Abstract: To improve urban transportation systems, transport mode identification (TMI) must infer user trajectories’ travel modes. Existing TMI methods use mobility features from densely sampled GPS trajectory points (1 second per GPS point) or IMU…
Abstract: Accurate spatio-temporal traffic forecasting underpins dynamic strategy and applications for intelligent transportation systems, improving traffic safety and reducing road congestion. Deep learning methods like CNN have improved traffic flow forecasting. These CNN-based methods learn…
Abstract: Real-time traffic demand can be revealed by accurate metro ridership forecasting. Deep learning algorithms are widely used to capture spatio-temporal features in metro riding behavior due to their superior performance. Current deep learning models…
Abstract: Autonomous vehicles, ridesharing, and self-driving technology will change how ride hailing platforms plan their services. However, advances in technology, road congestion, environmental concerns, fuel usage, vehicle emissions, and high vehicle usage costs have focused…
Abstract: Seizure prediction using graph theory and EEG signals is advanced. Recent deep learning approaches, which fail to fully explore EEG characterizations and electrode correlations simultaneously, neglect the spatial or temporal dependencies in an epileptic…
Abstract: Learning image similarity underpins many vision tasks. Discriminative metric learning seeks a class embedding. The biggest challenge is learning a metric that generalizes from training to novel, but related, test samples. It should transfer…
Abstract: Linear discriminant analysis (LDA) is a classical statistical machine-learning method that finds a linear data transformation that increases class discrimination in an optimal discriminant subspace. LDA assumes Gaussian class distributions and single-label data annotations….
Abstract: Deep learning’s success makes understanding its mechanism and rationality urgent. Deep learning requires data depth, structure, and volume. Most deep learning theoretical studies emphasize the importance and benefits of neural network depth and structure….
Abstract: Multiview fuzzy systems use multiview learning to effectively model fuzzy scenarios and obtain an interpretable model. Current multiview fuzzy system studies face several challenges, including how to efficiently collaborate between multiple views with few…
Abstract: Recently, social media research has grown. This article predicts active users in online social events, which is rare compared to previous work on emerging topics. This prediction task is a binary classification problem based…
Abstract: In various domains and settings, model-based approaches have produced effective recommendation lists. They can accurately recommend items using latent factors. Even if the model embeds content-based information, we lose references to the recommended item’s…
Abstract: Kernel methods have thrived for 20 years. Big data has greatly increased data collection. Existing kernel methods are not scalable for training or predicting. This paper introduces a general sparse kernel learning formulation based…
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