Machine Learning and Life Sciences

Ghosh, Shyamasree; Dasgupta, Rathi

doi:10.1007/978-981-16-8881-2_11

Cited by 2 publications

(1 citation statement)

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“…They have been applied also in a myriad of other fields of science and technology. Such fields go from life sciences (Gosh and Dasgupta, 2022) [15] to urban traffic (Genser, 2022) [16] and cybersecurity (Musser and Garriot, 2021) [17]. The difference between classical and ML approach in dealing with the solar-terrestrial environment is indicated in Figure 3.…”

Section: The Advent Of Machine Learningmentioning

confidence: 99%

New Ways to Modelling and Predicting Ionosphere Variables

Radicella

2023

Atmosphere

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The new way of thinking science from Newtonian determinism to nonlinear unpredictability and the dawn of advanced computer science and technology can be summarized in the words of the theoretical physicist Michel Baranger, who, in 2000, said in a conference: “Twenty-first-century theoretical physics is coming out of the chaos revolution; it will be about complexity and its principal tool will be the computer.”. This can be extended to natural sciences in general. Modelling and predicting ionosphere variables have been considered since many decades as a paramount objective of research by scientists and engineers. The new approach to natural sciences influenced also ionosphere research. Ionosphere as a part of the solar–terrestrial environment is recognized to be a complex chaotic system, and its study under this new way of thinking should become an important area of ionospheric research. After discussing the new context, this paper will try to review recent advances in the exploration of ionosphere parameter time series in terms of chaos theory and the use of machine-learning algorithms.

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Section: The Advent Of Machine Learningmentioning

confidence: 99%