scholarly journals Artificial neural network classification of asteroids in the M1:2 mean-motion resonance with Mars

2021 ◽  
Vol 504 (1) ◽  
pp. 692-700
Author(s):  
V Carruba ◽  
S Aljbaae ◽  
R C Domingos ◽  
W Barletta
Keyword(s):  
Supervised Machine Learning ◽  
Machine Learning Methods ◽  
Mean Motion ◽  
Artificial Neural ◽  
Astronomical Images ◽  
Asteroid Dynamics ◽  
First Time ◽  
Asteroid Families ◽  
Mean Motion Resonance

ABSTRACT Artificial neural networks (ANNs) have been successfully used in the last years to identify patterns in astronomical images. The use of ANN in the field of asteroid dynamics has been, however, so far somewhat limited. In this work, we used for the first time ANN for the purpose of automatically identifying the behaviour of asteroid orbits affected by the M1:2 mean-motion resonance with Mars. Our model was able to perform well above 85 per cent levels for identifying images of asteroid resonant arguments in term of standard metrics like accuracy, precision, and recall, allowing to identify the orbital type of all numbered asteroids in the region. Using supervised machine learning methods, optimized through the use of genetic algorithms, we also predicted the orbital status of all multi-opposition asteroids in the area. We confirm that the M1:2 resonance mainly affects the orbits of the Massalia, Nysa, and Vesta asteroid families.

scholarly journals Seeing It All: Evaluating Supervised Machine Learning Methods for the Classification of Diverse Otariid Behaviours

PLoS ONE ◽  
2016 ◽  
Vol 11 (12) ◽  
pp. e0166898 ◽  
Author(s):  
Monique A. Ladds ◽  
Adam P. Thompson ◽  
David J. Slip ◽  
David P. Hocking ◽  
Robert G. Harcourt
Keyword(s):  
Machine Learning ◽  
Supervised Machine Learning ◽  
Learning Methods ◽  
Machine Learning Methods

A New Approach Hybrid Based in Artificial Neural Networks to Detection and Classification of Failures in Aeronautical Structures

2015 ◽  
Vol 763 ◽  
pp. 175-181
Author(s):  
Simone Silva Frutuoso Souza ◽  
Fernando Parra dos Anjos Lima ◽  
Fábio Roberto Chavarette
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
New Approach ◽  
Inspection Process ◽  
Artificial Neural ◽  
Aluminum Beam ◽  
First Time ◽  
Hybrid Methodology

In this paper presents a new hybrid methodology to perform fault detection and classification of aircraft structures using the tool as ARTMAP-Fuzzyand Perceptron multi-layer artificial neural networks. This method is divided into two steps, the first step performed by the multi-layer Perceptron neural network, which consists in the detection of abnormalities in the structure. The second step is performed by ARTMAP-Fuzzyneural network and consists of the classification of faults structural detected in the first time. The main application of this hybrid methodology is to assist in the inspection process of aeronautical structures in order to identify and characterize flaws as well, make decision-making in order to avoid accidents or air crashes. To evaluate this method, the modeling and simulation was carried out signals from a numerical model of an aluminum beam. The results obtained by the methodology demonstrating robustness and accuracy structural flaws.

scholarly journals Supervised Methods Applied to the Construction of a Vision System for the Classification of Cocoa Beans in the Cut-Test

2019 ◽  
Author(s):  
Felipe Antunes ◽  
Anne Canuto ◽  
Benjamin Bedregal ◽  
Eduardo Palmeira ◽  
Iaslan Silva
Keyword(s):  
System Performance ◽  
Vision System ◽  
Attribute Selection ◽  
Supervised Machine Learning ◽  
Classification Algorithms ◽  
Cocoa Beans ◽  
Machine Learning Methods ◽  
Supervised Methods ◽  
Classification Tasks

Supervised machine learning methods, also known as classification algorithms, have been widely used in the literature for many classification tasks. In this context, some aspects of these algorithms, as the used attributes used and the form they were built, have a direct impact in the system performance. Therefore, in this paper, we evaluate the application of classification algorithms, along with attribute selection, to propose an improved version of a vision system that performs the classification of cocoa beans. The main aim of this investigation is to improve the performance of a cocoa classification system that aims at helping farmers to classify the different cocoa beans based on images of these beans.

scholarly journals An Innovative Method for Predicting and Classifying Inadequate Accuracy in Heart Disease by Using Decision Tree with K-Nearest Neighbors Algorithm

2021 ◽  
Vol 36 (1) ◽  
pp. 609-615
Author(s):  
Mandhapati Rajesh ◽  
Dr.K. Malathi
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Decision Tree ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Accuracy Rate ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Learning Techniques

Aim: Predicting the Heartdiseases using medical parameters of cardiac patients to get a good accuracy rate using machine learning methods like innovative Decision Tree (DT) algorithm. Materials and Methods: Supervised Machine learning Techniques with innovative Decision Tree (N = 20) and K Nearest Neighbour (KNN) (N = 20) are performed with five different datasets at each time to record five samples. Results: The Decision Tree is used to predict heart disease with the help of various medical conditions, the accuracy is achieved for DT is 98% and KNN is 72.2%. The two algorithms Decision Tree and KNN are statistically insignificant (=.737) with the independent sample T-Test value (p<0.005) with a confidence level of 95%. Conclusion: Prediction and classification of heart disease significantly seem to be better in DT than KNN.

Dynamical classification of the asteroids in the Hungaria group: The objects affected by the exterior mean-motion resonance 2:3 with Mars

Icarus ◽  
2021 ◽  
pp. 114564
Author(s):  
J.A. Correa-Otto ◽  
M. Cañada-Assandri ◽  
R.S. García ◽  
N.E. Trógolo ◽  
A.M. Leiva ◽  
...  
Keyword(s):  
Mean Motion ◽  
Mean Motion Resonance

scholarly journals Classification of blazar candidates of uncertain type from the Fermi LAT 8-yr source catalogue with an artificial neural network

2020 ◽  
Vol 493 (2) ◽  
pp. 1926-1935 ◽  
Author(s):  
M Kovačević ◽  
G Chiaro ◽  
S Cutini ◽  
G Tosti
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Supervised Machine Learning ◽  
Large Area ◽  
Bl Lacertae ◽  
Spectrum Radio ◽  
Γ Ray ◽  
Artificial Neural ◽  
Bl Lacs

ABSTRACT The Fermi Large Area Telescope (LAT) has detected more than 5000 γ-ray sources in its first 8 yr of operation. More than 3000 of them are blazars. About 60 per cent of the Fermi-LAT blazars are classified as BL Lacertae objects (BL Lacs) or Flat Spectrum Radio Quasars (FSRQs), while the rest remain of uncertain type. The goal of this study was to classify those blazars of uncertain type, using a supervised machine learning method based on an artificial neural network, by comparing their properties to those of known γ-ray sources. Probabilities for each of 1329 uncertain blazars to be a BL Lac or FSRQ are obtained. Using 90 per cent precision metric, 801 can be classified as BL Lacs and 406 as FSRQs while 122 still remain unclassified. This approach is of interest because it gives a fast preliminary classification of uncertain blazars. We also explored how different selections of training and testing samples affect the classification and discuss the meaning of network outputs.

scholarly journals Faint Object Classification Using Artificial Neural Networks

1994 ◽  
Vol 161 ◽  
pp. 249-252
Author(s):  
M. Serra-Ricart
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Neural Network Classifier ◽  
The Neural Network ◽  
Faint Object ◽  
Artificial Neural ◽  
Astronomical Images

Artificial Neural Network techniques are applied to the classification of faint objects, detected in digital astronomical images, and a Bayesian classifier (the neural network classifier, NNC hereafter) is proposed. This classifier can be implemented using a feedforward multilayered neural network trained by the back-propagation procedure (Werbos 1974).

scholarly journals An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Hui Yu ◽  
Jian Deng ◽  
Ran Nathan ◽  
Max Kröschel ◽  
Sasha Pekarsky ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Reduction ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Behavioural Research ◽  
Intermittent Sampling

Abstract Background Our understanding of movement patterns and behaviours of wildlife has advanced greatly through the use of improved tracking technologies, including application of accelerometry (ACC) across a wide range of taxa. However, most ACC studies either use intermittent sampling that hinders continuity or continuous data logging relying on tracker retrieval for data downloading which is not applicable for long term study. To allow long-term, fine-scale behavioural research, we evaluated a range of machine learning methods for their suitability for continuous on-board classification of ACC data into behaviour categories prior to data transmission. Methods We tested six supervised machine learning methods, including linear discriminant analysis (LDA), decision tree (DT), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and extreme gradient boosting (XGBoost) to classify behaviour using ACC data from three bird species (white stork Ciconia ciconia, griffon vulture Gyps fulvus and common crane Grus grus) and two mammals (dairy cow Bos taurus and roe deer Capreolus capreolus). Results Using a range of quality criteria, SVM, ANN, RF and XGBoost performed well in determining behaviour from ACC data and their good performance appeared little affected when greatly reducing the number of input features for model training. On-board runtime and storage-requirement tests showed that notably ANN, RF and XGBoost would make suitable on-board classifiers. Conclusions Our identification of using feature reduction in combination with ANN, RF and XGBoost as suitable methods for on-board behavioural classification of continuous ACC data has considerable potential to benefit movement ecology and behavioural research, wildlife conservation and livestock husbandry.

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