scholarly journals Banks Credit Risk Prediction with Optimized ANN Based on Improved Owl Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Pegah Sharifi ◽  
Vipin Jain ◽  
Mehdi Arab Poshtkohi ◽  
Erfan seyyedi ◽  
Vahid Aghapour
Keyword(s):  
Decision Tree ◽  
Credit Risk ◽  
Credit Rating ◽  
Search Algorithm ◽  
True Positive ◽  
Future Performance ◽  
The Neural Network ◽  
Novel Method ◽  
Problem Data ◽  
Risk Concept

Credit is one of the most significant elements in banks and financial institutions. It can also be described as unpredicted events, which mainly occur in the form of either assets or liabilities. The risk occurrence is that the facility recipients have no willingness and ability to repay their debt to the bank, which is a default that is synonymous with credit risk. Credit ratings are a way to decrease and measure credit risk and, therefore, manage it appropriately. Credit rating is an approach for estimating the features and recipients of facilities’ performance based on quantitative criteria, including the company’s financial information. The anticipated future performance allows the applicants to obtain facilities with the exact specifications. In this study, due to the need and significance of calculating the credit risk concept, a novel method based on the hybrid method of artificial neural networks and an improved version of Owl search algorithm (IOSA) and forecasting of C5 risk of decision tree credit is done. This algorithm has two major parts. The decision tree runs based on an IOSA to provide the best weighting of the neural network. The weights created along with the problem data are then given as the input to the main network, and the data are classified. The algorithm has the highest level of accuracy, 96% that is much higher than other algorithms. The results also show a precision of 0.885 and a recall of 0.83 for 618 true positive samples. The proposed method has the highest accuracy and reliability toward the other comparative methods. The study is based on actual data noticed in one of the branches of the Bank Melli, Iran.

scholarly journals A Novel Method on Hydrographic Survey Technology Selection Based on the Decision Tree Supervised Learning

2021 ◽  
Vol 11 (11) ◽  
pp. 4966
Author(s):  
Ivana Golub Medvešek ◽  
Igor Vujović ◽  
Joško Šoda ◽  
Maja Krčum
Keyword(s):  
Decision Tree ◽  
Supervised Learning ◽  
Autonomous Underwater Vehicle ◽  
Specific Area ◽  
Underwater Vehicle ◽  
Appropriate Technology ◽  
Technology Selection ◽  
Hydrographic Survey ◽  
Seabed Mapping ◽  
Novel Method

Hydrographic survey or seabed mapping plays an important role in achieving better maritime safety, especially in coastal waters. Due to advances in survey technologies, it becomes important to choose well-suited technology for a specific area. Moreover, various technologies have various ranges of equipment and manufacturers, as well as characteristics. Therefore, in this paper, a novel method of a hydrographic survey, i.e., identifying the appropriate technology, has been developed. The method is based on a reduced elimination matrix, decision tree supervised learning, and multicriteria decision methods. The available technologies were: remotely operated underwater vehicle (ROV), unmanned aerial vehicle (UAV), light detection and ranging (LIDAR), autonomous underwater vehicle (AUV), satellite-derived bathymetry (SDB), and multibeam echosounder (MBES), and they are applied as a case study of Kaštela Bay. Results show, considering the specifics of the survey area, that UAV is the best-suited technology to be used for a hydrographic survey. However, some other technologies, such as SDB come close and can be considered an alternative for hydrographic surveys.

scholarly journals Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm

2021 ◽  
Vol 11 (15) ◽  
pp. 6728
Author(s):  
Muhammad Asfand Hafeez ◽  
Muhammad Rashid ◽  
Hassan Tariq ◽  
Zain Ul Abideen ◽  
Saud S. Alotaibi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tabu Search ◽  
Decision Tree ◽  
Decision Trees ◽  
Search Algorithm ◽  
Learning Algorithms ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Tabu Search Algorithm

Classification and regression are the major applications of machine learning algorithms which are widely used to solve problems in numerous domains of engineering and computer science. Different classifiers based on the optimization of the decision tree have been proposed, however, it is still evolving over time. This paper presents a novel and robust classifier based on a decision tree and tabu search algorithms, respectively. In the aim of improving performance, our proposed algorithm constructs multiple decision trees while employing a tabu search algorithm to consistently monitor the leaf and decision nodes in the corresponding decision trees. Additionally, the used tabu search algorithm is responsible to balance the entropy of the corresponding decision trees. For training the model, we used the clinical data of COVID-19 patients to predict whether a patient is suffering. The experimental results were obtained using our proposed classifier based on the built-in sci-kit learn library in Python. The extensive analysis for the performance comparison was presented using Big O and statistical analysis for conventional supervised machine learning algorithms. Moreover, the performance comparison to optimized state-of-the-art classifiers is also presented. The achieved accuracy of 98%, the required execution time of 55.6 ms and the area under receiver operating characteristic (AUROC) for proposed method of 0.95 reveals that the proposed classifier algorithm is convenient for large datasets.

Bank Credit Risk Analysis and Bank Credit Rating

2020 ◽  
pp. 275-348
Author(s):  
Terence M. Yhip ◽  
Bijan M. D. Alagheband
Keyword(s):  
Risk Analysis ◽  
Credit Risk ◽  
Credit Rating ◽  
Bank Credit ◽  
Credit Risk Analysis

scholarly journals MODEL JARINGAN SARAF TIRUAN UNTUK EVALUASI RESIKO KREDIT

2018 ◽  
Vol 2 (1) ◽  
pp. 76
Author(s):  
Novan Wijaya
Keyword(s):  
Neural Network ◽  
Credit Risk ◽  
Risk Evaluation ◽  
Evaluation System ◽  
Financial Risk ◽  
Banking Sector ◽  
Major Focus ◽  
Backpropagation Algorithm ◽  
The Neural Network ◽  
Risk Evaluation System

Credit risk evaluation is an importanttopic in financial risk management and become a major focus in the banking sector. This research discusses a credit risk evaluation system using an artificial neural network model based on backpropagation algorithm. This system is to train and test the neural network to determine the predictive value of credit risk, whether high riskorlow risk. This neural network uses 14 input layers, nine hidden layers and an output layer, and the data used comes from the bank that has branches in EastJakarta. The results showed that neural network can be used effectively in the evaluation of credit risk with accuracy of 88% from 100 test data

scholarly journals Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module

2021 ◽  
Vol 22 (21) ◽  
pp. 12080
Author(s):  
Minzhe Yu ◽  
Yushuai Duan ◽  
Zhong Li ◽  
Yang Zhang
Keyword(s):  
Feature Learning ◽  
Peptide Identification ◽  
Performance Assessments ◽  
Network Training ◽  
The Neural Network ◽  
Proteomics Technology ◽  
Experimental Process ◽  
Quantitative Results ◽  
Novel Method ◽  
Peptide Detectability

According to proteomics technology, as impacted by the complexity of sampling in the experimental process, several problems remain with the reproducibility of mass spectrometry experiments, and the peptide identification and quantitative results continue to be random. Predicting the detectability exhibited by peptides can optimize the mentioned results to be more accurate, so such a prediction is of high research significance. This study builds a novel method to predict the detectability of peptides by complying with the capsule network (CapsNet) and the convolutional block attention module (CBAM). First, the residue conical coordinate (RCC), the amino acid composition (AAC), the dipeptide composition (DPC), and the sequence embedding code (SEC) are extracted as the peptide chain features. Subsequently, these features are divided into the biological feature and sequence feature, and separately inputted into the neural network of CapsNet. Moreover, the attention module CBAM is added to the network to assign weights to channels and spaces, as an attempt to enhance the feature learning and improve the network training effect. To verify the effectiveness of the proposed method, it is compared with some other popular methods. As revealed from the experimentally achieved results, the proposed method outperforms those methods in most performance assessments.

A local search algorithm for plug-and-play reconfiguration of interconnected systems

2016 ◽  
Vol 64 (12) ◽  
Author(s):  
Sven Bodenburg ◽  
Jan Lunze
Keyword(s):  
Search Algorithm ◽  
Interconnected Systems ◽  
Local Algorithm ◽  
Local Search Algorithm ◽  
Plug And Play ◽  
Interconnected System ◽  
Actuator Failures ◽  
Tank System ◽  
Novel Method ◽  
Set Up

AbstractThis paper proposes a novel method to organise the reconfiguration process of decentralised controllers after actuator failures have occurred in an interconnected system. If an actuator fails in a subsystem, only the corresponding control station should be reconfigured, although the fault has effects on other subsystems through the physical couplings. The focus of this paper is on the organisation of the reconfiguration process without a central coordinator. Design agents exist for each of the subsystems which store the subsystem model. A local algorithm is presented to gather models from neighbouring design agents with the aim to set-up a model which describes the behaviour of the faulty subsystem including its neighbours. Furthermore, local reconfiguration conditions are proposed to design a virtual actuator so as to guarantee stability of the overall system. As a consequence, the design agents “play” together to gather the model of the faulty subsystem before the reconfigured control station is “plugged-in” the control hardware. Plug-and-play reconfiguration is illustrated by an interconnected tank system.

scholarly journals The assessment of companies for external and internal purposes

2012 ◽  
Vol 50 (No. 3) ◽  
pp. 105-109
Author(s):  
H. Sůvová
Keyword(s):  
Risk Assessment ◽  
Credit Risk ◽  
Balanced Scorecard ◽  
Credit Rating ◽  
Single Track ◽  
Good Tool ◽  
The Third ◽  
Financial Perspective ◽  
Quality Award ◽  
Holistic Assessment

This article presents holistic concepts of companies’ assessments intended for two basic groups of users: internal and external. Companies’ assessments concentrated only on financial perspective are very single-track and already obsolete and therefore, further perspectives are used to complete companies’ assessments. Among concepts intended for internal assessments, the so-called balanced scorecard approach has developed since late nineties. This concept helps in company’s strategic management. Moreover, there is a concept of EFQM Excellence model introduced at the beginning of nineties for assessing applications for the European Quality Award, but has become widely used for company assessment and management. The third mentioned concept is intended for credit risk assessment is credit rating. The development of methodology of the holistic assessment of Czech farm businesses may be a good tool for different external and internal users.

A Graph Grammar Approach to Generate Neural Network Topologies

2007 ◽  
Author(s):  
Chaitanya Vempati ◽  
Matthew I. Campbell
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Process Modeling ◽  
Graph Transformation ◽  
Graph Grammar ◽  
Transformation Rules ◽  
The Neural Network ◽  
Network Topologies ◽  
Novel Method ◽  
Defining Graph

Neural networks are increasingly becoming a useful and popular choice for process modeling. The success of neural networks in effectively modeling a certain problem depends on the topology of the neural network. Generating topologies manually relies on previous neural network experience and is tedious and difficult. Hence there is a rising need for a method that generates neural network topologies for different problems automatically. Current methods such as growing, pruning and using genetic algorithms for this task are very complicated and do not explore all the possible topologies. This paper presents a novel method of automatically generating neural networks using a graph grammar. The approach involves representing the neural network as a graph and defining graph transformation rules to generate the topologies. The approach is simple, efficient and has the ability to create topologies of varying complexity. Two example problems are presented to demonstrate the power of our approach.

Part II The Financial Crisis of 2007–2011, 6 Market and Regulatory Failure

2018 ◽  
Author(s):  
Mccormick Roger ◽  
Stears Chris
Keyword(s):  
Risk Management ◽  
Financial Crisis ◽  
Credit Risk ◽  
Financial Market ◽  
Credit Rating ◽  
Third Party ◽  
Credit Rating Agencies ◽  
Rating Agencies ◽  
Market Participants ◽  
Regulatory Failure

This chapter first discusses the origins of the financial crisis, highlighting practice of ‘packaging and selling’ credit risk by financial market participants that led up to the crisis. It argues that although, in retrospect, many aspects of that practice look very bad indeed, the idea that banks might originate a credit exposure and then transfer the credit risk attached to it to a third party was, before the financial crisis, considered to be part and parcel of sound risk management. The discussion then turns to credit-rating agencies. Analysis of the financial crisis and ‘what went wrong’ has shown that rating agencies were too generous with their rating of many of the structured products that contributed to the collapse.

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