Fractional Fuzzy Neural System: Fractional Differential-Based Compensation Prediction for Reputation Infringement Cases
With the rise of social media and the internet, the rapid dissemination of information has increased the likelihood of reputation infringement. This study utilizes judicial big data and AI to analyze intrinsic connections in reputation infringement cases, aiding judges in delivering consistent rulin...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Fractal and Fractional |
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| Online Access: | https://www.mdpi.com/2504-3110/8/3/172 |
| _version_ | 1827306188311625728 |
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| author | Ni Zhang Wu-Yang Zhu Peng Jin Guo Huang Yi-Fei Pu |
| author_facet | Ni Zhang Wu-Yang Zhu Peng Jin Guo Huang Yi-Fei Pu |
| author_sort | Ni Zhang |
| collection | DOAJ |
| description | With the rise of social media and the internet, the rapid dissemination of information has increased the likelihood of reputation infringement. This study utilizes judicial big data and AI to analyze intrinsic connections in reputation infringement cases, aiding judges in delivering consistent rulings. The challenge lies in balancing freedom of speech with the right to reputation and addressing the ambiguity and subjectivity in infringement cases. This research constructs a structured reputation infringement case dataset from Chinese Judgments Online. It introduces a Fractional Fuzzy Neural System (FFNS) to tackle the vagueness in reputation infringement acts and judicial language, enhancing prediction accuracy for case outcomes. The FFNS, integrating fractional calculus, fuzzy logic, and neural networks, excels in adaptability and nonlinear modeling. It uses fractional order fuzzy membership functions to depict the extent and severity of reputation infringement accurately, combining these outputs with neural networks for predictive analysis. The result is a more precise adjudication tool, demonstrating significant potential for judicial application. |
| first_indexed | 2024-04-24T18:16:08Z |
| format | Article |
| id | doaj.art-b258cac3c8194628b8599901a1d2a02f |
| institution | Directory Open Access Journal |
| issn | 2504-3110 |
| language | English |
| last_indexed | 2024-04-24T18:16:08Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fractal and Fractional |
| spelling | doaj.art-b258cac3c8194628b8599901a1d2a02f2024-03-27T13:42:08ZengMDPI AGFractal and Fractional2504-31102024-03-018317210.3390/fractalfract8030172Fractional Fuzzy Neural System: Fractional Differential-Based Compensation Prediction for Reputation Infringement CasesNi Zhang0Wu-Yang Zhu1Peng Jin2Guo Huang3Yi-Fei Pu4Library of Sichuan University, Chengdu 610065, ChinaCollege of Computer Science, Sichuan University, Chengdu 610065, ChinaSichuan Provincial Key Lab of Philosophy and Social Science for Language Intelligence in Special Education, Leshan Normal University, Leshan 614099, ChinaSchool of Electronic Information and Artificial Intelligence, Leshan Normal University, Leshan 614099, ChinaCollege of Computer Science, Sichuan University, Chengdu 610065, ChinaWith the rise of social media and the internet, the rapid dissemination of information has increased the likelihood of reputation infringement. This study utilizes judicial big data and AI to analyze intrinsic connections in reputation infringement cases, aiding judges in delivering consistent rulings. The challenge lies in balancing freedom of speech with the right to reputation and addressing the ambiguity and subjectivity in infringement cases. This research constructs a structured reputation infringement case dataset from Chinese Judgments Online. It introduces a Fractional Fuzzy Neural System (FFNS) to tackle the vagueness in reputation infringement acts and judicial language, enhancing prediction accuracy for case outcomes. The FFNS, integrating fractional calculus, fuzzy logic, and neural networks, excels in adaptability and nonlinear modeling. It uses fractional order fuzzy membership functions to depict the extent and severity of reputation infringement accurately, combining these outputs with neural networks for predictive analysis. The result is a more precise adjudication tool, demonstrating significant potential for judicial application.https://www.mdpi.com/2504-3110/8/3/172fractional fuzzy systemfractional fuzzy neural networksimilar case similar judgmentsmart justice |
| spellingShingle | Ni Zhang Wu-Yang Zhu Peng Jin Guo Huang Yi-Fei Pu Fractional Fuzzy Neural System: Fractional Differential-Based Compensation Prediction for Reputation Infringement Cases Fractal and Fractional fractional fuzzy system fractional fuzzy neural network similar case similar judgment smart justice |
| title | Fractional Fuzzy Neural System: Fractional Differential-Based Compensation Prediction for Reputation Infringement Cases |
| title_full | Fractional Fuzzy Neural System: Fractional Differential-Based Compensation Prediction for Reputation Infringement Cases |
| title_fullStr | Fractional Fuzzy Neural System: Fractional Differential-Based Compensation Prediction for Reputation Infringement Cases |
| title_full_unstemmed | Fractional Fuzzy Neural System: Fractional Differential-Based Compensation Prediction for Reputation Infringement Cases |
| title_short | Fractional Fuzzy Neural System: Fractional Differential-Based Compensation Prediction for Reputation Infringement Cases |
| title_sort | fractional fuzzy neural system fractional differential based compensation prediction for reputation infringement cases |
| topic | fractional fuzzy system fractional fuzzy neural network similar case similar judgment smart justice |
| url | https://www.mdpi.com/2504-3110/8/3/172 |
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