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Innovative Topologies and Algorithms for Neural Networks

Author

Listed:
  • 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)

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.

Suggested Citation

  • Salvatore Graziani & Maria Gabriella Xibilia, 2020. "Innovative Topologies and Algorithms for Neural Networks," Future Internet, MDPI, vol. 12(7), pages 1-4, July.
    • Handle: RePEc:gam:jftint:v:12:y:2020:i:7:p:117-:d:383394
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    References listed on IDEAS

    as
    1. Qiao Meng & Huansheng Song & Gang Li & Yu’an Zhang & Xiangqing Zhang, 2019. "A Block Object Detection Method Based on Feature Fusion Networks for Autonomous Vehicles," Complexity, Hindawi, vol. 2019, pages 1-14, February.
    2. Fabíola Martins Campos de Oliveira & Edson Borin, 2019. "Partitioning Convolutional Neural Networks to Maximize the Inference Rate on Constrained IoT Devices," Future Internet, MDPI, vol. 11(10), pages 1-30, September.
    3. Dong Xu & Ruping Ge & Zhihua Niu, 2019. "Forward-Looking Element Recognition Based on the LSTM-CRF Model with the Integrity Algorithm," Future Internet, MDPI, vol. 11(1), pages 1-16, January.
    4. Xinyu Zhang & Xiaoqiang Li, 2019. "Dynamic Gesture Recognition Based on MEMP Network," Future Internet, MDPI, vol. 11(4), pages 1-11, April.
    5. Wenkuan Li & Peiyu Liu & Qiuyue Zhang & Wenfeng Liu, 2019. "An Improved Approach for Text Sentiment Classification Based on a Deep Neural Network via a Sentiment Attention Mechanism," Future Internet, MDPI, vol. 11(4), pages 1-15, April.
    6. Ying Zhang & Yimin Chen & Chen Huang & Mingke Gao, 2019. "Object Detection Network Based on Feature Fusion and Attention Mechanism," Future Internet, MDPI, vol. 11(1), pages 1-14, January.
    7. Hongwei Zhao & Weishan Zhang & Haoyun Sun & Bing Xue, 2019. "Embedded Deep Learning for Ship Detection and Recognition," Future Internet, MDPI, vol. 11(2), pages 1-12, February.
    8. Xiangpeng Song & Hongbin Yang & Congcong Zhou, 2019. "Pedestrian Attribute Recognition with Graph Convolutional Network in Surveillance Scenarios," Future Internet, MDPI, vol. 11(11), pages 1-13, November.
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    Cited by:

    1. Vidhi Tiwari & Kirti Pal, 2022. "Short-Term Load Forecasting for a Captive Power Plant Using Artificial Neural Network," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 12(1), pages 1-11, January.
    2. Marco Ferretti & Ugo Fiore & Francesca Perla & Marcello Risitano & Salvatore Scognamiglio, 2022. "Deep Learning Forecasting for Supporting Terminal Operators in Port Business Development," Future Internet, MDPI, vol. 14(8), pages 1-19, July.

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