Web Intelligence and Data Mining in Urban Areas

2019 ◽  
pp. 27-47 ◽  
Author(s):  
Anandakumar Haldorai ◽  
Arulmurugan Ramu ◽  
Suriya Murugan
Keyword(s):  
Data Mining ◽  
Urban Areas ◽  
Web Intelligence

Scientific Web Intelligence

2011 ◽  
pp. 995-999
Author(s):  
Mike Thelwall
Keyword(s):  
Data Mining ◽  
End Users ◽  
Research Field ◽  
Data Sources ◽  
Web Pages ◽  
Web Intelligence ◽  
Research Fields ◽  
Patterns Of Communication ◽  
The Impact ◽  
Citation Databases

Scientific Web Intelligence (SWI) is a research field that combines techniques from data mining, Web intelligence, and scientometrics to extract useful information from the links and text of academic-related Web pages using various clustering, visualization, and counting techniques. Its origins lie in previous scientometric research into mining off-line academic data sources such as journal citation databases. Typical scientometric objectives are either evaluative (assessing the impact of research) or relational (identifying patterns of communication within and among research fields). From scientometrics, SWI also inherits a need to validate its methods and results so that the methods can be justified to end users, and the causes of the results can be found and explained.

Data mining for Web intelligence

Computer ◽  
2002 ◽  
Vol 35 (11) ◽  
pp. 64-70 ◽  
Author(s):  
Jiawei Han ◽  
K.C.-C. Chang
Keyword(s):  
Data Mining ◽  
Web Intelligence

IMPENDING BRAIN INFORMATICS RESEARCH FROM WEB INTELLIGENCE PERSPECTIVE

2006 ◽  
Vol 05 (04) ◽  
pp. 713-727 ◽  
Author(s):  
NING ZHONG
Keyword(s):  
Data Mining ◽  
Research And Development ◽  
High Impact ◽  
Human Intelligence ◽  
Research Issues ◽  
Web Intelligence ◽  
Brain Sciences ◽  
Intelligence Models ◽  
Multi Agent

Web Intelligence (WI)-based portal techniques (e.g. the wisdom Web, data mining, multi-agent, and data/knowledge grids) will provide a new powerful platform for Brain Sciences. New understanding and discovery of the human intelligence models in Brain Sciences (e.g. cognitive science, neuroscience, brain informatics) will yield new WI research and development. In this paper, we briefly investigate three high-impact research issues as well as present a case study, to demonstrate the potentials of Brain Informatics (BI) research from WI perspective.

scholarly journals A SURVEY ON RURAL SCHOOL STUDENTS EDUCATION PERFORMANCE USING DATA MINING TECHNIQUES

2021 ◽  
pp. 209-212
Author(s):  
Suresh Solomon. G ◽  
Nancy Jasmine Goldina
Keyword(s):  
Data Mining ◽  
Rural Areas ◽  
Urban Areas ◽  
Research Study ◽  
Rural School ◽  
Vital Role ◽  
School Students ◽  
And Performance ◽  
Using Data ◽  
Education Performance

In India there exists a lot of Rural areas in which the educational performance of the rural school students are inferior when compared it to the performance of the urban areas due to the lack of facilities, environment, income, employment opportunities and exposure. Equality is one among the basic principle of our country, so it’s a mere responsibility of any research study to perform a detailed analysis towards the performance of rural school students by focusing on to the factors to be monitored and improved so that the Rural areas also raise to the equilant level of competition with the Urban areas. For this goal Data mining plays a vital role in order to handle the data in proper way for analysis and prediction of performances for the improvement of rural school student’s education domain results. This paper presents a survey on Data Mining strategies used for prediction and performance analysis of rural school students education improvements. KEYWORDS—Data Mining, Rural, Urban, Prediction, Performance

scholarly journals COVID-19 pandemic spread against countries’ non-pharmaceutical interventions responses: a data-mining driven comparative study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Konstantinos F. Xylogiannopoulos ◽  
Panagiotis Karampelas ◽  
Reda Alhajj
Keyword(s):  
Data Mining ◽  
Visual Analytics ◽  
Urban Areas ◽  
Health Care Systems ◽  
Clustering Methods ◽  
Whole Process ◽  
Death Case ◽  
Policies And Measures ◽  
Early Implementation ◽  
Care Systems

Abstract Background The first half of 2020 has been marked as the era of COVID-19 pandemic which affected the world globally in almost every aspect of the daily life from societal to economical. To prevent the spread of COVID-19, countries have implemented diverse policies regarding Non-Pharmaceutical Intervention (NPI) measures. This is because in the first stage countries had limited knowledge about the virus and its contagiousness. Also, there was no effective medication or vaccines. This paper studies the effectiveness of the implemented policies and measures against the deaths attributed to the virus between January and May 2020. Methods Data from the European Centre for Disease Prevention and Control regarding the identified cases and deaths of COVID-19 from 48 countries have been used. Additionally, data concerning the NPI measures related policies implemented by the 48 countries and the capacity of their health care systems was collected manually from their national gazettes and official institutes. Data mining, time series analysis, pattern detection, machine learning, clustering methods and visual analytics techniques have been applied to analyze the collected data and discover possible relationships between the implemented NPIs and COVID-19 spread and mortality. Further, we recorded and analyzed the responses of the countries against COVID-19 pandemic, mainly in urban areas which are over-populated and accordingly COVID-19 has the potential to spread easier among humans. Results The data mining and clustering analysis of the collected data showed that the implementation of the NPI measures before the first death case seems to be very effective in controlling the spread of the disease. In other words, delaying the implementation of the NPI measures to after the first death case has practically little effect on limiting the spread of the disease. The success of implementing the NPI measures further depends on the way each government monitored their application. Countries with stricter policing of the measures seems to be more effective in controlling the transmission of the disease. Conclusions The conducted comparative data mining study provides insights regarding the correlation between the early implementation of the NPI measures and controlling COVID-19 contagiousness and mortality. We reported a number of useful observations that could be very helpful to the decision makers or epidemiologists regarding the rapid implementation and monitoring of the NPI measures in case of a future wave of COVID-19 or to deal with other unknown infectious pandemics. Regardless, after the first wave of COVID-19, most countries have decided to lift the restrictions and return to normal. This has resulted in a severe second wave in some countries, a situation which requires re-evaluating the whole process and inspiring lessons for the future.

scholarly journals Experiment in Finding Look-Alike European Cities Using Urban Atlas Data

2020 ◽  
Vol 9 (6) ◽  
pp. 406
Author(s):  
Zdena Dobesova
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Data Mining ◽  
Land Use ◽  
Urban Areas ◽  
Nearest Neighbor ◽  
Land Use Patterns ◽  
Use Patterns ◽  
European Cities ◽  
Trained Neural Network

The integration of geography and machine learning can produce novel approaches in addressing a variety of problems occurring in natural and human environments. This article presents an experiment that identifies cities that are similar according to their land use data. The article presents interesting preliminary experiments with screenshots of maps from the Czech map portal. After successfully working with the map samples, the study focuses on identifying cities with similar land use structures. The Copernicus European Urban Atlas 2012 was used as a source dataset (data valid years 2015–2018). The Urban Atlas freely offers land use datasets of nearly 800 functional urban areas in Europe. To search for similar cities, a set of maps detailing land use in European cities was prepared in ArcGIS. A vector of image descriptors for each map was subsequently produced using a pre-trained neural network, known as Painters, in Orange software. As a typical data mining task, the nearest neighbor function analyzes these descriptors according to land use patterns to find look-alike cities. Example city pairs based on land use are also presented in this article. The research question is whether the existing pre-trained neural network outside cartography is applicable for categorization of some thematic maps with data mining tasks such as clustering, similarity, and finding the nearest neighbor. The article’s contribution is a presentation of one possible method to find cities similar to each other according to their land use patterns, structures, and shapes. Some of the findings were surprising, and without machine learning, could not have been evident through human visual investigation alone.

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