Big Data for the Magnetic Field Variations in Solar-Terrestrial Physics and Their Wavelet Analysis

Srebrov, Bozhidar; Kounchev, Ognyan; Simeonov, Georgi

doi:10.1016/b978-0-12-819154-5.00031-x

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…The principal goal of unsupervised learning is to discover hidden and interesting patterns in unlabeled data. Unlike supervised learning, unsupervised learning uses unlabeled, unclassified, and categorized training data [41]. Popular unsupervised learning algorithms include but are not limited to K-Means Clustering [42] and Dimensionality Reduction Algorithms [39].…”

Section: Figure 3 Artificial Neural Networkmentioning

confidence: 99%

Applications and Challenges of Artificial Intelligence in Space Missions

2024

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Artificial Intelligence (AI) is increasingly finding acceptance in the space community, especially Machine Learning (ML), a subdomain of AI. ML algorithms now find numerous applications in autonomous navigation, spacecraft health monitoring and operational management of satellite constellations. However, a large number of surveys on the applications of AI in space missions can be classified into two categories. The first category suffers from the limitation of being old and not covering some crucial and recent developments in the field; such as the contributions of Deep Learning (DL) and bioinspired AI algorithms. The challenge with the second category lies in its being too detailed with respect to the development and application of specific AI techniques or algorithms. These limitations have necessitated the need to have a concise survey with a wider scope for those interested in the applications and challenges of AI in the space industry, especially those with technical backgrounds in other fields. In this paper, we surveyed the use of traditional AI techniques in various domains of space missions without delving into formal methods. Some bioinspired AI algorithms were also surveyed and their potential application areas highlighted. Unlike similar surveys that focus only on technological challenges, we also addressed some crucial legal drawbacks that emanate from the reliance and use of AI in space. Summarily, while discussing future directions we reviewed some advancements in Internet of Things (IoT) and Blockchain technologies. Our review prioritized three application domains positioned to benefit immensely from the inevitable AI-Blockchain convergence in the space community. These include the Internet of Space Things (IoST), Satellite Communication and Spacecraft Data Security.

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Section: Figure 3 Artificial Neural Networkmentioning

confidence: 99%

Applications and Challenges of Artificial Intelligence in Space Missions

2024

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Lithosphere–atmosphere–ionosphere coupling during the September 2015 Coquimbo earthquake

Adhikari,

Klausner,

Cândido

et al. 2024

J Earth Syst Sci

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Big Data and Actuarial Science

Hassani

Unger

Beneki

2020

BDCC

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This article investigates the impact of big data on the actuarial sector. The growing fields of applications of data analytics and data mining raise the ability for insurance companies to conduct more accurate policy pricing by incorporating a broader variety of data due to increased data availability. The analyzed areas of this paper span from automobile insurance policy pricing, mortality and healthcare modeling to estimation of harvest-, climate- and cyber risk as well as assessment of catastrophe risk such as storms, hurricanes, tornadoes, geomagnetic events, earthquakes, floods, and fires. We evaluate the current use of big data in these contexts and how the utilization of data analytics and data mining contribute to the prediction capabilities and accuracy of policy premium pricing of insurance companies. We find a high penetration of insurance policy pricing in almost all actuarial fields except in the modeling and pricing of cyber security risk due to lack of data in this area and prevailing data asymmetries, for which we identify the application of artificial intelligence, in particular machine learning techniques, as a possible solution to improve policy pricing accuracy and results.

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Big Data for the Magnetic Field Variations in Solar-Terrestrial Physics and Their Wavelet Analysis

Cited by 3 publications

References 12 publications

Applications and Challenges of Artificial Intelligence in Space Missions

Applications and Challenges of Artificial Intelligence in Space Missions

Lithosphere–atmosphere–ionosphere coupling during the September 2015 Coquimbo earthquake

Big Data and Actuarial Science

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