MLP Neural Network Research Articles

Recognition efficiency enhancement of control chart pattern using ensemble MLP neural network

chart pattern for six different fundamental patterns have been proposed. The multilayer perceptron based neural network has applied as an entity to form the ensemble. The loss of generalization property of an individual neural network has been fulfilled by providing the diversity in the training data for the individual entity of ensemble. The outcome of individual recognizer has combined over their decision value to include the outcome knowledge of each and every entity instead of majority voting of decision. The proposed method has been verified over a large set of data experimentally and observed that there is significant improvement with proposed ensemble approach in compare to individual recognizer.

Predicting the Extent of Sidoarjo Mud Flow Using Remote Sensing

The Sidoarjo mud flow in East Java is the result of a natural phenomenon in which hot mudflow occurs due to volcanic activity. The Sidoarjo mud flow resulted in a considerable ecological disaster in the area. In this study, by using the Modification of Normalized Difference Water Index (MNDWI) technique we measured the extension of the mudflow area from 2013 to 2020 using Landsat 8 satellite data imagery. This study is meant to predict the extension of the mud flow area in the research site by comparing regression and neural network techniques in order to find the best approach. The RPROP MLP neural network technique was used to predict the Sidoarjo mud-flowing area in 2021 to 2025. Surprisingly the results of these calculations showed that the RPROP MLP neural network with three hidden layers and 20 neurons performed the best, with an R square value for training of 0.77915565 and for testing of 0.78321550.

An Expanded MLP Neural Network for Fast Angiodysplasia Lesions Segmentation in Capsule Endoscopy Images

In this paper, an expanded multilayer perceptron (EMLP) neural network is proposed to automatically segment angiodysplasia regions in wireless capsule endoscopy (WCE) images The main idea is to minimize the distance between the input image and the corresponding binary ground truth, i.e., the mask image. After the training phase, when a test image is given to the network, the lesion pixels will be close to “one” and the other pixels will be close to “zero” and finally, the lesion area can be segmented using thresholding. Since angiodysplasia lesions appear in images with different spectrums of red color, the classical MLP neural network cannot be trained with a wide range of red color, hence leads to undesirable network accuracy. To solve this problem, we proposed an EMLP neural network for image segmentation. In the EMLP neural network, neurons are divided into several groups, each of which is for learning a spectrum of the lesion. The EMLP is able to learn a wider range of red colors. The proposed method is able to segment WCE images containing angiodysplasia faster than the existing methods. Our investigation shows that our method also outperforms existing methods in terms of segmentation scores.

Air Quality Index Prediction Based on Multilayer Perceptron

Taking the comprehensive evaluation index AQI as the research goal, the data of Beijing is studied, and an AQI prediction model based on multilayer perceptron neural network (MLP neural network) is proposed. First, the Beijing air quality dataset is constructed, and the AQIs from 2014 to 2019 are extracted as input features. Then, the air quality index of Beijing in 2020 is predicted based on MLP. After that, through parameter adjustment and model comparison, the final prediction accuracy reached 0.861.

Multilayer perceptron applied to genotypes classification in diallel studies

In the last decades, a new trend to use more refined analytical procedures, such as artificial neural networks (ANN), has emerged to be most accurate, efficient, and extensively applied for mining and data prediction in different contexts, including plant breeding. Thus, this study was developed to establish a new classification proposal for targeting genotypes in breeding programs to approach classical models, such as a complete diallel and modern prediction techniques. The study was based on the standard deviation values of an interpopulation diallel and it also verified the possibility of training a neural network with the standardized genetic parameters for a discrete scale. We used 12 intercrossed maize populations in a complete diallel scheme (66 hybrids), evaluated during the 2005/2006 crop season in three different environments in southern Brazil. The implemented MLP architecture and other associated parameters allowed the development of a generalist model of genotype classification. The MLP neural network model was efficient in predicting parental and interpopulation hybrid classifications from average genetic components from a complete diallel, regardless of the evaluation environment.

Chaotic seismic signal modeling based on noise and earthquake anomaly detection

Since ancient times, people have tried to predict earthquakes using simple perceptions such as animal behavior. The prediction of the time and strength of an earthquake is of primary concern. In this study chaotic signal modeling is used based on noise and detecting anomalies before an earthquake using artificial neural networks (ANNs). Artificial neural networks are efficient tools for solving complex problems such as prediction and identification. In this study, the effective features of chaotic signal model is obtained considering noise and detection of anomalies five minutes before an earthquake occurrence. Neuro-fuzzy classifier and MLP neural network approaches showed acceptable accuracy of 84.6491% and 82.8947%, respectively. Results demonstrate that the proposed method is an effective seismic signal model based on noise and anomaly detection before an earthquake.

A COMPARATIVE STUDY FOR THE PREDICTION OF HEART ATTACK RISK AND ASSOCIATED FACTORS USING MLP AND RBF NEURAL NETWORKS

Abstract— Aim: The aim of this study is to develop a predictive classification model that can identify risk factors for heart attack disease.
 Materials and Methods: In the study, patients with low and high probability of having a heart attack were examined. Variable importance was calculated to identify risk factors. The radial basis function and multilayer perception neural networks were used to compare the classification prediction results.
 Results: MLP model criteria; Accuracy 0.911, F1 score 0.918, Specificity 0.92, Sensitivity 0.903, while RBF model criteria were obtained as accuracy 0.797, F1 score 0.812, Specificity 0.84, Sensitivity 0.765. The first three most important factors that may be associated with having a heart attack were obtained as trestbps, oldpeak, and chol. 
 Conclusion: According to the prediction results of the heart attack, it can be said that the model created with the MLP neural network has more successful predictions than the model created with the RBF neural network. In addition, estimating the importance values of the factors most associated with heart attack (obtaining the most important biomarkers that may cause heart attack) is a promising result for the diagnosis, treatment and prognosis of the disease.
 
 Keywords— Heart Attack, machine learning, neural networks, classification, variable importance.

Prediction of Chemical Production Based on Neural Network

As an important part of artificial intelligence and machine learning, artificial neural network (ANN) has been widely used because of its strong information processing and autonomous learning capabilities. In this paper, the development history of ANN is summarized, and the three common types of ANN are introduced: MLP neural network, BP neural network and recurrent neural network. Finally the practical application of ANN in chemical production forecasting are analyzed and an outlook on its development direction is prospected.

Prediction and optimization of heating and cooling loads in a residential building based on multi-layer perceptron neural network and different optimization algorithms

Since cooling and heating loads are regarded as significant parameters to examine the energy performance of buildings, the need to predict and analyze them for the residential buildings seems to be undeniable. Hence, the present paper wants to optimize the multi-layer perceptron neural network using several optimization methods to predict the heating and cooling of energy-efficient buildings. The dataset used in this study consists of eight independent factors: surface area, wall area, roof area, relative compactness, overall height, orientation, glazing area, and glazing area distribution. To prove the reliability and accuracy of the obtained results, test and training data are also considered. According to the statistical results, biogeography-based optimization has the highest value of R2 and the lowest values of RMSD, normalized RMSD, and MAE in both training data and test data for cooling and heating loads. Hence, the forecasting accuracy of the proposed MLP neural network optimized with the BBO optimization algorithm with the RMSD, R2, and MAE of 2.82, 0.920, 2.15 in the training phase of heating load and with the RMSD, R2, and MAE of 3.18, 0.887, 2.97 in the training phase of the cooling load is much better than those of the other models.

Leakage analysis of campus water supply system based on MLP neural network

Water supply system is an important part of campus public facilities, and the water supply pipeline will leak, not only increase the cost of water supply, but also cause water waste. This paper collects water consumption data of water meters at all levels in a school’s water supply system, establishes MLP multilayer perceptron neural network model, determines the water leakage rate according to the fluctuation of predicted value and actual value, so as to analyze the leakage situation of each school’s water supply system. When the fluctuation between the actual and predicted water consumption exceeds a certain threshold, water leakage occurs on that day. Through solving the model, the following conclusions are finally drawn: (1) the annual water leakage rate of the school is 10.74%, and the water leakage is 29131.418L. (2) The water leakage rate in the first quarter is the highest, and the water leakage in the second quarter is the highest.(3) The school aquaculture area is the most serious leakage phenomenon, and the maximum water leakage rate of each water meter node is more than 10%.

Application of neural network to simulate the behavior of hospitalizations and their costs under the effects of various polluting gases in the city of São Paulo.

This work aims to obtain an artificial neural network to simulate hospitalizations for respiratory diseases influenced by pollutant gaseous such as CO, PM10, PM2.5, NO2, O3, and SO2 emitted from 2011 to 2017, in the city of São Paulo. The hospitalization costs were also be calculated. MLP and RBF neural networks have been tested by varying the number of neurons in the hidden layer and the type of equation of the output function. The following pollutants and its concentration range were collected considering the supervision of Alto Tiete station set, in several neighborhoods in the city of São Paulo, from in the period 2011 to 2017: 28–63 µg/m3 of PM2.5, 52–110 µg/m3 of PM10, 49–135 µg/m3 of O3, 0.8–2.6 ppm CO, 41–98 µg/m3 of NO2, and 3–16 µg/m3 of SO2. Results showed that a RBF neural network with 6 input neurons, 13 hidden layer neurons, and 1 output neuron, using BFGS algorithm and a Gaussian function to neuronal activation, was the best fitted to the experimental datasets. So, knowing the monthly concentration of gaseous pollutions was possible to predict the hospitalization of 1464 to 3483 ± 510 patients, with costs between 570,447 and 1,357,151 ± 198,171 USD per month. This way, it is possible to use this neural network to predict the costs of hospitalizing patients for respiratory diseases and to contribute to the decision-making of how much the government should spend on health care.

Objective evaluation of optical illusion skirt based on image texture features

PurposeNowadays, most methods of illusion garment evaluation are based on the subjective evaluation of experienced practitioners, which consumes time and the results are too subjective to be accurate enough. It is necessary to explore a method that can quantify professional experience into objective indicators to evaluate the sensory comfort of the optical illusion skirt quickly and accurately. The purpose of this paper is to propose a method to objectively evaluate the sensory comfort of optical illusion skirt patterns by combining texture feature extraction and prediction model construction.Design/methodology/approachFirstly, 10 optical illusion sample skirts are produced, and 10 experimental images are collected for each sample skirt. Then a Likert five-level evaluation scale is designed to obtain the sensory comfort level of each skirt through the questionnaire survey. Synchronously, the coarseness, contrast, directionality, line-likeness, regularity and roughness of the sample image are calculated based on Tamura texture feature algorithm, and the mean, contrast and entropy are extracted of the image transformed by Gabor wavelet. Both are set as objective parameters. Two final indicators T1 and T2 are refined from the objective parameters previously obtained to construct the predictive model of the subjective comfort of the visual illusion skirt. The linear regression model and the MLP neural network model are constructed.FindingsResults show that the accuracy of the linear regression model is 92%, and prediction accuracy of the MLP neural network model is 97.9%. It is feasible to use Tamura texture features, Gabor wavelet transform and MLP neural network methods to objectively predict the sensory comfort of visual illusion skirt images.Originality/valueCompared with the existing uncertain and non-reproducible subjective evaluation of optical illusion clothing based on experienced experts. The main advantage of the authors' method is that this method can objectively obtain evaluation parameters, quickly and accurately obtain evaluation grades without repeated evaluation by experienced experts. It is a method of objectively quantifying the experience of experts.

Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland

The main purpose of this study is to investigate the relationships between key sources of air pollutant emissions (sources of energy production, factories which are particularly harmful to the environment, the fleets of cars, environmental protection expenditure) and the main environmental air pollution (SO2, NOx, CO and PM) in Poland. Models based on MLP neural networks were used as predictive models. Global sensitivity analysis was used to demonstrate the significant impact of individual network input variables on the output variable. To verify the effectiveness of the models created, the actual data were compared with the data obtained through modelling. Projected courses of changes in the variables under study correspond with the real data, which confirms that the proposed models generalize acquired knowledge well. The high MLP network quality parameters of 0.99–0.85 indicate that the network generalizes the acquired knowledge accurately. The sensitivity analysis for NOx, CO and PM pollutants indicates the significance of all input variables. For SO2, it showed significance for four of the six variables analysed. The predictions made by the neural models are not very different from the experimental values.

An Intelligent Model for Prediction of In-Vitro Fertilization Success using MLP Neural Network and GA Optimization

In Vitro Fertilization (IVF) is one of the scientifically known methods of infertility treatment. This study aimed at improving the performance of predicting the success of IVF using machine learning and its optimization through evolutionary algorithms. The Multilayer Perceptron Neural Network (MLP) were proposed to classify the infertility dataset. The Genetic algorithm was used to improve the performance of the Multilayer Perceptron Neural Network model. The proposed model was applied to a dataset including 594 eggs from 94 patients undergoing IVF, of which 318 were of good quality embryos and 276 were of lower quality embryos. For performance evaluation of the MLP model, an ROC curve analysis was conducted, and 10-fold cross-validation performed. The results revealed that this intelligent model has high efficiency with an accuracy of 96% for Multi-layer Perceptron neural network, which is promising compared to counterparts methods.

Predicting the Risk of Hypertension Based on Several Easy-to-Collect Risk Factors: A Machine Learning Method.

Hypertension is a widespread chronic disease. Risk prediction of hypertension is an intervention that contributes to the early prevention and management of hypertension. The implementation of such intervention requires an effective and easy-to-implement hypertension risk prediction model. This study evaluated and compared the performance of four machine learning algorithms on predicting the risk of hypertension based on easy-to-collect risk factors. A dataset of 29,700 samples collected through a physical examination was used for model training and testing. Firstly, we identified easy-to-collect risk factors of hypertension, through univariate logistic regression analysis. Then, based on the selected features, 10-fold cross-validation was utilized to optimize four models, random forest (RF), CatBoost, MLP neural network and logistic regression (LR), to find the best hyper-parameters on the training set. Finally, the performance of models was evaluated by AUC, accuracy, sensitivity and specificity on the test set. The experimental results showed that the RF model outperformed the other three models, and achieved an AUC of 0.92, an accuracy of 0.82, a sensitivity of 0.83 and a specificity of 0.81. In addition, Body Mass Index (BMI), age, family history and waist circumference (WC) are the four primary risk factors of hypertension. These findings reveal that it is feasible to use machine learning algorithms, especially RF, to predict hypertension risk without clinical or genetic data. The technique can provide a non-invasive and economical way for the prevention and management of hypertension in a large population.

A standardized analytics pipeline for reliable and rapid development and validation of prediction models using observational health data

Background and objectiveAs a response to the ongoing COVID-19 pandemic, several prediction models in the existing literature were rapidly developed, with the aim of providing evidence-based guidance. However, none of these COVID-19 prediction models have been found to be reliable. Models are commonly assessed to have a risk of bias, often due to insufficient reporting, use of non-representative data, and lack of large-scale external validation. In this paper, we present the Observational Health Data Sciences and Informatics (OHDSI) analytics pipeline for patient-level prediction modeling as a standardized approach for rapid yet reliable development and validation of prediction models. We demonstrate how our analytics pipeline and open-source software tools can be used to answer important prediction questions while limiting potential causes of bias (e.g., by validating phenotypes, specifying the target population, performing large-scale external validation, and publicly providing all analytical source code). MethodsWe show step-by-step how to implement the analytics pipeline for the question: ‘In patients hospitalized with COVID-19, what is the risk of death 0 to 30 days after hospitalization?’. We develop models using six different machine learning methods in a USA claims database containing over 20,000 COVID-19 hospitalizations and externally validate the models using data containing over 45,000 COVID-19 hospitalizations from South Korea, Spain, and the USA. ResultsOur open-source software tools enabled us to efficiently go end-to-end from problem design to reliable Model Development and evaluation. When predicting death in patients hospitalized with COVID-19, AdaBoost, random forest, gradient boosting machine, and decision tree yielded similar or lower internal and external validation discrimination performance compared to L1-regularized logistic regression, whereas the MLP neural network consistently resulted in lower discrimination. L1-regularized logistic regression models were well calibrated. ConclusionOur results show that following the OHDSI analytics pipeline for patient-level prediction modelling can enable the rapid development towards reliable prediction models. The OHDSI software tools and pipeline are open source and available to researchers from all around the world.

Calculation of the Population of Construction Scaffoldings Using Neural Networks

Building scaffoldings are temporary structures that are commonly used in the construction industry. A precise determination of the number of building scaffoldings in use is a very complex task. The literature survey showed that there is a lack of scientific studies concerning the estimation of the scaffolding population in the construction industry. This observation gave rise to the need to undertake such research, the aim of which was to develop a model of a neural network set which would in turn enable the number of used building scaffoldings to be estimated. In order to carry out such a research task, an original research methodology was developed, which used the results of empirical research that involved the counting of construction scaffoldings used in selected representative areas of the studied regions of Poland (the research was carried out in the period from 2016 to 2018), and also data taken from a publication of the Central Statistical Office on socio-economic indicators that characterize the analyzed regions (data from 2010 to 2018). The main element of the developed methodology is a set of five MLP neural networks, which was used to predict the number of used construction scaffoldings. The analysis of the sensitivity of the quantitative and qualitative variables of the model showed that they have a significant impact on the final result generated by the networks. The obtained results of the research and analyses showed the size of the population of building scaffoldings used in individual regions of Poland, and also the seasonality of their occurrence. The knowledge obtained on this basis can be used, among others, in economic analyses related to the use of construction scaffolding, as well as in the process of managing occupational safety on scaffoldings. The most important scientific aspect of the article concerns the development of an original methodology for estimating the population of building scaffoldings.

LDNM: A General Web Service Classification Framework via Deep Fusion of Structured and Unstructured Features

Classifying Web services plays a critical role in several fundamental service management tasks, such as service discovery, selection, ranking, and recommendation. However, traditional Web service classification approaches usually difficult to dispose unstructured sparse documents and underutilize the rich network relations. The consideration of multiple document representation schemes can ameliorate the former problem, whereas an appropriate network representation method could be a positive solution to the latter problem. In this paper, we propose a general Web service classification framework via deep fusion of structured and unstructured features, named LDNM. Firstly, we transform each service document into feature vectors by using two document representation methods: topic distribution based on LDA, and neural-network-based document embedding model known as Doc2vec. Then we obtain structured representation vectors which stem from service invoking and tagging graphs by applying Node2vec. Finally, we fuse these features and train a service classifier by using an MLP neural network. Comprehensive experiments are conducted on real-world datasets to demonstrate the effectiveness of the proposed approach.

Use of optimised MLP neural networks for spatiotemporal estimation of indoor environmental conditions of existing buildings

Controlling the indoor environmental quality in real time is essential for the health, well-being and productivity of occupants of a building. In recent years, research has focused on improving monitoring devices and strategies and developing techniques for estimating indoor conditions. The use of machine learning algorithms in this context has increased considerably. However, monitoring data in real time from large multizone working areas is challenging. The aim of this work is to provide an interpolation methodology based on the use of optimised multilayered perceptron neural networks to estimate the indoor environmental conditions of a building in real time. These estimations are obtained without the need for neither monitoring in the occupied working area nor human intervention and considering low-cost sensors. The neural network is optimised by implementing the multiobjective genetic algorithm NSGA-II to find the best architecture in terms of error and complexity. This method was applied to the building of a research centre in north-western Spain, where interpolated values for indoor air temperature, relative humidity and CO2 concentration were obtained. The results of this case study yielded relative errors close to 6% for temperature, 5% for relative humidity, and 12% for CO2 concentration. These values validate the methodology developed for the estimation of indoor environmental conditions and the contribution of this research to the improvement of the monitoring and control of the indoor environmental quality of a building.

Design of Ensemble Classifier Model Based on MLP Neural Network For Breast Cancer Diagnosis

Nowadays, breast cancer is one of the leading causes of death women in the worldwide. If breast cancer is detected at the beginning stage, it can ensure long-term survival. Numerous methods have been proposed for the early prediction of this cancer, however, efforts are still ongoing given the importance of the problem. Artificial Neural Networks (ANN) have been established as some of the most dominant machine learning algorithms, where they are very popular for prediction and classification work. In this paper, an Intelligent Ensemble Classification method based on Multi-Layer Perceptron neural network (IEC-MLP) is proposed for breast cancer diagnosis. The proposed method is split into two stages, parameters optimization and ensemble classification. In the first stage, the MLP Neural Network (MLP-NN) parameters, including optimal features, hidden layers, hidden nodes and weights, are optimized with an Evolutionary Algorithm (EA) for maximize the classification accuracy. In the second stage, an ensemble classification algorithm of MLP-NN is applied to classify the patient with optimized parameters. Our proposed IEC-MLP method which can not only help to reduce the complexity of MLP-NN and effectively selection the optimal feature subset, but it can also obtain the minimum misclassification cost. The classification results were evaluated using the IEC-MLP for different breast cancer datasets and the prediction results obtained were very promising (98.74% accuracy on the WBCD dataset). Meanwhile, the proposed method outperforms the GAANN and CAFS algorithms and other state-of-the-art classifiers. In addition, IEC-MLP could also be applied to other cancer diagnosis.