scholarly journals The Use of Artificial Neural Networks for Forecasting of Air Temperature inside a Heated Foil Tunnel

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 652 ◽  
Author(s):  
Sławomir Francik ◽  
Sławomir Kurpaska
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Mean Square ◽  
Internal Temperature ◽  
Data Set ◽  
External Temperature ◽  
Factors Influencing ◽  
Testing Data ◽  
Artificial Neural ◽  
Hidden Layer

It is important to correctly predict the microclimate of a greenhouse for control and crop management purposes. Accurately forecasting temperatures in greenhouses has been a focus of research because internal temperature is one of the most important factors influencing crop growth. Artificial Neural Networks (ANNs) are a powerful tool for making forecasts. The purpose of our research was elaboration of a model that would allow to forecast changes in temperatures inside the heated foil tunnel using ANNs. Experimental research has been carried out in a heated foil tunnel situated on the property of the Agricultural University of Krakow. Obtained results have served as data for ANNs. Conducted research confirmed the usefulness of ANNs as tools for making internal temperature forecasts. From all tested networks, the best is the three-layer Perceptron type network with 10 neurons in the hidden layer. This network has 40 inputs and one output (the forecasted internal temperature). As the networks input previous historical internal temperature, external temperature, sun radiation intensity, wind speed and the hour of making a forecast were used. These ANNs had the lowest Root Mean Square Error (RMSE) value for the testing data set (RMSE value = 3.7 °C).

scholarly journals IMPLEMENTASI JARINGAN SYARAF TIRUAN BACKPROPAGATION DENGAN ALGORITMA CONJUGATE GRADIENT UNTUK KLASIFIKASI KONDISI RUMAH (Studi Kasus di Kabupaten Cilacap Tahun 2018)

2020 ◽  
Vol 9 (1) ◽  
pp. 41-49
Author(s):  
Johanes Roisa Prabowo ◽  
Rukun Santoso ◽  
Hasbi Yasin
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Conjugate Gradient ◽  
Training Data ◽  
Gradient Algorithm ◽  
Output Layer ◽  
Average Accuracy ◽  
Testing Data ◽  
Artificial Neural ◽  
Hidden Layer

House is one aspect of the welfare of society that must be met, because house is the main need for human life besides clothing and food. The condition of the house as a good shelter can be known from the structure and facilities of buildings. This research aims to analyze the classification of house conditions is livable or not livable. The method used is artificial neural networks (ANN). ANN is a system information processing that has characteristics similar to biological neural networks. In this research the optimization method used is the conjugate gradient algorithm. The data used are data of Survei Sosial Ekonomi Nasional (Susenas) March 2018 Kor Keterangan Perumahan for Cilacap Regency. The data is divided into training data and testing data with the proportion that gives the highest average accuracy is 90% for training data and 10% for testing data. The best architecture obtained a model consisting of 8 neurons in input layer, 10 neurons in hidden layer and 1 neuron in output layer. The activation function used are bipolar sigmoid in the hidden layer and binary sigmoid in the output layer. The results of the analysis showed that ANN works very well for classification on house conditions in Cilacap Regency with an average accuracy of 98.96% at the training stage and 97.58% at the testing stage.Keywords: House, Classification, Artificial Neural Networks, Conjugate Gradient

scholarly journals Comparison of Artificial Neural Network and Box- Jenkins Models to Predict the Number of Patients with Hypertension in Kalar

2020 ◽  
Vol 33 (4) ◽  
pp. 110
Author(s):  
Layla A. Ahmed
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Percentage Error ◽  
Mean Square ◽  
Data Set ◽  
Intelligent Technique ◽  
Number Of Patients ◽  
Artificial Neural

    Artificial Neural Network (ANN) is widely used in many complex applications. Artificial neural network is a statistical intelligent technique resembling the characteristic of the human neural network.  The prediction of time series from the important topics in statistical sciences to assist administrations in the planning and make the accurate decisions, so the aim of this study is to analysis the monthly hypertension in Kalar for the period (January 2011- June 2018) by applying an autoregressive –integrated- moving average model  and artificial neural networks and choose the best and most efficient model for patients with hypertension in Kalar through the comparison between neural networks and Box- Jenkins models on a data set for predict. Comparisons between the models has been performed using Criterion indicator Akaike information Criterion, mean square of error,  root mean square of error, and mean absolute percentage error, concluding that the prediction for patients with hypertension by using artificial neural networks model is the best.

scholarly journals Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study

2021 ◽  
Vol 11 (15) ◽  
pp. 6723
Author(s):  
Ariana Raluca Hategan ◽  
Romulus Puscas ◽  
Gabriela Cristea ◽  
Adriana Dehelean ◽  
Francois Guyon ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Training Sample ◽  
Training Data ◽  
Elemental Content ◽  
Neural Structure ◽  
Food Authentication ◽  
Data Set ◽  
Artificial Neural

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model.

Optimization Artificial Neural Network Using Artificial Bee Colony in Letter Recognition Classification

2020 ◽  
Vol 8 (4) ◽  
pp. 469
Author(s):  
I Gusti Ngurah Alit Indrawan ◽  
I Made Widiartha
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Classification Accuracy ◽  
Artificial Bee Colony Algorithm ◽  
Artificial Bee Colony ◽  
Letter Recognition ◽  
Bee Colony ◽  
Artificial Neural ◽  
Hidden Layer

Artificial Neural Networks or commonly abbreviated as ANN is one branch of science from the field of artificial intelligence which is often used to solve various problems in fields that involve grouping and pattern recognition. This research aims to classify Letter Recognition datasets using Artificial Neural Networks which are weighted optimally using the Artificial Bee Colony algorithm. The best classification accuracy results from this study were 92.85% using a combination of 4 hidden layers with each hidden layer containing 10 neurons.

Predicting Students Grades Using Artificial Neural Networks and Support Vector Machine

2019 ◽  
pp. 751-766
Author(s):  
Sajid Umair ◽  
Muhammad Majid Sharif
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Artificial Neural Networks ◽  
Student Performance ◽  
Semantic Analysis ◽  
Vital Role ◽  
Support Vector ◽  
Data Set ◽  
Important Research Topic ◽  
Artificial Neural

Prediction of student performance on the basis of habits has been a very important research topic in academics. Studies show that selection of the correct data set also plays a vital role in these predictions. In this chapter, the authors took data from different schools that contains student habits and their comments, analyzed it using latent semantic analysis to get semantics, and then used support vector machine to classify the data into two classes, important for prediction and not important. Finally, they used artificial neural networks to predict the grades of students. Regression was also used to predict data coming from support vector machine, while giving only the important data for prediction.

scholarly journals Artificial Neural Networks in Drug Design 11: Influence of Learning Rate and Momentum Factor on the Predictive Ability

2000 ◽  
Vol 68 (1) ◽  
pp. 57-64 ◽  
Author(s):  
D. Kaiser ◽  
C. Tmej ◽  
P. Chiba ◽  
K.-J. Schaper ◽  
G. Ecker
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Power ◽  
Predictive Ability ◽  
Learning Rate ◽  
Data Set ◽  
Lead Compounds ◽  
Learning Rates ◽  
Artificial Neural ◽  
Nanomolar Range

A data set of 48 propafenone-type modulators of multidrug resistance was used to investigate the influence of learning rate and momentum factor on the predictive power of artificial neural networks of different architecture. Generally, small learning rates and medium sized momentum factors are preferred. Some of the networks showed higher cross validated Q2 values than the corresponding linear model (0.87 vs. 0.83). Screening of a 158 compound virtual library identified several new lead compounds with activities in the nanomolar range.

scholarly journals Application of Artificial Neural Networks for Accurate Prediction of Thermal and Rheological Properties of Nanofluids

2020 ◽  
Author(s):  
Behzad Vaferi
Keyword(s):  
Heat Transfer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Thermophysical Properties ◽  
Convective Heat Transfer Coefficient ◽  
Industrial Applications ◽  
Mean Square ◽  
Ann Models ◽  
Artificial Neural ◽  
Mean Square Errors

Nanofluids have recently been considered as one of the most popular working fluid in heat transfer and fluid mechanics. Accurate estimation of thermophysical properties of nanofluids is required for the investigation of their heat transfer performance. Thermal conductivity coefficient, convective heat transfer coefficient, and viscosity are some the most important thermophysical properties that directly influence on the application of nanofluids. The aim of the present chapter is to develop and validate artificial neural networks (ANNs) to estimate these thermophysical properties with acceptable accuracy. Some simple and easy measurable parameters including type of nanoparticle and base fluid, temperature and pressure, size and concentration of nanoparticles, etc. are used as independent variables of the ANN approaches. The predictive performance of the developed ANN approaches is validated with both experimental data and available empirical correlations. Various statistical indices including mean square errors (MSE), root mean square errors (RMSE), average absolute relative deviation percent (AARD%), and regression coefficient (R2) are used for numerical evaluation of accuracy of the developed ANN models. Results confirm that the developed ANN models can be regarded as a practical tool for studying the behavior of those industrial applications, which have nanofluids as operating fluid.

scholarly journals Prediction of Horizontal Daily Global Solar Irradiation Using Artificial Neural Networks (ANNs) in the Castile and León Region, Spain

Agronomy ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 96
Author(s):  
Francisco J. Diez ◽  
Luis M. Navas-Gracia ◽  
Leticia Chico-Santamarta ◽  
Adriana Correa-Guimaraes ◽  
Andrés Martínez-Rodríguez
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Predictive Modelling ◽  
Solar Irradiation ◽  
Data Series ◽  
Markov Analysis ◽  
Global Solar Irradiation ◽  
Ann Models ◽  
Artificial Neural ◽  
Hidden Layer

This article evaluates horizontal daily global solar irradiation predictive modelling using artificial neural networks (ANNs) for its application in agricultural sciences and technologies. An eight year data series (i.e., training networks period between 2004–2010, with 2011 as the validation year) was measured at an agrometeorological station located in Castile and León, Spain, owned by the irrigation advisory system SIAR. ANN models were designed and evaluated with different neuron numbers in the input and hidden layers. The only neuron used in the outlet layer was the global solar irradiation simulated the day after. Evaluated values of the input data were the horizontal daily global irradiation of the current day [H(t)] and two days before [H(t−1), H(t−2)], the day of the year [J(t)], and the daily clearness index [Kt(t)]. Validated results showed that best adjustment models are the ANN 7 model (RMSE = 3.76 MJ/(m2·d), with two inputs ([H(t), Kt(t)]) and four neurons in the hidden layer) and the ANN 4 model (RMSE = 3.75 MJ/(m2·d), with two inputs ([H(t), J(t)]) and two neurons in the hidden layer). Thus, the studied ANN models had better results compared to classic methods (CENSOLAR typical year, weighted moving mean, linear regression, Fourier and Markov analysis) and are practically easier as they need less input variables.

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