Dynamic selection of composite Web services based on a genetic algorithm optimized new structured neural network

2005 ◽  
Author(s):  
Lei Yang ◽  
Yu Dai ◽  
Bin Zhang ◽  
Yan Gao
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Web Services ◽  
Dynamic Selection ◽  
Composite Web Services ◽  
Selection Of

scholarly journals Classification of Bidikmisi Scholarship Acceptance using Neural Network Based on Hybrid Method of Genetic Algorithm

2021 ◽  
Vol 5 (2) ◽  
pp. 396-404
Author(s):  
N Cahyani ◽  
Sinta Septi Pangastuti ◽  
K Fithriasari ◽  
Irhamah Irhamah ◽  
N Iriawan
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimal Number ◽  
Connection Weight ◽  
The Neural Network ◽  
Network Method ◽  
Input Variables ◽  
Human Brains ◽  
Selection Of

A Neural network is a series of algorithms that endeavours to recognize underlying relationships in a set of data through processes that mimic the way human brains operate. In the case of classification, this method can provide a fit model through various factors, such as the variety of the optimal number of hidden nodes, the variety of relevant input variables, and the selection of optimal connection weights. One popular method to achieve the optimal selection of connection weights is using a Genetic Algorithm (GA), the basic concept is to iterate over Darwin's evolution. This research presents the Neural Network method with the Backpropagation Neural Network (BPNN) and the combined method of BPNN with GA, where GA is used to initialize and optimize the connection weight of BPNN. Based on accuracy value, the BPNN method combined with GA provides better classification, which is 90.51%, in the case of Bidikmisi Scholarship classification in East Java.

scholarly journals Using neural network for drowsiness detection based on EEG signals and optimization in the selection of its features using genetic algorithm

2020 ◽  
Vol 8 (1) ◽  
pp. 1-9
Author(s):  
Sepehr Sarabi ◽  
Milad Asadnejad ◽  
Saman Rajabi
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Traffic Accidents ◽  
Critical Factors ◽  
Eeg Signals ◽  
Statistical Population ◽  
Network Algorithms ◽  
Execution Speed ◽  
Genetic Algorithm Method ◽  
Selection Of

One of the major causes of traffic accidents is driver’s drowsiness. For this reason, detecting whether the driver's eyes are open or closed is one of the critical factors in reducing road deaths. One way to detect whether your eyes are open or closed is to use EEG signals. EEG signals are obtained from the recording of electrical activity in the human brain. The present study uses a neural network that is applied to the driver's EEG signals to detect whether the eye is open or closed. The data of the EEG signals used in this paper consist of 14 features that are based on a statistical population of 600 people. Various neural network algorithms have been implemented for clustering these data into two classes of open or closed eyes, which are described in this paper. Perceptron neural network and radial base neural network (RBF) are two types of networks used in this paper. Also, in order to improve the execution speed and reduce the occupied space of the microcontroller, the genetic algorithm method has been used to optimize the fitting function of Fisher’s discriminant rate, in which the optimized function provides better results in the less occupied time and space.

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