scholarly journals PERAMALAN KUNJUNGAN WISATAWAN MANCANEGARA KE PROVINSI BALI MENGGUNAKAN METODE ARTIFICIAL NEURAL NETWORK

2020 ◽  
Vol 9 (4) ◽  
pp. 213
Author(s):  
I KETUT RESTU WIRANATA ◽  
G.K. GANDHIADI ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Activation Function ◽  
Network Algorithms ◽  
Tourism Sector ◽  
Forecasting System ◽  
Artificial Neural ◽  
Tourism Potential ◽  
Sigmoid Activation Function

Bali has an increasing tourism potential. This is evidenced by the increasing number of foreign tourist visits to Bali Province each year. Although Bali's tourism trends have continued to increase over the past few years, efforts to improve the quality of Bali tourism need to be made. One way is to do forecasting. To support improvement efforts in Bali's tourism sector, the author created a forecasting system for foreign tourists to Bali province using artificial neural network methods with back propagation algorithms. Artificial Neural Networks with back propagation algorithms are neural network algorithms by finding optimal weight values. The forecast results using the binary sigmoid activation function were obtained by 489,862 foreign tourists in November 2019 with MAPE at 1.62% and 487,342 foreign tourists in December 2019 with MAPE of 11.78%. The forecast results using the bipolar sigmoid activation function were obtained by 493,200 foreign tourists in November 2019 with MAPE of 0.95% and 484,090 foreign tourists in December 2019 with MAPE of 12.37%.

scholarly journals Jaringan Saraf Tiruan dengan Backpropagation untuk Memprediksi Penyakit Asma

2015 ◽  
Vol 2 (1) ◽  
pp. 28
Author(s):  
Dahriani Hakim Tanjung
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Back Propagation ◽  
Activation Function ◽  
Learning Rate ◽  
Sigmoid Function ◽  
A Value ◽  
Artificial Neural ◽  
Require Output

Penelitian ini bertujuan untuk memprediksi penyakit asma menggunakan teknik pengenalan pola yaitu jaringan saraf tiruan dengan metode backpropagation. Data penilaian asma mengacu pada riwayat penyakit asma seseorang. Jaringan saraf tiruan dilakukan dengan menentukan jumlah unit untuk setiap lapisan dengan fungsi aktivasi sigmoid biner. Pengujian dilakukan menggunakan perangkat lunak matlab yang diuji dengan beberapa bentuk arsitektur jaringan. Arsitektur dengan konfigurasi terbaik terdiri dari 18 lapisan masukan, 8 lapisan tersembunyi dan 4 lapisan keluaran dengan nilai learning rate sebesar 0.5, nilai toleransi error 0.001, menghasilkan maksimal epoch 4707 dan MSE 0.00100139. MSE berada di bawah nilai error yaitu 0.001, Parameter tersebut dipilih menjadi parameter terbaik karena menghasilkan jumlah iterasi yang memiliki nilai akurasi MSE yang cukup baik, karena nilai MSE paling kecil dari arsitektur yang lain serta nilai MSE dibawah dari nilai error yang ditentukan. Sigmoid Biner Fungsi ini digunakan untuk jaringan saraf yang dilatih dengan menggunakan metode backpropagation. Fungsi sigmoid memiliki nilai range 0 sampai 1. Oleh karena itu, fungsi ini sering digunakan untuk jaringan saraf yang membutuhkan nilai output yang terletak pada interval 0 sampai 1.This study aims to predict asthma using pattern recognition techniques namely artificial neural network with back propagation method. Asthma assessment data refers to a person's history of asthma. Artificial neural network is done by determining the number of units for each layer with binary sigmoid activation function. Testing is done using matlab software being tested with some form of network architecture. Architecture with the best configuration consists of 18 layers of input, 8 hidden layer and output layer 4 with a value of learning rate of 0.5, the error tolerance value 0001, 4707 and resulted in the maximum epoch MSE .00100139. MSE is under the error value is 0.001, the parameter is chosen to be the best parameters for generating the number of iterations that have an accuracy value of MSE is quite good, because the smallest MSE value than other architectures as well as the value of the MSE under a specified error value. Binary sigmoid function is used for neural network trained using the backpropagation method. Sigmoid function has a value in the range 0 to 1. Therefore, this function is often used for neural networks that require output value lies in the interval 0 to 1.

scholarly journals Design and Performance Analysis of Low Power and High Throughput of Analog Data Compression and Decompression using ANN in 32nm FinFET Technology

2021 ◽  
Vol 15 ◽  
pp. 730-744
Author(s):  
G. K. Venkatesh ◽  
S. Bhargavi ◽  
Basavaraj V. Hiremath ◽  
C. Anil Kumar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Integrated Circuits ◽  
Back Propagation ◽  
Activation Function ◽  
Cell Number ◽  
Design Rule ◽  
Novel Technology ◽  
Artificial Neural ◽  
And Performance

The development and fabrication of integrated circuits for the applicational areas of VLSI such as processing of the signal, medicine tomography, telecommunication turn out to be a novel technology for the upcoming innovations. The fabrication of IC’s is attributable to the methodology in the technology of VLSI and when compared to artificial Neural Network, the genetic performance of these productions is approximately the same and are typically employed for diagnosing the syndrome, compression as well as the decompression of signal used in the medical domain. Techniques such as HMM, DCT, as well as PCA are employed for compression and decompression of signals but these approaches still possess some disadvantages. Therefore, to overcome these issues, a chip-level design for Artificial Neural Network is proposed that makes use of FinFET 32 nm technology and includes sigmoid activation function (SAF), Gilbert cell number, as well as bias circuits to prolong the compressed magnitude relation and accuracy. As a result, with the help of the Cadence Virtuoso analog tool, the Artificial Neural Network has been designed using FinFET 32nm technology along with all the details of sub-units such as Layout vs Schematic (LVS), Design rule check (DRC), RC extraction as well as chip level (GDS-II). Feed Forward Artificial Neural Network (FWANN) is considered as one of the most basic types of ANN and it is implemented using the concept of Back Propagation (BP). The simulation results of the suggested 16-bit 6TRAM cell were found to have 8%, 21%, and 0.9% improvement in consuming power, delay, and compressed data losses respectively.

scholarly journals Identification of fat-soluble vitamins deficiency using artificial neural network

2019 ◽  
Vol 8 (1) ◽  
pp. 6-11
Author(s):  
Noviyanti Sagala ◽  
Cynthia Hayat ◽  
Frahselia Tandipuang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Detection System ◽  
Back Propagation ◽  
Activation Function ◽  
Vitamin Deficiency ◽  
Training Data ◽  
Artificial Neural ◽  
Experienced Physician ◽  
Fat Soluble Vitamins

The fat-soluble vitamins (A, D, E, K) deficiency remain frequent universally and may have consequential adverse resultants and causing slow appearance symptoms gradually and intensify over time. The vitamin deficiency detection requires an experienced physician to notice the symptoms and to review a blood test’s result (high-priced). This research aims to create an early detection system of fat-soluble vitamin deficiency using artificial neural network Back-propagation. The method was implemented by converting deficiency symptoms data into training data to be used to produce a weight of ANN and testing data. We employed Gradient Descent and Logsig as an activation function. The distribution of training data and test data was 71 and 30, respectively. The best architecture generated an accuracy of 95 % in a combination of parameters using 150 hidden layers, 10000 epoch, error target 0.0001, learning rate 0.25.

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 626
Author(s):  
Svajone Bekesiene ◽  
Rasa Smaliukiene ◽  
Ramute Vaicaitiene
Keyword(s):  
Neural Network ◽  
Transfer Functions ◽  
Back Propagation ◽  
Armed Forces ◽  
Activation Function ◽  
Optimal Number ◽  
Ann Model ◽  
Back Propagation Algorithm ◽  
Classification Rate ◽  
Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

Test on Flood Prediction-Model Using Artificial Neural Network for ShiiLiAn Hydrologic Station on MinChiang,China

2010 ◽  
Vol 39 ◽  
pp. 555-561 ◽  
Author(s):  
Qing Hua Luan ◽  
Yao Cheng ◽  
Zha Xin Ima
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flood Control ◽  
Back Propagation ◽  
Forecast Model ◽  
Flood Forecast ◽  
Back Propagation Algorithm ◽  
Nonlinear Method ◽  
Flood Prediction ◽  
Artificial Neural

The establishing of a precise simulation model for runoff prediction in river with several tributaries is the difficulty of flood forecast, which is also one of the difficulties in hydrologic research. Due to the theory of Artificial Neural Network, using Back Propagation algorithm, the flood forecast model for ShiLiAn hydrologic station in Minjiang River is constructed and validated in this study. Through test, the result shows that the forecast accuracy is satisfied for all check standards of flood forecast and then proves the feasibility of using nonlinear method for flood forecast. This study provides a new method and reference for flood control and water resources management in the local region.

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