КФУ. Карточка публикации. Significant Features with M-Polar Neutrosophic Topological Spaces and Grey Wolf Optimization Algorithm for Bankruptcy Prediction Model

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SIGNIFICANT FEATURES WITH M-POLAR NEUTROSOPHIC TOPOLOGICAL SPACES AND GREY WOLF OPTIMIZATION ALGORITHM FOR BANKRUPTCY PREDICTION MODEL

Форма представления

Статьи в зарубежных журналах и сборниках

Год публикации

2025

Язык

английский

Авторы, сотрудники КФУ

Бахвалов Сергей Юрьевич, автор

Библиографическое описание на языке оригинала

Bakhvalov S, Shichiyakh R, Gladysheva I, Significant Features with M-Polar Neutrosophic Topological Spaces and Grey Wolf Optimization Algorithm for Bankruptcy Prediction Model//International Journal of Neutrosophic Science. - 2025. - Vol.25, Is.2. - P.290-302.

Аннотация

An interval neutrosophic set (INS) is an example of a NS, which is simplified from the theory of fuzzy set (FS), classical set, paradoxist set, intuitionistic FS, paraconsistent set, interval-valued FS, interval-valued intuitionistic FS, and tautological set. The association of an element to an INS is stated by 3 values such as t, i, and f. These values signify memberships of truth, indeterminacy, and false, correspondingly. Bankruptcy prediction is also called a corporate failure or bankruptcy prediction, which is a major focus in the area of finance and accounting, as the condition of a business is extremely substantial to its partners, shareholders, investors, creditors, even its suppliers, and buyers. Practitioners and researchers were reserved for emerging models and approaches to forecast the bankruptcy of companies more rapidly and precisely. With the excessive growth of contemporary information technology, it has developed to use machine learning (ML) or deep learning (DL) techniques to perform the prediction, from the preliminary study of economic statements. This study introduces an Optimized Bankruptcy Prediction using Feature Selection with m-Polar Neutrosophic Topological Spaces (OBPFS-MPNTS) method. The projected OBPFS-MPNTS system uses the parameter tuning and DL method to forecast the presence of bankruptcy. To achieve this, the OBPFS-MPNTS approach uses min-max normalization to convert input data into a uniform format. The OBPFS-MPNTS method begins with a grey wolf optimization (GWO) for selecting feature subsets. In addition, the OBPFS-MPNTS algorithm applies the m-polar neutrosophic topological space (MPNTS) system for bankruptcy prediction. To upsurge the performance of the MPNTS system, the whale optimizer algorithm (WOA) is employed. The experimentation outcome study of the OBPFS-MPNTS system is verified on a benchmark database and the outcomes pointed out the developments of the OBPFS-MPNTS algorithm over other current methodologies.

Ключевые слова

Bankruptcy Prediction , Feature Selection , Neutrosophic Set , m-Polar Neutrosophic Topological Spaces , Fuzzy Set

Название журнала

International Journal of Neutrosophic Science

URL

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204284826&doi=10.54216%2fIJNS.250225&partnerID=40&md5=6454f5a4d6fea6229aa26c1fca86f828

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https://repository.kpfu.ru/?p_id=305163

Полная запись метаданных

Поле DC	Значение	Язык
dc.contributor.author	Бахвалов Сергей Юрьевич	ru_RU
dc.date.accessioned	2025-01-01T00:00:00Z	ru_RU
dc.date.available	2025-01-01T00:00:00Z	ru_RU
dc.date.issued	2025	ru_RU
dc.identifier.citation	Bakhvalov S, Shichiyakh R, Gladysheva I, Significant Features with M-Polar Neutrosophic Topological Spaces and Grey Wolf Optimization Algorithm for Bankruptcy Prediction Model//International Journal of Neutrosophic Science. - 2025. - Vol.25, Is.2. - P.290-302.	ru_RU
dc.identifier.uri	https://repository.kpfu.ru/?p_id=305163	ru_RU
dc.description.abstract	International Journal of Neutrosophic Science	ru_RU
dc.description.abstract	An interval neutrosophic set (INS) is an example of a NS, which is simplified from the theory of fuzzy set (FS), classical set, paradoxist set, intuitionistic FS, paraconsistent set, interval-valued FS, interval-valued intuitionistic FS, and tautological set. The association of an element to an INS is stated by 3 values such as t, i, and f. These values signify memberships of truth, indeterminacy, and false, correspondingly. Bankruptcy prediction is also called a corporate failure or bankruptcy prediction, which is a major focus in the area of finance and accounting, as the condition of a business is extremely substantial to its partners, shareholders, investors, creditors, even its suppliers, and buyers. Practitioners and researchers were reserved for emerging models and approaches to forecast the bankruptcy of companies more rapidly and precisely. With the excessive growth of contemporary information technology, it has developed to use machine learning (ML) or deep learning (DL) techniques to perform the prediction, from the preliminary study of economic statements. This study introduces an Optimized Bankruptcy Prediction using Feature Selection with m-Polar Neutrosophic Topological Spaces (OBPFS-MPNTS) method. The projected OBPFS-MPNTS system uses the parameter tuning and DL method to forecast the presence of bankruptcy. To achieve this, the OBPFS-MPNTS approach uses min-max normalization to convert input data into a uniform format. The OBPFS-MPNTS method begins with a grey wolf optimization (GWO) for selecting feature subsets. In addition, the OBPFS-MPNTS algorithm applies the m-polar neutrosophic topological space (MPNTS) system for bankruptcy prediction. To upsurge the performance of the MPNTS system, the whale optimizer algorithm (WOA) is employed. The experimentation outcome study of the OBPFS-MPNTS system is verified on a benchmark database and the outcomes pointed out the developments of the OBPFS-MPNTS algorithm over other current methodologies.	ru_RU
dc.language.iso	ru	ru_RU
dc.subject	Bankruptcy Prediction	ru_RU
dc.subject	Feature Selection	ru_RU
dc.subject	Neutrosophic Set	ru_RU
dc.subject	m-Polar Neutrosophic Topological Spaces	ru_RU
dc.subject	Fuzzy Set	ru_RU
dc.title	Significant Features with M-Polar Neutrosophic Topological Spaces and Grey Wolf Optimization Algorithm for Bankruptcy Prediction Model	ru_RU
dc.type	Статьи в зарубежных журналах и сборниках	ru_RU