scholarly journals The Implementation of The Neuro Fuzzy Method Using Information Gain for Improving Accuracy in Determination of Landslide Prone Areas

2019 ◽  
Vol 6 (1) ◽  
pp. 95-105
Author(s):  
Winda Try Astuti ◽  
Much Aziz Muslim ◽  
Endang Sugiharti
Keyword(s):  
Information Gain ◽  
Technological Development ◽  
Training Data ◽  
Fuzzy Method ◽  
Neuro Fuzzy ◽  
Disaster Severity ◽  
Accuracy Increase ◽  
Improving Accuracy ◽  
Landslide Data

The accuracy of information is increasing rapidly as technological development. For the example, the information in determination of disaster severity. The disasters that can be determined is landslide. This determination can be conducted using the fuzzy method. One of method is neuro fuzzy. Neuro fuzzy is a combined method of two systems, fuzzy logic and artificial neural network. The accuracy of neuro fuzzy method can be increased by applying the information gain. The purpose of this study is to implement and to know the accuracy of the implementation of information gain as the selection of landslide data features. It conducted to the neuro fuzzy method in determining landslide prone areas. The distribution of training data and testing data was using 20 k-fold cross validation. The implementation of the neuro fuzzy method on landslide data was obtained an accuracy of 81.9231%. In the implementation of the neuro fuzzy method with information gain was conducted in classification process. The process will stop when the accuracy has decreased. The highest accuracy result was obtained of 88.489% by removing an attribute. So, it can be concluded the accuracy increase of 6.5659% in the implementation of the neuro fuzzy method and information gain in determination of landslide prone areas.

Operational Determination of the Activated Sludge Process Using Neural Networks

1992 ◽  
Vol 26 (9-11) ◽  
pp. 2461-2464 ◽  
Author(s):  
R. D. Tyagi ◽  
Y. G. Du
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Steady State ◽  
Activated Sludge ◽  
Feedforward Neural Network ◽  
Training Data ◽  
Activated Sludge Process

A steady-statemathematical model of an activated sludgeprocess with a secondary settler was developed. With a limited number of training data samples obtained from the simulation at steady state, a feedforward neural network was established which exhibits an excellent capability for the operational prediction and determination.

Diagnostics of the location of damage to tubing oil well pipes

2019 ◽  
Vol 5 (4) ◽  
pp. 115-128
Author(s):  
Timur A. Ishmuratov ◽  
Rif G. Sultanov ◽  
Milyausha N. Khusnutdinova
Keyword(s):  
Fluid Motion ◽  
Oil Well ◽  
General Belief ◽  
Oil Flow ◽  
Specialized Equipment ◽  
Improving Accuracy ◽  
Analytical Expressions ◽  
Technical Measures ◽  
Quick Determination

The study is devoted to the mathematical description of the process of oil outflow in places of leakage of the tubing string, which allows a computer to locate a leakage in the tubing. The authors propose methodology for identifying defects in the tubing and determining the location of the leak. The uniqueness of this methodology lies in quick determination of the place of leakage without the use of specialists, sophisticated and specialized equipment. Mathematical modeling of oil flow in the tubing requires the apparatus of continuum mechanics. It is a general belief that the movement of oil in the pipe flows at low speeds due to its outflow from the hole. Using the general equations of mass and energy balance, the authors have obtained differential equations of fluid motion in a vertical pipe in the process of its outflow from the tubing and in the process of injection. Analytical expressions are the solution to these equations, as they can help in estimating the degree of damage and its location, as well as the feasibility of its eliminating. The results show that an increase in the leakage and injection times leads to improving accuracy of locating damage. Thus, when conducting various geological and technical measures (GTM) at the well, it is possible to assess the presence of leakage and its intensity when deciding on the repair of tubing.

scholarly journals Evaluating Grayware Characteristics and Risks

2011 ◽  
Vol 2011 ◽  
pp. 1-28 ◽  
Author(s):  
Zhongqiang Chen ◽  
Zhanyan Liang ◽  
Yuan Zhang ◽  
Zhongrong Chen
Keyword(s):  
Information Gain ◽  
Feature Space ◽  
Training Data ◽  
Support Vector ◽  
Learning Models ◽  
Generalization Capability ◽  
Self Organizing Maps ◽  
Defense Strategies ◽  
Security Applications ◽  
Vector Machines

Grayware encyclopedias collect known species to provide information for incident analysis, however, the lack of categorization and generalization capability renders them ineffective in the development of defense strategies against clustered strains. A grayware categorization framework is therefore proposed here to not only classify grayware according to diverse taxonomic features but also facilitate evaluations on grayware risk to cyberspace. Armed with Support Vector Machines, the framework builds learning models based on training data extracted automatically from grayware encyclopedias and visualizes categorization results with Self-Organizing Maps. The features used in learning models are selected with information gain and the high dimensionality of feature space is reduced by word stemming and stopword removal process. The grayware categorizations on diversified features reveal that grayware typically attempts to improve its penetration rate by resorting to multiple installation mechanisms and reduced code footprints. The framework also shows that grayware evades detection by attacking victims' security applications and resists being removed by enhancing its clotting capability with infected hosts. Our analysis further points out that species in categoriesSpywareandAdwarecontinue to dominate the grayware landscape and impose extremely critical threats to the Internet ecosystem.

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