Digital twin paradigm: A systematic literature review
Manufacturing enterprises are facing the need to align themselves to the new information
technologies (IT) and respond to the new challenges of variable market demand. One of the …
technologies (IT) and respond to the new challenges of variable market demand. One of the …
Machine learning in medical applications: A review of state-of-the-art methods
Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …
complex challenges in recent years in various application areas, such as medical, financial …
An overview of multi-agent reinforcement learning from game theoretical perspective
Y Yang, J Wang - arxiv preprint arxiv:2011.00583, 2020 - arxiv.org
Following the remarkable success of the AlphaGO series, 2019 was a booming year that
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
witnessed significant advances in multi-agent reinforcement learning (MARL) techniques …
Machine learning for 5G/B5G mobile and wireless communications: Potential, limitations, and future directions
Driven by the demand to accommodate today's growing mobile traffic, 5G is designed to be
a key enabler and a leading infrastructure provider in the information and communication …
a key enabler and a leading infrastructure provider in the information and communication …
Deep learning enabled inverse design in nanophotonics
Deep learning has become the dominant approach in artificial intelligence to solve complex
data-driven problems. Originally applied almost exclusively in computer-science areas such …
data-driven problems. Originally applied almost exclusively in computer-science areas such …
[HTML][HTML] How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda
V Singh, SS Chen, M Singhania, B Nanavati… - International Journal of …, 2022 - Elsevier
Data availability and accessibility have brought in unseen changes in the finance systems
and new theoretical and computational challenges. For example, in contrast to classical …
and new theoretical and computational challenges. For example, in contrast to classical …
Deep reinforcement learning for dynamic scheduling of a flexible job shop
The ability to handle unpredictable dynamic events is becoming more important in pursuing
agile and flexible production scheduling. At the same time, the cyber-physical convergence …
agile and flexible production scheduling. At the same time, the cyber-physical convergence …
Machine learning techniques for 5G and beyond
Wireless communication systems play a very crucial role in modern society for
entertainment, business, commercial, health and safety applications. These systems keep …
entertainment, business, commercial, health and safety applications. These systems keep …
A survey of multi-objective sequential decision-making
Sequential decision-making problems with multiple objectives arise naturally in practice and
pose unique challenges for research in decision-theoretic planning and learning, which has …
pose unique challenges for research in decision-theoretic planning and learning, which has …
Connected automated vehicle cooperative control with a deep reinforcement learning approach in a mixed traffic environment
This paper proposes a cooperative strategy of connected and automated vehicles (CAVs)
longitudinal control for a mixed connected and automated traffic environment based on deep …
longitudinal control for a mixed connected and automated traffic environment based on deep …