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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Hodhod IntJofOnlineBiomedEng2019 15-3.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:CyberMaster: An expert system to guide the development of cybersecurity curricula|CyberMaster: An expert system to guide the development of cybersecurity curricula]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The growing number of reported cyberattacks poses a difficult challenge to individuals, governments, and organizations. Adequate protection of [[information]] systems urgently requires a [[cybersecurity]]-educated workforce trained using a curriculum that covers the essential skills required for different cybersecurity work roles. The goal of the CyberMaster [[expert system]] is to assist inexperienced instructors with cybersecurity course design. It is an intelligent system that uses visual feedback to guide the user through the design process. Initial test executions show the promise of such a system in addressing the enormous shortage of cybersecurity experts currently available for designing courses and training programs. ('''[[Journal:CyberMaster: An expert system to guide the development of cybersecurity curricula|Full article...]]''')<br />
[[Artificial intelligence]] (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and [[Data visualization|visualize]] large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in [[Information management|data management]] that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of [[machine learning]] (ML), which holds much promise for this domain ... ('''[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Full article...]]''')<br />
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Revision as of 17:50, 15 April 2024

Tab1 Williamson F1000Res2023 10.png

"Data management challenges for artificial intelligence in plant and agricultural research"

Artificial intelligence (AI) is increasingly used within plant science, yet it is far from being routinely and effectively implemented in this domain. Particularly relevant to the development of novel food and agricultural technologies is the development of validated, meaningful, and usable ways to integrate, compare, and visualize large, multi-dimensional datasets from different sources and scientific approaches. After a brief summary of the reasons for the interest in data science and AI within plant science, the paper identifies and discusses eight key challenges in data management that must be addressed to further unlock the potential of AI in crop and agronomic research, and particularly the application of machine learning (ML), which holds much promise for this domain ... (Full article...)
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