Betting models using AI: A review on ANN, SVM, and Markov Chain

Kollár, Aladár

doi:10.31219/osf.io/mr2v3

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The artificial neural network (ANN) is a modelling approach motivated by the human nervous system that permits learning by pattern from representative data that depicts a physical occurrence or a decision process. The ability to build actual correlations between independent and dependent variables, as well as extract delicate information and complicated knowledge from representative data sets, is a distinguishing feature of ANN (Srivastav, 2021) (Kollár, 2021a). Without making any assumptions regarding a formal representation of the events, the connections between independent and dependent variables can be established.…”

Section: Ann and Dann In Medical Diagnosismentioning

confidence: 99%

Adoption of machine learning for medical diagnosis

Shaheen¹

2021

Preprint

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The healthcare industry has historically been an early adopter of technology advancements and has reaped significant benefits. Machine learning (an artificial intelligence subset) is being used in a variety of health-related fields, including the invention of new medical treatments, the management of patient data and records, and the treatment of chronic diseases. One of the most important uses of machine learning in healthcare is the detection and diagnosis of diseases and conditions that are otherwise difficult to identify. This can range from tumors that are difficult to detect in their early stages to other hereditary illnesses. This research identifies and discusses the various usages of machine learning in medical diagnosis.

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Section: Ann and Dann In Medical Diagnosismentioning

confidence: 99%

Adoption of machine learning for medical diagnosis

Shaheen¹

2021

Preprint

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“…Numerous businesses are using machine learning to analyze massive quantities of data, from calculating credit for loan applications to scanning legal contracts for errors to analyzing employee interactions with customers to detect inappropriate behavior. New tools make it easier than ever for developers to design and deploy machine-learning algorithms [2] [3] .…”

Section: Introductionmentioning

confidence: 99%

Monitoring bias and fairness in machine learning models: A review

Bacelar

2021

Preprint

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Introduction Machine learning algorithms are quickly gaining traction in both the private and public sectors for their ability to automate both simple and complex decision-making processes. The vast majority of economic sectors, including transportation, retail, advertisement, and energy, are being disrupted by widespread data digitization and the emerging technologies that leverage it. Computerized systems are being introduced in government operations to improve accuracy and objectivity, and AI is having an impact on democracy and governance [1]. Numerous businesses are using machine learning to analyze massive quantities of data, from calculating credit for loan applications to scanning legal contracts for errors to analyzing employee interactions with customers to detect inappropriate behavior. New tools make it easier than ever for developers to design and deploy machine-learning algorithms [2] [3].

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A Deep Learning Model and Its Application to Predict the Monthly MCI Drought Index in the Yunnan Province of China

Mei

Liu

et al. 2022

Atmosphere

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The Yunnan province of China is a typical humid region but with several severe region-wide droughts. Drought indices are generally used to identify and characterize drought events, and then play a key role in drought prediction. Therefore, a novel prediction model was proposed to predict a comprehensive drought indicator (meteorological composite index, MCI) in Yunnan province. This model combined the recurrent neural networks (RNN) based on a gated recurrent neural unit (GRU) and convolutional neural networks (CNN) with optimization using the modified particle swarm optimization (PSO) algorithm. In this model, pre-processed predictor data were input into the GRU module to extract the time features of the sequences. Furthermore, the feature matrices were input into the CNN module to extract the deep local features and the inter-relationship of the predictors. The model was trained and used to predict the monthly MCI drought index of the representative five stations of Yunnan province from 1960 to 2020. The combined model was evaluated by comparison with traditional machine learning models such as the least absolute shrinkage and selection operator (LASSO) and random forest (RF), and the traditional GRU model. The results show significantly improved skills in root mean square error, mean absolute error and Nash–Sutcliffe efficiency coefficient. This novel method was valuable for the monthly drought prediction in Yunnan province and related climate-risk management.

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Betting models using AI: A review on ANN, SVM, and Markov Chain

Cited by 3 publications

References 7 publications

Adoption of machine learning for medical diagnosis

Adoption of machine learning for medical diagnosis

Monitoring bias and fairness in machine learning models: A review

A Deep Learning Model and Its Application to Predict the Monthly MCI Drought Index in the Yunnan Province of China

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