scholarly journals Machine-learning approaches to select Wolf-Rayet candidates

2016 ◽  
Vol 12 (S329) ◽  
pp. 422-422
Author(s):  
A. P. Marston ◽  
G. Morello ◽  
P. Morris ◽  
S. Van Dyk ◽  
J. Mauerhan
Keyword(s):  
Machine Learning ◽  
Detection Rate ◽  
Stellar Population ◽  
Broad Band ◽  
Stellar Populations ◽  
Learning Approaches ◽  
Nearest Neighbour ◽  
Learning Classifier ◽  
Selection Of

AbstractThe WR stellar population can be distinguished, at least partially, from other stellar populations by broad-band IR colour selection. We present the use of a machine learning classifier to quantitatively improve the selection of Galactic Wolf-Rayet (WR) candidates. These methods are used to separate the other stellar populations which have similar IR colours. We show the results of the classifications obtained by using the 2MASS J, H and K photometric bands, and the Spitzer/IRAC bands at 3.6, 4.5, 5.8 and 8.0μm. The k-Nearest Neighbour method has been used to select Galactic WR candidates for observational follow-up. A few candidates have been spectroscopically observed. Preliminary observations suggest that a detection rate of 50% can easily be achieved.

Machine learning approaches to understand and predict rate constants for organic processes in mixtures containing ionic liquids

2021 ◽  
Vol 23 (4) ◽  
pp. 2742-2752
Author(s):  
Tamar L. Greaves ◽  
Karin S. Schaffarczyk McHale ◽  
Raphael F. Burkart-Radke ◽  
Jason B. Harper ◽  
Tu C. Le
Keyword(s):  
Machine Learning ◽  
Ionic Liquids ◽  
Rate Constants ◽  
Learning Approaches ◽  
Learning Models ◽  
Organic Reaction ◽  
Machine Learning Models ◽  
Selection Of

Machine learning models were developed for an organic reaction in ionic liquids and validated on a selection of ionic liquids.

A Comparative Review of Various Machine Learning Approaches for Improving the Performance of Stego Anomaly Detection

2018 ◽  
pp. 351-371 ◽  
Author(s):  
Hemalatha Jeyaprakash ◽  
KavithaDevi M. K. ◽  
Geetha S.
Keyword(s):  
Machine Learning ◽  
Anomaly Detection ◽  
Detection Rate ◽  
High Dimensional ◽  
Learning Approaches ◽  
Learning Tools ◽  
High Detection Rate ◽  
Comparative Review ◽  
High Detection

In recent years, steganalyzers are intelligently detecting the stego images with high detection rate using high dimensional cover representation. And so the steganographers are working towards this issue to protect the cover element dependency and to protect the detection of hiding secret messages. Any steganalysis algorithm may achieve its success in two ways: 1) extracting the most sensitive features to expose the footprints of message hiding; 2) designing or building an effective classifier engine to favorably detect the stego images through learning all the stego sensitive features. In this chapter, the authors improve the stego anomaly detection using the second approach. This chapter presents a comparative review of application of the machine learning tools for steganalysis problem and recommends the best classifier that produces a superior detection rate.

scholarly journals Research Trend of Causal Machine Learning Method: A Literature Review

2020 ◽  
Vol 9 (2) ◽  
pp. 111-118
Author(s):  
Shindy Arti ◽  
Indriana Hidayah ◽  
Sri Suning Kusumawardhani
Keyword(s):  
Machine Learning ◽  
Learning System ◽  
Research Trend ◽  
Learning Approaches ◽  
Algorithm Performance ◽  
Combination Technique ◽  
Causal Impact ◽  
Learning Research ◽  
The Relationship ◽  
Selection Of

Machine learning is commonly used to predict and implement  pattern recognition and the relationship between variables. Causal machine learning combines approaches for analyzing the causal impact of intervention on the result, asumming a considerably ambigous variables. The combination technique of causality and machine learning is adequate for predicting and understanding the cause and effect of the results. The aim of this study is a systematic review to identify which causal machine learning approaches are generally used. This paper focuses on what data characteristics are applied to causal machine learning research and how to assess the output of algorithms used in the context of causal machine learning research. The review paper analyzes 20 papers with various approaches. This study categorizes data characteristics based on the type of data, attribute value, and the data dimension. The Bayesian Network (BN) commonly used in the context of causality. Meanwhile, the propensity score is the most extensively used in causality research. The variable value will affect algorithm performance. This review can be as a guide in the selection of a causal machine learning system.

scholarly journals Analytical framework for optimized feature extraction for upgrading occupancy sensing performance

2020 ◽  
Vol 10 (4) ◽  
pp. 4093
Author(s):  
Preethi Krishna Rao Mane ◽  
K. Narasimha Rao
Keyword(s):  
Machine Learning ◽  
Energy Management ◽  
Doppler Radar ◽  
Analytical Framework ◽  
Learning Approaches ◽  
Analytical Methodology ◽  
Efficient Selection ◽  
Accuracy Performance ◽  
Selection Of ◽  
Sensing Performance

The adoption of the occupancy sensors has become an inevitable in commercial and non-commercial security devices, owing to their proficiency in the energy management. It has been found that the usages of conventional sensors is shrouded with operational problems, hence the use of the Doppler radar offers better mitigation of such problems. However, the usage of Doppler radar towards occupancy sensing in existing system is found to be very much in infancy stage. Moreover, the performance of monitoring using Doppler radar is yet to be improved more. Therefore, this paper introduces a simplified framework for enriching the event sensing performance by efficient selection of minimal robust attributes using Doppler radar. Adoption of analytical methodology has been carried out to find that different machine learning approaches could be further used for improving the accuracy performance for the feature that has been extracted in the proposed system of occuancy system.

scholarly journals Virtual-screening workflow tutorials and prospective results from the Teach-Discover-Treat competition 2014 against malaria

F1000Research ◽  
2017 ◽  
Vol 6 ◽  
pp. 1136 ◽  
Author(s):  
Sereina Riniker ◽  
Gregory A. Landrum ◽  
Floriane Montanari ◽  
Santiago D. Villalba ◽  
Julie Maier ◽  
...  
Keyword(s):  
Machine Learning ◽  
Virtual Screening ◽  
High Throughput ◽  
Learning Approaches ◽  
High Throughput Screen ◽  
Hit Rate

The first challenge in the 2014 competition launched by the Teach-Discover-Treat (TDT) initiative asked for the development of a tutorial for ligand-based virtual screening, based on data from a primary phenotypic high-throughput screen (HTS) against malaria. The resulting Workflows were applied to select compounds from a commercial database, and a subset of those were purchased and tested experimentally for anti-malaria activity. Here, we present the two most successful Workflows, both using machine-learning approaches, and report the results for the 114 compounds tested in the follow-up screen. Excluding the two known anti-malarials quinidine and amodiaquine and 31 compounds already present in the primary HTS, a high hit rate of 57% was found.

scholarly journals Investigating Machine Learning Approaches for Bitcoin Ransomware Payment Detection Systems

2020 ◽  
Vol 5 (9) ◽  
pp. 1216-1222
Author(s):  
Kirat Jadhav
Keyword(s):  
Machine Learning ◽  
Detection Rate ◽  
Nearest Neighbor ◽  
Gradient Boosting ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Detection Systems ◽  
Digital World ◽  
F Measure ◽  
Machine Learning Models

Cryptocurrencies have revolutionized the process of trading in the digital world. Roughly one decade since the induction of the first bitcoin block, thousands of cryptocurrencies have been introduced. The anonymity offered by the cryptocurrencies also attracted the perpetuators of cybercrime. This paper attempts to examine the different machine learning approaches for efficiently identifying ransomware payments made to the operators using bitcoin transactions. Machine learning models may be developed based on patterns differentiating such cybercrime operations from normal bitcoin transactions in order to identify and report attacks. The machine learning approaches are evaluated on bitcoin ransomware dataset. Experimental results show that Gradient Boosting and XGBoost algorithms achieved better detection rate with respect to precision, recall and F-measure rates when compared with k-Nearest Neighbor, Random Forest, Naïve Bayes and Multilayer Perceptron approaches

scholarly journals Machine learning approaches to identify sleep genes

2021 ◽  
Author(s):  
Yin Yeng Lee ◽  
Mehari Endale ◽  
Gang Wu ◽  
Marc D Ruben ◽  
Lauren J Francey ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prior Knowledge ◽  
Molecular Mechanisms ◽  
Genetic Regulation ◽  
Learning Approaches ◽  
Sleep Regulation ◽  
Genome Wide ◽  
Genome Wide Data ◽  
Key Genes

Genetics impacts sleep, yet, the molecular mechanisms underlying sleep regulation remain elusive. We built machine learning (ML) models to predict genes based on their similarity to known sleep genes. Our predictions fit with prior knowledge of sleep regulation and also identify several key genes/pathways to pursue in follow-up studies. We tested one of our findings, the NF-κB pathway, and showed that its genetic alteration affects sleep duration in mice. Our study highlights the power of ML to integrate prior knowledge and genome-wide data to study genetic regulation of sleep and other complex behaviors.

scholarly journals Modelling of S&P 500 Index Price Based on U.S. Economic Indicators: Machine Learning Approach

2021 ◽  
Vol 32 (4) ◽  
pp. 362-375
Author(s):  
Ligita Gasparėnienė ◽  
Rita Remeikiene ◽  
Aleksejus Sosidko ◽  
Vigita Vėbraitė
Keyword(s):  
Machine Learning ◽  
Statistical Methods ◽  
Stock Prices ◽  
Statistical Significance ◽  
Economic Indicators ◽  
Learning Approaches ◽  
Statistical Machine Learning ◽  
New Methods ◽  
Machine Learning Approach ◽  
Selection Of

In order to forecast stock prices based on economic indicators, many studies have been conducted using well-known statistical methods. Meanwhile, since ~2010 as the power of computers improved, new methods of machine learning began to be used. It would be interesting to know how those algorithms using a variety of mathematical and statistical methods, are able to predict the stock market. The purpose of this article is to model the monthly price of the S&P 500 index based on U.S. economic indicators using statistical, machine learning, deep learning approaches and finally compare metrics of those models. After the selection of indicators according to the data visualization, multicollinearity tests, statistical significance tests, 3 out of 27 indicators remained. The main finding of the research is that the authors improved the baseline statistical linear regression model by 19 percent using a ML Random Forest algorithm. In this way, model achieved accuracy 97.68% of prediction S&P 500 index.

scholarly journals Predictors of attrition in a longitudinal population-based study of aging

2020 ◽  
pp. 1-12
Author(s):  
Erin Jacobsen ◽  
Xinhui Ran ◽  
Anran Liu ◽  
Chung-Chou H. Chang ◽  
Mary Ganguli
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Study Participant ◽  
Drop Out ◽  
Cognitive Test ◽  
Learning Approaches ◽  
Multivariable Logistic Regression ◽  
Study Results

ABSTRACT Background: Longitudinal studies predictably experience non-random attrition over time. Among older adults, risk factors for attrition may be similar to risk factors for outcomes such as cognitive decline and dementia, potentially biasing study results. Objective: To characterize participants lost to follow-up which can be useful in the study design and interpretation of results. Methods: In a longitudinal aging population study with 10 years of annual follow-up, we characterized the attrited participants (77%) compared to those who remained in the study. We used multivariable logistic regression models to identify attrition predictors. We then implemented four machine learning approaches to predict attrition status from one wave to the next and compared the results of all five approaches. Results: Multivariable logistic regression identified those more likely to drop out as older, male, not living with another study participant, having lower cognitive test scores and higher clinical dementia ratings, lower functional ability, fewer subjective memory complaints, no physical activity, reported hobbies, or engagement in social activities, worse self-rated health, and leaving the house less often. The four machine learning approaches using areas under the receiver operating characteristic curves produced similar discrimination results to the multivariable logistic regression model. Conclusions: Attrition was most likely to occur in participants who were older, male, inactive, socially isolated, and cognitively impaired. Ignoring attrition would bias study results especially when the missing data might be related to the outcome (e.g. cognitive impairment or dementia). We discuss possible solutions including oversampling and other statistical modeling approaches.

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