Deep Learning in Big Data, Image, and Signal Processing in the Modern Digital Age
At present, data is constantly generated across various industries, including the internet. New technologies have emerged to trace data origins and assess their potential for collection, quantification, decoding, and analysis. Big data, signals, and images are of particular importance due to the wea...
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| Other Authors: | , |
| Format: | eBook |
| Language: | English |
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Basel
MDPI - Multidisciplinary Digital Publishing Institute
2023
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| Collection: | Directory of Open Access Books - Collection details see MPG.ReNa |
| Summary: | At present, data is constantly generated across various industries, including the internet. New technologies have emerged to trace data origins and assess their potential for collection, quantification, decoding, and analysis. Big data, signals, and images are of particular importance due to the wealth of domain-specific information they hold. These data play a critical role in addressing issues like national security, cybersecurity, marketing, medical informatics, and fraud detection. Deep learning techniques have gained immense popularity. They empower the analysis and understanding of vast amounts of unsupervised data, making them invaluable for processing when raw data lack categorization and labels. These applications extend to working with medical images and signal processing for wellness devices, remote monitoring, and neural devices. In industrial settings, data can be used for early warning systems in assembly lines, while massive datasets can be derived from electronic health records and hospital information systems, for example. This reprint is dedicated to exploring the application of deep learning in tackling significant challenges related to big data, images, and signals. It seeks to unravel the potential of these cutting-edge technologies in addressing complex real-world problems across diverse sectors. |
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| Item Description: | Creative Commons (cc), https://creativecommons.org/licenses/by/4.0/ |
| Physical Description: | 1 electronic resource (452 p.) |
| ISBN: | books978-3-0365-9099-8 9783036590998 9783036590981 |