Prediction of the thermo-physical properties of deep-fat frying plantain chips (ipekere) using artificial neural network

Abstract This study uses artificial neural network (ANN) to predict the thermo-physical properties of deep-fat frying plantain chips (ipekere). The frying was conducted with temperature and time ranged of 150 to 190 °C and 2 to 4 minutes using factorial design. The result revealed that specific heat was most influenced by temperature and time with the value 2.002 kJ/kg°C at 150 °C and 2.5 minutes. The density ranged from 0.997 – 1.005 kg/m3 while thermal diffusivity and conductivity were least affected with 0.192 x 10−6 m2/s and 0.332 W/m°C respectively at 190 °C and 4 minutes. The ANN architecture was developed using Levenberg–Marquardt (TRAINLM) and Feed-forward back propagation algorithm. The experimentation based on the ANN model produced a desirable prediction of the thermo-physical properties through the application of diverse amount of neutrons in the hidden layer. The predictive experimentation of the computational model with R2 ≥ 0.7901 and MSE ≤ 0.1125 does not only show the validity in anticipating the thermo-physical properties, it also indicates the capability of the model to identify a relevant association between frying time, frying temperatures and thermo-physical properties. Hence, to avoid a time consuming and expensive experimental tests, the developed model in this study is efficient in prediction of the thermo-physical properties of deep-fat frying plantain chips.

Download Full-text

Simultaneous Quantitative Analysis of Clorsulon and Ivermectin in a Veterinary Formulation by Artificial Neural Network

Revista de Chimie ◽

10.37358/rc.08.10.1988 ◽

2008 ◽

Vol 59 (10) ◽

Author(s):

Gozde Pektas ◽

Erdal Dinc ◽

Dumitru Baleanu

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Transfer Function ◽

Back Propagation ◽

Ann Model ◽

Back Propagation Algorithm ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer

Simultaneaous spectrophotometric determination of clorsulon (CLO) and invermectin (IVE) in commercial veterinary formulation was performed by using the artificial neural network (ANN) based on the back propagation algorithm. In order to find the optimal ANN model various topogical networks were tested by using different hidden layers. A logsig input layer, a hidden layer of neurons using the logsig transfer function and an output layer of two neurons with purelin transfer function was found suitable for basic configuration for ANN model. A calibration set consisting of CLO and IVE in calibration set was prepared in the concentration range of 1-23 �g/mL and 1-14 �g/mL, repectively. This calibration set contains 36 different synthetic mixtures. A prediction set was prepared in order to evaluate the recovery of the investigated approach ANN chemometric calibration was applied to the simultaneous analysis of CLO and IVE in compounds in a commercial veterinary formulation. The experimental results indicate that the proposed method is appropriate for the routine quality control of the above mentioned active compounds.

Download Full-text

Prediction of Drape Coefficient by Artificial Neural Network

Autex Research Journal ◽

10.1515/aut-2015-0045 ◽

2015 ◽

Vol 15 (4) ◽

pp. 266-274 ◽

Cited By ~ 5

Author(s):

Adel Ghith ◽

Thouraya Hamdi ◽

Faten Fayala

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Ann Model ◽

Back Propagation Algorithm ◽

Propagation Algorithm ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Hidden Layer ◽

Different Diameters

Abstract An artificial neural network (ANN) model was developed to predict the drape coefficient (DC). Hanging weight, Sample diameter and the bending rigidities in warp, weft and skew directions are selected as inputs of the ANN model. The ANN developed is a multilayer perceptron using a back-propagation algorithm with one hidden layer. The drape coefficient is measured by a Cusick drape meter. Bending rigidities in different directions were calculated according to the Cantilever method. The DC obtained results show a good correlation between the experimental and the estimated ANN values. The results prove a significant relationship between the ANN inputs and the drape coefficient. The algorithm developed can easily predict the drape coefficient of fabrics at different diameters.

Download Full-text

Prediction of Neutron Yield of IR-IECF Facility in High Voltages Using Artificial Neural Network

Journal of Engineering ◽

10.1155/2014/798160 ◽

2014 ◽

Vol 2014 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

A. Sadighzadeh ◽

A. Salehizadeh ◽

M. Mohammadzadeh ◽

F. Shama ◽

S. Setayeshi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Discharge Current ◽

Back Propagation ◽

Ann Model ◽

Back Propagation Algorithm ◽

Cathode Voltage ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Maximum Voltage

Artificial neural network (ANN) is applied to predict the number of produced neutrons from IR-IECF device in wide discharge current and voltage ranges. Experimentally, discharge current from 20 to 100 mA had been tuned by deuterium gas pressure and cathode voltage had been changed from −20 to −82 kV (maximum voltage of the used supply). The maximum neutron production rate (NPR) of 1.46 × 107 n/s had occurred when the voltage was −82 kV and the discharge current was 48 mA. The back-propagation algorithm is used for training of the proposed multilayer perceptron (MLP) neural network structure. The obtained results show that the proposed ANN model has achieved good agreement with the experimental data. Results show that NPR of 1.855 × 108 n/s can be achieved in voltage and current of 125 kV and 45 mA, respectively. This prediction shows 52% increment in maximum voltage of power supply. Also, the optimum discharge current can increase 1270% NPR.

Download Full-text

APPLICABILITY OF ARTIFICIAL NEURAL NETWORK MODEL FOR SIMULATION OF MONTHLY RUNOFF IN COMPARISON WITH SOM OTHER TRADITIONAL MODELS

Science and Technology Development Journal ◽

10.32508/stdj.v12i4.2237 ◽

2009 ◽

Vol 12 (4) ◽

pp. 94-106 ◽

Cited By ~ 1

Author(s):

Duc Van Le

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Research Trend ◽

Rainfall Runoff ◽

Ann Model ◽

Back Propagation Algorithm ◽

Runoff Forecasting ◽

Artificial Neural ◽

Artificial Neural Network Ann

Artificial Neural Network (ANN) model along with Back Propagation Algorithm (BPA) has been applied in many fields, especially in hydrology and water resources management to simulate or forecast rainfall runoff process, discharge and water level - time series, and other hydrological variables. Several researches have recently been focusing to compare the applicability of ANN model with other theory-driven and data-driven approaches. The comparison of ANN with M5 model trees for rainfall-runoff forecasting, with ARMAX models for deriving flow series, with AR models and regression models for forecasting and estimating daily river flows have been carried out. The better results that were implemented by ANN model have been concluded. So, this research trend is continued for the comparison of ANN model with Tank, Harmonic, Thomas and Fiering models in simulation of the monthly runoffs at Dong Nai river basin, Viet Nam. The results proved ANN being the best choice among these models, if suitable and enough data sources were available.

Download Full-text

Modeling of Rainfall-Runoff Correlations Using Artificial Neural Network-A Case Study of Dharoi Watershed of a Sabarmati River Basin, India

Civil Engineering Journal ◽

10.28991/cej-2017-00000074 ◽

2017 ◽

Vol 3 (2) ◽

pp. 78-87 ◽

Cited By ~ 7

Author(s):

Ajaykumar Bhagubhai Patel ◽

Geeta S. Joshi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

River Basin ◽

Annual Rainfall ◽

Rainfall Runoff ◽

Ann Model ◽

Back Propagation Algorithm ◽

Feed Forward Back Propagation ◽

Sabarmati River ◽

Artificial Neural

The use of an Artificial Neural Network (ANN) is becoming common due to its ability to analyse complex nonlinear events. An ANN has a flexible, convenient and easy mathematical structure to identify the nonlinear relationships between input and output data sets. This capability could efficiently be employed for the different hydrological models such as rainfall-runoff models, which are inherently nonlinear in nature. Artificial Neural Networks (ANN) can be used in cases where the available data is limited. The present work involves the development of an ANN model using Feed-Forward Back Propagation algorithm for establishing monthly and annual rainfall runoff correlations. The hydrologic variables used were monthly and annual rainfall and runoff for monthly and annual time period of monsoon season. The ANN model developed in this study is applied to Dharoi reservoir watersheds of Sabarmati river basin of India. The hydrologic data were available for twenty-nine years at Dharoi station at Dharoi dam project. The model results yielding into the least error is recommended for simulating the rainfall-runoff characteristics of the watersheds. The obtained results can help the water resource managers to operate the reservoir properly in the case of extreme events such as flooding and drought.

Download Full-text

Forecasting Coconut Yield: A Comparative Study between the Use of Traditional Forecasting and Feed Forward Back Propagation Artificial Neural Network

UIC Research Journal ◽

10.17158/320 ◽

2014 ◽

Vol 18 (2) ◽

Author(s):

Eric John G. Emberda ◽

Den Ryan L. Dumas ◽

Timothy Pierce M. Rentillo

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Ann Model ◽

Feed Forward ◽

Feed Forward Back Propagation ◽

Forecast Data ◽

The Mean ◽

Artificial Neural ◽

Artificial Neural Network Ann

<p>This study compared the use of Linear Regression and Feed Forward Backpropagation Artificial Neural Network (ANN) in forecasting the coconut yield and copra yield of a selected area in Davao region. Raw data were gathered from the Philippine Coconut Authority, Davao Research Center. An ANN model was created and tested repeatedly to the best combination of nodes. Accuracy of the forecast between the two methods was compared by looking at the mean square error and the standard error for variable x and y. Results showed that the use of Feed Forward Back Propagation Artificial Neural Network gives better accuracy of the forecast data.</p>

Download Full-text

COVID-19 screening: use of an artificial neural network

BMJ Supportive & Palliative Care ◽

10.1136/bmjspcare-2021-003391 ◽

2021 ◽

pp. bmjspcare-2021-003391

Author(s):

Narges Roustaei ◽

Elahe Allahyari

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Clinical Symptoms ◽

Gastrointestinal Symptoms ◽

Sociodemographic Factors ◽

Ann Model ◽

Back Propagation Algorithm ◽

Feed Forward Back Propagation ◽

Artificial Neural ◽

At Home

ObjectivesCOVID-19 is the biggest pandemic of the 21st century. The disease can be influenced by various sociodemographic factors and can manifest as clinical, pulmonary and gastrointestinal symptoms. This study used an artificial neural network (ANN) model with important sociodemographic factors as well as clinical, pulmonary and gastrointestinal symptoms to screen patients for COVID-19. Patients themselves can screen for these symptoms at home.MethodsData on all registered patients were extracted in autumn. The best ANN model was selected from different combinations of connections, some hidden layers and some neurons in each hidden layer. In this study, 70% of the data were used in the network training process and the remaining 30% were used to evaluate the function of the multilayer, feed-forward, back-propagation algorithm.ResultsThe sensitivity and specificity of the ANN model in diagnosing patients with COVID-19 were 94.5% and 17.4%. In order of priority, clinical symptoms, sociodemographic factors, pulmonary symptoms and gastrointestinal symptoms were important predictive factors for COVID-19 using the ANN model. Screening patients for COVID-19 using clinical symptoms and sociodemographic factors (80% importance) remains essential.ConclusionsHome monitoring of oxygen saturation and body temperature as well as old age and drug addiction can be helpful in self-screening symptoms of COVID-19 at home, thereby preventing unnecessary visits to medical centres and reducing burden on medical services.

Download Full-text

Rainfall Prediction Using Artificial Neural Network (ANN) for tarai Region of Uttarakhand

Current Journal of Applied Science and Technology ◽

10.9734/cjast/2019/v33i530096 ◽

2019 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Pooja Yadav ◽

Atish Sagar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Back Propagation ◽

Back Propagation Algorithm ◽

Feed Forward ◽

Rainfall Prediction ◽

Feed Forward Back Propagation ◽

Propagation Algorithm ◽

Term Prediction ◽

Artificial Neural

Rainfall prediction is clearly of great importance for any country. One would like to make long term prediction, i.e. predict total monsoon rainfall a few weeks or months and in advance short term prediction, i.e. predict rainfall over different locations a few days in advance [1]. Predicted by using its correlation with observed parameter. Several regression and neural network based models are currently available. While Artificial Neural Network provide a great deal of promise, they also embody much uncertainty [2,3]. In this paper, different artificial neural network models have been created for the rainfall prediction of Uttarakhand region in India. These ANN models were created using training algorithms namely, feed-forward back propagation algorithm [4,5]. The number of neurons for all the models was kept at 10. The mean squared error was measured for each model and the best accuracy was obtained by the feed-forward back propagation algorithm with MSE value as low as 0.00547823.

Download Full-text

Optimization and Prediction of Daily Methane Production for Food Waste By using The Artificial Neural Network (ANN)

Journal of University of Shanghai for Science and Technology ◽

10.51201/jusst/21/07334 ◽

2021 ◽

Vol 23 (07) ◽

pp. 1365-1376

Author(s):

Fareed A.Radhi ◽

◽

Fawziea M. Hussien ◽

Johain J. Faraj ◽

◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Methane Production ◽

Biogas Production ◽

Back Propagation ◽

Cow Dung ◽

Back Propagation Algorithm ◽

Methane Generation ◽

Feed Forward Back Propagation ◽

Artificial Neural

This study aimed to know how a neural network model can be used to predict the behaviour of particular methane production using a three-layer (3:12:1) feed forward back-propagation algorithm and the logsig-purelin transfer function to maximize maximum methane prediction. The artificial neural network performs admirably compared to the daily methane generation of (vegetable waste 5%, lipids 12.5%, meat residues 12.5%, cow dung 10% and water 60%) at various temperatures (19–30 °C) for 64 days. Among the three input variables (feedstock, temperature, and time), feedstock has the strongest correlation with the particular methane production value. Thus, the research validates the ANN model’s ability to anticipate the biogas production curve’s behaviour and forecast the optimal substrate temperature for maximum biogas production. The regression value(R) for the digester was 0.95769. The capability of ANN modelling acts as a preliminary draft and significantly lowers the time required for methane generation in-line control. The daily methane production was predicted at three temperatures (20, 25, and 30 oC) for 75 days, and it found out the best temperature reaction was 30 oC.

Download Full-text

Using Artificial Neural Networks in Predicting the Level of Stress among Military Conscripts

Mathematics ◽

10.3390/math9060626 ◽

2021 ◽

Vol 9 (6) ◽

pp. 626

Author(s):

Svajone Bekesiene ◽

Rasa Smaliukiene ◽

Ramute Vaicaitiene

Keyword(s):

Neural Network ◽

Transfer Functions ◽

Back Propagation ◽

Armed Forces ◽

Activation Function ◽

Optimal Number ◽

Ann Model ◽

Back Propagation Algorithm ◽

Classification Rate ◽

Artificial Neural

The present study aims to elucidate the main variables that increase the level of stress at the beginning of military conscription service using an artificial neural network (ANN)-based prediction model. Random sample data were obtained from one battalion of the Lithuanian Armed Forces, and a survey was conducted to generate data for the training and testing of the ANN models. Using nonlinearity in stress research, numerous ANN structures were constructed and verified to limit the optimal number of neurons, hidden layers, and transfer functions. The highest accuracy was obtained by the multilayer perceptron neural network (MLPNN) with a 6-2-2 partition. A standardized rescaling method was used for covariates. For the activation function, the hyperbolic tangent was used with 20 units in one hidden layer as well as the back-propagation algorithm. The best ANN model was determined as the model that showed the smallest cross-entropy error, the correct classification rate, and the area under the ROC curve. These findings show, with high precision, that cohesion in a team and adaptation to military routines are two critical elements that have the greatest impact on the stress level of conscripts.

Download Full-text