scholarly journals Features selection by genetic algorithm optimization with k-nearest neighbour and learning ensemble to predict Parkinson disease

2022 ◽  
Vol 12 (2) ◽  
pp. 1982
Author(s):  
Nsiri Benayad ◽  
Zayrit Soumaya ◽  
Belhoussine Drissi Taoufiq ◽  
Ammoumou Abdelkrim
Keyword(s):  
Genetic Algorithm ◽  
Speech Signal ◽  
Wavelet Transforms ◽  
Predictive Coding ◽  
Discrete Wavelet ◽  
Features Selection ◽  
Nearest Neighbour ◽  
Algorithm Optimization ◽  
Linear Predictive Coding ◽  
Zero Crossing

<span lang="EN-US">Among the several ways followed for detecting Parkinson's disease, there is the one based on the speech signal, which is a symptom of this disease. In this paper focusing on the signal analysis, a data of voice records has been used. In these records, the patients were asked to utter vowels “a”, “o”, and “u”. Discrete wavelet transforms (DWT) applied to the speech signal to fetch the variable resolution that could hide the most important information about the patients. From the approximation a3 obtained by Daubechies wavelet at the scale 2 level 3, 21 features have been extracted: a <a name="_Hlk88480766"></a>linear predictive coding (LPC), energy, zero-crossing rate (ZCR), mel frequency cepstral coefficient (MFCC), and wavelet Shannon entropy. Then for the classification, the K-nearest neighbour (KNN) has been used. The KNN is a type of instance-based learning that can make a decision based on approximated local functions, besides the ensemble learning. However, through the learning process, the choice of the training features can have a significant impact on overall the process. So, here it stands out the role of the genetic algorithm (GA) to select the best training features that give the best accurate classification.</span>

scholarly journals Audio Hiding based on Wavelet Transform and Linear Predictive Coding

2016 ◽  
Vol 42 (1) ◽  
pp. 30-37
Author(s):  
Jamal Hasoon ◽  
Saad Al-Saad
Keyword(s):  
Wavelet Transform ◽  
Efficient Method ◽  
High Frequency ◽  
Signal To Noise Ratio ◽  
Predictive Coding ◽  
Discrete Wavelet ◽  
Linear Predictive Coding ◽  
Signal To Noise ◽  
Log Likelihood ◽  
Objective Tests

In this work an efficient method for hiding a speech in audio is proposed. The features of secretspeech is extracted with LPC (Linear Predictive Coding), and these parameters embedded in audio inchaotic order. Discrete Wavelet Transform (DWT) is applied on audio frames to split the signal in high andlow frequencies. The embedding parameters are embedded in high frequency. The stego audio isperceptually indistinguishable from the equivalent cover audio. The proposed method allows hiding a sameduration of speech (secret) and audio (cover). The stego audio is subjected to objective tests such signal to noiseratio (SNR), signal to noise ratio segmental (SNRseg), Segmental Spectral SNR, Log Likelihood Ratio (LLR)and Correlation (Rxy) to determine the similarity with original audio.

scholarly journals O USO DE REDES NEURAIS AUTO- ORGANIZÁVEIS NA VISUALIZAÇÃO DE FORMAÇÃO DE AGRUPAMENTOS A PARTIR DO CONHECIMENTO ACENTUAL DE APRENDIZES BRASILEIROS DE INGLÊS

Organon ◽  
2011 ◽  
Vol 26 (51) ◽  
Author(s):  
Ana Cristina Cunha
Keyword(s):  
Neural Network ◽  
Speech Signal ◽  
Early Stage ◽  
Predictive Coding ◽  
Lexical Stress ◽  
Self Organizing Map ◽  
L2 Acquisition ◽  
Linear Predictive Coding ◽  
Classi Cation ◽  
Basic Hypothesis

! is study aimed at investigating how Brazilian learners ofEnglish organize their knowledge about lexical stress of a speci" c wordcategory at an early stage of L2 acquisition with the help of an unsuper-vised neural network, a self-organizing map (SOM), also called Kohonennetwork. ! e basic hypothesis tested was whether the parameterization ofthe speech signal from learner’s utterances through processing techniquessuch as Linear Predictive Coding (LPC), which consisted of the input ofthe network, would be e# ective in the classi" cation of learners and theirutterances. ! e study consisted of an empirical part and a computationalone. ! e participants were beginner students aged between 18 and 25.Preliminary results indicate that the combination of LPC+SOM allowedthe creation of well-de" ned category clusters, which is an important stepin data classi" cation to aid language pro" ciency level determination, andcomputer-assisted pronunciation teaching.

scholarly journals Word recognition from speech signal using linear predictive coding and spectrum analysis

2018 ◽  
Vol 7 (3) ◽  
pp. 1531
Author(s):  
Mandeep Singh ◽  
Gurpreet Singh
Keyword(s):  
Word Recognition ◽  
Sample Size ◽  
Spectrum Analysis ◽  
Speech Signal ◽  
Predictive Coding ◽  
The Other ◽  
Hybrid Technique ◽  
Linear Predictive Coding ◽  
Statistical Parameters ◽  
Isolated Word

This paper presents a technique for isolated word recognition from speech signal using Spectrum Analysis and Linear Predictive Coding (LPC). In the present study, only those words have been analyzed which are commonly used during a telephonic conversations by criminals. Since each word is characterized by unique frequency spectrum signature, thus, spectrum analysis of a speech signal has been done using certain statistical parameters. These parameters help in recognizing a particular word from a speech signal, as there is a unique value of a feature for each word, which helps in distinguishing one word from the other. Second method used is based on LPC coefficients. Analysis of features extracted using LPC coefficients help in identification of a specific word from the input speech signal. Finally, a combination of best features from these two methods has been used and a hybrid technique is proposed. An accuracy of 94% has been achieved for sample size of 400 speech words.  

Pembangunan Satu Kaedah Cekap dalam Pengesanan dan Pengkelasan Gangguan Kualiti Kuasa

2012 ◽  
Author(s):  
Ramizi Mohamed ◽  
Azah Mohamed ◽  
Aini Hussain
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Power Quality ◽  
Predictive Coding ◽  
Discrete Wavelet ◽  
Linear Predictive Coding ◽  
Power Quality Disturbance ◽  
Voltage Swell ◽  
Power Quality Disturbances

Pengesanan dan pengkelasan data gangguan kualiti kuasa secara automatik telah menjadi penting terutamanya untuk menangani masalah gangguan pangkalan data yang besar. Kertas kerja ini membentangkan satu kaedah cekap dalam pengesanan dan pengkelasan gangguan kualiti kuasa. Kaedah yang dicadangkan untuk mengesan gangguan adalah berdasarkan penjelmaan anak gelombang diskrit dan pengekodan ramalan lelurus manakala kaedah yang telah dibangunkan untuk mengkelaskan gangguan adalah berdasarkan rangkaian neural tiruan (RNT). Sebelum pelaksaan RNT, isyarat gangguan dikesan terlebih dahulu untuk mendapatkan pekali anak gelombang kuasa dua dan pekali pengekodan ramalan lelurus. Pekali ini mewakili sifat bagi berbagai jenis gangguan dan digunakan sebagai data masukan kepada RNT yang telah dibina. Oleh itu, anak gelombang dan pengekodan ramalan lelurus digunakan sebagai prapemprosesan isyarat gangguan yang kemudiannya disambungkan kepada RNT. Dalam pelaksanan RNT, model rangkaian neural lapisan berbilang dengan algoritma perambatan ke belakang telah dipertimbangkan. Reka bentuk RNT yang telah dibangunkan adalah berbentuk hierarki dan modular supaya RNT yang berasingan dikhaskan untuk mengkelas berbagai jenis gangguan dan juga gangguan dengan kadar persampelan yang berbeza. Keputusan yang diperolehi menunjukkan bahawa kaedah anak gelombang dan pengekodan ramalan lelurus adalah sangat berkesan untuk mengesan gangguan kualiti kuasa dan kaedah RNT pula dapat mengkelaskan dengan jitu gangguan kualiti kuasa seperti lendut voltan, ampul voltan, fana dan takukan. Kata kunci: Kualiti kuasa; anak gelombang; pengekodan ramalan lelurus; rangkaian neural Automated power quality disturbance detection and classification is preferred so as to enable faster and more efficient analysis of a disturbance large database. This paper presents an efficient method to detect and classify some power quality disturbances. The proposed method for detecting the disturbances is based on discrete wavelet transform and linear predictive coding whereas the method for classifying the disturbances is based on artificial neural network (ANN). Prior to the ANN implementation, the disturbance signals are first detected by the discrete wavelet transform and the linear predictive coding techniques to obtain the squared wavelet transform coefficients and the linear predictive coding coefficients. These features represent the various disturbances and serve as inputs to the developed ANNs. Therefore, wavelets and linear predictive coding are employed as a preprocessing stage and is connected to the ANN. In the ANN implementation, the multilayer perceptron neural network model and the backpropagation algorithm are considered. The design of the developed ANNs are hierarchical as well as modular in nature so that separate ANNs are dedicated to classify the various types of disturbances and to handle the disturbances with different sampling rates. The results obtained show that the wavelets and the linear predictive coding methods are effective in detecting power quality disturbances and the ANNs can accurately classify the disturbances such as voltage sag, voltage swell, transients and notching. Key words: Power quality; wavelets; linear predictive coding; neural networks

scholarly journals Heart Rate Variability Classification using Support Vector Machine and Genetic Algorithm

2018 ◽  
Author(s):  
M Ashtiyani ◽  
S Navaei Lavasani ◽  
A Asgharzadeh Alvar ◽  
M R Deevband
Keyword(s):  
Genetic Algorithm ◽  
Heart Rate ◽  
Support Vector Machine ◽  
Heart Rate Variability ◽  
Cardiac Activity ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Features Selection

Background: Electrocardiogram (ECG) is defined as an electrical signal, which represents cardiac activity. Heart rate variability (HRV) as the variation of interval between two consecutive heartbeats represents the balance between the sympathetic and parasympathetic branches of the autonomic nervous system.Objective: In this study, we aimed to evaluate the efficiency of discrete wavelet transform (DWT) based features extracted from HRV which were further selected by genetic algorithm (GA), and were deployed by support vector machine to HRV classification.Materials and Methods: In this paper, 53 ECGs including 3 different beat types (ventricular fibrillation (VF), atrial fibrillation (AF) and also normal sinus rhythm (NSR)), were selected from the MIT/BIH arrhythmia database. The approach contains 4 stages including HRV signal extraction from each ECG signal, feature extraction using DWT (entropy, mean, variance, kurtosis and spectral component β), best features selection by GA and classification of normal and abnormal ECGs using the selected features by support vector machine (SVM).Results: The performance of the classification procedure employing the combination of selected features were evaluated using several measures including accuracy, sensitivity, specificity and precision which resulted in 97.14%, 97.54%, 96.9% and 97.64%, respectively.Conclusion: A comparative analysis with the related existing methods illustrates  the proposed method has a higher potential in the classification of AF and VF. The attempt to classify the ECG signal has been successfully achieved. The proposed method has shown a promising sensitivity of 97.54% which indicates that this technique is an excellent model for computer-aided diagnosis of cardiac arrhythmias.

scholarly journals Dual Heuristic Feature Selection Based on Genetic Algorithm and Binary Particle Swarm Optimization

2019 ◽  
Vol 27 (1) ◽  
pp. 171-183
Author(s):  
Ali Hakem Jabor ◽  
Ali Hussein Ali
Keyword(s):  
Genetic Algorithm ◽  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Selection Method ◽  
Binary Particle Swarm Optimization ◽  
Features Selection ◽  
Nearest Neighbour ◽  
Selection Algorithm ◽  
Swarm Optimization

The features selection is one of the data mining tools that used to select the most important features of a given dataset. It contributes to save time and memory during the handling a given dataset. According to these principles, we have proposed features selection method based on mixing two metaheuristic algorithms Binary Particle Swarm Optimization and Genetic Algorithm work individually. The K-Nearest Neighbour (K-NN) is used as an objective function to evaluate the proposed features selection algorithm. The Dual Heuristic Feature Selection based on Genetic Algorithm and Binary Particle Swarm Optimization (DHFS) test, and compared with 26 well-known datasets of UCI machine learning. The numeric experiments result imply that the DHFS better performance compared with full features and that selected by the mentioned algorithms (Genetic Algorithm and Binary Particle Swarm Optimization). 

Sign in / Sign up

Export Citation Format

Share Document