Deep Learning Enabled Financial Crisis Prediction Model for Small-Medium Sized Industries

作     者：Kavitha Muthukumaran K.Hariharanath 

作者机构：SSN School of ManagementKalavakkamChennai603110India 

出 版 物：《Intelligent Automation & Soft Computing》 (智能自动化与软计算（英文）)

年 卷 期：2023年第35卷第1期

页      面：521-536页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Small medium-sized enterprises deep learning FCP financial sector prediction metaheuristics sailfish optimization 

摘      要：Recently,data science techniques utilize artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to take an influence and grow their *** SMEs,owing to the inexistence of consistent data and other features,evaluating credit risks is difficult and *** the other hand,it becomes necessary to design efficient models for predicting business failures orfinancial crises of *** data classification approaches forfinancial crisis prediction(FCP)have been presented for predicting thefinancial status of the organization by the use of past data.A major process involved in the design of FCP is the choice of required features for enhanced classifier *** this motivation,this paper focuses on the design of an optimal deep learning-basedfinancial crisis prediction(ODL-FCP)model for *** proposed ODL-FCP technique incorporates two phases:Archimedes optimization algorithm based feature selection(AOA-FS)algorithm and optimal deep convo-lution neural network with long short term memory(CNN-LSTM)based data *** ODL-FCP technique involves a sailfish optimization(SFO)algorithm for the hyperparameter optimization of the CNN-LSTM *** performance validation of the ODL-FCP technique takes place using a benchmarkfinancial dataset and the outcomes are inspected in terms of various *** experimental results highlighted that the proposed ODL-FCP technique has out-performed the other techniques.