scholarly journals Machine learning-based prediction of toxicity of organic compounds towards fathead minnow

RSC Advances ◽  
2020 ◽  
Vol 10 (59) ◽  
pp. 36174-36180 ◽  
Author(s):  
Xingmei Chen ◽  
Limin Dang ◽  
Hai Yang ◽  
Xianwei Huang ◽  
Xinliang Yu
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Organic Compounds ◽  
Fathead Minnow ◽  
Support Vector ◽  
Quantitative Structure ◽  
Structure Toxicity Relationship ◽  
Relationship Of ◽  
Toxicity Relationship

A quantitative structure–toxicity relationship of 963 chemicals against fathead minnow was developed by using support vector machine and genetic algorithm.

scholarly journals Kynurenine and Hemoglobin as Sex-Specific Variables in COVID-19 Patients: A Machine Learning and Genetic Algorithms Approach

Diagnostics ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2197
Author(s):  
Jose M. Celaya-Padilla ◽  
Karen E. Villagrana-Bañuelos ◽  
Juan José Oropeza-Valdez ◽  
Joel Monárrez-Espino ◽  
Julio E. Castañeda-Delgado ◽  
...  
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Immune Response ◽  
Support Vector Machine ◽  
Clinical Manifestations ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Men And Women ◽  
Metabolic Alterations

Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.

scholarly journals Support vector machine in the anticipation of currency markets

2018 ◽  
Vol 7 (2.7) ◽  
pp. 66
Author(s):  
V Lalithendra Nadh ◽  
G Syam Prasad
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Stock Market ◽  
Support Vector ◽  
Learning Approaches ◽  
Currency Markets ◽  
Machine Learning Methods ◽  
Conventional Models

Various researchers have done an expansive research within the domain of stock market anticipation. The majority of the anticipated models is confronting some pivotal troubles because of the likelihood of the market. Numerous normal models are accurate when the data is linear. In any case, the expectation in view of nonlinear data could be a testing movement. From past twenty years with the progression of innovation and the artificial intelligence, including machine learning approaches like a Support Vector Machine it becomes conceivable to estimate in light of nonlinear data. Modern researchers are combining GA (Genetic Algorithm) with SVM to achieve highly precise outcomes. This analysis compares the SVM and ESVM with other conventional models and other machine learning methods in the domain of currency market prediction. Finally, the consequence of SVM when compared with different models it is demonstrated that SVM is the premier for foreseeing.

scholarly journals A hybrid method of genetic algorithm and support vector machine for intrusion detection

2021 ◽  
Vol 11 (1) ◽  
pp. 900
Author(s):  
Mushtaq Talb Tally ◽  
Haleh Amintoosi
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Support Vector Machine ◽  
Feature Selection ◽  
Intrusion Detection ◽  
Hybrid Method ◽  
Security Policies ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques

With the development of web applications nowadays, intrusions represent a crucial aspect in terms of violating the security policies. Intrusions can be defined as a specific change in the normal behavior of the network operations that intended to violate the security policies of a particular network and affect its performance. Recently, several researchers have examined the capabilities of machine learning techniques in terms of detecting intrusions. One of the important issues behind using the machine learning techniques lies on employing proper set of features. Since the literature has shown diversity of feature types, there is a vital demand to apply a feature selection approach in order to identify the most appropriate features for intrusion detection. This study aims to propose a hybrid method of Genetic Algorithm and Support Vector Machine. GA has been as a feature selection in order to select the best features, while SVM has been used as a classification method to categorize the behavior into normal and intrusion based on the selected features from GA. A benchmark dataset of intrusions (NSS-KDD) has been in the experiment. In addition, the proposed method has been compared with the traditional SVM. Results showed that GA has significantly improved the SVM classification by achieving 0.927 of f-measure.

Sign in / Sign up

Export Citation Format

Share Document