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:Fig17 Pinheiro Sensors2018 18-3.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Security architecture and protocol for trust verifications regarding the integrity of files stored in cloud services|Security architecture and protocol for trust verifications regarding the integrity of files stored in cloud services]]"'''
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


[[Cloud computing]] is considered an interesting paradigm due to its scalability, availability, and virtually unlimited storage capacity. However, it is challenging to organize a cloud storage service (CSS) that is safe from the client point-of-view and to implement this CSS in public clouds since it is not advisable to blindly consider this configuration as fully trustworthy. Ideally, owners of large amounts of data should trust their data to be in the cloud for a long period of time, without the burden of keeping copies of the original data, nor of accessing the whole content for verification regarding data preservation. Due to these requirements, [[Data integrity|integrity]], availability, [[Information privacy|privacy]], and trust are still challenging issues for the adoption of cloud storage services, especially when losing or leaking [[information]] can bring significant damage, be it legal or business-related. With such concerns in mind, this paper proposes an architecture for periodically monitoring both the information stored in the cloud and the service provider behavior. ('''[[Journal:Security architecture and protocol for trust verifications regarding the integrity of files stored in cloud services|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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