摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-调查人员发布了关于人工智能的新报告。根据Newsrx编辑在印度卡纳塔克邦的新闻报道，研究表明：“这项研究使用科学计量学的方法，对人工智能(AI)和机器学习(ML)在自然灾害预防中的应用进行了分析。科学核心收集网作为主要数据来源，产生了38456个从2003年到2022年的记录。我们的新闻记者从政府第一格拉德学院的研究中获得了一句话，“分析集中在高度有影响力的研究上，定义为获得100条或更多引用的论文，最终形成了1637篇公开文章。VOSviewer软件促进了作者、机构和国家之间合作模式的探索，随着新出现的研究主题和影响最大的文章的确定。这些高被引的论文分布在不同的来源(625篇)。总计被引443502篇,平均每篇文献被引270.92篇。有趣的是,年平均被引增长率呈现负趋势(-1.02%),随着时间的推移，引文模式可能发生变化。平均6.9岁的论文撰写年龄表明，大多数研究都是相对较新的。合作成为该领域的一个突出特征，每篇文献共有5.09名合著者，46.55%的合作是国际性的。这突出了该领域研究固有的合作性质。学术文章(1263篇)代表了主要的研究类型，其次是评论(323篇)，表明该领域在同行评议文献中有坚实的基础。该研究的发现对未来的研究和实际应用具有重要意义。确定文献中的差距，强调有必要进一步探索开发适合特定类型自然灾害的人工智能和D ML模型，并在现实世界中评估这些模型。强调国际合作和国际通用方法是推进这一关键领域的关键组成部分。这种方法承认其关注高引用论文和单一数据库的局限性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – Investigators publish new report on Ar tificial Intelligence. According to news reporting out of Karnataka, India, by N ewsRx editors, research stated, “This study conducts an analysis of artificial i ntelligence (AI) and machine learning (ML) applications in natural disaster pred iction using a scientometric approach. The Web of Science Core Collection served as the primary data source, yielding 38,456 records spanning from 2003 to 2022. ” Our news journalists obtained a quote from the research from Government First Gr ade College, “The analysis concentrated on highly influential research, defined by papers garnering 100 or more citations, resulting in a final set of 1,637 pub lications. VOSviewer software facilitated the exploration of collaboration patte rns among authors, institutions, and countries, along with the identification of emerging research topics and the most impactful articles. These highly cited pa pers were distributed across various sources (625). A total of 443,502 citations were counted, with an average of 270.92 citations per document. Interestingly, the average annual citation growth rate exhibited a negative trend (-1.02% ), suggesting a potential shift in citation patterns over time. The average docu ment age of 6.9 years indicates that the majority of the research is relatively recent. Collaboration emerges as a prominent feature within the field, with an a verage of 5.09 co-authors per document and 46.55% of collaboration s being international. This underscores the collaborative nature inherent in res earch within this domain. Scholarly articles (1263) represent the predominant do cument type, followed by reviews (323), indicative of the field’s solid foundati on in peer-reviewed literature. The study’s findings hold significant implicatio ns for future research and practical applications, identifying gaps in the liter ature and underscoring the necessity for further exploration in developing AI an d ML models tailored to specific types of natural disasters, as well as assessin g these models in real-world scenarios. International collaboration and interdis ciplinary approaches are highlighted as pivotal components in advancing this cri tical field. While providing valuable insights, this approach acknowledges limit ations associated with its focus on highly cited papers and a single database.”
NETL
NSTL