Research on the Construction Scheme of Wuhan Emergency Logistics System Under the Background of Public Health Emergencies

In order to improve the emergency logistics support capacity of Wuhan city and build a transportation power pilot, based on the background of public health emergencies and on the basis of comprehensively summarizing the experience, practices and prominent problems of emergency logistics support work of COVID-19 in Wuhan City, this paper studies from the aspects of development foundation, overall thinking and main tasks, Put forward the systematic framework and specific implementation path of emergency logistics system construction of "building three guarantee systems of reserve facilities, transportation capacity and command and dispatching, and building an information platform”. At the same time, in the construction of emergency logistics command and coordination information platform, K-means clustering method is adopted to achieve scientific matching and efficient connection between emergency materials transit stations and demand points. For other cities It is of practical significance to improve the regional emergency logistics system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Machine Learning Approach: Consumer Buying Behavior Analysis

The rise of multiple company competitors during the COVID-19 outbreak resulted in fierce competition among competing firms for new clients and the retention of current ones. As a result of the foregoing, exceptional customer service is required, regardless of the size of the organization. Furthermore, any company's ability to know each of its customers' desires will provide it an advantage when it comes to providing specialized customer care and establishing customized marketing plans for them. The term 'Consumer Buying Behavior Analysis' refers to a comprehensive assessment of the company's ideal clients/customers. In this project, we're utilizing the K-Means Algorithm to divide clients into two groups: 'Highly Active Customers' and 'Least Active Customers.' Then, utilizing the Apriori Algorithm, we use Association Rule Mining to recommend the best goods to clients based on their purchasing history and associations. We take one step further and use Logistic Regression to validate our Clustering operation by doing Binary Classification with our clusters as the label, resulting in accuracy and an F1 score of 91%. © 2022 IEEE.

COVID-19 and Arabic Twitter: How can Arab world governments and Public Health Organizations learn from social media?

In March 2020, the World Health Organization announced the COVID-19 outbreak as a pandemic. Most previous social media related research has been on English tweets and COVID-19. In this study, we collect approximately 1 million Arabic tweets from the Twitter streaming API related to COVID-19. Focussing on outcomes that we believe will be useful for Public Health Organizations, we analyse them in three different ways: identifying the topics discussed during the period, detecting rumours, and predicting the source of the tweets. We use the k-means algorithm for the first goal with k=5. The topics discussed can be grouped as follows: COVID-19 statistics, prayers for God, COVID-19 locations, advise and education for prevention, and advertising. We sample 2000 tweets and label them manually for false information, correct information, and unrelated. Then, we apply three different machine learning algorithms, Logistic Regression, Support Vector Classification, and Naïve Bayes with two sets of features, word frequency approach and word embeddings. We find that Machine Learning classifiers are able to correctly identify the rumour related tweets with 84% accuracy. We also try to predict the source of the rumour related tweets depending on our previous model which is about classifying tweets into five categories: academic, media, government, health professional, and public. Around (60%) of the rumour related tweets are classified as written by health professionals and academics. © ACL 2020.All right reserved.

Voting Classification Approach for Covid-19 Prediction with K-mean and PCA

Coronavirus Disease 2019 is occurred as a challenging disease among the scientist worldwide. The disease is developed at an extensive level. Thus, the disease must be detected, reported, isolated, diagnosed and cured at initial phase for mitigating its growth rate. This research paper is conducted on the basisof predicting covid-19 ML algorithms. The methods of predicting this disease consist of diverse stages inwhich data is added as input, pre-processed, attributes are extracted and data is classified. This research work focuses on gathering the authentic dataset which get pre-processed for the classification. In the phase of feature extraction,PCA and k-mean algorithms are applied. The votingclassification method is applied in this work in which GNB, BNB, RF and Support Vector Machine algorithms are integrated. Python is executed to implement the introduced method. Diverse metrics are considered to analyze the outcomes. Using supervised machine learning, we create this model. The branch of ML focuses on implementing intelligent models so that various complicated issues can be tackled. The introduced method offers higher accuracy, precisionand recall in comparison with other classifiers. © 2022 IEEE.

The Labour Market Consequences of the COVID-19 Pandemic in the European Union Countries: Selected Issues

The massive lockdowns of economies as a result of the COVID-19 pandemic are unprecedented on a global scale. Such actions have unfortunately had their negative consequences for the labour market. This is expressed, among other things, through the deterioration of labour market indicators. The aim of the presented study is to assess the size of differences in changes in selected labour market indicators across EU countries over the period 2019–2020 and to assess the heterogeneity of EU countries due to the responses of these indicators. Given that EU countries have used isolation strategies and job support with different intensities, and that their labour markets are characterised by quite different elasticities, the response of these markets is characterised by considerable heterogeneity. In the analysis, we consider labour market characteristics such as economic activity, employment level, share of part-time workers, share of temporary workers or share of self-employed. The k-means algorithm is applied as a research tool. In turn, we use the silhouette index to assess the quality of the obtained divisions. The results obtained indicate a diverse response of national labour markets to the restrictions introduced as a result of COVID-19. The largest negative changes we observe in the group includes PIIGS countries (Portugal, Ireland, Italy, Greece and Spain) and Bulgaria, Czech Republic, and Slovakia. The countries in the group in which Luxembourg, Hungary and the Netherlands are classified have done relatively well, where apart from a reduction in the number of temporary workers, changes in other characteristics are positive. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Clustering Analysis in the Student Academic Activities on COVID-19 Pandemic in Mexico

The pandemic caused by the COVID-19 disease has affected all aspects of the life of the people in every region of the world. The academic activities at universities in Mexico have been particularly disturbed by two years of confinement;all activities were migrated to an online modality where improvised actions and prolonged isolation have implied a significant threat to the educational institutions. Amid this pandemic, some opportunities to use Artificial Intelligence tools for understanding the associated phenomena have been raised. In this sense, we use the K-means algorithm, a well-known unsupervised machine learning technique, to analyze the data obtained from questionaries applied to students in a Mexican university to understand their perception of how the confinement and online academic activities have affected their lives and their learning. Results indicate that the K-means algorithm has better results when the number of groups is bigger, leading to a lower error in the model. Also, the analysis helps to make evident that the lack of adequate computing equipment, internet connectivity, and suitable study spaces impact the quality of the education that students receive, causing other problems, including communication troubles with teachers and classmates, unproductive classes, and even accentuate psychological issues such as anxiety and depression. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling

K-means algorithm is one of the well-known unsupervised machine learning algorithms. The algorithm typically finds out distinct non-overlapping clusters in which each point is assigned to a group. The minimum squared distance technique distributes each point to the nearest clusters or subgroups. One of the K-means algorithm’s main concerns is to find out the initial optimal centroids of clusters. It is the most challenging task to determine the optimum position of the initial clusters’ centroids at the very first iteration. This paper proposes an approach to find the optimal initial centroids efficiently to reduce the number of iterations and execution time. To analyze the effectiveness of our proposed method, we have utilized different real-world datasets to conduct experiments. We have first analyzed COVID-19 and patient datasets to show our proposed method’s efficiency. A synthetic dataset of 10M instances with 8 dimensions is also used to estimate the performance of the proposed algorithm. Experimental results show that our proposed method outperforms traditional kmeans++ and random centroids initialization methods regarding the computation time and the number of iterations. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Analyzing and Predicting the Deaths and Cases of Covid-19 in Different Countries Using Fuzzy K Means Algorithm and Exponential Method

Covid-19 disease, since it first appearance in the Chinese city of Wuhan, has led to many infections and deaths, not only in China, but also in most countries of the world. The most prominent symptoms of this disease are headache, fever, strong cough, and perhaps the strongest of it is difficulty breathing in the event that the virus reaches the lung, which leads to death in many cases if the patient's condition is late, or he does not have strong immunity. The purpose of this study is to use Fuzzy k Means (FKM) and predictive algorithm representing in Simple Exponential Smoothing Method (SESM) to evaluate confirmed cases and deaths in different countries. This study's findings show that the FKM approach can evaluate data and produce reliable results, in addition to the SESM can give good prediction. According to this study, machine learning technologies and predicting methodologies achieved good results when used together. © 2021 IEEE.

An Analysis on the Weibo Topic Detection Based on K-means Algorithm

Sina Weibo, as a platform for netizens to express their opinions, generates a large amount of public opinion data and constantly generates new topics. How to detect new and hot topics on Weibo is a meaningful studied issue. Document Clustering is a widely studied problem in Text Categorization. K-means is one of the most famous unsupervised learning algorithms, partitions a given dataset into disjoint clusters following a simple and easy way. But the traditional K-means algorithm assigns initial centroids randomly, which cannot guarantee to choose the maximum dissimilar documents as the centroids for the clusters. A modified K-means algorithm is proposed, which uses Jaccard distance measure for assigning the most dissimilar k documents as centroids, and uses Word2vec as the Chinese text vectorization model. The experimental results demonstrate that the proposed K-means algorithm improves the clustering performance, and is able to detect new and hot topics based on Weibo COVID-19 data. © 2022 IEEE.

Predicting Creditworthiness of Smartphone Users in Indonesia during the COVID-19 pandemic using Machine Learning

In this research work, we attempted to predict the creditworthiness of smartphone users in Indonesia during the COVID-19 pandemic using machine learning. Principal Component Analysis (PCA) and Kmeans algorithms are used for the prediction of creditworthiness with the used a dataset of 1050 respondents consisting of twelve questions to smartphone users in Indonesia during the COVID-19 pandemic. The four different classification algorithms (Logistic Regression, Support Vector Machine, Decision Tree, and Naive Bayes) were tested to classify the creditworthiness of smartphone users in Indonesia. The tests carried out included testing for accuracy, precision, recall, F1-score, and Area Under Curve Receiver Operating Characteristics (AUCROC) assesment. Logistic Regression algorithm shows the perfect performances whereas Naïve Bayes (NB) shows the least. The results of this research also provide new knowledge about the influential and non-influential variables based on the twelve questions conducted to the respondents of smartphone users in Indonesia during the COVID-19 pandemic. © 2022 IEEE.

Educational Data Mining to Identify the Patterns of Use made by the University Professors of the Moodle Platform

Due to the events caused by the COVID-19 pandemic and social distancing measures, learning management systems have gained importance, preserving quality standards, they can be used to implement remote education or as support for face-to-face education. Consequently, it is important to know how teachers and students use them. In this work, clustering techniques are used to analyze the use, made by university professors, of the resources and activities of the Moodle platform. The CRISP-DM methodology was applied to implement a data mining process, based on the Simple K-Means algorithm;to identify associated groups of teachers it was necessary to categorize the data obtained from the platform. The Apriori algorithm was applied to identify associations in the use of resources and activities. Performance scales were established in the use of Moodle functionalities, the results show the use made by teachers was very low. Rules were generated to identify the associations between activities and resources. As a result the functionalities that need to be enhanced in the teacher training processes were identified. Having identified the patterns of use of the Moodle platform, it is concluded that it was necessary to use a Likert scale to transform the frequency of use of activities and resources and identify the rules of association that establish profiles of teachers and tools that should be promoted in future training actions © 2022,International Journal of Advanced Computer Science and Applications.All Rights Reserved

Clustering of Student Perceptions on Developing a Physics Laboratory Based on Information Technology and Local Wisdom

Distance education has been driven by the Covid-19 outbreak on an unprecedented scale. This condition presents its own challenges for learning related to experiments such as physics. Based on these conditions, a physics laboratory based on information technology and local wisdom will be developed which can be an alternative for learning physics with experiments that can be done at home. For this reason, students' perceptions of the use of laboratories and information technology in learning physics and the development of physics laboratories based on information technology and local wisdom are clustered using the K mean algorithm. Research questionnaires were conducted on 211 first year students at Duta Bangsa University who took physics courses. The results showed that there were 3 clusters produced that correlated with students' perceptions of being neutral, agreeing, and strongly agreeing. Based on the results of the cluster, 101 students (45.70%) strongly agree, 78 students (35.29%) agree, and 42 students (19.01%) are neutral in using laboratories and information technology in learning physics. In addition, 103 students (46.61%) agreed, 62 students (28.05%) were neutral, and 56 students (25.34%) agreed to develop a physics laboratory based on information technology and local wisdom. further research in the development of a physics laboratory based on information technology and local wisdom. © 2021 IEEE.