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

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.

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Intellectually combined face recognition using curvelet based principle component analysis for feature extraction and Bayesian Classifier

2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies ◽

10.1109/icsccn.2011.6024578 ◽

2011 ◽

Cited By ~ 4

Author(s):

N. Rajkumar ◽

S. Vijayakumar ◽

C. Murukesh

Keyword(s):

Feature Extraction ◽

Face Recognition ◽

Principle Component Analysis ◽

Component Analysis ◽

Bayesian Classifier ◽

Principle Component

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

Materials Today Proceedings ◽

10.1016/j.matpr.2021.03.341 ◽

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

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

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v10i5.3013 ◽

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.

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Image Feature Extraction using Non Linear Principle Component Analysis

Procedia Engineering ◽

10.1016/j.proeng.2012.06.114 ◽

2012 ◽

Vol 38 ◽

pp. 911-917 ◽

Cited By ~ 3

Author(s):

M. Sivasathya ◽

S. Mary Joans

Keyword(s):

Feature Extraction ◽

Principle Component Analysis ◽

Component Analysis ◽

Image Feature ◽

Image Feature Extraction ◽

Principle Component ◽

Non Linear

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Feature extraction and selection algorithms in biomedical data classifiers based on time-frequency and principle component analysis.

11th Mediterranean Conference on Medical and Biomedical Engineering and Computing 2007 - IFMBE Proceedings ◽

10.1007/978-3-540-73044-6_19 ◽

2007 ◽

pp. 70-73 ◽

Cited By ~ 1

Author(s):

Pawel Kostka ◽

E.J. Tkacz

Keyword(s):

Feature Extraction ◽

Principle Component Analysis ◽

Component Analysis ◽

Biomedical Data ◽

Time Frequency ◽

Principle Component ◽

Feature Extraction And Selection ◽

Selection Algorithms

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

Chinese Journal of Chemistry ◽

10.1002/cjoc.19960140607 ◽

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

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

Behavioral and Brain Sciences ◽

10.1017/s0140525x03370153 ◽

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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