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.

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 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 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 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.

Detection of Loss Zones while Drilling Using Different Machine Learning Techniques

2021 ◽  
pp. 1-29
Author(s):  
Ahmed Alsaihati ◽  
Mahmoud Abughaban ◽  
Salaheldin Elkatatny ◽  
Abdulazeez Abdulraheem
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Random Forests ◽  
Nearest Neighbors ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Learning Techniques ◽  
Vector Machines ◽  
Testing Set

Abstract Fluid loss into formations is a common operational issue that is frequently encountered when drilling across naturally or induced fractured formations. This could pose significant operational risks, such as well-control, stuck pipe, and wellbore instability, which, in turn, lead to an increase of well time and cost. This research aims to use and evaluate different machine learning techniques, namely: support vector machines, random forests, and K-nearest neighbors in detecting loss circulation occurrences while drilling using solely drilling surface parameters. Actual field data of seven wells, which had suffered partial or severe loss circulation, were used to build predictive models, while Well-8 was used to compare the performance of the developed models. Different performance metrics were used to evaluate the performance of the developed models. Recall, precision, and F1-score measures were used to evaluate the ability of the developed model to detect loss circulation occurrences. The results showed the K-nearest neighbors classifier achieved a high F1-score of 0.912 in detecting loss circulation occurrence in the testing set, while the random forests was the second-best classifier with almost the same F1-score of 0.910. The support vector machines achieved an F1-score of 0.83 in predicting the loss circulation occurrence in the testing set. The K-nearest neighbors outperformed other models in detecting the loss circulation occurrences in Well-8 with an F1-score of 0.80. The main contribution of this research as compared to previous studies is that it identifies losses events based on real-time measurements of the active pit volume.

scholarly journals Using Linear Discriminant Analysis for Dimensionality Reduction for Predicting Anomalies of BGP data

2019 ◽  
Vol 8 (11) ◽  
pp. 1989-1995
Keyword(s):  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Multi Layer Perceptron ◽  
High Dimensions ◽  
K Nearest Neighbors ◽  
Linear Discriminant ◽  
Data Packets ◽  
Vector Machines

Border Gateway Protocol (BGP) is a vital protocol on the internet for transfer of data packets among Autonomous System (AS). Security is a major concern for the transmission of BGP packets which are often attacked by worms or are hijacked by an attacker which results in requests entering black holes or loss of connection to the particular sites. The BGP anomalies can be reduced by analyzing the BGP datasets. Since, ASes communicate through messages, therefore, the anomalies can be reduced by identifying the corrupted BGP message in the dataset. In this paper, BGP anomalies have been classified by applying Machine learning (ML) algorithms. The dataset contains information about the sending and receiving time between ASes. The classifiers were used to predict the anomalies. Since the dataset had high dimensions, the dimensions were reduced using Linear Discriminant Analysis (LDA) and then Support Vector Machines (SVM), K-Nearest Neighbors (KNN), Linear Regression, Logistic Regression and Multi-Layer Perceptron (MLP) have been used to classify the anomalies.

scholarly journals APTITUDE Framework for Learning Data Classification Based on Machine Learning

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Machine Learning ◽  
Data Classification ◽  
Nearest Neighbors ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Course Content ◽  
Applied Model ◽  
Vector Machines ◽  
Learning Data

Learning analytics refers to the machine learning to provide predictions of learner success and prescriptions to learners and teachers. The main goal of paper is to proposed APTITUDE framework for learning data classification in order to achieve an adaptation and recommendations a course content or flow of course activities. This framework has applied model for student learning prediction based on machine learning. The five machine learning algorithms are used to provide learning data classification: random forest, Naïve Bayes, k-nearest neighbors, logistic regression and support vector machines

scholarly journals Hybrid Classifier System: Support Vector Machines Dikombinasikan dengan K-Nearest Neighbors untuk Menentukan Kelayakan Nasabah Bank dalam Pengajuan Kredit

2018 ◽  
Vol 7 (1) ◽  
pp. 9-16
Author(s):  
Selvia Lorena Br Ginting ◽  
Aldi Azhar Permana
Keyword(s):  
Data Mining ◽  
Support Vector Machines ◽  
Nearest Neighbors ◽  
Support Vector ◽  
System Support ◽  
K Nearest Neighbors ◽  
Hybrid Classifier ◽  
Classifier System ◽  
Vector Machines

Riset ini dilakukan dengan maksud membangun aplikasi yang dapat manganalisis data nasabah bank kemudian menentukan kelayakan nasabah tersebut dalam hal pemberian pinjaman, agar terhindar dari masalah kredit macet dikemudian hari. Metode yang digunakan adalah metode hybrid yang menggabungkan 2 teknik klasifikasi Data Mining yaitu Support Vector Machines (SVM) dan K-Nearest Neighbors (KNN). SVM bekerja dengan cara menemukan hyperplane yang optimal dan support vector. Lebih lanjut, algoritma KNN akan melakukan klasifikasi data nasabah bank berdasarkan pengidentifikasian support vector tersebut. Dengan 2000 data latih dan 103 data uji: nilai parameter cost=0,1, gamma=2, sistem mengidentifikasi 1998 support vector, kemudian dengan nilai K=16 sistem memberikan hasil 88,35% data yang cocok (91 data dari 103). Dapat disimpulkan bahwa aplikasi ini bekerja dengan cukup baik dan dapat membantu credit analyst dalam merekomendasikan nasabah yang layak memperoleh pinjaman. Kata Kunci - aplikasi; data mining; klasifikasi; metode hybrid; SVM-KNN  

Autonomous Navigation in Unknown Road-Like Environments Using Support Vector Machines

2010 ◽  
Author(s):  
Srinivas Tennety ◽  
Manish Kumar
Keyword(s):  
Support Vector Machines ◽  
Autonomous Navigation ◽  
Nearest Neighbors ◽  
Field Method ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
The Road ◽  
Vector Machines ◽  
Planning Algorithm ◽  
Path Planning Algorithm

In this paper enhancements have been done to the previously proposed Support Vector Machines (SVM) based path planning algorithm [1]. SVM, a data classification technique has been applied in conjunction with k-Nearest Neighbors (k-NN) algorithm for autonomous navigation in unknown road-like environments. The features of the road such as lane markers and the obstacles in the robot’s visibility are divided into two classes using k-nearest neighbors (k-NN) algorithm and then a maximum margin hyperplane is obtained using SVM that optimally separates both the classes. This hyperplane represents a collision-free path. The proposed algorithm has been tested in a variety of environments and the scenarios where it was unsuccessful have been identified and addressed for improvement. The simulation results of the enhanced algorithm have been presented and its performance has been compared with vfh+, a purely local obstacle avoidance algorithm based on artificial potential field method.

scholarly journals On relationships between imbalance and overlapping of datasets

10.29007/h71z ◽  
2020 ◽  
Author(s):  
Waleed Almutairi ◽  
Ryszard Janicki
Keyword(s):  
Support Vector Machines ◽  
Performance Indicators ◽  
Imbalanced Data ◽  
Nearest Neighbors ◽  
Support Vector ◽  
Classification Algorithms ◽  
Data Sets ◽  
K Nearest Neighbors ◽  
Imbalanced Data Sets ◽  
Vector Machines

The paper deals with problems that imbalanced and overlapping datasets often en- counter. Performance indicators as accuracy, precision and recall of imbalanced data sets, both with and without overlapping, are discussed and compared with the same performance indicators of balanced datasets with overlapping. Three popular classification algorithms, namely, Decision Tree, KNN (k-Nearest Neighbors) and SVM (Support Vector Machines) classifiers are analyzed and compared.

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