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WALCOM: Algorithms and Computation : 16th International Conference and Workshops, WALCOM 2022, Jember, Indonesia, March 24–26, 2022, Proceedings
Authors: --- ---
ISBN: 3030967301 303096731X Year: 2022 Publisher: Cham : Springer International Publishing : Imprint: Springer,

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Abstract

This book constitutes the proceedings of the 16th International Conference on Algorithms and Computation, WALCOM 2022, which was held in Jember, Indonesia, during March 24-26, 2022. This proceedings volume contains 30 full papers which were carefully reviewed and selected from a total of 89 submissions and 3 invited papers. They cover diverse areas of algorithms and computation, such as approximation algorithms, computational complexity, computational geometry, graph algorithms, graph drawing and visualization, online algorithms, parameterized complexity and property testing.

Keywords

Computer science. --- Computer science—Mathematics. --- Discrete mathematics. --- Data structures (Computer science). --- Information theory. --- Computer graphics. --- Computer engineering. --- Computer networks. --- Theory of Computation. --- Discrete Mathematics in Computer Science. --- Data Structures and Information Theory. --- Computer Graphics. --- Computer Engineering and Networks. --- Informatics --- Science --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Electronic data processing --- Network computers --- Computers --- Automatic drafting --- Graphic data processing --- Graphics, Computer --- Computer art --- Graphic arts --- Engineering graphics --- Image processing --- Communication theory --- Communication --- Cybernetics --- Information structures (Computer science) --- Structures, Data (Computer science) --- Structures, Information (Computer science) --- File organization (Computer science) --- Abstract data types (Computer science) --- Discrete mathematical structures --- Mathematical structures, Discrete --- Structures, Discrete mathematical --- Numerical analysis --- Distributed processing --- Design and construction --- Digital techniques --- Computer science --- Mathematics.


Book
Machine Learning under Resource Constraints.
Authors: --- --- --- --- --- et al.
ISBN: 3110785986 3110785978 Year: 2022 Publisher: Berlin ; Boston : De Gruyter,

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Machine Learning under Resource Constraints addresses novel machine learning algorithms that are challenged by high-throughput data, by high dimensions, or by complex structures of the data in three volumes. Resource constraints are given by the relation between the demands for processing the data and the capacity of the computing machinery. The resources are runtime, memory, communication, and energy. Hence, modern computer architectures play a significant role. Novel machine learning algorithms are optimized with regard to minimal resource consumption. Moreover, learned predictions are executed on diverse architectures to save resources. It provides a comprehensive overview of the novel approaches to machine learning research that consider resource constraints, as well as the application of the described methods in various domains of science and engineering. Volume 3 describes how the resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples. In the areas of health and medicine, it is demonstrated how machine learning can improve risk modelling, diagnosis, and treatment selection for diseases. Machine learning supported quality control during the manufacturing process in a factory allows to reduce material and energy cost and save testing times is shown by the diverse real-time applications in electronics and steel production as well as milling. Additional application examples show, how machine-learning can make traffic, logistics and smart cities more effi cient and sustainable. Finally, mobile communications can benefi t substantially from machine learning, for example by uncovering hidden characteristics of the wireless channel.

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