目的应用文献计量学方法分析近10年来近视研究领域的现状、热点和未来的发展方向。方法检索Web of Science核心数据库中2013年1月1日至2022年12月31日近视相关的研究类和综述类文献,使用VOSviewer软件对国家、研究机构、作者进行共现分...目的应用文献计量学方法分析近10年来近视研究领域的现状、热点和未来的发展方向。方法检索Web of Science核心数据库中2013年1月1日至2022年12月31日近视相关的研究类和综述类文献,使用VOSviewer软件对国家、研究机构、作者进行共现分析,使用CiteSpace软件对关键词和共被引参考文献进行聚类分析。结果最终纳入9745篇文献,涉及123个国家或地区,7150个机构和29343位作者。通过分析发现全球在近视领域的发文量整体呈增长趋势,中国是发文量最多的国家,来自美国的研究总被引用次数最多。关键词分析结果表明,早期近视研究热点主要集中于屈光手术、并发症的诊断与治疗、遗传学研究以及流行病学特征,而近年来研究重点已迅速转向近视的预防和控制。共被引文献聚类分析结果显示,近视领域包含多个聚类模块,如#0学龄儿童、#1小切口角膜基质透镜取出术、#2近视控制、#3屈光不正、#4接触镜等研究方向。研究前沿主要聚焦于近视管理技术、近视与视网膜和脉络膜血管、人工智能在近视领域的应用等方面。结论近十年近视研究领域涵盖眼科学、分子生物学、遗传学、眼视光学、流行病学等多个学科领域。未来需要进一步探索近视的病因和发病机制、早期识别和筛查、管理技术、人工智能辅助诊断等,以制定更加有效、安全的近视防控策略。展开更多
A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (I...A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (IoT) devices. The term “big data security” refers to all the safeguards and instruments used to protect both the data and analytics processes against intrusions, theft, and other hostile actions that could endanger or adversely influence them. Beyond being a high-value and desirable target, protecting Big Data has particular difficulties. Big Data security does not fundamentally differ from conventional data security. Big Data security issues are caused by extraneous distinctions rather than fundamental ones. This study meticulously outlines the numerous security difficulties Large Data analytics now faces and encourages additional joint research for reducing both big data security challenges utilizing Ontology Web Language (OWL). Although we focus on the Security Challenges of Big Data in this essay, we will also briefly cover the broader Challenges of Big Data. The proposed classification of Big Data security based on ontology web language resulting from the protégé software has 32 classes and 45 subclasses.展开更多
This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed a...This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.展开更多
To improve question answering （QA） performance based on real-world web data sets,a new set of question classes and a general answer re-ranking model are defined.With pre-defined dictionary and grammatical analysis,t...To improve question answering （QA） performance based on real-world web data sets,a new set of question classes and a general answer re-ranking model are defined.With pre-defined dictionary and grammatical analysis,the question classifier draws both semantic and grammatical information into information retrieval and machine learning methods in the form of various training features,including the question word,the main verb of the question,the dependency structure,the position of the main auxiliary verb,the main noun of the question,the top hypernym of the main noun,etc.Then the QA query results are re-ranked by question class information.Experiments show that the questions in real-world web data sets can be accurately classified by the classifier,and the QA results after re-ranking can be obviously improved.It is proved that with both semantic and grammatical information,applications such as QA, built upon real-world web data sets, can be improved,thus showing better performance.展开更多
文摘目的应用文献计量学方法分析近10年来近视研究领域的现状、热点和未来的发展方向。方法检索Web of Science核心数据库中2013年1月1日至2022年12月31日近视相关的研究类和综述类文献,使用VOSviewer软件对国家、研究机构、作者进行共现分析,使用CiteSpace软件对关键词和共被引参考文献进行聚类分析。结果最终纳入9745篇文献,涉及123个国家或地区,7150个机构和29343位作者。通过分析发现全球在近视领域的发文量整体呈增长趋势,中国是发文量最多的国家,来自美国的研究总被引用次数最多。关键词分析结果表明,早期近视研究热点主要集中于屈光手术、并发症的诊断与治疗、遗传学研究以及流行病学特征,而近年来研究重点已迅速转向近视的预防和控制。共被引文献聚类分析结果显示,近视领域包含多个聚类模块,如#0学龄儿童、#1小切口角膜基质透镜取出术、#2近视控制、#3屈光不正、#4接触镜等研究方向。研究前沿主要聚焦于近视管理技术、近视与视网膜和脉络膜血管、人工智能在近视领域的应用等方面。结论近十年近视研究领域涵盖眼科学、分子生物学、遗传学、眼视光学、流行病学等多个学科领域。未来需要进一步探索近视的病因和发病机制、早期识别和筛查、管理技术、人工智能辅助诊断等,以制定更加有效、安全的近视防控策略。
文摘A vast amount of data (known as big data) may now be collected and stored from a variety of data sources, including event logs, the internet, smartphones, databases, sensors, cloud computing, and Internet of Things (IoT) devices. The term “big data security” refers to all the safeguards and instruments used to protect both the data and analytics processes against intrusions, theft, and other hostile actions that could endanger or adversely influence them. Beyond being a high-value and desirable target, protecting Big Data has particular difficulties. Big Data security does not fundamentally differ from conventional data security. Big Data security issues are caused by extraneous distinctions rather than fundamental ones. This study meticulously outlines the numerous security difficulties Large Data analytics now faces and encourages additional joint research for reducing both big data security challenges utilizing Ontology Web Language (OWL). Although we focus on the Security Challenges of Big Data in this essay, we will also briefly cover the broader Challenges of Big Data. The proposed classification of Big Data security based on ontology web language resulting from the protégé software has 32 classes and 45 subclasses.
文摘This work leveraged predictive modeling techniques in machine learning (ML) to predict heart disease using a dataset sourced from the Center for Disease Control and Prevention in the US. The dataset was preprocessed and used to train five machine learning models: random forest, support vector machine, logistic regression, extreme gradient boosting and light gradient boosting. The goal was to use the best performing model to develop a web application capable of reliably predicting heart disease based on user-provided data. The extreme gradient boosting classifier provided the most reliable results with precision, recall and F1-score of 97%, 72%, and 83% respectively for Class 0 (no heart disease) and 21% (precision), 81% (recall) and 34% (F1-score) for Class 1 (heart disease). The model was further deployed as a web application.
基金Microsoft Research Asia Internet Services in Academic Research Fund(No.FY07-RES-OPP-116)the Science and Technology Development Program of Tianjin(No.06YFGZGX05900)
文摘To improve question answering （QA） performance based on real-world web data sets,a new set of question classes and a general answer re-ranking model are defined.With pre-defined dictionary and grammatical analysis,the question classifier draws both semantic and grammatical information into information retrieval and machine learning methods in the form of various training features,including the question word,the main verb of the question,the dependency structure,the position of the main auxiliary verb,the main noun of the question,the top hypernym of the main noun,etc.Then the QA query results are re-ranked by question class information.Experiments show that the questions in real-world web data sets can be accurately classified by the classifier,and the QA results after re-ranking can be obviously improved.It is proved that with both semantic and grammatical information,applications such as QA, built upon real-world web data sets, can be improved,thus showing better performance.
