Principle component analysis-based optimized feature extraction merged with nonlinear regression model for improved state-of-health prediction

2022 ◽  
Vol 48 ◽  
pp. 104026
Author(s):  
Pyeong-Yeon Lee ◽  
Sanguk Kwon ◽  
Deokhun Kang ◽  
Inho Cho ◽  
Jonghoon Kim
Keyword(s):  
Feature Extraction ◽  
Regression Model ◽  
Nonlinear Regression ◽  
Principle Component Analysis ◽  
Component Analysis ◽  
Nonlinear Regression Model ◽  
State Of Health ◽  
Principle Component

ANALYSIS OF VOICE CUES IN RECOGNITION OF SARCASM

2019 ◽  
Vol 12 ◽  
Author(s):  
Basavaraj N Hiremath ◽  
Malini M Patilb
Keyword(s):  
Feature Extraction ◽  
Computer Program ◽  
Principle Component Analysis ◽  
Voice Recognition ◽  
Component Analysis ◽  
Recognition System ◽  
Principle Component ◽  
Whole Process ◽  
The Voice

The voice recognition system is about cognizing the signals, by feature extraction and identification of related parameters. The whole process is referred to as voice analytics. The paper aims at analysing and synthesizing the phonetics of voice using a computer program called “PRAAT”. The work carried out in the paper also supports the analysis of voice segmentation labelling, analyse the unique features of voice cues, understanding physics of voice, further the process is carried out to recognize sarcasm. Different unique features identified in the work are, intensity, pitch, formants related to read, speak, interactive and declarative sentences by using principle component analysis.

Feature extraction of underwater images using principle component analysis with image registration

2021 ◽  
Author(s):  
M. Islabudeen ◽  
P. Vigneshwaran ◽  
G. Sindhu Madhuri ◽  
B. Muthu Kumar ◽  
J. Ragaventhiran ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Image Registration ◽  
Principle Component Analysis ◽  
Component Analysis ◽  
Principle Component

scholarly journals Brain tumor identification with a hybrid feature extraction method based on discrete wavelet transform and principle component analysis

2021 ◽  
Vol 10 (5) ◽  
pp. 2588-2597
Author(s):  
Dalia Mohammad Toufiq ◽  
Ali Makki Sagheer ◽  
Hadi Veisi
Keyword(s):  
Feature Extraction ◽  
Wavelet Transform ◽  
Brain Tumors ◽  
Discrete Wavelet Transform ◽  
Principle Component Analysis ◽  
Component Analysis ◽  
Discrete Wavelet ◽  
Principle Component ◽  
Hybrid Feature Extraction ◽  
Mr Brain

The Identification of brain tumors is a critical step that relies on the expertise and abilities of the physician. In order to enable radiologists to spot brain tumors, an automated tumor arrangement is extremely important. This paper presents a technique for MR brain image segmentation and classification to identify images as normal and abnormal. The proposed technique is a hybrid feature extraction submitted to enhance the classification results and basically consists of three stages. The first stage is used a 3-level of discrete wavelet transform (DWT) to extract image characteristics. In the second stage, the principle component analysis (PCA) is applied to reduce the size of characteristics. Finally, a random forest classifier (RF) was used with a feature selection for identification. 181 MR brain images are collected (81 normal and 100 abnormal), in distinguishing normal and abnormal tissues, the experimental results obtained an accuracy of 98%, the sensitivity achieved is 99.2%, specificity achieved is 97.8%, and showed the effectiveness of the proposed technique compared with many kinds of literature. The results show that the 3L-DWT+PCA+RF still achieved the best classification results. The proposed model could apply to the brain MRI sphere classification, which will help doctors to diagnose a tumor if it is normal or abnormal in certain degrees. 

A nonlinear regression model applied to kinetic multi-component analysis of esters with a SAW-impedance sensor

2010 ◽  
Vol 14 (6) ◽  
pp. 525-531
Author(s):  
Cai Qing-Yun ◽  
Peng Li ◽  
Wang Rong-Hui ◽  
Nie Li-Hua ◽  
Yao Shou-Zhuo
Keyword(s):  
Regression Model ◽  
Nonlinear Regression ◽  
Component Analysis ◽  
Nonlinear Regression Model ◽  
Impedance Sensor

Four challenges for cognitive neuroscience and the cortico-hippocampal division of memory

2003 ◽  
Vol 26 (6) ◽  
pp. 681-682
Author(s):  
Harry Howard
Keyword(s):  
Independent Component Analysis ◽  
Recent Work ◽  
Cognitive Neuroscience ◽  
Principle Component Analysis ◽  
Pattern Analysis ◽  
Component Analysis ◽  
Independent Component ◽  
Principle Component ◽  
Complementary Methods ◽  
Current State

Jackendoff's criticisms of the current state of theorization in cognitive neuroscience are defused by recent work on the computational complementarity of the hippocampus and neocortex. Such considerations lead to a grounding of Jackendoff's processing model in the complementary methods of pattern analysis effected by independent component analysis (ICA) and principle component analysis (PCA).

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