scholarly journals A Set of Features Extraction Methods for the Recognition of the Isolated Handwritten Digits

2014 ◽  
Vol 3 (5) ◽  
pp. 349-355 ◽  
Author(s):  
S. Ouchtati ◽  
M. Redjimi ◽  
M. Bedda
Keyword(s):  
Extraction Methods ◽  
Features Extraction

scholarly journals Evaluation of speech signal features extraction methods

2020 ◽  
Vol 2 (1) ◽  
pp. 69-78
Author(s):  
Ismail Shayeb ◽  
Naseem Asad ◽  
Ziad Alqadi ◽  
Qazem Jaber
Keyword(s):  
Speech Signal ◽  
High Speed ◽  
Research Paper ◽  
Extraction Methods ◽  
Features Extraction ◽  
Experimental Results ◽  
Extraction Time ◽  
Digital Signals ◽  
Human Speech ◽  
Signal Features

Human speech digital signals are famous and important digital types, they are used in many vital applications which require a high speed processing, so creating a speech signal features is a needed issue. In this research paper we will study more widely used methods of features extraction, we will implement them, and the obtained experimental results will be compared, efficiency parameters such as extraction time and throughput will be obtained and a speedup of each method will be calculated. Speech signal histogram will be used to improve some methods efficiency.

scholarly journals A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

2019 ◽  
Vol 9 (12) ◽  
pp. 2518 ◽  
Author(s):  
Yuan hong Zhong ◽  
Shun Zhang ◽  
Rongbu He ◽  
Jingyi Zhang ◽  
Zhaokun Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Electronic Tongue ◽  
Extraction Methods ◽  
Features Extraction ◽  
Superior Performance ◽  
Discrete Wavelet ◽  
Features Selection ◽  
Time Frequency ◽  
Value Decomposition

Feature extraction is a key part of the electronic tongue system. Almost all of the existing features extraction methods are “hand-crafted”, which are difficult in features selection and poor in stability. The lack of automatic, efficient and accurate features extraction methods has limited the application and development of electronic tongue systems. In this work, a convolutional neural network-based auto features extraction strategy (CNN-AFE) in an electronic tongue (e-tongue) system for tea classification was proposed. First, the sensor response of the e-tongue was converted to time-frequency maps by short-time Fourier transform (STFT). Second, features were extracted by convolutional neural network (CNN) with time-frequency maps as input. Finally, the features extraction and classification results were carried out under a general shallow CNN architecture. To evaluate the performance of the proposed strategy, experiments were held on a tea database containing 5100 samples for five kinds of tea. Compared with other features extraction methods including features of raw response, peak-inflection point, discrete cosine transform (DCT), discrete wavelet transform (DWT) and singular value decomposition (SVD), the proposed model showed superior performance. Nearly 99.9% classification accuracy was obtained and the proposed method is an approximate end-to-end features extraction and pattern recognition model, which reduces manual operation and improves efficiency.

Analysis of Brain Tumor Using MR Images: A Brief Survey

2021 ◽  
pp. 2250023
Author(s):  
B. J. Shivaprasad ◽  
M. Ravikumar ◽  
D. S. Guru
Keyword(s):  
Brain Tumor ◽  
Extraction Methods ◽  
Features Extraction ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Brain Tumor Segmentation

In this paper, we have discussed in detail about detection and extraction of brain tumor from MRI technique, where the importance of using MRI is also highlighted. Various features extraction methods and classifiers are explained in brain tumor segmentation. This paper mainly focuses on challenges involved in brain tumor analysis, which is helpful for researchers and those who are interested to carry out their research on this topic.

Speaker identification features extraction methods: A systematic review

2017 ◽  
Vol 90 ◽  
pp. 250-271 ◽  
Author(s):  
Sreenivas Sremath Tirumala ◽  
Seyed Reza Shahamiri ◽  
Abhimanyu Singh Garhwal ◽  
Ruili Wang
Keyword(s):  
Systematic Review ◽  
Speaker Identification ◽  
Extraction Methods ◽  
Features Extraction

REVIEW OF OFFLINE TEXT INDEPENDENT WRITER IDENTIFICATION TECHNIQUES

2016 ◽  
Vol 78 (4-3) ◽  
Author(s):  
Shehzad Khalid ◽  
Uzma Naqv ◽  
Sana Shokat
Keyword(s):  
Extraction Methods ◽  
Features Extraction ◽  
Writer Identification ◽  
Future Directions ◽  
Identification Techniques

This paper reviews various text independent writer identification techniques through offline documents. Different features extraction methods are discussed. Classification approaches that are mainly used for identification by the researchers and verification by different groups and individuals are presented.  Identification rates achieved by the reviewed papers are tabulated and analyzed. A survey of different databases used in the reviewed papers is performed. Application of writer identification in different language domains is also discussed. Future directions for the automated writer identification are presented in the end.

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