Difference between revisions of "Template:Article of the week"

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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig1 Annane IntJInterMobileTech2019 13-4.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Virtualization-based security techniques on mobile cloud computing: Research gaps and challenges|Virtualization-based security techniques on mobile cloud computing: Research gaps and challenges]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


The principle constraints of mobile devices are their limited resources, including processing capability, storage space, and battery life. However, [[cloud computing]] offers a means of vast computing resources and services. With it a new idea emerged, the inclusion of cloud computing into mobile devices such as smartphones, tablet, and other personal digital assistants (PDA) to augment their capacities, providing a robust technology called mobile cloud computing (MCC). Although MCC has brought many advantages to mobile users, it also still suffers from the security and privacy issues of data while hosted on virtual machines (VM) on remote cloud’s servers. Currently, the eyes of security experts are turned towards the virtualization-based security techniques used either on the cloud or on mobile devices. The new challenge is to develop secure methods in order to authenticate highly sensitive digital content. ('''[[Journal:Virtualization-based security techniques on mobile cloud computing: Research gaps and challenges|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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