Artificial Neural Network Modeling To Predict And Optimize Phenolic Acids Production From Callus Culture of Lactuca Undulata

Abstract The present study aims to model and optimize phenolic acids productions from Lactuca undulate root and leaf-derived callus using the feed-forward Artificial Neural Network (ANN) model. For this purpose, the effect of different concentrations (0, 0.1, 0.5, 1, and 2 mg/l) of Kin in combinations with or without 2,4-D and/or NAA was investigated on callus induction and phenolic acids production. A multi-layer perceptron ANN was applied to correlate the output parameters (cichoric acid, chlorogenic acid and caffeic acid contents) to input (Kin, 2,4-D and NAA) training parameters. A single hidden layer with 5, 10, 15, 20, 25, 30, 35 and 40 neurons was used to optimize ANN architecture. Sum squared error (SSE), Relative Error (RE) and correlation factor (R2) were applied to identify the performance of ANN models. According to the obtained data, the feed-forward neural network with tangent-sigmoid (3-30-1), tangent-tangent (3-15-1) and tangent-tangent (3-35-1) activation function was found as the best model to predict cichoric acid, chlorogenic acid and caffeic acid production from leaf-derived callus, respectively. Meanwhile, ANN with activation function of tangent-tangent (3-20-1), tangent-tangent (3-25-1) and sigmoid-sigmoid (3-20-1) were the most effective models to predict the amount of cichoric acid, chlorogenic acid and caffeic acid from root-derived callus, respectively. In the current study, there was a strong correlation between experimental and predicted data. These results demonstrated that the selected ANN model could predict the effects of plant growth regulators on phenolic acids production using callus culture method.

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Power Forecasting from Solar Panels Using Artificial Neural Network in UTHM Parit Raja

Journal of Advanced Industrial Technology and Application ◽

10.30880/jaita.2021.02.01.003 ◽

2021 ◽

Vol 02 (01) ◽

Author(s):

Natasha Munirah Mohd Fahmi ◽

◽

Nor Aira Zambri ◽

Norhafiz Salim ◽

Sim Sy Yi ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Climatic Factors ◽

Activation Function ◽

Solar Panels ◽

Ann Model ◽

Power Characteristics ◽

Step Procedure ◽

Artificial Neural ◽

Artificial Neural Network Ann

This paper presents a step-by-step procedure for the simulation of photovoltaic modules with numerical values, using MALTAB/Simulink software. The proposed model is developed based on the mathematical model of PV module, which based on PV solar cell employing one-diode equivalent circuit. The output current and power characteristics curves highly depend on some climatic factors such as radiation and temperature, are obtained by simulation of the selected module. The collected data are used in developing Artificial Neural Network (ANN) model. Multilayer Perceptron (MLP) and Radial Basis Function (RBF) are the techniques used to forecast the outputs of the PV. Various types of activation function will be applied such as Linear, Logistic Sigmoid, Hyperbolic Tangent Sigmoid and Gaussian. The simulation results show that the Logistic Sigmoid is the best technique which produce minimal root mean square error for the system.

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Estimation of Moisture Ratio of a Mushroom Undergoing Microwave-vacuum Drying Using Artificial Neural Network and Regression Models

Chemical Product and Process Modeling ◽

10.2202/1934-2659.1057 ◽

2007 ◽

Vol 2 (3) ◽

Cited By ~ 3

Author(s):

Poonpat Poonnoy ◽

Ampawan Tansakul ◽

Manjeet Chinnan

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Moisture Ratio ◽

Vacuum Pressure ◽

Ann Model ◽

Drying Time ◽

Feed Forward ◽

Marquardt Algorithm ◽

Ann Models ◽

Artificial Neural

The drying rate of a mushroom undergoing microwave-vacuum (MV) drying (MVD) was controlled by moisture dissipation and was dependent on vacuum pressure levels. The main objective of this work was to develop artificial neural network (ANN) model to predict moisture ratio of MV-dried mushrooms. One-hidden-layer feed-forward ANN models were trained and validated with experimental data. The Levenberg-Marquardt algorithm was utilized in regulating the ANN model weights and biases. Inputs for ANN models were vacuum pressure and drying time. Output from ANN models was moisture ratio at a given drying time. Reduced chi-square (X 2) and root mean square error (RMSE), and residual sum of squares (RSS) of the results from ANN models were calculated and compared with those of a modified Page's model (an experimental-based mathematical model), which is commonly used in the literature. The X 2, RMSE, and RSS of the ANN model (2.272 x 10 -5, 4.023 x 10 -3, and 3.204 x 10 -3, respectively) were found to be lower than those of the modified Page's model (6.692 x 10 -4, 2.561 x 10 -2, and 12.98 x 10 -2, respectively). These results indicate that the feed-forward ANN model represented the drying characteristics of mushrooms better than the modified Page's model. Therefore, the ANN model could be considered as a better tool for estimation of the moisture content of mushrooms than by the modified Page's model.

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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>

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Artificial Neural Network Modelling of Green Synthesised Silver Nanoparticles in Bentonite/Starch Bio-Nanocomposite

Current Nanoscience ◽

10.2174/1573413713666171103103141 ◽

2018 ◽

Vol 14 (3) ◽

pp. 239-251 ◽

Cited By ~ 4

Author(s):

Anupama Thapliyal ◽

Roop Krishen Khar ◽

Amrish Chandra

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Silver Nanoparticles ◽

Mean Square Error ◽

Reduction Method ◽

Mean Square ◽

Ann Model ◽

Ag Nps ◽

Feed Forward ◽

Artificial Neural

Background: In this study, computational Artificial Neural Network (ANN) model is applied for optimisation and evaluation of silver nanoparticles (AgNPs) size in the bionanocomposite matrix. The primary purpose of this study is used a feed-forward ANN model to create a connection between the output as the size of Ag–NPs, with four inputs variables, including AgNO3 concentration, the weight percentage of starch, Bentonite amount and Gallic acid concentration. Method: Silver nanoparticles were synthesised via biogenic green reduction method. The fast Levenberg– Marquardt (LM) backpropagation algorithm applied for the training of ANN model in this research. The optimised ANN is a multilayer perceptron (MLP) which is a kind of feed forward (4- 10-1) network has an input layer with 4 nodes, hidden layers with 10 neurones, and an output layer with 1 node found a fitness function. Results: The output results of developed computational ANN model were compared to its predictive values of the size of silver nanoparticles regarding two statistical parameters, the coefficient of determination (R2) and mean square error (MSE) of data set. It observed that ANN predicted values are close to the actual values and well fitted to the data. The mean square error(MSE) is 0.03, and a regression is about 1. Conclusion: AgNO3 concentration has the most likely factor affecting the size of silver nanoparticles (Ag–NPs) and this makes possible to develop a green reduction method for the preparation of silver nanoparticles. This study confirms that employing ANN method with LM feed forward (4-10-1) network is a useful tool with cost-effective for predicting the results of analysis and modelling of the chemical reactions.

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Estimation of unsteady hydromagnetic Williamson fluid flow in a radiative surface through numerical and artificial neural network modeling

Scientific Reports ◽

10.1038/s41598-021-93790-9 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Anum Shafiq ◽

Andaç Batur Çolak ◽

Tabassum Naz Sindhu ◽

Qasem M. Al-Mdallal ◽

T. Abdeljawad

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Heat Generation ◽

Back Propagation ◽

Neural Network Modeling ◽

Multi Layer Perceptron ◽

Ann Model ◽

Feed Forward ◽

Williamson Fluid ◽

Artificial Neural

AbstractIn current investigation, a novel implementation of intelligent numerical computing solver based on multi-layer perceptron (MLP) feed-forward back-propagation artificial neural networks (ANN) with the Levenberg–Marquard algorithm is provided to interpret heat generation/absorption and radiation phenomenon in unsteady electrically conducting Williamson liquid flow along porous stretching surface. Heat phenomenon is investigated by taking convective boundary condition along with both velocity and thermal slip phenomena. The original nonlinear coupled PDEs representing the fluidic model are transformed to an analogous nonlinear ODEs system via incorporating appropriate transformations. A data set for proposed MLP-ANN is generated for various scenarios of fluidic model by variation of involved pertinent parameters via Galerkin weighted residual method (GWRM). In order to predict the (MLP) values, a multi-layer perceptron (MLP) artificial neural network (ANN) has been developed. There are 10 neurons in hidden layer of feed forward (FF) back propagation (BP) network model. The predictive performance of ANN model has been analyzed by comparing the results obtained from the ANN model using Levenberg-Marquard algorithm as the training algorithm with the target values. When the obtained Mean Square Error (MSE), Coefficient of Determination (R) and error rate values have been analyzed, it has been concluded that the ANN model can predict SFC and NN values with high accuracy. According to the findings of current analysis, ANN approach is accurate, effective and conveniently applicable for simulating the slip flow of Williamson fluid towards the stretching plate with heat generation/absorption. The obtained results showed that ANNs are an ideal tool that can be used to predict Skin Friction Coefficients and Nusselt Number values.

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Artificial Neural Network Hyperparameter Effectiveness Determination And Optimization Algorithm

10.32920/ryerson.14635770 ◽

2021 ◽

Author(s):

James VanderVeen

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Model Performance ◽

Activation Function ◽

Ann Model ◽

Reproductive Process ◽

Hyperparameter Selection ◽

Artificial Neural ◽

Individual Variables

Machine learning models can contain many layers and branches. Each branch and layer, contain individual variables, know as hyperparameters, that require manual tuning. For instance, the genetic algorithm designed by Unit Amin [2] was designed to mimic the reproductive process of living organisms. The genetic algorithm and the Artificial Neural Network (ANN) training processes contain inherent randomness that reduces the replicability of results. Combined with the sheer magnitude of hyperparameter permutations, confidence that model has arrived at the best solution may be low. The algorithm designed for this thesis was designed to isolate portions of a complex ANN model and generate results showing the effect each hyperparameter has on the performance of the model as a whole. The results of this thesis show that the algorithm effectively generates data correlating model performance to hyperparameter selection. This is evident in section 3.1, and 3.2, where it is shown that using the sigmoid activation function with CNN layers, regardless of the number of filters, or hyperparameters used in the subsequent LSTM layers, produces superior RMSE scores. Section 3.2 also reveals that the model does not improve in performance as the number of CNN and LSTM layers are added to the model. Finally, the results in section 3.3 show that the rmsprop optimizer generates superior results regardless of the hyperparameters used in the rest of the model.

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Application of Artificial Neural Network to Predict Biodiesel Yield from Waste Frying Oil Transesterification

Indonesian Journal of Science and Technology ◽

10.17509/ijost.v5i1.23099 ◽

2020 ◽

Vol 5 (1) ◽

pp. 62-74

Author(s):

Agus Haryanto ◽

Tri Wahyu Saputra ◽

Mareli Telaumbanua ◽

Amiera Citra Gita

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Reaction Time ◽

Activation Function ◽

Frying Oil ◽

Biodiesel Production ◽

Ann Model ◽

Data Set ◽

Ann Models ◽

Artificial Neural

Used frying oil (UFO) has a great potential as feedstock for biodiesel production. This study aims to develop an artificial neural network (ANN) model to predict biodiesel yield produced from base-catalyzed transesterification of UFO. The experiment was performed with 100 mL of UFO at three different molar ratios (oil:methanol) (namely 1:4, 1:5, and 1:6), conducted with reaction temperatures of 30 to 55oC (raised by 5oC), and reaction time of 0.25, 0.5, 1, 2, 3, 6, 8, and 10 minutes. Prediction model was based on ANN model consisting of three layers with 27 combinations of three activation functions (tansig, logsig, purelin). All activation function architectures were trained using Levenberg- Marquardt train type with 126 data set (87.5%) and learning rate of 0.001. Model validation used 18 data set (12.5%) measured at reaction time of 8 min. Results showed that two ANN models with activation function of logsig-purelin-logsig and purelin-logsig-tansig be the best with RRMSE of 2.41% and 2.44% with R2 of 0.9355 and 0.9391, respectively. Predictions of biodiesel yield using ANN models are significantly better than those of first-order kinetics.

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Artificial Neural Network Hyperparameter Effectiveness Determination And Optimization Algorithm

10.32920/ryerson.14635770.v1 ◽

2021 ◽

Author(s):

James VanderVeen

Keyword(s):

Neural Network ◽

Genetic Algorithm ◽

Artificial Neural Network ◽

Model Performance ◽

Activation Function ◽

Ann Model ◽

Reproductive Process ◽

Hyperparameter Selection ◽

Artificial Neural ◽

Individual Variables

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Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

10.31227/osf.io/dp63x ◽

2018 ◽

Author(s):

Rizki Eka Putri ◽

Denny Darlis

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Low Cost ◽

Image Resolution ◽

Blood Type ◽

Feed Forward ◽

Testing Facility ◽

System Input ◽

Artificial Neural ◽

Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

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Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

Recent Patents on Mechanical Engineering ◽

10.2174/2212797612666190530115739 ◽

2019 ◽

Vol 12 (3) ◽

pp. 248-261

Author(s):

Baomin Wang ◽

Xiao Chang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Temperature Rise ◽

High Speed ◽

Ball Bearing ◽

Influence Factors ◽

Ball Bearings ◽

Ann Model ◽

Angular Contact Ball Bearing ◽

Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

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