Horse Optimization Algorithm Based Recurrent Neural Network Method for Epileptic Seizures Classification

2022 ◽  
pp. 183-190
Author(s):  
Dorin Moldovan
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Optimization Algorithm ◽  
Epileptic Seizures ◽  
Neural Network Method ◽  
Network Method

scholarly journals Website Visitors Forecasting using Recurrent Neural Network Method

2021 ◽  
Vol 6 (2) ◽  
pp. 137-145
Author(s):  
Putu Bagus Arya ◽  
Wayan Firdaus Mahmudy ◽  
Achmad Basuki
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Comparison Method ◽  
Backpropagation Neural Network ◽  
Short Term ◽  
Term Memory ◽  
Neural Network Method ◽  
Network Method ◽  
Long Short Term Memory

Abstract. The number of visitors and content accessed by users on a site shows the performance of the site. Therefore, forecasting needs to be done to find out how many users a website will come. This study applies the Long Short Term Memory method which is a development of the Recurrent Neural Network method. Long Short Term Memory has the advantage that there is an architecture of remembering and forgetting the output to be processed back into the input. In addition, the ability of another Long Short Term Memory is to be able to maintain errors that occur when doing backpropagation so that it does not allow errors to increase. The comparison method used in this study is Backpropagation. Neural Network method that is often used in various fields. The testing using new visitor data and first time visitors from 2018 to 2019 with vulnerable time per month. The computational experiment prove that the Long Short Term Memory produces better result in term of the mean square error (MSE) comparable to those achieved by Backpropagation Neural Network method.

scholarly journals An Enhanced Algorithm of RNN Using Trend in Time-Series

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 912 ◽  
Author(s):  
Yi ◽  
Bu ◽  
Kim
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Recurrent Neural Network ◽  
Time Series Data ◽  
Real Life ◽  
Series Data ◽  
Term Memory ◽  
Neural Network Method ◽  
Network Method ◽  
Learning Sets

The concept of trend in data and a novel neural network method for the forecasting ofupcoming time-series data are proposed in this paper. The proposed method extracts two datasets—the trend and the remainder—resulting in two separate learning sets for training. This methodworks sufficiently, even when only using a simple recurrent neural network (RNN). The proposedscheme is demonstrated to achieve better performance in selected real-life examples, compared toother averaging-based statistical forecast methods and other recurrent methods, such as longshort-term memory (LSTM).

Methods for evaluation the manufacturability of a vehicle

2020 ◽  
pp. 152-160
Keyword(s):  
Neural Network ◽  
Initial Data ◽  
Comparative Data ◽  
Training Algorithm ◽  
Neural Network Method ◽  
The Neural Network ◽  
Network Method ◽  
Calculation Results ◽  
Production And Operation

Methods for evaluation the manufacturability of a vehicle in the field of production and operation based on an energy indicator, expert estimates and usage of a neural network are stated. By using the neural network method the manufacturability of a car in a complex and for individual units is considered. The preparation of the initial data at usage a neural network for predicting the manufacturability of a vehicle is shown; the training algorithm and the architecture for calculating the manufacturability of the main units are given. According to the calculation results, comparative data on the manufacturability vehicles of various brands are given.

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