Review and evaluation of reinforcement learning frameworks on smart grid applications
With the rise in electricity, gas and oil prices and the persistently high levels of carbon
emissions, there is an increasing demand for effective energy management in energy …
emissions, there is an increasing demand for effective energy management in energy …
A new vaccine supply chain network under COVID-19 conditions considering system dynamic: Artificial intelligence algorithms
With the discovery of the COVID-19 vaccine, what has always been worrying the decision-
makers is related to the distribution management, the vaccination centers' location, and the …
makers is related to the distribution management, the vaccination centers' location, and the …
A survey on providing customer and public administration based services using AI: chatbot
A chatbot is emerged as an effective tool to address the user queries in automated, most
appropriate and accurate way. Depending upon the complexity of the subject domain …
appropriate and accurate way. Depending upon the complexity of the subject domain …
Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system
New ways to integrate energy systems to maximize efficiency are being sought to meet
carbon emissions goals. Nuclear-renewable integrated energy system (NR-IES) concepts …
carbon emissions goals. Nuclear-renewable integrated energy system (NR-IES) concepts …
[图书][B] Deep learning: A beginners' guide
D Meedeniya - 2023 - books.google.com
This book focuses on deep learning (DL), which is an important aspect of data science, that
includes predictive modeling. DL applications are widely used in domains such as finance …
includes predictive modeling. DL applications are widely used in domains such as finance …
Robust berth scheduling using machine learning for vessel arrival time prediction
In this work, the potentials of data-driven optimization for the well-known berth allocation
problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel …
problem are studied. The aim of robust berth scheduling is to derive conflict-free vessel …
Traffic congestion analysis based on a web-gis and data mining of traffic events from twitter
J Salazar-Carrillo, M Torres-Ruiz, CA Davis Jr… - Sensors, 2021 - mdpi.com
Smart cities are characterized by the use of massive information and digital communication
technologies as well as sensor networks where the Internet and smart data are the core …
technologies as well as sensor networks where the Internet and smart data are the core …
[Retracted] Telehealth for COVID‐19: A Conceptual Framework
W Yousaf, AI Umar, SH Shirazi, M Fayaz… - Journal of …, 2023 - Wiley Online Library
The world has been going through the global crisis of the coronavirus (COVID‐19). It is a
challenging situation for every country to tackle its healthcare system. COVID‐19 spreads …
challenging situation for every country to tackle its healthcare system. COVID‐19 spreads …
Reinforcement learning for high-quality reality mapping of indoor construction using unmanned ground vehicles
A Ibrahim, W Torres-Calderon… - Automation in …, 2023 - Elsevier
Recent advances in reality capture technology focused on automating reality capture and
devising robust computational models to convert the collected data into usable formats …
devising robust computational models to convert the collected data into usable formats …
Error-related potential-based shared autonomy via deep recurrent reinforcement learning
Objective. Error-related potential (ErrP)-based brain–computer interfaces (BCIs) have
received a considerable amount of attention in the human–robot interaction community. In …
received a considerable amount of attention in the human–robot interaction community. In …