Modeling eye gaze velocity trajectories using GANs with spectral loss for enhanced fidelity by Shailendra Bhandari, Pedro Lencastre, Rujeena Mathema, Alexander Szorkovszky, Anis Yazidi, Pedro G. Lind

“…Abstract Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. …”
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Interpretable Machine Learning for Multi-Crop Yield Prediction in Semi-Arid Regions: A Hierarchical Approach to Handle Climate Data Sparsity by Rachid Ed-daoudi, M’barek El Haloui

“…Performance declines during extreme weather events (e.g., unprecedented droughts) that are absent from historical data, and soil moisture dynamics remain excluded. Future work will address these limitations through integration of soil moisture sensors, development of transfer learning approaches for extreme weather events. …”
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Spatial-and-local-aware deep learning approach for Ground-Level NO2 estimation in England with multisource data from satellite-based observations and chemical transport models by Siying Wang, Shuangyin Zhang, Dawei Wang, Weifeng Li

“…To address the translation invariance of convolutional neural networks (CNNs), we combined a local channel to identify spatial heterogeneity in the model. …”
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Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning by Teemu Koivisto, Marcin Mińkowski, Lasse Laurson

“…Here, we study the relationship between the atomic structure and the elastic and plastic properties of polycrystalline tantalum nanopillars under uniaxial compression by means of interpretable machine learning. We first train a convolutional neural network using data from molecular dynamics simulations to learn the mapping from the sample-specific initial atomic structure to features of the stress–strain curve. …”
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Integrating principal component analysis, fuzzy inference systems, and advanced neural networks for enhanced estuarine water quality assessment by Richard O. Usang, Bamidele I. Olu-Owolabi, Kayode O. Adebowale

“…Study focus: The research develops a comprehensive framework for assessing estuarine water quality by integrating Principal Component Analysis (PCA), Fuzzy Inference Systems (FIS), and advanced neural network models, specifically Long Short-Term Memory (LSTM) and a hybrid LSTM-Convolutional Neural Network (CNN). The study employs SHAP (SHapley Additive exPlanations) analysis to interpret the contributions of individual water quality parameters to model predictions, addressing the challenge of handling large and complex datasets from water quality monitoring programs and aiming to provide robust predictions and insights into water quality dynamics. …”
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Accurate total consumer price index forecasting with data augmentation, multivariate features, and sentiment analysis: A case study in Korea. by Injae Seo, Minkyoung Kim, Jong Wook Kim, Beakcheol Jang

“…This heterogeneity makes it difficult to account for all variations in consumer behavior and price dynamics. Second, the monthly frequency of CPI data results in a relatively sparse time series, limiting the performance of the analysis. …”
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Medium density EMG armband for gesture recognition by Eisa Aghchehli, Eisa Aghchehli, Milad Jabbari, Chenfei Ma, Matthew Dyson, Kianoush Nazarpour

“…To enhance decoding accuracy, we introduced a novel spatio-temporal convolutional neural network that integrates spatial information from additional EMG sensors with temporal dynamics. …”
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RoPT: Route-Planning Model with Transformer by Zuyun Xiong, Yan Wang, Yuxuan Tian, Lijuan Liu, Shunzhi Zhu

“…However, traditional route-planning methods, such as graph search and Recurrent Neural Network (RNN)-based methods, struggle to capture the complex dynamics of road networks. Specifically in A*-type methods, routes should be searched instantly on the whole graph for the sake of dynamic changes in edge time consumption. …”
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Applying deep learning to teleseismic phase detection and picking: PcP and PKiKP cases by Congcong Yuan, Jie Zhang

“…The scheme consists of three steps: 1) latent phase traces are truncated from the whole seismogram with theoretical arrival times; 2) latent phases are recognized and evaluated by convolutional neural network (CNN) models; 3) arrivals of good or fair phase are picked with another CNN models. …”
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A Multi-Factor-Fusion Framework for Efficient Prediction of Pedestrian-Level Wind Environment Based on Deep Learning by Zhen-Zhong Hu, Yan-Tao Min, Shuo Leng, Sunwei Li, Jia-Rui Lin

“…Numerical simulations, such as computational fluid dynamics and multi-scale modeling techniques, are commonly used for wind environment analysis. …”
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Systematic Review on Automation of Central Tire Inflation System Based on Terrain Conditions by Carl Luis C. Ledesma, Charlothe John I. Tablizo, Marites B. Tabanao, Emmanuel A. Salcedo, Emmy Grace T. Requillo, John Paul T. Cruz

“…Incorrect vehicle tire pressure affects vehicle dynamics, fuel efficiency, and driver safety across different terrain conditions. …”
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DeepLASD countermeasure for logical access audio spoofing by Hamed Al-Tairi, Ali Javed, Tasawer Khan, Abdul Khader Jilani Saudagar

“…The model incorporates a SincConv layer for interpretable spectral processing, along with residual convolutional blocks that integrate attention for improved feature extraction. …”
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Improving trend prediction of agricultural futures price using image encoding and attention mechanisms by Dabin Zhang, Huiqiang Xie, Huanling Hu, Zehui Yu

“…This paper introduces a novel hybrid deep learning model named ImgEnc-AttNet, which integrates image encoding techniques with attention mechanisms to enhance the model’s capability to interpret agricultural futures price dynamics. By transforming one-dimensional time series data into image representations, the model employs Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM) to extract rich visual patterns and temporal dependencies from the encoded images. …”
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A Hybrid Spatial–Temporal Deep Learning Method for Metro Tunnel Displacement Prediction Under “Dual Carbon” Background by Jianyong Chai, Limin Jia, Jianfeng Liu, Enguang Hou, Zhe Chen

“…The model leverages the strengths of GCNs in capturing spatial correlations and LSTM networks in processing temporal dynamics, offering a robust framework for accurate displacement prediction. …”
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Fusion of Recurrence Plots and Gramian Angular Fields with Bayesian Optimization for Enhanced Time-Series Classification by Maria Mariani, Prince Appiah, Osei Tweneboah

“…Experiments on seven univariate datasets show that our method significantly outperforms traditional classifiers such as one-nearest neighbor with Dynamic Time Warping, Shapelet Transform, and RP-based convolutional neural networks. …”
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A deep learning approach for automated scoring of the Rey–Osterrieth complex figure by Nicolas Langer, Maurice Weber, Bruno Hebling Vieira, Dawid Strzelczyk, Lukas Wolf, Andreas Pedroni, Jonathan Heitz, Stephan Müller, Christoph Schultheiss, Marius Troendle, Juan Carlos Arango Lasprilla, Diego Rivera, Federica Scarpina, Qianhua Zhao, Rico Leuthold, Flavia Wehrle, Oskar Jenni, Peter Brugger, Tino Zaehle, Romy Lorenz, Ce Zhang

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Financial accounting management strategy based on business intelligence technology for sustainable development strategy by Jianben Feng

“…To solve this problem, the study proposes a corporate financial distress prediction model based on graph convolutional neural networks and dynamic time regularization. …”
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Optimizing Deep Learning Models for Fire Detection, Classification, and Segmentation Using Satellite Images by Abdallah Waleed Ali, Sefer Kurnaz

“…Over the past decade, satellite data have been systematically utilized to monitor wildfire dynamics and evaluate their impacts, leading to substantial advancements in wildfire management strategies. …”
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Evaluation of machine learning techniques for real-time prediction of implanted lower limb mechanics by Chase Maag, Clare K. Fitzpatrick, Paul J. Rullkoetter

“…Compared to traditional FE or rigid body dynamics models, these predictive models offered significantly faster prediction speeds, enabling near-instantaneous insights into the TKR system’s performance. …”
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Deep learning from three-dimensional lithium-ion battery multiphysics model part I: Data development by Yiheng Pang, Yun Wang, Zhiqiang Niu

“…The developed model proves to be capable of providing insightful and reliable data for the training of convolutional neural network and long short-term memory (CNN-LSTM) in part II.…”
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