Accuracy Comparison of Classification Techniques for Mouse Dynamics-Based Biometric CaRP

2020 ◽  
pp. 701-711
Author(s):  
Sushama Kulkarni ◽  
Hanmant Fadewar
Keyword(s):  
Classification Techniques ◽  
Accuracy Comparison ◽  
Mouse Dynamics

A Comparison of Classification Technique for Metacognitive Knowledge

2011 ◽  
Vol 403-408 ◽  
pp. 4538-4542
Author(s):  
Phongthanat Sae Joo ◽  
Charan Sanrach ◽  
Sumalee Chaijaroen
Keyword(s):  
Bayesian Classifier ◽  
Metacognitive Knowledge ◽  
Rule Based ◽  
Classification Techniques ◽  
General Classification ◽  
Classification Technique ◽  
Accuracy Comparison ◽  
Self Study ◽  
Self Knowledge ◽  
Metacognitive Learning

Metcognitive learning has been developed to 1) enhance students to have awareness for conducting self study, 2) verify metacognitive knowledge and 3) provide proper lessons for each student. The test of metacognitive knowledge was implemented, and at least two out of three metacognitive knowledges; knowledge of self, knowledge of task, and knowledge of strategy, should be presented so that students’ metacognitive regulation can be proved. Therefore classification techniques were proposed to classify metacognitive knowledge of students via accuracy comparison of four classification techniques: Bayesian classifier, Decision Tree, Rule Based, and General Classification as 92.04%, 91.22%, 86.56%, and 92.87% respectively. Nonetheless, Bayesian Classifier is selected to be algorithm for metacognitive learning environment.

Advancement of Criminal Profiling Classification Techniques Using a Multi-Method Scaling Framework

2012 ◽  
Author(s):  
Alasdair Matthew Goodwill ◽  
Skye Stephens ◽  
Sandra Oziel ◽  
Nicola Bowes
Keyword(s):  
Criminal Profiling ◽  
Classification Techniques

HYBRID CLASSIFICATION SCHEMES FOR HEART MURMUR DETECTION TO ASSIST PHONOCARDIOGRAM BASED SIGNAL ACQUISITION

2017 ◽  
Vol 13 (9) ◽  
pp. 6480-6488 ◽  
Author(s):  
A.D. Jeyarani ◽  
Reena Daphne ◽  
Solomon Roach
Keyword(s):  
Neural Network ◽  
Signal Acquisition ◽  
Heart Murmur ◽  
Classification Techniques ◽  
Classification Schemes ◽  
Neural Network Classification ◽  
Nonlinear Classification ◽  
Processing Step ◽  
System Characterization ◽  
Hybrid Classification

The main contribution of this paper has been to introduce nonlinear classification techniques to extract more information from the PCG signal. Especially, Artificial Neural Network classification techniques have been used to reconstruct the underlying system’s state space based on the measured PCG signal. This processing step provides a geometrical interpretation of the dynamics of the signal, whose structure can be utilized for both system characterization and classification as well as for signal processing tasks such as detection and prediction.

Sign in / Sign up

Export Citation Format

Share Document