RAMSEY Volume 1, Issue 2, Aug 2022
A review of intentions to use artificial intelligence in Big Data Analytics for Thailand agriculture
Parichat Jaipong, Patcharavadee Sriboonruang, Sutithep Siripipattanakul, Tamonwan Sitthipon, Pichart Kaewpuang, Pichakoon Auttawechasakoon
Abstract
This study explains the essentials of artificial intelligence (AI) in big data analytics for the agriculture industry during the COVID-19 pandemic. The systematic review approach was employed for the documentary and analysed using content analysis. In addition, the literature and previous studies were obtained from various research articles on EBSCO, Google Scholar, Scopus, Web of Science, and ScienceDirect. The criteria for inclusion were studies that were related to AI in big data analytics for agriculture, were published in English, and were peer-reviewed. Five independent reviewers assessed search results, extracted data, and evaluated the studies’ quality to summarise and report the findings. The results reveal that the COVID-19 pandemic has increased the utility of AI in big data for agricultural business operations. For instance, AI-enabled supply chain management enables businesses to accurately predict demand spikes and decreases and adjust material volumes and routes. AI can collect more extensive data, which can assist the production team in predicting more accurate delivery times and inventory adjustments. Adaptability, high performance, precision, and cost-effectiveness are the central concepts of AI in agriculture. AI agriculture applications include soil, crop, weed, and disease management. The direct application of AI throughout the agricultural sector could represent a paradigm shift in agricultural practices. AI-powered farming solutions enable farmers to accomplish more with fewer resources, enhance crop quality, and guarantee a rapid GTM (go-to-market) crop strategy. Thus, AI adoption is based on the TAM model’s perceived ease of use, usefulness, and social influence. The recommendation is to research qualitative and quantitative approaches for further study and clarification of the essence of AI in the agriculture industry.
Keywords: big data analytics, artificial intelligence (AI), agriculture industry, intentions to use, systematic review
In-text Citation
According to new research… (Jaipong, Sriboonruang, et al., 2022).
In research from Jaipong, Sriboonruang, et al. (2022) …
Bibliography
Jaipong, P., Sriboonruang, P., Siripipattanakul, S., Sitthipon, T., Kaewpuang, P., & Auttawechasakoon, P. (2022). A review of intentions to use artificial intelligence in Big Data Analytics for Thailand agriculture. Review of Advanced Multidisciplinary Sciences, Engineering & Innovation, 1(2), 1–8.
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Artificial Intelligence (AI) Adoption in the Medical Education during the Digital Era: A Review Article
Tamonwan Sitthipon, Pichart Kaewpuang, Parichat Jaipong, Patcharavadee Sriboonruang, Sutithep Siripipattanakul, Pichakoon Auttawechasakoon
Abstract
This study explains the essentials of artificial intelligence (AI) in medical education during the digital era. This article contains a comprehensive systematic review and content analysis. The literature and previous studies were obtained from various research articles on EBSCO, Google Scholar, Scopus, Web of Science, and ScienceDirect. The criteria for inclusion were studies that clearly defined artificial intelligence related to the medical education sector and were published in English with peer-reviewed. Five independent reviewers evaluated search results, extracted data, and the quality of the studies to summarise and report the findings. The results reveal that as the practice of medicine enters the era of AI, the use of data to improve clinical decision-making will increase, thereby increasing the need for skilled medicine-machine interaction. As the rate of medical knowledge growth accelerates, technologies such as AI are required to enable healthcare professionals to apply the knowledge in practice effectively. Medical professionals must receive adequate training on this new technology, its advantages to improving cost, quality, and access to healthcare, and its disadvantages, such as lack of transparency and liability. AI must be seamlessly integrated into the curriculum’s various facets. The recommendation is to address the current state of education on AI in the medical curriculum. Moreover, both quantitative and qualitative studies are necessary for further research.
Keywords: artificial intelligence, medical education, digital era, TAM, intention to use
In-text Citation
According to new research… (Jaipong, Sriboonruang, et al., 2022).
In research from Jaipong, Sriboonruang, et al. (2022) …
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