Stock market prediction using deep reinforcement learning

AL Awad, SM Elkaffas, MW Fakhr - Applied System Innovation, 2023 - mdpi.com
Stock value prediction and trading, a captivating and complex research domain, continues to
draw heightened attention. Ensuring profitable returns in stock market investments demands …

A Q-learning and fuzzy logic-based hierarchical routing scheme in the intelligent transportation system for smart cities

AM Rahmani, RA Naqvi, E Yousefpoor, MS Yousefpoor… - Mathematics, 2022 - mdpi.com
A vehicular ad hoc network (VANET) is the major element of the intelligent transportation
system (ITS). The purpose of ITS is to increase road safety and manage the movement of …

Impacts of robot learning on user attitude and behavior

N Moorman, E Hedlund-Botti, M Schrum… - Proceedings of the …, 2023 - dl.acm.org
With an aging population and a growing shortage of caregivers, the need for in-home robots
is increasing. However, it is intractable for robots to have all functionalities pre-programmed …

Towards Green AI. A methodological survey of the scientific literature

E Barbierato, A Gatti - IEEE Access, 2024 - ieeexplore.ieee.org
The pervasive deployment of Deep Learning models has recently prompted apprehensions
regarding their ecological footprint, owing to the exorbitant levels of energy consumption …

Intra-and inter-association attention network-enhanced policy learning for social group recommendation

Y Wang, Z Dai, J Cao, J Wu, H Tao, G Zhu - World Wide Web, 2023 - Springer
Abstract Social Group Recommendation (SGR) is a critical task to recommend items to a
group of users in social network platforms, such as Meetup, Douban, Mofengwo, etc …

AI-driven warehouse automation: A comprehensive review of systems

EO Sodiya, UJ Umoga, OO Amoo… - … Research and Reviews, 2024 - gsconlinepress.com
This comprehensive review explores the profound impact of artificial intelligence (AI) on
warehouse automation, providing an in-depth examination of various AI-driven systems. As …

Off-policy evaluation for action-dependent non-stationary environments

Y Chandak, S Shankar, N Bastian… - Advances in …, 2022 - proceedings.neurips.cc
Methods for sequential decision-making are often built upon a foundational assumption that
the underlying decision process is stationary. This limits the application of such methods …

An efficient energy saving scheme using reinforcement learning for 5G and beyond in H-CRAN

H Fourati, R Maaloul, N Trabelsi, L Chaari, M Jmaiel - Ad Hoc Networks, 2024 - Elsevier
Maximizing the energy saving is one of the most important metrics in 5G and Beyond (B5G)
cellular mobile networks. In order to satisfy the diverse requirements of 5G/B5G in dynamic …

[HTML][HTML] Reinforcement learning design framework for nacre-like structures optimized for pre-existing crack resistance

BY Tseng, YC Cai, CWC Guo, E Zhao, CH Yu - Journal of Materials …, 2023 - Elsevier
Nacre is known for its uniquely high toughness and lightweight capabilities. Its unique
structure is composed of soft nacre proteins and stiff calcium carbonates, allowing it to …

Improving the performance of autonomous driving through deep reinforcement learning

A Tammewar, N Chaudhari, B Saini, D Venkatesh… - Sustainability, 2023 - mdpi.com
Reinforcement learning (RL) is revolutionizing the artificial intelligence (AI) domain and
significantly aiding in building autonomous systems with a higher level comprehension of …