Smart Tourism using Attractive and Safe Travel Recommendation Technology

The tourism sector was among the most affected sector during the COVID-19 pandemic and has lost up to USD 5.87 billion potential revenue. Since many countries closed the borders, including Indonesia, by applying travel restrictions and thus tourists postponed their visits. Whereas vaccine distribution has shown good progress as the vaccination percentage in Jakarta and Bali has shown promising results since the majority of its population has been vaccinated, and it helps many industries, including tourism, recover. However, the pandemic might change tourist behavior. In addition, information about tourist destinations is spread poorly in various sources, and it psychologically affects tourists' decision to visit. Many works have been published to address this issue with the recommendation system. However, it does not provide geopolitical variables such as PPKM in Indonesia to ensure safeness for the tourist. Therefore, this research aims to enhance innovations in the tourism industry by considering the geopolitics factor into the system using Multiple Linear Regression. The result of this research demonstrates the effectiveness of geopolitics added variable on three different cities Jakarta, Java, and Bali. It can be implemented in a wide area in Indonesia. For further research, the proposed model can be used in a wide area in Indonesia and developed for a more comprehensive recommendation system. © 2022 IEEE.

TEST ON JUDICIAL ACTIVISM IN SOCIETY IN A CRISIS AGGRAVATED BY PANDEMIA: NECESSARY REFLECTIONS ON RECOMMENDATION 62/2020, NATIONAL COUNCIL OF JUSTICE (CNJ)

After experiences of significant violations of the essential rights to the person as a human being that marked, especially in the 18th and 19th centuries, taking as a landmark the post-World War II, there is an expansion of rights and instruments for their effectiveness, both internally, or international. In this context, a different state stance was demanded: of power centered on an authoritarian monarch followed by the non-interventionist to the State of Social Welfare where the promotion, expansion and instrumentation of rights became essential. In Brazil, with the democratization of the country and the promulgation of a Constitution, the essential foundation of which is the dignity of the human person, the realization of fundamental rights and the respect for separation come to permeate the entire legal system. In that same step, before the legislative option of providing for indeterminate legal concepts plus the "doctrine of the enforcement of fundamental rights”, it allowed the judicial protagonism to expand in the country, more commonly called "judicial activism” which, a priori, has a negative and needs to be contained. With the social and economic crisis aggravated by the covid-19 pandemic, this activist role of the Judiciary is also present, so reflections need to be made. Thus, the present study will be dedicated to the analysis of judicial activism in the context of a pandemic crisis, pointing out positive and negative aspects, based on a qualified doctrinal review and Recommendation 62/2020, by CNJ. © 2022, Universidade do Estado do Rio de Janeiro. All rights reserved.

S2k-Guideline SARS-CoV-2, COVID-19 and (early) rehabilitation - a consensus-based guideline for Germany.

The consensus-based guideline "SARS-CoV-2, COVID-19 and (early) rehabilitation" for Germany has two sections: In the first part, the guideline addresses infection protection-related procedures during the COVID-19 pandemic. In the second part, it provides practice recommendations for rehabilitation after COVID-19. The specific recommendations for rehabilitation after COVID-19 as issued by 13 German medical societies and two patient-representative organizations are presented together with general background information for their development.

Artificial-Intelligence-Driven Algorithms for Predicting Response to Corticosteroid Treatment in Patients with Post-Acute COVID-19.

Pulmonary fibrosis is one of the most severe long-term consequences of COVID-19. Corticosteroid treatment increases the chances of recovery; unfortunately, it can also have side effects. Therefore, we aimed to develop prediction models for a personalized selection of patients benefiting from corticotherapy. The experiment utilized various algorithms, including Logistic Regression, k-NN, Decision Tree, XGBoost, Random Forest, SVM, MLP, AdaBoost, and LGBM. In addition easily human-interpretable model is presented. All algorithms were trained on a dataset consisting of a total of 281 patients. Every patient conducted an examination at the start and three months after the post-COVID treatment. The examination comprised a physical examination, blood tests, functional lung tests, and an assessment of health state based on X-ray and HRCT. The Decision tree algorithm achieved balanced accuracy (BA) of 73.52%, ROC-AUC of 74.69%, and 71.70% F1 score. Other algorithms achieving high accuracy included Random Forest (BA 70.00%, ROC-AUC 70.62%, 67.92% F1 score) and AdaBoost (BA 70.37%, ROC-AUC 63.58%, 70.18% F1 score). The experiments prove that information obtained during the initiation of the post-COVID-19 treatment can be used to predict whether the patient will benefit from corticotherapy. The presented predictive models can be used by clinicians to make personalized treatment decisions.

Exploration of college students' psychological problems based on online education under COVID-19

As a result of the COVID-19 pandemic, many college students have been isolated at home and unable to walk into class as usual. This series of protective measures to avoid the spread of the disease may have an additional psychological impact on the lives of college students. The purpose of this study was to propose a strategy for using an intelligent online learning system based on content recommendations and electronic questionnaires in the educational domain. We invited 3000 isolated university students (47.6% male and 52.4% female) to an online trial. It proved to be effective in helping us intervene in students' psychological problems quickly, objectively, efficiently, and in real-time. In addition, our analysis of the data collected from the intelligent online learning system showed that the degree to which college students' psychological problems were affected by isolation was closely related to students' grade level, family background, major category, and computer proficiency. The current study suggests that the mental health of college students should be well monitored during segregation. Targeted psychological counseling is more necessary for students in upper grades, low-income families, liberal arts majors, and those with weak computer proficiency to reduce the emotional impact of segregation on students.

Factors Predicting Hotel Recommendations: A Comparison of Guest Feedback Before and After the Hotel Closures During the COVID-19 Pandemic

The COVID-19 pandemic adversely impacted the hospitality industry. The current study explored potential factors (i.e. cleanliness, location, room, service, and value) influencing guests' hotel recommendations before the shutdown of hotels due to COVID-19 and after the reopening of hotels. This study employed secondary data and random forest analysis to test the hypotheses. The results indicated that before COVID-19, the value factor had the most significant effect on a guest's willingness to recommend, followed by service, cleanliness, room, and location. A year after the shutdown period, the value factor still had the most significant effect, followed by service, room, and cleanliness, while location had an insignificant effect. Hotel managers can utilize the findings to create new strategies to attract guests and allocate resources to better address guests' evolving expectations. [ FROM AUTHOR] Copyright of International Journal of Hospitality & Tourism Administration is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

What is needed to build a personalized recommender system for K-12 students' E-Learning? Recommendations for future systems and a conceptual framework.

Online learning has significantly expanded along with the spread of the coronavirus disease (COVID-19). Personalization becomes an essential component of learning systems due to students' different learning styles and abilities. Recommending materials that meet the needs and are tailored to learners' styles and abilities is necessary to ensure a personalized learning system. The study conducted a systematic literature review (SLR) of papers on recommendation systems for e-learning in the K12 setting published between 2017 and 2021 and aims to identify the most important component of a personalized recommender system for school students' e-learning. Recommendations for later studies were proposed based on the identified components, namely a personalized conceptual framework for providing materials to school students. The proposed framework comprised four stages: student profiling, material collection, material filtering, and validation.

Personalized exercise recommendation method based on causal deep learning: Experiments and implications

The COVID-19 pandemic has accelerated innovations for supporting learning and teaching online. However, online learning also means a reduction of opportunities in direct communication between teachers and students. Given the inevitable diversity in learning progress and achievements for individual online learners, it is difficult for teachers to give personalized guidance to a large number of students. The personalized guidance may cover many aspects, including recommending tailored exercises to a specific student according to the student's knowledge gaps on a subject. In this paper, we propose a personalized exercise recommendation method named causal deep learning (CDL) based on the combination of causal inference and deep learning. Deep learning is used to train and generate initial feature representations for the students and the exercises, and intervention algorithms based on causal inference are then applied to further tune these feature representations. Afterwards, deep learning is again used to predict individual students' score ratings on exercises, from which the Top-N ranked exercises are recommended to similar students who likely need enhancing of skills and understanding of the subject areas indicated by the chosen exercises. Experiments of CDL and four baseline methods on two real-world datasets demonstrate that CDL is superior to the existing methods in terms of capturing students' knowledge gaps in learning and more accurately recommending appropriate exercises to individual students to help bridge their knowledge gaps. © 2022 The Author(s).

Learning Peer Recommendation Based on Weighted Heterogeneous Information Networks on Online Learning Platforms

With the development of online education, there is an urgent need to solve the problem of the low completion rate of online learning courses. Although learning peer recommendation can effectively address this problem, prior studies of learning peer-recommendation methods extract only a portion of the interaction information and fail to take into account the heterogeneity of the various types of objects (e.g., students, teachers, videos, exercises, and knowledge points). To better motivate students to complete online learning courses, we propose a novel method to recommend learning peers based on a weighted heterogeneous information network. First, we integrate the above different objects, various relationships between objects, and the attribute values to links in a weighted heterogeneous information network. Second, we propose a method for automatically generating all meaningful weighted meta-paths to extract and identify meaningful meta-paths. Finally, we use the Bayesian Personalized Ranking (BPR) optimization framework to discover the personalized weights of target students on different meaningful weighted meta-paths. We conducted experiments using three real datasets, and the experimental results demonstrate the effectiveness and interpretability of the proposed method.

Overview of the adoption of online food ordering services in spain and india. An analytical approach based on the stimulus-organism-response model

COVID-19 pandemic, the foodservice industry has had to modify the way it offers its services. The aim of this paper is to examine the drivers of intention to use and recommendation of online food delivery (OFD) using the SOR model, to analyze the perceived risk of COVID-19 and its relationship with the perceived risk for online purchase of OFD as well as to analyze the cultural effect between Spain and India. For this purpose, an online questionnaire was developed by obtaining a sample of 422 users and structural equation modeling (PLS-SEM) was used to determine which variables had a significant influence on the adoption of the OFD. The results confirm that attitude is the main antecedent of intention to use and recommendation, in contrast to the subjective norm relationships, where it was only confirmed by recommendation. This finding demonstrates how individuals' attitude toward intention and recommendation is more favorable than influence of third parties on decisions. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Execution of Market Basket Analysis and Recommendation Systems in Physical Retail Stores to Advance Sales Revenues

In the early months of 2020, pandemic covid-19 hit many parts of the world. Especially developing countries like India observed a negative growth rate in few quarters of last financial year. Retailing is one of the key sectors that contribute to Indian GDP with a share of nearly 10 percent. Hence there is a need for the retail sector to bounce back which is possible with the efficient use of new digital technologies. Market basket analysis is used here to extract the association rules which can be directly used for formulating discount and combo offers. Along with that, these rules can be used to decide the product positioning in the retail store. Items which are bought together can be placed next to each other to increase sales. Recommendation systems are most commonly used in ecommerce websites like Amazon, Flipkart, etc, and streaming platforms like Netflix to recommend the items that are to be purchased by users. Although recommendation engines are implemented in multiple web and mobile applications, these are not in the implementation stage in offline retail stores due to many implications associated with them like infrastructure, cost, etc. In this project, we have used market basket analysis and recommendation systems to propose a model to implement in retail stores to increase sales revenues and enhance customer experience. © 2022 IEEE.

Recommendations for the Diagnosis and Treatment of Maternal SARS-CoV-2 Infection

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has spread worldwide and threatened human's health. With the passing of time, the epidemiology of coronavirus disease 2019 evolves and the knowledge of SARS-CoV-2 infection accumulates. To further improve the scientific and standardized diagnosis and treatment of maternal SARS-CoV-2 infection in China, the Chinese Society of Perinatal Medicine of Chinese Medical Association commissioned leading experts to develop the Recommendations for the Diagnosis and Treatment of Maternal SARS-CoV-2 Infection under the guidance of the Maternal and Child Health Department of the National Health Commission. This recommendations includes the epidemiology, diagnosis, management, maternal care, medication treatment, care of birth and newborns, and psychological support associated with maternal SARS-CoV-2 infection. It is hoped that the recommendations will effectively help the clinical management of maternal SARS-CoV-2 infection.Copyright © Wolters Kluwer Health, Inc. All rights reserved.

Factors Contributing to Recommendation Intention of Foreign Tourists in Times of Crisis: A Moderated Moderation Analysis

Purpose - As a leading source of foreign exchange and investment, tourism has grown in importance as a component of international trade. Accordingly, in recent decades much attention has been directed toward attracting foreign tourists and, in turn, positively affecting the recommendation intentions of foreign tourists. Despite such interests, there remains a dearth of empirical research on this issue. Moreover, prior research has focused primarily on the simple main effect of a certain factor on recommendation intentions. Therefore, the present study aims to (1) investigate the effect of overall satisfaction on the recommendation intentions of foreign tourists, and (2) examine the potential moderating effects of personal factors (i.e., age and destination image) on the association between overall satisfaction and recommendation intention. Design/methodology - Using a moderated moderation analysis of the data drawn from the 2018 International Visitor Survey conducted by the Korea Tourism Organization, this study proposes the three-way interaction effects of overall satisfaction, age, and destination image on recommendation intention. Findings - The findings of the study indicate that overall satisfaction is positively associated with recommendation intention and this relationship becomes stronger among younger tourists. The findings further indicate that the moderating effect of age on the relationship between overall satisfaction and recommendation intention depends on changes in the image of the destination. Specifically, the destination image exerts a positive moderating impact on the influence of age that moderates the overall satisfaction and recommendation intention relationship. Originality/value - Considering that the tourism economy has been severely affected by the current COVID-19 pandemic, this study contributes to a more accurate understanding of the factors affecting the recommendation intention, especially in times of crisis.

Semantic-based Thai Recipe Recommendation

Nowadays, people are constantly affected by epidemics such as COVID-19. To reduce the risk of acquiring germs in the community, people's lifestyles have been changed, and they are more inclined to cook for themselves. Typically, people can usually quickly and easily find recipe information via websites and applications. The resulting recipes consist of ingredients as specified by the user. Unfortunately, users often have ingredients that disappear in available cooking recipes. This makes the system is unable to recommend all relevant recipes to users, although the users can use the existing ingredients instead of the ingredients specified in the recipes. Based on this limitation, this research proposes a semantic-based Thai cooking recipe recommendation system which can recommend recipes based on the ingredient substitutes. This research uses existing Thai food ontology to retrieve substitute ingredients based on three different ingredient properties, such as smell, taste, and texture. To recommend cooking recipes, the system expands the given user queries with substitute ingredients and then calculates similarities between all queries and each cooking recipe. Recipes with high similarities are presented and ranked to users. To evaluate the performances, precision, recall and f-measure are applied. The experiments demonstrate that the proposed method performs well with 0.96, 0.72, and 0.82 in precision, recall, and f-measure respectively.

A Session-Based Recommender System for Learning Resources

Learning resource recommendation systems can help learners find suitable resources (e.g., books, journals,.) for learning and research. In particular, in the context of online learning due to the impact of the COVID-19 pandemic, the learning resource recommendation is very necessary. In this study, we propose using session-based recommendation systems to suggest the learning resources to the learners. Experiments are performed on a learning resource dataset collected at a local university and a public dataset. After preprocessing the data to convert it to session form, the Neural Attentive Session-based Recommendation (NARM) and Recurrent Neural Networks (GRU4Rec) models were used for training, testing, and comparison. The results show that recommending learning resources according to the NARM model is more effective than that of the GRU4Rec model, and thus, using the session-based recommendation system would be a promising approach for learning resource recommendation.

Ensemble Learning for Interpretable Concept Drift and Its Application to Drug Recommendation

During the COVID-19 epidemic, the online prescription pattern of Internet healthcare pro-vides guarantee for the patients with chronic diseases and reduces the risk of cross-infection, but it also raises the burden of decision-making for doctors. Online drug recommendation system can effectively assist doctors by analysing the electronic medical records (EMR) of patients. Unlike commercial recommendations, the accuracy of drug recommendations should be very high due to their relevance to patient health. Besides, concept drift may occur in the drug treatment data streams, handling drift and location drift causes is critical to the accuracy and reliability of the rec-ommended results. This paper proposes a multi-model fusion online drug recommendation system based on the association of drug and pathological features with online-nearline-offline architecture.The system transforms drug recommendation into pattern classification and adopts interpretable concept drift detection and adaptive ensemble classification algorithms. We apply the system to the Percutaneous Coronary Intervention (PCI) treatment process. The experiment results show our system performs nearly as good as doctors, the accuracy is close to 100%.

Online learning resource recommendation method based on multi-similarity metric optimization under the COVID-19 epidemic.

With the continuous COVID-19 pneumonia epidemic, online learning has become a normal choice for many learners. However, the problems of information overload and knowledge maze have been aggravated in the process of online learning. A learning resource recommendation method based on multi similarity measure optimization is proposed in this paper. We optimize the user score similarity by introducing information entropy, and use particle swarm optimization algorithm to determine the comprehensive similarity weight, and determine the nearest neighbor user with both score similarity and interest similarity through secondary screening in this method. The ultimate goal is to improve the accuracy of recommendation results, and help learners learn more effectively. We conduct experiments on public data sets. The experimental results show that the algorithm in this paper can significantly improve the recommendation accuracy on the basis of maintaining a stable recommendation coverage.

HPV vaccine recommendations by age: A survey of providers in federally qualified health centers.

Clinician recommendation remains a critical factor in improving HPV vaccine uptake. Clinicians practicing in federally qualified health centers were surveyed between October 2021 and July 2022. Clinicians were asked how they recommended HPV vaccination for patients aged 9-10, 11-12, 13-18, 19-26, and 27-45 y (strongly recommend, offer but do not recommend strongly, discuss only if the patient initiates the conversation, or recommend against). Descriptive statistics were assessed, and exact binomial logistic regression analyses were utilized to examine factors associated with HPV vaccination recommendation in 9-10-y-old patients. Respondents (n = 148) were primarily female (85%), between the ages of 30-39 (38%), white, non-Hispanic (62%), advanced practice providers (55%), family medicine specialty (70%), and practicing in the Northeast (63%). Strong recommendations for HPV vaccination varied by age: 65% strongly recommended for ages 9-10, 94% for ages 11-12, 96% for ages 13-18, 82% for age 19-26, and 26% for ages 27-45 y. Compared to Women's Health/OBGYN specialty, family medicine clinicians were less likely to recommend HPV vaccination at ages 9-10 (p = .03). Approximately two-thirds of clinicians practicing in federally qualified health centers or safety net settings strongly recommend HPV vaccine series initiation at ages 9-10. Additional research is needed to improve recommendations in younger age groups.

Uses of Social Media and Computer Technologies for Guest Satisfaction and Recommendation Analysis using Machine Learning in Hotels Industries

Guest loyalty has great effect on their satisfaction as well as it helps to increase the efficiency, and it leads to improved profit and sale of hotel and create positive impact on customers. Loyalty is a long-Term commitment which helps to stand the business in market for a long time and make business successful. Hospitality industry is the one of the fastest and largest job generating and revenue generating industry in among all sectors. In hospitality industry, employees come with the contact with guest either in front of the house (Front Office F and B service) or back of the house (Food Production Housekeeping) and perform their duties in the best professional way. Customer satisfaction is one of the important objectives to sustain the guest loyalty for repetition. In this research primary data was collected and it belongs to empirical research. Data were collected from the UttrakahndGarhwal region and the major cities covered are Mansoori, Dehradun, Rishikesh and Haridwar. The findings of this study are how customer retention/loyalty relates to the recommendation to the others in addition with the overall outlets performance. The study defines the impact of the overall behavioral variables related to F and B outlets in relation to the satisfaction. The research was conducted with the help of online offline filled questionnaires with the population size of 110. The population covered the regular travelers who have visited any of the Hotels and stayed at least for one night. The data was analyzed by using correlation, ANOVAs and T-Test. The study has reflected the Gap in constraint of the data collection and the COVID-19 Government guidelines. © 2022 IEEE.

Research and Application of Online Course Recommendation System Based on TF-IDF Algorithm

In recent years, with the rapid development of Internet technology, a large number of online learning resources have emerged. Especially affected by the COVID-19 epidemic, online learning has become a very effective learning means. However, a large number of learning platforms and massive online teaching resources have the following three problems: 1) The quality of these courses is uneven and the evaluation standards are different;2) There are so many similar courses that it is difficult for learners to distinguish them;3) These classes are lack of unity and integration, and it is hard to recommend any hierarchical, coherent and systematic course resources to learners. Therefore, a recommendation model based on TF-IDF algorithm is designed to extract personalized-featured courses, use the nearest neighbor similarity to cluster the similarity of similar courses, and conduct the featured portrait of learners to realize online courses recommendation. Combined with the model design, this paper presents a tag-based online course resource recommendation system, which can fully explore learners' explicit and implicit preferences according to course tags, and recommend satisfactory MOOC resources for them with good application value. © 2022 IEEE.