An investigation of factors affecting the satisfaction of school meals catering service by using artificial neural network

2018 ◽  
Author(s):  
Kuang-Tai Liu ◽  
Chiu-Chi We ◽  
Pin-Chang Chen
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
School Meals ◽  
Factors Affecting ◽  
Artificial Neural

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

Towards Developing the Piece-Wise Linear Neural Network Algorithm for Rule Extraction

2020 ◽  
pp. 1632-1649
Author(s):  
Veronica Chan ◽  
Christine W. Chan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Equations ◽  
Rule Extraction ◽  
Ann Model ◽  
Factors Affecting ◽  
Artificial Neural ◽  
Linear Neural Network ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

This paper discusses development and application of a decomposition neural network rule extraction algorithm for nonlinear regression problems. The algorithm is called the piece-wise linear artificial neural network or PWL-ANN algorithm. The objective of the algorithm is to “open up” the black box of a neural network model so that rules in the form of linear equations are generated by approximating the sigmoid activation functions of the hidden neurons in an artificial neural network (ANN). The preliminary results showed that the algorithm gives high fidelity and satisfactory results on sixteen of the nineteen tested datasets. By analyzing the values of R2 given by the PWL approximation on the hidden neurons and the overall output, it is evident that in addition to accurate approximation of each individual node of a given ANN model, there are more factors affecting the fidelity of the PWL-ANN algorithm Nevertheless, the algorithm shows promising potential for domains when better understanding about the problem is needed.

Factors affecting algal blooms in a man-made lake and prediction using an artificial neural network

Measurement ◽  
2014 ◽  
Vol 53 ◽  
pp. 224-233 ◽  
Author(s):  
Sohyun Cho ◽  
Byungjin Lim ◽  
Jaewoon Jung ◽  
Sangdon Kim ◽  
Hyunmi Chae ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Algal Blooms ◽  
Factors Affecting ◽  
Artificial Neural

Exploring the major factors affecting fly-ash concrete carbonation using artificial neural network

2017 ◽  
Vol 31 (S2) ◽  
pp. 969-988 ◽  
Author(s):  
Yasmina Kellouche ◽  
Bakhta Boukhatem ◽  
Mohamed Ghrici ◽  
Arezki Tagnit-Hamou
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fly Ash ◽  
Factors Affecting ◽  
Fly Ash Concrete ◽  
Concrete Carbonation ◽  
Artificial Neural ◽  
Major Factors

scholarly journals Identifying the factors affecting occupational accidents: An artificial neural network model

2021 ◽  
Vol 10 (4) ◽  
pp. 195
Author(s):  
Gholamhossein Pourtaghi ◽  
Soheil Hassanipour ◽  
Mojtaba Sepandi ◽  
Hadiseh Rabiei ◽  
Mahdi Malakoutikhah
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Occupational Accidents ◽  
Factors Affecting ◽  
Artificial Neural

scholarly journals Determining the Yield of the Crop using Artificial Neural Network Method

2019 ◽  
Vol 9 (1) ◽  
pp. 2959-2965
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Crop Yield ◽  
Crop Yields ◽  
Back Propagation ◽  
Large Data ◽  
Machine Learning Techniques ◽  
Agricultural System ◽  
Factors Affecting ◽  
Artificial Neural

The agricultural system is complex and comprehend since it deals with large data that comes from several factors. Lot of techniques and have been used to identify any interactions between factors affecting yields with crop performance. The major objective of this paper is to help us predict the yield of a particular crop before even cultivating it for its production. We are using artificial neural networks for forwarding and implementing a system that will help the farmers in finding their crop yields according to their given data as input in the system and the system will give output based on previous data. The method used in this crop yield system is an artificial neural network and the algorithm used is feed forward and back propagation. Provide the input of data sets and the desired outcome of the system. Compute the error between the actual and desired outcome of the system. Amendment of the weight associated with different inputs and different functions. Compare the errors and the tolerance ratio of the output. Various machine learning techniques have been used in the past for calculating the crop yield using remote data. However, these methods are less useful and effective for predicting the yield of maize and for some other crops, which is cultivated at different times in various fields.The major application of this crop yield system is that it will help us to predict the yield before even cultivating it by studying the previous data collected such as soil fertility, pH level.

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