of medical technology, the diagnosis of lung diseases relies more on the determination of medical images. With increasingly huge data, a powerful data processing model is urgently needed to provide favorable support for this field. The goal of this study is to develop a computer-assisted method to identify COVID-19 from X-ray pictures of the lungs at the very beginning of the disease. The architecture is implemented as a software system on a computer that can assist in the affordable and accurate early identification of cardiac illness. The performance of CNN architecture is best among all other classification algorithms to detect COVID-9 from Lung X-ray images. The datasets consist of COVID-19 established cases for 4 weeks which included the X-ray images of the chest. Then the distribution of the data was examined according to the statistical distribution. For this prediction, time series models are used for forecasting the pandemic situation. The performances of the methods were compared according to the MSE metric and it was seen that the Convolutional Neural Networks (CNN) achieved the optimal trend pattern.

Quantitative Identification Study of Epidemic Risk in the Spatial Environment of Harbin City

Global pandemics pose a threat to the sustainable development of urban health. As urban spaces are important places for people to interact, overcrowding in these spaces can increase the risk of disease transmission, which is detrimental to the sustainable development of urban health. Therefore, it is crucial to identify potential epidemic risk areas and assess their risk levels for future epidemic prevention and the sustainable development of urban health. This article takes the main urban area of Harbin as the research object and conducts a cluster spatial analysis from multiple perspectives, including building density, functional density, functional mix, proximity, intermediacy, and thermal intensity, proposing a comprehensive identification method. The study found that (1) functional density is the most significant influencing factor in the formation of epidemic risks. Among various urban functions, commercial and public service functions have the strongest impact on the generation and spread of epidemic risks, and their distribution also has the widest impact range. (2) The spaces with higher levels of epidemic risk in Harbin are mainly distributed in the core urban areas, while the peripheral areas have relatively lower levels of risk, showing a decreasing trend from the center to the periphery. At the same time, the hierarchical distribution of urban space also has an impact on the spatial distribution of the epidemic. (3) The method proposed in this study played an important role in identifying the spatial aggregation of epidemic risks in Harbin and successfully identified the risk levels of epidemic distribution in the city. In spatial terms, it is consistent with high-risk locations of epidemic outbreaks, which proves the effectiveness and feasibility of the proposed method. These research findings are beneficial for measures to promote sustainable urban development, improve the city's epidemic prevention capabilities and public health levels, and make greater contributions to the sustainable development of global public health, promoting global health endeavors.

Building social capital in cruise travel via social network sites

The purpose of this study is to investigate what type of Facebook posts help cruise lines build bridging and bonding social capital. The study applies the Chi-Square Automatic Interaction Detection (CHAID) method to identify which types of posts establish bridging and bonding social capital. The analysis is conducted on an international cruise line's official Facebook posts posted between 1 January 2018 and 1 January 2020 before the Covid-19 pandemic. The results highlight that media type, embedding passenger motivation, and a ship image help establish both bridging and bonding social capital, while content type helps establish bridging social capital. The paper is original because it helps understand how cruise lines can improve bonding and bridging social capital via social media. The paper also enhances understanding of social capital theory in the travel industry by investigating the relationship between Facebook post types and social capital in cruise shipping.

Spatial Identification, Prevention and Control of Epidemics in High-Rise Residential Areas Based on Wind Environments

The wind environment in residential areas can exert a direct or indirect influence on the spread of epidemics, with some scholars paying particular attention to the epidemic prevention and control of residential areas from the perspective of wind environments. As a result, it is urgent to re-examine the epidemic prevention response of residential spaces. Taking high-rise residential areas in Xi'an as an example, the article defines the air flow field area based on on-site wind environment measurements, crowd behavior annotation, and CFD simulation. Using the double-effect superposition of crowd behavior and risk space, the paper undertook a multiple identification strategy of epidemic prevention space. The identification methods and management and control strategies of epidemic prevention in high-rise residential areas are proposed. Additionally, the living environment of residential areas is optimized, and a healthy residential space is created. The transformation from concept and calls for action to space implementation is made to provide a reference for improving the space management and control capabilities in high-rise residential areas in China. The results of this study can be used as a guideline for future residential planning and design from the perspective of preventing airborne diseases.

Contactless non-invasive method to identify abnormal tongue area using K-mean and problem identification in COVID-19 scenario

Due to the spread of COVID-19 all around the world, there is a need of automatic system for primary tongue ulcer cancerous cell detection since everyone do not go to hospital due to the panic and fear of virus spread. These diseases if avoided may spread soon. So, in such a situation, there is global need of improvement in disease sensing through remote devices using non-invasive methods. Automatic tongue analysis supports the examiner to identify the problem which can be finally verified using invasive methods. In automated tongue analysis image quality, segmentation of the affected region plays an important role for disease identification. This paper proposes mobile-based image sensing and sending the image to the examiner, if examiner finds an issue in the image, the examiner may guide the user to go for further treatment. For segmentation of abnormal area, K-mean clustering is used by varying its parameters.

Diagnosing COVID-19 From Chest CT Scan Images Using Deep Learning Models

A novel coronavirus named COVID-19 has spread speedily and has triggered a worldwide outbreak of respiratory illness. Early diagnosis is always crucial for pandemic control. Compared to RT-PCR, chest computed tomography (CT) imaging is the more consistent, concrete, and prompt method to identify COVID-19 patients. For clinical diagnostics, the information received from computed tomography scans is critical. So there is a need to develop an image analysis technique for detecting viral epidemics from computed tomography scan pictures. Using DenseNet, ResNet, CapsNet, and 3D-ConvNet, four deep machine learning-based architectures have been proposed for COVID-19 diagnosis from chest computed tomography scans. From the experimental results, it is found that all the architectures are providing effective accuracy, of which the COVID-DNet model has reached the highest accuracy of 99%. Proposed architectures are accessible at https://github.com/shamiktiwari/CTscanCovi19 can be utilized to support radiologists and reserachers in validating their initial screening.

Identification of spaces with cluster infection risks in small cities in China based on spatial syntax and GIS

Among modern cities developing in a large-scale, extensive and unbalanced manner, smaller cities are relatively lagged behind due to relatively underdeveloped infrastructure, inadequate capital and technology talents, and insufficient attention from the national government, and thus they are more vulnerable when hit by unexpected disasters. The rampant pandemic of coronavirus disease 2019 (COVID-19) has made it even clearer that small cities must be equipped with stronger abilities to timely identify and prevent potential disease outbreaks. This paper takes Zhaodong City as an example to study how to better locate spaces with cluster infection risks in small cities. It combines spatial syntax, points of interest (POI), and geographical information system (GIS), and adopts hotspot analysis, average nearest neighbour analysis, kernel density estimation and other methods, to identify and locate potentially vulnerable spaces in neighbourhoods with relatively frequent people-to-people contact and thus higher disease transmission risks. Results show that there are three point-space, four line-space, and one plane-space with high risk of outbreaks in Zhaodong City, verifying the efficacy of the identification method for small cities.

An Automatic Detection Method for COVID-19 in CT Images

There are many new cases of new coronary pneumonia (named COVID-19) every day around the world. In such a severe situation, effective detection of COVID-19 has become extremely important. Studies have shown that chest CT images can be used for COVID-19 detection because they can show bilateral changes in the lungs of people infected with COVID-19. It is not difficult for experienced radiologists to make preliminary judgments based on CT images. However, with the emergence of a large number of suspected cases, the explosive demand has overwhelmed doctors. Therefore, the automatic diagnosis of COVID-19 CT images is of great significance to realize early diagnosis, early isolation, and early treatment. In this paper, we study an automatic identification method that uses transfer learning technology, which transfers the pre-trained VGG16 network for feature extraction, and combines VAE augmentation data to reduce over-fitting, and finally uses integrated technology to achieve better detection results. Our experiments have shown that the accuracy of our method for identifying whether a patient’s CT image is positive or negative for COVID-19 is 91%, the precision is 88%, the recall rate is 94%, and the F1-score is 91%. Compared with other the state of art methods, the method proposed in this article can provide more efficient classification and identification of COVID-19.