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利用大数据分析理解用户生成的地理信息在医疗保健中的应用。

Understanding the User-Generated Geographic Information by Utilizing Big Data Analytics for Health Care.

机构信息

Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Istanbul Sabahattin Zaim University, Istanbul, Turkey.

Department of Computer Engineering, Istinye University, Istanbul, Turkey.

出版信息

Comput Intell Neurosci. 2022 Oct 6;2022:2532580. doi: 10.1155/2022/2532580. eCollection 2022.

DOI:10.1155/2022/2532580
PMID:36248930
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9560849/
Abstract

There are two main ways to achieve an active lifestyle, the first is to make an effort to exercise and second is to have the activity as part of your daily routine. The study's major purpose is to examine the influence of various kinds of physical engagements on density dispersion of participants in Shanghai, China, and even prototype check-in data from a Location-Based Social Network (LBSN) utilizing a mix of spatial, temporal, and visualization methodologies. This paper evaluates Weibo used for big data evaluation and its dependability in some types rather than physically collected proofs by investigating the relationship between time, class, place, frequency, and place of check-in built on geographic features and related consequences. Kernel density estimation has been used for geographical assessment. Physical activities and frequency allocation are formed as a result of hour-to-day consumption habits. Our observations are based on customer check-in activities in physical venues such as gyms, parks, and playing fields, the prevalence of check-ins, peak times for visiting fun parks, and gender disparities, and we applied relative difference formulation to reveal the gender difference in a much better way. The purpose of this research is to investigate the influence of physical activity and health-related standard of living on well-being in a selection of Shanghai inhabitants.

摘要

有两种主要的方法可以实现积极的生活方式,第一种是努力锻炼,第二种是将活动作为日常生活的一部分。本研究的主要目的是利用空间、时间和可视化方法的组合,检查各种身体活动对中国上海参与者密度分布的影响,甚至利用基于位置的社交网络(LBSN)的原型签到数据。本文通过研究基于地理特征的签到时间、地点、频率和地点与相关后果之间的关系,评估了微博在大数据评估中的应用及其在某些类型中的可靠性,而不是通过物理收集的证据。核密度估计已用于地理评估。体育活动和频率分配是根据日常消费习惯形成的。我们的观察基于客户在健身房、公园和运动场等实体场所的签到活动,签到的流行程度,访问游乐园的高峰时间以及性别差异,并应用相对差异公式以更好地揭示性别差异。本研究的目的是调查身体活动和与健康相关的生活水平对上海居民幸福感的影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/430c/9560849/670a5f21ca40/CIN2022-2532580.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/430c/9560849/8e716e72c62b/CIN2022-2532580.001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/430c/9560849/6573c0113df7/CIN2022-2532580.008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/430c/9560849/58ac1a400911/CIN2022-2532580.009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/430c/9560849/670a5f21ca40/CIN2022-2532580.010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/430c/9560849/8e716e72c62b/CIN2022-2532580.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/430c/9560849/102f2832db06/CIN2022-2532580.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/430c/9560849/5a6aa965f24d/CIN2022-2532580.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/430c/9560849/daf5105ee0c2/CIN2022-2532580.004.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/430c/9560849/43ca10595898/CIN2022-2532580.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/430c/9560849/3ea8b71bf884/CIN2022-2532580.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/430c/9560849/6573c0113df7/CIN2022-2532580.008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/430c/9560849/670a5f21ca40/CIN2022-2532580.010.jpg

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Accessing Artificial Intelligence for Fetus Health Status Using Hybrid Deep Learning Algorithm (AlexNet-SVM) on Cardiotocographic Data.利用心音图数据的混合深度学习算法(AlexNet-SVM)获取胎儿健康状况的人工智能
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