KFU. Card publication. Jellyfish Search Algorithm Based Feature Selection with Optimal Deep Learning for Predicting Financial Crises in the Economy and Society

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JELLYFISH SEARCH ALGORITHM BASED FEATURE SELECTION WITH OPTIMAL DEEP LEARNING FOR PREDICTING FINANCIAL CRISES IN THE ECONOMY AND SOCIETY

Form of presentation

Articles in international journals and collections

Year of publication

2024

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Authors, employees of KFU

Bakhvalov Sergey Yurevich, author

Osadchiy Eduard Aleksandrovich, author

Bibliographic description in the original language

Osadchy E, Abdullayev I, Bakhvalov S, Jellyfish Search Algorithm Based Feature Selection with Optimal Deep Learning for Predicting Financial Crises in the Economy and Society//Fusion: Practice and Applications. - 2024. - Vol.14, Is.2. - P.186-198.

Annotation

The financial crises has emphasized the part of financial relationship as a potential source of macroeconomic variability and systemic risk worldwide. Predicting financial crises using deep learning (DL) infers leveraging neural network (NN) to identify patterns indicative of future financial crisis and analyse complicated financial data. DL approaches such as recurrent neural network (RNN) or long short-term memory (LSTM) that process a massive quantity of past financial data such as geopolitical events, economic indicators, and market prices. These models target to identify refined connections and signals that can lead to an economic recession by learning from earlier crisis and their precursors. The problem resides in the complex and dynamic nature of financial market, demanding continuous training and modification of methods to retain significance in the aspect of developing financial condition. Although DL shows the potential to increase prediction capabilities, it's vital to accept the inherent ambiguity in financial market and the requirement for cutting-edge development of models to enhance their accuracy and reliability. This study proposes a jellyfish search algorithm based feature selection with optimum deep learning algorithm (JSAFS-ODL) for financial crisis prediction (FCP). The objective of JSAFSODL technique is classified the presence of financial crises or non-financial crises. To accomplish this, the JSAFSODL technique applies JSA based feature selection (JSA-FS) to choose an optimum set of features. Besides, RNNGRU model can be used for the FCP. For enhancing the detection results of the RNN-GRU approach, chimp optimization algorithm (COA) can be utilized for the optimal tuning of the hyperparameters correlated to the RNNGRU model. To guarantee the better performance of the JSAFS-ODL procedure, a series of tests were involved. The obtained values highlighted that the JSAFS-ODL technique reaches significant performance of the JSAFSODL technique.

Keywords

Financial Crises

The name of the journal

Fusion: Practice and Applications

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186212510&doi=10.54216%2fFPA.140215&partnerID=40&md5=0aee84890a345f36208670e6f3160028

Please use this ID to quote from or refer to the card

https://repository.kpfu.ru/eng/?p_id=301483&p_lang=2

Full metadata record

Field DC	Value	Language
dc.contributor.author	Bakhvalov Sergey Yurevich	ru_RU
dc.contributor.author	Osadchiy Eduard Aleksandrovich	ru_RU
dc.date.accessioned	2024-01-01T00:00:00Z	ru_RU
dc.date.available	2024-01-01T00:00:00Z	ru_RU
dc.date.issued	2024	ru_RU
dc.identifier.citation	Osadchy E, Abdullayev I, Bakhvalov S, Jellyfish Search Algorithm Based Feature Selection with Optimal Deep Learning for Predicting Financial Crises in the Economy and Society//Fusion: Practice and Applications. - 2024. - Vol.14, Is.2. - P.186-198.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/eng/?p_id=301483&p_lang=2	ru_RU
dc.description.abstract	Fusion: Practice and Applications	ru_RU
dc.description.abstract	The financial crises has emphasized the part of financial relationship as a potential source of macroeconomic variability and systemic risk worldwide. Predicting financial crises using deep learning (DL) infers leveraging neural network (NN) to identify patterns indicative of future financial crisis and analyse complicated financial data. DL approaches such as recurrent neural network (RNN) or long short-term memory (LSTM) that process a massive quantity of past financial data such as geopolitical events, economic indicators, and market prices. These models target to identify refined connections and signals that can lead to an economic recession by learning from earlier crisis and their precursors. The problem resides in the complex and dynamic nature of financial market, demanding continuous training and modification of methods to retain significance in the aspect of developing financial condition. Although DL shows the potential to increase prediction capabilities, it's vital to accept the inherent ambiguity in financial market and the requirement for cutting-edge development of models to enhance their accuracy and reliability. This study proposes a jellyfish search algorithm based feature selection with optimum deep learning algorithm (JSAFS-ODL) for financial crisis prediction (FCP). The objective of JSAFSODL technique is classified the presence of financial crises or non-financial crises. To accomplish this, the JSAFSODL technique applies JSA based feature selection (JSA-FS) to choose an optimum set of features. Besides, RNNGRU model can be used for the FCP. For enhancing the detection results of the RNN-GRU approach, chimp optimization algorithm (COA) can be utilized for the optimal tuning of the hyperparameters correlated to the RNNGRU model. To guarantee the better performance of the JSAFS-ODL procedure, a series of tests were involved. The obtained values highlighted that the JSAFS-ODL technique reaches significant performance of the JSAFSODL technique.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject		ru_RU
dc.title	Jellyfish Search Algorithm Based Feature Selection with Optimal Deep Learning for Predicting Financial Crises in the Economy and Society	ru_RU
dc.type	Articles in international journals and collections	ru_RU