[HTML][HTML] Plant disease detection and classification by deep learning
Plant diseases affect the growth of their respective species, therefore their early identification
is very important. Many Machine Learning (ML) models have been employed for the …
is very important. Many Machine Learning (ML) models have been employed for the …
[HTML][HTML] A comprehensive survey on the application of deep and reinforcement learning approaches in autonomous driving
Abstract Recent advances in Intelligent Transport Systems (ITS) and Artificial Intelligence
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …
(AI) have stimulated and paved the way toward the widespread introduction of Autonomous …
Developing a deep Q-learning and neural network framework for trajectory planning
VSR Kosuru, AK Venkitaraman - European Journal of Engineering and …, 2022 - ej-eng.org
Autonomy field, every vehicle is occupied with some kind or alter driver assist features in
order to compensate driver comfort. Expansion further to fully Autonomy is extremely …
order to compensate driver comfort. Expansion further to fully Autonomy is extremely …
Augmented and virtual reality in construction: drivers and limitations for industry adoption
Augmented and virtual reality have the potential to provide a step-change in productivity in
the construction sector; however, the level of adoption is very low. This paper presents a …
the construction sector; however, the level of adoption is very low. This paper presents a …
[HTML][HTML] Deep reinforcement learning in transportation research: A review
Applying and adapting deep reinforcement learning (DRL) to tackle transportation problems
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
is an emerging interdisciplinary field. While rapidly growing, a comprehensive and synthetic …
Challenges and countermeasures for adversarial attacks on deep reinforcement learning
Deep reinforcement learning (DRL) has numerous applications in the real world, thanks to
its ability to achieve high performance in a range of environments with little manual …
its ability to achieve high performance in a range of environments with little manual …
Autonomous vehicles perception (avp) using deep learning: Modeling, assessment, and challenges
HH Jebamikyous, R Kashef - IEEE Access, 2022 - ieeexplore.ieee.org
Perception is the fundamental task of any autonomous driving system, which gathers all the
necessary information about the surrounding environment of the moving vehicle. The …
necessary information about the surrounding environment of the moving vehicle. The …
Constrained motion planning of free-float dual-arm space manipulator via deep reinforcement learning
Abstract Space manipulator has complex kinetic and dynamic properties due to its free-float
base. Moreover, the motion planning of a dual-arm manipulator is even more challenging …
base. Moreover, the motion planning of a dual-arm manipulator is even more challenging …
Curiosity-driven and victim-aware adversarial policies
Recent years have witnessed great potential in applying Deep Reinforcement Learning
(DRL) in various challenging applications, such as autonomous driving, nuclear fusion …
(DRL) in various challenging applications, such as autonomous driving, nuclear fusion …
Genetic algorithm-based hyperparameter optimization of deep learning models for PM2.5 time-series prediction
C Erden - International Journal of Environmental Science and …, 2023 - Springer
Since air pollution negatively affects human health and causes serious diseases, accurate
air pollution prediction is essential regarding environmental sustainability. Although …
air pollution prediction is essential regarding environmental sustainability. Although …