A Robust Neonatal Facial Pain Cues Classification

2013 ◽  
Vol 475-476 ◽  
pp. 1110-1117
Author(s):  
Muhammad Naufal Mansor ◽  
Mohd Nazri Rejab
Keyword(s):  
Nearest Neighbor ◽  
Early Stage ◽  
Principal Component ◽  
Experimental Result ◽  
K Nearest Neighbor ◽  
Feed Forward Neural Network ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Pain State ◽  
Infant Pain

Late of infant pain detection on the early stage may affect newborns growth. Regarding of this matter, different techniques have been proposed such as facial expressions, speech production variation, and physiological signals to detect the pain states of a person. For past 2 decades, the determination of pain state through images has been undergone substantial research and development. Various techniques are used in the literature to classify pain states on the basis of images. In this paper, a feature extraction method using Principal Component Analysis (PCA) was adopted for identifying the pain states of an infant. In this study images samples are taken from Classification of Pain Expressions (COPE) database. Fuzzy k-NN, k Nearest Neighbor (k-NN), Feed Forward Neural network (FFNN) and Linear Discriminant analysis (LDA) based classifier is used to test usefulness of suggested features. Experimental result shows that the suggested methods can be used to identify the pain states of an infant.

scholarly journals Pengenalan Wajah Menggunakan Metode Linear Discriminant Analysis dan k Nearest Neighbor

2017 ◽  
Vol 9 (1) ◽  
pp. 1-9
Author(s):  
Fandiansyah Fandiansyah ◽  
Jayanti Yusmah Sari ◽  
Ika Putri Ningrum
Keyword(s):  
Feature Extraction ◽  
Face Recognition ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Nearest Neighbor ◽  
Experimental Result ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Nearest Neighbor Classifier ◽  
Neighbor Classifier

Face recognition is one of the biometric system that mostly used for individual recognition in the absent machine or access control. This is because the face is the most visible part of human anatomy and serves as the first distinguishing factor of a human being. Feature extraction and classification are the key to face recognition, as they are to any pattern classification task. In this paper, we describe a face recognition method based on Linear Discriminant Analysis (LDA) and k-Nearest Neighbor classifier. LDA used for feature extraction, which directly extracts the proper features from image matrices with the objective of maximizing between-class variations and minimizing within-class variations. The features of a testing image will be compared to the features of database image using K-Nearest Neighbor classifier. The experiments in this paper are performed by using using 66 face images of 22 different people. The experimental result shows that the recognition accuracy is up to 98.33%. Index Terms—face recognition, k nearest neighbor, linear discriminant analysis.

scholarly journals A Study on the Psychological Analysis System Using Machine Learning

2018 ◽  
Vol 7 (3.33) ◽  
pp. 128
Author(s):  
Ki Young Lee ◽  
Kyu Ho Kim ◽  
Jeong Jin Kang ◽  
Sung Jai Choi ◽  
Yong Soon Im ◽  
...  
Keyword(s):  
Machine Learning ◽  
Nearest Neighbor ◽  
Smart Phone ◽  
Principal Component ◽  
Digital Data ◽  
Expression Recognition ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Psychological Analysis ◽  
Analysis System

Real-time facial expression recognition and analysis technology is recently drawing attention in areas of computer vision, computer graphics, and HCI. Recognition of user’s emotion on the basis of video and voice is drawing particular interest. The technology may help managers of households or hospitals. In the present study, video and voice were converted into digital data through MATLAB by using PCA(Principal Component Analysis), LDA(Linear Discriminant Analysis), KNN(K Nearest Neighbor) algorithms to analyze emotions through machine learning. The manager of the psychological analysis counseling system may understand a user’s emotion in an smart phone environment. This system of the present study may help the manager to have a smooth conversation or develop a smooth relationship with a user on the basis of the provided psychological analysis results. 

scholarly journals Analysis of classification methods suitable for band limited spatially filtered EEG signal applicable to non-invasive BCI

2019 ◽  
Vol 8 (9) ◽  
pp. 1689-1694
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Common Spatial Pattern ◽  
Linear Discriminant ◽  
Feature Extraction Method ◽  
Non Invasive ◽  
Band Limited ◽  
Ca Sensitivity

Electroencephalographic (EEG) signals are the preferred input for non-invasive Brain-Computer Interface (BCI). Efficient signal processing strategies, including feature extraction and classification, are required to distinguish the underlying task of BCI. This work proposes the optimized common spatial pattern(CSP) filtering technique as the feature extraction method for collecting the spatially spread variation of the signal. The bandpass filter (BPF) designed for this work assures the availability of event-related synchronized (ERS) and event-related desynchronized (ERD) signal as input to the spatial filter. This work takes consideration of the area-specific electrodes for feature formation. This work further proposes a comparative analysis of classifier algorithms for classification accuracy(CA), sensitivity and specificity and the considered algorithms are Support Vector Machine(SVM), Linear Discriminant Analysis(LDA), and K-Nearest Neighbor(KNN). Performance parameters considered are CA, sensitivity, and selectivity, which can judge the method not only for high CA but also inclining towards the particular class. Thus it will direct in the selection of appropriate classifier as well as tuning the classifier to get the balanced results. In this work, CA, the prior performance parameter is obtained to be 88.2% sensitivity of 94.2% and selectivity 82.2% for the cosine KNN classifier. SVM with linear kernel function also gives the comparable results, thus concluding that the robust classifiers perform well for all parameters in case of CSP for feature extraction.

scholarly journals Biometric authenticator algorithm based on multiresolution analysis

2020 ◽  
Vol 20 (3) ◽  
pp. 1332
Author(s):  
Soumia Kerrache ◽  
Beladgham Mohammed ◽  
Hamza Aymen ◽  
Kadri Ibrahim
Keyword(s):  
Feature Extraction ◽  
Multiresolution Analysis ◽  
Nearest Neighbor ◽  
Curvelet Transform ◽  
Principal Component ◽  
Image Features ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Feature Extraction Method ◽  
Fusion Approach

Features extraction is an essential process in identifying person biometrics because the effectiveness of the system depends on it. Multiresolution Analysis success can be used in the system of a person’s identification and pattern recognition. In this paper, we present a feature extraction method for two-dimensional face and iris authentication.  Our approach is a combination of principal component analysis (PCA) and curvelet transform as an improved fusion approach for feature extraction. The proposed fusion approach involves image denoising using 2D-Curvelet transform to achieve compact representations of curves singularities. This is followed by the application of PCA as a fusion rule to improve upon the spatial resolution. The limitations of the only PCA algorithm are a poor recognition speed and complex mathematical calculating load, to reduce these limitations, we are applying the curvelet transform. <br /> To assess the performance of the presented method, we have employed three classification techniques: Neural networks (NN), K-Nearest Neighbor (KNN) and Support Vector machines (SVM).<br />The results reveal that the extraction of image features is more efficient using Curvelet/PCA.

scholarly journals Identification of Mint Scents Using a QCM Based E-Nose

2020 ◽  
Author(s):  
Salih Okur ◽  
Mohammed Sarheed ◽  
Robert Huber ◽  
Zejun Zhang ◽  
Lars Heinke ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
K Nearest Neighbor ◽  
Efficient Manner ◽  
Mentha Spicata ◽  
Electronic Noses ◽  
Linear Discriminant ◽  
Nepeta Cataria ◽  
Metal Oxide Nanoparticle ◽  
Nanoparticle Dispersions

Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO2, ITO), one Metal-Organic Frame as Cu(BPDC), and 7 different polymer films including PVA, PEDOT: PSS, PFO, SB, SW, SG, PB were used for functionalizing of QCM sensors. The purpose was to discriminate six economically relevant Mint species (Mentha x piperita, Mentha spicata, Mentha spicata ssp. crispa, Mentha longifolia, Agastache rugosa, and Nepeta cataria). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models including Principal Component Analy-sis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner.

scholarly journals Detecting Types of Sleep Apneas Through Nonlinear Features of Electrocardiogram and Electroencephalogram Signals

2021 ◽  
Author(s):  
Shaghayegh Saghafi ◽  
Fereidoun Nowshiravan Rahatabad ◽  
Keivan Maghooli
Keyword(s):  
Sleep Apnea ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Majority Voting ◽  
Support Vector ◽  
Svm Classifier ◽  
Common Disease ◽  
K Nearest Neighbor ◽  
Linear Discriminant ◽  
Nonlinear Features

Purpose: Sleep apnea is a common disease among women, and mainly men. The most dangerous complication of this disorder is heart stroke. Other complications include insufficient sleep and resulting daytime tiredness and illness that affect the individual's activities during the day, disrupt their life. Therefore, identifying this disease is important. Materials and Methods: We used Electroencephalogram (EEG) and Electrocardiogram (ECG) channels from the data of 25 patients with sleep apnea, for each type of sleep apnea, 8 nonlinear-like features, including fractal dimension, correlation dimension, certainty, recurrence rate, mean diagonal lines, the entropy of recursive quantification analysis, sample Entropy, and Shannon entropy were extracted. Then, feature matrices were sorted using principal component analysis in the order of linear combination of features, and the 20 selected features were chosen, normalized using common methods, and fed to different classifiers. Two 5-class and 2-class classification methods were assessed. In the 5-classification, three classifiers were used; the support vector machine, k-nearest neighbor, and multilayer perceptron. Results: The results showed that the highest mean validity, accuracy, sensitivity, and specificity for the SVM classifier was 88.45%, 88.35%, 88.33%, and 88.32%, respectively. In the 2-class approach, in addition to the mentioned classifiers, linear discriminant analysis, Bayes, and majority voting were used, and each class was considered against all classes. The highest average validity, average accuracy, average sensitivity, average specificity using the majority rule voting was 94.35%, 94.30%, 94.32%, and 94.15% respectively. Conclusion: When the results of classifiers are combined with the majority voting method, the validity of identifying the classes increases. The average validity for this method was obtained at 94.42%, which was higher than several other studies. It is recommended that databases with a larger sample size be used. This would lead to increased reliability of the proposed analysis method. Moreover, using novel deep-learning-based methods could help obtain better results.

scholarly journals Preprocessing of Skin Images and Feature Selection for Early Stage of Melanoma Detection using Color Feature Extraction

2021 ◽  
Vol 4 (2) ◽  
pp. 95
Author(s):  
Yuita Arum Sari ◽  
Anggi Gustiningsih Hapsani ◽  
Sigit Adinugroho ◽  
Lukman Hakim ◽  
Siti Mutrofin
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Normal Skin ◽  
Feature Selection Method ◽  
K Nearest Neighbor ◽  
Color Feature ◽  
K Value ◽  
Linear Discriminant

Preprocessing is an essential part to achieve good segmentation since it affects the feature extraction process. Melanoma have various shapes and their extracted features from image are used for early stage detection. Due to the fact that melanoma is one of dangerous diseases, early detection is required to prevent further phase of cancer from developing. In this paper, we propose a new framework to detect cancer on skin images using color feature extraction and feature selection. The default color space of skin images is RGB, then brightness is added to distinguish the normal and darken area on the skin. After that, average filter and histogram equalization are applied as well for attaining a good color intensities which are capable of determining normal skin from suspicious one. Otsu thresholding is utilized afterwards for melanoma segmentation. There are 147 features extracted from segmented images. Those features are reduced using three types of feature selection algorithms: Linear Discriminant Analysis (LDA), Correlation based Feature Selection (CFS), and Relief. All selected features are classified using k-Nearest Neighbor  (k-NN). Relief is known to be the best feature selection method among others and the optimal k value is 7 with 10-cross validation with accuracy of 0.835 and 0.845, without and with feature selection respectively. The result indicates that the frameworks is applicable for early skin cancer detection.

scholarly journals Machine Learning-Based Fault Diagnosis of Self-Aligning Bearings for Rotating Machinery Using Infrared Thermography

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Ankush Mehta ◽  
Deepam Goyal ◽  
Anurag Choudhary ◽  
B. S. Pabla ◽  
Safya Belghith
Keyword(s):  
Fault Diagnosis ◽  
Infrared Thermography ◽  
Condition Monitoring ◽  
Nearest Neighbor ◽  
Principal Component ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Mechanical Equipment ◽  
K Nearest Neighbor ◽  
Linear Discriminant

Bearings are considered as indispensable and critical components of mechanical equipment, which support the basic forces and dynamic loads. Across different condition monitoring (CM) techniques, infrared thermography (IRT) has gained the limelight due to its noncontact nature, high accuracy, and reliability. This article presents the use of IRT for the bearing fault diagnosis. A two-dimensional discrete wavelet transform (2D-DWT) has been applied for the decomposition of the thermal image. Principal component analysis (PCA) has been used for the reduction of dimensionality of extracted features, and thereafter the most relevant features are accomplished. Furthermore, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbor (KNN) as the classifiers were considered for classification of faults and performance assessment. The results reveal that the SVM outperformed LDA as well as KNN. Noncontact condition monitoring shows a great potential to be implemented in determining the health of machine. The utilization of noncontact thermal imaging-based instruments has enormous potential in anticipating the maintenance and increased machine availability.

scholarly journals Identification of Mint Scents Using a QCM Based E-Nose

Chemosensors ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 31
Author(s):  
Salih Okur ◽  
Mohammed Sarheed ◽  
Robert Huber ◽  
Zejun Zhang ◽  
Lars Heinke ◽  
...  
Keyword(s):  
Nearest Neighbor ◽  
Principal Component ◽  
K Nearest Neighbor ◽  
Efficient Manner ◽  
Mentha Spicata ◽  
Electronic Noses ◽  
Linear Discriminant ◽  
Nepeta Cataria ◽  
Metal Oxide Nanoparticle ◽  
Nanoparticle Dispersions

Mints emit diverse scents that exert specific biological functions and are relevance for applications. The current work strives to develop electronic noses that can electronically discriminate the scents emitted by different species of Mint as alternative to conventional profiling by gas chromatography. Here, 12 different sensing materials including 4 different metal oxide nanoparticle dispersions (AZO, ZnO, SnO2, ITO), one Metal Organic Frame as Cu(BPDC), and 7 different polymer films, including PVA, PEDOT:PSS, PFO, SB, SW, SG, and PB were used for functionalizing of Quartz Crystal Microbalance (QCM) sensors. The purpose was to discriminate six economically relevant Mint species (Mentha x piperita, Mentha spicata, Mentha spicata ssp. crispa, Mentha longifolia, Agastache rugosa, and Nepeta cataria). The adsorption and desorption datasets obtained from each modified QCM sensor were processed by three different classification models, including Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and k-Nearest Neighbor Analysis (k-NN). This allowed discriminating the different Mints with classification accuracies of 97.2% (PCA), 100% (LDA), and 99.9% (k-NN), respectively. Prediction accuracies with a repeating test measurement reached up to 90.6% for LDA, and 85.6% for k-NN. These data demonstrate that this electronic nose can discriminate different Mint scents in a reliable and efficient manner.

scholarly journals Towards a Support System for Digital Mammogram Classification

2021 ◽  
Vol 66 (2) ◽  
pp. 19
Author(s):  
A. Bajcsi
Keyword(s):  
Feature Selection ◽  
Nearest Neighbor ◽  
Early Stage ◽  
Region Of Interest ◽  
Principal Component ◽  
Region Growing ◽  
Pectoral Muscle ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Similar System

Cancer is the illness of the 21th century. With the development of technology some of these lesions became curable, if they are in an early stage. Researchers involved with image processing started to conduct experiments in the field of medical imaging, which contributed to the appearance of systems that can detect and/or diagnose illnesses in an early stage. This paper’s aim is to create a similar system to help the detection of breast cancer. First, the region of interest is defined using filtering and two methods, Seeded Region Growing and Sliding Window Algorithm, to remove the pectoral muscle. The region of interest is segmented using k-means and further used together with the original image. Gray-Level Run-Length Matrix features (in four direction) are extracted from the image pairs. To filter the important features from resulting set Principal Component Analysis and a genetic algorithm based feature selection is used. For classification K-Nearest Neighbor, Support Vector Machine and Decision Tree classifiers are experimented. To train and test the system images of Mammographic Image Analysis Society are used. The best performance is achieved features for directions {45◦ , 90◦ , 135◦ }, applying GA feature selection and DT classification (with a maximum depth of 30). This paper presents a comprehensive analysis of the different combinations of the algorithms mentioned above, where the best performence repored is 100% and 59.2% to train and test accuracies respectively.

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