The Role of Statistical Methods in Enhancing Artificial Intelligence: Techniques and Applications
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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.
Ashrafi, F., & Javadi, A. (2024). Correct characteristics of the newly involved artificial intelligence methods in science and technology using statistical data sets. International Journal of Modern Engineering Technologies, 1(1). https://icdst.ir/OAJ/index.php/IJMET/article/view/30
Carta, S., Consoli, S., Podda, A. S., Recupero, D. R., & Stanciu, M. M. (2022). Statistical arbitrage powered by explainable artificial intelligence. Expert Systems with Applications, 206, 117763. https://doi.org/10.1016/j.eswa.2022.117763
Chen, W., Shahabi, H., Shirzadi, A., Hong, H., Akgun, A., Tian, Y., ... & Li, S. (2019). Novel hybrid artificial intelligence approach of bivariate statistical-methods-based kernel logistic regression classifier for landslide susceptibility modeling. Bulletin of Engineering Geology and the Environment, 78, 4397-4419. https://doi.org/10.1007/s10064-018-1401-8
Colosimo, B. M., del Castillo, E., Jones-Farmer, L. A., & Paynabar, K. (2021). Artificial intelligence and statistics for quality technology: an introduction to the special issue. Journal of Quality Technology, 53(5), 443-453. https://doi.org/10.1080/00224065.2021.1987806
El-Bahloul, S. A. (2020). Optimization of wire electrical discharge machining using statistical methods coupled with artificial intelligence techniques and soft computing. SN Applied Sciences, 2, 1-8. https://doi.org/10.1007/s42452-019-1849-6
Faes, L., Sim, D. A., van Smeden, M., Held, U., Bossuyt, P. M., & Bachmann, L. M. (2022). Artificial intelligence and statistics: just the old wine in new wineskins?. Frontiers in Digital Health, 4, 833912. https://doi.org/10.3389/fdgth.2022.833912
Friedrich, S., Antes, G., Behr, S., Binder, H., Brannath, W., Dumpert, F., ... & Friede, T. (2022). Is there a role for statistics in artificial intelligence?. Advances in Data Analysis and Classification, 16(4), 823-846. https://doi.org/10.1007/s11634-021-00455-6
Fu, X. (2022). Research on artificial intelligence classification and statistical methods of financial data in smart cities. Computational Intelligence and Neuroscience, 2022(1), 9965427. https://doi.org/10.1155/2022/9965427
Grebovic, M., Filipovic, L., Katnic, I., Vukotic, M., & Popovic, T. (2022, November). Overcoming limitations of statistical methods with artificial neural networks. In 2022 International Arab Conference on Information Technology (ACIT) (pp. 1-6). IEEE. https://doi.org/10.1109/ACIT57182.2022.9994218
Harjule, P., Rahman, A., Agarwal, B., & Tiwari, V. (Eds.). (2023). Computational statistical methodologies and modeling for artificial intelligence. CRC Press.
Hong, Y., Lian, J., Xu, L., Min, J., Wang, Y., Freeman, L. J., & Deng, X. (2023). Statistical perspectives on reliability of artificial intelligence systems. Quality Engineering, 35(1), 56-78. https://doi.org/10.1080/08982112.2022.2089854
Haddaway, N. R., Page, M. J., Pritchard, C. C., & McGuinness, L. A. (2022). PRISMA2020: An R package and Shiny app for producing PRISMA 2020-compliant flow diagrams, with interactivity for optimised digital transparency and Open Synthesis Campbell Systematic Reviews, 18, e1230. https://doi.org/10.1002/cl2.1230
Download citation (.ris)
Hou, L. (2021, October). Research on Artificial Intelligence Forecasting Method Integrating Data Mining and Statistical Analysis. In 2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT) (pp. 505-508). IEEE. https://doi.org/10.1109/ACAIT53529.2021.9731349
Martha, S., & Nuthana Priya, M. (2023). Role of statistics in artificial intelligence. International Journal of Engineering Applied Sciences and Technology, 8(1), 96-98. https://www.ijeast.com/papers/96-98,%20Tesma0712,IJEAST.pdf
Nassif, A. B., Soudan, B., Azzeh, M., Attilli, I., & AlMulla, O. (2021). Artificial intelligence and statistical techniques in short-term load forecasting: a review. arXiv preprint arXiv:2201.00437. https://doi.org/10.48550/arXiv.2201.00437
Paul, R., Arya, P., & Kumar, S. (2019). Use of Artificial Intelligence in statistical research. Indian Farming, 69(3).
Poduval, B., Pitman, K. M., Verkhoglyadova, O., & Wintoft, P. (2023). Applications of statistical methods and machine learning in the space sciences. Frontiers in Astronomy and Space Sciences, 10, 1163530. https://doi.org/10.3389/fspas.2023.1163530
Poursaeid, M., Poursaeid, A. H., & Shabanlou, S. (2022). A comparative study of artificial intelligence models and a statistical method for groundwater level prediction. Water Resources Management, 36(5), 1499-1519. https://doi.org/10.1007/s11269-022-03070-y
Sujatha, R., & Chatterjee, J. M. (2021). Role of Artificial Intelligence in COVID-19 Prediction Based on Statistical Methods. Applications of Artificial Intelligence in COVID-19, 73-97. https://doi.org/10.1007/978-981-15-7317-0_5
Tavazzi, E., Longato, E., Vettoretti, M., Aidos, H., Trescato, I., Roversi, C., ... & Di Camillo, B. (2023). Artificial intelligence and statistical methods for stratification and prediction of progression in amyotrophic lateral sclerosis: A systematic review. Artificial Intelligence in Medicine, 142, 102588. https://doi.org/10.1016/j.artmed.2023.102588
Yu, B., & Kumbier, K. (2018). Artificial intelligence and statistics. Frontiers of Information Technology & Electronic Engineering, 19(1), 6-9. https://doi.org/10.1631/FITEE.1700813
Zhang, Y., & Zhang, T. (2024). A Comprehensive Review of Assessing Storm Surge Disasters: From Traditional Statistical Methods to Artificial Intelligence-Based Techniques. Atmosphere, 15(3), 359. https://doi.org/10.3390/atmos15030359
Ezam, Z. , Totakhail, A. , Ghafory, H. & Hakimi, M. (2024). Transformative Impact of Artificial Intelligence on IoT Applications: A Systematic Reviewof Advancements, Challenges, and Future Trends. International Journal of Academic and Practical Research, 3(1), 155-164. https://doi.org/10.5281/zenodo.11397763
Amiri, G. A., Hakimi, M., Rajaee, S. M. K., & Hussaini, M. F. (2024). Artificial Intelligence and Technological Evolution: A Comprehensive Analysis of Modern Challenges and Future Opportunities. Journal of Social Science Utilizing Technology, 2(3), 301-316. https://doi.org/10.70177/jssut.v2i3.1265
Hakimi, M., Zarinkhail, M. S., Ghafory, H., & Hamidi, S. A. (2024). Revolutionizing technology education with artificial intelligence and machine learning: A comprehensive systematic literature review. TIERS, 6(2), 94–110. Retrieved November 30, 2024, from https://journal.undiknas.ac.id/index.php/tiers/article/view/5640
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