scholarly journals A Catalog of 323 Cataclysmic Variables from LAMOST DR6

2021 ◽  
Vol 257 (2) ◽  
pp. 65
Author(s):  
Yongkang Sun ◽  
Zhenghao Cheng ◽  
Shuo Ye ◽  
Ruobin Ding ◽  
Yijiang Peng ◽  
...  
Keyword(s):  
Binary Stars ◽  
Classification Accuracy ◽  
Template Matching ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Cataclysmic Variables ◽  
Emission Spectra ◽  
Machine Learning Algorithm ◽  
Data Release ◽  
Validation Set

Abstract In this work, we present a catalog of cataclysmic variables (CVs) identified from the sixth data release (DR6) of the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). To single out the CV spectra, we introduce a novel machine-learning algorithm called UMAP to screen out a total of 169,509 Hα emission spectra, and obtain a classification accuracy of the algorithm of over 99.6% from the cross-validation set. We then apply the template-matching program PyHammer v2.0 to the LAMOST spectra to obtain the optimal spectral type with metallicity, which help us identify the chromospherically active stars and potential binary stars from the 169,509 spectra. After visually inspecting all of the spectra, we identify 323 CV candidates from the LAMOST database, among them 52 objects are new. We further classify the new CV candidates in subtypes based on their spectral features, including five DN subtypes during outbursts, five NL subtypes, and four magnetic CVs (three AM Her type and one IP type). We also find two CVs that have been previously identified by photometry and confirm their previous classification with the LAMOST spectra.

scholarly journals Teaching Evaluation System by use of Machine Learning and Artificial Intelligence Methods

2021 ◽  
Vol 16 (05) ◽  
pp. 87
Author(s):  
Jingjing Hu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Classification Accuracy ◽  
Evaluation System ◽  
Learning Algorithm ◽  
Evaluation Model ◽  
Teaching Evaluation ◽  
Bp Algorithm ◽  
Machine Learning Algorithm ◽  
Existing Problems

To explore the adoption of artificial intelligence (AI) technology in the field of teacher teaching evaluation, the machine learning algorithm is proposed to construct a teaching evaluation model, which is suitable for the current educational model, and can help colleges and universities to improve the existing problems in teaching. Firstly, the existing problems in the current teaching evaluation system are put forward and a novel teaching evaluation model is designed. Then, the relevant theories and techniques required to build the model are introduced. Finally, the experiment methods and process are carried out to find out the appropriate machine learning algorithm and optimize the obtained weighted naive Bayes (WNB) algorithm, which is compared with traditional naive Bayes (NB) algorithm and back propagation (BP) algorithm. The results reveal that compared with NB algorithm, the average classification accuracy of WNB algorithm is 0.817, while that of NB algorithm is 0.751. Compared with BP algorithm, WNB algorithm has a classification accuracy of 0.800, while that of BP algorithm is 0.680. Therefore, it is proved that WNB algorithm has favorable effect in teaching evaluation model.

Classification of P2P Traffic Based on a Heteromorphic Ensemble Learning Model

2014 ◽  
Vol 687-691 ◽  
pp. 2693-2697
Author(s):  
Li Ding ◽  
Li Mao ◽  
Xiao Feng Wang
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Ensemble Learning ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Learning Model ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Environment

One single machine learning algorithm presents shortcomings when the data environment changes in the process of application. This article puts forward a heteromorphic ensemble learning model made up of bayes, support vector machine (SVM) and decision tree which classifies P2P traffic by voting principle. The experiment shows that the model can significantly improve the classification accuracy, and has a good stability.

scholarly journals Classification of Aggressive Behaviors Based on sEMG Feature Extraction and Machine Learning Algorithm

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 656-656
Author(s):  
Youngjun Kim ◽  
Uchechukuwu David ◽  
Yeonsik Noh
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Learning Machine

Abstract New surface electromyography (sEMG) feature extraction approach combined with Empirical Mode Decomposition (EMD) and Dispersion Entropy (DisEn) is proposed for classifying aggressive and normal behaviors from sEMG data. In this study, we used the sEMG physical action dataset from the UC Irvine Machine Learning repository. The raw sEMG was decomposed with EMD to obtain a set of Intrinsic Mode Functions (IMF). The IMF, which includes the most discriminant feature for each action, was selected based on the analysis by Hibert Transform (HT) in the time-frequency domain. Next, the DisEn of the selected IMF was calculated as a corresponding feature. Finally, the DisEn value was tested using five different classifiers, such as LDA, Quadratic DA, k-NN, SVM, and Extreme Learning Machine (ELM) for the classification task. Among these ML algorithms, we achieved classification accuracy, sensitivity, and specificity with ELM as 98.44%, 100%, and 96.72%, respectively.

scholarly journals Radiomics Features Predict Telomerase Reverse Transcriptase Promoter Mutations in World Health Organization Grade II Gliomas via a Machine-Learning Approach

2021 ◽  
Vol 10 ◽  
Author(s):  
Shengyu Fang ◽  
Ziwen Fan ◽  
Zhiyan Sun ◽  
Yiming Li ◽  
Xing Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Learning Algorithm ◽  
World Health ◽  
Machine Learning Algorithm ◽  
Who Grade ◽  
Grade Ii ◽  
Health Organization ◽  
Who Grade Ii Gliomas ◽  
Fold Cross Validation

The detection of mutations in telomerase reverse transcriptase promoter (pTERT) is important since preoperative diagnosis of pTERT status helps with evaluating prognosis and determining the surgical strategy. Here, we aimed to establish a radiomics-based machine-learning algorithm and evaluated its performance with regard to the prediction of mutations in pTERT in patients with World Health Organization (WHO) grade II gliomas. In total, 164 patients with WHO grade II gliomas were enrolled in this retrospective study. We extracted a total of 1,293 radiomics features from multi-parametric magnetic resonance imaging scans. Elastic net (used for feature selection) and support vector machine with linear kernel were applied in nested 10-fold cross-validation loops. The predictive model was evaluated by receiver operating characteristic and precision-recall analyses. We performed an unpaired t-test to compare the posterior predictive probabilities among patients with differing pTERT statuses. We selected 12 valuable radiomics features using nested 10-fold cross-validation loops. The area under the curve (AUC) was 0.8446 (95% confidence interval [CI], 0.7735–0.9065) with an optimal summed value of sensitivity of 0.9355 (95% CI, 0.8802–0.9788) and specificity of 0.6197 (95% CI, 0.5071–0.7371). The overall accuracy was 0.7988 (95% CI, 0.7378–0.8598). The F1-score was 0.8406 (95% CI, 0.7684–0.902) with an optimal precision of 0.7632 (95% CI, 0.6818–0.8364) and recall of 0.9355 (95% CI, 0.8802–0.9788). Posterior probabilities of pTERT mutations were significantly different between patients with wild-type and mutant TERT promoters. Our findings suggest that a radiomics analysis with a machine-learning algorithm can be useful for predicting pTERT status in patients with WHO grade II glioma and may aid in glioma management.

scholarly journals Pediatric Severe Sepsis Prediction Using Machine Learning

2017 ◽  
Author(s):  
Thomas Desautels ◽  
Jana Hoffman ◽  
Christopher Barton ◽  
Qingqing Mao ◽  
Melissa Jay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Severe Sepsis ◽  
San Francisco ◽  
High Performance ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Medical Center ◽  
Prediction Algorithm ◽  
Machine Learning Algorithm ◽  
Logistic Organ Dysfunction

Early detection of pediatric severe sepsis is necessary in order to administer effective treatment. In this study, we assessed the efficacy of a machine-learning-based prediction algorithm applied to electronic healthcare record (EHR) data for the prediction of severe sepsis onset. The resulting prediction performance was compared with the Pediatric Logistic Organ Dysfunction score (PELOD-2) and pediatric Systemic Inflammatory Response Syndrome score (SIRS) using cross-validation and pairwise t-tests. EHR data were collected from a retrospective set of de-identified pediatric inpatient and emergency encounters drawn from the University of California San Francisco (UCSF) Medical Center, with encounter dates between June 2011 and March 2016. Patients (n = 11,127) were 2-17 years of age and 103 [0.93%] were labeled severely septic. In four-fold cross-validation evaluations, the machine learning algorithm achieved an AUROC of 0.912 for discrimination between severely septic and control pediatric patients at onset and AUROC of 0.727 four hours before onset. Under the same measure, the prediction algorithm also significantly outperformed PELOD-2 (p < 0.05) and SIRS (p < 0.05) in the prediction of severe sepsis four hours before onset. This machine learning algorithm has the potential to deliver high-performance severe sepsis detection and prediction for pediatric inpatients.

scholarly journals Machine learning algorithm for improving performance on 3 AQ-screening classification

2019 ◽  
Vol 4 (2) ◽  
pp. 44-49
Author(s):  
Taftazani Ghazi Pratama ◽  
Rudy Hartanto ◽  
Noor Akhmad Setiawan
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Autism Spectrum ◽  
Support Vector ◽  
Autism Spectrum Quotient ◽  
Machine Learning Algorithm ◽  
Study Support ◽  
Artificial Neural Network Ann ◽  
Fold Cross Validation

Autism Spectrum Disorder (ASD) classification using machine learning can help parents, caregivers, psychiatrists, and patients to obtain the results of early detection of ASD. In this study, the dataset used is the autism-spectrum quotient for child, adolescent and adult, namely AQ-child, AQ-adolescent, AQ-adult. This study aims to improve the sensitivity and specificity of previous studies so that the classification results of ASD are better characterized by the reduced misclassification. The algorithm applied in this study: support vector machine (SVM), random forest (RF), artificial neural network (ANN). The evaluation results using 10-fold cross validation showed that RF succeeded in producing higher adult AQ sensitivity, which was 87.89%. The increase in the specificity level of AQ-Adolescents is better produced using an SVM of 86.33%.

Machine Learning Algorithm to Predict Early Complications after Brain Tumor Surgery

2018 ◽  
Author(s):  
C.H.B. van Niftrik ◽  
F. van der Wouden ◽  
V. Staartjes ◽  
J. Fierstra ◽  
M. Stienen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Tumor Surgery ◽  
Early Complications ◽  
Brain Tumor Surgery

Feature extraction and prediction of Dengue Outbreaks

2020 ◽  
pp. 216-222
Author(s):  
Kunal Parikh ◽  
Tanvi Makadia ◽  
Harshil Patel
Keyword(s):  
Public Health ◽  
Machine Learning ◽  
Developing Countries ◽  
Feature Extraction ◽  
Predictive Analytics ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Health Concerns ◽  
The World ◽  
Dengue Outbreaks

Dengue is unquestionably one of the biggest health concerns in India and for many other developing countries. Unfortunately, many people have lost their lives because of it. Every year, approximately 390 million dengue infections occur around the world among which 500,000 people are seriously infected and 25,000 people have died annually. Many factors could cause dengue such as temperature, humidity, precipitation, inadequate public health, and many others. In this paper, we are proposing a method to perform predictive analytics on dengue’s dataset using KNN: a machine-learning algorithm. This analysis would help in the prediction of future cases and we could save the lives of many.

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