Multilayer Perceptron Neural Network Algorithm Research Articles

Prediction of maize cultivar yield based on machine learning algorithms for precise promotion and planting

This study proposed a model that utilized machine learning algorithms to predict the yield of maize (Zea mays L.) cultivars. This will enable the selection of good cultivars with high yields that are suitable for planting in specific areas, such as a district or county. The breeding value of the cultivars and 11 types of time-series meteorological variables were selected as the input parameters of the model. The yield was set as the output parameter of the model. Random forest (RF), Levenberg–Marquardt neural network, and multilayer perceptron neural network algorithms were used to construct the model. The results showed that the RF outperformed the other algorithms in predicting the yield of maize cultivars by achieving the maximum coefficient of determination (R2) of 0.77 and minimal root-mean-square error of 320.25 kg/acre, mean absolute error of 229.84 kg/acre, and mean absolute percentage error of 7.1%. The constructed model can be used to effectively predict the yield of specific varieties to enable the selection of good varieties in specific areas, such as a district or county. A prediction of the yield of a specific maize cultivar in a particular planting environment can have considerable value. It can facilitate the objective identification of better adapting cultivars among farmers and support the precise promotion and planting of cultivars.

Prediction of potentially toxic elements in water resources using MLP-NN, RBF-NN, and ANFIS: a comprehensive review.

Water resources are constantly threatened by pollution of potentially toxic elements (PTEs). In efforts to monitor and mitigate PTEs pollution in water resources, machine learning (ML) algorithms have been utilized to predict them. However, reviewstudies have not paid attention to the suitability of input variables utilized for PTE prediction. Therefore, the present review analyzed studies that employed three ML algorithms: MLP-NN (multilayer perceptron neural network), RBF-NN (radial basis function neural network), and ANFIS (adaptive neuro-fuzzy inference system) to predict PTEs in water. A total of 139 models were analyzed to ascertain the input variables utilized, the suitability of the input variables, the trends of the ML model applications, and the comparison of their performances. The present study identified seven groups of input variables commonly used to predict PTEs in water. Group 1 comprised of physical parameters (P), chemical parameters (C), and metals (M). Group 2 contains only P and C; Group 3 contains only P and M; Group 4 contains only C and M; Group 5 contains only P; Group 6 contains only C; and Group 7 contains only M. Studies that employed the three algorithms proved that Groups 1, 2, 3, 5, and 7 parameters are suitable input variables for forecasting PTEs in water. The parameters of Groups 4 and 6 also proved to be suitable for the MLP-NN algorithm. However, their suitability with respect to the RBF-NN and ANFIS algorithms could not be ascertained. The most commonly predicted PTEs using the MLP-NN algorithm were Fe, Zn, and As. For the RBF-NN algorithm, they were NO3, Zn, and Pb, and for the ANFIS, they were NO3, Fe, and Mn. Based on correlation and determinationcoefficients (R,R2), the overall order of performance of the three ML algorithms was ANFIS > RBF-NN > MLP-NN, even though MLP-NN was the most commonly used algorithm.

Prediction of Air Pollution with Machine Learning Algorithms

Air pollution has become an important problem due to its threats. Air pollutants are in complex interaction with atmosphere and environment. For this reason, it is important to study air pollution issues. In recent years, studies on prediction of air pollutants with machine learning methods have gained momentum. In this study, some air pollutants are predicted with various machine learning algorithms considering meteorological factors. In machine learning phase, a separate study is conducted with various machine learning algorithms (multilayer perceptron neural network, stochastic gradient descent, ridge regression, cross decomposition) considering temperature, relative humidity, wind, pressure and air pollutant measurements of previous hour. Consistencies of these algorithms in estimating pollutant concentrations are compared. Various statistical metrics are used to analyze the consistencies. As a result, the coefficient of determination of all algorithms are found above 0.67, considering the test section. It is found that the coefficient of determination of the multilayer perceptron neural network algorithm provides better results than other algorithms.

Toxicity source apportionment of fugitive dust PM2.5-bound polycyclic aromatic hydrocarbons using multilayer perceptron neural network analysis in Guanzhong Plain urban agglomeration, China

Polycyclic aromatic hydrocarbons (PAHs) in urban fugitive dust, known for their toxicity and ability to generate reactive oxygen species (ROS), are a major public health concern. This study assessed the spatial distribution and health risks of 15 PAHs in construction dust (CD) and road dust (RD) samples collected from June to November 2021 over the cities of Tongchuan (TC), Baoji (BJ), Xianyang (XY), and Xi'an (XA) in the Guanzhong Plain, China. The average concentration of ΣPAHs in RD was 39.5 ± 20.0 μg g−1, approximately twice as much as in CD. Four-ring PAHs from fossil fuels combustion accounted for the highest proportion of ΣPAHs in fugitive dust over all four cities. Health-related indicators including benzo(a)pyrene toxic equivalency factors (BAPTEQ), oxidative potential (OP), and incremental lifetime cancer risk (ILCR) all presented higher risk in RD than those in CD. The multilayer perceptron neural network algorithm quantified that vehicular and industrial emissions contributed 86 % and 61 % to RD and CD BAPTEQ, respectively. For OP, the sources of biomass and coal combustion were the key generator which accounted for 31–54 %. These findings provide scientific evidence for the direct efforts toward decreasing the health risks of fugitive dust in Guanzhong Plain urban agglomeration, China.

A novel wearable ERP-based BCI approach to explicate hunger necessity

This study aimed to design a Brain-Computer Interface system to detect people's hunger status. EEG signals were recorded in various scenarios to create a database. We extracted the time-domain and frequency-domain features from these signals and applied them to the inputs of various Machine Learning algorithms. We compared the classification performances and reached the best-performing algorithm. The highest success score of 97.62% was achieved using the Multilayer Perceptron Neural Network algorithm in Event-Related Potential analysis.

Application of machine learning algorithms and feature selection in rapeseed (Brassica napus L.) breeding for seed yield

BackgroundStudying the relationships between rapeseed seed yield (SY) and its yield-related traits can assist rapeseed breeders in the efficient indirect selection of high-yielding varieties. However, since the conventional and linear methods cannot interpret the complicated relations between SY and other traits, employing advanced machine learning algorithms is inevitable. Our main goal was to find the best combination of machine learning algorithms and feature selection methods to maximize the efficiency of indirect selection for rapeseed SY.ResultsTo achieve that, twenty-five regression-based machine learning algorithms and six feature selection methods were employed. SY and yield-related data from twenty rapeseed genotypes were collected from field experiments over a period of 2 years (2019–2021). Root mean square error (RMSE), mean absolute error (MAE), and determination coefficient (R2) were used to evaluate the performance of the algorithms. The best performance with all fifteen measured traits as inputs was achieved by the Nu-support vector regression algorithm with quadratic polynomial kernel function (R2 = 0.860, RMSE = 0.266, MAE = 0.210). The multilayer perceptron neural network algorithm with identity activation function (MLPNN-Identity) using three traits obtained from stepwise and backward selection methods appeared to be the most efficient combination of algorithms and feature selection methods (R2 = 0.843, RMSE = 0.283, MAE = 0.224). Feature selection suggested that the set of pods per plant and days to physiological maturity along with plant height or first pod height from the ground are the most influential traits in predicting rapeseed SY.ConclusionThe results of this study showed that MLPNN-Identity along with stepwise and backward selection methods can provide a robust combination to accurately predict the SY using fewer traits and therefore help optimize and accelerate SY breeding programs of rapeseed.

Estimation of service length with the machine learning algorithms and neural networks for patients who receiving home health care

The main purpose of the study is to develop an estimation model using machine learning algorithms and to ensure the effective and efficient implementation of home health care service planning in hospitals with these algorithms. The necessary approvals for the study were obtained. The data set was created by obtaining patient data (except for data such as Turkish Republic identification number) from 14 hospitals providing Home Health Care Services in the city of Diyarbakır. The data set was subjected to necessary pre-processing and descriptive statistics were applied. For the estimation model, Decision Tree, Random Forest and Multi-layer Perceptron Neural Network algorithms were used. It was found that the number of days of home health care service, which the patients received, varied depending on their age and gender. It was observed that the patients were generally in the disease groups that required Physiotherapy and Rehabilitation treatments. It was determined that the length of service for patients can be predicted with a high reliability rate (Multi-Layer Model Acc: 90.4%, Decision Tree Model Acc: 86.4%, Random Forest Model Acc: 88.5%) using machine learning algorithms. In the light of the findings and data patterns obtained in the study, it is thought that effective and efficient planning will be made in terms of health management. In addition, it is believed that estimating the average length of service for patients will contribute to strategic planning of human resources for health, and to reducing medical consumables, drugs and hospital expenses.

Greenhouse Energy Analysis and Neural Networks Modelling in Northern Iraq

Abstract This study aims to analyse the energy of cucumber production in a greenhouse and examine the application of a multilayer perceptron to predict the productivity of an agricultural region in Nineveh Governorate. The research data were collected from experiments including fuel, fertilisers, pesticides, seeds, workers, electricity, and the number of hours worked in agricultural processes to produce cucumber crops. The results showed that the total energy consumption of the cucumber was 46,432.013 MJ·ha−1, while the output energy was 53,127.727 MJ·ha−1. The fungicide energy consumption, herbicide energy consumption and electricity energy consumption are considered the most critical variable in cucumber plantation procedures; its significance is the relative values of 100%, 99.7% and 93.3%. The impacts of human labour, P fertiliser, diesel fuel and N fertiliser on cucumber operation were 25,725 MJ·ha−1, 548.596 MJ·ha−1, 3,011.178 MJ·ha−1 and 7,244.545 MJ·ha−1, respectively. This research concludes that a multilayer perceptron neural network algorithm helps predict cucumber production and shows that the trained neural network produced minimal errors, indicating that the test model could predict a cucumber crop yield in Nineveh province.

Diagnosis of Breast Cancer Using Computational Intelligence Models and IoT Applications.

The use of computer-aided diagnostic (CAD) models has been proposed to aid in the detection and classification of breast cancer. In this work, we evaluated the performance of multilayer perceptron neural network and nonlinear support vector machine models to classify breast cancer nodules. From the contour of 569 samples, ten morphological features were used as input to the classifiers. The average results obtained in the set of 50 simulations performed show that the proposed models showed good performance (all exceeded 90.0%) in terms of accuracy in the test set. The nonlinear support vector machine algorithm stands out when compared to the proposed multilayer perceptron neural network algorithm, with 99% accuracy and a 2% false-negative rate. The neural network model presented lower performance than the nonlinear support vector machine classifier. With the application of the proposed models, the average results obtained are promising in the classification of breast cancer.

Machine learning approach to predict the heat transfer coefficients pertaining to a radiant cooling system coupled with mixed and forced convection

Mixed convection phenomenon over radiant cooled surfaces with displacement ventilation in living environments is becoming a popular issue due to the airborne viruses and energy economy. Artificial neural networks are one of the machine learning methods that are widely evaluated as an engineering tool. In the current study, heat transfer coefficients for a radiant wall cooling system coupled with mixed and forced convection have been predicted by a machine learning approach. This approach should be noted as a first experimental investigation couple with an artificial neural network analysis in the open sources in which mixed convection systems in real sized living environments is examined. Experimentally obtained heat transfer coefficients have been used in the development of the feed forward back propagation multi-layer perceptron network structure. So as to analyze the impact of the input factors on the prediction performance, two neural network structures with dissimilar input parameters such as various temperatures, velocities, and heat transfer rates have been developed. By means of feed forward back propagation multi-layer perceptron neural network algorithms, convection, radiation, and total heat transfer coefficients have been predicted using the experimentally acquired dataset including 35 data points belonging to the mixed and forced convection conditions. Training, validation, and test data groups include 70%, 15%, and 15% of the dataset, in turn. Training algorithm has been computed via Levenberg-Marquardt one with 10 neurons in the hidden layer. The findings obtained from the computational solution have been evaluated as a result of the contrast with the target data with in the ±5% deviation band for all heat transfer coefficients. The performance factors have been computed and the estimation precision of the numerical models has been thoroughly examined.

Supervised mineral exploration targeting and the challenges with the selection of deposit and non-deposit sites thereof

Selection of non-deposit sites is a challenging issue affecting the application of supervised algorithms for modeling mineral exploration targets. For this, equal number of deposit and non-deposit sites has been widely applied for training purposes. In this paper, we investigated the effect of changes in the number of non-deposit sites on the effectiveness of exploration targeting models while the number of deposit sites is constant. The results obtained demonstrated that exploration targeting models are affected by the ratio of non-deposit and deposit sites. Thus, balancing between the number of deposit and non-deposit sites is an efficient way to produce more-reliable exploration targets when supervised algorithms are applied for modeling. The idea of this research came from the fact that mineralization is a rare event, and therefore, in a region of interest number of non-deposit sites is much more than that of deposit events. To illustrate the procedure proposed, we used an exploration dataset of porphyry Cu mineralization in Chahargonbad area, SE Iran. A sequence application of self-organizing map and multilayer perceptron neural network algorithm was applied to better illustration of the changing effects of the number of non-deposit sites on the ensuing exploration targeting models.

A new machine learning-based healthcare monitoring model for student’s condition diagnosis in Internet of Things environment

Advancement in sensor technologies has resulted in rapid evolution of Internet of Things (IoT) applications for developing behavioral and physiological monitoring systems such as IoT-based student healthcare monitoring system. Nowadays, a growing number of students living alone scattered over wide geographical areas, and tracking their health function status is necessary. In this paper, an IoT-based student healthcare monitoring model is proposed to continuously check student vital signs and detect biological and behavioral changes via smart healthcare technologies. In this model, vital data are collected via IoT devices and data analysis is carried out through the machine learning methods for detecting the probable risks of student’s physiological and behavioral changes. The experimental results reveal that the proposed model meets the efficiency and proper accuracy for detecting the students’ condition. After evaluating the proposed model, the support vector machine has achieved the highest accuracy of 99.1% which is a promising result for our purpose. The results outperformed decision tree, random forest, and multilayer perceptron neural network algorithms as well.

Fault Diagnosis of Bevel Gears Using Neural Pattern Recognition and MLP Neural Network Algorithms

Gear mechanisms are key components for rotating machinery ranging from automotive, hydraulic systems to aviation systems. As a more reliable, safer, economical fault diagnostic method, vibration and acoustic signatures of such systems have been widely studied. There are only a few numbers of studies incorporating sound and vibration monitoring together, for different working hours of the mechanism, rotating at different operational parameters. A bevel gear test setup was developed in-house to observe the effect of different operating conditions as shaft loading, shaft speed, lubrication level and abrasive contamination along with different operating hours. The system operating condition was also monitored, by obtaining visual photographs of gear teeth. Vibration and sound signals were recorded followed by fast Fourier Transform and Power Spectrum Density computations to extract the features used in developing a Multi-Layer Perceptron (MLP) based Neural Network and a Neural Pattern Recognition algorithm for fault classification purposes. It has been shown that sound and vibration measurements can be confidently used to predict bevel gear fault conditions.

Scavenging carbon deposition on alumina supported cobalt catalyst during renewable hydrogen-rich syngas production by methane dry reforming using artificial intelligence modeling technique

Methane dry reforming is a thermo-catalytical process that utilizes two principal components of greenhouse gases for the production of hydrogen-rich syngas . One major shortcoming of the methane dry reforming as a potential route for renewable hydrogen-rich syngas production is catalyst deactivation through carbon deposition . In this study, an artificial neural network approach was employed for predictive modeling of the deactivation of alumina supported cobalt catalyst used to catalyze methane dry reforming reaction. The effect of methane/carbon dioxide (CH 4 /CO 2 ) ratio, reaction temperature and nitrogen (N 2 ) flowrate on the carbon deposition were investigated using full factorial experimental design . Two artificial neural network modeling techniques namely multilayer perceptron neural network (MLPNN) and radial basis function (RBFNN) were employed for the prediction of carbon deposition per gram catalyst using data obtained from 170 experimental runs. The hidden neurons were optimized to obtain 16 and 20 units respectively for the MLPNN and the RBFNN resulting in the network architecture of 3, 16, 1 and 3, 20, 1, respectively. The statistical analysis of the network performance resulted in mean standard error (MSE) values of 0.048 and 0.00285 for training the MLPNN algorithm above and below stoichiometric conditions with corresponding R 2 values of 0.945 and 0.965. While MSE values of 0.0073 and 0.00015 were obtained for the training of the RBFNN algorithm above and below stoichiometric conditions with R 2 of 0.987 for both cases. Base on the statistical analysis the RBFNN model was adjudicated as a better predictor of the carbon deposition during the hydrogen-rich syngas production than the MLPNN model. The three input parameters were found to have varying levels of importance in the prediction of the carbon deposition. The reaction temperature was observed to be the most important parameters that influence the prediction of carbon deposition above stochiometric while CH 4 /CO 2 was the most important parameters that influence the prediction of carbon deposition below stoichiometric conditions. • MLPNN and RBFNN models were developed and employed to predict carbon deposition on Co/αAl 2 O 3 . • Optimized hidden neurons with 16 and 20 units respectively were obtained for MLPNN and RBFNN. • Levenberg-Marquardt algorithm was employed to train the MLPNN and RBFNN. • Both MLPNN and RBFNN accurately predict carbon deposition with minimal residuals. • RBFNN with MSE of 0.00015 and R 2 of 0.985 predicts better than MLPNN with MSE of 0.00285 and R 2 of 0.965

Predictive Land Use Change under Business-As-Usual and Afforestation Scenarios in the Vea Catchment, West Africa

This study aimed to assess the historical Land use/land cover (LULC) changes and project the future (2025) LULC pattern in the Vea catchment based on Business as Usual (BAU) and afforestation scenarios of land use. Landsat Imagery of 1990, 2001, 2011 and 2016 were classified at overall accuracy assessment of 82%, 86%, 85% and 88% respectively. Major transitions were modeled using the Multi-layer Perceptron Neural Network algorithm, and the future scenarios maps of LULC were projected based on the Markov chain after validation of the Land Change Modeler. The results indicate the conversion of forest/mixed vegetation (23.1%) and grassland (76.9%) to cropland as the dominant LULC conversion from 1990 to 2016. An increase in cropland, built-up areas, and water bodies were observed while grassland and forest/ mixed vegetation decreased over the last 27 years. The 2025 LULC simulation indicates continuous expansion of cropland at the expense of forest/mixed vegetation which is projected to decrease by 4.5% in 2025 for the BAU scenario. Under afforestation scenario, where forest/mixed vegetation and grassland are expected to increase, cropland is projected to decrease by 20% in 2025. These findings set a reference ground for sustainable land use governance through responsible planning and management of land and water resources by considering trade-offs between cropland expansion and ecosystems’ preservation in the Vea catchment.

Estimating a civil aircraft’s development cost with a GM(1, N) model and an MLP neural network

PurposeThe purpose of this paper is to study a new approach by combining a multilayer perceptron neural network (MLPNN) algorithm with a GM(1, N) model in order to estimate the development cost of a new type of aircraft.Design/methodology/approachFirst, data about developing costs and their influencing factors were collected for several types of Boeing and Airbus aircraft. Second, a GM(1, N) model was constructed to simulate development costs for a civil aircraft. Then, an MLPNN algorithm was added to optimize and revise the simulative and forecasting values. Finally, a combined approach, using both a GM(1, N) model and an MLPNN algorithm was adopted to forecast development costs for new civil aircraft.FindingsThe results show that the proposed approach could do the work of cost estimation for new types of aircraft. Rather than using a single model, the combined approach could improve simulative and forecasting accuracy.Practical implicationsScientific cost estimation could improve management efficiency and promote the success of a new type of civil aircraft development. Considering that China’s civil aircraft research and development is at its very beginning stages, only very limited data could be collected. The development costs for civil aircraft are affected by a series of factors. The approach outlined by this paper could be applied to development cost estimations in China’s civil aircraft industry.Originality/valueThe paper has succeeded by constructing a cost estimation index system and proposing a novel combined cost estimation approach comprised of a GM(1, N) model and an MLPNN. It has undoubtedly contributed to improving the accuracy of cost estimations.

Forecasting Drought Using Multilayer Perceptron Artificial Neural Network Model

These days human beings are facing many environmental challenges due to frequently occurring drought hazards. It may have an effect on the country’s environment, the community, and industries. Several adverse impacts of drought hazard are continued in Pakistan, including other hazards. However, early measurement and detection of drought can provide guidance to water resources management for employing drought mitigation policies. In this paper, we used a multilayer perceptron neural network (MLPNN) algorithm for drought forecasting. We applied and tested MLPNN algorithm on monthly time series data of Standardized Precipitation Evapotranspiration Index (SPEI) for seventeen climatological stations located in Northern Area and KPK (Pakistan). We found that MLPNN has potential capability for SPEI drought forecasting based on performance measures (i.e., Mean Average Error (MAE), the coefficient of correlation (R), and Root Mean Square Error (RMSE)). Water resources and management planner can take necessary action in advance (e.g., in water scarcity areas) by using MLPNN model as part of their decision-making.

Determination of near-surface structures from multi-channel surface wave data using multi-layer perceptron neural network (MLPNN) algorithm

This study proposes the use of multi-layer perceptron neural networks (MLPNN) to invert dispersion curves obtained via multi-channel analysis of surface waves (MASW) for shear S-wave velocity profile. The dispersion curve used in inversion includes the fundamental-mode dispersion data. In order to investigate the applicability and performance of the proposed MLPNN algorithm, test studies were performed using both synthetic and field examples. Gaussian random noise with a standard deviation of 4 and 8% was added to the noise-free test data to make the synthetic test more realistic. The model parameters, such as S-wave velocities and thicknesses of the synthetic layered-earth model, were obtained for different S/N ratios and noise-free data. The field survey was performed over the natural gas pipeline, located in the Germencik district of Aydin city, western Turkey. The results show that depth, velocity, and location of the embedded natural gas pipe are successfully estimated with reasonably good approximation.

Prediction of Influent Flow Rate: Data-Mining Approach

In this paper, models for short-term prediction of influent flow rate in a wastewater-treatment plant are discussed. The prediction horizon of the model is up to 180 min. The influent flow rate, rainfall rate, and radar reflectivity data are used to build the prediction model by different data-mining algorithms. The multilayer perceptron neural network algorithm has been selected to build the prediction models for different time horizons. The computational results show that the prediction model performs well for horizons up to 150 min. Both the peak values and the trends are accurately predicted by the model. There is a small lag between the predicted and observed influent flow rate for horizons exceeding 30 min. The lag becomes larger with the increase of the prediction horizon. DOI: 10.1061/(ASCE)EY.1943-7897 .0000103. © 2013 American Society of Civil Engineers. CE Database subject headings: Data collection; Algorithms; Flow rates; Neural networks; Radar; Rainfall; Wastewater management; Water treatment plants; Predictions. Author keywords: Data-mining algorithms; Influent flow rate; Multilayer perceptron neural networks; Radar reflectivity; Rainfall; Wastewater-treatment plant.

Prediction of mortality in stroke patients using multilayer perceptron neural networks

We aim ed to predict mortality in stroke patients by using multilayer perceptron (MLP) neural networks. Materials and methods: A data set consisting of 584 stroke patients was analyzed using MLP neural networks. The effect of prognostic factors (age, hospitalization time, sex, hypertension, atrial fibrillation, embolism, stroke type, infection, diabetes mellitus, and ischemic heart disease) on mortality in stroke were trained with 6 different MLP algorithms [quick propagation (QP), Levenberg-Marquardt (LM), backpropagation (BP), quasi-Newton (QN), delta bar delta (DBD), and conjugate gradient descent (CGD)]. The performances of the MLP neural network algorithms were compared using the receiver operating characteristic (ROC) curve method. Results: Among the 6 algorithms that were trained with the MLP, QP achieved the highest specificity (81.3%), sensitivity (78.4%), accuracy (80.7%), and area under the curve (AUC) (0.869) values, while CGD achieved the lowest specificity (61.5%), sensitivity (58.7%), accuracy (60.8%), and AUC (0.636) values. The AUC of the QP algorithm was statistically significantly higher than the AUCs of the QN, DBD, and CGD algorithms (P < 0.05 for all of the pairwise comparisons). Conclusion: The MLP trained with the QP algorithm achieved the highest specificity, sensitivity, accuracy, and AUC values. This can be helpful in the prediction of mortality in stroke.