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Kohistani, Jaamay
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The Role of Statistical Methods in Enhancing Artificial Intelligence: Techniques and Applications Fazil, Abdul Wajid; Kohistani, Jaamay; Rahmani, Bilal
Journal of Social Science Utilizing Technology Vol. 2 No. 4 (2024)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v2i4.1608

Abstract

Background. The undeniable infiltration of artificial intelligence into numerous career fields underlines statistical methods as an important tool in optimizing accurate results from AI. Therefore, the simulation of sound statistical practices is, therefore, unavoidable in healthcare, finance, and environmental sciences for such purposes as model validation performance improvement and uncertainty analysis, among other reasons. Purpose. The purpose of this proposal is to collaboratively analyze the role of statistical methods, like regression, Bayesian inference, Fi-Parsing, etc., in optimizing AI. Some examples will further aid in reinforcing the moment of reliability and firmness of any AI application. Method. A full systematic literature review (SLR) was conducted that analyzed scholarly publication articles from 2019 to early 2024 in reputed databases such as Springer, MDPI, ScienceDirect, and Wiley. The focus of the review is on the application of statistical techniques on the AI systems for improved performance and decision-making reliability. Results. The findings show that statistical methods highly recommend their role in AI model validation uncertainty representation, prediction, and optimal performance enhancement. The evidence for improved performance in critical areas such as healthcare, finance, and environmental science creates great hurdles for high-stakes decision-making. Conclusion. The study upholds the fundamentally critical role that statistical methods occupy and their role in AI development towards future pursuits of research and practical work. A clear-cut pathway to institutionalizing these methods in AI technology is proposed as a guarantee of its reliability and sustainability in diverse applications.
The Role of Statistical Methods in Enhancing Artificial Intelligence: Techniques and Applications Fazil, Abdul Wajid; Kohistani, Jaamay; Rahmani, Bilal
Journal of Social Science Utilizing Technology Vol. 2 No. 4 (2024)
Publisher : Yayasan Pendidikan Islam Daarut Thufulah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70177/jssut.v2i4.1608

Abstract

Background. The undeniable infiltration of artificial intelligence into numerous career fields underlines statistical methods as an important tool in optimizing accurate results from AI. Therefore, the simulation of sound statistical practices is, therefore, unavoidable in healthcare, finance, and environmental sciences for such purposes as model validation performance improvement and uncertainty analysis, among other reasons. Purpose. The purpose of this proposal is to collaboratively analyze the role of statistical methods, like regression, Bayesian inference, Fi-Parsing, etc., in optimizing AI. Some examples will further aid in reinforcing the moment of reliability and firmness of any AI application. Method. A full systematic literature review (SLR) was conducted that analyzed scholarly publication articles from 2019 to early 2024 in reputed databases such as Springer, MDPI, ScienceDirect, and Wiley. The focus of the review is on the application of statistical techniques on the AI systems for improved performance and decision-making reliability. Results. The findings show that statistical methods highly recommend their role in AI model validation uncertainty representation, prediction, and optimal performance enhancement. The evidence for improved performance in critical areas such as healthcare, finance, and environmental science creates great hurdles for high-stakes decision-making. Conclusion. The study upholds the fundamentally critical role that statistical methods occupy and their role in AI development towards future pursuits of research and practical work. A clear-cut pathway to institutionalizing these methods in AI technology is proposed as a guarantee of its reliability and sustainability in diverse applications.
Co-Authors Fazil, Abdul Wajid Rahmani, Bilal