A reinforcement learning and deep learning based intelligent system for the support of impaired patients in home treatment

2020 ◽  
pp. 114285
Author(s):  
Muddasar Naeem ◽  
Giovanni Paragliola ◽  
Antonio Coronato
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Intelligent System ◽  
Home Treatment

Deep Learning-Based Ground Vibration Monitoring: Reinforcement Learning and RNN-CNN Approach

2021 ◽  
pp. 1-5
Author(s):  
Sangseok Yun ◽  
Jae-Mo Kang ◽  
Jeongseok Ha ◽  
Sangho Lee ◽  
Dong-Woo Ryu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Ground Vibration ◽  
Vibration Monitoring

scholarly journals Diversity oriented Deep Reinforcement Learning for targeted molecule generation

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Tiago Pereira ◽  
Maryam Abbasi ◽  
Bernardete Ribeiro ◽  
Joel P. Arrais
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Reinforcement Learning ◽  
Deep Neural Networks ◽  
Chemical Space ◽  
Biological Properties ◽  
Training Process ◽  
Training Strategy ◽  
Inhibitory Power ◽  
Exploratory Strategy

AbstractIn this work, we explore the potential of deep learning to streamline the process of identifying new potential drugs through the computational generation of molecules with interesting biological properties. Two deep neural networks compose our targeted generation framework: the Generator, which is trained to learn the building rules of valid molecules employing SMILES strings notation, and the Predictor which evaluates the newly generated compounds by predicting their affinity for the desired target. Then, the Generator is optimized through Reinforcement Learning to produce molecules with bespoken properties. The innovation of this approach is the exploratory strategy applied during the reinforcement training process that seeks to add novelty to the generated compounds. This training strategy employs two Generators interchangeably to sample new SMILES: the initially trained model that will remain fixed and a copy of the previous one that will be updated during the training to uncover the most promising molecules. The evolution of the reward assigned by the Predictor determines how often each one is employed to select the next token of the molecule. This strategy establishes a compromise between the need to acquire more information about the chemical space and the need to sample new molecules, with the experience gained so far. To demonstrate the effectiveness of the method, the Generator is trained to design molecules with an optimized coefficient of partition and also high inhibitory power against the Adenosine $$A_{2A}$$ A 2 A and $$\kappa$$ κ opioid receptors. The results reveal that the model can effectively adjust the newly generated molecules towards the wanted direction. More importantly, it was possible to find promising sets of unique and diverse molecules, which was the main purpose of the newly implemented strategy.

A survey on deep learning and deep reinforcement learning in robotics with a tutorial on deep reinforcement learning

2021 ◽  
Author(s):  
Eduardo F. Morales ◽  
Rafael Murrieta-Cid ◽  
Israel Becerra ◽  
Marco A. Esquivel-Basaldua
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning

scholarly journals Deep learning-based decision support system for weeds detection in wheat fields

2022 ◽  
Vol 12 (1) ◽  
pp. 816
Author(s):  
Brahim Jabir ◽  
Noureddine Falih
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Decision Support ◽  
Large Scale ◽  
Intelligent System ◽  
Management Program ◽  
Wheat Crop ◽  
Smart System ◽  
Mechanical Methods ◽  
Hand Weeding

<span>In precision farming, identifying weeds is an essential first step in planning an integrated pest management program in cereals. By knowing the species present, we can learn about the types of herbicides to use to control them, especially in non-weeding crops where mechanical methods that are not effective (tillage, hand weeding, and hoeing and mowing). Therefore, using the deep learning based on convolutional neural network (CNN) will help to automatically identify weeds and then an intelligent system comes to achieve a localized spraying of the herbicides avoiding their large-scale use, preserving the environment. In this article we propose a smart system based on object detection models, implemented on a Raspberry, seek to identify the presence of relevant objects (weeds) in an area (wheat crop) in real time and classify those objects for decision support including spot spray with a chosen herbicide in accordance to the weed detected.</span>

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