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'''"[[Journal:Smart information systems in cybersecurity: An ethical analysis|Smart information systems in cybersecurity: An ethical analysis]]"'''
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Tab1 Williamson F1000Res2023 10.png|240px]]</div>
'''"[[Journal:Data management challenges for artificial intelligence in plant and agricultural research|Data management challenges for artificial intelligence in plant and agricultural research]]"'''


This report provides an overview of the current implementation of smart information systems (SIS) in the field of [[cybersecurity]]. It also identifies the positive and negative aspects of using SIS in cybersecurity, including ethical issues which could arise while using SIS in this area. One company working in the industry of telecommunications (Company A) is analysed in this report. Further specific ethical issues that arise when using SIS technologies in Company A are critically evaluated. Finally, conclusions are drawn on the case study, and areas for improvement are suggested. Increasing numbers of items are becoming connected to the internet. Cisco—a global leader in information technology, networking, and [[cybersecurity]]—estimates that more than 8.7 billion devices were connected to the internet by the end of 2012, a number that will likely rise to over 40 billion in 2020. ('''[[Journal:Smart information systems in cybersecurity: An ethical analysis|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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