Algorithm for the Signal Validation in the Emergency Situation Using Unsupervised Learning Methods

2021 ◽  
pp. 259-268
Author(s):  
Younhee Choi ◽  
Gyeongmin Yoon ◽  
Jonghyun Kim
Keyword(s):  
Unsupervised Learning ◽  
Emergency Situation ◽  
Learning Methods ◽  
Signal Validation

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 Methods for Identification of Defects in Heterogeneous Materials

2020 ◽  
Author(s):  
MUTHU RAM PRABHU ELENCHEZHIAN ◽  
VAMSEE VADLAMUDI ◽  
RASSEL RAIHAN ◽  
KENNETH REIFSNIDER
Keyword(s):  
Unsupervised Learning ◽  
Heterogeneous Materials ◽  
Learning Methods

CLUSTERING-BASED NETWORK INTRUSION DETECTION

2007 ◽  
Vol 14 (02) ◽  
pp. 169-187 ◽  
Author(s):  
SHI ZHONG ◽  
TAGHI M. KHOSHGOFTAAR ◽  
NAEEM SELIYA
Keyword(s):  
Data Mining ◽  
Network Security ◽  
Intrusion Detection ◽  
Unsupervised Learning ◽  
Network Traffic ◽  
Clustering Algorithms ◽  
Network Intrusion Detection ◽  
Learning Methods ◽  
High Detection Rate ◽  
Network Intrusion

Recently data mining methods have gained importance in addressing network security issues, including network intrusion detection — a challenging task in network security. Intrusion detection systems aim to identify attacks with a high detection rate and a low false alarm rate. Classification-based data mining models for intrusion detection are often ineffective in dealing with dynamic changes in intrusion patterns and characteristics. Consequently, unsupervised learning methods have been given a closer look for network intrusion detection. We investigate multiple centroid-based unsupervised clustering algorithms for intrusion detection, and propose a simple yet effective self-labeling heuristic for detecting attack and normal clusters of network traffic audit data. The clustering algorithms investigated include, k-means, Mixture-Of-Spherical Gaussians, Self-Organizing Map, and Neural-Gas. The network traffic datasets provided by the DARPA 1998 offline intrusion detection project are used in our empirical investigation, which demonstrates the feasibility and promise of unsupervised learning methods for network intrusion detection. In addition, a comparative analysis shows the advantage of clustering-based methods over supervised classification techniques in identifying new or unseen attack types.

A Bayes-true data generator for evaluation of supervised and unsupervised learning methods

2011 ◽  
Vol 32 (11) ◽  
pp. 1523-1531 ◽  
Author(s):  
Janick V. Frasch ◽  
Aleksander Lodwich ◽  
Faisal Shafait ◽  
Thomas M. Breuel
Keyword(s):  
Unsupervised Learning ◽  
Learning Methods ◽  
Supervised And Unsupervised Learning ◽  
Data Generator
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