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Found 9 papers, 3 papers with code
Deep reinforcement learning-based longitudinal control strategy for automated vehicles at signalised intersections
Overall, the results confirm that DRL-based longitudinal vehicle control strategy at SI can help to improve traffic safety, efficiency, and comfort.
RiM: Record, Improve and Maintain Physical Well-being using Federated Learning
In academic settings, the demanding environment often forces students to prioritize academic performance over their physical well-being.
SPIRIT: Short-term Prediction of solar IRradIance for zero-shot Transfer learning using Foundation Models
Traditional solar forecasting models are based on several years of site-specific historical irradiance data, often spanning five or more years, which are unavailable for newer photovoltaic farms.
Subject Representation Learning from EEG using Graph Convolutional Variational Autoencoders
We propose GC-VASE, a graph convolutional-based variational autoencoder that leverages contrastive learning for subject representation learning from EEG data.
Patent Novelty Assessment Accelerating Innovation and Patent Prosecution
By equipping users with efficient access to comprehensive patent claim information, our transformative platform seeks to ignite informed exploration and innovation in the ever-evolving domain of intellectual property.
Towards Infusing Auxiliary Knowledge for Distracted Driver Detection
Distracted driving is a leading cause of road accidents globally.
DECODE: Data-driven Energy Consumption Prediction leveraging Historical Data and Environmental Factors in Buildings
In summary, our research contributes to energy prediction by offering a robust LSTM model that outperforms alternative methods and operates with remarkable efficiency, generalizability, and reliability.
Using LSTM for the Prediction of Disruption in ADITYA Tokamak
Major disruptions in tokamak pose a serious threat to the vessel and its surrounding pieces of equipment.
Robust Regression with Compositional Covariates
To this end, we propose a sparse robust statistical regression framework that considers compositional and non-compositional measurements as predictors and identifies outliers in continuous response variables.
Methodology Applications
