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Found 13 papers, 5 papers with code
WildfireGPT: Tailored Large Language Model for Wildfire Analysis
The recent advancement of large language models (LLMs) represents a transformational capability at the frontier of artificial intelligence (AI) and machine learning (ML).
Wavelet-Inspired Multiscale Graph Convolutional Recurrent Network for Traffic Forecasting
Traffic forecasting is the foundation for intelligent transportation systems.
Analyzing Regional Impacts of Climate Change using Natural Language Processing Techniques
Understanding the multifaceted effects of climate change across diverse geographic locations is crucial for timely adaptation and the development of effective mitigation strategies.
A Comparative Study of Loss Functions: Traffic Predictions in Regular and Congestion Scenarios
While accurate forecasting of regular traffic conditions is crucial, a reliable AI system must also accurately forecast congestion scenarios to maintain safe and efficient transportation.
Analyzing the impact of climate change on critical infrastructure from the scientific literature: A weakly supervised NLP approach
In comparison with a months-long process of subject-matter expert labeling, we assign category labels to the whole corpus using weak supervision and supervised learning in about 13 hours.
Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting
Moreover, most of the existing deep learning traffic forecasting models are black box, presenting additional challenges related to explainability and interpretability.
Deep-Ensemble-Based Uncertainty Quantification in Spatiotemporal Graph Neural Networks for Traffic Forecasting
Our approach uses a scalable Bayesian optimization method to perform hyperparameter optimization, selects a set of high-performing configurations, fits a generative model to capture the joint distributions of the hyperparameter configurations, and trains an ensemble of models by sampling a new set of hyperparameter configurations from the generative model.
A data-centric weak supervised learning for highway traffic incident detection
To that end, we focus on a data-centric approach to improve the accuracy and reduce the false alarm rate of traffic incident detection on highways.
Dynamic Graph Neural Network for Traffic Forecasting in Wide Area Networks
Wide area networking infrastructures (WANs), particularly science and research WANs, are the backbone for moving large volumes of scientific data between experimental facilities and data centers.
Bharatanatyam Dance Transcription using Multimedia Ontology and Machine Learning
We first attempt to capture the concepts of the basic steps of an Indian Classical Dance form, named Bharatanatyam Adavus, in an ontological model.
Transfer Learning with Graph Neural Networks for Short-Term Highway Traffic Forecasting
To that end, we develop a new transfer learning approach for DCRNN, where a single model trained on data-rich regions of the highway network can be used to forecast traffic on unseen regions of the highway network.
Posture and sequence recognition for Bharatanatyam dance performances using machine learning approach
To develop an application for dance, three aspects of dance analysis need to be addressed: 1) Segmentation of the dance video to find the representative action elements, 2) Matching or recognition of the detected action elements, and 3) Recognition of the dance sequences formed by combining a number of action elements under certain rules.
Graph-Partitioning-Based Diffusion Convolutional Recurrent Neural Network for Large-Scale Traffic Forecasting
We demonstrate the efficacy of the graph-partitioning-based DCRNN approach to model the traffic on a large California highway network with 11, 160 sensor locations.
