scholarly journals Small Lung Nodules Detection Based on Fuzzy-Logic and Probabilistic Neural Network With Bioinspired Reinforcement Learning

2020 ◽  
Vol 28 (6) ◽  
pp. 1178-1189 ◽  
Author(s):  
Giacomo Capizzi ◽  
Grazia Lo Sciuto ◽  
Christian Napoli ◽  
Dawid Polap ◽  
Marcin Wozniak
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Probabilistic Neural Network ◽  
Lung Nodules

Small lung nodules detection based on local variance analysis and probabilistic neural network

2018 ◽  
Vol 161 ◽  
pp. 173-180 ◽  
Author(s):  
Marcin Woźniak ◽  
Dawid Połap ◽  
Giacomo Capizzi ◽  
Grazia Lo Sciuto ◽  
Leon Kośmider ◽  
...  
Keyword(s):  
Neural Network ◽  
Probabilistic Neural Network ◽  
Variance Analysis ◽  
Lung Nodules ◽  
Local Variance

Reinforcement Learning in Social Media Marketing

2021 ◽  
pp. 30-48
Author(s):  
Patrik Eklund
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Action Selection ◽  
Social Media Marketing ◽  
Rule Bases ◽  
Facebook Marketing

In this chapter, the authors describe an architecture for reinforcement learning in social media marketing. The rule bases used for action selection within the architecture build upon many-valued (fuzzy) logic. Action evaluation and internal learning is based on neural network like structures. In using variables measuring the effect of advertising, we must understand direction of influence between advertiser, owning the content of the advertisement, and advertisee, as the target of an advertisement, and as facilitated by social media marketing. Examples are drawn from Facebook marketing.

scholarly journals A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Na Guo ◽  
Caihong Li ◽  
Tengteng Gao ◽  
Guoming Liu ◽  
Yongdi Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Reinforcement Learning ◽  
Path Planning ◽  
Mobile Robots ◽  
Success Rate ◽  
Fuzzy Logic Control ◽  
Logic Control ◽  
Local Path Planning ◽  
Local Path

Due to the limitation of mobile robots’ understanding of the environment in local path planning tasks, the problems of local deadlock and path redundancy during planning exist in unknown and complex environments. In this paper, a novel algorithm based on the combination of a long short-term memory (LSTM) neural network, fuzzy logic control, and reinforcement learning is proposed, and uses the advantages of each algorithm to overcome the other’s shortcomings. First, a neural network model including LSTM units is designed for local path planning. Second, a low-dimensional input fuzzy logic control (FL) algorithm is used to collect training data, and a network model (LSTM_FT) is pretrained by transferring the learned method to learn the basic ability. Then, reinforcement learning is combined to learn new rules from the environments autonomously to better suit different scenarios. Finally, the fusion algorithm LSTM_FTR is simulated in static and dynamic environments, and compared to FL and LSTM_FT algorithms, respectively. Numerical simulations show that, compared to FL, LSTM_FTR can significantly improve decision-making efficiency, improve the success rate of path planning, and optimize the path length. Compared to the LSTM_FT, LSTM_FTR can improve the success rate and learn new rules.

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