scholarly journals A Comparative Study on Discrete Shmaliy Moments and Their Texture-Based Applications

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Germán González ◽  
Rodrigo Nava ◽  
Boris Escalante-Ramírez
Keyword(s):  
Support Vector ◽  
K Nearest Neighbors ◽  
Orthogonal Moments ◽  
Krawtchouk Moments ◽  
Polynomial Coefficients ◽  
New Family ◽  
Vector Machines ◽  
Discrete Orthogonal ◽  
Polynomial Bases ◽  
Fold Cross Validation

In recent years, discrete orthogonal moments have attracted the attention of the scientific community because they are a suitable tool for feature extraction. However, the numerical instability that arises because of the computation of high-order moments is the main drawback that limits their wider application. In this article, we propose an image classification method that avoids numerical errors based on discrete Shmaliy moments, which are a new family of moments derived from Shmaliy polynomials. Shmaliy polynomials have two important characteristics: one-parameter definition that implies a simpler definition than popular polynomial bases such as Krawtchouk, Hahn, and Racah; a linear weight function that eases the computation of the polynomial coefficients. We use IICBU-2008 database to validate our proposal and include Tchebichef and Krawtchouk moments for comparison purposes. The experiments are carried out through 5-fold cross-validation, and the results are computed using random forest, support vector machines, naïve Bayes, and k-nearest neighbors classifiers.

Persian Handwritten Number Recognition Using Adapted Framing Feature and Support Vector Machines

2016 ◽  
Vol 15 (01) ◽  
pp. 1650004 ◽  
Author(s):  
Hedieh Sajedi ◽  
Mehran Bahador
Keyword(s):  
Support Vector Machines ◽  
Recognition Rate ◽  
Nearest Neighbors ◽  
Polynomial Kernel ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
New Approach ◽  
Number Recognition ◽  
Vector Machines

In this paper, a new approach for segmentation and recognition of Persian handwritten numbers is presented. This method utilizes the framing feature technique in combination with outer profile feature that we named this the adapted framing feature. In our proposed approach, segmentation of the numbers into digits has been carried out automatically. In the classification stage of the proposed method, Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN) are used. Experimentations are conducted on the IFHCDB database consisting 17,740 numeral images and HODA database consisting 102,352 numeral images. In isolated digit level on IFHCDB, the recognition rate of 99.27%, is achieved by using SVM with polynomial kernel. Furthermore, in isolated digit level on HODA, the recognition rate of 99.07% is achieved by using SVM with polynomial kernel. The experiments illustrate that applying our proposed method resulted higher accuracy compared to previous researches.

scholarly journals Recognition of Gait Activities Using Acceleration Data from A Smartphone and A Wearable Device

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 60 ◽  
Author(s):  
Irvin Hussein Lopez-Nava ◽  
Matias Garcia-Constantino ◽  
Jesus Favela
Keyword(s):  
Assisted Living ◽  
Inertial Sensor ◽  
Ambient Assisted Living ◽  
Human Gait ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Acceleration Data ◽  
Vector Machines ◽  
Young Subjects ◽  
Physical Spaces

Activity recognition is an important task in many fields, such as ambient intelligence, pervasive healthcare, and surveillance. In particular, the recognition of human gait can be useful to identify the characteristics of the places or physical spaces, such as whether the person is walking on level ground or walking down stairs in which people move. For example, ascending or descending stairs can be a risky activity for older adults because of a possible fall, which can have more severe consequences than if it occurred on a flat surface. While portable and wearable devices have been widely used to detect Activities of Daily Living (ADLs), few research works in the literature have focused on characterizing only actions of human gait. In the present study, a method for recognizing gait activities using acceleration data obtained from a smartphone and a wearable inertial sensor placed on the ankle of people is introduced. The acceleration signals were segmented based on the automatic detection of strides, also called gait cycles. Subsequently, a feature vector of the segmented signals was extracted, which was used to train four classifiers using the Naive Bayes, C4.5, Support Vector Machines, and K-Nearest Neighbors algorithms. Data was collected from seven young subjects who performed five gait activities: (i) going down an incline, (ii) going up an incline, (iii) walking on level ground, (iv) going down stairs, and (v) going up stairs. The results demonstrate the viability of using the proposed method and technologies in ambient assisted living contexts.

scholarly journals Assessment of Interventions in Fuel Management Zones Using Remote Sensing

2020 ◽  
Vol 9 (9) ◽  
pp. 533 ◽  
Author(s):  
Ricardo Afonso ◽  
André Neves ◽  
Carlos Viegas Damásio ◽  
João Moura Pires ◽  
Fernando Birra ◽  
...  
Keyword(s):  
Satellite Images ◽  
Vegetation Indices ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Fuel Management ◽  
K Nearest Neighbors ◽  
Management Zones ◽  
Vector Machines ◽  
Sentinel 2

Every year, wildfires strike the Portuguese territory and are a concern for public entities and the population. To prevent a wildfire progression and minimize its impact, Fuel Management Zones (FMZs) have been stipulated, by law, around buildings, settlements, along national roads, and other infrastructures. FMZs require monitoring of the vegetation condition to promptly proceed with the maintenance and cleaning of these zones. To improve FMZ monitoring, this paper proposes the use of satellite images, such as the Sentinel-1 and Sentinel-2, along with vegetation indices and extracted temporal characteristics (max, min, mean and standard deviation) associated with the vegetation within and outside the FMZs and to determine if they were treated. These characteristics feed machine-learning algorithms, such as XGBoost, Support Vector Machines, K-nearest neighbors and Random Forest. The results show that it is possible to detect an intervention in an FMZ with high accuracy, namely with an F1-score ranging from 90% up to 94% and a Kappa ranging from 0.80 up to 0.89.

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 Regression Models for Symbolic Interval-Valued Variables

Entropy ◽  
2021 ◽  
Vol 23 (4) ◽  
pp. 429
Author(s):  
Jose Emmanuel Chacón ◽  
Oldemar Rodríguez
Keyword(s):  
Regression Models ◽  
Mean Squared Error ◽  
Nearest Neighbors ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
R Language ◽  
Squared Error ◽  
Vector Machines ◽  
Synthetic Datasets ◽  
Interval Valued

This paper presents new approaches to fit regression models for symbolic internal-valued variables, which are shown to improve and extend the center method suggested by Billard and Diday and the center and range method proposed by Lima-Neto, E.A.and De Carvalho, F.A.T. Like the previously mentioned methods, the proposed regression models consider the midpoints and half of the length of the intervals as additional variables. We considered various methods to fit the regression models, including tree-based models, K-nearest neighbors, support vector machines, and neural networks. The approaches proposed in this paper were applied to a real dataset and to synthetic datasets generated with linear and nonlinear relations. For an evaluation of the methods, the root-mean-squared error and the correlation coefficient were used. The methods presented herein are available in the the RSDA package written in the R language, which can be installed from CRAN.

scholarly journals Identification of Urdu Ghazal Poets using SVM

2020 ◽  
Vol 38 (4) ◽  
pp. 935-944
Author(s):  
Nida Tariq ◽  
Iqra Ijaz ◽  
Muhammad Kamran Malik ◽  
Zubair Malik ◽  
Faisal Bukhari
Keyword(s):  
Feature Selection ◽  
Support Vector ◽  
Sentence Structure ◽  
Writing Style ◽  
K Nearest Neighbors ◽  
Chi Square ◽  
Urdu Literature ◽  
Vector Machines ◽  
Two Factors ◽  
Feature Selection Techniques

Urdu literature has a rich tradition of poetry, with many forms, one of which is Ghazal. Urdu poetry structures are mainly of Arabic origin. It has complex and different sentence structure compared to our daily language which makes it hard to classify. Our research is focused on the identification of poets if given with ghazals as input. Previously, no one has done this type of work. Two main factors which help categorize and classify a given text are the contents and writing style. Urdu poets like Mirza Ghalib, Mir Taqi Mir, Iqbal and many others have a different writing style and the topic of interest. Our model caters these two factors, classify ghazals using different classification models such as SVM (Support Vector Machines), Decision Tree, Random forest, Naïve Bayes and KNN (K-Nearest Neighbors). Furthermore, we have also applied feature selection techniques like chi square model and L1 based feature selection. For experimentation, we have prepared a dataset of about 4000 Ghazals. We have also compared the accuracy of different classifiers and concluded the best results for the collected dataset of Ghazals.

scholarly journals Descripción del movimiento humano basado en el marco de Frenet Serret y datos tipo MOCAP

2021 ◽  
Vol 17 (34) ◽  
pp. 170-180
Author(s):  
Juan Camilo Hernandez-Gomez ◽  
Alejandro Restrepo-Martínez ◽  
Juliana Valencia-Aguirre
Keyword(s):  
Motion Capture ◽  
Nearest Neighbors ◽  
Human Movement ◽  
The Body ◽  
Microsoft Kinect ◽  
Support Vector ◽  
Motion Capture Data ◽  
K Nearest Neighbors ◽  
Vector Machines ◽  
Optical Markers

Clasificar el movimiento humano se ha convertido en una necesidad tecnológica, en donde para definir la posición de un sujeto requiere identificar el recorrido de las extremidades y el tronco del cuerpo, y tener la capacidad de diferenciar esta posición respecto a otros sujetos o movimientos, generándose la necesidad tener datos y algoritmos que faciliten su clasificación. Es así, como en este trabajo, se evalúa la capacidad discriminante de datos de captura de movimiento en rehabilitación física, donde la posición de los sujetos es adquirida con el Kinect de Microsoft y marcadores ópticos, y atributos del movimiento generados con el marco de Frenet Serret, evaluando su capacidad discriminante con los algoritmos máquinas de soporte vectorial, redes neuronales y k vecinos más cercanos. Los resultados presentan porcentajes de acierto del 93.5% en la clasificación con datos obtenidos del Kinect, y un éxito del 100% para los movimientos con marcadores ópticos. Classify human movement has become a technological necessity, where defining the position of a subject requires identifying the trajectory of the limbs and trunk of the body, having the ability to differentiate this position from other subjects or movements, which generates the need to have data and algorithms that help their classification. Therefore, the discriminant capacity of motion capture data in physical rehabilitation is evaluated, where the position of the subjects is acquired with the Microsoft Kinect and optical markers. Attributes of the movement generated with the Frenet Serret framework. Evaluating their discriminant capacity by means of support vector machines, neural networks, and k nearest neighbors algorithms. The obtained results present an accuracy of 93.5% in the classification with data obtained from the Kinect, and success of 100% for movements where the position is defined with optical markers.

scholarly journals Perbandingan Algoritma k-Nearest Neighbors (k-NN) dan Support Vector Machines (SVM) untuk Klasifikasi Pengenalan Citra Wajah

2021 ◽  
Vol 20 (1) ◽  
pp. 186-191
Author(s):  
Parasian DP Silitonga ◽  
Romanus Damanik
Keyword(s):  
Image Recognition ◽  
Machine Learning Algorithms ◽  
Public Image ◽  
Support Vector ◽  
Facial Image ◽  
K Nearest Neighbors ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Significant Research ◽  
The Moment

Abstract- The study of face recognition is one of the areas of computer vision that requires significant research at the moment. Numerous researchers have conducted studies on facial image recognition using a variety of techniques or methods to achieve the highest level of accuracy possible when recognizing a person's face from existing images. However, recognizing the image of a human face is not easy for a computer. As a result, several approaches were taken to resolve this issue. This study compares two (two) machine learning algorithms for facial image recognition to determine which algorithm has the highest level of accuracy, precision, recall, and AUC. The comparison is carried out in the following steps: image acquisition, preprocessing, feature extraction, face classification, training, and testing. Based on the stages and experiments conducted on public image datasets, it is concluded that the SVM algorithm, on average, has a higher level of accuracy, precision, and recall than the k-NN algorithm when the dataset proportion is 90:10. While the k-NN algorithm has the highest similarity in terms of accuracy, precision, and recall at 80%: 20% and 70%: 30% of 99.20. However, for the highest AUC percentage level, the k-NN algorithm outperforms SVM at a dataset proportion of 80%: 20% at 100%.

scholarly journals Cardiovascular Disease Prediction Using Machine Learning

2020 ◽  
pp. 46-54
Author(s):  
Digvijay Kumar ◽  
Bavithra
Keyword(s):  
Nearest Neighbors ◽  
Support Vector ◽  
Disease Prediction ◽  
Proper Treatment ◽  
Huge Number ◽  
K Nearest Neighbors ◽  
Vector Machines ◽  
The World ◽  
Supervised Learning Algorithms ◽  
Feasible System

Heart-related diseases or Cardiovascular Diseases (CVDs) are the most common and main reasons for a huge number of deaths in the world, not only in India but in the whole world. So, there is a need for a reliable, accurate, and feasible system to diagnose such diseases in time for proper treatment. This research paper represents the various models based on such algorithms and techniques to analyze their performance. Such as Logistic Regression, Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Naive Bayes, Random Forest, and ensemble models which are Supervised Learning algorithms. Using various important features that are necessary for the prediction of CVDs (like a person is having CVDs or not), which we will further discuss in this paper.

scholarly journals Analyzing the Effectiveness of the Brain–Computer Interface for Task Discerning Based on Machine Learning

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2403
Author(s):  
Jakub Browarczyk ◽  
Adam Kurowski ◽  
Bozena Kostek
Keyword(s):  
Feature Extraction ◽  
Principal Component ◽  
Component Analysis ◽  
Mental States ◽  
Extraction Methods ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbors ◽  
Vector Machines

The aim of the study is to compare electroencephalographic (EEG) signal feature extraction methods in the context of the effectiveness of the classification of brain activities. For classification, electroencephalographic signals were obtained using an EEG device from 17 subjects in three mental states (relaxation, excitation, and solving logical task). Blind source separation employing independent component analysis (ICA) was performed on obtained signals. Welch’s method, autoregressive modeling, and discrete wavelet transform were used for feature extraction. Principal component analysis (PCA) was performed in order to reduce the dimensionality of feature vectors. k-Nearest Neighbors (kNN), Support Vector Machines (SVM), and Neural Networks (NN) were employed for classification. Precision, recall, F1 score, as well as a discussion based on statistical analysis, were shown. The paper also contains code utilized in preprocessing and the main part of experiments.

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