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Sensors Data Processing Using Machine Learning

Sensors Data Processing Using Machine Learning

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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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Keywords

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

Links

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

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