Artificial Intelligence and Deep Learning in Sensors and Applications
Materialtyp:
ArtikelUtgivningsinformation: MDPI - Multidisciplinary Digital Publishing Institute 2024Innehållstyp: - text
- computer
- online resource
- 9783725814510
- 9783725814527
- Computing and Information Technology
- Computer science
- CT scan
- Generative Adversarial Network
- Kinect V4 sensor
- XAI recommendation system
- XAI scoring system
- adversarial attack
- adversarial examples
- adversarial patches
- anomaly classification
- anomaly detection
- approach
- attention mechanism
- audio super-resolution
- bone-conduction microphone
- classification
- clinical data
- contrastive learning
- convolutional LSTM
- convolutional neural network
- data augmentation
- deep learning
- deep neural network
- deep reinforcement learning
- dense prediction
- down sampling
- eXplainable Artificial Intelligence (XAI)
- face recognition
- feature fusion
- generalization
- human activity recognition
- industrial control system
- lung cancer
- machine learning
- medical XAI
- mmWave radar
- multi-attention block
- multi-head attention
- multimodal
- multimodality
- multivariate time-series
- network
- network randomization
- one-way consistency
- perturbation
- point cloud
- point clouds
- real-time system
- residual block
- scene understanding
- self-attention
- self-supervised learning
- semantic segmentation
- semi-supervised learning
- short-time Fourier
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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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eng
Freely available e-book