Computational Intelligence-Based Financial Crisis Prediction Model Using Feature Subset Selection with Optimal Deep Belief Network

Metawa, Noura; Pustokhina, Irina V.; Pustokhin, Denis A.; Shankar, K.; Elhoseny, Mohamed

doi:10.1089/big.2020.0158

Cited by 22 publications

(20 citation statements)

References 29 publications

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“…Accurateness of FCP plays a significant process to regulate the financial organization success and production. For instance, a minor positive alteration in the accurateness level of probable client via default credit will decrease the future loss of a firm (Metawa et al, 2018(Metawa et al, , 2021. FCP is treated as a data classification problem (Ala'raj & Abbod, 2016).…”

Section: Role Of Artificial Intelligence In Fcpmentioning

confidence: 99%

RETRACTED ARTICLE: Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

Pustokhina

Pustokhin

Mohanty³

et al. 2021

Ann Oper Res

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Financial Technology (FinTech) is treated as a distinctive taxonomy which majorly examines the financial technology sectors in a broader set of operations for enterprises by the use of Information Technology (IT) applications. Since the Internet of Things (IoT) is increasing tremendously, artificial intelligence (AI) assisted agile IoT is the way forward for sustainable finance. The deepness of the agile IoT has probably transformed the financial market today, and it may rapidly develop as a dominant tool in the future. The integration of AI and IoT techniques will considerably extract valued financial data and avail better services to the customers. One of the important concepts involved in FinTech is financial crisis prediction (FCP), which is a process of determining the financial status of a company. With this motivation, this paper designs a novel artificial intelligence assisted IoT based FCP (AIAIoT-FCP) model in the FinTech environment. The proposed AIAIoT-FCP model encompasses different stages such as data collection, data preprocessing, feature selection, and classification. At the primary stage, the financial data of the enterprises are collected by the use of the IoT devices such as smartphones and laptops. Besides, a chaotic Henry gas solubility optimization based feature selection (CHGSO-FS) technique is applied to select optimum features. In addition, a deep extreme learning machine (DELM) based classifier is used to determine the class labels of the financial data. Finally, the Nesterov-accelerated Adaptive Moment Estimation (NADAM) based hyperparameter optimizer of the DELM model is involved to boost the classification performance of the DELM model. An extensive simulation analysis is carried out on the benchmark financial dataset to highlight the betterment of the AIAIoT-FCP model. The resultant values portrayed the superior performance of the AIAIoT-FCP model over the state of art techniques in a considerable way.

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Section: Role Of Artificial Intelligence In Fcpmentioning

confidence: 99%

RETRACTED ARTICLE: Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

Pustokhina

Pustokhin

Mohanty³

et al. 2021

Ann Oper Res

Self Cite

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“…Support vector machines and artificial neural networks based on artificial intelligence have been widely applied in the field of financial crisis prediction in recent years, which has improved prediction efficiency significantly. ese methods, however, are challenging to generate satisfying results due to the imbalance of financial forecasting difficulties and the complexity of data noise and distribution [3][4][5]. Financial crisis prediction entails an analysis of an enterprise's situation based on financial statements, business plans, and other relevant accounting materials provided by the enterprise, as well as the use of accounting, statistics, finance, enterprise management, factor analysis, comparative analysis, and other analysis methods to timely address problems identified in the enterprise.…”

Section: Introductionmentioning

confidence: 99%

Research on Intelligent Prediction Method of Financial Crisis of Listed Enterprises Based on Random Forest Algorithm

Jiang¹,

Wang²

2021

Security and Communication Networks

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Traditional financial crisis prediction approaches have a tough time extracting the properties of financial data, resulting in financial crisis prediction with insufficient accuracy. As a result, based on the random forest algorithm, an intelligent financial crisis prediction approach for listed enterprises is proposed. The random forest method is used to mine the characteristics of financial data based on financial index data from publicly traded companies. This research develops a financial crisis prediction index system based on the findings of data feature mining. The CCR model is used to assess the efficiency of listed firms’ decision-making units with more input and output, and the efficiency index of each decision-making unit is calculated. The efficiency evaluation index of publicly traded companies is used to divide the severity of the financial crisis. The experimental results reveal that, when compared to standard prediction methods, this method’s forecast accuracy is commensurate with the actual state of businesses, and it can reduce the time it takes to predict financial crises.

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“…In traditional DBN is same as Restricted Boltzmann Machine (RBM) method which is composed of output layer. Moreover, DBN applies robust, greedy unsupervised learning method for training RMB and supervised finetuning scheme to change the system by labeled data [21][22][23]. The RBM is comprised of visible layer v and hidden layer h, linked by undirected weights.…”

Section: Optimal Dbn Based Traffic Flow Prediction Techniquementioning

confidence: 99%

An Optimal Deep Learning for Cooperative Intelligent Transportation System

Lakshmi¹,

Nagineni²,

Lydia³

et al. 2022

Computers, Materials &Amp; Continua

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Cooperative Intelligent Transport System (C-ITS) plays a vital role in the future road traffic management system. A vital element of C-ITS comprises vehicles, road side units, and traffic command centers, which produce a massive quantity of data comprising both mobility and service-related data. For the extraction of meaningful and related details out of the generated data, data science acts as an essential part of the upcoming C-ITS applications. At the same time, prediction of short-term traffic flow is highly essential to manage the traffic accurately. Due to the rapid increase in the amount of traffic data, deep learning (DL) models are widely employed, which uses a non-parametric approach for dealing with traffic flow forecasting. This paper focuses on the design of intelligent deep learning based short-term traffic flow prediction (IDL-STFLP) model for C-ITS that assists the people in various ways, namely optimization of signal timing by traffic signal controllers, travelers being able to adapt and alter their routes, and so on. The presented IDL-STFLP model operates on two main stages namely vehicle counting and traffic flow prediction. The IDL-STFLP model employs the Fully Convolutional Redundant Counting (FCRC) based vehicle count process. In addition, deep belief network (DBN) model is applied for the prediction of short-term traffic flow. To further improve the performance of the DBN in traffic flow prediction, it will be optimized by Quantum-behaved bat algorithm (QBA) which optimizes the tunable parameters of DBN. Experimental results based on benchmark dataset show that the presented method can count vehicles and predict traffic flow in real-time with a maximum performance under dissimilar environmental situations. 20

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Computational Intelligence-Based Financial Crisis Prediction Model Using Feature Subset Selection with Optimal Deep Belief Network

Cited by 22 publications

References 29 publications

RETRACTED ARTICLE: Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

RETRACTED ARTICLE: Artificial intelligence assisted Internet of Things based financial crisis prediction in FinTech environment

Research on Intelligent Prediction Method of Financial Crisis of Listed Enterprises Based on Random Forest Algorithm

An Optimal Deep Learning for Cooperative Intelligent Transportation System

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