Deep learning in the industrial internet of things: Potentials, challenges, and emerging applications
Recent advances in the Internet of Things (IoT) are giving rise to a proliferation of
interconnected devices, allowing the use of various smart applications. The enormous …
interconnected devices, allowing the use of various smart applications. The enormous …
Deep reinforcement learning techniques for vehicular networks: Recent advances and future trends towards 6G
Employing machine learning into 6G vehicular networks to support vehicular application
services is being widely studied and a hot topic for the latest research works in the literature …
services is being widely studied and a hot topic for the latest research works in the literature …
[HTML][HTML] Financial portfolio optimization with online deep reinforcement learning and restricted stacked autoencoder—DeepBreath
F Soleymani, E Paquet - Expert Systems with Applications, 2020 - Elsevier
The process of continuously reallocating funds into financial assets, aiming to increase the
expected return of investment and minimizing the risk, is known as portfolio management. In …
expected return of investment and minimizing the risk, is known as portfolio management. In …
Optimization of the electricity generation of a wave energy converter using deep reinforcement learning
Ocean wave energy is one of the sustainable energy sources which continues to attract
increasing research interests. Traditionally, the model-based controls of Wave Energy …
increasing research interests. Traditionally, the model-based controls of Wave Energy …
An assessment of multistage reward function design for deep reinforcement learning-based microgrid energy management
Reinforcement learning based energy management strategy has been an active research
subject in the past few years. Different from the baseline reward function (BRF), the work …
subject in the past few years. Different from the baseline reward function (BRF), the work …
[HTML][HTML] Deep reinforcement learning for multi-objective placement of virtual machines in cloud datacenters
The ubiquitous diffusion of cloud computing requires suitable management policies to face
the workload while guaranteeing quality constraints and mitigating costs. The typical trade …
the workload while guaranteeing quality constraints and mitigating costs. The typical trade …
[HTML][HTML] Intelligent transportation systems: A survey on modern hardware devices for the era of machine learning
The increasing complexity of Intelligent Transportation Systems (ITS), that comprise a wide
variety of applications and services, has imposed a necessity for high-performance Modern …
variety of applications and services, has imposed a necessity for high-performance Modern …
Deep reinforcement learning-based radar network target assignment
F Meng, K Tian, C Wu - IEEE sensors journal, 2021 - ieeexplore.ieee.org
This study focuses on the problem of target assignment when a phased-array radar network
detects hypersonic-glide vehicles in near-space and proposes a method for target …
detects hypersonic-glide vehicles in near-space and proposes a method for target …
Urbanenqosplace: A deep reinforcement learning model for service placement of real-time smart city iot applications
Multi-access Edge Computing (MEC) enables IoT applications to place their services in the
edge servers of mobile networks, balancing Quality-of-Service (QoS) and energy-efficiency …
edge servers of mobile networks, balancing Quality-of-Service (QoS) and energy-efficiency …
Learning to traverse over graphs with a Monte Carlo tree search-based self-play framework
The combinatorial optimization (CO) problems on the graph are the core and classic
problems in artificial intelligence (AI) and operations research (OR). For example, the …
problems in artificial intelligence (AI) and operations research (OR). For example, the …