Artificial Intelligence and Deep Learning in Sensors and Applications

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 develo...

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Dettagli Bibliografici
Natura:	Online
Lingua:	inglese
Pubblicazione:	MDPI - Multidisciplinary Digital Publishing Institute 2024
Soggetti:	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 neural network multi-attention block residual block audio super-resolution bone-conduction microphone real-time system convolutional neural network face recognition adversarial attack perturbation adversarial examples adversarial patches Generative Adversarial Network semi-supervised learning semantic segmentation dense prediction one-way consistency scene understanding human activity recognition mmWave radar Kinect V4 sensor point clouds skeleton data multimodal two stream weed detection machine learning systematic literature review multivariate time-series short-time Fourier transform 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 multimodality feature fusion lung cancer CT scan clinical data n/a thema EDItEUR::U Computing and Information Technology thema EDItEUR::U Computing and Information Technology::UY Computer science
Accesso online:	ONIX_20240906_9783725814510_173
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Riassunto:	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.

Artificial Intelligence and Deep Learning in Sensors and Applications

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