scholarly journals Wearable Intelligent System for the Diagnosis of Cardiac Diseases Working in Real Time and with Low Energy Cost

Proceedings ◽  
2018 ◽  
Vol 2 (19) ◽  
pp. 513
Author(s):  
Olga Valenzuela ◽  
Beatriz Prieto ◽  
Elvira Delgado-Marquez ◽  
Hector Pomares ◽  
Ignacio Rojas
Keyword(s):  
Intelligent System ◽  
Principal Component ◽  
Developed Countries ◽  
Support Vector ◽  
Average Life ◽  
Non Invasive ◽  
Wide Range ◽  
Vector Machines ◽  
Advanced System ◽  
Source Of Information

Heart disease is currently one of the leading causes of death in developed countries. The electrocardiogram is an important source of information for identifying these conditions, therefore, becomes necessary to seek an advanced system of diagnosis based on these signals. In this paper we used samples of electrocardiograms of MIT-related database with ten types of pathologies and a rate corresponding to normal (healthy patient), which are processed and used for extraction from its two branches of a wide range of features. Next, various techniques have been applied to feature selection based on genetic algorithms, principal component analysis and mutual information. To carry out the task of intelligent classification, 3 different scenarios have been considered. These techniques allow us to achieve greater efficiency in the classification methods used, namely support vector machines (SVM) and decision trees (DT) to perform a comparative analysis between them. Finally, during the development of this contribution, the use of very non-invasive devices (2 channel ECG) was analyzed, we could practically classify them as wearable, which would not need interaction by the user, and whose energy consumption is very small to extend the average life of the user been on it.

scholarly journals Multiscale Supervised Classification of Point Clouds with Urban and Forest Applications

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4523 ◽  
Author(s):  
Carlos Cabo ◽  
Celestino Ordóñez ◽  
Fernando Sáchez-Lasheras ◽  
Javier Roca-Pardiñas ◽  
and Javier de Cos-Juez
Keyword(s):  
Random Forest ◽  
Laser Scanning ◽  
Supervised Classification ◽  
Computing Time ◽  
Principal Component ◽  
Point Clouds ◽  
Support Vector ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Input Variables

We analyze the utility of multiscale supervised classification algorithms for object detection and extraction from laser scanning or photogrammetric point clouds. Only the geometric information (the point coordinates) was considered, thus making the method independent of the systems used to collect the data. A maximum of five features (input variables) was used, four of them related to the eigenvalues obtained from a principal component analysis (PCA). PCA was carried out at six scales, defined by the diameter of a sphere around each observation. Four multiclass supervised classification models were tested (linear discriminant analysis, logistic regression, support vector machines, and random forest) in two different scenarios, urban and forest, formed by artificial and natural objects, respectively. The results obtained were accurate (overall accuracy over 80% for the urban dataset, and over 93% for the forest dataset), in the range of the best results found in the literature, regardless of the classification method. For both datasets, the random forest algorithm provided the best solution/results when discrimination capacity, computing time, and the ability to estimate the relative importance of each variable are considered together.

Data-Driven Analysis of Engine Mission Severity Using Non-Dimensional Groups

2021 ◽  
Author(s):  
Tim Brandes ◽  
Stefano Scarso ◽  
Christian Koch ◽  
Stephan Staudacher
Keyword(s):  
Support Vector Machine ◽  
Computation Time ◽  
Principal Component ◽  
General Term ◽  
Support Vector ◽  
Physical Parameters ◽  
Machine Model ◽  
Precision Error ◽  
Operational Conditions ◽  
Wide Range

Abstract A numerical experiment of intentionally reduced complexity is used to demonstrate a method to classify flight missions in terms of the operational severity experienced by the engines. In this proof of concept, the general term of severity is limited to the erosion of the core flow compressor blade and vane leading edges. A Monte Carlo simulation of varying operational conditions generates a required database of 10000 flight missions. Each flight is sampled at a rate of 1 Hz. Eleven measurable or synthesizable physical parameters are deemed to be relevant for the problem. They are reduced to seven universal non-dimensional groups which are averaged for each flight. The application of principal component analysis allows a further reduction to three principal components. They are used to run a support-vector machine model in order to classify the flights. A linear kernel function is chosen for the support-vector machine due to its low computation time compared to other functions. The robustness of the classification approach against measurement precision error is evaluated. In addition, a minimum number of flights required for training and a sensible number of severity classes are documented. Furthermore, the importance to train the algorithms on a sufficiently wide range of operations is presented.

scholarly journals pIPredict: a computer tool for predicting isoelectric points of peptides and proteins

2015 ◽  
Vol 61 (1) ◽  
pp. 83-91 ◽  
Author(s):  
V.S. Skvortsov ◽  
N.N. Alekseychuk ◽  
D.V. Khudyakov ◽  
I.V. Romero Reyes
Keyword(s):  
Amino Acid ◽  
Posttranslational Modifications ◽  
Support Vector ◽  
Amino Acid Residues ◽  
Computer Tool ◽  
Wide Range ◽  
Vector Machines ◽  
Isoelectric Points ◽  
Peptide Fractionation ◽  
Made In

The data on approximate values of isoelectric point (pI) of peptides obtained during their fractionation by isoelectric focusing can be successfully used for the calculation of the pKa’s scale for amino acid residues. This scale can be used for pI prediction. The data of peptide fractionation also provides information about various posttranslational modifications (PTM), so that the prediction of pI may be performed for a wide range of protein forms. In this study, pKa values were calculated using a set of 13448 peptides (including 300 peptides with PTMs significant for pI calculation). The pKa constants were calculated for N-terminal, internal and C-terminal amino acid residues separately. The comparative analysis has shown that our scale increases the accuracy of pI prediction for peptides and proteins and successfully competes with traditional scales and such methods as support vector machines and artificial neural networks. The prediction performed by this scale, can be made in our program pIPredict with GUI written in JAVA as executable jar-archive. The program is freely available for academic users at http://www.ibmc.msk.ru/LPCIT/pIPredict. The software has also the possibility of pI predicting by some other scales; it recognizes some PTM and has the ability to use a custom scale.

scholarly journals Cascade Support Vector Machines with Dimensionality Reduction

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Oliver Kramer
Keyword(s):  
Support Vector Machines ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Large Data ◽  
Locally Linear Embedding ◽  
Benchmark Problems ◽  
Support Vector ◽  
Training Set ◽  
Support Vectors ◽  
Vector Machines

Cascade support vector machines have been introduced as extension of classic support vector machines that allow a fast training on large data sets. In this work, we combine cascade support vector machines with dimensionality reduction based preprocessing. The cascade principle allows fast learning based on the division of the training set into subsets and the union of cascade learning results based on support vectors in each cascade level. The combination with dimensionality reduction as preprocessing results in a significant speedup, often without loss of classifier accuracies, while considering the high-dimensional pendants of the low-dimensional support vectors in each new cascade level. We analyze and compare various instantiations of dimensionality reduction preprocessing and cascade SVMs with principal component analysis, locally linear embedding, and isometric mapping. The experimental analysis on various artificial and real-world benchmark problems includes various cascade specific parameters like intermediate training set sizes and dimensionalities.

scholarly journals Arousal Detection in Elderly People from Electrodermal Activity Using Musical Stimuli

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4788
Author(s):  
Almudena Bartolomé-Tomás ◽  
Roberto Sánchez-Reolid ◽  
Alicia Fernández-Sotos ◽  
José Miguel Latorre ◽  
Antonio Fernández-Caballero
Keyword(s):  
Elderly People ◽  
Electrodermal Activity ◽  
Well Being ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Wide Range ◽  
Vector Machines ◽  
Musical Genres ◽  
Commercial Device ◽  
Arousal Detection

The detection of emotions is fundamental in many areas related to health and well-being. This paper presents the identification of the level of arousal in older people by monitoring their electrodermal activity (EDA) through a commercial device. The objective was to recognize arousal changes to create future therapies that help them to improve their mood, contributing to reduce possible situations of depression and anxiety. To this end, some elderly people in the region of Murcia were exposed to listening to various musical genres (flamenco, Spanish folklore, Cuban genre and rock/jazz) that they heard in their youth. Using methods based on the process of deconvolution of the EDA signal, two different studies were carried out. The first, of a purely statistical nature, was based on the search for statistically significant differences for a series of temporal, morphological, statistical and frequency features of the processed signals. It was found that Flamenco and Spanish Folklore presented the highest number of statistically significant parameters. In the second study, a wide range of classifiers was used to analyze the possible correlations between the detection of the EDA-based arousal level compared to the participants’ responses to the level of arousal subjectively felt. In this case, it was obtained that the best classifiers are support vector machines, with 87% accuracy for flamenco and 83.1% for Spanish Folklore, followed by K-nearest neighbors with 81.4% and 81.5% for Flamenco and Spanish Folklore again. These results reinforce the notion of familiarity with a musical genre on emotional induction.

scholarly journals Cultivar Discrimination of Single Alfalfa (Medicago sativa L.) Seed via Multispectral Imaging Combined with Multivariate Analysis

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6575
Author(s):  
Lingjie Yang ◽  
Zuxin Zhang ◽  
Xiaowen Hu
Keyword(s):  
Multivariate Analysis ◽  
Multispectral Imaging ◽  
Principal Component ◽  
Nondestructive Method ◽  
Morphological Features ◽  
Support Vector ◽  
Linear Discrimination ◽  
Vector Machines ◽  
Alfalfa Seed ◽  
Medicago Sativa L

Rapid and accurate discrimination of alfalfa cultivars is crucial for producers, consumers, and market regulators. However, the conventional routine of alfalfa cultivars discrimination is time-consuming and labor-intensive. In this study, the potential of a new method was evaluated that used multispectral imaging combined with object-wise multivariate image analysis to distinguish alfalfa cultivars with a single seed. Three multivariate analysis methods including principal component analysis (PCA), linear discrimination analysis (LDA), and support vector machines (SVM) were applied to distinguish seeds of 12 alfalfa cultivars based on their morphological and spectral traits. The results showed that the combination of morphological features and spectral data could provide an exceedingly concise process to classify alfalfa seeds of different cultivars with multivariate analysis, while it failed to make the classification with only seed morphological features. Seed classification accuracy of the testing sets was 91.53% for LDA, and 93.47% for SVM. Thus, multispectral imaging combined with multivariate analysis could provide a simple, robust and nondestructive method to distinguish alfalfa seed cultivars.

A Proof of Convergence of the Concave-Convex Procedure Using Zangwill's Theory

2012 ◽  
Vol 24 (6) ◽  
pp. 1391-1407 ◽  
Author(s):  
Bharath K. Sriperumbudur ◽  
Gert R. G. Lanckriet
Keyword(s):  
Iterative Algorithms ◽  
Principal Component ◽  
Convergence Theory ◽  
Support Vector ◽  
Sparse Principal Component Analysis ◽  
Convex Programs ◽  
Difference Of Convex Functions ◽  
Vector Machines ◽  
Difference Of Convex ◽  
Proof Of Convergence

The concave-convex procedure (CCCP) is an iterative algorithm that solves d.c. (difference of convex functions) programs as a sequence of convex programs. In machine learning, CCCP is extensively used in many learning algorithms, including sparse support vector machines (SVMs), transductive SVMs, and sparse principal component analysis. Though CCCP is widely used in many applications, its convergence behavior has not gotten a lot of specific attention. Yuille and Rangarajan analyzed its convergence in their original paper; however, we believe the analysis is not complete. The convergence of CCCP can be derived from the convergence of the d.c. algorithm (DCA), proposed in the global optimization literature to solve general d.c. programs, whose proof relies on d.c. duality. In this note, we follow a different reasoning and show how Zangwill's global convergence theory of iterative algorithms provides a natural framework to prove the convergence of CCCP. This underlines Zangwill's theory as a powerful and general framework to deal with the convergence issues of iterative algorithms, after also being used to prove the convergence of algorithms like expectation-maximization and generalized alternating minimization. In this note, we provide a rigorous analysis of the convergence of CCCP by addressing two questions: When does CCCP find a local minimum or a stationary point of the d.c. program under consideration? and when does the sequence generated by CCCP converge? We also present an open problem on the issue of local convergence of CCCP.

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