Intelligent decision-making for liver fibrosis stadialization based on tandem feature selection and evolutionary-driven neural network

2012 ◽  
Vol 39 (17) ◽  
pp. 12824-12832 ◽  
Author(s):  
Florin Gorunescu ◽  
Smaranda Belciug ◽  
Marina Gorunescu ◽  
Radu Badea
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Feature Selection ◽  
Liver Fibrosis ◽  
Intelligent Decision Making ◽  
Intelligent Decision

scholarly journals Critical Decision Making Using Neural Networks

2018 ◽  
Vol 7 (4.10) ◽  
pp. 15 ◽  
Author(s):  
Rajat Bhati ◽  
Shubham Saraff ◽  
Chhandak Bagchi ◽  
V. Vijayarajan
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Making ◽  
Emotional Intelligence ◽  
Final Decision ◽  
Computing Systems ◽  
Intelligent Decision Making ◽  
The Neural Network ◽  
Intelligent Decision ◽  
Car Accident

Decision Making influenced by different scenarios is an important feature that needs to be integrated in the computing systems. In this paper, the system takes prompt decisions in emotionally motivated use-cases like in an unavoidable car accident. The system extracts the features from the available visual and processes it in the Neural network. In addition to that the facial recognition plays a key role in returning factors critical to the scenario and hence alter the final decision. Finally, each recognized subject is categorized into six distinct classes which is utilised by the system for intelligent decision-making. Such a system can form the basis of dynamic and intelligent decision-making systems of the future which include elements of emotional intelligence.  

Research on Intelligent Decision-Making System of Variable Spraying for Mobile Robot in Greenhouse

2012 ◽  
Vol 241-244 ◽  
pp. 1835-1838
Author(s):  
Guo Qin Gao ◽  
Hai Yan Zhou ◽  
Xue Mei Niu ◽  
Zhi Ming Fang
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Mobile Robot ◽  
Fuzzy Neural Network ◽  
Plant Diseases ◽  
Chemical Pollution ◽  
Utilization Rate ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Fuzzy Neural

In order to improve the pesticide effective utilization rate and reduce the pesticide residues and the chemical pollution, an intelligent decision-making method for variable spraying of mobile robot based on a fuzzy neural network is proposed. The system is built by integrating the level of plant diseases and insect pests and the spraying target’s distance and area. The intelligent decision-making is achieved by self-learning and self-correcting the fuzzy rules of the fuzzy neural network. The simulation experiment results show that the intelligent decision-making method can realize real-time and quick decision. It has the greater decision accuracy than the fuzzy decision system on the samples not appearing in training and has a good fit for the uncertain work environment in greenhouse.

scholarly journals A Brain-Inspired Decision-Making Linear Neural Network and Its Application in Automatic Drive

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 794
Author(s):  
Tianjun Sun ◽  
Zhenhai Gao ◽  
Fei Gao ◽  
Tianyao Zhang ◽  
Siyan Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Decision Making ◽  
Driving Simulator ◽  
Fuzzy Classification ◽  
Intelligent Decision Making ◽  
Driving Behaviors ◽  
Intelligent Decision ◽  
Linear Neural Network ◽  
And Control

Brain-like intelligent decision-making is a prevailing trend in today’s world. However, inspired by bionics and computer science, the linear neural network has become one of the main means to realize human-like decision-making and control. This paper proposes a method for classifying drivers’ driving behaviors based on the fuzzy algorithm and establish a brain-inspired decision-making linear neural network. Firstly, different driver experimental data samples were obtained through the driving simulator. Then, an objective fuzzy classification algorithm was designed to distinguish different driving behaviors in terms of experimental data. In addition, a brain-inspired linear neural network was established to realize human-like decision-making and control. Finally, the accuracy of the proposed method was verified by training and testing. This study extracts the driving characteristics of drivers through driving simulator tests, which provides a driving behavior reference for the human-like decision-making of an intelligent vehicle.

Sign in / Sign up

Export Citation Format

Share Document