TY - BOOK ID - 133330385 TI - Innovative Topologies and Algorithms for Neural Networks AU - Xibilia, Maria Gabriella AU - Graziani, Salvatore PY - 2021 PB - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute DB - UniCat KW - Information technology industries KW - facial image analysis KW - facial nerve paralysis KW - deep convolutional neural networks KW - image classification KW - Chinese text classification KW - long short-term memory KW - convolutional neural network KW - Arabic named entity recognition KW - bidirectional recurrent neural network KW - GRU KW - LSTM KW - natural language processing KW - word embedding KW - CNN KW - object detection network KW - attention mechanism KW - feature fusion KW - LSTM-CRF model KW - elements recognition KW - linguistic features KW - POS syntactic rules KW - action recognition KW - fused features KW - 3D convolution neural network KW - motion map KW - long short-term-memory KW - tooth-marked tongue KW - gradient-weighted class activation maps KW - ship identification KW - fully convolutional network KW - embedded deep learning KW - scalability KW - gesture recognition KW - human computer interaction KW - alternative fusion neural network KW - deep learning KW - sentiment attention mechanism KW - bidirectional gated recurrent unit KW - Internet of Things KW - convolutional neural networks KW - graph partitioning KW - distributed systems KW - resource-efficient inference KW - pedestrian attribute recognition KW - graph convolutional network KW - multi-label learning KW - autoencoders KW - long-short-term memory networks KW - convolution neural Networks KW - object recognition KW - sentiment analysis KW - text recognition KW - IoT (Internet of Thing) systems KW - medical applications UR - https://www.unicat.be/uniCat?func=search&query=sysid:133330385 AB - The introduction of new topologies and training procedures to deep neural networks has solicited a renewed interest in the field of neural computation. The use of deep structures has significantly improved state-of-the-art applications in many fields, such as computer vision, speech and text processing, medical applications, and IoT (Internet of Things). The probability of a successful outcome from a neural network is linked to selection of an appropriate network architecture and training algorithm. Accordingly, much of the recent research on neural networks has been devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. This book gives significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications. ER -