A macro–micro spatio-temporal neural network for traffic prediction
Accurate traffic prediction is crucial for planning, management and control of intelligent
transportation systems. Most state-of-the-art methods for traffic prediction effectively capture …
transportation systems. Most state-of-the-art methods for traffic prediction effectively capture …
Real-time safety assessment for dynamic systems with limited memory and annotations
Z Liu, X He - IEEE Transactions on Intelligent Transportation …, 2023 - ieeexplore.ieee.org
Real-time safety assessment of dynamic systems has recently received increasing attention.
However, the performance of existing advanced approaches is often negatively affected by …
However, the performance of existing advanced approaches is often negatively affected by …
Auto-learning communication reinforcement learning for multi-intersection traffic light control
Multi-agent reinforcement learning is a promising solution to achieve intelligent traffic light
control by regarding each intersection as an independent agent. However, agents encounter …
control by regarding each intersection as an independent agent. However, agents encounter …
Concept drift adaptation by exploiting drift type
Concept drift is a phenomenon where the distribution of data streams changes over time.
When this happens, model predictions become less accurate. Hence, models built in the …
When this happens, model predictions become less accurate. Hence, models built in the …
Learn-to-adapt: Concept drift adaptation for hybrid multiple streams
Existing concept drift adaptation (CDA) methods aim to continually update outdated
classifiers in a single-labeled stream scenario. However, real-world data streams are …
classifiers in a single-labeled stream scenario. However, real-world data streams are …
A novel continual reinforcement learning-based expert system for self-optimization of soft real-time systems
Z Masood, Z Jiangbin, I Ahmad, C Dongdong… - Expert Systems with …, 2024 - Elsevier
Virtual globes are soft real-time systems, which stream multi-resolution data sets and render
world-scale landscapes in real-time. Such systems require an adaptation mechanism to …
world-scale landscapes in real-time. Such systems require an adaptation mechanism to …
A self-adaptive ensemble for user interest drift learning
User interest reflects user preference which plays an important role in commercial decision-
making. Learning and predicting user interest has attracted significant attention in recent …
making. Learning and predicting user interest has attracted significant attention in recent …
Fuzzy Machine Learning: A Comprehensive Framework and Systematic Review
Machine learning draws its power from various disciplines, including computer science,
cognitive science, and statistics. Although machine learning has achieved great …
cognitive science, and statistics. Although machine learning has achieved great …
COSIGN: Contextual Facts Guided Generation for Knowledge Graph Completion
Abstract Knowledge graph completion (KGC) aims to infer missing facts based on existing
facts within a KG. Recently, research on generative models (GMs) has addressed the …
facts within a KG. Recently, research on generative models (GMs) has addressed the …
ACDC: Online unsupervised cross-domain adaptation
We consider the problem of online unsupervised cross-domain adaptation, where two
independent but related data streams with different feature spaces–a fully labeled source …
independent but related data streams with different feature spaces–a fully labeled source …