Granular Computing in Neural Networks

2001 ◽  
pp. 275-305 ◽  
Author(s):  
Scott Dick ◽  
Abraham Kandel
Keyword(s):  
Neural Networks ◽  
Granular Computing

scholarly journals On a Hybridization of Deep Learning and Rough Set Based Granular Computing

Algorithms ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 63 ◽  
Author(s):  
Krzysztof Ropiak ◽  
Piotr Artiemjew
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Rough Set ◽  
Granular Computing ◽  
High Efficiency ◽  
Opportunity To Learn ◽  
Original Data ◽  
Learning Tools ◽  
Approximation Techniques ◽  
Decision Systems

The set of heuristics constituting the methods of deep learning has proved very efficient in complex problems of artificial intelligence such as pattern recognition, speech recognition, etc., solving them with better accuracy than previously applied methods. Our aim in this work has been to integrate the concept of the rough set to the repository of tools applied in deep learning in the form of rough mereological granular computing. In our previous research we have presented the high efficiency of our decision system approximation techniques (creating granular reflections of systems), which, with a large reduction in the size of the training systems, maintained the internal knowledge of the original data. The current research has led us to the question whether granular reflections of decision systems can be effectively learned by neural networks and whether the deep learning will be able to extract the knowledge from the approximated decision systems. Our results show that granulated datasets perform well when mined by deep learning tools. We have performed exemplary experiments using data from the UCI repository—Pytorch and Tensorflow libraries were used for building neural network and classification process. It turns out that deep learning method works effectively based on reduced training sets. Approximation of decision systems before neural networks learning can be important step to give the opportunity to learn in reasonable time.

Fuzzy Moves Using Compensatory Granular Reasoning

2003 ◽  
Vol 03 (03) ◽  
pp. 249-263 ◽  
Author(s):  
Yan-Qing Zhang ◽  
Abraham Kandel
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Granular Computing ◽  
Fault Tolerant ◽  
The Future ◽  
Fuzzy Game

In this paper, compensatory granular reasoning methods are proposed based on fuzzy logic, neural networks, genetic algorithms, compensatory computing and granular computing. The compensatory operation can be reduced to the relevant general compensatory AND operations which replace the traditional AND operations in the compensatory granular reasoning. Different compensatory AND operations such as fuzzy compensatory AND, neural compensatory AND, genetic compensatory AND, linear compensatory AND, and exponential compensatory AND are defined. The compensatory granular reasoning methods can be used to make more reliable decisions. For example, the compensatory granular reasoning methods can make more fault-tolerant fuzzy moves in a fuzzy game if a player carefully chooses a reasonable compensatory operation. In the future, different compensatory granular reasoning methods will be used in different decision-making applications.

scholarly journals Brain Tumor Detection: 2 Novel Approaches

2020 ◽  
Author(s):  
Donghyun Kim
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Brain Tumor ◽  
Complexity Theory ◽  
Granular Computing ◽  
Circuit Complexity ◽  
Ensemble Methods ◽  
Tumor Detection ◽  
Classification Algorithms ◽  
Novel Approaches

In this paper, we propose 2 novel methods for brain tumor detection in MRI images. In the first proposed approach, we build upon prior research on ensemble methods by testing the concatenation of pre-trained models: features extracted via transfer learning are merged and segmented by classification algorithms or a stacked ensemble of those algorithms. In the second approach, we expand upon prior studies on convolutional neural networks: a convolutional neural network involving a specific module of layers is used for classification. The first approach achieved accuracy scores of 0.98 and the second approach achieved a score of 0.863, outperforming a benchmark VGG-16 model. Considerations to granular computing and circuit complexity theory are given in the paper as well.

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