ROGER: Reconstructing orbits of galaxies in extreme regions using machine learning techniques

ABSTRACT We present the ROGER (Reconstructing Orbits of Galaxies in Extreme Regions) code, which uses three different machine learning techniques to classify galaxies in, and around, clusters, according to their projected phase-space position. We use a sample of 34 massive, M200 > 1015h−1M⊙, galaxy clusters in the MultiDark Planck 2 (MDLP2) simulation at redshift zero. We select all galaxies with stellar mass M⋆ ≥ 108.5h−1M⊙, as computed by the semi-analytic model of galaxy formation SAG, that are located in, and in the vicinity of, these clusters and classify them according to their orbits. We train ROGER to retrieve the original classification of the galaxies from their projected phase-space positions. For each galaxy, ROGER gives as output the probability of being a cluster galaxy, a galaxy that has recently fallen into a cluster, a backsplash galaxy, an infalling galaxy, or an interloper. We discuss the performance of the machine learning methods and potential uses of our code. Among the different methods explored, we find the K-Nearest Neighbours algorithm achieves the best performance.

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Machine Classification of Transient Images

Proceedings of the International Astronomical Union ◽

10.1017/s1743921314013842 ◽

2014 ◽

Vol 10 (S306) ◽

pp. 288-291

Author(s):

Lise du Buisson ◽

Navin Sivanandam ◽

Bruce A. Bassett ◽

Mathew Smith

Keyword(s):

Machine Learning ◽

Naive Bayes ◽

Machine Learning Techniques ◽

Imaging Data ◽

Transient Detection ◽

Minimum Error ◽

Learning Techniques ◽

Nearest Neighbours ◽

Transient Imaging

AbstractUsing transient imaging data from the 2nd and 3rd years of the SDSS supernova survey, we apply various machine learning techniques to the problem of classifying transients (e.g. SNe) from artefacts, one of the first steps in any transient detection pipeline, and one that is often still carried out by human scanners. Using features mostly obtained from PCA, we show that we can match human levels of classification success, and find that a K-nearest neighbours algorithm and SkyNet perform best, while the Naive Bayes, SVM and minimum error classifier have performances varying from slightly to significantly worse.

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

Alinteri Journal of Agricultural Sciences ◽

10.47059/alinteri/v36i1/ajas21086 ◽

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.

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A Brief Survey on Text Classification Using Various Machine Learning Techniques

International Journal of Advanced Research in Computer Science and Software Engineering ◽

10.23956/ijarcsse.v8i1.521 ◽

2018 ◽

Vol 8 (1) ◽

pp. 14

Author(s):

Padmavathi .S ◽

M. Chidambaram

Keyword(s):

Machine Learning ◽

Text Classification ◽

Fixed Number ◽

Machine Learning Techniques ◽

Online Information ◽

Rule Based ◽

Learning Techniques ◽

Machine Learning Approach ◽

Rule Based Approach

Text classification has grown into more significant in managing and organizing the text data due to tremendous growth of online information. It does classification of documents in to fixed number of predefined categories. Rule based approach and Machine learning approach are the two ways of text classification. In rule based approach, classification of documents is done based on manually defined rules. In Machine learning based approach, classification rules or classifier are defined automatically using example documents. It has higher recall and quick process. This paper shows an investigation on text classification utilizing different machine learning techniques.

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Classification of Parkinson's Disease using Speech Attributes with Parametric and Nonparametric Machine Learning Techniques

2020 3rd International Conference on Intelligent Sustainable Systems (ICISS) ◽

10.1109/iciss49785.2020.9316078 ◽

2020 ◽

Author(s):

S. Sharanyaa ◽

P N. Renjith ◽

K. Ramesh

Keyword(s):

Machine Learning ◽

Parkinson’S Disease ◽

Parkinson's Disease ◽

Machine Learning Techniques ◽

Learning Techniques

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Predictive modeling for peri-implantitis by using machine learning techniques

Scientific Reports ◽

10.1038/s41598-021-90642-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Tomoaki Mameno ◽

Masahiro Wada ◽

Kazunori Nozaki ◽

Toshihito Takahashi ◽

Yoshitaka Tsujioka ◽

...

Keyword(s):

Machine Learning ◽

Demographic Data ◽

Risk Indicators ◽

Machine Learning Techniques ◽

Support Vector ◽

Machine Learning Methods ◽

Complex Interactions ◽

Learning Techniques ◽

Increased Risk ◽

Vector Machines

AbstractThe purpose of this retrospective cohort study was to create a model for predicting the onset of peri-implantitis by using machine learning methods and to clarify interactions between risk indicators. This study evaluated 254 implants, 127 with and 127 without peri-implantitis, from among 1408 implants with at least 4 years in function. Demographic data and parameters known to be risk factors for the development of peri-implantitis were analyzed with three models: logistic regression, support vector machines, and random forests (RF). As the results, RF had the highest performance in predicting the onset of peri-implantitis (AUC: 0.71, accuracy: 0.70, precision: 0.72, recall: 0.66, and f1-score: 0.69). The factor that had the most influence on prediction was implant functional time, followed by oral hygiene. In addition, PCR of more than 50% to 60%, smoking more than 3 cigarettes/day, KMW less than 2 mm, and the presence of less than two occlusal supports tended to be associated with an increased risk of peri-implantitis. Moreover, these risk indicators were not independent and had complex effects on each other. The results of this study suggest that peri-implantitis onset was predicted in 70% of cases, by RF which allows consideration of nonlinear relational data with complex interactions.

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