MACHINE LEARNING METHODS FOR SYSTEMIC RISK ANALYSIS IN FINANCIAL SECTORS

Financial systemic risk is an important issue in economics and financial systems. Trying to detect and respond to systemic risk with growing amounts of data produced in financial markets and systems, a lot of researchers have increasingly employed machine learning methods. Machine learning methods study the mechanisms of outbreak and contagion of systemic risk in the financial network and improve the current regulation of the financial market and industry. In this paper, we survey existing researches and methodologies on assessment and measurement of financial systemic risk combined with machine learning technologies, including big data analysis, network analysis and sentiment analysis, etc. In addition, we identify future challenges, and suggest further research topics. The main purpose of this paper is to introduce current researches on financial systemic risk with machine learning methods and to propose directions for future work.

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Machine Learning Methods for Prediction of Hospital Mortality in Patients with Coronary Heart Disease after Coronary Artery Bypass Grafting

Kardiologiia ◽

10.18087/cardio.2020.10.n1170 ◽

2020 ◽

Vol 60 (10) ◽

pp. 38-46

Author(s):

B. I. Geltser ◽

K. J. Shahgeldyan ◽

V. Y. Rublev ◽

V. N. Kotelnikov ◽

A. B. Krieger ◽

...

Keyword(s):

Machine Learning ◽

Heart Disease ◽

Clinical Laboratory ◽

Artery Bypass ◽

Fatal Outcome ◽

Left Ventricular ◽

Learning Technologies ◽

Relative Mass ◽

Learning Methods ◽

Machine Learning Methods

Aim To compare the accuracy of predicting an in-hospital fatal outcome for models based on current machine-learning technologies in patients with ischemic heart disease (IHD) after coronary bypass (CB) surgery.Material and methods A retrospective analysis of 866 electronic medical records was performed for patients (685 men and 181 women) who have had a CB surgery for IHD in 2008–2018. Results of clinical, laboratory, and instrumental evaluations obtained prior to the CB surgery were analyzed. Patients were divided into two groups: group 1 included 35 (4 %) patients who died within the first 20 days of CB, and group 2 consisted of 831 (96 %) patients with a beneficial outcome of the surgery. Predictors of the in-hospital fatal outcome were identified by a multistep selection procedure with analysis of statistical hypotheses and calculation of weight coefficients. For construction of models and verification of predictors, machine-learning methods were used, including the multifactorial logistic regression (LR), random forest (RF), and artificial neural networks (ANN). Model accuracy was evaluated by three metrics: area under the ROC curve (AUC), sensitivity, and specificity. Cross validation of the models was performed on test samples, and the control validation was performed on a cohort of patients with IHD after CB, whose data were not used in development of the models.Results The following 7 risk factors for in-hospital fatal outcome with the greatest predictive potential were isolated from the EuroSCORE II scale: ejection fraction (EF) <30 %, EF 30-50 %, age of patients with recent MI, damage of peripheral arterial circulation, urgency of CB, functional class III-IV chronic heart failure, and 5 additional predictors, including heart rate, systolic blood pressure, presence of aortic stenosis, posterior left ventricular (LV) wall relative thickness index (RTI), and LV relative mass index (LVRMI). The models developed by the authors using LR, RF and ANN methods had higher AUC values and sensitivity compared to the classical EuroSCORE II scale. The ANN models including the RTI and LVRMI predictors demonstrated a maximum level of prognostic accuracy, which was illustrated by values of the quality metrics, AUC 93 %, sensitivity 90 %, and specificity 96 %. The predictive robustness of the models was confirmed by results of the control validation.Conclusion The use of current machine-learning technologies allowed developing a novel algorithm for selection of predictors and highly accurate models for predicting an in-hospital fatal outcome after CB.

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Advances of Quantum Machine Learning

Limitations and Future Applications of Quantum Cryptography - Advances in Information Security, Privacy, and Ethics ◽

10.4018/978-1-7998-6677-0.ch013 ◽

2021 ◽

pp. 257-275

Author(s):

Bhanu Chander

Keyword(s):

Machine Learning ◽

Kernel Methods ◽

High Quality ◽

Learning Methods ◽

Machine Learning Methods ◽

Quantum Machine Learning ◽

Time Quantum ◽

Computational Resources ◽

Future Consequences ◽

Future Work

The basic idea of artificial intelligence and machine learning is that machines have the talent to learn from data, previous experience, and perform the work in future consequences. In the era of the digitalized world which holds big data has long-established machine learning methods consistently with requisite high-quality computational resources in numerous useful and realistic tasks. At the same time, quantum machine learning methods work exponentially faster than their counterparts by making use of quantum mechanics. Through taking advantage of quantum effects such as interference or entanglement, quantum computers can proficiently explain selected issues that are supposed to be tough for traditional machines. Quantum computing is unexpectedly related to that of kernel methods in machine learning. Hence, this chapter provides quantum computation, advance of QML techniques, QML kernel space and optimization, and future work of QML.

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ANALYSIS OF MACHINE LEARNING METHODS FOR PREDICTIONS OF STOCK EXCHANGE SHARE PRICES

Scientific Journal of Astana IT University ◽

10.37943/aitu.2021.47.22.009 ◽

2021 ◽

pp. 94-100

Author(s):

V. Serbin ◽

U. Zhenisserov

Keyword(s):

Machine Learning ◽

Stock Market ◽

Financial Markets ◽

Stock Prices ◽

Stock Price ◽

Learning Algorithms ◽

Support Vector ◽

Learning Methods ◽

Machine Learning Methods ◽

Price Patterns

Since the stock market is one of the most important areas for investors, stock market price trend prediction is still a hot subject for researchers in both financial and technical fields. Lately, a lot of work has been analyzed and done in the field of machine learning algorithms for analyzing price patterns and predicting stock prices and index changes. Currently, machine-learning methods are receiving a lot of attention for predicting prices in financial markets. The main goal of current research is to improve and develop a system for predicting future prices in financial markets with higher accuracy using machine-learning methods. Precise predicting stock market returns is a very difficult task due to the volatile and non-linear nature of financial stock markets. With the advent of artificial intelligence and machine learning, forecasting methods have become more effective at predicting stock prices. In this article, we looked at the machine learning techniques that have been used to trade stocks to predict price changes before an actual rise or fall in the stock price occurs. In particular, the article discusses in detail the use of support vector machines, linear regression, and prediction using decision stumps, classification using the nearest neighbor algorithm, and the advantages and disadvantages of each method. The paper introduces parameters and variables that can be used to recognize stock price patterns that might be useful in future stock forecasting, and how the boost can be combined with other learning algorithms to improve the accuracy of such forecasting systems.

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A Systematic Review of Defensive and Offensive Cybersecurity with Machine Learning

Applied Sciences ◽

10.3390/app10175811 ◽

2020 ◽

Vol 10 (17) ◽

pp. 5811

Author(s):

Imatitikua D. Aiyanyo ◽

Hamman Samuel ◽

Heuiseok Lim

Keyword(s):

Machine Learning ◽

Systematic Review ◽

Domain Knowledge ◽

Supervised Machine Learning ◽

Detection Methods ◽

Data Sets ◽

Research Topics ◽

Research Papers ◽

Learning Methods ◽

Machine Learning Methods

This is a systematic review of over one hundred research papers about machine learning methods applied to defensive and offensive cybersecurity. In contrast to previous reviews, which focused on several fragments of research topics in this area, this paper systematically and comprehensively combines domain knowledge into a single review. Ultimately, this paper seeks to provide a base for researchers that wish to delve into the field of machine learning for cybersecurity. Our findings identify the frequently used machine learning methods within supervised, unsupervised, and semi-supervised machine learning, the most useful data sets for evaluating intrusion detection methods within supervised learning, and methods from machine learning that have shown promise in tackling various threats in defensive and offensive cybersecurity.

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Machine learning approaches for parsing comorbidity/heterogeneity in antisociality and substance use disorders: A primer

Personality Neuroscience ◽

10.1017/pen.2021.2 ◽

2021 ◽

Vol 4 ◽

Author(s):

Matthew S. Shane ◽

William J. Denomme

Keyword(s):

Machine Learning ◽

Substance Use ◽

Data Driven ◽

Learning Approaches ◽

Learning Methods ◽

Machine Learning Methods ◽

High Level ◽

Future Work ◽

Interacting Features ◽

Research Domain

Abstract By some accounts, as many as 93% of individuals diagnosed with antisocial personality disorder (ASPD) or psychopathy also meet criteria for some form of substance use disorder (SUD). This high level of comorbidity, combined with an overlapping biopsychosocial profile, and potentially interacting features, has made it difficult to delineate the shared/unique characteristics of each disorder. Moreover, while rarely acknowledged, both SUD and antisociality exist as highly heterogeneous disorders in need of more targeted parcellation. While emerging data-driven nosology for psychiatric disorders (e.g., Research Domain Criteria (RDoC), Hierarchical Taxonomy of Psychopathology (HiTOP)) offers the opportunity for a more systematic delineation of the externalizing spectrum, the interrogation of large, complex neuroimaging-based datasets may require data-driven approaches that are not yet widely employed in psychiatric neuroscience. With this in mind, the proposed article sets out to provide an introduction into machine learning methods for neuroimaging that can help parse comorbid, heterogeneous externalizing samples. The modest machine learning work conducted to date within the externalizing domain demonstrates the potential utility of the approach but remains highly nascent. Within the paper, we make suggestions for how future work can make use of machine learning methods, in combination with emerging psychiatric nosology systems, to further diagnostic and etiological understandings of the externalizing spectrum. Finally, we briefly consider some challenges that will need to be overcome to encourage further progress in the field.

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Research Progress of Gliomas in Machine Learning

Cells ◽

10.3390/cells10113169 ◽

2021 ◽

Vol 10 (11) ◽

pp. 3169

Author(s):

Ning Zhang ◽

Yameng Wu ◽

Yu Guo ◽

Yu Sa ◽

Qifeng Li ◽

...

Keyword(s):

Machine Learning ◽

Learning Technologies ◽

Research Progress ◽

Literature Mining ◽

Learning Methods ◽

Machine Learning Methods ◽

Integrate Analysis ◽

Speed Up ◽

Biomedical Publications ◽

Future Orientations

In the field of gliomas research, the broad availability of genetic and image information originated by computer technologies and the booming of biomedical publications has led to the advent of the big-data era. Machine learning methods were applied as possible approaches to speed up the data mining processes. In this article, we reviewed the present situation and future orientations of machine learning application in gliomas within the context of workflows to integrate analysis for precision cancer care. Publicly available tools or algorithms for key machine learning technologies in the literature mining for glioma clinical research were reviewed and compared. Further, the existing solutions of machine learning methods and their limitations in glioma prediction and diagnostics, such as overfitting and class imbalanced, were critically analyzed.

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Machine learning methods, databases and tools for drug combination prediction

Briefings in Bioinformatics ◽

10.1093/bib/bbab355 ◽

2021 ◽

Author(s):

Lianlian Wu ◽

Yuqi Wen ◽

Dongjin Leng ◽

Qinglong Zhang ◽

Chong Dai ◽

...

Keyword(s):

Machine Learning ◽

Drug Combination ◽

Predictive Performance ◽

Search Space ◽

Drug Combinations ◽

Learning Methods ◽

Machine Learning Methods ◽

High Throughput Screens ◽

Novel Drug ◽

Future Work

Abstract Combination therapy has shown an obvious efficacy on complex diseases and can greatly reduce the development of drug resistance. However, even with high-throughput screens, experimental methods are insufficient to explore novel drug combinations. In order to reduce the search space of drug combinations, there is an urgent need to develop more efficient computational methods to predict novel drug combinations. In recent decades, more and more machine learning (ML) algorithms have been applied to improve the predictive performance. The object of this study is to introduce and discuss the recent applications of ML methods and the widely used databases in drug combination prediction. In this study, we first describe the concept and controversy of synergism between drug combinations. Then, we investigate various publicly available data resources and tools for prediction tasks. Next, ML methods including classic ML and deep learning methods applied in drug combination prediction are introduced. Finally, we summarize the challenges to ML methods in prediction tasks and provide a discussion on future work.

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