Multilayer Perceptron Neural Network Research Articles

Predicting dyslipidemia incidence: unleashing machine learning algorithms on Lifestyle Promotion Project data

BackgroundDyslipidemia, characterized by variations in plasma lipid profiles, poses a global health threat linked to millions of deaths annually.ObjectivesThis study focuses on predicting dyslipidemia incidence using machine learning methods, addressing the crucial need for early identification and intervention.MethodsThe dataset, derived from the Lifestyle Promotion Project (LPP) in East Azerbaijan Province, Iran, undergoes a comprehensive preprocessing, merging, and null handling process. Target selection involves five distinct dyslipidemia-related variables. Normalization techniques and three feature selection algorithms are applied to enhance predictive modeling.ResultThe study results underscore the potential of different machine learning algorithms, specifically multi-layer perceptron neural network (MLP), in reaching higher performance metrics such as accuracy, F1 score, sensitivity and specificity, among other machine learning methods. Among other algorithms, Random Forest also showed remarkable accuracies and outperformed K-Nearest Neighbors (KNN) in metrics like precision, recall, and F1 score. The study’s emphasis on feature selection detected meaningful patterns among five target variables related to dyslipidemia, indicating fundamental shared unities among dyslipidemia-related factors. Features such as waist circumference, serum vitamin D, blood pressure, sex, age, diabetes, and physical activity related to dyslipidemia.ConclusionThese results cooperatively highlight the complex nature of dyslipidemia and its connections with numerous factors, strengthening the importance of applying machine learning methods to understand and predict its incidence precisely.

Predicting the Magnitude of Possible Earthquakes in the Shahrood District of Khalkhal County, Ardabil, Iran, Using Artificial Neural Networks

Background and objective Earthquakes are natural disasters that can cause high damage. Prediction of earthquakes is necessary to reduce damages, develop resistant infrastructure, and prevent the loss of life in human societies. This research aims to predict the magnitude of possible earthquakes in the Shahrood district of Khalkhal County using artificial neural networks (ANN). Method A multi-layer perceptron neural network was used to predict earthquake magnitude. Results The results showed that in the fault areas, especially the Kalur fault, earthquakes with a magnitude of 1-3 on the Richter scale are more likely to occur (70%). The likelihood of earthquakes with a magnitude of 4-6 on the Richter scale is moderate (26%) and the earthquakes with a magnitude of 7-10 on the Richter scale have a very low probability (4%). Conclusion In future studies, it is recommended to use machine learning models to better and more accurately predict earthquakes in the studied area.

Machine learning approaches for asthma disease prediction among adults in Sri Lanka.

Objectives: Addressing the challenge of cost-effective asthma diagnosis amidst diverse symptom patterns among patients, this study aims to develop a machine learning-based asthma prediction tool for self-detection of asthma. Methods: Data from 6,665 participants in the Sri Lanka Health and Ageing Study (2018-2019) are used for this research. Thirteen machine learning algorithms, including Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, Naïve Bayes, K-Nearest Neighbors, Gradient Boost, XGBoost, AdaBoost, CatBoost, LightGBM, Multi-Layer Perceptron, and Probabilistic Neural Network, are employed. Results: A hybrid version of Logistic Regression and LightGBM outperformed other models, achieving an AUC of 0.9062 and 79.85% sensitivity. Key predictive features for asthma include wheezing, breathlessness with wheezing, shortness of breath attacks, coughing attacks, chest tightness, nasal allergies, physical activity, passive smoking, ethnicity, and residential sector. Conclusion: Combining Logistic Regression and LightGBM models can effectively predict adult asthma based on self-reported symptoms and demographic and behavioural characteristics. The proposed expert system assists clinicians and patients in diagnosing potential asthma cases.

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm

Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method was used to determine the significant inputs (time series lagged data) to the model. To compare the performance of Boruta-XGB-CNN-LSTM models, three machine learning approaches—multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) were used. Different statistical metrics, such as correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error, were used to assess the models' performance. From 10 years of data in both rivers, 7 years (2012–2018) were used as a training set, and 3 years (2019–2021) were used for testing the models. Application of the Boruta-XGB-CNN-LSTM model in forecasting one day ahead of EC showed that in both stations, Boruta-XGB-CNN-LSTM can forecast the EC parameter better than other machine learning models for the test dataset (R = 0.9429, RMSE = 45.6896, MAPE = 5.9749 for Albert River, and R = 0.9215, RMSE = 43.8315, MAPE = 7.6029 for Barratta Creek). Considering the better performance of the Boruta-XGB-CNN-LSTM model in both rivers, this model was used to forecast 3–10 days ahead of EC. The results showed that the Boruta-XGB-CNN-LSTM model is very capable of forecasting the EC for the next 10 days. The results showed that by increasing the forecasting horizon from 3 to 10 days, the performance of the Boruta-XGB-CNN-LSTM model slightly decreased. The results of this study show that the Boruta-XGB-CNN-LSTM model can be used as a good soft computing method for accurately predicting how the EC will change in rivers.

MODELLING DOMESTIC WATER DEMAND IN MALAYSIA TO IDENTIFY INFLUENCING FACTORS: A COMPARATIVE ANALYSIS

Water crises are often experienced by many developing countries worldwide. Predicting future domestic water demand and identifying the influential factors are vital to managing water supply effectively. This study aims to determine the best predictive models among Multiple Linear Regression (MLR), Multi-layer Perceptron (MLP), and Radial Basis Function (RBF) Neural Networks as well as to identify the significant influential factors towards domestic water demand. Based on the yearly records from 2000 to 2018 obtained from the Malaysian Water Association, the Department of Environment, and the Department of Statistics Malaysia the analysis results indicate an increasing pattern of domestic water in Malaysia with the demand for non-domestic water twice lower than domestic water. Based on RMSE and R-squared, Multi-layer Perceptron is the best model for predicting domestic water demand. The MLR model shows that the two most significant influential factors towards domestic water demand are price and design capacity, with negative and positive relationships. The results describe that an increase in price affects a decrease in water demand, while an increase in design capacity will reduce the water demand. The findings suggest that the water utilities in Malaysia should focus more on these identified factors.

Pilot Area Formation Evaluation: Upper Shale Member/ Rumaila Oil Field

This study aims to conduct a comprehensive formation evaluation of a pilot area within the Upper Shale Member of the Rumaila Oil Field. This evaluation is an essential step in the full development of the field. The application of well-log data and core analyses can help in obtaining the desired information about the geological characteristics of the formation. The process begins with measuring the formation temperature and water resistance utilizing Schlumberger’s charts and equations. The volume of shale was determined by two different methods, which were then used together to obtain the final shale volume. The porosity was determined using the conventional porosity equations from the porosity logs and the saturation was estimated based on Archie's equation. In core data analysis, an unconventional technique was utilized to determine rock type and permeability. The core porosity and the permeability were classified into four groups mainly using a self-organizing map and an unsupervised machine-learning method, and selected regression equations of each group were applied to estimate permeability in the core. The method depicted a good agreement between the core and estimated permeabilities, proving it as an effective tool. A complicated training data set was constructed based on the use of a multilayer perceptron neural network on coreless wells to identify rock types and permeability. Analyzing the petrophysical properties of the study area showed evidence that this area is characterized by heterogeneity. The heterogeneity of this formation is due to the presence of a considerable amount of shale, in addition to the significant characteristic differences in the layers and the same layer in different locations. The abundance of shale rock poses challenges during drilling operations, particularly due to shale washout which can lead to mechanical issues with the drilling string. Therefore, caution is advised when drilling new wells in the area to mitigate shale washout risks. Furthermore, the analysis identified layers with high hydrocarbon saturation that are viable for production. Conversely, some layers, characterized by shale presence and poor rock quality, are deemed unsuitable for production, and should not be considered as reservoir rock.

Artificial Neural Network Modeling Techniques for Drying Kinetics of Citrus medica Fruit during the Freeze-Drying Process

The main objective of this study is to analyze the drying kinetics of Citrus medica by using the freeze-drying method at various thicknesses (3, 5, and 7 mm) and cabin pressures (0.008, 0.010, and 0.012 mbar). Additionally, the study aims to evaluate the efficacy of an artificial neural network (ANN) in estimating crucial parameters like dimensionless mass loss ratio (MR), moisture content, and drying rate. Feedforward multilayer perceptron (MLP) neural network architecture was employed to model the freeze-drying process of Citrus medica. The ANN architecture was trained using a dataset covering various drying conditions and product characteristics. The training process, including hyperparameter optimization, is detailed and the performance of the ANN is evaluated using robust metrics such as RMSE and R². As a result of comparing the experimental MR with the predicted MR of the ANN modeling created by considering various product thicknesses and cabin pressures, the R² was found to be 0.998 and the RMSE was 0.010574. Additionally, color change, water activity, and effective moisture diffusivity were examined in this study. As a result of the experiments, the color change in freeze-dried Citrus medica fruits was between 6.9 ± 0.2 and 21.0 ± 0.6, water activity was between 0.4086 ± 0.0104 and 0.5925 ± 0.0064, effective moisture diffusivity was between 4.19 × and 21.4 × , respectively. In freeze-drying experiments conducted at various cabin pressures, it was observed that increasing the slice thickness of Citrus medica fruit resulted in longer drying times, higher water activity, greater color changes, and increased effective moisture diffusivity. By applying the experimental data to mathematical models and an ANN, the optimal process conditions were determined. The results of this study indicate that ANNs can potentially be applied to characterize the freeze-drying process of Citrus medica.

Multilayer Perceptron-Based Wearable Exercise-Related Heart Rate Variability Predicts Anxiety and Depression in College Students.

(1) Background: This study aims to investigate the correlation between heart rate variability (HRV) during exercise and recovery periods and the levels of anxiety and depression among college students. Additionally, the study assesses the accuracy of a multilayer perceptron-based HRV analysis in predicting these emotional states. (2) Methods: A total of 845 healthy college students, aged between 18 and 22, participated in the study. Participants completed self-assessment scales for anxiety and depression (SAS and PHQ-9). HRV data were collected during exercise and for a 5-min period post-exercise. The multilayer perceptron neural network model, which included several branches with identical configurations, was employed for data processing. (3) Results: Through a 5-fold cross-validation approach, the average accuracy of HRV in predicting anxiety levels was 89.3% for no anxiety, 83.6% for mild anxiety, and 74.9% for moderate to severe anxiety. For depression levels, the average accuracy was 90.1% for no depression, 84.2% for mild depression, and 82.1% for moderate to severe depression. The predictive R-squared values for anxiety and depression scores were 0.62 and 0.41, respectively. (4) Conclusions: The study demonstrated that HRV during exercise and recovery in college students can effectively predict levels of anxiety and depression. However, the accuracy of score prediction requires further improvement. HRV related to exercise can serve as a non-invasive biomarker for assessing psychological health.

Analysis of Correlation between Electron Diffraction Images of Corneocytes and Skin Barrier Properties by Multilayer Perceptron and Convolutional Neural Network

Analysis of Correlation between Electron Diffraction Images of Corneocytes and Skin Barrier Properties by Multilayer Perceptron and Convolutional Neural Network

Variational temporal convolutional networks for I-FENN thermoelasticity

Machine learning (ML) has been used to solve multiphysics problems like thermoelasticity through multi-layer perceptron (MLP) networks. However, MLPs have high computational costs and need to be trained for each prediction instance. To overcome these limitations, we introduced an integrated finite element neural network (I-FENN) framework to solve transient thermoelasticity problems in Abueidda and Mobasher (2024). This approach used a physics-informed temporal convolutional network (PI-TCN) within a finite element scheme for solving transient thermoelasticity problems. In this paper, we introduce an I-FENN framework using a new variational TCN model trained to minimize the thermoelastic variational form rather than the strong form of the energy balance. We mathematically prove that the I-FENN setup based on minimizing the variational form of transient thermoelasticity still leads to the same solution as the strong form. Introducing the variational form to the ML model brings the advantages of lower requirement for the differentiability of the basis function and, thus, lower memory requirement and higher computational efficiency. Also, it automatically satisfies zero Neumann boundary conditions, thus reducing the complexity of the loss function. The formulation based on the variational form complies with thermodynamic requirements. The proposed loss function reduces the difference between predicted and target data while minimizing the variational form of thermoelasticity equations, combining the benefits of both data-driven and variational methods. In addition, this study uses finite element shape functions for spatial gradient calculations and compares their performance against automatic differentiation. Our results reveal that models leveraging shape functions exhibit higher accuracy in capturing the behavior of the thermoelasticity problem and faster convergence. Adding the variational term and using shape functions for gradient calculations ensure better adherence to the underlying physics. We demonstrate the capabilities of this I-FENN framework through multiple numerical examples. Additionally, we discuss the convergence of the proposed variational TCN model and the impact of hyperparameters on its performance. The proposed approach offers a well-founded and flexible platform for solving fully coupled thermoelasticity problems while retaining computational efficiency, where the efficiency scales proportional to the model size.

Unveiling significant risk factors for intensive care unit-acquired weakness: Advancing preventive care

In this editorial, we discuss an article titled, “Significant risk factors for intensive care unit-acquired weakness: A processing strategy based on repeated machine learning,” published in a recent issue of the World Journal of Clinical Cases . Intensive care unit-acquired weakness (ICU-AW) is a debilitating condition that affects critically ill patients, with significant implications for patient outcomes and their quality of life. This study explored the use of artificial intelligence and machine learning techniques to predict ICU-AW occurrence and identify key risk factors. Data from a cohort of 1063 adult intensive care unit (ICU) patients were analyzed, with a particular emphasis on variables such as duration of ICU stay, duration of mechanical ventilation, doses of sedatives and vasopressors, and underlying comorbidities. A multilayer perceptron neural network model was developed, which exhibited a remarkable impressive prediction accuracy of 86.2% on the training set and 85.5% on the test set. The study highlights the importance of early prediction and intervention in mitigating ICU-AW risk and improving patient outcomes.

Artificial Neural Network-Based Real-Time Power Management for a Hybrid Renewable Source Applied for a Water Desalination System

Water desalination systems integrated with stand-alone hybrid energy sources offer a remarkable solution to the water–energy challenge. Given the complexity of these systems, selecting an appropriate energy management system is crucial. In this regard, employing artificial intelligence techniques to develop and validate an energy management system can be an effective approach for handling such intricate systems. Therefore, this paper presents an ANN-based energy management system (ANNEMS) for a pumping and desalination system connected to an isolated hybrid renewable energy source. Thus, a parametric sensitivity algorithm was developed to identify the optimal neural network architecture. The water–energy management-based supervised multi-layer perceptron neural network demonstrated effective power sharing within a short time frame, achieving accuracy criteria of RMSE, R, and R² between the actual and estimated electrical power of the three motor pumps. The ANNEMS is defined to facilitate real-time power sharing distribution among the various system motor pumps on the test bench, considering the generated power profile and water tank levels. The proposed strategy employs power field oriented control to maintain DC bus voltage stability. Experimental results from the implementation of the proposed ANNEMS are provided. Therein, the power levels of the three motor pumps demonstrated consistent adherence to their reference values. In summary, this study highlights the significance of selecting appropriate energy management for real-time experimental validation.

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.

Antiviral drugs preformulation: thermal energy effects by multilayer perceptron network approach and Hirshfeld surface properties

Antiviral drugs preformulation: thermal energy effects by multilayer perceptron network approach and Hirshfeld surface properties

Fast Prediction of Airfoil Aerodynamic Characteristics Based on a Combined Autoencoder

Aircraft airfoils are classified into two main categories: symmetrical and asymmetrical. Both types of airfoils have a significant impact on the flight performance and safety of the aircraft. The fast prediction of the aerodynamic coefficients and pressure distributions of airfoils is crucial for the design of aircraft. The traditional wind tunnel test and CFD methods have the disadvantages of high test cost and high time consumption. To solve these problems, a combined autoencoder (CAE) network is proposed in this paper, which can achieve the fast prediction of airfoil aerodynamic coefficients and pressure distributions. The network consists of an airfoil shape autoencoder (AE) network and a multilayer perceptron (MLP) network. Firstly, an autoencoder network reflecting the characteristics of the airfoil shape is established, and the effects of different latent variables on the performance of the autoencoder network are investigated. Then, the latent variables obtained from the autoencoder are concatenated with the inflow conditions such as the Reynolds number and the angle of attack to be used as inputs to the MLP network, and the aerodynamic coefficients of different airfoils in different inflow conditions are predicted. The effects of various latent variable inputs, as well as the direct input of the airfoil shape into the MLP network, on the prediction performance of aerodynamic coefficients are compared and analyzed. The optimal aerodynamic coefficient prediction network is then obtained. Finally, the CAE network is also applied to predict the pressure distributions of different airfoils in different inflow conditions and the effects of different latent variables and input conditions on the prediction performance of the pressure distributions are analyzed and compared with the advantages and disadvantages of the CAE network and the conditional variational autoencoder (CVAE) network. The results demonstrate that the proposed method is capable of accurately predicting aerodynamic characteristics in a shorter time, offering a valuable reference for the fast and efficient design of aircraft airfoils.

Analyzing the effects of streetscape and land use on urban accidents and predicting future accidents by using machine learning algorithms (case study: Mashhad)

In general, land use and layout of streets can have a significant impact on the behavior of drivers and pedestrians. In particular, streetscape has often been overlooked that recognizing the role of streetscape on street accident in urban areas is important. The aim of this research is to investigate the influence of streetscape and land use on urban accidents that occurred in Mashhad between the years 2017 and 2021. To achieve this objective, the study focused on analyzing accidents in three different urban zones. It also considered the land use types adjacent to both closed and open streets, including residential, commercial, and mixed land uses. The research employed various surveys to gather the necessary data and insights related to the targeted areas. Statistics on accident in three zones show that among the mentioned land uses, commercial areas have experienced the highest number of accidents, with their share being approximately three times that of accidents in residential areas. Additionally, 75 % of all accidents took place in areas with open streetscape, whereas accidents in areas with enclosed view accounted for one third of the number of accidents in open streetscape areas. In this research, analysis and modeling were conducted using machine learning algorithms implemented in the Python programming language. Several models were employed, and the best models were selected based on their performance and accuracy, which include Random Forest Regression (RFR), Multilayer Neural Network Perceptron Regression (MLP) and Extreme Boost Gradient Regression (XGBoost). The accuracy of the machine learning models which successfully predicted future outcomes was as follows: Random Forest Regression (RFR) achieved 85 % accuracy, Extreme Boost Gradient Regression (XGBoost) achieved 81 % accuracy, and finally, Neural Network Multilayer Perceptron Regression (MLP) achieved 75 % accuracy.

Application of Multilayer Perceptron Neural Network in Geological Modeling of Categorical Variables: A Case Study in Peru

Application of Multilayer Perceptron Neural Network in Geological Modeling of Categorical Variables: A Case Study in Peru

Past and future land use change dynamics: assessing the impact of urban development on agricultural land in the Pantura Jabar region, Indonesia.

The conversion of large-scale agricultural land into urban areas poses a significant challenge to achieving national and global food security targets, as outlined in Sustainable Development Goal number 2, which aims to eradicate hunger. Indonesia has experienced a significant decline in rice field areas, with a reduction of approximately 650 thousand hectares within a year (2017-2018), the largest being in Java. Hence, this study aims to examine the impact of urban expansion on agricultural land in the north coast region of West Java Province from 2013 to 2020 and develop a predictive model for 2030 to support sustainable land use planning. The primary methods employed were random forest (RF) analysis using Google Earth Engine, intensity analysis, multilayer perceptron-neural network (MLP-NN), Markov chains-cellular automata (Markov-CA), and stakeholder interviews. The model also evaluated the influence of "distance to tollgates" as a previously unexplored driving factor in existing land use modeling studies. Landsat image classification results using the RF algorithm showed 87-88% accuracy. Cropland has historically been and is projected to remain the primary target for the expansion of built-up areas. Spatial planning irregularities were found in the growth of these areas that adversely affected farmers' socioeconomic and environmental conditions. Evaluation of land use models using MLP-NN and Markov-CA demonstrated an accuracy rate of 86.29-86.23%. The distance to tollgates factor significantly impacts the models, albeit less than population density. The 2030 intervention scenario, which implements a firm policy for sustainable agricultural land use, offers the potential to maintain the predicted cropland loss compared to business as usual.

Real-time in situ monitoring of ash content in coal via dual-energy X-rays and an MLP neural network

ABSTRACT The accurate estimation and measurement of coal ash are crucial for fuel selection, combustion efficiency assessment and quality control. However, the most widely used ash measurement method is combustion; this method is highly accurate but has a certain lag; additionally, an older radiation measurement method has a certain error. In this regard, in this study, a detection model and measurement method are proposed based on the combination of dual-energy X-ray measurement results and artificial intelligence algorithms, i.e. a coal ash detection model based on a multilayer perceptron (MLP) neural network. A model training database was created, and 6468 raw data points were measured with an experimental apparatus and organized. The results showed that the root-mean-square error between the predicted value and the true value of the trained model was 0.0857. By comparing several indices with the traditional backpropagation (BP) neural network, the root-mean-square error was reduced by 1.27%, and the model’s errors in different predicted output values were uniformly distributed without evident systematic deviation; these results demonstrated and confirmed that our proposed ash prediction model achieved high estimation accuracy and had strong robustness.

Cognitive analytics enabled responsible artificial intelligence for business model innovation: A multilayer perceptron neural networks estimation

Cognitive analytics employs and analyses complex and heterogeneous data sources generating deeper insights that mimic the natural intelligence of the human brain. Cognitive analytics-enabled Artificial Intelligence (AI) that promotes Business Model Innovation (BMI) for the efficiency of the healthcare system is a nascent and undertheorized domain. Within the healthcare management systems, stakeholders’ engagement with AI, particularly with responsible AI, to optimize BMI and improve business performance is bounded by several caveats. Using the Technology Acceptance Model (TAM) and Social Network Theory (SNT) as our conceptual foci, we empirically examine through the Multilayer Perceptron Neural Network the extent to which responsible AI leads to Business Model Innovation (BMI) through the stakeholders’ engagement. Our contributions are novel which demonstrate that cognitive analytics-enabled responsible AI is central to innovation, and healthcare stakeholders exhibit a robust propensity to reorientate and innovate their existing BMI to achieve improved business performance. It has significant implications for innovation, AI and cognitive analytics literature.