Pub Date : 2022-03-31DOI: 10.1142/s2424922x2250005x
Fulya Gokalp-Yavuz, Y. Güney, Ş. Özdemir, Y. Tuaç, O. Arslan
Spreading of novel coronavirus disease started in China and moved to Korea and Japan, then several countries in Europe, and the last step to the countries in the North and South American continents. Since the virus spread worldwide, we simultaneously use all available daily confirmed cases, recovered cases, and death data to cluster countries in time and spatial dimensions after adjusting for population. For this aim, time-series clustering with the dynamic time warping method is implemented and relevant clusters are marked on the world maps for a better visual understanding in this paper. Grouping countries will give an idea of the spread of the virus, guide decision-makers to implement future prevention vaccination policies, and help them generate global solutions against new virus variants. One of the main results obtained from the cluster analysis is that the European, North and South American continents have homogeneous structures regarding the number of daily confirmed cases per million and relatively more heterogeneous regarding the daily number of recoveries per million such that the overwhelming majority of countries are in the very high cluster. The absence of countries from the low or middle clusters indicates that these continents have to fight the virus more fiercely. African and Asian continents are heterogeneous in all cases. Therefore, these continents should focus on country-specific protections to fight against the virus.
{"title":"The Clustering Structure of the COVID-19 Outbreak in Global Scale","authors":"Fulya Gokalp-Yavuz, Y. Güney, Ş. Özdemir, Y. Tuaç, O. Arslan","doi":"10.1142/s2424922x2250005x","DOIUrl":"https://doi.org/10.1142/s2424922x2250005x","url":null,"abstract":"Spreading of novel coronavirus disease started in China and moved to Korea and Japan, then several countries in Europe, and the last step to the countries in the North and South American continents. Since the virus spread worldwide, we simultaneously use all available daily confirmed cases, recovered cases, and death data to cluster countries in time and spatial dimensions after adjusting for population. For this aim, time-series clustering with the dynamic time warping method is implemented and relevant clusters are marked on the world maps for a better visual understanding in this paper. Grouping countries will give an idea of the spread of the virus, guide decision-makers to implement future prevention vaccination policies, and help them generate global solutions against new virus variants. One of the main results obtained from the cluster analysis is that the European, North and South American continents have homogeneous structures regarding the number of daily confirmed cases per million and relatively more heterogeneous regarding the daily number of recoveries per million such that the overwhelming majority of countries are in the very high cluster. The absence of countries from the low or middle clusters indicates that these continents have to fight the virus more fiercely. African and Asian continents are heterogeneous in all cases. Therefore, these continents should focus on country-specific protections to fight against the virus.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"27 1","pages":"2250005:1-2250005:15"},"PeriodicalIF":0.6,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74733296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Education is a dynamic system by which students perceive the factors necessary to fit them into the society. Education is mainly intentional learning that grooms individuals to achieve success in their adult lives. Evaluation of teaching techniques, course management (CM), communication, and student monitoring are the main characteristics of today’s education system. The aim to plan the curriculum of education management in both schools and colleges leads to the implementation of an MS-BDA. The development process for evaluation of teaching techniques and CM includes the use of the sentiment analysis method, which assesses the emotional feelings of students studying the course by managing curriculum quality. The big data analysis with MNN is developed by considering the communication and student monitoring system. This system evaluates the monitoring model provided in MS-BDA for assessing student communication on merging the voice-over with the communication language processing system. The simulation analysis is performed based on accessibility, adaptability, and efficiency, proving the proposed framework’s reliability. Therefore, the system outputs an accuracy of 99.1% when compared to the existing methods.
{"title":"Research on the Automation Integration Terminal of the Education Management Platform Based on Big Data Analysis","authors":"Huizhong Zhang, Fanrong Meng, Guizhen Wang, Beenu Mago, Thendral Puyalnithi","doi":"10.1142/s2424922x22500036","DOIUrl":"https://doi.org/10.1142/s2424922x22500036","url":null,"abstract":"Education is a dynamic system by which students perceive the factors necessary to fit them into the society. Education is mainly intentional learning that grooms individuals to achieve success in their adult lives. Evaluation of teaching techniques, course management (CM), communication, and student monitoring are the main characteristics of today’s education system. The aim to plan the curriculum of education management in both schools and colleges leads to the implementation of an MS-BDA. The development process for evaluation of teaching techniques and CM includes the use of the sentiment analysis method, which assesses the emotional feelings of students studying the course by managing curriculum quality. The big data analysis with MNN is developed by considering the communication and student monitoring system. This system evaluates the monitoring model provided in MS-BDA for assessing student communication on merging the voice-over with the communication language processing system. The simulation analysis is performed based on accessibility, adaptability, and efficiency, proving the proposed framework’s reliability. Therefore, the system outputs an accuracy of 99.1% when compared to the existing methods.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"347 1","pages":"2250003:1-2250003:24"},"PeriodicalIF":0.6,"publicationDate":"2022-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79699994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-19DOI: 10.1142/s2424922x22500024
P. Peng
After entering the new century, the country’s requirements for environmental protection are increasingly stringent. Many provinces with weak industrial bases regard tourism as an important industry for economic development and make great efforts to support and promote it. In order to adapt to the changes in the demand of the tourism industry under the internet, this paper is based on the research of the characteristics of cultural tourism planning strategies under the background of big data. After analyzing the advantages of the traditional ant colony algorithm in the design of tourism routes, in order to improve the rationality of the planning of travel routes, an optimized ant colony algorithm model is established to solve the characteristic tourism planning routes, making route planning more scientific and efficient. The final simulation experiment proves that the improvement of the ant colony algorithm in this study can effectively improve the effectiveness of the route planning and formulate special tourist routes that are more popular with tourists.
{"title":"Study on the Characteristics of Special Cultural Tourism Securing and Enhancing Operations Based on Big Data","authors":"P. Peng","doi":"10.1142/s2424922x22500024","DOIUrl":"https://doi.org/10.1142/s2424922x22500024","url":null,"abstract":"After entering the new century, the country’s requirements for environmental protection are increasingly stringent. Many provinces with weak industrial bases regard tourism as an important industry for economic development and make great efforts to support and promote it. In order to adapt to the changes in the demand of the tourism industry under the internet, this paper is based on the research of the characteristics of cultural tourism planning strategies under the background of big data. After analyzing the advantages of the traditional ant colony algorithm in the design of tourism routes, in order to improve the rationality of the planning of travel routes, an optimized ant colony algorithm model is established to solve the characteristic tourism planning routes, making route planning more scientific and efficient. The final simulation experiment proves that the improvement of the ant colony algorithm in this study can effectively improve the effectiveness of the route planning and formulate special tourist routes that are more popular with tourists.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"24 1","pages":"2250002:1-2250002:17"},"PeriodicalIF":0.6,"publicationDate":"2022-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83265950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-03-19DOI: 10.1142/s2424922x22500048
Huixia Yu
As a result of gathering information from multiple consumer centers, big data (BD) assists in analyzing traveler patterns and developing a unique marketing plan tailored to the target demographic. BD tourism forecasting is a relatively new academic field because of the challenges in capturing, gathering, and modeling this sort of data due to its inherent privacy and economic importance. The growth rate of cruise tourists has slowed down after years of rapid expansion. Investing in homeports, cruise ships, and promotional activities carries a growing danger of financial loss. To make investment decisions and prepare for the future, it is necessary to predict tourism demand. We present the least-squares vector regression (LSVR) model with the gravitational search method for forecasting demand for cruise tourism (FCT) based on BD to improve forecasting performance. As a part of the proposed model forecasting demand for cruise tourism based on big data (FDCT-BD), hyper-parameters of the LSVR model are improved using an algorithm and by comparing these models with various configuration combinations. This paper forecasts tourist arrivals based on internet BD from a search engine and online review platforms and the comparative advantage of multi-platform forecasting over single-platform forecasting based on online review data. However, the results show that the methodology’s recommended framework is successful and that BD may estimate cruise tourist demand with enhanced performance and accuracy 93.8% and 97.9%, respectively.
{"title":"An Effective Model for Forecasting Travel Consumer Demand Using Big Data Analysis","authors":"Huixia Yu","doi":"10.1142/s2424922x22500048","DOIUrl":"https://doi.org/10.1142/s2424922x22500048","url":null,"abstract":"As a result of gathering information from multiple consumer centers, big data (BD) assists in analyzing traveler patterns and developing a unique marketing plan tailored to the target demographic. BD tourism forecasting is a relatively new academic field because of the challenges in capturing, gathering, and modeling this sort of data due to its inherent privacy and economic importance. The growth rate of cruise tourists has slowed down after years of rapid expansion. Investing in homeports, cruise ships, and promotional activities carries a growing danger of financial loss. To make investment decisions and prepare for the future, it is necessary to predict tourism demand. We present the least-squares vector regression (LSVR) model with the gravitational search method for forecasting demand for cruise tourism (FCT) based on BD to improve forecasting performance. As a part of the proposed model forecasting demand for cruise tourism based on big data (FDCT-BD), hyper-parameters of the LSVR model are improved using an algorithm and by comparing these models with various configuration combinations. This paper forecasts tourist arrivals based on internet BD from a search engine and online review platforms and the comparative advantage of multi-platform forecasting over single-platform forecasting based on online review data. However, the results show that the methodology’s recommended framework is successful and that BD may estimate cruise tourist demand with enhanced performance and accuracy 93.8% and 97.9%, respectively.","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"206 1","pages":"2250004:1-2250004:21"},"PeriodicalIF":0.6,"publicationDate":"2022-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79731932","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-01-01DOI: 10.1142/S2424922X22410017
Ruyi Li, Tao Zou
{"title":"An Analysis of the Influence of Internet Plus on Chorus Art","authors":"Ruyi Li, Tao Zou","doi":"10.1142/S2424922X22410017","DOIUrl":"https://doi.org/10.1142/S2424922X22410017","url":null,"abstract":"","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"34 1","pages":"2241001:1-2241001:10"},"PeriodicalIF":0.6,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79751939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-30DOI: 10.1142/s2424922x22420013
Jin Chen
{"title":"Research on Teaching Methods of Teachers' Ideological Education Based on Large Data","authors":"Jin Chen","doi":"10.1142/s2424922x22420013","DOIUrl":"https://doi.org/10.1142/s2424922x22420013","url":null,"abstract":"","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"45 1","pages":"2242001:1-2242001:14"},"PeriodicalIF":0.6,"publicationDate":"2021-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88345809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-30DOI: 10.1142/s2424922x21420043
Huawen Chen, Jianyun Xie, S. Wang, Sakkaravarthi Ramanathan, Ronald Mutegeki
{"title":"Research on Intelligent Management System of Meteorological Archives Based on Big Data Framework","authors":"Huawen Chen, Jianyun Xie, S. Wang, Sakkaravarthi Ramanathan, Ronald Mutegeki","doi":"10.1142/s2424922x21420043","DOIUrl":"https://doi.org/10.1142/s2424922x21420043","url":null,"abstract":"","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"10 1","pages":"2142004:1-2142004:24"},"PeriodicalIF":0.6,"publicationDate":"2021-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78542701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-30DOI: 10.1142/s2424922x21420031
Wei Wang, X. Liu
{"title":"Social-Interactive Sports Monitor for Children Using Augmentative and Alternative Communication","authors":"Wei Wang, X. Liu","doi":"10.1142/s2424922x21420031","DOIUrl":"https://doi.org/10.1142/s2424922x21420031","url":null,"abstract":"","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"61 1","pages":"2142003:1-2142003:20"},"PeriodicalIF":0.6,"publicationDate":"2021-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77673896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-30DOI: 10.1142/s2424922x21430026
Mohammad Al-Sarem, A. Alsaeedi, Faisal Saeed
{"title":"A Deep Learning Artificial Neural Network Algorithm for Instance-based Arabic Language Authorship Attribution","authors":"Mohammad Al-Sarem, A. Alsaeedi, Faisal Saeed","doi":"10.1142/s2424922x21430026","DOIUrl":"https://doi.org/10.1142/s2424922x21430026","url":null,"abstract":"","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"19 1","pages":"2143002:1-2143002:14"},"PeriodicalIF":0.6,"publicationDate":"2021-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75935491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-10-30DOI: 10.1142/s2424922x2142002x
Xiaolong Zheng, Liliya Pan, Bin Lu, Hanqing Hu, Adnan Khurshid
{"title":"Research on Enterprise Human Resource Decision-Making Technology based on Machine Learning Big Data Analysis Model","authors":"Xiaolong Zheng, Liliya Pan, Bin Lu, Hanqing Hu, Adnan Khurshid","doi":"10.1142/s2424922x2142002x","DOIUrl":"https://doi.org/10.1142/s2424922x2142002x","url":null,"abstract":"","PeriodicalId":47145,"journal":{"name":"Advances in Data Science and Adaptive Analysis","volume":"28 1","pages":"2142002:1-2142002:21"},"PeriodicalIF":0.6,"publicationDate":"2021-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84383139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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