SPAM

2015 ◽  
pp. 245-263 ◽  
Author(s):  
Ritu Chauhan ◽  
Harleen Kaur
Keyword(s):  
Remote Sensing ◽  
Feature Selection ◽  
Geographic Information ◽  
Business Organizations ◽  
Medical Databases ◽  
Large Databases ◽  
Database Technology ◽  
New Methodologies ◽  
Feature Selection Techniques ◽  
Shape And Size

The tremendous increase in spatial database technology has created furious interest among the researchers to adopt new methodologies for discovery of interesting patterns among large databases. But the raw data gathered from various resources such as Geographic Information Systems (GIS), business organizations, medical databases, climatic, market survey, remote sensing and several other resources might consist of data, which can be relevant, irrelevant or noisy in nature. However, retrieval of patterns from such databases can lead to serious concerns, which can frame inconsistent or irrelevant futuristic results. To deal with such issues, feature selection techniques are adopted to remove irrelevant, redundant and noisy features. Our approach focuses on retrieval of effective and efficient spatial clusters from large number of medical databases. In this chapter, we have defined our novel framework SpaGrid and SPAM algorithm to retrieve clusters of variant shape and size from large databases. The application of our framework is used with spatial medical databases where the implementation details are discussed with Matlab 7.1.

SPAM

2018 ◽  
pp. 1323-1337
Author(s):  
Ritu Chauhan ◽  
Harleen Kaur
Keyword(s):  
Remote Sensing ◽  
Feature Selection ◽  
Geographic Information ◽  
Business Organizations ◽  
Medical Databases ◽  
Large Databases ◽  
Database Technology ◽  
New Methodologies ◽  
Feature Selection Techniques ◽  
Shape And Size

The tremendous increase in spatial database technology has created furious interest among the researchers to adopt new methodologies for discovery of interesting patterns among large databases. But the raw data gathered from various resources such as Geographic Information Systems (GIS), business organizations, medical databases, climatic, market survey, remote sensing and several other resources might consist of data, which can be relevant, irrelevant or noisy in nature. However, retrieval of patterns from such databases can lead to serious concerns, which can frame inconsistent or irrelevant futuristic results. To deal with such issues, feature selection techniques are adopted to remove irrelevant, redundant and noisy features. Our approach focuses on retrieval of effective and efficient spatial clusters from large number of medical databases. In this chapter, we have defined our novel framework SpaGrid and SPAM algorithm to retrieve clusters of variant shape and size from large databases. The application of our framework is used with spatial medical databases where the implementation details are discussed with Matlab 7.1.

A lazy feature selection method for multi-label classification

2021 ◽  
Vol 25 (1) ◽  
pp. 21-34
Author(s):  
Rafael B. Pereira ◽  
Alexandre Plastino ◽  
Bianca Zadrozny ◽  
Luiz H.C. Merschmann
Keyword(s):  
Feature Selection ◽  
Text Categorization ◽  
Feature Selection Method ◽  
Selection Method ◽  
Video Classification ◽  
Classification Problems ◽  
Class Label ◽  
New Feature ◽  
Feature Selection Techniques ◽  
Biomolecular Analysis

In many important application domains, such as text categorization, biomolecular analysis, scene or video classification and medical diagnosis, instances are naturally associated with more than one class label, giving rise to multi-label classification problems. This has led, in recent years, to a substantial amount of research in multi-label classification. More specifically, feature selection methods have been developed to allow the identification of relevant and informative features for multi-label classification. This work presents a new feature selection method based on the lazy feature selection paradigm and specific for the multi-label context. Experimental results show that the proposed technique is competitive when compared to multi-label feature selection techniques currently used in the literature, and is clearly more scalable, in a scenario where there is an increasing amount of data.

scholarly journals Children’s Activity Classification for Domestic Risk Scenarios Using Environmental Sound and a Bayesian Network

Healthcare ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 884
Author(s):  
Antonio García-Domínguez ◽  
Carlos E. Galván-Tejada ◽  
Ramón F. Brena ◽  
Antonio A. Aguileta ◽  
Jorge I. Galván-Tejada ◽  
...  
Keyword(s):  
Feature Selection ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Classification Model ◽  
Activity Classification ◽  
Environmental Sound ◽  
Non Invasive ◽  
Akaike Criterion ◽  
Data Source ◽  
Feature Selection Techniques

Children’s healthcare is a relevant issue, especially the prevention of domestic accidents, since it has even been defined as a global health problem. Children’s activity classification generally uses sensors embedded in children’s clothing, which can lead to erroneous measurements for possible damage or mishandling. Having a non-invasive data source for a children’s activity classification model provides reliability to the monitoring system where it is applied. This work proposes the use of environmental sound as a data source for the generation of children’s activity classification models, implementing feature selection methods and classification techniques based on Bayesian networks, focused on the recognition of potentially triggering activities of domestic accidents, applicable in child monitoring systems. Two feature selection techniques were used: the Akaike criterion and genetic algorithms. Likewise, models were generated using three classifiers: naive Bayes, semi-naive Bayes and tree-augmented naive Bayes. The generated models, combining the methods of feature selection and the classifiers used, present accuracy of greater than 97% for most of them, with which we can conclude the efficiency of the proposal of the present work in the recognition of potentially detonating activities of domestic accidents.

Effective combining of feature selection techniques for machine learning-enabled IoT intrusion detection

2021 ◽  
Author(s):  
Md Arafatur Rahman ◽  
A. Taufiq Asyhari ◽  
Ong Wei Wen ◽  
Husnul Ajra ◽  
Yussuf Ahmed ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Feature Selection Techniques

scholarly journals A Survey of Remote Sensing and Geographic Information System Applications for Flash Floods

2021 ◽  
Vol 13 (9) ◽  
pp. 1818
Author(s):  
Lisha Ding ◽  
Lei Ma ◽  
Longguo Li ◽  
Chao Liu ◽  
Naiwen Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Information System ◽  
Geographic Information System ◽  
Flash Flood ◽  
Flash Floods ◽  
Geographic Information ◽  
Flood Disaster ◽  
Future Research ◽  
Time Zone ◽  
Gis Technologies

Flash floods are among the most dangerous natural disasters. As climate change and urbanization advance, an increasing number of people are at risk of flash floods. The application of remote sensing and geographic information system (GIS) technologies in the study of flash floods has increased significantly over the last 20 years. In this paper, more than 200 articles published in the last 20 years are summarized and analyzed. First, a visualization analysis of the literature is performed, including a keyword co-occurrence analysis, time zone chart analysis, keyword burst analysis, and literature co-citation analysis. Then, the application of remote sensing and GIS technologies to flash flood disasters is analyzed in terms of aspects such as flash flood forecasting, flash flood disaster impact assessments, flash flood susceptibility analyses, flash flood risk assessments, and the identification of flash flood disaster risk areas. Finally, the current research status is summarized, and the orientation of future research is also discussed.

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