CAPABILITIES AND APPLICATION OF ARTIFICIAL INTELLIGENCE (AI) MODELS IN QUALITATIVE AND QUANTITATIVE DATA MINING, DATA PROCESSING AND DATA ANALYSIS

Authors

  • Moises C. Torrentira Jr. EnP, PhD., University of Southeastern Philippines, Philippines

DOI:

https://doi.org/10.46827/ejes.v11i9.5521

Keywords:

capabilities and application, artificial intelligence (AI) models, qualitative, quantitative, data mining, data processing, and data analysis

Abstract

The study was conducted to determine the capabilities and application of Artificial Intelligence in the development of themes in qualitative and quantitative data analysis. Multi-stage data mining as a tool in AI data collection was employed by the researcher to process data reduction and the extraction of codes. This technique was able to generate the final themes pertaining to the capabilities and application of AI models, which include pattern recognition analysis, thematic analysis, sentiment analysis, efficient data processing, objectivity and bias reduction, and insight generation. These capabilities and applications of AI models can be used to highlight the benefits and ethical use of AI in research, especially in data mining, data processing, and data analysis.

 

Article visualizations:

Hit counter

Author Biography

  • Moises C. Torrentira Jr., EnP, PhD., University of Southeastern Philippines, Philippines

    About the Author

    Dr. Torrentira is a licensed environmental planner and a professional teacher. He earned a Doctorate Degree in Development Administration while his Master’s degrees include Master in Environmental Planning, Master in Business Administration and Master in Public Administration. His Orcid ID is https://orcid.org/0000-0003-3812-0182. His works are searchable in Academia and ResearchGate. He has been consistently cited by other authors in various academic fields.

References

"Artificial intelligence technology behind ChatGPT was built in Iowa — with a lot of water". AP News. September 9, 2023. Archived from the original on September 10, 2023. Retrieved on September 10, 2023.

Asatryan, B., Bleijendaal, H., & Wilde, A. A. (2023). Towards advanced diagnosis and management of inherited arrhythmia syndromes: Harnessing the capabilities of artificial intelligence and machine learning. Heart Rhythm.

Chowdhary, K., & Chowdhary, K. R. (2020). Natural language processing. Fundamentals of artificial intelligence, 603-649. https://doi.org/10.1016/j.hrthm.2023.07.001

Baviskar, D., Ahirrao, S., Potdar, V., & Kotecha, K. (2021). Efficient automated processing of the unstructured documents using artificial intelligence: A systematic literature review and future directions. IEEE Access, 9, 72894-72936. Retrieved from https://ieeexplore.ieee.org/document/9402739

Belotto, M. J. (2018). Data analysis methods for qualitative research: Managing the challenges of coding, interrater reliability, and thematic analysis. The qualitative report, 23(11), 2622-2633. http://dx.doi.org/10.46743/2160-3715/2018.3492

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer 645-678. Retrieved from https://link.springer.com/book/9780387310732

Chakriswaran, P., Vincent, D. R., Srinivasan, K., Sharma, V., Chang, C. Y., & Reina, D. G. (2019). Emotion AI-driven sentiment analysis: A survey, future research directions, and open issues. Applied Sciences, 9(24), 5462. Retrieved from https://www.mdpi.com/2076-3417/9/24/5462

Huang, CJ., Chen, PW. & Pan, WT. Using multi-stage data mining technique to build forecast model for Taiwan stocks. Neural Comput & Applic 21, 2057–2063 (2012). https://doi.org/10.1007/s00521-011-0628-0

Kelly, S., Kaye, S.A., Oviedo-Trespalacios, O. (2023). What factors contribute to the acceptance of artificial intelligence? A systematic review. Telematics and Informatics, Vol. 77. https://doi.org/10.1016/j.tele.2022.101925

Ma, L., & Sun, B. (2020). Machine learning and AI in marketing–Connecting computing power to human insights. International Journal of Research in Marketing, 37(3), 481-504. https://doi.org/10.1016/j.ijresmar.2020.04.005

Marshall, D. T., & Naff, D. B. (2024). The Ethics of Using Artificial Intelligence in Qualitative Research. Journal of empirical research on human research ethics: JERHRE, 19(3), 92–102. https://doi.org/10.1177/15562646241262659

Riger, S., & Sigurvinsdottir, R. (2016). Thematic analysis. In L. A. Jason & D. S. Glenwick (Eds.), Handbook of methodological approaches to community-based research: Qualitative, quantitative, and mixed methods (pp. 33–41). Oxford University Press.

Silberg, J., & Manyika, J. (2019). Notes from the AI frontier: Tackling bias in AI (and in humans). McKinsey Global Institute, 1(6), 1-31. Retrieved from https://www.mckinsey.com/~/media/mckinsey/featured%20insights/artificial%20intelligence/tackling%20bias%20in%20artificial%20intelligence%20and%20in%20humans/mgi-tackling-bias-in-ai-june-2019.pdf

Sun, W., Nasraoui, O., & Shafto, P. (2020). Evolution and impact of bias in human and machine learning algorithm interaction. Plos one, 15(8), e0235502. https://doi.org/10.1371/journal.pone.0235502

Taherdoost, H., & Madanchian, M. (2023). Artificial intelligence and sentiment analysis: A review in competitive research. Computers, 12(2), 37. https://doi.org/10.3390/computers12020037

Terry, G., Hayfield, N., Clarke, V., & Braun, V. (2017). Thematic analysis. The SAGE handbook of qualitative research in psychology, 2(17-37), 25.

Turner, S. F., Cardinal, L. B., & Burton, R. M. (2017). Research design for mixed methods: A triangulation-based framework and roadmap. Organizational research methods, 20(2), 243-267. https://doi.org/10.4135/9781526405555

Wu, T., He, S., Liu, J., Sun, S., Liu, K., Han, Q. L., & Tang, Y. (2023). A brief overview of ChatGPT: The history, status quo and potential future development. IEEE/CAA Journal of Automatica Sinica, 10(5), 1122-1136. Retrieved from https://ieeexplore.ieee.org/document/10113601

Downloads

Published

2024-08-11

Issue

Section

Articles