Academic Journal
Predicting Student Behavior Using a Neutrosophic Deep Learning Model.
| Title: | Predicting Student Behavior Using a Neutrosophic Deep Learning Model. |
|---|---|
| Authors: | Shitaya, Ahmed Mohamed, Wahed, Mohamed El Syed, Abd El khalek, Saied Helemy, Ismail, Amr, Shams, Mahmoud Y., Salama, A. A. |
| Source: | Neutrosophic Sets & Systems; 2025, Vol. 76, p288-310, 23p |
| Subject Terms: | Artificial neural networks, Data mining, Distributed databases, Object-oriented programming languages, School dropouts, Deep learning |
| Abstract: | We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory--a mathematical framework that accounts for truth, falsity, and indeterminacy--with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. [ABSTRACT FROM AUTHOR] |
| Copyright of Neutrosophic Sets & Systems is the property of Multimedia Larga and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: Predicting Student Behavior Using a Neutrosophic Deep Learning Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Shitaya%2C+Ahmed+Mohamed%22">Shitaya, Ahmed Mohamed</searchLink><br /><searchLink fieldCode="AR" term="%22Wahed%2C+Mohamed+El+Syed%22">Wahed, Mohamed El Syed</searchLink><br /><searchLink fieldCode="AR" term="%22Abd+El+khalek%2C+Saied+Helemy%22">Abd El khalek, Saied Helemy</searchLink><br /><searchLink fieldCode="AR" term="%22Ismail%2C+Amr%22">Ismail, Amr</searchLink><br /><searchLink fieldCode="AR" term="%22Shams%2C+Mahmoud+Y%2E%22">Shams, Mahmoud Y.</searchLink><br /><searchLink fieldCode="AR" term="%22Salama%2C+A%2E+A%2E%22">Salama, A. A.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Neutrosophic Sets & Systems; 2025, Vol. 76, p288-310, 23p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Data+mining%22">Data mining</searchLink><br /><searchLink fieldCode="DE" term="%22Distributed+databases%22">Distributed databases</searchLink><br /><searchLink fieldCode="DE" term="%22Object-oriented+programming+languages%22">Object-oriented programming languages</searchLink><br /><searchLink fieldCode="DE" term="%22School+dropouts%22">School dropouts</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: We developed an information system using an object-oriented programming language and a distributed database (DDB) consisting of multiple interconnected databases across a computer network, managed by a distributed database management system (DDBMS) for easy access. An intelligent system was designed to assess the difficulty level of preliminary exams and select top-performing advanced students using a Neutrosophic Deep Learning Model. The dataset was randomly split into training (80%) and testing (20%) sets, and the model, trained with the Adam optimizer at a 0.001 learning rate over 50 epochs, incorporated early stopping based on validation loss. This system, implemented at a traditional Egyptian university, achieved a 95% accuracy in predicting student dropout. Student behavior, influenced by personal, environmental, and academic factors, is often evaluated subjectively, leading to inconsistent results. Traditional machine learning approaches struggle with the inherent uncertainty in behavioral data. To address this, we combined neutrosophic theory--a mathematical framework that accounts for truth, falsity, and indeterminacy--with deep learning, which excels at learning complex data relationships, to predict student outcomes such as dropout rates. Evaluating the model on student data, including attendance and grades, showed superior accuracy, achieving a determination coefficient of 0.95, demonstrating the approach's potential for identifying at-risk students and enabling targeted interventions. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Neutrosophic Sets & Systems is the property of Multimedia Larga and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 23 StartPage: 288 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Data mining Type: general – SubjectFull: Distributed databases Type: general – SubjectFull: Object-oriented programming languages Type: general – SubjectFull: School dropouts Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Predicting Student Behavior Using a Neutrosophic Deep Learning Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Shitaya, Ahmed Mohamed – PersonEntity: Name: NameFull: Wahed, Mohamed El Syed – PersonEntity: Name: NameFull: Abd El khalek, Saied Helemy – PersonEntity: Name: NameFull: Ismail, Amr – PersonEntity: Name: NameFull: Shams, Mahmoud Y. – PersonEntity: Name: NameFull: Salama, A. A. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: 2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 23316055 Numbering: – Type: volume Value: 76 Titles: – TitleFull: Neutrosophic Sets & Systems Type: main |
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