Machine learning classification of new asteroid families members

ABSTRACT Asteroid families are groups of asteroids that are the product of collisions or of the rotational fission of a parent object. These groups are mainly identified in proper elements or frequencies domains. Because of robotic telescope surveys, the number of known asteroids has increased from ${\simeq}10\, 000$ in the early 1990s to more than $750\, 000$ nowadays. Traditional approaches for identifying new members of asteroid families, like the hierarchical clustering method (HCM), may struggle to keep up with the growing rate of new discoveries. Here we used machine learning classification algorithms to identify new family members based on the orbital distribution in proper (a, e, sin (i)) of previously known family constituents. We compared the outcome of nine classification algorithms from stand-alone and ensemble approaches. The extremely randomized trees (ExtraTree) method had the highest precision, enabling to retrieve up to 97 per cent of family members identified with standard HCM.

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Machine-learning identification of asteroid groups

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stz1795 ◽

2019 ◽

Vol 488 (1) ◽

pp. 1377-1386 ◽

Cited By ~ 2

Author(s):

V Carruba ◽

S Aljbaae ◽

A Lucchini

Keyword(s):

Machine Learning ◽

Hierarchical Clustering ◽

Clustering Algorithms ◽

Family Members ◽

Parent Body ◽

Clustering Methods ◽

New Family ◽

Hierarchical Clustering Methods ◽

Rotational Failure ◽

Asteroid Families

ABSTRACT Asteroid families are groups of asteroids that share a common origin. They can be the outcome of a collision or be the result of the rotational failure of a parent body or its satellites. Collisional asteroid families have been identified for several decades using hierarchical clustering methods (HCMs) in proper elements domains. In this method, the distance of an asteroid from a reference body is computed, and, if it is less than a critical value, the asteroid is added to the family list. The process is then repeated with the new object as a reference, until no new family members are found. Recently, new machine-learning clustering algorithms have been introduced for the purpose of cluster classification. Here, we apply supervised-learning hierarchical clustering algorithms for the purpose of asteroid families identification. The accuracy, precision, and recall values of results obtained with the new method, when compared with classical HCM, show that this approach is able to found family members with an accuracy above 89.5 per cent, and that all asteroid previously identified as family members by traditional methods are consistently retrieved. Values of the areas under the curve coefficients below Receiver Operating Characteristic curves are also optimal, with values consistently above 85 per cent. Overall, we identify 6 new families and 13 new clumps in regions where the method can be applied that appear to be consistent and homogeneous in terms of physical and taxonomic properties. Machine-learning clustering algorithms can, therefore, be very efficient and fast tools for the problem of asteroid family identification.

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5 Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images

Sir Syed Research Journal of Engineering & Technology ◽

10.33317/ssurj.v1i1.36 ◽

2018 ◽

Vol 1 (1) ◽

pp. 6 ◽

Cited By ~ 5

Author(s):

Lubna Farhi ◽

Razia Zia ◽

Zain Anwar Ali

Keyword(s):

Machine Learning ◽

Support Vector ◽

Classification Algorithms ◽

Mr Images ◽

Data Set ◽

Machine Learning Classification ◽

Machine Learning Classifiers ◽

Brain Mr Images ◽

Artificial Neural Network Ann

Brain cancer has remained one of the key causes ofdeaths in people of all ages. One way to survival amongst patientsis to correctly diagnose cancer in its early stages. Recentlymachine learning has become a very important tool in medicalimage classification. Our approach is to examine and comparevarious machine learning classification algorithms that help inbrain tumor classification of Magnetic Resonance (MR) images.We have compared Artificial Neural Network (ANN), K-nearestNeighbor (KNN), Decision Tree (DT), Support Vector Machine(SVM) and Naïve Bayes (NB) classifiers to determine theaccuracy of each classifier and find the best amongst them forclassification of cancerous and noncancerous brain MR images.We have used 86 MR images and extracted a large number offeatures for each image. Since the equal number of images, havebeen used thus there is no suspicion of results being biased. Forour data set the most accurate results were provided by ANN. Itwas found that ANN provides better results for medium to largedatabase of Brain MR Images.

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Human–Machine Interface: Multiclass Classification by Machine Learning on 1D EOG Signals for the Control of an Omnidirectional Robot

Sensors ◽

10.3390/s21175882 ◽

2021 ◽

Vol 21 (17) ◽

pp. 5882

Author(s):

Francisco David Pérez-Reynoso ◽

Liliam Rodríguez-Guerrero ◽

Julio César Salgado-Ramírez ◽

Rocío Ortega-Palacios

Keyword(s):

Machine Learning ◽

Learning Curve ◽

Severe Disabilities ◽

Routine Activities ◽

Human Machine Interface ◽

Classification Algorithms ◽

Machine Learning Classification ◽

Machine Interface ◽

The One

People with severe disabilities require assistance to perform their routine activities; a Human–Machine Interface (HMI) will allow them to activate devices that respond according to their needs. In this work, an HMI based on electrooculography (EOG) is presented, the instrumentation is placed on portable glasses that have the task of acquiring both horizontal and vertical EOG signals. The registration of each eye movement is identified by a class and categorized using the one hot encoding technique to test precision and sensitivity of different machine learning classification algorithms capable of identifying new data from the eye registration; the algorithm allows to discriminate blinks in order not to disturb the acquisition of the eyeball position commands. The implementation of the classifier consists of the control of a three-wheeled omnidirectional robot to validate the response of the interface. This work proposes the classification of signals in real time and the customization of the interface, minimizing the user’s learning curve. Preliminary results showed that it is possible to generate trajectories to control an omnidirectional robot to implement in the future assistance system to control position through gaze orientation.

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Accident Detection and Number Plate Recognition using Image Processing and Machine Learning

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e6964.018520 ◽

2020 ◽

Vol 8 (5) ◽

pp. 5079-5083

Keyword(s):

Machine Learning ◽

Image Processing ◽

Classification Algorithms ◽

Machine Learning Classification ◽

Image Enhancements ◽

Canny Edge ◽

Continuous Frames ◽

Processing Techniques ◽

Accident Detection

The purpose of this project is to detect the accident before it happens along with theextraction the number plate. Different image processing techniques along with morphological operators and Canny Edge Detection are used for image enhancements and object outline detections. With analysis of continuous frames, the relative velocity and the distance from which the leading vehicles are moving could be computed which is further helpful in accident detection and thus prevention too. Histogram of Oriented Gradients (HOG features) are used for feature extraction. Different machine learning classification algorithms like SVM, MLP, and XGBoost are used for classification of the object. Different standard OCR tools like Pytesseract, PyOCR, TesserOCR are used for the retrieval of the vehicle number from the extracted licence plate sub-image.

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Classification of Australian Native Forest Species Using Hyperspectral Remote Sensing and Machine-Learning Classification Algorithms

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2013.2282166 ◽

2014 ◽

Vol 7 (6) ◽

pp. 2481-2489 ◽

Cited By ~ 52

Author(s):

Xiao Shang ◽

Laurie A. Chisholm

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Hyperspectral Remote Sensing ◽

Classification Algorithms ◽

Native Forest ◽

Forest Species ◽

Machine Learning Classification

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5 Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images

Sir Syed Research Journal of Engineering & Technology ◽

10.33317/ssurj.v8i1.36 ◽

2018 ◽

Vol 8 (1) ◽

pp. 6

Author(s):

Lubna Farhi ◽

Razia Zia ◽

Zain Anwar Ali

Keyword(s):

Machine Learning ◽

Support Vector ◽

Classification Algorithms ◽

Mr Images ◽

Data Set ◽

Machine Learning Classification ◽

Machine Learning Classifiers ◽

Brain Mr Images ◽

Artificial Neural Network Ann

Brain cancer has remained one of the key causes ofdeaths in people of all ages. One way to survival amongst patientsis to correctly diagnose cancer in its early stages. Recentlymachine learning has become a very important tool in medicalimage classification. Our approach is to examine and comparevarious machine learning classification algorithms that help inbrain tumor classification of Magnetic Resonance (MR) images.We have compared Artificial Neural Network (ANN), K-nearestNeighbor (KNN), Decision Tree (DT), Support Vector Machine(SVM) and Naïve Bayes (NB) classifiers to determine theaccuracy of each classifier and find the best amongst them forclassification of cancerous and noncancerous brain MR images.We have used 86 MR images and extracted a large number offeatures for each image. Since the equal number of images, havebeen used thus there is no suspicion of results being biased. Forour data set the most accurate results were provided by ANN. Itwas found that ANN provides better results for medium to largedatabase of Brain MR Images.

Download Full-text