Genetically Optimized Rule-Based Fuzzy Polynomial Neural Networks: Synthesis of Computational Intelligence Technologies

2007 ◽  
pp. 437-444
Author(s):  
Sung-Kwun Oh ◽  
James F. Peters ◽  
Witold Pedrycz ◽  
Tae-Chon Ahn
Keyword(s):  
Neural Networks ◽  
Computational Intelligence ◽  
Rule Based

scholarly journals Transformer-based artificial neural networks for the conversion between chemical notations

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lev Krasnov ◽  
Ivan Khokhlov ◽  
Maxim V. Fedorov ◽  
Sergey Sosnin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rapid Development ◽  
Performance Level ◽  
Rule Based ◽  
Development Costs ◽  
Overall Performance ◽  
Artificial Neural

AbstractWe developed a Transformer-based artificial neural approach to translate between SMILES and IUPAC chemical notations: Struct2IUPAC and IUPAC2Struct. The overall performance level of our model is comparable to the rule-based solutions. We proved that the accuracy and speed of computations as well as the robustness of the model allow to use it in production. Our showcase demonstrates that a neural-based solution can facilitate rapid development keeping the required level of accuracy. We believe that our findings will inspire other developers to reduce development costs by replacing complex rule-based solutions with neural-based ones.

scholarly journals Extraction of organic chemistry grammar from unsupervised learning of chemical reactions

2021 ◽  
Vol 7 (15) ◽  
pp. eabe4166
Author(s):  
Philippe Schwaller ◽  
Benjamin Hoover ◽  
Jean-Louis Reymond ◽  
Hendrik Strobelt ◽  
Teodoro Laino
Keyword(s):  
Organic Chemistry ◽  
Neural Networks ◽  
Chemical Synthesis ◽  
Unsupervised Learning ◽  
Chemical Reactions ◽  
Data Driven ◽  
Experimental Task ◽  
Rule Based ◽  
Atom Mapping ◽  
Mapping Information

Humans use different domain languages to represent, explore, and communicate scientific concepts. During the last few hundred years, chemists compiled the language of chemical synthesis inferring a series of “reaction rules” from knowing how atoms rearrange during a chemical transformation, a process called atom-mapping. Atom-mapping is a laborious experimental task and, when tackled with computational methods, requires continuous annotation of chemical reactions and the extension of logically consistent directives. Here, we demonstrate that Transformer Neural Networks learn atom-mapping information between products and reactants without supervision or human labeling. Using the Transformer attention weights, we build a chemically agnostic, attention-guided reaction mapper and extract coherent chemical grammar from unannotated sets of reactions. Our method shows remarkable performance in terms of accuracy and speed, even for strongly imbalanced and chemically complex reactions with nontrivial atom-mapping. It provides the missing link between data-driven and rule-based approaches for numerous chemical reaction tasks.

A hybrid computational intelligence approach for structural damage detection using marine predator algorithm and feedforward neural networks

2021 ◽  
Vol 252 ◽  
pp. 106568
Author(s):  
Long Viet Ho ◽  
Duong Huong Nguyen ◽  
Mohsen Mousavi ◽  
Guido De Roeck ◽  
Thanh Bui-Tien ◽  
...  
Keyword(s):  
Neural Networks ◽  
Damage Detection ◽  
Computational Intelligence ◽  
Structural Damage ◽  
Feedforward Neural Networks ◽  
Structural Damage Detection ◽  
Marine Predator ◽  
Intelligence Approach

Computational intelligence approach using Levenberg–Marquardt backpropagation neural networks to solve the fourth-order nonlinear system of Emden–Fowler model

2021 ◽  
Author(s):  
Zulqurnain Sabir ◽  
Mohamed R. Ali ◽  
Muhammad Asif Zahoor Raja ◽  
Muhammad Shoaib ◽  
Rafaél Artidoro Sandoval Núñez ◽  
...  
Keyword(s):  
Neural Networks ◽  
Nonlinear System ◽  
Fourth Order ◽  
Computational Intelligence ◽  
Backpropagation Neural Networks ◽  
Levenberg Marquardt ◽  
Intelligence Approach

Rule-based artificial neural networks

1993 ◽  
Vol 24 (7) ◽  
pp. 1415-1418 ◽  
Author(s):  
REUVEN R. LEVARY
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Rule Based ◽  
Artificial Neural

scholarly journals Human Activities Recognition Based on Neuro-Fuzzy Finite State Machine

Technologies ◽  
2018 ◽  
Vol 6 (4) ◽  
pp. 110 ◽  
Author(s):  
Gadelhag Mohmed ◽  
Ahmad Lotfi ◽  
Amir Pourabdollah
Keyword(s):  
Neural Networks ◽  
Human Activities ◽  
Finite State Machine ◽  
Fuzzy Rule ◽  
State Machine ◽  
Machine Learning Techniques ◽  
Rule Based ◽  
Neuro Fuzzy ◽  
Finite State ◽  
Learning Capabilities

Human activity recognition and modelling comprise an area of research interest that has been tackled by many researchers. The application of different machine learning techniques including regression analysis, deep learning neural networks, and fuzzy rule-based models has already been investigated. In this paper, a novel method based on Fuzzy Finite State Machine (FFSM) integrated with the learning capabilities of Neural Networks (NNs) is proposed to represent human activities in an intelligent environment. The proposed approach, called Neuro-Fuzzy Finite State Machine (N-FFSM), is able to learn the parameters of a rule-based fuzzy system, which processes the numerical input/output data gathered from the sensors and/or human experts’ knowledge. Generating fuzzy rules that represent the transition between states leads to assigning a degree of transition from one state to another. Experimental results are presented to demonstrate the effectiveness of the proposed method. The model is tested and evaluated using a dataset collected from a real home environment. The results show the effectiveness of using this method for modelling the activities of daily living based on ambient sensory datasets. The performance of the proposed method is compared with the standard NNs and FFSM techniques.

Human Skin Detection in Color Images Using Deep Learning

2015 ◽  
Vol 5 (2) ◽  
pp. 1-13 ◽  
Author(s):  
Mohammadreza Hajiarbabi ◽  
Arvin Agah
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Human Skin ◽  
Skin Color ◽  
Color Image ◽  
Gaussian Model ◽  
Color Images ◽  
Skin Detection ◽  
Learning Methods ◽  
Rule Based

Human skin detection is an important and challenging problem in computer vision. Skin detection can be used as the first phase in face detection when using color images. The differences in illumination and ranges of skin colors have made skin detection a challenging task. Gaussian model, rule based methods, and artificial neural networks are methods that have been used for human skin color detection. Deep learning methods are new techniques in learning that have shown improved classification power compared to neural networks. In this paper the authors use deep learning methods in order to enhance the capabilities of skin detection algorithms. Several experiments have been performed using auto encoders and different color spaces. The proposed technique is evaluated compare with other available methods in this domain using two color image databases. The results show that skin detection utilizing deep learning has better results compared to other methods such as rule-based, Gaussian model and feed forward neural network.

scholarly journals P6388Cardiac arrhythmia classification using computational intelligence: neural networks and fuzzy logic techniques

2017 ◽  
Vol 38 (suppl_1) ◽  
Author(s):  
P. Melin ◽  
E. Ramirez ◽  
G. Prado-Arechiga
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Computational Intelligence
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