A study of the emotional evaluation models of color patterns based on the adaptive fuzzy system and the neural network

With the exciting development of Internet and the increasing use of it for providing or acquiring information, we are witnessing an enormous volume of text documents and online images. This is considered as information redundancy, which is one of the prominent features of modern day life. In this regard, fast and accurate access to important and favorite resources is one of the concerns of users of these enormous resources of information. Today, what is of great importance is the lack of methods to find and optimally exploit the information available, rather than the shortage or lack of information. The problem with big image data, the effort to eliminate noise and visual disturbances such as parameters from inappropriate light sources, the inadequacy of color combinations, and many other factors in received images, are very important issues in working on images and processing them. In this regard, the method of classification of the texts from the images using a fuzzy system and neural network based algorithm is suggested. In this method, the location of the fuzzy system is introduced at the begin and end of the neural network synchronized with fuzzification operation and fuzzy inversion. In fact, the main idea in this article is to eliminate or minimize noise in classifying the documents with high inaccuracy.

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An Approach of Anomaly Detection and Neural Network Classifiers to Measure Cellulolytic Activity

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666181219160406 ◽

2019 ◽

Vol 21 (9) ◽

pp. 681-692

Author(s):

Luis Francisco Barbosa-Santillán ◽

María de los Angeles Calixto-Romo ◽

Juan Jaime Sánchez-Escobar ◽

Liliana Ibeth Barbosa-Santillán

Keyword(s):

Neural Network ◽

Enzymatic Activity ◽

Real Time ◽

High Throughput Screening ◽

Cellulolytic Activity ◽

Color Patterns ◽

Cellulolytic Microorganisms ◽

The Neural Network ◽

Neural Network Classifiers

Aim and Objective: A common method used for massive detection of cellulolytic microorganisms is based on the formation of halos on solid medium. However, this is a subjective method and real-time monitoring is not possible. The objective of this work was to develop a method of computational analysis of the visual patterns created by cellulolytic activity through artificial neural networks description. Materials and Methods: Our method learns by an adaptive prediction model and automatically determines when enzymatic activity on a chromogenic indicator such as the hydrolysis halo occurs. To achieve this goal, we generated a data library with absorbance readings and RGB values of enzymatic hydrolysis, obtained by spectrophotometry and a prototype camera-based equipment (Enzyme Vision), respectively. We used the first part of the library to generate a linear regression model, which was able to predict theoretical absorbances using the RGB color patterns, which agreed with values obtained by spectrophotometry. The second part was used to train, validate, and test the neural network model in order to predict cellulolytic activity based on color patterns. Results: As a result of our model, we were able to establish six new descriptors useful for the prediction of the temporal changes in the enzymatic activity. Finally, our model was evaluated on one halo from cellulolytic microorganisms, achieving the regional classification of the generated halo in three of the six classes learned by our model. Conclusion: We assume that our approach can be a viable alternative for high throughput screening of enzymatic activity in real time.

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