Recurrent Neural Networks: Algorithms Design and Applications for Safety Critical Systems
A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Combinatorial Optimization, Graph, and Network Algorithms".
Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 3594
Special Issue Editor
Interests: machine learning; active learning; computational intelligence; big data; health diagnostics; anomalies; traffic; hot spots; bio-inspired computation; meta-learning; behaviour identification
Special Issue Information
Dear Colleagues,
Recurrent Neural Networks (RNNs) are a category of Neural Networks that allow for capturing temporal dynamic behaviour from data. It has been widely applied to sequential data or time series data. Applications include natural language processing, prognostic and health management, healthcare, human behaviour detection, and other safety critical systems. RNNs are distinguished by their memory mechanism, as information from prior input layers of the network influence subsequent inputs and outputs. Several variations and modifications of RNNs are now found in the literature, such as GRUs, LSTMS, Bi-directional RNN, expanding the domains of applicability, as well as the effectiveness of these approaches to temporal data.
This Special Issue invites researchers to submit their recent advances in RNNs for safety critical systems. Potential topics of interest include, but are not limited to:
- Novel algorithms and applications;
- Stacked and/or hybrid architectures (e.g., ConvLSTM);
- RNNs for sensor healthcare data;
- Exploration of RNN and transformers for fault detection and/or anomaly detection;
- Novel algorithms that address uncertainty using Bayesian Techniques, Gaussian Processes, etc.;
- Engineering applications of RNN, such as prognostic and health management, remaining useful life prediction;
- Multi-view RNNs;
- Attention mechanisms;
- RNN explanation/interpretation
Dr. Grazziela Patrocinio Figueredo
Guest Editor
Manuscript Submission Information
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