scholarly journals Machine learning approach to drivers of bank lending: evidence from an emerging economy

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Onder Ozgur ◽  
Erdal Tanas Karagol ◽  
Fatih Cemil Ozbugday
Keyword(s):  
Machine Learning ◽  
Bank Lending ◽  
Machine Learning Algorithms ◽  
Bank Loans ◽  
Policy Implications ◽  
Bank Managers ◽  
Machine Learning Approach ◽  
Macroeconomic Indicators ◽  
Lending Behavior ◽  
Partial Dependence

AbstractThe study analyzes the performance of bank-specific characteristics, macroeconomic indicators, and global factors to predict the bank lending in Turkey for the period 2002Q4–2019Q2. The objective of this study is first, to clarify the possible nonlinear and nonparametric relationships between outstanding bank loans and bank-specific, macroeconomic, and global factors. Second, it aims to propose various machine learning algorithms that determine drivers of bank lending and benefits from the advantages of these techniques. The empirical findings indicate favorable evidence that the drivers of bank lending exhibit some nonlinearities. Additionally, partial dependence plots depict that numerous bank-specific characteristics and macroeconomic indicators tend to be important variables that influence bank lending behavior. The study’s findings have some policy implications for bank managers, regulatory authorities, and policymakers.

scholarly journals A Machine Learning Approach to Study Glycosidase Activities from Bifidobacterium

2021 ◽  
Vol 9 (5) ◽  
pp. 1034
Author(s):  
Carlos Sabater ◽  
Lorena Ruiz ◽  
Abelardo Margolles
Keyword(s):  
Machine Learning ◽  
Supervised Classification ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Human Milk Oligosaccharides ◽  
Future Studies ◽  
High Fiber ◽  
Machine Learning Approach ◽  
Prebiotic Oligosaccharides

This study aimed to recover metagenome-assembled genomes (MAGs) from human fecal samples to characterize the glycosidase profiles of Bifidobacterium species exposed to different prebiotic oligosaccharides (galacto-oligosaccharides, fructo-oligosaccharides and human milk oligosaccharides, HMOs) as well as high-fiber diets. A total of 1806 MAGs were recovered from 487 infant and adult metagenomes. Unsupervised and supervised classification of glycosidases codified in MAGs using machine-learning algorithms allowed establishing characteristic hydrolytic profiles for B. adolescentis, B. bifidum, B. breve, B. longum and B. pseudocatenulatum, yielding classification rates above 90%. Glycosidase families GH5 44, GH32, and GH110 were characteristic of B. bifidum. The presence or absence of GH1, GH2, GH5 and GH20 was characteristic of B. adolescentis, B. breve and B. pseudocatenulatum, while families GH1 and GH30 were relevant in MAGs from B. longum. These characteristic profiles allowed discriminating bifidobacteria regardless of prebiotic exposure. Correlation analysis of glycosidase activities suggests strong associations between glycosidase families comprising HMOs-degrading enzymes, which are often found in MAGs from the same species. Mathematical models here proposed may contribute to a better understanding of the carbohydrate metabolism of some common bifidobacteria species and could be extrapolated to other microorganisms of interest in future studies.

scholarly journals A Machine Learning Approach to Predict Deep Venous Thrombosis Among Hospitalized Patients

2021 ◽  
Vol 27 ◽  
pp. 107602962199118
Author(s):  
Logan Ryan ◽  
Samson Mataraso ◽  
Anna Siefkas ◽  
Emily Pellegrini ◽  
Gina Barnes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Stratification ◽  
Venous Thrombosis ◽  
Deep Venous Thrombosis ◽  
Primary Outcome ◽  
Scoring Systems ◽  
Hospitalized Patients ◽  
Machine Learning Algorithms ◽  
Cancer History ◽  
Machine Learning Approach

Deep venous thrombosis (DVT) is associated with significant morbidity, mortality, and increased healthcare costs. Standard scoring systems for DVT risk stratification often provide insufficient stratification of hospitalized patients and are unable to accurately predict which inpatients are most likely to present with DVT. There is a continued need for tools which can predict DVT in hospitalized patients. We performed a retrospective study on a database collected from a large academic hospital, comprised of 99,237 total general ward or ICU patients, 2,378 of whom experienced a DVT during their hospital stay. Gradient boosted machine learning algorithms were developed to predict a patient’s risk of developing DVT at 12- and 24-hour windows prior to onset. The primary outcome of interest was diagnosis of in-hospital DVT. The machine learning predictors obtained AUROCs of 0.83 and 0.85 for DVT risk prediction on hospitalized patients at 12- and 24-hour windows, respectively. At both 12 and 24 hours before DVT onset, the most important features for prediction of DVT were cancer history, VTE history, and internal normalized ratio (INR). Improved risk stratification may prevent unnecessary invasive testing in patients for whom DVT cannot be ruled out using existing methods. Improved risk stratification may also allow for more targeted use of prophylactic anticoagulants, as well as earlier diagnosis and treatment, preventing the development of pulmonary emboli and other sequelae of DVT.

Using Supervised Machine Learning Algorithms for Automated Lithology Prediction from Wireline Log Data

2021 ◽  
Author(s):  
Marian Popescu ◽  
Rebecca Head ◽  
Tim Ferriday ◽  
Kate Evans ◽  
Jose Montero ◽  
...  
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Training Dataset ◽  
Depth Interval ◽  
Log Data ◽  
Machine Learning Approach ◽  
Lithology Prediction ◽  
Logging While Drilling

Abstract This paper presents advancements in machine learning and cloud deployment that enable rapid and accurate automated lithology interpretation. A supervised machine learning technique is described that enables rapid, consistent, and accurate lithology prediction alongside quantitative uncertainty from large wireline or logging-while-drilling (LWD) datasets. To leverage supervised machine learning, a team of geoscientists and petrophysicists made detailed lithology interpretations of wells to generate a comprehensive training dataset. Lithology interpretations were based on applying determinist cross-plotting by utilizing and combining various raw logs. This training dataset was used to develop a model and test a machine learning pipeline. The pipeline was applied to a dataset previously unseen by the algorithm, to predict lithology. A quality checking process was performed by a petrophysicist to validate new predictions delivered by the pipeline against human interpretations. Confidence in the interpretations was assessed in two ways. The prior probability was calculated, a measure of confidence in the input data being recognized by the model. Posterior probability was calculated, which quantifies the likelihood that a specified depth interval comprises a given lithology. The supervised machine learning algorithm ensured that the wells were interpreted consistently by removing interpreter biases and inconsistencies. The scalability of cloud computing enabled a large log dataset to be interpreted rapidly; >100 wells were interpreted consistently in five minutes, yielding >70% lithological match to the human petrophysical interpretation. Supervised machine learning methods have strong potential for classifying lithology from log data because: 1) they can automatically define complex, non-parametric, multi-variate relationships across several input logs; and 2) they allow classifications to be quantified confidently. Furthermore, this approach captured the knowledge and nuances of an interpreter's decisions by training the algorithm using human-interpreted labels. In the hydrocarbon industry, the quantity of generated data is predicted to increase by >300% between 2018 and 2023 (IDC, Worldwide Global DataSphere Forecast, 2019–2023). Additionally, the industry holds vast legacy data. This supervised machine learning approach can unlock the potential of some of these datasets by providing consistent lithology interpretations rapidly, allowing resources to be used more effectively.

scholarly journals Employing a Machine Learning Approach to Detect Combined Internet of Things Attacks against Two Objective Functions Using a Novel Dataset

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
John Foley ◽  
Naghmeh Moradpoor ◽  
Henry Ochenyi
Keyword(s):  
Machine Learning ◽  
Low Power ◽  
Vulnerability Analysis ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Lossy Networks ◽  
Global Networks ◽  
Machine Learning Approach ◽  
Iot Devices ◽  
Network Metrics

One of the important features of routing protocol for low-power and lossy networks (RPLs) is objective function (OF). OF influences an IoT network in terms of routing strategies and network topology. On the contrary, detecting a combination of attacks against OFs is a cutting-edge technology that will become a necessity as next generation low-power wireless networks continue to be exploited as they grow rapidly. However, current literature lacks study on vulnerability analysis of OFs particularly in terms of combined attacks. Furthermore, machine learning is a promising solution for the global networks of IoT devices in terms of analysing their ever-growing generated data and predicting cyberattacks against such devices. Therefore, in this paper, we study the vulnerability analysis of two popular OFs of RPL to detect combined attacks against them using machine learning algorithms through different simulated scenarios. For this, we created a novel IoT dataset based on power and network metrics, which is deployed as part of an RPL IDS/IPS solution to enhance information security. Addressing the captured results, our machine learning approach is successful in detecting combined attacks against two popular OFs of RPL based on the power and network metrics in which MLP and RF algorithms are the most successful classifier deployment for single and ensemble models.

Endogenous Asset Fire Sales and Bank Lending Incentives

2020 ◽  
Vol 10 (04) ◽  
pp. 2150002
Author(s):  
Zhongzhi Song
Keyword(s):  
Cash Holdings ◽  
Bank Lending ◽  
Bank Loans ◽  
Policy Implications ◽  
Liquidity Shocks ◽  
Fire Sales ◽  
The 2008 Financial Crisis ◽  
Lending Decisions ◽  
The Impact ◽  
Capital Injections

This paper examines the impact of banks’ lending incentives on asset prices and bank cash holdings under liquidity risk. Banks make lending decisions based on the tradeoff between costs (fire sales of illiquid assets) and benefits (high returns from bank loans). This paper shows fire sales of assets can be an endogenous outcome, even if banks are endowed with enough cash to meet liquidity shocks. This paper also helps explain why banks have kept a large amount of cash without lending after government capital injections in the 2008 financial crisis. The model further provides policy implications for government intervention.

A machine learning approach to achieve accurate time series forecast of sea-wave conditions

2020 ◽  
Author(s):  
Giulia Cremonini ◽  
Giovanni Besio ◽  
Daniele Lagomarsino ◽  
Agnese Seminara
Keyword(s):  
Machine Learning ◽  
Environmental Variables ◽  
Machine Learning Algorithms ◽  
Accurate Estimation ◽  
Time Series Forecast ◽  
Machine Learning Approach ◽  
Physically Based ◽  
Physically Based Models ◽  
Wave Conditions ◽  
Sea Wave

<p>Reliable forecast of environmental variables is fundamental in managing<br>risk associated with hazard scenarios. In this work, we use state of the<br>art machine learning algorithms to build forecasting models and to get<br>accurate estimation of sea wave conditions. We exploit multivariate time<br>series of environmental variables, extracted either from hindcast<br>database (provided by MeteOcean Group at DICCA) or observed data from<br>sparse buoys. In this way, future values of sea wave height can be<br>predicted in order to evaluate the risk associated with incoming<br>scenarios. The aim is to provide new forecasting tools representing an<br>alternative to physically based models which have higher computational<br>cost.</p>

scholarly journals The Effects of Bank-Level and Macroeconomic Variables on Commercial Bank Lending Based on Type of Use

2021 ◽  
Vol 24 (1) ◽  
pp. 105
Author(s):  
Arintoko Arintoko
Keyword(s):  
Interest Rates ◽  
Bank Lending ◽  
Bank Loans ◽  
Capital Adequacy ◽  
Working Capital ◽  
Macroeconomic Variables ◽  
Distributed Lag ◽  
Real Gdp ◽  
Long Run ◽  
Lending Behavior

ABSTRACTThe purpose of this study was to analyze the effect of interest rates, bank-level and macroeconomic variables on bank lending based on the type of use. The analysis method uses an autoregressive distributed lag (ARDL) model with quarterly data for the period of 2011Q1 - 2020Q1. The results show that investment lending behavior can be explained well by all bank-level and macroeconomic variables for the long run. The bank-level variable also reflects the performance and soundness of the bank, namely the capital adequacy ratio and loan to deposit ratio. Meanwhile, macroeconomic variables include inflation and real GDP. Consumer lending behavior is better explained by macroeconomic variables than bank-level variables. Meanwhile, GDP is the only variable that has a significant effect on working capital loans, which means that the behavior of working capital loans is more influenced by the business cycle as indicated by changes in real GDP. GDP is the only variable that consistently has a significant positive effect on bank loans for the three types of loans. Banks need to continue to emphasize the principle of prudence in providing credit by taking into account the term and credit risk, as well as internal and external factors.

scholarly journals Phenotyping Cardiogenic Shock

2021 ◽  
Author(s):  
Elric Zweck ◽  
Katherine L. Thayer ◽  
Ole K. L. Helgestad ◽  
Manreet Kanwar ◽  
Mohyee Ayouty ◽  
...  
Keyword(s):  
Machine Learning ◽  
Hospital Mortality ◽  
Cardiogenic Shock ◽  
Treatment Strategies ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
Tailored Treatment ◽  
Machine Learning Approach ◽  
Clinical Profiles ◽  
Patient Enrollment

Background Cardiogenic shock (CS) is a heterogeneous syndrome with varied presentations and outcomes. We used a machine learning approach to test the hypothesis that patients with CS have distinct phenotypes at presentation, which are associated with unique clinical profiles and in‐hospital mortality. Methods and Results We analyzed data from 1959 patients with CS from 2 international cohorts: CSWG (Cardiogenic Shock Working Group Registry) (myocardial infarction [CSWG‐MI; n=410] and acute‐on‐chronic heart failure [CSWG‐HF; n=480]) and the DRR (Danish Retroshock MI Registry) (n=1069). Clusters of patients with CS were identified in CSWG‐MI using the consensus k means algorithm and subsequently validated in CSWG‐HF and DRR. Patients in each phenotype were further categorized by their Society of Cardiovascular Angiography and Interventions staging. The machine learning algorithms revealed 3 distinct clusters in CS: "non‐congested (I)", "cardiorenal (II)," and "cardiometabolic (III)" shock. Among the 3 cohorts (CSWG‐MI versus DDR versus CSWG‐HF), in‐hospital mortality was 21% versus 28% versus 10%, 45% versus 40% versus 32%, and 55% versus 56% versus 52% for clusters I, II, and III, respectively. The "cardiometabolic shock" cluster had the highest risk of developing stage D or E shock as well as in‐hospital mortality among the phenotypes, regardless of cause. Despite baseline differences, each cluster showed reproducible demographic, metabolic, and hemodynamic profiles across the 3 cohorts. Conclusions Using machine learning, we identified and validated 3 distinct CS phenotypes, with specific and reproducible associations with mortality. These phenotypes may allow for targeted patient enrollment in clinical trials and foster development of tailored treatment strategies in subsets of patients with CS.

scholarly journals A machine learning approach to predict ethnicity using personal name and census location in Canada

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0241239
Author(s):  
Kai On Wong ◽  
Osmar R. Zaïane ◽  
Faith G. Davis ◽  
Yutaka Yasui
Keyword(s):  
Machine Learning ◽  
First Nations ◽  
Predictive Value ◽  
Large Scale ◽  
Performance Metrics ◽  
Characteristic Curve ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approach ◽  
Machine Learning Approach

Background Canada is an ethnically-diverse country, yet its lack of ethnicity information in many large databases impedes effective population research and interventions. Automated ethnicity classification using machine learning has shown potential to address this data gap but its performance in Canada is largely unknown. This study conducted a large-scale machine learning framework to predict ethnicity using a novel set of name and census location features. Methods Using census 1901, the multiclass and binary class classification machine learning pipelines were developed. The 13 ethnic categories examined were Aboriginal (First Nations, Métis, Inuit, and all-combined)), Chinese, English, French, Irish, Italian, Japanese, Russian, Scottish, and others. Machine learning algorithms included regularized logistic regression, C-support vector, and naïve Bayes classifiers. Name features consisted of the entire name string, substrings, double-metaphones, and various name-entity patterns, while location features consisted of the entire location string and substrings of province, district, and subdistrict. Predictive performance metrics included sensitivity, specificity, positive predictive value, negative predictive value, F1, Area Under the Curve for Receiver Operating Characteristic curve, and accuracy. Results The census had 4,812,958 unique individuals. For multiclass classification, the highest performance achieved was 76% F1 and 91% accuracy. For binary classifications for Chinese, French, Italian, Japanese, Russian, and others, the F1 ranged 68–95% (median 87%). The lower performance for English, Irish, and Scottish (F1 ranged 63–67%) was likely due to their shared cultural and linguistic heritage. Adding census location features to the name-based models strongly improved the prediction in Aboriginal classification (F1 increased from 50% to 84%). Conclusions The automated machine learning approach using only name and census location features can predict the ethnicity of Canadians with varying performance by specific ethnic categories.

A Machine Learning Approach for One-Stop Learning

2008 ◽  
pp. 333-357 ◽  
Author(s):  
Marco A. Alvarez ◽  
SeungJin Lim
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approach ◽  
Internet Users ◽  
Machine Learning Approach ◽  
Supervised Learning Algorithms ◽  
One Stop ◽  
The Subject

Current search engines impose an overhead to motivated students and Internet users who employ the Web as a valuable resource for education. The user, searching for good educational materials for a technical subject, often spends extra time to filter irrelevant pages or ends up with commercial advertisements. It would be ideal if, given a technical subject by user who is educationally motivated, suitable materials with respect to the given subject are automatically identified by an affordable machine processing of the recommendation set returned by a search engine for the subject. In this scenario, the user can save a significant amount of time in filtering out less useful Web pages, and subsequently the user’s learning goal on the subject can be achieved more efficiently without clicking through numerous pages. This type of convenient learning is called One-Stop Learning (OSL). In this paper, the contributions made by Lim and Ko in (Lim and Ko, 2006) for OSL are redefined and modeled using machine learning algorithms. Four selected supervised learning algorithms: Support Vector Machine (SVM), AdaBoost, Naive Bayes and Neural Networks are evaluated using the same data used in (Lim and Ko, 2006). The results presented in this paper are promising, where the highest precision (98.9%) and overall accuracy (96.7%) obtained by using SVM is superior to the results presented by Lim and Ko. Furthermore, the machine learning approach presented here, demonstrates that the small set of features used to represent each Web page yields a good solution for the OSL problem.

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