[PDF] Top 20 Improving robustness of artificial neural networks model using genetic algorithm
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Improving robustness of artificial neural networks model using genetic algorithm
... In this work, an ANN model is used for inferential estimation of product composition in a distillation column. The ain is to address an issue in process industries, i.e.[r] ... See full document
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Modeling and Optimization of Roll-bonding Parameters for Bond Strength of Ti/Cu/Ti Clad Composites by Artificial Neural Networks and Genetic Algorithm
... ANN methods are based on some significant conceptions that have been provided by neuroscientists. In the ANN method, a simulation of a small part of the central nervous system is performed wherein stimulation data are ... See full document
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A Hybrid Model using Artificial Neural Network and Genetic Algorithm for Degree of Injury Determination
... Differences in the criteria used for assessing injuries are very necessary because they are related to the possibility of death, costs, negligence, quality of life, and disability. The method used is an ICISS (ICD-based ... See full document
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Research on Classification of E-shopper Based on Neural Networks and Genetic Algorithm
... optimal algorithm of modeling dynamic architecture for artificial neural networks (ANN) and a novel machine-learning algorithm for extracting rules from databases via using ... See full document
8
Sign Language Recognition using Hybrid Neural Networks
... Since, artificial neural networks are best suited for automated pattern recognition problems; they are used as a classification tool for this ...important algorithm for training neural ... See full document
7
Face Recognition using Genetic Algorithm and Neural Networks
... the Genetic Algorithms technique application in facial detection, which will solve the one step for face ...the Genetic Algorithms (GA's) are characterized as one search technique inspired by Darwin ... See full document
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Feature selection of microarray data using genetic algorithms and artificial neural networks
... larger neural network had been used, the epoch number would have to be adjusted accordingly to achieve accurate ...the model could quickly fit. A good balance was found by using the [2,1] ...However ... See full document
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Optimizing the Multilayer Feed Forward Artificial Neural Networks Architecture and Training Parameters using Genetic Algorithm
... feed-forward neural network model for fault detection NN of a deep-trough hydroponic system and a predictive modeling NN system of a similar hydroponic system has also been ...a genetic ... See full document
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IMPROVING BUSINESS RULES MANAGEMENT THROUGH THE APPLICATION OF ADAPTIVE BUSINESS INTELLIGENCE TECHNIQUE
... into account past, present and possible future infor- mation on that asset. Future information can be ex- pectations of individual or market participant. The in- formation technologies let the users find various types of ... See full document
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Title: CLASSIFICATION ON BREAST CANCER USING GENETIC ALGORITHM TRAINED NEURAL NETWORK
... training neural networks are based on local search, population methods, and others such as cooperative coevolutionary models ...where Genetic Programming is used to obtain graphs that represent ... See full document
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Mitigating Economic Denial of Sustainability (EDoS) in Cloud Environment using Genetic Algorithm and Artificial Neural Network
... section. Artificial Neural Network (ANN) to detect affected route: An ANN is a computational model in which the functions depend on biological neural ...the neural network is the number ... See full document
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Accuracy Enhancement of Artificial Neural Network using Genetic Algorithm
... of genetic algorithm and neural ...forward neural networks are used to classify the complex ...the neural network are driven using genetic algorithm because ... See full document
5
A New Method for Intrusion Detection Using Genetic Algorithm and Neural network
... evolutionary algorithm to determine the characteristics, the ability of the algorithm to search all the parts of the search space in the network and its ability to exploit the best ...This algorithm ... See full document
10
Performance Enhancement of RSA Algorithm Using Artificial Neural Networks
... RSA algorithm by using An Artificial Neural Network (ANN), we provided an extensive quantitative evaluation of execution time for both normal RSA and ANN RSA ...An Artificial ... See full document
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Experimental Investigation of Classification Algorithms for Predicting Lesion Type on Breast DCE MR Images
... classifier model and the test set of 35 is used to verify the trained classifier ...but artificial bee colony algorithm optimized neural network based classifier technique shows comparably ... See full document
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A Comparative Study of Call Admission Control in Mobile Multimedia Networks using Soft Computing
... hybrid neural network approache’s to estimate cell loss rate of variable bit rates video traffic for CAC in ATM ...ATM networks, it’s very difficult to estimate CLR from a limited number of training ... See full document
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Online identification of heat dissipaters using artificial neural networks
... box model is ...to neural based techniques which are adapted from standard ARX (AutoRegressive structure with eXtra inputs) and OE (Output-Error) ...the Neural Network ARX (NNARX) model, only ... See full document
6
Estimating of Scour in Downstream of the Water Level Regulation Structures
... equations, artificial neural networkis used to calculate maximum scour ...MLP networks by error back propagation (BP) training algorithm were ... See full document
8
Simulation and Optimization Techniques for Sawmill Yard Operations—A Literature Review
... supply networks that are characterized by a high degree of interdependences and where logistic processes play important role, and also there is a need for process optimization ...the model or to evaluate ... See full document
14
Intrusion Detection System Using Hybrid Approach by MLP and K-Means Clustering
... This technique proposes a combination of the K-means clustering and Naïve Bayes classifiers (KM+NB), this means a hybrid learning approach. The evaluation and comparison of this approach was done by using KDD ... See full document
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