From google gemini to openai q*(q-star): A survey of reshaping the generative artificial intelligence (ai) research landscape
This comprehensive survey explored the evolving landscape of generative Artificial
Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts …
Intelligence (AI), with a specific focus on the transformative impacts of Mixture of Experts …
[HTML][HTML] Scope of machine learning in materials research—A review
This comprehensive review investigates the multifaceted applications of machine learning in
materials research across six key dimensions, redefining the field's boundaries. It explains …
materials research across six key dimensions, redefining the field's boundaries. It explains …
A reinforcement learning model for the reliability of blockchain oracles
Smart contracts struggle with the major limitation of operating on data that is solely residing
on the blockchain network. The need of recruiting third parties, known as oracles, to assist …
on the blockchain network. The need of recruiting third parties, known as oracles, to assist …
Multi-agent deep reinforcement learning with demonstration cloning for target localization
In target localization applications, readings from multiple sensing agents are processed to
identify a target location. The localization systems using stationary sensors use data fusion …
identify a target location. The localization systems using stationary sensors use data fusion …
Reinforcement learning framework for UAV-based target localization applications
Smart environmental monitoring has gained prominence, where target localization is of
utmost importance. Employing UAVs for localization tasks is appealing owing to their low …
utmost importance. Employing UAVs for localization tasks is appealing owing to their low …
Influence-and interest-based worker recruitment in crowdsourcing using online social networks
Workers recruitment remains a significant issue in Mobile Crowdsourcing (MCS), where the
aim is to recruit a group of workers that maximizes the expected Quality of Service (QoS) …
aim is to recruit a group of workers that maximizes the expected Quality of Service (QoS) …
Deep reinforcement learning for the computation offloading in MIMO-based Edge Computing
Abstract Multi-access Edge Computing (MEC) has recently emerged as a potential
technology to serve the needs of mobile devices (MDs) in 5G and 6G cellular networks. By …
technology to serve the needs of mobile devices (MDs) in 5G and 6G cellular networks. By …
Variable Speed Limit Control for the Motorway–Urban Merging Bottlenecks Using Multi-Agent Reinforcement Learning
X Fang, T Péter, T Tettamanti - Sustainability, 2023 - mdpi.com
Traffic congestion is a typical phenomenon when motorways meet urban road networks. At
this special location, the weaving area is a recurrent traffic bottleneck. Numerous research …
this special location, the weaving area is a recurrent traffic bottleneck. Numerous research …
Automatic parameter optimization using genetic algorithm in deep reinforcement learning for robotic manipulation tasks
Learning agents can make use of Reinforcement Learning (RL) to decide their actions by
using a reward function. However, the learning process is greatly influenced by the elect of …
using a reward function. However, the learning process is greatly influenced by the elect of …
Explainable AI for Event and Anomaly Detection and Classification in Healthcare Monitoring Systems
Artificial intelligence (AI) has the potential to revolutionize healthcare by automating the
detection and classification of events and anomalies. In the scope of this work, events and …
detection and classification of events and anomalies. In the scope of this work, events and …