首页|Reports on Artificial Intelligence Findings from Government First Grade College Provide New Insights (Artificial Intelligence and Machine Learning for Disaster Prediction: a Scientometric Analysis of Highly Cited Papers)
Reports on Artificial Intelligence Findings from Government First Grade College Provide New Insights (Artificial Intelligence and Machine Learning for Disaster Prediction: a Scientometric Analysis of Highly Cited Papers)
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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.”
KarnatakaIndiaAsiaArtificial Intel ligenceCyborgsEmerging TechnologiesMachine LearningGovernment First Grad e College
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