scholarly journals Functional Classification of Urban Parks Based on Urban Functional Zone and Crowd-Sourced Geographical Data

2021 ◽  
Vol 10 (12) ◽  
pp. 824
Author(s):  
Su Cao ◽  
Shihong Du ◽  
Shuwen Yang ◽  
Shouhang Du
Keyword(s):  
Data Fusion ◽  
Language Processing ◽  
Data Extraction ◽  
Urban Parks ◽  
Urban Park ◽  
Social Functions ◽  
Functional Zone ◽  
Two Cities ◽  
Different Types

Urban parks have important impacts on urban ecosystems and in disaster prevention. They also have diverse social functions that are important to the living conditions and spatial structures of cities. Identifying and classifying the different types of urban parks are important for analyzing the sustainable development and the greening progress in cities. Existing studies have predominantly focused on the data extraction of urban green spaces as a whole, while there have been relatively few studies that have considered different categories of urban parks and their impact, which makes it difficult to characterize or predict the spatial distribution and structures of urban parks and limits further refinement of urban research. At present, the classification of urban parks relies on the physical features observed in remote sensing images, but these methods are limited when mapping the diverse functions and attributes of urban parks. Crowd-sourced geographic data may more accurately express the social functions of points of interest (POIs) in cities, and, therefore, employing open data sources may assist in data extraction and the classification of different types of urban parks. This paper proposed a multi-source data fusion approach for urban park classification including POI and urban functional zone (UFZ) data. First, the POI data were automatically reclassified using improved natural language processing (NLP) (i.e., text similarity measurements and topic modeling) to establish the links between urban park green-space types and POIs. The reclassified POI data as well as the UFZ data were then subjected to scene-based data fusion, and various types of urban parks were extracted using data attribute analysis and social attribute recognition for urban park mapping. Experimental analysis was conducted across Beijing and Hangzhou to verify the effectiveness of the proposed method, which had an overall classification accuracy of 82.8%. Finally, the urban park types of the two cities were compared and analyzed to obtain the characteristics of urban park types and structures in the two cities, which have different climates and urban structures.

scholarly journals Condition of urban park infrastructure in the context of perceived security of park users

2021 ◽  
Vol 900 (1) ◽  
pp. 012036
Author(s):  
P Polko ◽  
K Kimic
Keyword(s):  
Urban Parks ◽  
Likert Scale ◽  
High Impact ◽  
Urban Park ◽  
Personal Security ◽  
Urban Greenery ◽  
Sense Of Security ◽  
Different Types ◽  
Perceived Security ◽  
Key Aspects

Abstract Personal security is one of the key aspects affected by the perception of urban greenery, which plays an important role for city dwellers. The survey conducted in Poland in 2020 (N=394) aimed to check how important for park users in context of perceived security are selected factors related to maintenance of different types of park infrastructure (condition of equipment and pavement, also condition of greenery), level of park cleanliness (filling of the rubbish bins, litter on the ground, and graffiti on different types of facilities), and elements related to the use of park space (paths, varied topography, functional aids, and water). The condition of equipment was assessed as a factor of high impact (average of 4.13 in 5-point Likert scale), as well as the presence of park paths (4.02). The results indicate that all 10 of the examined factors are recognized as important (3 and higher). They also show that both women (compared to men) and older respondents (compared to those under 60) assessed higher the importance of factors related to the condition of elements of infrastructure and pavement, as well as the level of cleanliness in urban parks in shaping their personal sense of security.

MICE

2004 ◽  
Vol 9 (1) ◽  
pp. 53-68 ◽  
Author(s):  
Montserrat Arévalo Rodríguez ◽  
Montserrat Civit Torruella ◽  
Maria Antònia Martí
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Language Processing ◽  
Corpus Linguistics ◽  
Proper Names ◽  
Named Entity ◽  
Different Types

In the field of corpus linguistics, Named Entity treatment includes the recognition and classification of different types of discursive elements like proper names, date, time, etc. These discursive elements play an important role in different Natural Language Processing applications and techniques such as Information Retrieval, Information Extraction, translations memories, document routers, etc.

scholarly journals A Sentence Classification Framework to Identify Geometric Errors in Radiation Therapy from Relevant Literature

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 139
Author(s):  
Tanmay Basu ◽  
Simon Goldsworthy ◽  
Georgios V. Gkoutos
Keyword(s):  
Language Processing ◽  
Systematic Reviews ◽  
Data Extraction ◽  
Research Question ◽  
Relevant Literature ◽  
Support Vector ◽  
Svm Classifier ◽  
Geometric Errors ◽  
Training Corpus ◽  
Different Types

The objective of systematic reviews is to address a research question by summarizing relevant studies following a detailed, comprehensive, and transparent plan and search protocol to reduce bias. Systematic reviews are very useful in the biomedical and healthcare domain; however, the data extraction phase of the systematic review process necessitates substantive expertise and is labour-intensive and time-consuming. The aim of this work is to partially automate the process of building systematic radiotherapy treatment literature reviews by summarizing the required data elements of geometric errors of radiotherapy from relevant literature using machine learning and natural language processing (NLP) approaches. A framework is developed in this study that initially builds a training corpus by extracting sentences containing different types of geometric errors of radiotherapy from relevant publications. The publications are retrieved from PubMed following a given set of rules defined by a domain expert. Subsequently, the method develops a training corpus by extracting relevant sentences using a sentence similarity measure. A support vector machine (SVM) classifier is then trained on this training corpus to extract the sentences from new publications which contain relevant geometric errors. To demonstrate the proposed approach, we have used 60 publications containing geometric errors in radiotherapy to automatically extract the sentences stating the mean and standard deviation of different types of errors between planned and executed radiotherapy. The experimental results show that the recall and precision of the proposed framework are, respectively, 97% and 72%. The results clearly show that the framework is able to extract almost all sentences containing required data of geometric errors.

scholarly journals Big Data-Based Evaluation of Urban Parks: A Chinese Case Study

2019 ◽  
Vol 11 (7) ◽  
pp. 2125
Author(s):  
Zening Xu ◽  
Xiaolu Gao ◽  
Zhiqiang Wang ◽  
Jie Fan
Keyword(s):  
Data Extraction ◽  
Urban Parks ◽  
Urban Park ◽  
Public Parks ◽  
Digital Maps ◽  
Point Of Interest ◽  
Population Sizes ◽  
Definition Of

Urban parks play a key role in urban sustainable development. This paper proposes a method for the evaluation of public parks from the perspective of accessibility and quality. The method includes the data extraction of urban park locations and the delineation of urban built-up areas. The processing of urban park data not only involves the extraction from digital maps, but also the classification of urban parks using a semi-automated model in ArcGIS. The urban area is identified using the Point of Interest (POI) data in digital maps, taking economic and human activities into consideration. The service area and its overlapped time is included in the evaluation indicators. With a clear definition of park and urban built-up area, the evaluation result of urban parks is of great comparability. Taking China as an example, the quality of urban parks in 273 prefecture-level cities has been evaluated. The results show that the average service coverage of urban parks in Chinese cities is 64.8%, and that there are significant disparities between cities with different population sizes and locations. The results suggest the necessity to improve public parks in small-and-medium sized cities and inland areas to strengthen the coordination of urbanization and regional development.

scholarly journals Data Extraction and Sentimental Analysis from “Twitter” using Web Scrapping.

2019 ◽  
Vol 9 (1) ◽  
pp. 6451-6455
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Data Collection ◽  
Language Processing ◽  
Presidential Elections ◽  
Data Extraction ◽  
Textual Data ◽  
Spell Check ◽  
By Products

In this paper , we attempt to do the sentimental analysis of the 2016 US presidential elections. Sentimental analysis requires the data to be extracted from websites or sources where people present their opinions, views ,complaints about the subjects that need to analyzed .Furthermore, it is necessary to ensure that the sample size of the data is large enough to get conclusive results .It is also necessary to ensure that the data is cleaned before it is used to make predictions. Cleaning is done using common techniques like tokenization, spell check ,etc. Sentimental Analysis is one of the by-products of Natural Language Processing . This paper includes data collection as well as classification of textual data based on machine learning .

Dual stain kit for the microscopic gram classification of ocular infection bacteria

1993 ◽  
Vol 51 ◽  
pp. 248-249
Author(s):  
Jacob S. Hanker ◽  
Dale N. Holdren ◽  
Kenneth L. Cohen ◽  
Beverly L. Giammara
Keyword(s):  
Contact Lenses ◽  
Ocular Infection ◽  
Gram Negative Bacteria ◽  
Gram Negative ◽  
Gram Staining ◽  
Ocular Infections ◽  
Different Types ◽  
Culture Positive ◽  
Increased Susceptibility

Keratitis and conjunctivitis (infections of the cornea or conjunctiva) are ocular infections caused by various bacteria, fungi, viruses or parasites; bacteria, however, are usually prominent. Systemic conditions such as alcoholism, diabetes, debilitating disease, AIDS and immunosuppressive therapy can lead to increased susceptibility but trauma and contact lens use are very important factors. Gram-negative bacteria are most frequently cultured in these situations and Pseudomonas aeruginosa is most usually isolated from culture-positive ulcers of patients using contact lenses. Smears for staining can be obtained with a special swab or spatula and Gram staining frequently guides choice of a therapeutic rinse prior to the report of the culture results upon which specific antibiotic therapy is based. In some cases staining of the direct smear may be diagnostic in situations where the culture will not grow. In these cases different types of stains occasionally assist in guiding therapy.

Explanatory Power for Medical Expert Systems: Studies in the Representation of Causal Relationships for Clinical Consultations

1982 ◽  
Vol 21 (03) ◽  
pp. 127-136 ◽  
Author(s):  
J. W. Wallis ◽  
E. H. Shortliffe
Keyword(s):  
Explanatory Power ◽  
Prototype System ◽  
Inference Rules ◽  
Medical Expert ◽  
Comprehensive Knowledge ◽  
Different Types ◽  
Medical Expert Systems ◽  
Explanation System ◽  
Systems Studies

This paper reports on experiments designed to identify and implement mechanisms for enhancing the explanation capabilities of reasoning programs for medical consultation. The goals of an explanation system are discussed, as is the additional knowledge needed to meet these goals in a medical domain. We have focussed on the generation of explanations that are appropriate for different types of system users. This task requires a knowledge of what is complex and what is important; it is further strengthened by a classification of the associations or causal mechanisms inherent in the inference rules. A causal representation can also be used to aid in refining a comprehensive knowledge base so that the reasoning and explanations are more adequate. We describe a prototype system which reasons from causal inference rules and generates explanations that are appropriate for the user.

Speaker-Oriented Attitude Datives in Social Context

2018 ◽  
Author(s):  
Youssef A. Haddad
Keyword(s):  
Social Context ◽  
Shared Values ◽  
Social Functions ◽  
Rapport Management ◽  
The Social ◽  
Different Types ◽  
Levantine Arabic ◽  
Values And Beliefs ◽  
Social Acts

This chapter examines the social functions of speaker-oriented attitude datives in Levantine Arabic. It analyzes these datives as perspectivizers used by a speaker to instruct her hearer to view her as a form of authority in relation to him, to the content of her utterance, and to the activity they are both involved in. The nature of this authority depends on the sociocultural, situational, and co-textual context, including the speaker’s and hearer’s shared values and beliefs, their respective identities, and the social acts employed in interaction. The chapter analyzes specific instances of speaker-oriented attitude datives as used in different types of social acts (e.g., commands, complaints) and in different types of settings (e.g., family talk, gossip). It also examines how these datives interact with facework, politeness, and rapport management.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

scholarly journals Preventing corneal blindness caused by keratitis using artificial intelligence

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Zhongwen Li ◽  
Jiewei Jiang ◽  
Kuan Chen ◽  
Qianqian Chen ◽  
Qinxiang Zheng ◽  
...  
Keyword(s):  
Early Diagnosis ◽  
Vision Loss ◽  
Learning System ◽  
Automated Classification ◽  
Slit Lamp ◽  
Resource Limited ◽  
The World ◽  
Different Types ◽  
Corneal Blindness

AbstractKeratitis is the main cause of corneal blindness worldwide. Most vision loss caused by keratitis can be avoidable via early detection and treatment. The diagnosis of keratitis often requires skilled ophthalmologists. However, the world is short of ophthalmologists, especially in resource-limited settings, making the early diagnosis of keratitis challenging. Here, we develop a deep learning system for the automated classification of keratitis, other cornea abnormalities, and normal cornea based on 6,567 slit-lamp images. Our system exhibits remarkable performance in cornea images captured by the different types of digital slit lamp cameras and a smartphone with the super macro mode (all AUCs>0.96). The comparable sensitivity and specificity in keratitis detection are observed between the system and experienced cornea specialists. Our system has the potential to be applied to both digital slit lamp cameras and smartphones to promote the early diagnosis and treatment of keratitis, preventing the corneal blindness caused by keratitis.

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