Machine Learning Methods in Systems
Proceedings of 13th Computer Science On-line Conference 2024, Vol. 4
Silhavy, Radek.
editor.
edt
http://id.loc.gov/vocabulary/relators/edt
Silhavy, Petr.
editor.
edt
http://id.loc.gov/vocabulary/relators/edt
SpringerLink (Online service)
text
sz
2024
1st ed. 2024.
monographic
eng
access
XV, 520 p. 193 illus., 139 illus. in color. online resource.
This book requires an in-depth exploration of machine learning and its integration into system engineering. This book presents contemporary research methodologies, with a strong focus on the innovative application of machine learning techniques in developing and optimizing systems. It includes the meticulously reviewed proceedings from the Machine Learning Methods in Systems session of the 13th Computer Science Online Conference 2024 (CSOC 2024), held virtually in April 2024.
-- 1: Extrapolation of periodic signal with Poisson noise using neural networks. -- 2: Method for Complexing Information From Intelligent Sensors of Mobile Components of Monitoring Systems. -- 3: Using regression models to analyze data. -- 4: Improving Password Generation Algorithm with Parallellism: comparative performance study. -- 5: Assessing the Feasibility of Implementing Information Systems and Management Systems Projects Using Fuzzy Modeling Tools. -- 6: Semi-phenomenological approach to the description of gold nanoclusters. -- 7: “Imaginary boundary” method in studying the optical properties of ordered nanostructures. -- 8: A method for controlling the efficiency of second harmonic generation by controlled change in the refractive index of an external dielectric medium. -- 9: Study of the properties of selectively transparent metasurfaces tunable through external control of the properties of 2D materials, etc.
edited by Radek Silhavy, Petr Silhavy.
Computational intelligence
Software engineering
Computational Intelligence
Software Engineering
Q342
Springer Nature eBook
Lecture Notes in Networks and Systems, 1126
9783031705953
https://doi.org/10.1007/978-3-031-70595-3
https://doi.org/10.1007/978-3-031-70595-3
241024
20251030113242.0
978-3-031-70595-3