论文征集:医学数据分析的人工智能医疗物联网特刊

IF 7.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data Mining and Analytics Pub Date : 2022-01-01 DOI:10.26599/BDMA.2022.9020001
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引用次数: 0

摘要

在现代大数据时代,教育、能源、环境、医疗、欺诈、检测和流量等各个领域产生和存储的数据量总是呈指数级增长。与此同时,由于人工智能、机器学习、深度学习和数据分析等各个领域的巨大增长,世界各地的商业和社会发生了重大的范式转变。这给决策带来了重大挑战,并为经济、政府和行业带来了潜在的转型。人工智能工具、技术、技术和大数据提高了所创建系统的预测能力,使政府、公共和私营部门能够发现新的模式和趋势。它提高了公共价值观,如问责制、安全性、安保和透明度,以实现更好的决策、政策和治理。它们还有许多能力来执行人类技能无法完成的复杂任务。它们可以用来收集、组织和分析各种各样的大型数据集,以发现模式和趋势。它通过确定新的收入来源,扩大业务、产品审查和促销的客户群,解决了与经济发展有关的几个问题。它有助于预测和预防疾病,预测气候变化,并提供能源解决方案。本期特刊旨在征集涵盖大数据和人工智能最新数据分析概念和技术的高质量论文。这期特刊是全球研究人员讨论他们的工作和该领域最新进展的论坛。邀请来自人工智能和大数据分析在产品、金融、健康和环境方面的最佳论文。根据主题的创新性和相关性,将选出最佳的高质量论文。
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Call for papers: Special issue on AI-enabled Internet of medical things for medical data analytics
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来源期刊
Big Data Mining and Analytics
Big Data Mining and Analytics Computer Science-Computer Science Applications
CiteScore
20.90
自引率
2.20%
发文量
84
期刊介绍: Big Data Mining and Analytics, a publication by Tsinghua University Press, presents groundbreaking research in the field of big data research and its applications. This comprehensive book delves into the exploration and analysis of vast amounts of data from diverse sources to uncover hidden patterns, correlations, insights, and knowledge. Featuring the latest developments, research issues, and solutions, this book offers valuable insights into the world of big data. It provides a deep understanding of data mining techniques, data analytics, and their practical applications. Big Data Mining and Analytics has gained significant recognition and is indexed and abstracted in esteemed platforms such as ESCI, EI, Scopus, DBLP Computer Science, Google Scholar, INSPEC, CSCD, DOAJ, CNKI, and more. With its wealth of information and its ability to transform the way we perceive and utilize data, this book is a must-read for researchers, professionals, and anyone interested in the field of big data analytics.
期刊最新文献
Contents Front Cover Incremental Data Stream Classification with Adaptive Multi-Task Multi-View Learning Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals Gender-Based Analysis of User Reactions to Facebook Posts
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