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.  

scholarly journals A Neural Network Decomposition Algorithm for Mapping on Crossbar-Based Computing Systems

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1526 ◽  
Author(s):  
Choongmin Kim ◽  
Jacob A. Abraham ◽  
Woochul Kang ◽  
Jaeyong Chung
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Predictive Performance ◽  
Fixed Number ◽  
Decomposition Algorithm ◽  
Computing Systems ◽  
Network Decomposition ◽  
Von Neumann ◽  
Analog Computing ◽  
The Neural Network

Crossbar-based neuromorphic computing to accelerate neural networks is a popular alternative to conventional von Neumann computing systems. It is also referred as processing-in-memory and in-situ analog computing. The crossbars have a fixed number of synapses per neuron and it is necessary to decompose neurons to map networks onto the crossbars. This paper proposes the k-spare decomposition algorithm that can trade off the predictive performance against the neuron usage during the mapping. The proposed algorithm performs a two-level hierarchical decomposition. In the first global decomposition, it decomposes the neural network such that each crossbar has k spare neurons. These neurons are used to improve the accuracy of the partially mapped network in the subsequent local decomposition. Our experimental results using modern convolutional neural networks show that the proposed method can improve the accuracy substantially within about 10% extra neurons.

scholarly journals NEAT Snake: a both evolutionary and neural network adaptation approach

2020 ◽  
Author(s):  
Alisson Steffens Henrique ◽  
Vinicius Almeida dos Santos ◽  
Rodrigo Lyra
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Neural Networks ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Real Time ◽  
Relative Position ◽  
Network Adaptation ◽  
The Neural Network

There are several challenges when modeling artificial intelligencemethods for autonomous players on games (bots). NEAT is one ofthe models that, combining genetic algorithms and neural networks,seek to describe a bot behavior more intelligently. In NEAT, a neuralnetwork is used for decision making, taking relevant inputs fromthe environment and giving real-time decisions. In a more abstractway, a genetic algorithm is applied for the learning step of the neuralnetworks’ weights, layers, and parameters. This paper proposes theuse of relative position as the input of the neural network, basedon the hypothesis that the bot profit will be improved.

scholarly journals Effect of Emotional Intelligence and Leadership Styles on Risk Intelligent Decision Making and Risk Management

2021 ◽  
Vol 11 (1) ◽  
pp. 71-81
Author(s):  
Jayet Moon
Keyword(s):  
Decision Making ◽  
Emotional Intelligence ◽  
Leadership Styles ◽  
Risk Perceptions ◽  
Risk Attitude ◽  
Success Factor ◽  
Chi Square ◽  
Intelligent Decision Making ◽  
Intelligent Decision ◽  
Transactional Leaders

AbstractIn today’s world, uncertainty abounds. It is therefore incumbent on managers to take decisions using unbiased considerations in dealing with organizational risks. Often, risk decisions are replete with assumptions and biases, leading to incorrect decisions. Leaders who apply emotional intelligence (EI) skills are better poised to challenge internal biases and assumptions to improve decision-making, but limited empirical evidence exists that accounts for the nexus between EI, leadership styles and risk perceptions of managers. The purpose of the paper was to explore the relevance of the theory of EI in risk-based decision-making, while comparing various leadership styles. The research adopted a questionnaire survey administered to 173 employed individuals. The research hypotheses analyzed the mediating roles of EI and leadership styles in risk perceptions using ‘t’ statistic and where applicable, Chi-square testing. The results of the analysis confirmed the role of EI in filtering deleterious internal biases and confirmed EI’s presence as a success factor in leadership and decision-making. Transformational leaders are, however, more emotionally intelligent and less biased. These attributes allow for the generation of a suitable risk attitude and enhance risk-intelligent decisions as compared to transactional leaders. This study, while being descriptive, is exploratory in nature and opens pathways for further targeted research based on specific EI abilities or traits and various situational risk attitudes.

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.

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