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.

Customer Load Profile Clustering Using K-means Algorithm: A Case Study in an Electric Distribution Company in the Philippines Amidst the COVID-19 Pandemic

Most of the electric distribution companies in the Philippines are interested in analyzing customer load profile, they are concerned in classifying their customer's profile into different categories based on the energy consumption, Also the user's profile will help to understand how the consumption of energy may affect the electric distribution grid. In the current condition right now, facing the COVID-19 pandemic, most Filipinos are inclined to work at home, thus the consumption of energy increased. In this paper, residential data were collected in one of the electric distribution companies in the Philippines amidst the COVID-19 pandemic conditions. The data consist of 1,048,575 customer profiles from the year 2021. This study aims to use clustering methods such as the K-means algorithm in grouping customers' profiles and validate the suitable amount of clusters using the proposed method, such as the multi-criteria model and elbow method. Results show that 2 and 7 clusters, respectively, were fitted in the data. © 2022 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.

Cluster Analysis on Covid-19 Outbreak Sentiments from Twitter Data using K-means Algorithm

With the remarkable advances on the Internet and recent computer technologies, social media has become prominent platforms to share opinions. Nowadays, COVID-19 is considered as one of the major crises in the world. People use social media to express their thoughts about COVID-19 and actions that have been taken to control it. There is an immense need to discover and understand the public sentiment related to COVID-19 to give better insights for the decision makers and governments in making accurate decisions. In regard to this interest, several researches have explored COVID-19 outbreak sentiment analysis, however most of these studies have used classification approach which require the data to be manually labelled. In analyzing large number of data, labelling process can be an intricate task and expert dependent. This study aims to explore COVID-19 pandemic sentiment by using clustering approach. The data is obtained by crawling COVID-19 related posts from Twitter. The crawled data is pre-processed, and terms are extracted by using Term Frequency-Inverse Document Frequency (TF-IDF) technique. Singular Value Decomposition (SVD) technique is then used to reduce irrelevant features. K-means algorithm is employed to cluster the tweets into k clusters. The results of each cluster are plotted using t-Distributed Stochastic Neighbour Embedding (t-SNE) technique and lexicon-based sentiment analysis has been applied to discover sentiments of these clusters. The results showed relatively 9 clusters were obtained with different topics ranging highest score of 83.25% positivity and 16.75% of negativity are reported. Dominant topics are explored using word cloud and the clustering results have been evaluated with 0.0070 Silhouette coefficient. In future, this study suggests in using other word embedding technique as a data representation to deal with sparsity and high dimensionality of textual data. © 2021, Success Culture Press. All rights reserved.

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