Effects of extreme value loss on long-term correlated time series are analyzed by means of detrended fluctuation analysis (DFA) and power spectral density analysis. Weaker memory can be detected after removing of extreme values for the artificial long-term correlated data, indicating the emergence ...
Crop yield estimation Deep Learning Long short-term memory Multi-task learning Extreme yield loss Attribution analysis 1. Introduction Large-scale crop yield estimation is critical for understanding the dynamics of global food security. The food supply has become more vulnerable under global warming with...
The AI model developed for this study uses long short-term memory (LSTM) networks21to predict daily streamflow through a 7-day forecast horizon. The model is described in detail inMethods, and a version of the model suitable for research is implemented in the open-source NeuralHydrology reposit...
The authors further applied the proposed double time-scale travel time distribution to analyzing network resilience by showing the short-term and long-term impacts of disruption and systematic changes on road network performance. 1.1.2. Travel time variability and the treatment of extreme events ...
In addition, AttentionXML uses bidirectional long short-term memory (BiLSTM) with an attention mechanism to capture a specific text representation for each label. X-Transformer [7] employs Transformer encoders to match relevant clusters and then uses sparse TF-IDF features and neural embeddings to ...
the present study is aimed to propose a deep learning model using a long short-term memory (LSTM)-based deep learning model and recurrent neural network (RNN) in order to increase the predictive accuracy of short and long-term annual windows by enhancing deep learning (two-dimensional). In ...
multiple global vegetation models and machine learning products to analyze the cause of the sensitivity change. We found that a threefold increase inγCGRTemerged due to the long-term changes in the magnitude of CGR variability (i.e., indicated by one standard deviation of CGR; STDCGR), which...
Slope multi-step excavation displacement prediction surrogate model based on a long short-term memory neural network: for small sample data and multi-feature multi-task learning. Georisk, 2024. DOI:10.1080/17499518.2024.2356543 196. Liu, C., Su, H. Prediction of martensite start temperature of ...
Malware classification using word embeddings algorithms and long-short term memory networks 2022, Computational Intelligence Designing and validating a cost effective safe network: Application to a PACS system 2019, International Conference on Advances in Biomedical Engineering, ICABMEAbout the author Steve ...
Microscopy with extreme ultraviolet (EUV) radiation holds promise for high-resolution imaging with excellent material contrast, due to the short wavelength and numerous element-specific absorption edges available in this spectral range. At the same time,