Innovative Topologies and Algorithms for Neural Networks

Salvatore Graziani and Maria Gabriella Xibilia
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Salvatore Graziani: Dipartimento di Ingegneria Elettrica, Elettronica e Informatica, University of Catania, Viale Andrea Doria 6, 95125 Catania, Italy
Maria Gabriella Xibilia: Dipartimento di Ingegneria, University of Messina, Contrada di Dio, S. Agata, 98166 Messina ME, Italy

Future Internet, 2020, vol. 12, issue 7, 1-4

Abstract: 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 the state of the art in many applications, 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 is devoted to the study and proposal of novel architectures, including solutions tailored to specific problems. The papers of this Special Issue make significant contributions to the above-mentioned fields by merging theoretical aspects and relevant applications. Twelve papers are collected in the issue, addressing many relevant aspects of the topic.

Keywords: autoencoders; long-short-term memory networks; convolution neural Networks; object recognition; sentiment analysis; text recognition; gesture recognition; IoT (Internet of Thing) systems; medical applications (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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