Artificial Intelligence and Deep Learning in Sensors and Applications

doi:10.3390/books978-3-7258-1452-7

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Artificial Intelligence and Deep Learning in Sensors and Applications

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July 2024

272 pages

ISBN978-3-7258-1451-0 (Hardback)
ISBN978-3-7258-1452-7 (PDF)

https://doi.org/10.3390/books978-3-7258-1452-7 (registering)

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This book is a reprint of the Special Issue Artificial Intelligence and Deep Learning in Sensors and Applications that was published in

Chemistry & Materials Science

Engineering

Environmental & Earth Sciences

Summary

The aim of this reprint is to address increasingly complex human problems by utilizing various sensors to collect data, enabling the formulation of solutions through deep learning and artificial intelligence (AI). This trend creates a high demand for sensors while presenting new challenges in developing sensor devices and applications across various fields, such as healthcare, manufacturing, agriculture, transportation, construction, and environmental monitoring. For instance, in environmental monitoring, AI-integrated sensors rapidly analyze large datasets to identify real-time patterns and trends, enhancing weather forecasting accuracy by gathering data from multiple sources. In industrial settings, AI-enhanced sensors optimize manufacturing by monitoring equipment health, predicting failures, and proactively scheduling maintenance. This reprint compiles contributions on AI and sensor technology, sharing ideas, designs, applications, and deployment experiences across various fields, including smart manufacturing, construction, autonomous vehicles, traffic monitoring, object recognition, image classification, speech processing, and human behavior analysis.

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Hardback

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Keywords

traffic flow prediction; deep learning; convolutional LSTM; attention mechanism; eXplainable Artificial Intelligence (XAI); XAI recommendation system; XAI scoring system; medical XAI; survey; approach; anomaly detection; anomaly classification; industrial control system; deep learning; deep neural network; multi-attention block; residual block; audio super-resolution; bone-conduction microphone; real-time system; convolutional neural network; deep learning; face recognition; adversarial attack; perturbation; adversarial examples; adversarial patches; Generative Adversarial Network; semi-supervised learning; semantic segmentation; dense prediction; one-way consistency; deep learning; scene understanding; human activity recognition; mmWave radar; Kinect V4 sensor; point clouds; skeleton data; multimodal; two stream; attention mechanism; weed detection; deep learning; machine learning; systematic literature review; multivariate time-series; anomaly detection; short-time Fourier transform; deep learning; transformer; self-attention; multi-head attention; point cloud; down sampling; classification; network; deep reinforcement learning; self-supervised learning; contrastive learning; generalization; data augmentation; network randomization; deep learning; multimodality; feature fusion; lung cancer; CT scan; clinical data; n/a