This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of co...This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].展开更多
Text mining is a text data analysis,found that the relationship between concepts and underlying concepts from unstructured text,it is extracted from large text database has not yet been realized patterns or associatio...Text mining is a text data analysis,found that the relationship between concepts and underlying concepts from unstructured text,it is extracted from large text database has not yet been realized patterns or associations,some information retrieval and text processing system can find the relationship between words and paragraphs.This article first describes the data sources and a brief introduction to the related platforms and functional components.Secondly,it explains the Chinese word segmentation and the Korean word segmentation system.At last,it takes the news,documents and materials of the Korean Peninsula as well as the various public opinion data on the network as the basic data for the research.The examples of word frequency graph and word cloud graph is carried out to show the results of text mining through Chinese word segmentation system and Korean word segmentation system.展开更多
The Internet of Medical Things(IoMT)is a collection of smart healthcare devices,hardware infrastructure,and related software applications,that facilitate the connection of healthcare information technology system via ...The Internet of Medical Things(IoMT)is a collection of smart healthcare devices,hardware infrastructure,and related software applications,that facilitate the connection of healthcare information technology system via the Internet.It is also called IoT in healthcare,facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare data.In other words,IoMT is an amalgam of medical devices and applications,which improves overall healthcare outcomes.However,this system is prone to securityand privacy-related attacks on healthcare data.Therefore,providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is essential.To mitigate this,we proposed a new Artificial-Intelligence envisioned secure communication scheme for IoMT.The discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to IoMT.Furthermore,we provide the security analysis of the proposed scheme to show its security against different possible attacks.Moreover,a comparative study of the proposed scheme with other similar schemes is presented.Our results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs,and security and functionality attributes.Finally,we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.展开更多
本文利用Web of Science数据库,对国际大数据研究领域的文献进行收集,分别按照论文的年代、著者、国别与机构进行统计分析,并利用SPSS软件对文献的高频关键词进行聚类分析和多维尺度分析,利用Ucinet软件予以可视化呈现,总结了国际大数...本文利用Web of Science数据库,对国际大数据研究领域的文献进行收集,分别按照论文的年代、著者、国别与机构进行统计分析,并利用SPSS软件对文献的高频关键词进行聚类分析和多维尺度分析,利用Ucinet软件予以可视化呈现,总结了国际大数据研究的现状与热点,以期对国内大数据的研究提供有益的参考和借鉴。展开更多
目的:检索大数据与健康相关的SCI论文,分析当前该领域的研究方向。方法:对Web of Science^(TM)核心合集数据库中该主题的SCI论文进行文献计量学分析,统计频次大于等于28次的高频主题词并生成共现矩阵,使用SPSS软件进行聚类分析,进而获...目的:检索大数据与健康相关的SCI论文,分析当前该领域的研究方向。方法:对Web of Science^(TM)核心合集数据库中该主题的SCI论文进行文献计量学分析,统计频次大于等于28次的高频主题词并生成共现矩阵,使用SPSS软件进行聚类分析,进而获得该领域的研究热点。结果:经检索得出相关论文979篇,高频主题词14个,获得该领域六个主要研究热点。结论:近年来有关大数据与健康相关的SCI论文主要研究方向包括:人口患病率研究、儿童健康领域研究、卫生保健领域研究、护理质量的效果与控制、健康和风险研究、疾病死亡率研究。展开更多
文摘This research paper has provided the methodology and design for implementing the hybrid author recommender system using Azure Data Lake Analytics and Power BI. It offers a recommendation for the top 1000 Authors of computer science in different fields of study. The technique used in this paper is handling the inadequate Information for citation;it removes the problem of cold start, which is encountered by very many other recommender systems. In this paper, abstracts, the titles, and the Microsoft academic graphs have been used in coming up with the recommendation list for every document, which is used to combine the content-based approaches and the co-citations. Prioritization and the blending of every technique have been allowed by the tuning system parameters, allowing for the authority in results of recommendation versus the paper novelty. In the end, we do observe that there is a direct correlation between the similarity rankings that have been produced by the system and the scores of the participant. The results coming from the associated scrips of analysis and the user survey have been made available through the recommendation system. Managers must gain the required expertise to fully utilize the benefits that come with business intelligence systems [1]. Data mining has become an important tool for managers that provides insights about their daily operations and leverage the information provided by decision support systems to improve customer relationships [2]. Additionally, managers require business intelligence systems that can rank the output in the order of priority. Ranking algorithm can replace the traditional data mining algorithms that will be discussed in-depth in the literature review [3].
文摘Text mining is a text data analysis,found that the relationship between concepts and underlying concepts from unstructured text,it is extracted from large text database has not yet been realized patterns or associations,some information retrieval and text processing system can find the relationship between words and paragraphs.This article first describes the data sources and a brief introduction to the related platforms and functional components.Secondly,it explains the Chinese word segmentation and the Korean word segmentation system.At last,it takes the news,documents and materials of the Korean Peninsula as well as the various public opinion data on the network as the basic data for the research.The examples of word frequency graph and word cloud graph is carried out to show the results of text mining through Chinese word segmentation system and Korean word segmentation system.
基金The authors would like to thank the reviewers and the Associate Editor for their valuable suggestions that helped in improving the quality,readability and presentation of the paper.This work was supported by FCT/MCTES through national funds and when applicable co-funded EU funds under the Project UIDB/50008/2020by the Brazilian National Council for Research and Development(CNPq)via Grants No.431726/2018-3 and 313036/2020-9.
文摘The Internet of Medical Things(IoMT)is a collection of smart healthcare devices,hardware infrastructure,and related software applications,that facilitate the connection of healthcare information technology system via the Internet.It is also called IoT in healthcare,facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare data.In other words,IoMT is an amalgam of medical devices and applications,which improves overall healthcare outcomes.However,this system is prone to securityand privacy-related attacks on healthcare data.Therefore,providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is essential.To mitigate this,we proposed a new Artificial-Intelligence envisioned secure communication scheme for IoMT.The discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to IoMT.Furthermore,we provide the security analysis of the proposed scheme to show its security against different possible attacks.Moreover,a comparative study of the proposed scheme with other similar schemes is presented.Our results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs,and security and functionality attributes.Finally,we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.
文摘本文利用Web of Science数据库,对国际大数据研究领域的文献进行收集,分别按照论文的年代、著者、国别与机构进行统计分析,并利用SPSS软件对文献的高频关键词进行聚类分析和多维尺度分析,利用Ucinet软件予以可视化呈现,总结了国际大数据研究的现状与热点,以期对国内大数据的研究提供有益的参考和借鉴。
文摘目的:检索大数据与健康相关的SCI论文,分析当前该领域的研究方向。方法:对Web of Science^(TM)核心合集数据库中该主题的SCI论文进行文献计量学分析,统计频次大于等于28次的高频主题词并生成共现矩阵,使用SPSS软件进行聚类分析,进而获得该领域的研究热点。结果:经检索得出相关论文979篇,高频主题词14个,获得该领域六个主要研究热点。结论:近年来有关大数据与健康相关的SCI论文主要研究方向包括:人口患病率研究、儿童健康领域研究、卫生保健领域研究、护理质量的效果与控制、健康和风险研究、疾病死亡率研究。