scholarly journals Data analysis methods in astronomic objects classification (Sloan Digital Sky Survey DR14)

2021 ◽  
Vol 9 (3) ◽  
pp. 66-77
Author(s):  
V. A. Golov ◽  
D. A. Petrusevich
Keyword(s):  
Machine Learning ◽  
Statistical Information ◽  
Classification Systems ◽  
Sloan Digital Sky Survey ◽  
Gradient Boosting ◽  
Lagrange Points ◽  
Machine Learning Classification ◽  
Ensembles Of Classifiers ◽  
The Earth ◽  
Sky Survey

In the paper Sloan Digital Sky Survey DR14 dataset was investigated. It contains statistical information about many astronomical objects. The information was obtained within the framework of the Sloan Digital Sky Survey project. There are telescopes at the Earth surface, at the Earth orbit and in the Lagrange points of some systems (Earth–Moon, Sun–Earth). The telescopes gain information in different frequency ranges. The large quantity of statistical information leads to the demand for analytical algorithms and systems capable of making classification. Such information is marked up well enough to build machine learning classification systems. The paper presents the results of a number of classifiers. The handled data contains measures of three types of astronomical objects of the Sloan Digital Sky Survey DR14 dataset (star, quasar, galaxy). The CART decision tree, logistic regression, naïve Bayes classifiers and ensembles of classifiers (random forest, gradient boosting) were implemented. Conclusions about special features of each machine learning classifier trained to solve this task are made at the end of the paper. In some cases, classifiers’ structure can be explained physically. The accuracy of the classifiers built in this research is more than 90% (metrics F1, precision and recall are implemented, because the classes are unbalanced). Taking these values into account classification task is supposed to be successfully solved. At the same time, the structure of classifiers and importance of features can be used as a physical explanation of the solution.

Short- and Medium-range Prediction of Relativistic Electron Flux in the Earth’s Outer Radiation Belt by Machine Learning Methods

2021 ◽  
Vol 3 ◽  
pp. 47-57
Author(s):  
I. N. Myagkova ◽  
◽  
V. R. Shirokii ◽  
Yu. S. Shugai ◽  
O. G. Barinov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Radiation Belt ◽  
Gradient Boosting ◽  
Relativistic Electrons ◽  
Learning Methods ◽  
Outer Radiation Belt ◽  
Machine Learning Methods ◽  
The Earth ◽  
Skill Scores ◽  
Medium Range

The ways are studied to improve the quality of prediction of the time series of hourly mean fluxes and daily total fluxes (fluences) of relativistic electrons in the outer radiation belt of the Earth 1 to 24 hours ahead and 1 to 4 days ahead, respectively. The prediction uses an approximation approach based on various machine learning methods, namely, artificial neural networks (ANNs), decision tree (random forest), and gradient boosting. A comparison of the skill scores of short-range forecasts with the lead time of 1 to 24 hours showed that the best results were demonstrated by ANNs. For medium-range forecasting, the accuracy of prediction of the fluences of relativistic electrons in the Earth’s outer radiation belt three to four days ahead increases significantly when the predicted values of the solar wind velocity near the Earth obtained from the UV images of the Sun of the AIA (Atmospheric Imaging Assembly) instrument of the SDO (Solar Dynamics Observatory) are included to the list of the input parameters.

scholarly journals Morpho-photometric redshifts

2019 ◽  
Vol 489 (4) ◽  
pp. 4802-4808 ◽  
Author(s):  
Kristen Menou
Keyword(s):  
Poor Performance ◽  
Sloan Digital Sky Survey ◽  
Gradient Boosting ◽  
Learning Tools ◽  
Multi Layer Perceptron ◽  
Comparable Data ◽  
Photometric Redshifts ◽  
Data Set ◽  
Photometric Redshift ◽  
Sky Survey

ABSTRACT Machine learning (ML) is one of two standard approaches (together with SED fitting) for estimating the redshifts of galaxies when only photometric information is available. ML photo-z solutions have traditionally ignored the morphological information available in galaxy images or partly included it in the form of hand-crafted features, with mixed results. We train a morphology-aware photometric redshift machine using modern deep learning tools. It uses a custom architecture that jointly trains on galaxy fluxes, colours, and images. Galaxy-integrated quantities are fed to a Multi-Layer Perceptron (MLP) branch, while images are fed to a convolutional (convnet) branch that can learn relevant morphological features. This split MLP-convnet architecture, which aims to disentangle strong photometric features from comparatively weak morphological ones, proves important for strong performance: a regular convnet-only architecture, while exposed to all available photometric information in images, delivers comparatively poor performance. We present a cross-validated MLP-convnet model trained on 130 000 SDSS-DR12 (Sloan Digital Sky Survey – Data Release 12) galaxies that outperforms a hyperoptimized Gradient Boosting solution (hyperopt+XGBoost), as well as the equivalent MLP-only architecture, on the redshift bias metric. The fourfold cross-validated MLP-convnet model achieves a bias δz/(1 + z) = −0.70 ± 1 × 10−3, approaching the performance of a reference ANNZ2 ensemble of 100 distinct models trained on a comparable data set. The relative performance of the morphology-aware and morphology-blind models indicates that galaxy morphology does improve ML-based photometric redshift estimation.

Prediction of hepatocellular carcinoma patient survival using machine learning classification rules.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e15649-e15649
Author(s):  
Wei Zhou ◽  
Huan Chen ◽  
Wenbo Han ◽  
Ji He ◽  
Henghui Zhang
Keyword(s):  
Machine Learning ◽  
Hepatocellular Carcinoma ◽  
Gradient Boosting ◽  
Great Promise ◽  
Survival Prediction ◽  
Surgical Removal ◽  
Features Selection ◽  
Machine Learning Classification ◽  
Learning Techniques ◽  
Pathologic Features

e15649 Background: The outcome prediction of hepatocellular carcinoma (HCC) is conventionally determined by evaluating tissue samples obtained during surgical removal of the primary tumor focusing on their clinical and pathologic features. Recently, accumulating evidence suggests that cancer development is comprehensively modulated by the host’s immune system underlying the importance of immunological biomarkers for the prediction of HCC prognosis. However, an integrated predictive algorism incorporating clinical characteristic and immune features still remain to be established. Methods: We obtained respectable stage II HCC specimens, along with adjacent para-tumor tissues from 221 patients who underwent surgical resection at Eastern Hepatobiliary Surgery Hospital, (Shanghai, China) from 2015 through April 2018. Characteristics such as CD8+, CD163+, tumor-infiltrating lymphocytes (TILs) were obtained for further model construction used to predict the status of 3 survival indexes: Overall Survival (OS ,≤ 24 or > 24 month), Progression Free Survival (PFS, ≤ 6 or > 6 month), and Recurrence/Death (RD). Mutual information and coefficient between each feature and the survival indexes were tested to remove low scoring features after data cleaning and standardization. Furthermore, recursive features selection was preformed to obtain the optimal features combination. Finally, supervised learning techniques include either boosting or bagging strategy were used to fit and predict model with a grid-search method optimizing the parameters. Meanwhile, a cross validation procedure with 0.2 proportion of test cohort was randomly carried out for 10 times to evaluate the model. Results: We finally confirmed 15 biomarkers from the 46 candidates as features for the survival status prediction by using a 221 patients cohort. Among them, the top 10 most important biomarkers, included both clinical and immune attributes. The AUC of our model for survival indexes (OS, PFS, RD) was ranged from 0.76 (RD) to 0.8 (PFS), and the accuracy was above 0.85. Conclusions: We describe the integrative analysis of the clinical and immune features which collectively contribute to the survival index of HCC. Machine learning techniques, such as Gradient Boosting and random forest classifier , have a great promise for using in HCC cancer survival prediction.

scholarly journals Gully Erosion Susceptibility Mapping in Highly Complex Terrain Using Machine Learning Models

2021 ◽  
Vol 10 (10) ◽  
pp. 680
Author(s):  
Annan Yang ◽  
Chunmei Wang ◽  
Guowei Pang ◽  
Yongqing Long ◽  
Lei Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Complex Terrain ◽  
Large Scale ◽  
Area Under The Curve ◽  
Gully Erosion ◽  
Susceptibility Mapping ◽  
Weight Of Evidence ◽  
Gradient Boosting ◽  
Machine Learning Classification ◽  
Extreme Gradient Boosting

Gully erosion is the most severe type of water erosion and is a major land degradation process. Gully erosion susceptibility mapping (GESM)’s efficiency and interpretability remains a challenge, especially in complex terrain areas. In this study, a WoE-MLC model was used to solve the above problem, which combines machine learning classification algorithms and the statistical weight of evidence (WoE) model in the Loess Plateau. The three machine learning (ML) algorithms utilized in this research were random forest (RF), gradient boosted decision trees (GBDT), and extreme gradient boosting (XGBoost). The results showed that: (1) GESM were well predicted by combining both machine learning regression models and WoE-MLC models, with the area under the curve (AUC) values both greater than 0.92, and the latter was more computationally efficient and interpretable; (2) The XGBoost algorithm was more efficient in GESM than the other two algorithms, with the strongest generalization ability and best performance in avoiding overfitting (averaged AUC = 0.947), followed by the RF algorithm (averaged AUC = 0.944), and GBDT algorithm (averaged AUC = 0.938); and (3) slope gradient, land use, and altitude were the main factors for GESM. This study may provide a possible method for gully erosion susceptibility mapping at large scale.

scholarly journals Count Vectorized Spam and Ham Discernment of Short Message Service using Machine Learning Classification

2019 ◽  
Vol 8 (4) ◽  
pp. 557-561
Keyword(s):  
Machine Learning ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Gradient Boosting ◽  
Svm Classifier ◽  
Naive Bayes Classifier ◽  
Bayes Classifier ◽  
Naïve Bayes Classifier ◽  
Machine Learning Classification ◽  
Tree Classifier

With the growing volume and the amount of spam message, the demand for identifying the effective method for spam detection is in claim. The growth of mobile phone and Smartphone has led to the drastic increase in the SMS spam messages. The advancement and the clean process of mobile message servicing channel have attracted the hackers to perform their hacking through SMS messages. This leads to the fraud usage of other accounts and transaction that result in the loss of service and profit to the owners. With this background, this paper focuses on predicting the Spam SMS messages. The SMS Spam Message Detection dataset from KAGGLE machine learning Repository is used for prediction analysis. The analysis of Spam message detection is achieved in four ways. Firstly, the distribution of the target variable Spam Type the dataset is identified and represented by the graphical notations. Secondly, the top word features for the Spam and Ham messages in the SMS messages is extracted using Count Vectorizer and it is displayed using spam and Ham word cloud. Thirdly, the extracted Counter vectorized feature importance SMS Spam Message detection dataset is fitted to various classifiers like KNN classifier, Random Forest classifier, Linear SVM classifier, Ada Boost classifier, Kernel SVM classifier, Logistic Regression classifier, Gaussian Naive Bayes classifier, Decision Tree classifier, Extra Tree classifier, Gradient Boosting classifier and Multinomial Naive Bayes classifier. Performance analysis is done by analyzing the performance metrics like Accuracy, FScore, Precision and Recall. The implementation is done by python in Anaconda Spyder Navigator. Experimental Results shows that the Multinomial Naive Bayes classifier have achieved the effective prediction with the precision of 0.98, recall of 0.98, FScore of 0.98 , and Accuracy of 98.20%..

scholarly journals Supervised machine learning based liver disease prediction approach with LASSO feature selection

2021 ◽  
Vol 10 (6) ◽  
pp. 3369-3376
Author(s):  
Saima Afrin ◽  
F. M. Javed Mehedi Shamrat ◽  
Tafsirul Islam Nibir ◽  
Mst. Fahmida Muntasim ◽  
Md. Shakil Moharram ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Liver Disease ◽  
Decision Tree ◽  
Medical Science ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Machine Learning Classification ◽  
Prediction Approach

In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system. 

Machine Learning based Multivariate Seismic Site Classification System for South Korea

2020 ◽  
Author(s):  
Han-Saem Kim ◽  
Chang-Guk Sun ◽  
Hyung-Ik Cho ◽  
Moon-Gyo Lee
Keyword(s):  
Machine Learning ◽  
South Korea ◽  
Site Effect ◽  
Classification Criteria ◽  
Classification Systems ◽  
Supervised Machine Learning ◽  
Site Classification ◽  
Geospatial Information ◽  
Site Class ◽  
Machine Learning Classification

<p>Earthquake-induced land deformation and structure failure are more severe over soft soils than over firm soils and rocks owing to the seismic site effect and liquefaction. The site-specific seismic site effect related to the amplification of ground motion has spatial uncertainty depend on the local subsurface, surface geological, and topographic conditions. When the 2017 Pohang earthquake (M 5.4), South Korea’s second-strongest earthquake in decades, occurred, the severe damages influencing by variable site effect indicators were observed focusing on the basin or basin-edge region deposited unconsolidated Quaternary sediments. Thus, the site characterization is essential considering empirical correlations with geotechnical site response parameters and surface proxies. Furthermore, in the case of so many variables and tenuously related correlations, machine learning classification models can prove to be very precise than the parametric methods. In this study, the multivariate seismic site classification system was established using the machine learning technique based on the geospatial big data platform.</p><p>The supervised machine learning classification techniques and more specifically, random forest, support vector machine (SVM), and artificial neural network (ANN) algorithms have been adopted. Supervised machine learning algorithms analyze a set of labeled training data consisting of a set of input data and desired output values, and produce an inferred function which can be used for predictions from given input data. To optimize the classification criteria by considering the geotechnical uncertainty and local site effects, the training datasets applying principal component analysis (PCA) were verified with k-fold cross-validation. Moreover, the optimized training algorithm, proved by loss estimators (receiver operating characteristic curve (ROC), the area under the ROC curve (AUC)) based on the confusion matrix, was selected.</p><p>For the southeastern region in South Korea, the boring log information (strata, standard penetration test, etc.), geological map (1:50k scale), digital terrain model (having 5 m × 5 m), soil map (1:250k scale) were collected and constructed as geospatial big data. Preliminarily, to build spatially coincided datasets with geotechnical response parameters and surface proxies, the mesh-type geospatial information was built by the advanced geostatistical interpolation and simulation methods.</p><p>Site classification systems use seismic response parameters related to the geotechnical characteristics of the study area as the classification criteria. The current site classification systems in South Korea and the United States recommend Vs30, which is the average shear wave velocity (Vs) up to 30 m underground. This criterion uses only the dynamic characteristics of the site without considering its geometric distribution characteristics. Thus, the geospatial information for the input layer included the geo-layer thickness, surface proxies (elevation, slope, geological category, soil category), average Vs for soil layer (Vs,soil) and site period (TG). The Vs30-based site class was defined as categorical labeled data. Finally, the site class can be predicted using only proxies based on the optimized classification techniques.</p>

scholarly journals Data augmentation for machine learning redshifts applied to Sloan Digital Sky Survey galaxies

2015 ◽  
Vol 450 (1) ◽  
pp. 305-316 ◽  
Author(s):  
Ben Hoyle ◽  
Markus Michael Rau ◽  
Christopher Bonnett ◽  
Stella Seitz ◽  
Jochen Weller
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Sloan Digital Sky Survey ◽  
Sky Survey

scholarly journals Development of Prediction Models for Unplanned Hospital Readmission within 30 Days Based on Common Data Model: A Feasibility Study

2021 ◽  
Author(s):  
Sooyoung Yoo ◽  
Jinwook Choi ◽  
Borim Ryu ◽  
Seok Kim
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Data Model ◽  
Hospital Readmission ◽  
Prediction Models ◽  
External Validation ◽  
Gradient Boosting ◽  
Common Data Model ◽  
Time Interval ◽  
Machine Learning Classification

Abstract Background Unplanned hospital readmission after discharge reflects low satisfaction and reliability in care and the possibility of potential medical accidents, and is thus indicative of the quality of patient care and the appropriateness of discharge plans. Objectives The purpose of this study was to develop and validate prediction models for all-cause unplanned hospital readmissions within 30 days of discharge, based on a common data model (CDM), which can be applied to multiple institutions for efficient readmission management. Methods Retrospective patient-level prediction models were developed based on clinical data of two tertiary general university hospitals converted into a CDM developed by Observational Medical Outcomes Partnership. Machine learning classification models based on the LASSO logistic regression model, decision tree, AdaBoost, random forest, and gradient boosting machine (GBM) were developed and tested by manipulating a set of CDM variables. An internal 10-fold cross-validation was performed on the target data of the model. To examine its transportability, the model was externally validated. Verification indicators helped evaluate the model performance based on the values of area under the curve (AUC). Results Based on the time interval for outcome prediction, it was confirmed that the prediction model targeting the variables obtained within 30 days of discharge was the most efficient (AUC of 82.75). The external validation showed that the model is transferable, with the combination of various clinical covariates. Above all, the prediction model based on the GBM showed the highest AUC performance of 84.14 ± 0.015 for the Seoul National University Hospital cohort, yielding in 78.33 in external validation. Conclusions This study showed that readmission prediction models developed using machine-learning techniques and CDM can be a useful tool to compare two hospitals in terms of patient-data features.

scholarly journals Classification of Cotton Leaf Diseases Using AlexNet and Machine Learning Models

2021 ◽  
pp. 29-37
Author(s):  
Premkumar Borugadda ◽  
R. Lakshmi ◽  
Surla Govindu
Keyword(s):  
Machine Learning ◽  
Precision Agriculture ◽  
Performance Model ◽  
Gradient Boosting ◽  
Support Vector ◽  
Cotton Leaf ◽  
Decision Tree Classifier ◽  
Machine Learning Classification ◽  
Tree Classifier ◽  
Fully Connected

Computer vision has been demonstrated as state-of-the-art technology in precision agriculture in recent years. In this paper, an Alex net model was implemented to identify and classify cotton leaf diseases. Cotton Dataset consists of 2275 images, in which 1952 images were used for training and 324 images were used for validation. Five convolutional layers of the AlexNet deep learning technique is applied for features extraction from raw data. They were remaining three fully connected layers of AlexNet and machine learning classification algorithms such as Ada Boost Classifier (ABC), Decision Tree Classifier (DTC), Gradient Boosting Classifier (GBC). K Nearest Neighbor (KNN), Logistic Regression (LR), Random Forest Classifier (RFC), and Support Vector Classifier (SVC) are used for classification. Three fully connected layers of Alex Net provided the best performance model with a 94.92% F1_score at the training time of about 51min.  

Sign in / Sign up

Export Citation Format

Share Document