scholarly journals The Use of Arabic Vowels to Model the Pathological Effect of Influenza Disease by Wavelets

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Khaled Daqrouq ◽  
Abdel-Rahman Al-Qawasmi ◽  
Ahmed Balamesh ◽  
Ali S. Alghamdi ◽  
Mohamed A. Al-Amoudi
Keyword(s):  
Linear Prediction ◽  
Nearest Neighbor ◽  
Vocal Tract ◽  
Characteristic Curve ◽  
Voice Disorder ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Pathological Effect ◽  
Power Spectral ◽  
Linear Prediction Coding

Speech parameters may include perturbation measurements, spectral and cepstral modeling, and pathological effects of some diseases, like influenza, that affect the vocal tract. The verification task is a very good process to discriminate between different types of voice disorder. This study investigated the modeling of influenza’s pathological effects on the speech signals of the Arabic vowels “A” and “O.” For feature extraction, linear prediction coding (LPC) of discrete wavelet transform (DWT) subsignals denoted by LPCW was used. k-Nearest neighbor (KNN) and support vector machine (SVM) classifiers were used for classification. To study the pathological effects of influenza on the vowel “A” and vowel “O,” power spectral density (PSD) and spectrogram were illustrated, where the PSD of “A” and “O” was repressed as a result of the pathological effects. The obtained results showed that the verification parameters achieved for the vowel “A” were better than those for vowel “O” for both KNN and SVM for an average. The receiver operating characteristic curve was used for interpretation. The modeling by the speech utterances as words was also investigated. We can claim that the speech utterances as words could model the influenza disease with a good quality of the verification parameters with slightly less performance than the vowels “A” as speech utterances. A comparison with state-of-the-art method was made. The best results were achieved by the LPCW method.

scholarly journals Recognition of Consumer Preference by Analysis and Classification EEG Signals

2021 ◽  
Vol 14 ◽  
Author(s):  
Mashael Aldayel ◽  
Mourad Ykhlef ◽  
Abeer Al-Nafjan
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Consumer Preference ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Detection Systems ◽  
Power Spectral ◽  
Selection Of

Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography (EEG)-based brain-computer interface (BCI) research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (DWT) and power spectral density (PSD), which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF). We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network (DNN) outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset.

scholarly journals Modelling of Amplitude Modulated Vocal Fry Glottal Area Waveforms Using an Analysis-by-Synthesis Approach

2021 ◽  
Vol 11 (5) ◽  
pp. 1990
Author(s):  
Vinod Devaraj ◽  
Philipp Aichinger
Keyword(s):  
Voice Quality ◽  
Low Frequency ◽  
Voice Disorder ◽  
Support Vector ◽  
Svm Classifier ◽  
Power Spectral ◽  
Analysis By Synthesis ◽  
Simple Support ◽  
Frequency Modulations ◽  
Synthesis Approach

The characterization of voice quality is important for the diagnosis of a voice disorder. Vocal fry is a voice quality which is traditionally characterized by a low frequency and a long closed phase of the glottis. However, we also observed amplitude modulated vocal fry glottal area waveforms (GAWs) without long closed phases (positive group) which we modelled using an analysis-by-synthesis approach. Natural and synthetic GAWs are modelled. The negative group consists of euphonic, i.e., normophonic GAWs. The analysis-by-synthesis approach fits two modelled GAWs for each of the input GAW. One modelled GAW is modulated to replicate the amplitude and frequency modulations of the input GAW and the other modelled GAW is unmodulated. The modelling errors of the two modelled GAWs are determined to classify the GAWs into the positive and the negative groups using a simple support vector machine (SVM) classifier with a linear kernel. The modelling errors of all vocal fry GAWs obtained using the modulating model are smaller than the modelling errors obtained using the unmodulated model. Using the two modelling errors as predictors for classification, no false positives or false negatives are obtained. To further distinguish the subtypes of amplitude modulated vocal fry GAWs, the entropy of the modulator’s power spectral density and the modulator-to-carrier frequency ratio are obtained.

Recognition of Moving Tracked and Wheeled Vehicles Based on Sound Analysis and Machine Learning Algorithms

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Jacek Jakubowski ◽  
Jerzy Jackowski
Keyword(s):  
Linear Prediction ◽  
Machine Learning Algorithms ◽  
Spectral Density Estimation ◽  
Power Spectral ◽  
Separable Problems ◽  
Characteristic Features ◽  
Iterative Procedures ◽  
Linear Prediction Coding ◽  
Signal Processing Methods ◽  
Wheeled Vehicles

The paper presents results of a preliminary study on verification of the possibility to establish simple methods to process acquired sound signals that were generated by a vehicle in motion; to determine its characteristic features for classification as a wheeled or tracked one. The analysis covered 220 signals acquired from real experiment and pre-processed with the use of power spectral density estimation (PSD) and linear prediction coding (LPC). The signal processing methods were used to generate features for which applicability in the classification process was assessed using a statistical method. The set of features was then optimised to reduce the dimensionality of data. Results of recognition obtained with the proposed non-iterative procedures for solving linearly separable problems were compared with results from standard methods, including SVM and k-NN. The developed features as well as selected methods of classification were proposed with respect to the possibility to implement them in low computational power computers for embedded applications.

scholarly journals Identification of optimum segment in single channel EEG biometric system

2021 ◽  
Vol 23 (3) ◽  
pp. 1847
Author(s):  
Muhammad Afif Hendrawan ◽  
Pramana Yoga Saputra ◽  
Cahya Rahmad
Keyword(s):  
Single Channel ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Eeg Signal ◽  
Alpha Band ◽  
Biometric System ◽  
Linear Discriminant ◽  
Power Spectral ◽  
Eyes Open ◽  
High Level

Nowadays, biometric modalities have gained popularity in security systems. Nevertheless, the conventional commercial-grade biometric system addresses some issues. The biggest problem is that they can be imposed by artificial biometrics. The electroencephalogram (EEG) is a possible solution. It is nearly impossible to replicate because it is dependent on human mental activity. Several studies have already demonstrated a high level of accuracy. However, it requires a large number of sensors and time to collect the signal. This study proposed a biometric system using single-channel EEG recorded during resting eyes open (EO) conditions. A total of 45 EEG signals from 9 subjects were collected. The EEG signal was segmented into 5 second lengths. The alpha band was used in this study. Discrete wavelet transform (DWT) with Daubechies type 4 (db4) was employed to extract the alpha band. Power spectral density (PSD) was extracted from each segment as the main feature. Linear discriminant analysis (LDA) and support vector machine (SVM) were used to classify the EEG signal. The proposed method achieved 86% accuracy using LDA only from the third segment. Therefore, this study showed that it is possible to utilize single-channel EEG during a resting EO state in a biometric system.

scholarly journals Deep Learning for EEG-Based Preference Classification in Neuromarketing

2020 ◽  
Vol 10 (4) ◽  
pp. 1525 ◽  
Author(s):  
Mashael Aldayel ◽  
Mourad Ykhlef ◽  
Abeer Al-Nafjan
Keyword(s):  
Deep Learning ◽  
Nearest Neighbor ◽  
Consumer Preferences ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Consumer Responses ◽  
The Real ◽  
Power Spectral ◽  
Real Behavior ◽  
Real Purchasing

The traditional marketing methodologies (e.g., television commercials and newspaper advertisements) may be unsuccessful at selling products because they do not robustly stimulate the consumers to purchase a particular product. Such conventional marketing methods attempt to determine the attitude of the consumers toward a product, which may not represent the real behavior at the point of purchase. It is likely that the marketers misunderstand the consumer behavior because the predicted attitude does not always reflect the real purchasing behaviors of the consumers. This research study was aimed at bridging the gap between traditional market research, which relies on explicit consumer responses, and neuromarketing research, which reflects the implicit consumer responses. The EEG-based preference recognition in neuromarketing was extensively reviewed. Another gap in neuromarketing research is the lack of extensive data-mining approaches for the prediction and classification of the consumer preferences. Therefore, in this work, a deep-learning approach is adopted to detect the consumer preferences by using EEG signals from the DEAP dataset by considering the power spectral density and valence features. The results demonstrated that, although the proposed deep-learning exhibits a higher accuracy, recall, and precision compared with the k-nearest neighbor and support vector machine algorithms, random forest reaches similar results to deep learning on the same dataset.

scholarly journals Segmentation and Classification of Vowel Phonemes of Assamese Speech Using a Hybrid Neural Framework

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
Mousmita Sarma ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Neural Network ◽  
Linear Prediction ◽  
Vocal Tract ◽  
Probabilistic Neural Network ◽  
Recognition Performance ◽  
Spoken Word Recognition ◽  
Discrete Wavelet ◽  
Self Organizing Map ◽  
Formant Frequency ◽  
Artificial Neural Network Ann

In spoken word recognition, one of the crucial points is to identify the vowel phonemes. This paper describes an Artificial Neural Network (ANN) based algorithm developed for the segmentation and recognition of the vowel phonemes of Assamese language from some words containing those vowels. Self-Organizing Map (SOM) trained with a various number of iterations is used to segment the word into its constituent phonemes. Later, Probabilistic Neural Network (PNN) trained with clean vowel phonemes is used to recognize the vowel segment from the six different SOM segmented phonemes. One of the important aspects of the proposed algorithm is that it proves the validation of the recognized vowel by checking its first formant frequency. The first formant frequency of all the Assamese vowels is predetermined by estimating pole or formant location from the linear prediction (LP) model of the vocal tract. The proposed algorithm shows a high recognition performance in comparison to the conventional Discrete Wavelet Transform (DWT) based segmentation.

A New Algorithm for Analysis of MiRNA Expression Profiles—SVM-RFE-FKNN

2021 ◽  
Author(s):  
Duan Mei ◽  
Qiang Liu
Keyword(s):  
Mirna Expression ◽  
Nearest Neighbor ◽  
Expression Profiles ◽  
Binary Classification ◽  
Characteristic Curve ◽  
Classification Performance ◽  
Recursive Feature Elimination ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Mirna Expression Profiles

Based on MicroRNA (miRNA) expression profiles, this article proposes a new algorithm—SVM-RFE-FKNN, which combines the support vector machine-recursive feature elimination (SVM-RFE) algorithm and the fuzzy K -nearest neighbor (FKNN) algorithm, to realize binary classification of tumors. First, the SVM-RFE algorithm was used to select features from the miRNA expression profile dataset to constitute feature subsets and to determine the maximum number of support vectors. Next, this maximum number was regarded as the upper limit of the parameter K in the FKNN algorithm that was then used to classify the samples to be tested. Finally, the leave-one-out cross-validation method was adopted to assess the classification performance of the proposed algorithm. Through experiments, our proposed algorithm was compared with other twelve classification methods, and the result shows that our algorithm had better classification performance. Specifically, with only a few miRNA biomarkers, the proposed algorithm could reach an accuracy of 99.46% and an area under the receiver operating characteristic curve (AUC) of 0.9874.

scholarly journals A machine-learning approach to predict postprandial hypoglycemia

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Wonju Seo ◽  
You-Bin Lee ◽  
Seunghyun Lee ◽  
Sang-Man Jin ◽  
Sung-Min Park
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Learning Algorithm ◽  
Characteristic Curve ◽  
Artificial Pancreas ◽  
Individual Performance ◽  
Machine Learning Algorithms ◽  
Prediction Algorithm ◽  
Support Vector ◽  
K Nearest Neighbor

Abstract Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.

scholarly journals Automated detection of colon cancer using genomic signal processing

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Safaa M. Naeem ◽  
Mai S. Mabrouk ◽  
Mohamed A. Eldosoky ◽  
Ahmed Y. Sayed
Keyword(s):  
Colon Cancer ◽  
Nearest Neighbor ◽  
Haar Wavelet ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Colon Cells ◽  
Genomic Signal Processing ◽  
Dna Mutations ◽  
Representation Method

Abstract Background Disorders in deoxyribonucleic acid (DNA) mutations are the common cause of colon cancer. Detection of these mutations is the first step in colon cancer diagnosis. Differentiation among normal and cancerous colon gene sequences is a method used for mutation identification. Early detection of this type of disease can avoid complications that can lead to death. In this study, 55 healthy and 55 cancerous genes for colon cells obtained from the national center for biotechnology information GenBank are used. After applying the electron–ion interaction pseudopotential (EIIP) numbering representation method for the sequences, single-level discrete wavelet transform (DWT) is applied using Haar wavelet. Then, some statistical features are obtained from the wavelet domain. These features are mean, variance, standard deviation, autocorrelation, entropy, skewness, and kurtosis. The resulting values are applied to the k-nearest neighbor (KNN) and support vector machine (SVM) algorithms to obtain satisfactory classification results. Results Four important parameters are calculated to evaluate the performance of the classifiers. Accuracy (ACC), F1 score, and Matthews correlation coefficient (MCC) are 95%, 94.74%, and 0.9045%, respectively, for SVM and 97.5%, 97.44%, and 0.9512%, respectively, for KNN. Conclusion This study has created a novel successful system for colorectal cancer classification and detection with the well-satisfied results. The K-nearest network results are the best with low error for the generated classification system, even though the results of the SVM network are acceptable.

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