A Novel PCA based Multi-layer perceptron algorithm for Maintainability Prediction

2016 ◽  
Vol 37 (2) ◽  
pp. 90-96
Author(s):  
Deeksha Datyal ◽  
◽  
Aman Kaushik ◽  
Abhishek Tomar
Keyword(s):  
Multi Layer Perceptron ◽  
Perceptron Algorithm

scholarly journals HISTOGRAM OF ORIENTED GRADIENT UNTUK DETEKSI EKSPRESI WAJAH MANUSIA

2018 ◽  
Vol 10 (2) ◽  
pp. 81-86
Author(s):  
Marlinda Vasty Overbeek
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Facial Expressions ◽  
Moving Objects ◽  
Image Data ◽  
Gradient Algorithm ◽  
Multi Layer Perceptron ◽  
Histogram Of Oriented Gradient ◽  
Perceptron Algorithm ◽  
Feature Extracting

This research focuses on the detection of human facial expressions using the Histogram of Oriented Gradient algorithm. Whereas for the classification algorithm, Convolutional Neural Network is used. Image data used in the form of seven different expressions of humans with the extraction of 48x48 pixels. The use of Histogram of Oriented Gradient as a feature extracting algorithm, because Histogram of Oriented Gradient is good to be used in detecting moving objects. Whereas Convolutional Neural Network is used because it is an improvement of the Multi Layer Perceptron algorithm. Of the three epoches done, it produced the best accuracy of 77% re-introduction of human facial expressions. These results are quite convincing because it only uses three epochs.

Role Comparison between Deep Belief Neural Network and NeuroEvolution of Augmenting Topologies to Detect Diabetes

2021 ◽  
Vol 5 (2) ◽  
Author(s):  
A.B.M. Wijaya ◽  
D.S. Ikawahyuni ◽  
Rospita Gea ◽  
Febe Maedjaja
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
The Other ◽  
Multi Layer Perceptron ◽  
Initial Weight ◽  
Restricted Boltzmann Machines ◽  
Regression Problem ◽  
Perceptron Algorithm ◽  
Other Hand ◽  
The Neural Network

Diabetes in Indonesia has been perceived as a grave health problem and has been a concern since the early 1980’s [2]. The prevalence of diabetes in adults in Indonesia, as stated by IDF, was 6.2% with the total case amounting to 10.681.400. Moreover, Indonesia is also in the top ten global countries with the highest diabetes case in 2013. This research will investigate the role of Deep Belief Network (DBN) and NeuroEvolution of Augmenting Topology (NEAT) in solving regression problems in detecting diabetes. DBN works by processing the data in unsupervised network architectures. The algorithm puts Restricted Boltzmann Machines (RBM) into a stacked process. The output of the first RBM will be the input for the next RBM. On the other hand, the NEAT algorithm works by investigating the neural network architecture and evaluating the architecture using a multi-layer perceptron algorithm. Collaboration with a Genetic Algorithm in NEAT is the key process in architecture development. The research results showed that DBN could be utilized as the initial weight for Backpropagation Neural Network at 22.61% on average. On the other hand, the NEAT algorithm could be used by collaborating with a multi-layer perceptron to solve this regression problem by providing 74.5% confidence. This work also reveals potential works in the future by combining the Backpropagation algorithm with NEAT as an evaluation function and by combining it with DBN algorithms to process the produced initial weight.

scholarly journals Distance transform-watershed segmentation and multi-layer perceptron algorithm to separate touching orange fruit in digital images

2021 ◽  
Vol 922 (1) ◽  
pp. 012047
Author(s):  
I S Nasution ◽  
C Keke
Keyword(s):  
National Standard ◽  
Distance Transform ◽  
Multi Layer Perceptron ◽  
Citrus Fruits ◽  
Watershed Segmentation ◽  
Corn Kernel ◽  
Perceptron Algorithm ◽  
Class A ◽  
Orange Fruit ◽  
Accuracy Rates

Abstract An algorithm to separate touching oranges using a distance transform-watershed segmentation is presented. In this study, there are four classes of oranges, such as class A, B, C, and D, respectively. The size of each class is based on the Indonesian National Standard (SNI), the sample used is 168 oranges of which 140 are for training and 28 oranges are for testing. The image of citrus fruits was captured using Kinect v2 camera with a camera resolution of 1920 × 1080 pixels, the distance from the camera to the background is 23 cm. The images were captured in PNG format. The watersheds were computed based on the distance transformed by orange regions. The corresponding basins were finally used to split the falsely connected corn kernel by intersecting the basins with the corn kernel regions. Experimental results show that the multi-layer perceptrons have classification accuracy rates of 92.85%. The algorithm appears to be robust enough to separate most of the multiple touching scenarios.

Aplicação de Multi-Layer Perceptron para Previsão de Emissão de Gases derivados de Veículos a Diesel

2017 ◽  
Vol 3 (2) ◽  
pp. 1-11
Author(s):  
Luís Otávio Rigo Jr. ◽  
Jesuina Cássia Santiago de Araújo ◽  
Leandro Nogueira dos Santos ◽  
Mona Lisa Moura de Oliveira
Keyword(s):  
Selective Catalytic Reduction ◽  
Catalytic Reduction ◽  
Back Propagation ◽  
Multi Layer Perceptron

Fontes veiculares movidos a Diesel têm contribuido significativamente para o aumento da poluição atmosférica. A tendência mundial de utilizar motor Diesel se deve ao rendimento real alcançado por esta máquina (~34%), quando comparada com motores Otto (~26%). Em termos de poluição, tais motores apresentam a vantagem de emitir menor concentração de hidrocarbonetos e CO2. Por outro lado, o motor Diesel apresenta a desvantagem de emitir materiais particulados e NOx. Com fins de atender a legislação, tem sido incorporado aos veículos a Diesel um sistema catalítico, que injeta uréia nos gases de escape. Tal processo, conhecido como SCR (Selective Catalytic Reduction), tem por finalidade transformar NOx em N2 e H2O. Órgãos governamentais têm atuado como agentes controladores, exigindo dos fabricantes de motores soluções tecnológicas, capazes de reduzir os níveis de emissões destes poluentes. Essas soluções estão atreladas a uma série de testes experimentais onerosos. Tendo-se em vista que as taxas de emissão de NOx dependem de fatores que se correlacionam de forma complexa, faz-se necessário à utilização de ferramentas de simulação para prever tais taxas. Neste trabalho, foi utilizada uma Rede Neural Artificial, denominada Multi-Layer Perceptron, com algoritmo de aprendizado supervisionado Back Propagation, para estimar as taxas de emissão dos gases NOx, NH3 e N2O em veículos a Diesel. Os resultados mostraram que parâmetros de entrada (velocidade espacial, temperatura, concentração de NOx, de NH3, de O2 , de SO2 e de H2O) se correcionam fortemente com as taxas de emissão de NOx e NH3 na saída. Este fator foi comprovado pela grande capacidade de aprendizado das redes testadas, com erro médio próximo de 0,01 no conjunto de aprendizado. Os resultados sobre o conjunto de teste demonstraram, também, grande capacidade de generalização das redes. O melhor resultado encontrado foi de 2,9% para NOx e NH3 e de N2O de 5,1%. Estes resultados revelam que a RNA demonstrou ser um método eficiente para prever as taxas de emissão de poluentes em perímetro urbano e rodovias.

A neural computing-based cash price prediction using multi-layer perceptron (MLP) and feature selection for health economics

2019 ◽  
Vol 14 ◽  
Author(s):  
Zinat Ansari
Keyword(s):  
Feature Selection ◽  
Health Economics ◽  
Cash Flow ◽  
Multi Layer Perceptron ◽  
Asset Sales ◽  
Fixed Asset ◽  
Academic Fields ◽  
Investment Returns ◽  
Short And Long Term

Background: The present study proceeds to incorporate feature selection as a means for selecting the most relevant features affecting the prediction of cash prices in Iran in terms of health economics. Health economics are between academic fields that can aid in ameliorating conditions so as to perform better decisions in regards to the economy such as determining cash prices. Methods: Accordingly, a series of search algorithms, namely the Best-First, Greedy-Stepwise, and Ranker methods, are deployed in order to extract the most relevant features from among a 500 data samples. The validity of the methods was evaluated via the LMT procedure. The corresponding dataset used for this study constitutes a variety of features including net cash flow, dividends, revenue from short and long-term deposits, cash flow from investment returns, income tax, fixed asset purchases, fixed asset sales, long-term investment purchases, long-term investment sales, total cash flow from investment activities, financial facilities, and repayment of financial facilities. Results: The results were indicative of the superiority of the Ranker model using the RelieF-Attribute-Eval tool in Weka over the remaining classification methods. Ergo, the LMT approach could be employed to remove data redundancies and thereby accelerate the estimation process, while saving time and money. The results of the multi-layer perceptron (MLP) further confirmed the high accuracy of the proposed method in estimating cash prices. Conclusions: The present research attempted to reduce the volume of data required for predicting end cash by means of employing a feature selection so as to save both precious money and time.

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