scholarly journals Penerapan Jaringan Saraf Tiruan Dalam Memprediksi Indikator Utama Ekonomi Dunia

2018 ◽  
Vol 2 (2) ◽  
pp. 169
Author(s):  
Alan Boy Sandy Damanik ◽  
Agung Bimantoro
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Mean Squared Error ◽  
Economic Indicator ◽  
Squared Error ◽  
The World ◽  
Artificial Neural Network Method ◽  
World Economic ◽  
Architectural Models ◽  
The Mean

Economics is one of the most important aspects in the world. Economics greatly determines the progress and development of a country. However, there are still many countries with low economic levels. Therefore the aim of this study is to predict and determine the level of the main indicators of the world economy as one of the anticipatory steps to further increase the level of the country's economy. World Economic Indicator Data to be used is sourced from Bloomberg and Bank Indonesia. To find out further developments, it is necessary to research the existing data. The algorithm used is Backpropagatian Neural Network. Data analysis was carried out using artificial neural network method using Matlab R2011b software. The study uses 5 architectural models. The best network architecture produced is 3-43-1 with an accuracy rate of 86% and the Mean Squared Error (MSE) value is 1.336593.

scholarly journals Real-time diameter of the fetal aorta from ultrasound

2019 ◽  
Vol 32 (11) ◽  
pp. 6735-6744
Author(s):  
Nicoló Savioli ◽  
Enrico Grisan ◽  
Silvia Visentin ◽  
Erich Cosmi ◽  
Giovanni Montana ◽  
...  
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Network Architecture ◽  
Mean Squared Error ◽  
Ultrasound Images ◽  
Imaging Features ◽  
Execution Speed ◽  
The Mean ◽  
Temporal Redundancy ◽  
Gated Recurrent Unit

AbstractThe automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. This article presents an attempt to automate the challenging task of measuring the vascular diameter of the fetal abdominal aorta from ultrasound images. We propose a neural network architecture consisting of three blocks: a convolutional neural network (CNN) for the extraction of imaging features, a convolution gated recurrent unit (C-GRU) for exploiting the temporal redundancy of the signal, and a regularized loss function, called CyclicLoss, to impose our prior knowledge about the periodicity of the observed signal. The solution is investigated with a cohort of 25 ultrasound sequences acquired during the third-trimester pregnancy check, and with 1000 synthetic sequences. In the extraction of features, it is shown that a shallow CNN outperforms two other deep CNNs with both the real and synthetic cohorts, suggesting that echocardiographic features are optimally captured by a reduced number of CNN layers. The proposed architecture, working with the shallow CNN, reaches an accuracy substantially superior to previously reported methods, providing an average reduction of the mean squared error from 0.31 (state-of-the-art) to 0.09 $$\mathrm{mm}^2$$mm2, and a relative error reduction from 8.1 to 5.3%. The mean execution speed of the proposed approach of 289 frames per second makes it suitable for real-time clinical use.

Predicting The Number of Tourists Based on Backpropagation Algorithm

2021 ◽  
Vol 5 (3) ◽  
pp. 439-445
Author(s):  
Dwi Marlina ◽  
Fatchul Arifin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Architecture ◽  
Mean Squared Error ◽  
Predictive Accuracy ◽  
Training Process ◽  
Backpropagation Algorithm ◽  
Squared Error ◽  
Artificial Neural ◽  
Target Data

The number of tourists always fluctuates every month, as happened in Kaliadem Merapi, Sleman. The purpose of this research is to develop a prediction system for the number of tourists based on artificial neural networks. This study uses an artificial neural network for data processing methods with the backpropagation algorithm. This study carried out two processes, namely the training process and the testing process with stages consisting of: (1) Collecting input and target data, (2) Normalizing input and target data, (3) Creating artificial neural network architecture by utilizing GUI (Graphical User Interface) Matlab facilities. (4) Conducting training and testing processes, (5) Normalizing predictive data, (6) Analysis of predictive data. In the data analysis, the MSE (Mean Squared Error) value in the training process is 0.0091528 and in the testing process is 0.0051424. Besides, the validity value of predictive accuracy in the testing process is around 91.32%. The resulting MSE (Mean Squared Error) value is relatively small, and the validity value of prediction accuracy is relatively high, so this system can be used to predict the number of tourists in Kaliadem Merapi, Sleman.  

scholarly journals Penerapan Algoritma Backpropagation dalam Memprediksi Persentase Penduduk Buta Huruf di Indonesia

2019 ◽  
Vol 4 (2) ◽  
Author(s):  
Imelda Asih Rohani Simbolon ◽  
Fikri Yatussa’ada ◽  
Anjar Wanto
Keyword(s):  
Neural Network ◽  
Data Analysis ◽  
Network Architecture ◽  
Mean Squared Error ◽  
Central Bureau ◽  
Network Data ◽  
Backpropagation Neural Network ◽  
Matlab Software ◽  
Squared Error

Illiteracy is one of the most serious issues in Indonesia. The government's ignorance of illiterate people makes the illiteracy rate quite high. It should be one of the government's targets for reducing illiteracy in order to reduce the number of illiterate people. Illiteracy rate in Indonesia itself has reached 34.55% in Papua province. One way to suppress illiteracy rate in Indonesia is by predicting illiterate figures for subsequent years. The data to be predicted is the data of illiterate figures of each province in Indonesia which is sourced from the Indonesian Central Bureau of Statistics from 2011 to 2017. The method used in the prediction is Backpropagation Neural Network. Data analysis was done with the help of matlab software R2011b (7.13). This study uses 5 architectures, 4-5-1, 4-6-1, 4-9-1, 4-14-1 and 4-18-1. From these 5 models the best network architecture is 4-14-1 with 91% accuracy and Mean Squared Error 0,00274166.

scholarly journals DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Cédric Arisdakessian ◽  
Olivier Poirion ◽  
Breck Yunits ◽  
Xun Zhu ◽  
Lana X. Garmire
Keyword(s):  
Neural Network ◽  
Single Cell ◽  
Deep Neural Network ◽  
Mean Squared Error ◽  
Single Cells ◽  
Rna Seq ◽  
Squared Error ◽  
Study Gene Expression ◽  
Network Method ◽  
The Mean

Abstract Single-cell RNA sequencing (scRNA-seq) offers new opportunities to study gene expression of tens of thousands of single cells simultaneously. We present DeepImpute, a deep neural network-based imputation algorithm that uses dropout layers and loss functions to learn patterns in the data, allowing for accurate imputation. Overall, DeepImpute yields better accuracy than other six publicly available scRNA-seq imputation methods on experimental data, as measured by the mean squared error or Pearson’s correlation coefficient. DeepImpute is an accurate, fast, and scalable imputation tool that is suited to handle the ever-increasing volume of scRNA-seq data, and is freely available at https://github.com/lanagarmire/DeepImpute.

scholarly journals Perceptual Image Super Resolution Using Deep Learning and Super Resolution Convolution Neural Networks (SRCNN)

2020 ◽  
Author(s):  
Nagaraj P ◽  
Muthamilsudar K ◽  
Naga Nehanth S ◽  
Mohammed Shahid R ◽  
Sujith Kumar V
Keyword(s):  
Neural Network ◽  
Mean Squared Error ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Convolution Neural Network ◽  
Resolution Image ◽  
Squared Error ◽  
Image Super Resolution ◽  
The Mean ◽  
State Of Art

The main objective of Perceptual Image Super Resolution is to obtain a high resoluted image from a normal low resolution image. The task is very simple that we just want to make a Low firmness appearance into a extraordinary resolution image. To perform this task we have various methods like Classical Approach in which we try to maximize the mean squared error, evaluate by PSNR(Peak-Signal-to-Noise-Ratio). The first method used to perform this operation was SRCNN (Super Resolution Convolution Neural Network) and these days many of them use DRCN and VDSR which are slightly upgraded methods. Another technique used for the purpose of upscaling to get a high resoluted image from normal little resolution image is the state of art by PSNR. This method was a quite simple one in which we take a low determination image as input and place in a convolution neural network(CNN) and produce a high resolution image as the output. In this technique the edges will be clearly defined, but the whole image will be blurred. This method is unable to produce good-looking textures.

scholarly journals Analisis Jaringan Saraf Dalam Estimasi Tingkat Pengangguran Terbuka Penduduk Sumatera Utara

2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Juli Wahyuni ◽  
Yuri Widya Paranthy ◽  
Anjar Wanto
Keyword(s):  
Neural Network ◽  
Unemployment Rate ◽  
Network Architecture ◽  
Total Population ◽  
Mean Squared Error ◽  
Human Life ◽  
Learning Rate ◽  
Squared Error ◽  
The Government ◽  
North Sumatra

Abstrak — Pengangguran merupakan salah satu masalah ekonomi yang mempengaruhi kehidupan manusia secara langsung. Di Indonesia tingkat persentase pengangguran cukup tinggi, khususnya pada provinsi Sumatera Utara. Contohnya tercatat pada tahun 2010, kota sibolga memilik tingkat pengangguran yang paling tinggi yaitu berada di angka 17.50% dari total penduduknya. Berbeda dengan Samosir yang hanya memilik 0.55% pengangguran dari total penduduknya. Untuk dapat mengurangi jumlah pengangguran, khususnya di Sumatera Utara maka perlu dilakukan estimasi tingkat pengangguran untuk tahun-tahun mendatang, agar pemerintah memiliki acuan dalam menentukan kebijakan sehingga dapat melakukan penanggulangan terhadap jumlah pengangguran. Data yang digunakan pada penelitian ini terfokus pada data tingkat pengangguran terbuka penduduk umur 15 tahun keatas dari tahun 2010-2015 di Sumatera Utara. Metode yang digunakan dalam penelitian ini yaitu Jaringan Saraf Tiruan Backpropagation. Analisa data dilakukan dengan algoritma backpropagation menggunakan Matlab. Arsitektur jaringan yang digunakan ada 5 model (4-55-1, 4-57-1, 4-59-1, 4-61-1 dan 4-77-1), dengan model yang terbaik adalah 4-55-1 dengan Learning Rate yang digunakan 0.01. Sehingga menghasilkan tingkat akurasi 88% dengan nilai Mean Squared Error (MSE) adalah 0,55701127.Kata kunci— Pengangguran, Estimasi, Penduduk, Jaringan Saraf, Sumatera Utara.Abstract — Unemployment is one of the economic problems that affect human life directly. In Indonesia the level of unemployment is quite high, especially in North Sumatra province. For example, recorded in 2010, sibolga city has the highest unemployment rate that is at 17.50% of the total population. In contrast to Samosir who only have 0.55% unemployment out of the total population. In order to reduce the number of unemployment, especially in North Sumatra, it is necessary to estimate the unemployment rate for the coming years, so that the government has a reference in determining the policy so that it can handle the number of unemployed. The data used in this study focuses on open unemployment rate data of the population aged 15 years and over from 2010-2015 in North Sumatra. The method used in this research is Artificial Neural Network Backpropagation. Data analysis is done by backpropagation algorithm using Matlab. Network architecture used there are 5 models (4-55-1, 4-57-1, 4-59-1, 4-61-1 and 4-77-1), with the best model is 4-55-1 with Learning Rate used 0.01. So as to produce an accuracy of 88% with the Mean Squared Error (MSE) is 0.55701127.Keywords— Unemployment, Estimation, Population, Neural Network, North Sumatera.

Fault Diagnosis for the Oil System of Aviation Piston Engine Based on BP Neural Network

2014 ◽  
Vol 1030-1032 ◽  
pp. 1185-1188
Author(s):  
Yan Feng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Failure Modes ◽  
Mean Squared Error ◽  
Piston Engine ◽  
Squared Error ◽  
Training Samples ◽  
The Mean ◽  
The Given

Introduced the composition and the principle of operation of the oil system of aviation piston engine. Analysed common faults of the oil system including high oil pressure indication,low oil pressure indication, high oil temperature indication and excessive oil consumption.Failure causes for above faults were analysed separately.Symbols were stood for failure modes and failure causes. Constructed the BP neural network.Symbols of failure modes were inputs of the BP neural network,and symbols of failure causes were outputs of the BP neural network.Builded a mapping relationship between failure modes and failure causes by training samples studying.Four training samples were selected based on common faults and fault effects.A given mode was as a input of the network,and by adjusting connection weights and the threshold of every neuron,an ideal result could be gotten.Then other mode was as a input of the network which carried on studying until the epochs was 369,and the mean squared error fast converged and the value of mean squared error was.The failure causes for the given failure mode can be confirmed by this BP neural network.By engineering verification, the BP neural network is applicable to fault diagnosis for oil system of aviation piston engine.

Computing trade-offs in robust design: Perspectives of the mean squared error

2011 ◽  
Vol 60 (2) ◽  
pp. 248-255 ◽  
Author(s):  
Sangmun Shin ◽  
Funda Samanlioglu ◽  
Byung Rae Cho ◽  
Margaret M. Wiecek
Keyword(s):  
Robust Design ◽  
Mean Squared Error ◽  
Squared Error ◽  
Trade Offs ◽  
The Mean

scholarly journals Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception

2018 ◽  
Vol 10 (12) ◽  
pp. 4863 ◽  
Author(s):  
Chao Huang ◽  
Longpeng Cao ◽  
Nanxin Peng ◽  
Sijia Li ◽  
Jing Zhang ◽  
...  
Keyword(s):  
Power Plants ◽  
Mean Squared Error ◽  
Absolute Error ◽  
Multilayer Perception ◽  
Squared Error ◽  
The Mean ◽  
Effectiveness And Efficiency ◽  
Mlp Network ◽  
Grid Operation ◽  
Better Than

Photovoltaic (PV) modules convert renewable and sustainable solar energy into electricity. However, the uncertainty of PV power production brings challenges for the grid operation. To facilitate the management and scheduling of PV power plants, forecasting is an essential technique. In this paper, a robust multilayer perception (MLP) neural network was developed for day-ahead forecasting of hourly PV power. A generic MLP is usually trained by minimizing the mean squared loss. The mean squared error is sensitive to a few particularly large errors that can lead to a poor estimator. To tackle the problem, the pseudo-Huber loss function, which combines the best properties of squared loss and absolute loss, was adopted in this paper. The effectiveness and efficiency of the proposed method was verified by benchmarking against a generic MLP network with real PV data. Numerical experiments illustrated that the proposed method performed better than the generic MLP network in terms of root mean squared error (RMSE) and mean absolute error (MAE).

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