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Comparison between some machine learning algorithms on predicting the spectra of quark–anti-quark bound states.

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Title: Comparison between some machine learning algorithms on predicting the spectra of quark–anti-quark bound states.
Authors: Nahool, T. A.1,2 (AUTHOR) tarek.abdelwahab@sci.svu.edu.eg, Ismail, Atef3 (AUTHOR) atef@usa.com, Elshamndy, Samah K.4 (AUTHOR), Yasser, A. M.1,2 (AUTHOR) Yasser.mostafa@sci.svu.edu.eg
Source: International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics. 6/10/2023, Vol. 38 Issue 15/16, p1-13. 13p.
Subjects: Bound states, Machine learning, Random forest algorithms, K-nearest neighbor classification, Regression analysis
Abstract: This study is devoted to investigate the implementation of machine learning methodologies in the prediction of Quark–anti-Quark bound state spectrum. Predictions are produced by using variety of machine learning (ML) approaches, such as ridge regression, random forest regression, linear regression and K-nearest neighbors regression methods. The forecasts are then evaluated and contrasted in order to determine the optimal performance. Furthermore, systematic comparison of the considered ML methods in terms of percentage of performance is done. Each of the four strategies yielded comparable results. With accuracy of 99%, the ridge regression model exhibited the highest level of predictive performance. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics is the property of World Scientific Publishing Company 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: Engineering Source
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DbLabel: Engineering Source
An: 170041575
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Comparison between some machine learning algorithms on predicting the spectra of quark–anti-quark bound states.
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  Data: This study is devoted to investigate the implementation of machine learning methodologies in the prediction of Quark–anti-Quark bound state spectrum. Predictions are produced by using variety of machine learning (ML) approaches, such as ridge regression, random forest regression, linear regression and K-nearest neighbors regression methods. The forecasts are then evaluated and contrasted in order to determine the optimal performance. Furthermore, systematic comparison of the considered ML methods in terms of percentage of performance is done. Each of the four strategies yielded comparable results. With accuracy of 99%, the ridge regression model exhibited the highest level of predictive performance. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Modern Physics A: Particles & Fields; Gravitation; Cosmology; Nuclear Physics is the property of World Scientific Publishing Company 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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        Value: 10.1142/S0217751X23500884
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: K-nearest neighbor classification
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      – SubjectFull: Regression analysis
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              Text: 6/10/2023
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              Y: 2023
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