Sensors Data Processing Using Machine Learning

doi:10.3390/books978-3-7258-1172-4

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Sensors Data Processing Using Machine Learning

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May 2024

248 pages

ISBN978-3-7258-1171-7 (Hardback)
ISBN978-3-7258-1172-4 (PDF)

https://doi.org/10.3390/books978-3-7258-1172-4

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This book is a reprint of the Special Issue Sensors Data Processing Using Machine Learning that was published in

Chemistry & Materials Science

Engineering

Environmental & Earth Sciences

Summary

The main aim of this reprint was to collect research focusing on data processing using machine learning and deep learning. We invited investigators to contribute both original and review articles, covering the research and development in the areas of data processing using machine learning (ML) and deep learning (DL). These areas include solutions that are designed for smart devices. In this reprint, leading experts in the field share their insights, research findings, and visions for the future. Together, we embark on a journey to unlock the potential of effective data processing that involves transforming data from a given format into a more usable and desirable form, rendering them more meaningful and informative. Machine learning (ML), deep learning (DL), and artificial intelligence (AI) have proven to be effective methods for this purpose. Through the utilization of machine learning algorithms, mathematical modeling, or various statistical techniques, the entire process can be automated.

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Hardback

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Keywords

web mining; detection of degrees of toxicity; machine learning; lexicon approach; text data processing; cooperative, connected and automated mobility; infrastructure readiness assessment; connectivity data; positioning data; convolutional neural network; IoT-based system; IoT nodes; Raspberry Pi; Arduino-based module; COVID-19; big data; pre-trained model; BERT; DistilBERT; BERTimbau; DistilBERTimbau; transformer-based machine learning; rare earth extraction; time delay identification; grey correlation analysis; time-correlation; discrete state transition algorithm; wavelet neural network; deep learning; text classification; two-stream networks; feature fusion; sentiment classification; sarcasm detection; H.264/AVC; H.265/HEVC; QoE; QoS; packet loss rate; video quality; deep learning; 3DCNN; ConvLSTM; human activity recognition; IoT; smart systems; indoor navigation; mobile application; machine learning; deep learning; neural processing unit; neural processing cores; NPU benchmark; processor architectures; Apple M1; Apple M2; CoreML; neural engine; teaching evaluation system; student learning behavior; data augmentation; smart classrooms; ductile cast iron pipe; defect classification; self-supervised; CutPaste-Mix; remote sensing classification; sample selection method; classification model; sample size; n/a

Sensors Data Processing Using Machine Learning

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