Risk Prediction for Ship Encounter Situation Awareness Using Long Short-Term Memory Based Deep Learning on Intership Behaviors by Jie Ma, Wenkai Li, Chengfeng Jia, Chunwei Zhang, Yu Zhang

“…The benefit of our approach is that it transforms the complex problem for risk prediction into a time series classification task, which makes collision risk prediction reliable and easier to implement. …”
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Dynamics of salt intrusion in complex estuarine networks: an idealised model applied to the Rhine–Meuse Delta by B. Biemond, W. M. Kranenburg, W. M. Kranenburg, Y. Huismans, Y. Huismans, H. E. de Swart, H. A. Dijkstra

“…The model is able to capture the characteristics of the tide-dominated water level variations due to the main tidal component and the salinity time series for 1 year of observations. Quantification of tidally averaged salt transport components shows that transport related to exchange flow is dominant in the seaward, deep parts of the network, but tidal dispersion is dominant in shallower channels further inland. …”
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Intelligent information systems for power grid fault analysis by computer communication technology by Ronglong Xu, Jing Zhang

“…The Transformer’s self-attention mechanism processes time-series operational data from the grid, enabling precise identification of temporal dependencies in fault characteristics. …”
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The Studies of Chlorogenic Acid Antitumor Mechanism by Gene Chip Detection: The Immune Pathway Gene Expression by Tian Yi Kang, Hua Rong Yang, Jie Zhang, Dan Li, Jie Lin, Li Wang, XiaoPing Xu

“…Differentially expressed genes screened out by time-series analysis, GO analysis, and pathway analysis, and four immune-related genes were selected for further confirmation using RT-qPCR. …”
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Intelligent Ensemble Deep Learning System for Blood Glucose Prediction Using Genetic Algorithms by Dae-Yeon Kim, Dong-Sik Choi, Ah Reum Kang, Jiyoung Woo, Yechan Han, Sung Wan Chun, Jaeyun Kim

“…Although there are numerous deep learning algorithms available, this study applied five algorithms, namely, recurrent neural network (RNN), which is optimized for sequence data (e.g., time-series), and RNN-based algorithms (e.g., long short-term memory (LSTM), stacked LSTM, bidirectional LSTM, and gated recurrent unit). …”
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A Novel Rapeseed Mapping Framework Integrating Image Fusion, Automated Sample Generation, and Deep Learning in Southwest China by Ruolan Jiang, Xingyin Duan, Song Liao, Ziyi Tang, Hao Li

“…First, MODIS and Landsat data were used to fill the gaps in Sentinel-2 imagery, creating time-series images through the object-level processing version of the spatial and temporal adaptive reflectance fusion model (OL-STARFM). …”
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Heart rate variability is more sensitive to stress than heart rate in specialist police undergoing selection. by Colin Tomes, Ben Schram, Elisa Canetti, Robin Orr

“…As illustrated by a time-series plot, HR values were generally unremarkable, but HRV values were potentially depressed, and tentatively indicated overstress when count data from consecutive short-term analyses were derived. …”
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Intelligent deep federated learning model for enhancing security in internet of things enabled edge computing environment by Nasser Nammas Albogami

“…To accomplish this, the IDFLM-ES technique introduces a federated hybrid deep belief network (FHDBN) model using FL on time series data produced by the IoT edge devices. Besides, the IDFLM-ES technique uses data normalization and golden jackal optimization (GJO) based feature selection as a pre-processing step. …”
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ReLTER: An R interface for environmental observation in long term ecological research by Alessandro Oggioni, Micha Silver, Paolo Tagliolato, Arnon Karnieli

“…Ecological datasets are inherently complex, heterogeneous, and often span different time series. The links between spatial and temporal data and ecological/environmental science are framed in the discipline of ecoinformatics. …”
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Airport Arrival Flow Prediction considering Meteorological Factors Based on Deep-Learning Methods by Zhao Yang, Yifan Wang, Jie Li, Liming Liu, Jiyang Ma, Yi Zhong

“…The XGBoost method is applied to identify the relative importance of various variables. The historic time-series data of airport arrival flow and selected features are taken as input variables, and the subsequent flight arrival flow is the output variable. …”
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Predicting Screening Efficiency of Probability Screens Using KPCA-GRNN with WP-EE Feature Reconstruction by Qingtang Chen, Yijian Huang

“…Subsequently, empirical mode decomposition energy entropy (EMD-EE), variational mode decomposition energy entropy (VMD-EE), and wavelet packet energy entropy (WP-EE) features are extracted from the time series vibration signals, and three single input energy entropy-generalized regressive neural network (GRNN) prediction accuracy models are established and compared. …”
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Attention-Based Multi-Learning Approach for Speech Emotion Recognition With Dilated Convolution by Samuel, Kakuba, Alwin, Poulose

“…Recurrent neural networks (RNN) like long short-term memory (LSTM), gated recurrent units (GRU) and their variants that operate sequentially are often used to learn time series sequences of the signal, analyze long-term dependencies and the contexts of the utterances in the speech signal. …”
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Characterising the short- and long-term impacts of tropical cyclones on mangroves using the Landsat archive by Emma Asbridge, Claire Krause, Richard Lucas, Christopher J. Owers, Kerrylee Rogers, Leo Lymburner, Norman Mueller, Emma Ai, Sebastian Wong

“…Improvements in Earth observation data, particularly satellite-based sensors and datacube environments, have enhanced capacity to classify time-series data and advanced landscape monitoring. …”
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3D Web Application of Wind Prediction Under Typhoon on Coastal Cities Based on Ensemble k-NN and OpenFOAM by Mamad Tamamadin, Seong-Hoon Kee, Jurng-Jae Yee

“…This study also produces a more sophisticated web-based virtual globe and 3D city model to demonstrate a current typhoon prediction and past typhoons, online simulation results of ensemble k-NN algorithm, a volumetric profile of typhoons, wind flow, streamlines, wind pressure around buildings and a bridge, warning status, and time series of predicted wind velocity for supporting the disaster early warning system.…”
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Detecting tropical freshly-opened swidden fields using a combined algorithm of continuous change detection and support vector machine by Ningsang Jiang, Peng Li, Zhiming Feng

“…The integration CCDC with SVM represents a novelty in combining time series analysis and machine learning techniques and helps monitor annual swidden agriculture in the tropics.…”
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The impact of the COVID-19 pandemic on TB notifications in Ukraine in 2020 by A.N. Shapiro, M. Dolynska, S.S. Chiang, N. Rybak, V. Petrenko, C.R. Horsburgh Jr, J. Kobe, I. Terleieva, O. Sakalska, H.E. Jenkins

“…DESIGN/METHODS: We analyzed data from Ukraine’s National TB Program from January 2015 to December 2020 using interrupted time series models. We compared observed cases to counterfactual estimated cases had the pandemic not occurred and estimated trends through December 2020 nationally and by various demographics. …”
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Effects of Climate Variability on Normalized Difference Vegetation Index (NDVI) in the Gojeb River Catchment, Omo-Gibe Basin, Ethiopia by Melku Dagnachew, Asfaw Kebede, Awdenegest Moges, Adane Abebe

“…The spatiotemporal trend in NDVI and rainfall time series was assessed using a Theil–Sen (Sen) slope and Mann–Kendall (MK) statistical significance test at a 95% confidence interval. …”
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Comparative miRNAome combined with transcriptome and degradome analysis reveals a novel miRNA-mRNA regulatory network associated with starch metabolism affecting pre-harvest sprout... by Longqing Sun, Ting Zhao, Dandan Qin, Jing Dong, Daorong Zhang, Xifeng Ren

“…In order to further obtain candidate miRNAs, the different datasets of differentially expressed miRNAs were excavated by using differential-expression and time-series analysis. Combined with degradome data, the miRNA-mRNA networks analysis was performed after genome-wide screening of target genes, and then KEGG enrichment highlighted that the starch and sucrose metabolism pathway related to PHS was specifically enriched in an especial target-gene dataset derived from R12R18-HE miRNAs. …”
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Comparative transcriptomics in serial organs uncovers early and pan-organ developmental changes associated with organ-specific morphological adaptation by Marie Sémon, Marion Mouginot, Manon Peltier, Claudine Corneloup, Philippe Veber, Laurent Guéguen, Sophie Pantalacci

“…By comparing the dynamics of molar development with transcriptome time series, we found at least three early changes in mouse upper molar development. …”
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Development of measures to assess listening-related effort and fatigue in daily life among hearing aid users: protocol for a quantitative field trial by Trine Flensborg-Madsen, Andreea Micula, Jeppe Høy Konvalinka Christensen, Dorothea Wendt, Hamish Innes-Brown, Ingrid Johnsrude, Matthew Apps, Torsten Dau

“…Associations between physiological responses, ambient acoustics and momentary assessments, as well as the potential influence of participant characteristics, will be analysed using multilevel regression models and time-series analyses.Ethics and dissemination Informed consent will be obtained from all participants. …”
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