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Found 7 papers, 2 papers with code
Strategizing EV Charging and Renewable Integration in Texas
Exploring the convergence of electric vehicles (EVs), renewable energy, and smart grid technologies in the context of Texas, this study addresses challenges hindering the widespread adoption of EVs.
Empowering Distributed Solutions in Renewable Energy Systems and Grid Optimization
This study delves into the shift from centralized to decentralized approaches in the electricity industry, with a particular focus on how machine learning (ML) advancements play a crucial role in empowering renewable energy sources and improving grid management.
Implicit Poisoning Attacks in Two-Agent Reinforcement Learning: Adversarial Policies for Training-Time Attacks
In this paper, we study targeted poisoning attacks in a two-agent setting where an attacker implicitly poisons the effective environment of one of the agents by modifying the policy of its peer.
PALMER: Perception-Action Loop with Memory for Long-Horizon Planning
For training these perceptual representations, we combine Q-learning with contrastive representation learning to create a latent space where the distance between the embeddings of two states captures how easily an optimal policy can traverse between them.
Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniques
Both methods are able to identify UCDs/GCs with a precision and a recall of >93 percent and provide relevances that reflect the importance of each feature dimension %(colors and angular sizes) for the classification.
Medical Imaging and Computational Image Analysis in COVID-19 Diagnosis: A Review
The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis based on the accuracy and the method used, 4) to express the research limitations in this field and the methods used to overcome them.
An expert system for recommending suitable ornamental fish addition to an aquarium based on aquarium condition
Expert systems prove to be suitable replacement for human experts when human experts are unavailable for different reasons.
