Artificial Neural Network (ANN) Modeling of Odor Threshold Property of Diverse Chemical Constituents of Black Tea and Coffee

The authors have developed an artificial neural network model using odor threshold (OT) property data for diverse odorant components present in black tea (76 components) and coffee (46 components). The models were validated in terms of both internal and external validation criteria signifying acceptable results. The authors found the significant features controlling the OT property using Mean Absolute Error (MAE)-based criteria in a backward elimination of descriptors, one in each turn. The present results well-corroborated the previously published PLS-regression based chemometric model results.

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Using Artificial Neural Network to Predict Blast-Induced Ground Vibration

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.170-173.1013 ◽

2012 ◽

Vol 170-173 ◽

pp. 1013-1016

Author(s):

Fu Qiang Gao ◽

Xiao Qiang Wang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Ground Vibration ◽

Absolute Error ◽

Coefficient Of Determination ◽

Ann Model ◽

Monitoring Point ◽

Blasting Operation ◽

Artificial Neural ◽

Artificial Neural Network Ann

Prediction of peak particle velocity (PPV) is very complicated due to the number of influencing parameters affecting seism wave propagation. In this paper, artificial neural network (ANN) is implemented to develop a model to predict PPV in a blasting operation. Based on the measured parameters of maximum explosive charge used per delay and distance between blast face to monitoring point, a three-layer ANN was found to be optimum with architecture 2-5-1. Through the analysis of coefficient of determination (CoD) and mean absolute error (MAE) between monitored and predicted values of PPV, it indicates that the forecast data by the ANN model is close to the actua1 values.

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Use of Artificial Neural Network for Estimation of Propeller Torque Values in a CODLAG Propulsion System

Journal of Maritime & Transportation Science ◽

10.18048/2020.58.02. ◽

2020 ◽

Vol 58 (1) ◽

pp. 25-38

Author(s):

Sandi Baressi Šegota ◽

Daniel Štifanić ◽

Kazuhiro Ohkura ◽

Zlatan Car

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Absolute Error ◽

Activation Function ◽

Propulsion System ◽

Coefficient Of Determination ◽

Decay State ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Torque Values

An artificial neural network (ANN) approach is proposed to the problem of estimating the propeller torques of a frigate using combined diesel, electric and gas (CODLAG) propulsion system. The authors use a multilayer perceptron (MLP) feed-forward ANN trained with data from a dataset which describes the decay state coefficients as outputs and system parameters as inputs – with a goal of determining the propeller torques, removing the decay state coefficients and using the torque values of the starboard and port propellers as outputs. A total of 53760 ANNs are trained – 26880 for each of the propellers, with a total 8960 parameter combinations. The results are evaluated using mean absolute error (MAE) and coefficient of determination (R2). Best results for the starboard propeller are MAE of 2.68 [Nm], and MAE of 2.58 [Nm] for the port propeller with following ANN configurations respectively: 2 hidden layers with 32 neurons and identity activation and 3 hidden layers with 16, 32 and 16 neurons and identity activation function. Both configurations achieve R2 value higher than 0.99.

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Prediction of annual runoff using Artificial Neural Network and Regression approaches

MAUSAM ◽

10.54302/mausam.v62i1.4711 ◽

2021 ◽

Vol 62 (1) ◽

Author(s):

N. VIVEKANANDAN

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Absolute Error ◽

Annual Runoff ◽

Rainfall Runoff ◽

Statistical Parameters ◽

Runoff Prediction ◽

Model Efficiency ◽

Artificial Neural ◽

Artificial Neural Network Ann

Prediction of runoff is often important for optimal design of water storage and drainage works andmanagement of extreme events like floods and droughts. Rainfall-runoff (RR) models are considered to be most effectiveand expedient tool for runoff prediction. Number of models like stochastic, conceptual, deterministic, black-box, etc. iscommonly available for RR modelling. This paper details a study involving the use of Artificial Neural Network (ANN)and Regression (REG) approaches for prediction of runoff for Betwa and Chambal regions. Model performanceindicators such as model efficiency, correlation coefficient, root mean square error and root mean absolute error are usedto evaluate the performance of ANN and REG for runoff prediction. Statistical parameters are employed to find theaccuracy in prediction by ANN and REG for the data under study. The paper presents that ANN approach is found to besuitable for prediction of runoff for Betwa and Chambal regions.

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Using Artificial Neural Network (ANN) for prediction of soil coefficient of consolidation

Vietnam Journal of Earth Sciences ◽

10.15625/0866-7187/42/3/15008 ◽

2020 ◽

Vol 42 (3) ◽

Cited By ~ 2

Author(s):

Thai Binh Pham ◽

Sushant K. Singh ◽

Hai-Bang Ly

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Absolute Error ◽

Coefficient Of Determination ◽

Ann Model ◽

Coefficient Of Consolidation ◽

Experimental Approaches ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Accuracy Performance

Soil Coefficient of Consolidation (Cv) is a crucial mechanical parameter and used to characterize whether the soil undergoes consolidation or compaction when subjected to pressure. In order to define such a parameter, the experimental approaches are costly, time-consuming, and required appropriate equipment to perform the tests. In this study, the development of an alternative manner to estimate the Cv, based on Artificial Neural Network (ANN), was conducted. A database containing 188 tests was used to develop the ANN model. Two structures of ANN were considered, and the accuracy of each model was assessed using common statistical measurements such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In performing 600 simulations in each case, the ANN structure containing 14 neurons was statistically superior to the other one. Finally, a typical ANN result was presented to prove that it can be an excellent predictor of the problem, with a satisfying accuracy performance that yielded of RMSE = 0.0614, MAE = 0.0415, and R2 = 0.99727. This study might help in quick and accurate prediction of the Cv used in civil engineering problems.

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Biomedical Prediction of Radial Size of Powdered Element using Artificial Neural Network

Biomedical & Pharmacology Journal ◽

10.13005/bpj/1526 ◽

2018 ◽

Vol 11 (3) ◽

pp. 1593-1602

Author(s):

Yaagyanika Gehlot ◽

Bhairvi Sharma ◽

P. Muthu ◽

Hariharan Muthusamy ◽

S. Latha

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Transfer Functions ◽

Curcuma Longa ◽

Absolute Error ◽

Ann Model ◽

Ag Nps ◽

Optimum Algorithm ◽

Artificial Neural ◽

Artificial Neural Network Ann

Silver nitrous aqueous solution is used to biosynthesize Silver nanoparticles (Ag-NPs) through a green and easy way using tuber powder extracts of Curcuma Longa (C. longa). The aim is to model an Artificial Neural Network (ANN) using seven existing algorithms in MATLAB for forecasting the size of the silver nanoparticle with volume of both C. longa extraction and AgNO3, time of stirring and temperature of reaction as input functions. Several techniques including Quasi-Newton, Conjugate Gradient and Levenberg-Maquardt are employed for training the designed ANN model, a feed-forward backpropagation network with different combinations of architecture and transfer functions. Each algorithm is fashioned to obtain the best performance by calculating the Regression (R), Mean Square Error (MSE), Mean Absolute Error (MAE) and Error Sum of Squares (SSE), thereby comparing the results and propounding the optimum algorithm technique for the discussed application in nanoengineering. Finally, based on the findings, the optimum network is proposed through the simulation results.

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Using Artificial Neural Network (ANN) for prediction of soil coefficient of consolidation

Vietnam Journal of Earth Sciences ◽

10.15625/0866-7187/42/4/15008 ◽

2020 ◽

Vol 42 (4) ◽

Cited By ~ 2

Author(s):

Thai Binh Pham ◽

Sushant K. Singh ◽

Hai-Bang Ly

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Absolute Error ◽

Coefficient Of Determination ◽

Ann Model ◽

Coefficient Of Consolidation ◽

Experimental Approaches ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Accuracy Performance

Soil Coefficient of Consolidation (Cv) is a crucial mechanical parameter and used to characterize whether the soil undergoes consolidation or compaction when subjected to pressure. In order to define such a parameter, the experimental approaches are costly, time-consuming, and required appropriate equipment to perform the tests. In this study, the development of an alternative manner to estimate the Cv, based on Artificial Neural Network (ANN), was conducted. A database containing 188 tests was used to develop the ANN model. Two structures of ANN were considered, and the accuracy of each model was assessed using common statistical measurements such as the coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE). In performing 600 simulations in each case, the ANN structure containing 14 neurons was statistically superior to the other one. Finally, a typical ANN result was presented to prove that it can be an excellent predictor of the problem, with a satisfying accuracy performance that yielded of RMSE = 0.0614, MAE = 0.0415, and R2 = 0.99727. This study might help in quick and accurate prediction of the Cv used in civil engineering problems.

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