scholarly journals Neural Network-Based Model Reduction of Hydrodynamics Forces on an Airfoil

Fluids ◽  
2021 ◽  
Vol 6 (9) ◽  
pp. 332
Author(s):  
Hamayun Farooq ◽  
Ahmad Saeed ◽  
Imran Akhtar ◽  
Zafar Bangash
Keyword(s):  
Neural Network ◽  
Flow Field ◽  
Surface Pressure ◽  
Numerical Data ◽  
Sample Data ◽  
Out Of Sample ◽  
Temporal Pressure ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Lift And Drag

In this paper, an artificial neural network (ANN)-based reduced order model (ROM) is developed for the hydrodynamics forces on an airfoil immersed in the flow field at different angles of attack. The proper orthogonal decomposition (POD) of the flow field data is employed to obtain pressure modes and the temporal coefficients. These temporal pressure coefficients are used to train the ANN using data from three different angles of attack. The trained network then takes the value of angle of attack (AOA) and past POD coefficients as an input and predicts the future temporal coefficients. We also decompose the surface pressure modes into lift and drag components. These surface pressure modes are then employed to calculate the pressure component of lift CLp and drag CDp coefficients. The train model is then tested on the in-sample data and out-of-sample data. The results show good agreement with the true numerical data, thus validating the neural network based model.

scholarly journals SEIRDQ: A COVID-19 case projection modeling framework using ANN to model quarantine

2021 ◽  
Author(s):  
Harish Chandra ◽  
Xianwei Meng ◽  
Arman Margaryan
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Sources ◽  
Modeling Framework ◽  
Novel Approach ◽  
Out Of Sample ◽  
Artificial Neural ◽  
Model Versus ◽  
Artificial Neural Network Ann ◽  
The Impact

We propose and implement a novel approach to model the evolution of COVID-19 pandemic and predict the daily COVID-19 cases (infected, recovered and dead). Our model builds on the classical SEIR-based framework by adding additional compartments to capture recovered, dead and quarantined cases. Quarantine impacts are modeled using an Artificial Neural Network (ANN), leveraging alternative data sources such as the Google mobility reports. Since our model captures the impact of lockdown policies through the quarantine functions we designed, it is able to model and predict future waves of COVID-19 cases. We also benchmark out-of-sample predictions from our model versus those from other popular COVID-19 case projection models.

scholarly journals Data Mining using Artificial Neural Network POS_NEG Rules

2013 ◽  
Vol 11 (6) ◽  
pp. 2709-2714
Author(s):  
Pushkar Shinde ◽  
Dr. Varsha Patil
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Sensitivity Analysis ◽  
Artificial Neural Network ◽  
Backpropagation Algorithm ◽  
The Neural Network ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Sensitivity Method

Diabetes patients are increasing in number so it is necessary to predict , treat and diagnose the disease. Data Mining can help to provide knowledge about this disease. The knowledge extracted using Data Mining can help in treating and preventing the disease. Artificial Neural Network (ANN) can be used to create an classifier from the data. The neural network is trained using backpropagation algorithm The knowledge stored in the neural network is used to predict the disease. The knowledge stored in neural network is extracted using Pos-Neg sensitivity method. The knowledge extracted is in form of sensitivity analysis to analyze the disease and in turn help in treating the disease.

Using a GMDH-type neural network and ARIMA model to forecasting GDP in Algeria during the period of 1990-2019

2020 ◽  
Vol 3 (4) ◽  
pp. 37-47
Author(s):  
Abdelkader Sahed
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Arima Model ◽  
Time Series Forecasting ◽  
Group Method ◽  
Data Handling ◽  
Performance Indices ◽  
Promising Technique ◽  
Using Data ◽  
Artificial Neural Network Ann

Forecasting is a method to predict the future using data and the last information as a tool assists in planning to be effective. GMDH-Type (Group Method of Data Handling) artificial neural network (ANN) and Box-Jenkins method are among the know methods for time series forecasting of mathematical modeling. in the present study  GMDH-type neural network and ARIMA method has been used to forecasted GDP in Algeria during the period 1990 to2019 (Time series of quarterly observations on Gross Domestic Product (GDP) is used). Root mean square error (RMSE) was used as performance indices to test the accuracy of the forecast. The empirical results for both models showed that the GMDH model is a powerful tool in forecasting GDP and it provides a promising technique in time series forecasting methods.

River flow forecasting and estimation using different artificial neural network techniques

2008 ◽  
Vol 39 (1) ◽  
pp. 27-40 ◽  
Author(s):  
Özgür Kişi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
River Flow ◽  
Streamflow Forecasting ◽  
Black Sea Region ◽  
Generalized Regression Neural Networks ◽  
Artificial Neural ◽  
Using Data ◽  
Artificial Neural Network Ann

This paper demonstrates the application of different artificial neural network (ANN) techniques for the estimation of monthly streamflows. In the first part of the study, three different ANN techniques, namely, feed forward neural networks (FFNN), generalized regression neural networks (GRNN) and radial basis ANN (RBF) are used in one-month ahead streamflow forecasting and the results are evaluated. Monthly flow data from two stations, Gerdelli Station on Canakdere River and Isakoy Station on Goksudere River, in the Eastern Black Sea region of Turkey are used in the study. Based on the results, the GRNN was found to be better than the other ANN techniques in monthly flow forecasting. The effect of periodicity on the model's forecasting performance was also investigated. In the second part of the study, the performance of the ANN techniques was tested for river flow estimation using data from the nearby river.

scholarly journals Predictive Ability of Improved Neural Network Models to Simulate Pollutant Dispersion

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Khandaker M. A. Hossain
Keyword(s):  
Neural Network ◽  
Predictive Ability ◽  
Network Models ◽  
Pollutant Dispersion ◽  
Neural Network Models ◽  
Coastal Cities ◽  
Ann Models ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Urban Atmospheres

This paper describes the ability of artificial neural network (ANN) models to simulate the pollutant dispersion characteristics in varying urban atmospheres at different regions. ANN models are developed based on twelve meteorological (including rainfall/precipitation) and six traffic parameters/variables that have significant influence on emission/pollutant dispersion. The models are trained to predict concentration of carbon monoxide and particulate matters in urban atmospheres using field meteorological and traffic data. Training, validation, and testing of ANN models are conducted using data from the Dhaka city of Bangladesh. The models are used to simulate concentration of pollutants as well as the effect of rainfall on emission dispersion throughout the year and inversion condition during the night. The predicting ability and robustness of the models are then determined by using data of the coastal cities of Chittagong and Dhaka. ANN models based on both meteorological and traffic variables exhibit the best performance and are capable of resolving patterns of pollutant dispersion to the atmosphere for different cities.

scholarly journals An Autoencoder and Artificial Neural Network-based Method to Estimate Parity Status of Wild Mosquitoes from Near-infrared Spectra

2020 ◽  
Author(s):  
Masabho P. Milali ◽  
Samson S. Kiware ◽  
Nicodem J. Govella ◽  
Fredros Okumu ◽  
Naveen Bansal ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Burkina Faso ◽  
Near Infrared ◽  
Animal Blood ◽  
Near Infrared Spectra ◽  
Out Of Sample ◽  
Ann Models ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractBackgroundAfter mating, female mosquitoes need animal blood to develop their eggs. In the process of acquiring blood, they may acquire pathogens, which may cause different diseases to humans such as malaria, zika, dengue, and chikungunya. Therefore, knowing the parity status of mosquitoes is useful in control and evaluation of infectious diseases transmitted by mosquitoes, where parous mosquitoes are assumed to be potentially infectious. Ovary dissections, which currently are used to determine the parity status of mosquitoes, are very tedious and limited to very few experts. An alternative to ovary dissections is near-infrared spectroscopy (NIRS), which can estimate the age in days and the infectious state of laboratory and semi-field reared mosquitoes with accuracies between 80 and 99%. No study has tested the accuracy of NIRS for estimating the parity status of wild mosquitoes.Methods and resultsIn this study, we train artificial neural network (ANN) models on NIR spectra to estimate the parity status of wild mosquitoes. We use four different datasets: An. arabiensis collected from Minepa, Tanzania (Minepa-ARA); An. gambiae collected from Muleba, Tanzania (Muleba-GA); An. gambiae collected from Burkina Faso (Burkina-GA); and An.gambiae from Muleba and Burkina Faso combined (Muleba-Burkina-GA). We train ANN models on datasets with spectra preprocessed according to previous protocols. We then use autoencoders to reduce the spectra feature dimensions from 1851 to 10 and re-train ANN models. Before the autoencoder was applied, ANN models estimated parity status of mosquitoes in Minepa-ARA, Muleba-GA, Burkina-GA and Muleba-Burkina-GA with out-of-sample accuracies of 81.9 ± 2.8% (N=927), 68.7 ± 4.8% (N=140), 80.3 ± 2.0% (N=158), and 75.7 ± 2.5% (N=298), respectively. With the autoencoder, ANN models tested on out-of-sample data achieved 97.1 ± 2.2%, (N=927), 89.8 ± 1.7% (N=140), 93.3 ± 1.2% (N=158), and 92.7 ± 1.8% (N=298) accuracies for Minepa-ARA, Muleba-GA, Burkina-GA, and Muleba-Burkina-GA, respectively.ConclusionThese results show that a combination of an autoencoder and an ANN trained on NIR spectra to estimate parity status of wild mosquitoes yields models that can be used as an alternative tool to estimate parity status of wild mosquitoes, especially since NIRS is a high-throughput, reagent-free, and simple-to-use technique compared to ovary dissections.

scholarly journals MODELING OF SOLAR RADIATION WITH A NEURAL NETWORK

2018 ◽  
Vol 24 (3) ◽  
Author(s):  
VALENTIN STOYANOV ◽  
IVAYLO STOYANOV ◽  
TEODOR ILIEV
Keyword(s):  
Neural Network ◽  
Solar Radiation ◽  
Training Data ◽  
Training Algorithms ◽  
Data Set ◽  
Tilted Surface ◽  
Using Data ◽  
Artificial Neural Network Ann ◽  
Hidden Neurons

<p>Modeling of solar radiation with neural network could be used for real-time calculations of the radiation on tilted surfaces with different orientations. In the artificial neural network (ANN), latitude, day of the year, slope, surface azimuth and average daily radiation on horizontal surface are inputs, and average daily radiation on tilted surface of definite orientation is output. The possible ANN structure, the size of training data set, the number of hidden neurons, and the type of training algorithms were analyzed in order to identify the most appropriate model. The same ANN structure was trained and tested using data generated from the Klein and Theilacker model and long-term measurements. Reasonable accuracy was obtained for all predictions for practical need.</p>

scholarly journals Use of ANN in Predicting Life Expectancy: The Case of Turkey

2018 ◽  
Vol 1 (1) ◽  
pp. 1-7
Author(s):  
Ayhan AYDIN ◽  
Ümit ATİLA ◽  
Serpil AYDIN
Keyword(s):  
Social Sciences ◽  
Neural Network ◽  
Time Series ◽  
Artificial Neural Network ◽  
Life Expectancy ◽  
Social Economic ◽  
Time Series Models ◽  
Negative Effects ◽  
Using Data ◽  
Artificial Neural Network Ann

Life expectancy is a good measure for comparing parameters such as welfare, health level and development of countries. The high value for this indicator can only be achieved by identifying the positive and negative effects of these determinants and by making initiatives in this direction. In our study, we observed life expectancy estimation by using time series models and Artificial Neural Network (ANN) in terms of social, economic and environmental factors, that affect the life expectancy. Comparison of two estimator models performed using data compiled from OECD and WORLDBANK of Turkey between 1960-2016. In the application performed on social sciences data, meaningful indicators were interpreted together with the success of the ANN method. As a result of the study, a number of suggestions and development recommendations are presented in order to increase the life expectancy from birth, which is a decisive criterion for the country's level of prosperity, in a positive way.

scholarly journals Detection of Suicidality Among Opioid Users on Reddit: Machine Learning–Based Approach

10.2196/15293 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e15293
Author(s):  
Hannah Yao ◽  
Sina Rashidian ◽  
Xinyu Dong ◽  
Hongyi Duanmu ◽  
Richard N Rosenthal ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Suicidal Ideation ◽  
Suicide Ideation ◽  
Opioid Abuse ◽  
Amazon Mechanical Turk ◽  
Opioid Use ◽  
Sample Data ◽  
Out Of Sample ◽  
Opioid Users

Background In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult to classify as intentional or unintentional. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas that push individuals away from seeking help. These individuals may instead use web-based means to articulate their concerns. Objective This study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. The performance of the models is derivative of the data purity, and the results will help us to better understand the rationale of these users, providing new insights into individuals who are part of the opioid epidemic. Methods Reddit posts between June 2017 and June 2018 were collected from r/suicidewatch, r/depression, a set of opioid-related subreddits, and a control subreddit set. We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Amazon Mechanical Turk was used to provide labels for the out-of-sample data. Results Classification results were at least 90% across all models for at least one combination of input; the best classifier was convolutional neural network, which obtained an F1 score of 96.6%. When predicting out-of-sample data for posts containing both suicidal ideation and signs of opioid addiction, NN classifiers produced more false positives and traditional methods produced more false negatives, which is less desirable for predicting suicidal sentiments. Conclusions Opioid abuse is linked to the risk of unintentional overdose and suicide risk. Social media platforms such as Reddit contain metadata that can aid machine learning and provide information at a personal level that cannot be obtained elsewhere. We demonstrate that it is possible to use NNs as a tool to predict an out-of-sample target with a model built from data sets labeled by characteristics we wish to distinguish in the out-of-sample target.

The Effect of Hyper-Parameters on the Performance of Third Order Neural Network Algorithms on Medical Classification Data

2021 ◽  
Vol 02 (01) ◽  
Author(s):  
Nazri Mohd Nawi ◽  
◽  
Prihastuti Harsani ◽  
Eneng Tita Tosida ◽  
Khairina Mohamad Roslan ◽  
...  
Keyword(s):  
Neural Network ◽  
Back Propagation ◽  
Numerical Data ◽  
Bp Algorithm ◽  
Back Propagation Algorithm ◽  
Classification Problems ◽  
Third Order ◽  
Learning Speed ◽  
Medical Classification ◽  
Artificial Neural Network Ann

The artificial neural network (ANN) particularly back propagation (BP) algorithm has recently been applied in many areas. It is known that BP is an excellent classifier for nonlinear input and output numerical data. However, the popularity of BP comes with some drawbacks such as slow in learning and easily getting stuck in local minima. Improving training efficiency of BP algorithm is an active area of research and numerous papers have been reviewed in the literature. Furthermore, the performance of BP algorithm also highly influenced by the size of the datasets and the data preprocessing techniques that been chosen. This paper presents an improvement of BP by adjusting the two term parameters on the performance of third order neural network methods. This work also demonstrates the advantages of using preprocessing dataset in order to improve the BP convergence. The efficiency of the proposed method is verified by means of simulation on medical classification problems. The results show that the proposed implementation significantly improves the learning speed of the general back-propagation algorithm.

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