Probabilistic Calibration and Genetic Algorithm-based Bank Credit Strategies for MSMEs and Enlightenment to Tobacco Enterprise Management

2021 ◽  
Vol 7 (6) ◽  
pp. 5726-5740
Author(s):  
Liu Haixu ◽  
Zhang Yong ◽  
Li Hui ◽  
Mao Tianjun ◽  
Zheng Wenhui ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Risk Model ◽  
Credit Ratings ◽  
Principal Component ◽  
Measurement Model ◽  
Classification Model ◽  
Bank Credit ◽  
Probability Of Default ◽  
Multiple Perspectives ◽  
Credit Portfolio

Objectives: To further strengthen the role of Micro, Small and Medium Enterprises (MSMEs) in maintaining the vitality of national economy, governments around the world introduced many special policies. They kept guiding the banking industry to increase the support for MSMEs and reduce their financing difficulties in banks. Basing on the analysis of the bank's credit strategy for small and medium-sized enterprises of similar size, this paper gives the management strategy for small and medium-sized enterprises in tobacco industry to obtain bank credit when they cannot expand their turnover. In this paper, we proposed a binary classification model-based probabilistic calibration algorithm to calculate the default probability of enterprises in the formation of risk measurement model, and found the optimal solution of credit strategy using an improved genetic algorithm. Firstly, we discovered the enterprise’s information and invoice data of 123 micro and medium-sized enterprises with existing credit ratings. We extracted several features from multiple perspectives, such as size, relationship in supply chain, profitability, performance ability, and level of development, and removed the correlations among the indicators using principal component analysis (PCA). Secondly, the retained principal components were used as covariates, and we determined the credit ratings of the firms and the probability of default using discrete variables such as the credit ratings of the firms and whether they defaulted. Finally, we substituted the probability of default into the credit risk model to calculate the loss expectation and profit expectation of the credit portfolio, and used the profit expectation of the credit portfolio as the objective function of the 0-1 programming equation to derive the credit strategy with the lowest risk exposure and the highest return basing on the genetic algorithm.

scholarly journals Modeling of Bank Credit Risk Management Using the Cost Risk Model

2021 ◽  
Vol 14 (5) ◽  
pp. 211
Author(s):  
Iryna Yanenkova ◽  
Yuliia Nehoda ◽  
Svetlana Drobyazko ◽  
Andrii Zavhorodnii ◽  
Lyudmyla Berezovska
Keyword(s):  
Risk Management ◽  
Decision Support ◽  
Credit Risk ◽  
Risk Model ◽  
Bank Credit ◽  
Credit Risk Management ◽  
Loan Default ◽  
Increased Risk ◽  
Credit Portfolio ◽  
Cost Risk

This article deals with the issue of managing bank credit risk using a cost risk model. Modeling of bank credit risk management was proposed based on neural-cell technologies, which expand the possibilities of modeling complex objects and processes and provide high reliability of credit risk determination. The purpose of the article is to improve and develop methodical support and practical recommendations for reducing the level of risk based on the value-at-risk (VaR) methodology and its subsequent combination with methods of fuzzy programming and symbiotic methodical support. The model makes it possible to create decision support subsystems for nonperforming loan management based on the neuro-fuzzy approach. For this paper, economic and mathematical tools (based on the VaR methodology) were used, which made it possible to analyze and forecast the dynamics of overdue payment; assess the quality of the credit portfolio of the bank; determine possible trends in bank development. A scientific and practical approach is taken to assess and forecast the degree of credit problematicity by qualitative criteria using a mathematical model based on a fuzzy technology, which can forecast the increased risk of loan default at an early stage in the process of monitoring the loan portfolio and model forecasting changes in the degree of credit problematicity on change of indicators. A methodology is proposed for the analysis and forecasting of indicators of troubled loan debt, which should be implemented as software and included in the decision support system during the process of monitoring the risk of the bank’s credit portfolio.

scholarly journals Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1809
Author(s):  
Mohammed El Amine Senoussaoui ◽  
Mostefa Brahami ◽  
Issouf Fofana
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Oil Quality ◽  
Principal Component ◽  
Condition Assessment ◽  
Classification Performance ◽  
Transformer Oil ◽  
Classification Model ◽  
Insulation Degradation ◽  
Transformer Oils

Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The service-aged transformer oils were classified into four groups: the oils that can be maintained in service, the oils that should be reconditioned or filtered, the oils that should be reclaimed, and the oils that must be discarded. From the two algorithms, random forest exhibited a better performance and high accuracy with only a small amount of data. Good performance was achieved through not only the application of the proposed algorithm but also the approach of data preprocessing. Before feeding the classification model, the available data were transformed using the simple k-means method. Subsequently, the obtained data were filtered through correlation-based feature selection (CFsSubset). The resulting features were again retransformed by conducting the principal component analysis and were passed through the CFsSubset filter. The transformation and filtration of the data improved the classification performance of the adopted algorithms, especially random forest. Another advantage of the proposed method is the decrease in the number of the datasets required for the condition assessment of transformer oils, which is valuable for transformer condition monitoring.

scholarly journals Full spectrum and genetic algorithm-selected spectrum-based chemometric methods for simultaneous determination of azilsartan medoxomil, chlorthalidone, and azilsartan: Development, validation, and application on commercial dosage form

2021 ◽  
Vol 19 (1) ◽  
pp. 205-213
Author(s):  
Hany W. Darwish ◽  
Abdulrahman A. Al Majed ◽  
Ibrahim A. Al-Suwaidan ◽  
Ibrahim A. Darwish ◽  
Ahmed H. Bakheit ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simultaneous Determination ◽  
Predictive Power ◽  
Principal Component Regression ◽  
Selection Procedure ◽  
Principal Component ◽  
Chemometric Methods ◽  
Full Spectrum ◽  
Azilsartan Medoxomil

Abstract Five various chemometric methods were established for the simultaneous determination of azilsartan medoxomil (AZM) and chlorthalidone in the presence of azilsartan which is the core impurity of AZM. The full spectrum-based chemometric techniques, namely partial least squares (PLS), principal component regression, and artificial neural networks (ANN), were among the applied methods. Besides, the ANN and PLS were the other two methods that were extended by genetic algorithm procedure (GA-PLS and GA-ANN) as a wavelength selection procedure. The models were developed by applying a multilevel multifactor experimental design. The predictive power of the suggested models was evaluated through a validation set containing nine mixtures with different ratios of the three analytes. For the analysis of Edarbyclor® tablets, all the proposed procedures were applied and the best results were achieved in the case of ANN, GA-ANN, and GA-PLS methods. The findings of the three methods were revealed as the quantitative tool for the analysis of the three components without any intrusion from the co-formulated excipient and without prior separation procedures. Moreover, the GA impact on strengthening the predictive power of ANN- and PLS-based models was also highlighted.

scholarly journals Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Chen ◽  
Zan Lin ◽  
Chao Tan
Keyword(s):  
Cancer Diagnosis ◽  
Near Infrared ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
Classification Model ◽  
Support Vector ◽  
Biomedical Analysis ◽  
First Derivative ◽  
Svm Model ◽  
High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

Aspects of project learning in construction: a socio-technical model

2014 ◽  
Vol 14 (2) ◽  
pp. 229-244 ◽  
Author(s):  
Ali Mohammed Alashwal ◽  
Hamzah Abdul-Rahman
Keyword(s):  
Latent Variable ◽  
Construction Projects ◽  
Team Building ◽  
Principal Component ◽  
Measurement Model ◽  
Survey Method ◽  
Path Modeling ◽  
Content Type ◽  
Measurement Models ◽  
Project Learning

Purpose – The purpose of this paper is to determine the measurement constructs of learning within construction projects' milieu. The literature indicated some mechanisms of learning in projects under four aspects, namely knowledge sharing, knowledge creation, team action to learn, and learning support. The empirical study attempts to verify whether intra-project learning can be measured through these aspects. Design/methodology/approach – The study used a survey method to collect the data from 36 mega-sized building projects in Malaysia. In total, 203 questionnaires were collected from professionals working in the sites of these projects. The data were analysed using principal component analysis (PCA) to determine the constructs of intra-project learning. Partial least squares-path modeling was used then to confirm the results of PCA and determine the contribution of each construct to intra-project learning. Findings – The results affirmed two constructs of intra-project learning, named, social and technical and each consisted of four indicators of learning. Originality/value – The paper emphasized the socio-technical perspective of learning and contributed to developing a hierarchical measurement model of learning in construction project. A project manager can propose new initiatives in response to the new perspective of learning for team building and continuous development. Lastly, the paper provides a comprehensive presentation of how to estimate the hierarchical measurement models of project learning as a latent variable.

scholarly journals M6a Regulator-Associated Methylation Modification Patterns Shape Tumor Microenvironment Characteristics in Hepatocellular Carcinoma

2021 ◽  
Author(s):  
Bobin Ning ◽  
Yonggan Xue ◽  
Hongyi Liu ◽  
Hongyu Sun ◽  
Baoqing Jia
Keyword(s):  
Hepatocellular Carcinoma ◽  
Tumor Microenvironment ◽  
Single Cell ◽  
Signaling Pathways ◽  
Principal Component ◽  
Unsupervised Clustering ◽  
Survival Prediction ◽  
Multiple Perspectives ◽  
Single Cell Sequencing ◽  
Survival Expectations

Abstract Although substantial achievements in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC) have led to fundamental improvements both in the basic research and clinical management, the potential mechanisms and regulatory relationships between m6A regulators and the TME are still unknown. We first conducted unsupervised clustering on the samples according to the core m6A expression, and then compared the signaling pathways, differential genes (DEGs), and TME between the m6A phenotypes, and re-validated the relationship between m6A regulators and TME by single cell sequencing. Then, the geneCluster was obtained by another unsupervised clustering of the DEGs, and the clinical as well as TME traits were evaluated among the geneClusters. Finally, the m6A scores of individual patients were calculated by principal component analysis (PCA) to verify the correlation from multiple perspectives, including survivals, clinical characters, mutations, TME, immunotherapy, and chemotherapy. Through a comprehensive analysis of 729 samples, we classified HCC patients into three m6A clusters and three geneClusters. Each group exhibited remarkable variations in terms of signaling pathways, clinical traits, and survival expectations. Notably, the m6A phenotypes corresponded to three different types of TME, namely immune-inflamed, immune-excluded, and immune-desert, respectively. In addition, the m6A regulator can accurately reflect the individualized microenvironment in HCC, and present supreme expression levels in the stromal microenvironment. However, the m6A score system is able to make accurate predictions not only in terms of clinical traits, survival prediction, and TME mentioned above, but also in the sensitivity of HCC patients to immunotherapy and chemotherapy. This study revealed the uniqueness and pluripotency of m6A regulators in the TME of HCC by combining single-cell sequencing and bulk sequencing. The quantified m6A modification indices were able to accurately predict patient survival expectations, clinical traits, TME, and sensitivity to immunotherapy and chemotherapy.

scholarly journals WORLD EXPERIENCE: COMMERCIAL BANK CREDIT AND ECONOMIC GROWTH RELATIONSHIP

2020 ◽  
pp. 6-19
Author(s):  
Davit Aslanishvili
Keyword(s):  
Economic Growth ◽  
Large Scale ◽  
Banking Sector ◽  
Point Of View ◽  
Bank Credit ◽  
Main Function ◽  
Economic Point ◽  
Market Factors ◽  
Credit Portfolio ◽  
The Impact

This research focuses on the problem of large scale disproportion of success in the development of the banking sector and mostly unsuccessful development of the real sector of the economy. It should be noted that this disproportion is a subject of consideration in contemporary economic literature and our research is an attempt to broaden the issue and share ideas inside the international scientific circles. The main problem in the research is the impact of the banking sector's credit portfolio and the functioning of credit markets on the economic growth of the country. In this regard, it is very important to identify, study the macroeconomic stabilization and accelerated economic growth of the country and analyse the impact mechanisms of the credit market factors on economic growth. The conclusion that combines many of the research and opinions given in the survey can be as follows: From the economic point of view, the main function of banks is to increase the financing/lending of funds as the core point to increase investments in the economy. Thus, the development of the country in economic terms depends on the increase of investments. At present, it is in the hands of the banking sector whether to lead us to economic immobility or to accelerate the country's economic development through efficient allocation of resources.

scholarly journals Does the Financial Status of Company Affect the Bond Credit Rating? ——Empirical Evidence from China's Shanghai and Shenzhen Stock Exchanges

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yuyan Cai
Keyword(s):  
Credit Rating ◽  
Credit Ratings ◽  
Corporate Bonds ◽  
Principal Component ◽  
Corporate Bond ◽  
Stock Exchanges ◽  
Analysis Method ◽  
Financial Status ◽  
Financial Variables ◽  
The Impact

This article takes the companies that publicly issued corporate bonds on the Shanghai and Shenzhen Stock Exchanges from 2006 to 2018 as the research objects selecting six aspects that comprehensively reflect the 17 financial variables in 6 aspects: profitability, operating ability, bond repayment ability, development ability, cash flow and market value of the company. Principal component analysis method and factor analysis method are used to extract the principal factors of these financial indicator variables. That is how an ordered multi-classification Logistic regression model is constructed to test the impact of the Shanghai and Shenzhen Stock Exchanges’ financial status on the corporate bond credit rating. It turns out that the financial status of the Shanghai and Shenzhen Stock Exchanges have an important impact on the credit rating of corporate bonds. The financial status has a greater impact on corporate bonds with credit ratings of A- and AA-, while it has a smaller impact on corporate bonds with credit ratings above AA. The results of this article can help individual and institutional investors prevent risks from investing.

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