Regularized Stacked Auto-Encoder Based Pre-training for Generalization of Multi-layer Perceptron

2017 ◽  
pp. 232-242 ◽  
Author(s):  
Prasenjit Dey ◽  
Abhijit Ghosh ◽  
Tandra Pal
Keyword(s):  
Multi Layer Perceptron

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.

scholarly journals The Classification of Medicinal Plant Leaves Based on Multispectral and Texture Feature Using Machine Learning Approach

Agronomy ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 263
Author(s):  
Samreen Naeem ◽  
Aqib Ali ◽  
Christophe Chesneau ◽  
Muhammad H. Tahir ◽  
Farrukh Jamal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Medicinal Plant ◽  
Texture Feature ◽  
Stevia Rebaudiana ◽  
Ocimum Sanctum ◽  
Multi Layer Perceptron ◽  
Plant Leaves ◽  
Chi Square ◽  
Lemon Balm

This study proposes the machine learning based classification of medical plant leaves. The total six varieties of medicinal plant leaves-based dataset are collected from the Department of Agriculture, The Islamia University of Bahawalpur, Pakistan. These plants are commonly named in English as (herbal) Tulsi, Peppermint, Bael, Lemon balm, Catnip, and Stevia and scientifically named in Latin as Ocimum sanctum, Mentha balsamea, Aegle marmelos, Melissa officinalis, Nepeta cataria, and Stevia rebaudiana, respectively. The multispectral and digital image dataset are collected via a computer vision laboratory setup. For the preprocessing step, we crop the region of the leaf and transform it into a gray level format. Secondly, we perform a seed intensity-based edge/line detection utilizing Sobel filter and draw five regions of observations. A total of 65 fused features dataset is extracted, being a combination of texture, run-length matrix, and multi-spectral features. For the feature optimization process, we employ a chi-square feature selection approach and select 14 optimized features. Finally, five machine learning classifiers named as a multi-layer perceptron, logit-boost, bagging, random forest, and simple logistic are deployed on an optimized medicinal plant leaves dataset, and it is observed that the multi-layer perceptron classifier shows a relatively promising accuracy of 99.01% as compared to the competition. The distinct classification accuracy by the multi-layer perceptron classifier on six medicinal plant leaves are 99.10% for Tulsi, 99.80% for Peppermint, 98.40% for Bael, 99.90% for Lemon balm, 98.40% for Catnip, and 99.20% for Stevia.

Multi-layer Perceptron based Photovoltaic Forecasting for Rooftop PV Applications in Smart Grid

2020 ◽  
Author(s):  
Imtiaz Parvez ◽  
Arif Sarwat ◽  
Anjan Debnath ◽  
Temitayo Olowu ◽  
Md Golam Dastgir ◽  
...  
Keyword(s):  
Smart Grid ◽  
Multi Layer Perceptron

scholarly journals A deep learning integrated radiomics model for identification of coronavirus disease 2019 using computed tomography

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Xiaoguo Zhang ◽  
Dawei Wang ◽  
Jiang Shao ◽  
Song Tian ◽  
Weixiong Tan ◽  
...  
Keyword(s):  
Deep Learning ◽  
Ct Images ◽  
Community Acquired Pneumonia ◽  
Machine Learning Algorithms ◽  
Multi Layer Perceptron ◽  
Selection Methods ◽  
Medical Centers ◽  
Global Pandemic ◽  
Diagnostic Time ◽  
Logistic Regression Classifier

AbstractSince its first outbreak, Coronavirus Disease 2019 (COVID-19) has been rapidly spreading worldwide and caused a global pandemic. Rapid and early detection is essential to contain COVID-19. Here, we first developed a deep learning (DL) integrated radiomics model for end-to-end identification of COVID-19 using CT scans and then validated its clinical feasibility. We retrospectively collected CT images of 386 patients (129 with COVID-19 and 257 with other community-acquired pneumonia) from three medical centers to train and externally validate the developed models. A pre-trained DL algorithm was utilized to automatically segment infected lesions (ROIs) on CT images which were used for feature extraction. Five feature selection methods and four machine learning algorithms were utilized to develop radiomics models. Trained with features selected by L1 regularized logistic regression, classifier multi-layer perceptron (MLP) demonstrated the optimal performance with AUC of 0.922 (95% CI 0.856–0.988) and 0.959 (95% CI 0.910–1.000), the same sensitivity of 0.879, and specificity of 0.900 and 0.887 on internal and external testing datasets, which was equivalent to the senior radiologist in a reader study. Additionally, diagnostic time of DL-MLP was more efficient than radiologists (38 s vs 5.15 min). With an adequate performance for identifying COVID-19, DL-MLP may help in screening of suspected cases.

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