scholarly journals An Unsupervised Learning Model for Pattern Recognition in Routinely Collected Healthcare Data

2018 ◽  
Author(s):  
Sara Khalid ◽  
Andrew Judge ◽  
Rafael Pinedo-Villanueva
Keyword(s):  
Pattern Recognition ◽  
Unsupervised Learning ◽  
Learning Model ◽  
Healthcare Data

Evaluating disaster-related tweet credibility using content-based and user-based features

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Nasser Assery ◽  
Yuan (Dorothy) Xiaohong ◽  
Qu Xiuli ◽  
Roy Kaushik ◽  
Sultan Almalki
Keyword(s):  
Machine Learning ◽  
Unsupervised Learning ◽  
Supervised Learning ◽  
Emergency Response ◽  
Learning Model ◽  
Performance Comparison ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Content Type ◽  
Machine Learning Classification

Purpose This study aims to propose an unsupervised learning model to evaluate the credibility of disaster-related Twitter data and present a performance comparison with commonly used supervised machine learning models. Design/methodology/approach First historical tweets on two recent hurricane events are collected via Twitter API. Then a credibility scoring system is implemented in which the tweet features are analyzed to give a credibility score and credibility label to the tweet. After that, supervised machine learning classification is implemented using various classification algorithms and their performances are compared. Findings The proposed unsupervised learning model could enhance the emergency response by providing a fast way to determine the credibility of disaster-related tweets. Additionally, the comparison of the supervised classification models reveals that the Random Forest classifier performs significantly better than the SVM and Logistic Regression classifiers in classifying the credibility of disaster-related tweets. Originality/value In this paper, an unsupervised 10-point scoring model is proposed to evaluate the tweets’ credibility based on the user-based and content-based features. This technique could be used to evaluate the credibility of disaster-related tweets on future hurricanes and would have the potential to enhance emergency response during critical events. The comparative study of different supervised learning methods has revealed effective supervised learning methods for evaluating the credibility of Tweeter data.

Unsupervised Learning for Wafer Surface Defect Pattern Recognition

2021 ◽  
pp. 275-285
Author(s):  
Sheng Geng ◽  
Huaping Liu ◽  
Feng Wang ◽  
Shimin Zhao ◽  
Hu Liu
Keyword(s):  
Pattern Recognition ◽  
Unsupervised Learning ◽  
Surface Defect ◽  
Wafer Surface ◽  
Defect Pattern

Aspects of Intelligence in an "SP" Database System

2009 ◽  
pp. 725-754
Author(s):  
J. Gerard Wolff
Keyword(s):  
Pattern Recognition ◽  
Information Retrieval ◽  
Natural Language ◽  
Unsupervised Learning ◽  
Probabilistic Reasoning ◽  
Database System ◽  
Theory Of Computing ◽  
New Knowledge ◽  
Database Models

This chapter describes some of the kinds of “intelligence” that may be exhibited by an intelligent database system based on the SP theory of computing and cognition. The chapter complements an earlier paper on the SP theory as the basis for an intelligent database system (Wolff, forthcoming b) but it does not depend on a reading of that earlier paper. The chapter introduces the SP theory and its main attractions as the basis for an intelligent database system: that it uses a simple but versatile format for diverse kinds of knowledge, that it integrates and simplifies a range of AI functions, and that it supports established database models when that is required. Then with examples and discussion, the chapter illustrates aspects of “intelligence” in the system: pattern recognition and information retrieval, several forms of probabilistic reasoning, the analysis and production of natural language, and the unsupervised learning of new knowledge.

Unsupervised learning in nongaussian pattern recognition

1972 ◽  
Vol 4 (4) ◽  
pp. 401-416 ◽  
Author(s):  
P.K. Rajasekaran ◽  
M.D. Srinath
Keyword(s):  
Pattern Recognition ◽  
Unsupervised Learning
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