scholarly journals Application of a multilayer perceptron artificial neural network for identification of peach cultivars based on physical characteristics

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11529
Author(s):  
Adel M. Al-Saif ◽  
Mahmoud Abdel-Sattar ◽  
Abdulwahed M. Aboukarima ◽  
Dalia H. Eshra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Activation Function ◽  
Cross Entropy ◽  
Output Layer ◽  
Entropy Error ◽  
Artificial Neural ◽  
The Cross ◽  
Hidden Layer

In the fresh fruit industry, identification of fruit cultivars and fruit quality is of vital importance. In the current study, nine peach cultivars (Dixon, Early Grande, Flordaprince, Flordastar, Flordaglo, Florda 834, TropicSnow, Desertred, and Swelling) were evaluated for differences in skin color, firmness, and size. Additionally, a multilayer perceptron (MLP) artificial neural network was applied for identification of the cultivars according to these attributes. The MLP was trained with an input layer including six input nodes, a single hidden layer with six hidden nodes, and an output layer with nine output nodes. A hyperbolic tangent activation function was used in the hidden layer and the cross entropy error was given because the softmax activation function was functional to the output layer. Results showed that the cross entropy error was 0.165. The peach identification process was significantly affected by the following variables in order of contribution (normalized importance): polar diameter (100%), L∗ (89.0), b∗ (88.0%), a∗ (78.5%), firmness (71.3%), and cross diameter (37.5.3%). The MLP was found to be a viable method of peach cultivar identification and classification because few identifying attributes were required and an overall classification accuracy of 100% was achieved in the testing phase. Measurements and quantitative discrimination of peach properties are provided in this research; these data may help enhance the processing efficiency and quality of processed peaches.

Experimental and Comparison Based Study on Diabetes Prediction Using Artificial Neural Network

2019 ◽  
Vol 12 ◽  
Author(s):  
Nitesh Pradhan ◽  
VijayPal Singh Dhaka ◽  
Satish Chandra Kulhari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Nearest Neighbor ◽  
Activation Function ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Output Layer ◽  
Diabetes Prediction ◽  
Artificial Neural ◽  
Hidden Layer

Background: Diabetes is spreading in the entire world. In a survey, it is observed that every generation from child to old age people are suffering from diabetes. If diabetes is not identified in time, it may lead to deadliest disease. Prediction of diabetes is of the utmost challenging task by machines. In the human body, diabetes is one of the perilous maladies that creates depended disease such as kidney disease, heart attack, blindness etc. Thus it is very important to diagnose diabetes in time. Objective: Our target is to develop a system using Artificial Neural Network(ANN), with the ability to predict whether a patient suffers from diabetes or not. Method: This paper illustrates various machine learning techniques in form of literature review; such as Support Vector Machine, Naïve Bayes, K Nearest Neighbor, Decision Tree, Random Forest Etc. We applied ANN to predict diabetes. In this paper, the architecture of ANN consists of four hidden layers each of six neurons and one output layer with one neuron. Optimizer used for the architecture is ‘Adam’. Results: We have Pima Indian diabetes dataset of sufficient number of patients with nine different symptoms with respect to the patients and nine different features in connection with the mathematical computation/prediction. Hence we bifurcate the dataset into training and testing set in majority and minority ratio of 80:20 respectively. It facilitates us the majority patient’s data to be used as training set and minority data to be used as testing set. We train our network for multiple epoch with different activation function. We used four hidden layers with six neurons in each hidden layer and one output layer. On the hidden layer, we used multiple activation functions such as sigmoid, ReLU etc. and obtained beat accuracy (88.71%) in 600 epochs with ReLU activation function. On the output layer, we used only sigmoid activation function because we have only two classes in our dataset. Conclusion: Diabetes prediction by machine is a challenging task. So many machine learning algorithms exist to predict the diabetes such as Naïve Bayes, decision tree, K nearest neighbor, support vector machine etc. This paper presents a novel approach to predict whether a patient has diabetes or not based on Pima Indian diabetes dataset. In this paper, we used artificial neural network to train out network and it is observed that artificial neural network approach performs better than all other classifiers

scholarly journals The effect of Kurtosis on the accuracy of artificial neural network predictive model

2018 ◽  
Vol 204 ◽  
pp. 02018
Author(s):  
Aisyah Larasati ◽  
Anik Dwiastutik ◽  
Darin Ramadhanti ◽  
Aal Mahardika
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Predictive Model ◽  
Predictive Models ◽  
Output Layer ◽  
Input Layer ◽  
Artificial Neural ◽  
Misclassification Rates ◽  
Hidden Layer ◽  
Program Data

This study aims to explore the effect of kurtosis level of the data in the output layer on the accuracy of artificial neural network predictive models. The artificial neural network predictive models are comprised of one node in the output layer and six nodes in the input layer. The number of hidden layer is automatically built by the program. Data are generated using simulation approach. The results show that the kurtosis level of the node in the output layer is significantly affect the accuracy of the artificial neural network predictive model. Platycurtic and leptocurtic data has significantly higher misclassification rates than mesocurtic data. However, the misclassification rates between platycurtic and leptocurtic is not significantly different. Thus, data distribution with kurtosis nearly to zero results in a better ANN predictive model.

Classification of Coronary Artery Disease Using Multilayer Perceptron Neural Network

2021 ◽  
Vol 12 (3) ◽  
pp. 35-43
Author(s):  
Pratibha Verma ◽  
Vineet Kumar Awasthi ◽  
Sanat Kumar Sahu
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Artificial Neural Network ◽  
Coronary Artery ◽  
Multilayer Perceptron ◽  
Artificial Neural ◽  
Artery Disease ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

Coronary artery disease (CAD) has been the leading cause of death worldwide over the past 10 years. Researchers have been using several data mining techniques to help healthcare professionals diagnose heart disease. The neural network (NN) can provide an excellent solution to identify and classify different diseases. The artificial neural network (ANN) methods play an essential role in recognizes diseases in the CAD. The authors proposed multilayer perceptron neural network (MLPNN) among one hidden layer neuron (MLP) and four hidden layers neurons (P-MLP)-based highly accurate artificial neural network (ANN) method for the classification of the CAD dataset. Therefore, the ten-fold cross-validation (T-FCV) method, P-MLP algorithms, and base classifiers of MLP were employed. The P-MLP algorithm yielded very high accuracy (86.47% in CAD-56 and 98.35% in CAD-59 datasets) and F1-Score (90.36% in CAD-56 and 98.83% in CAD-59 datasets) rates, which have not been reported simultaneously in the MLP.

Rapid Method for Prediction of Escherichia coli Numbers Using an Electronic Sensor Array and an Artificial Neural Network

2004 ◽  
Vol 67 (8) ◽  
pp. 1604-1609 ◽  
Author(s):  
UBONRATANA SIRIPATRAWAN ◽  
JOHN E. LINZ ◽  
BRUCE R. HARTE
Keyword(s):  
Neural Network ◽  
Escherichia Coli ◽  
Artificial Neural Network ◽  
Transfer Function ◽  
Sensor Array ◽  
E Coli ◽  
Output Layer ◽  
Electronic Sensor ◽  
Artificial Neural ◽  
Hidden Layer

An electronic sensor array with 12 nonspecific metal oxide sensors was evaluated for its ability to monitor volatile compounds in super broth alone and in super broth inoculated with Escherichia coli (ATCC 25922) at 37°C for 2 to 12 h. Using discriminant function analysis, it was possible to differentiate super broth alone from that containing E. coli when cell numbers were 105 CFU or more. There was a good agreement between the volatile profiles from the electronic sensor array and a gas chromatography–mass spectrometer method. The potential to predict the number of E. coli and the concentration of specific metabolic compounds was investigated using an artificial neural network (ANN). The artificial neural network was composed of an input layer, one hidden layer, and an output layer, with a hyperbolic tangent sigmoidal transfer function in the hidden layer and a linear transfer function in the output layer. Good prediction was found as measured by a regression coefficient (R2 = 0.999) between actual and predicted data.

scholarly journals Initial Optimal Parameters of Artificial Neural Network and Support Vector Regression

2018 ◽  
Vol 8 (5) ◽  
pp. 3341 ◽  
Author(s):  
Edy Fradinata ◽  
Sakesun Suthummanon ◽  
Wannarat Suntiamorntut
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Support Vector Regression ◽  
Kernel Function ◽  
Activation Function ◽  
Support Vector ◽  
Optimal Parameters ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Linear Loss

This paper presents architecture of backpropagation Artificial Neural Network (ANN) and Support Vector Regression (SVR) models in supervised learning process for cement demand dataset. This study aims to identify the effectiveness of each parameter of mean square error (MSE) indicators for time series dataset. The study varies different random sample in each demand parameter in the network of ANN and support vector function as well. The variations of percent datasets from activation function, learning rate of sigmoid and purelin, hidden layer, neurons, and training function should be applied for ANN. Furthermore, SVR is varied in kernel function, lost function and insensitivity to obtain the best result from its simulation. The best results of this study for ANN activation function is Sigmoid. The amount of data input is 100% or 96 of data, 150 learning rates, one hidden layer, trinlm training function, 15 neurons and 3 total layers. The best results for SVR are six variables that run in optimal condition, kernel function is linear, loss function is ౬-insensitive, and insensitivity was 1. The better results for both methods are six variables. The contribution of this study is to obtain the optimal parameters for specific variables of ANN and SVR.

scholarly journals Konstruksi Model Matematika Pola Curah Hujan Menggunakan Artificial Neural Network (ANN) dengan Metode Backpropagation

2018 ◽  
Vol 1 (1) ◽  
pp. 10
Author(s):  
Habibi Ratu Perwira Negara ◽  
Irzani Irzani ◽  
Ripai Ripai
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Rainfall Pattern ◽  
Output Layer ◽  
South Central ◽  
Southern Central ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
The Mathematical Model

Abstrak: Penelitian ini bertujuan untuk menentukan model pola curah hujan menggunakan Artificial Neural Network (ANN) dengan metode Backpropagatiaon. Sampel dalam penelitian ini adalah 5 pos pencataan hujan di daerah Lombok Tengah bagian Selatan dan 2 pos pencatatan hujan di  daerah Lombok Timur bagian Selatan. Data penelitian berupa data setengah bulanan yang dicatat dari tahun 1973 sampai tahun 2010 untuk daerah Lombok Tengah bagian Selatan dan data dari tahun 1974 sampai tahun 2010 untuk daerah Lombok Timur bagian Selatan. Metode penilitian dilakukan dengan melakukan pembelajaran data curah hujan menggunakan 5 arsitektur yang berbeda. Pembelajaran dilakukan untuk menemukan arsitektur terbaik. Arsitektur untuk daerah Lombok Tengah bagian Selatan adalah 120 layer inputan, 240 layer hidden 1, 12 layer hidden 2, dan 1 layer output. Sedangkan arsitektur untuk daerah Lombok Timur bagian Selatan adalah 363 layer inputan, 54 layer hidden 1, 24 layer hidden 2, dan 1 layer output. Model matematika pola curah hujan yang diperoleh adalah .Abstract:  This study aims to determine the model of rainfall pattern using Artificial Neural Network (ANN) with Backpropagatiaon method. The sample in this research is 5 rainfall checkpoint in South Central Lombok and 2 post recording of rain in South East of Lombok. The research data are semi-monthly data recorded from 1973 to 2010 for the southern part of Central Lombok and data from 1974 to 2010 for the southern part of Lombok Timur. Methods of research conducted by conducting rainfall data learning using 5 different architectures. Learning is done to find the best architecture. The architecture for the Southern Central Lombok area is 120 layers of input, 240 hidden layers 1, 12 hidden layers 2, and 1 output layer. While the architecture for the southern part of East Lombok is 363 layers inputan, 54 hidden layer 1, 24 hidden layer 2, and 1 output layer. The mathematical model of the obtained rainfall pattern is .

scholarly journals Live to learn: learning rules-based artificial neural network

2021 ◽  
Vol 21 (1) ◽  
pp. 558
Author(s):  
Aseel Shakir I. Hilaiwah ◽  
Hanan Abed Alwally Abed Allah ◽  
Basim Akhudir Abbas ◽  
Tole Sutikno
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Ann Model ◽  
Output Layer ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer ◽  
Intelligent Model ◽  
Human Nervous System

<span>An extensive review of the artificial neural network (ANN) is presented in this paper. Previous studies review the artificial neural network (ANN) based on the approaches (algorithms) used or based on the types of the artificial neural network (ANN). The presented paper reviews the ANN based on the goal of the ANN (methods, and layers), which become the main objective of this paper. As a famous artificial intelligent model, ANN mimics the human nervous system in handling the information transmited by different nodes (also known as neurons) in this model. These nodes are stacked in layers and work collectively to bring about solution to complex problems. Numerous structures exist for ANN and each of these structures is designed to addressa a specific task. Basically, the ANN architecture is comprised of 3 different layers wherein the first layer rpresents the input layer that consist of several input nodes that represent the input parameterfor the model. The hidden layer is te second layer and consists of a hidden layer of neurons. The neurons in this layer are directly connected to the neurons in the output layer. The third layer is the output layer which is the models’ response layer. The output layer neurons have the activation functions for the calculation of the ANN final output. The connection between the nodes in the ANN model is mediated by synaptic weights. This paper is a comprehensive study of ANN models and their layers.</span>

scholarly journals Pengujian Algoritma Artificial Neural Network (ANN) Untuk Prediksi Kecepatan Angin

2019 ◽  
Vol 2 (1) ◽  
pp. 43
Author(s):  
Syukri Syukri ◽  
Samsuddin Samsuddin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Output Layer ◽  
Input Layer ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

<span lang="EN-US">Angin memiliki peran yang penting dalam kehidupan manusia, antara lain pada pembangkit listrik, pelayaran dan penerbangan. Ketiga sektor tersebut erat kaitannya dengan  kondisi angin. Angin dapat muncul setiap saat dan setiap waktu serta perubahan geografis pada suatu wilayah. Hal ini mengakibatkan sulitnya menentukan kecepatan angin, maka untuk mengatasi masalah tersebut diperlukan prediksi kecepatan angin. Saat ini berbagai metode prediksi telah banyak dikembangkan, salah satu metode yang dapat digunakan untuk melakukan prediksi dengan akurasi yang tinggi yaitu algoritma <em>Artificial Neural Network</em> (ANN) <em>Backpropagation</em>. Arsitektur ANN yang digunakan adalah  4 parameter <em>input layer</em>, <em>hidden layer</em> (5, 10, 15, 20, 25 dan 30) dan <em>output layer</em> (1 parameter). Data pembelajaran dan pengujian didapatkan dari stasiun BMKG Blang Bintang Aceh Besar, berupa data kecepatan angin jam per harian periode Januari 2011 sampai dengan Desember 2015 yang terdiri dari arah angin, suhu, tekanan, kelembaban dan suhu. Hasil pengujian menunjukkan bahwa metode ANN <em>Backpropagation </em>cukup baik diterapkan untuk proses prediksi, kemampuan ANN dalam melakukan prediksi memiliki tingkat akurasi rata – rata yang lebih baik yaitu 96 %. Sedangkan nilai rata – rata kerapatan daya angin jam per harian yaitu </span><span lang="EN-US">45.030 W/m<sup>2</sup></span>

scholarly journals PEMODELAN JARINGAN SYARAF TIRUAN DENGAN CASCADE FORWARD BACKPROPAGATION PADA KURS RUPIAH TERHADAP DOLAR AMERIKA SERIKAT

2018 ◽  
Vol 7 (1) ◽  
pp. 64-72
Author(s):  
Ekky Rosita Singgih Wigati ◽  
Budi Warsito ◽  
Rita Rahmawati
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Processing System ◽  
Training Data ◽  
Neural Network Modeling ◽  
Feed Forward Neural Network ◽  
Output Layer ◽  
Input Layer ◽  
Artificial Neural ◽  
Hidden Layer

Neural Network Modeling (NN) is an information-processing system that has characteristics in common with human brain. Cascade Forward Neural Network (CFNN) is an artificial neural network that its architecture similar to Feed Forward Neural Network (FFNN), but there is also a direct connection from input layer and output layer. In this study, we apply CFNN in time series field. The data used isexchange rate of rupiah against US dollar period of January 1st, 2015 until December 31st, 2017. The best model was built from 1 unit input layer with input Zt-1, 4 neurons in the hidden layer, and 1 unit output layer. The activation function used are the binary sigmoid in the hidden layer and linear in the output layer. The model produces MAPE of training data equal to 0.2995% and MAPE of testing data equal to 0.1504%. After obtaining the best model, the data is foreseen for January 2018 and produce MAPE equal to0.9801%. Keywords: artificial neural network, cascade forward, exchange rate, MAPE 

scholarly journals PERAMALAN JUMLAH PERMINTAAN UDANG BEKU PND MENGGUNAKAN METODE JARINGAN SYARAF TIRUAN (JST) BACKPROPAGATION

2017 ◽  
Vol 11 (1) ◽  
pp. 17 ◽  
Author(s):  
Iid Mufidah ◽  
Sony Suwasono ◽  
Yuli Wibowo ◽  
Deddy Wirawan Soedibyo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Series Data ◽  
Architecture Design ◽  
Neural Network Architecture ◽  
Output Layer ◽  
Artificial Neural Network Architecture ◽  
Artificial Neural ◽  
Hidden Layer

Forecasting is the art or science to estimate how many needs will come in order to meet the demand for goods or services, often based on historical time series data. The growing number of emerging companies in Indonesia today has created a very tight business competition in both services and products. Consumers choose the best service and high quality and low price. Consumer demand is always uncertain or varied in each subsequent period. The aim of this research was to determind the best backpropagation neural network architecture design and to predict the demand of frozen product of PND 26/30. This research used the method of Neural Network (ANN) and Processing ANN using MATLAB software. Implementation of ANN method in PT.XYZ using Backpropagation algorithm. Artificial neural network architecture used was 12 input layer, 1 output layer, and 12 hidden layer and activation function used tansig and purelin. Tansig for hidden layer and purelin for output layer. The best artificial neural network architecture design for product demand for PND 31/40 was a multi layer feedforward value of Mean Square Error (MSE) network training value of 0.01 with MAPE 3.35. The result of JST forecasting period 2017 were 960 MC, 637 MC, 572 MC, 993 MC, 1386 MC, 480 MC, 135 MC, 1209 MC, 1476 MC, 1029 MC, 290 MC, and 952 MC. Keywords: artificial neural network, PND 26/30, backpropagation, MSE, MAPE

Sign in / Sign up

Export Citation Format

Share Document