Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis

2021 ◽  
pp. 103598
Author(s):  
Kai Zhang ◽  
Zhen Qian ◽  
Yue Yang ◽  
Min Chen ◽  
Teng Zhong ◽  
...  
Keyword(s):  
Geospatial Analysis ◽  
Ensemble Classification ◽  
Classification Model ◽  
Road Noise ◽  
Noise Barriers ◽  
Street View

scholarly journals Dispersion at the edges of near road noise barriers

2020 ◽  
Author(s):  
Akula Venkatram ◽  
David K. Heist ◽  
Steven G. Perry ◽  
Lydia Brouwer
Keyword(s):  
Road Noise ◽  
Noise Barriers

scholarly journals Mapping the Essential Urban Land Use in Changchun by Applying Random Forest and Multi-Source Geospatial Data

2020 ◽  
Vol 12 (15) ◽  
pp. 2488 ◽  
Author(s):  
Shouzhi Chang ◽  
Zongming Wang ◽  
Dehua Mao ◽  
Kehan Guan ◽  
Mingming Jia ◽  
...  
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Urban Space ◽  
Semantic Segmentation ◽  
Urban Land ◽  
Urban Land Use ◽  
Geospatial Data ◽  
Classification Model ◽  
Rapid Acquisition ◽  
Street View

Understanding urban spatial pattern of land use is of great significance to urban land management and resource allocation. Urban space has strong heterogeneity, and thus there were many researches focusing on the identification of urban land use. The emergence of multiple new types of geospatial data provide an opportunity to investigate the methods of mapping essential urban land use. The popularization of street view images represented by Baidu Maps is benificial to the rapid acquisition of high-precision street view data, which has attracted the attention of scholars in the field of urban research. In this study, OpenStreetMap (OSM) was used to delineate parcels which were recognized as basic mapping units. A semantic segmentation of street view images was combined to enrich the multi-dimensional description of urban parcels, together with point of interest (POI), Sentinel-2A, and Luojia-1 nighttime light data. Furthermore, random forest (RF) was applied to determine the urban land use categories. The results show that street view elements are related to urban land use in the perspective of spatial distribution. It is reasonable and feasible to describe urban parcels according to the characteristics of street view elements. Due to the participation of street view, the overall accuracy reaches 79.13%. The contribution of street view features to the optimal classification model reached 20.6%, which is more stable than POI features.

scholarly journals (Un)Trendy Japan: Twitter bots and the 2017 Japanese general election

2019 ◽  
Vol 15 (3) ◽  
pp. 497-514
Author(s):  
Jozef Michal Mintal ◽  
Róbert Vancel
Keyword(s):  
User Profile ◽  
General Election ◽  
Ensemble Classification ◽  
Classification Model ◽  
Supervised Machine Learning ◽  
Political Actors ◽  
Political Processes ◽  
Political Events ◽  
The Uk ◽  
The Impact

AbstractSocial networking services (SNSs) can significantly impact public life during important political events. Thus, it comes as no surprise that different political actors try to exploit these online platforms for their benefit. Bots constitute a popular tool on SNSs that appears to be able to shape public opinion and disrupt political processes. However, the role of bots during political events in a non-Western context remains largely under-studied. This article addresses the question of the involvement of Twitter bots during electoral campaigns in Japan. In our study, we collected Twitter data over a fourteen-day period in October 2017 using a set of hashtags related to the 2017 Japanese general election. Our dataset includes 905,215 tweets, 665,400 of which were unique tweets. Using a supervised machine learning approach, we first built a custom ensemble classification model for bot detection based on user profile features, with an area under curve (AUC) for the test set of 0.998. Second, in applying our model, we estimate that the impact of Twitter bots in Japan was minor overall. In comparison with similar studies conducted during elections in the US and the UK, the deployment of Twitter bots involved in the 2017 Japanese general election seems to be significantly lower. Finally, given our results on the level of bots on Twitter during the 2017 Japanese general election, we provide various possible explanations for their underuse within a broader socio-political context.

Concept Attribute Labeling and Context-Aware Named Entity Recognition in Electronic Health Records

2018 ◽  
Vol 7 (1) ◽  
pp. 1-15 ◽  
Author(s):  
Alexandra Pomares-Quimbaya ◽  
Rafael A. Gonzalez ◽  
Oscar Mauricio Muñoz Velandia ◽  
Angel Alberto Garcia Peña ◽  
Julián Camilo Daza Rodríguez ◽  
...  
Keyword(s):  
Electronic Health Records ◽  
Ad Hoc ◽  
Named Entity Recognition ◽  
Ensemble Classification ◽  
Entity Recognition ◽  
Classification Model ◽  
Health Records ◽  
Named Entity ◽  
Electronic Health ◽  
Concept Attribute

Extracting valuable knowledge from Electronic Health Records (EHR) represents a challenging task due to the presence of both structured and unstructured data, including codified fields, images and test results. Narrative text in particular contains a variety of notes which are diverse in language and detail, as well as being full of ad hoc terminology, including acronyms and jargon, which is especially challenging in non-English EHR, where there is a dearth of annotated corpora or trained case sets. This paper proposes an approach for NER and concept attribute labeling for EHR that takes into consideration the contextual words around the entity of interest to determine its sense. The approach proposes a composition method of three different NER methods, together with the analysis of the context (neighboring words) using an ensemble classification model. This contributes to disambiguate NER, as well as labeling the concept as confirmed, negated, speculative, pending or antecedent. Results show an improvement of the recall and a limited impact on precision for the NER process.

Study of Financial Warning Ensemble Model for Listed Companies Based on Unbalanced Classification Perspective

2020 ◽  
Vol 16 (1) ◽  
pp. 32-48
Author(s):  
Wei Cong
Keyword(s):  
Decision Tree ◽  
Listed Companies ◽  
Hellinger Distance ◽  
Financial Data ◽  
Ensemble Classification ◽  
Classification Model ◽  
Base Classifier ◽  
C4.5 Decision Tree ◽  
Unbalanced Classification ◽  
Warning Model

Using the ensemble learning method to mine valuable information from a sea of financial data accumulated on the market of financial securities is very important for studying data processing. On the basis of financial data from A-share companies listed on Shanghai Stock Market, this article takes the perspective of unbalanced classification of ST stocks to carry out a study of the construction of a financial warning model for the listed companies. In our experiment, HDRF (HDRandom Forest, Hellinger Distance based Random Forest), ensemble classification models of Bagging, AdaBoost, and Rotation Forest, which take Hellinger distance decision tree (HDDT) as the base classifier, and the ensemble classification model which takes the C4.5 decision tree as the base classifier, are compared in respect of both the area under the ROC curve and the F-measure. As shown in the experimental results, the HDRF and the HDDT based classifier, as an ensemble method, are effective for financial data of listed companies.

A stacking ensemble classification model for detection and classification of power quality disturbances in PV integrated power network

Measurement ◽  
2021 ◽  
Vol 175 ◽  
pp. 109025
Author(s):  
Padmavathi Radhakrishnan ◽  
Kalaivani Ramaiyan ◽  
Arangarajan Vinayagam ◽  
Veerapandiyan Veerasamy
Keyword(s):  
Power Quality ◽  
Ensemble Classification ◽  
Classification Model ◽  
Power Network ◽  
Power Quality Disturbances
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