Perceptions of built environment and health outcomes for older Chinese in Beijing: A big data approach with street view images and deep learning technique

2019 ◽  
Vol 78 ◽  
pp. 101386 ◽  
Author(s):  
Ruoyu Wang ◽  
Ye Liu ◽  
Yi Lu ◽  
Jinbao Zhang ◽  
Penghua Liu ◽  
...  
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Built Environment ◽  
Health Outcomes ◽  
Environment And Health ◽  
Street View ◽  
Learning Technique ◽  
Older Chinese

Research on O2O Platform and Promotion Algorithm of Sports Venues Based on Deep Learning Technique

2018 ◽  
Vol 13 (3) ◽  
pp. 73-84
Author(s):  
Kaiyan Han ◽  
Qin Wang
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analysis ◽  
Joint Distribution ◽  
Big Data Analysis ◽  
Practical Significance ◽  
Hidden Variable ◽  
Optimal Value ◽  
Learning Technique ◽  
Sports Venues

In the era of big data, intelligent sports venues have a practical significance to provide personalized service for users and build up a platform for stadium management. This article proposes a new parallel big data promotion algorithm based on the latest achievements of big data analysis. The proposed algorithm calculates the optimal value by using the observed variables Y, the hidden variable data Z, the joint distribution P (Y, Z | θ) and distribution conditions P (Z | Y | θ). The experimental results show that the proposed algorithm has higher accuracy of big data analysis, and can serve the intelligent sports venues better.

scholarly journals Incremental autoencoders for text streams clustering in social networks

2021 ◽  
Vol 27 (11) ◽  
pp. 1203-1221
Author(s):  
Amal Rekik ◽  
Salma Jamoussi
Keyword(s):  
Social Networks ◽  
Big Data ◽  
Deep Learning ◽  
Data Streams ◽  
High Speed ◽  
Clustering Method ◽  
Text Streams ◽  
Data Field ◽  
Learning Technique ◽  
Clustering Data

Clustering data streams in order to detect trending topic on social networks is a chal- lenging task that interests the researchers in the big data field. In fact, analyzing such data needs several requirements to be addressed due to their large amount and evolving nature. For this purpose, we propose, in this paper, a new evolving clustering method which can take into account the incremental nature of the data and meet with its principal requirements. Our method explores a deep learning technique to learn incrementally from unlabelled examples generated at high speed which need to be clustered instantly. To evaluate the performance of our method, we have conducted several experiments using the Sanders, HCR and Terr-Attacks datasets.

scholarly journals Using Google Street View to examine associations between built environment characteristics and U.S. health outcomes

2019 ◽  
Vol 14 ◽  
pp. 100859 ◽  
Author(s):  
Quynh C. Nguyen ◽  
Sahil Khanna ◽  
Pallavi Dwivedi ◽  
Dina Huang ◽  
Yuru Huang ◽  
...  
Keyword(s):  
Built Environment ◽  
Health Outcomes ◽  
Google Street View ◽  
Street View

scholarly journals Google Street View Derived Built Environment Indicators and Associations with State-Level Obesity, Physical Activity, and Chronic Disease Mortality in the United States

2020 ◽  
Vol 17 (10) ◽  
pp. 3659
Author(s):  
Lynn Phan ◽  
Weijun Yu ◽  
Jessica M. Keralis ◽  
Krishay Mukhija ◽  
Pallavi Dwivedi ◽  
...  
Keyword(s):  
Physical Activity ◽  
Built Environment ◽  
Health Outcomes ◽  
State Level ◽  
Premature Mortality ◽  
The United States ◽  
Single Family ◽  
Google Street View ◽  
Street View ◽  
Neighborhood Built Environment

Previous studies have demonstrated that there is a high possibility that the presence of certain built environment characteristics can influence health outcomes, especially those related to obesity and physical activity. We examined the associations between select neighborhood built environment indicators (crosswalks, non-single family home buildings, single-lane roads, and visible wires), and health outcomes, including obesity, diabetes, cardiovascular disease, and premature mortality, at the state level. We utilized 31,247,167 images collected from Google Street View to create indicators for neighborhood built environment characteristics using deep learning techniques. Adjusted linear regression models were used to estimate the associations between aggregated built environment indicators and state-level health outcomes. Our results indicated that the presence of a crosswalk was associated with reductions in obesity and premature mortality. Visible wires were associated with increased obesity, decreased physical activity, and increases in premature mortality, diabetes mortality, and cardiovascular mortality (however, these results were not significant). Non-single family homes were associated with decreased diabetes and premature mortality, as well as increased physical activity and park and recreational access. Single-lane roads were associated with increased obesity and decreased park access. The findings of our study demonstrated that built environment features may be associated with a variety of adverse health outcomes.

scholarly journals Digital Excavation of Mediatized Urban Heritage: Automated Recognition of Buildings in Image Sources

2020 ◽  
Vol 5 (2) ◽  
pp. 24-34 ◽  
Author(s):  
Tino Mager ◽  
Carola Hein
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Built Environment ◽  
Citizen Participation ◽  
Urban Landscapes ◽  
Planning History ◽  
Ongoing Research ◽  
Automated Recognition ◽  
Digital Representations ◽  
Qualitative Approaches

Digital technologies provide novel ways of visualizing cities and buildings. They also facilitate new methods of analyzing the built environment, ranging from artificial intelligence (AI) to crowdsourced citizen participation. Digital representations of cities have become so refined that they challenge our perception of the real. However, computers have not yet become able to detect and analyze the visible features of built structures depicted in photographs or other media. Recent scientific advances mean that it is possible for this new field of computer vision to serve as a critical aid to research. Neural networks now meet the challenge of identifying and analyzing building elements, buildings and urban landscapes. The development and refinement of these technologies requires more attention, simultaneously, investigation is needed in regard to the use and meaning of these methods for historical research. For example, the use of AI raises questions about the ways in which computer-based image recognition reproduces biases of contemporary practice. It also invites reflection on how mixed methods, integrating quantitative and qualitative approaches, can be established and used in research in the humanities. Finally, it opens new perspectives on the role of crowdsourcing in both knowledge dissemination and shared research. Attempts to analyze historical big data with the latest methods of deep learning, to involve many people—laymen and experts—in research via crowdsourcing and to deal with partly unknown visual material have provided a better understanding of what is possible. The article presents findings from the ongoing research project ArchiMediaL, which is at the forefront of the analysis of historical mediatizations of the built environment. It demonstrates how the combination of crowdsourcing, historical big data and deep learning simultaneously raises questions and provides solutions in the field of architectural and urban planning history.

scholarly journals Application of Machine Learning in Rhinology: A State of the Art Review

2020 ◽  
Vol 63 (8) ◽  
pp. 341-349
Author(s):  
Myeong Sang Yu
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Medical Records ◽  
State Of The Art ◽  
Machine Learning Techniques ◽  
Machine Learning Technique ◽  
Learning Techniques ◽  
Learning Technique ◽  
Machine Learning Applications

The revolutionary development of artificial intelligence (AI) such as machine learning and deep learning have been one of the most important technology in many parts of industry, and also enhance huge changes in health care. The big data obtained from electrical medical records and digitalized images accelerated the application of AI technologies in medical fields. Machine learning techniques can deal with the complexity of big data which is difficult to apply traditional statistics. Recently, the deep learning techniques including convolutional neural network have been considered as a promising machine learning technique in medical imaging applications. In the era of precision medicine, otolaryngologists need to understand the potentialities, pitfalls and limitations of AI technology, and try to find opportunities to collaborate with data scientists. This article briefly introduce the basic concepts of machine learning and its techniques, and reviewed the current works on machine learning applications in the field of otolaryngology and rhinology.

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