scholarly journals Artificial neural network-assisted Fourier transform infrared spectroscopy for differentiation of Salmonella serogroups and its application on epidemiological tracing of Salmonella Bovismorbificans outbreak isolates from fresh sprouts

2019 ◽  
Vol 366 (15) ◽  
Author(s):  
Helene Oberreuter ◽  
Jörg Rau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Ftir Spectroscopy ◽  
Fourier Transform Infrared ◽  
Salmonella Infections ◽  
Salmonella Serotypes ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

ABSTRACT Salmonellae represent one of the most common bacterial infection reagents in both humans and animals. For detection and epidemiological elucidation of Salmonella infections, determination of Salmonella serotypes and differentiation between different Salmonella isolates is crucial. In the first part of this study, Artificial Neural Network (ANN)-assisted Fourier transform infrared (FTIR) spectroscopy was used to establish a method for subtyping Salmonella isolates according to their serogroups. For this, 290 Salmonella strains from 35 different serogroups were used to establish an ANN for differentiation between infrared spectra of 10 different Salmonella serogroups (B, C1, C2-C3, D1/D2, E1, E4, F, G, H, O:55) vs. the remaining serogroups. In the final ANN, sensitivity values ranged between 90 and 100% for most of the 10 serogroups under investigation. In the second part of this study, ANN-assisted FTIR spectroscopy was applied for epidemiological distinction of Salmonella Bovismorbificans outbreak isolates from fresh sprouts vs. isolates from other sources. Four Salmonella Bovismorbificans isolates from human and food origin in the context of a Southern German outbreak were successfully discriminated from other S. Bovismorbificans isolates from various sources. ANN-assisted FTIR spectroscopy is thus an effective tool for discrimination of Salmonella isolates at or even below serogroup level.

scholarly journals Determination of the Factors Affecting King Abdul Aziz University Published Articles in ISI by Multilayer Perceptron Artificial Neural Network

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 766
Author(s):  
Rashad A. R. Bantan ◽  
Ramadan A. Zeineldin ◽  
Farrukh Jamal ◽  
Christophe Chesneau
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Multilayer Perceptron ◽  
Research Study ◽  
Real Data ◽  
Factors Affecting ◽  
King Abdulaziz University ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Deanship of scientific research established by the King Abdulaziz University provides some research programs for its staff and researchers and encourages them to submit proposals in this regard. Distinct research study (DRS) is one of these programs. It is available all the year and the King Abdulaziz University (KAU) staff can submit more than one proposal at the same time up to three proposals. The rules of the DSR program are simple and easy so it contributes in increasing the international rank of KAU. The authors are offered financial and moral reward after publishing articles from these proposals in Thomson-ISI journals. In this paper, multiplayer perceptron (MLP) artificial neural network (ANN) is employed to determine the factors that have more effect on the number of ISI published articles. The proposed study used real data of the finished projects from 2011 to April 2019.

scholarly journals Application of artificial neural network to predict properties of diesel-biodiesel blends

1970 ◽  
Vol 6 (2) ◽  
pp. 98-103 ◽  
Author(s):  
Jatinder Kumar ◽  
Ajay Bansal
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Least Squares ◽  
Statistical Technique ◽  
Training Algorithms ◽  
Biodiesel Blends ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The experimental determination of various properties of diesel-biodiesel mixtures is very time consuming as well as tedious process. Any tool helpful in estimation of these properties without experimentation can be of immense utility. In present work, other tools of determination of properties of diesel-biodiesel blends were tried. A traditional statistical technique of linear regression (principle of least squares) was used to estimate the flash point, fire point, density and viscosity of diesel and biodiesel mixtures. A set of seven neural network architectures, three training algorithms along with ten different sets of weight and biases were examined to choose best Artificial Neural Network (ANN) to predict the above-mentioned properties of dieselbiodiesel mixtures. The performance of both of the traditional linear regression and ANN techniques were then compared to check their validity to predict the properties of various mixtures of diesel and biodiesel. Key words: Biodiesel; Artificial Neural Network; Principle of least squares; Diesel; Linear Regression. DOI: 10.3126/kuset.v6i2.4017Kathmandu University Journal of Science, Engineering and Technology Vol.6. No II, November, 2010, pp.98-103

scholarly journals Comparison of partial least squares and artificial neural network chemometric techniques in determination of sulfamethoxazole and trimethoprim in pharmaceutical suspension by ATR–FTIR spectrometry

2011 ◽  
Vol 26 (2) ◽  
pp. 105-114 ◽  
Author(s):  
M. Khanmohammadi ◽  
N. Dallali ◽  
A. Bagheri Garmarudi ◽  
M. Zarnegar ◽  
K. Ghasemi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Partial Least Square ◽  
Least Square ◽  
Statistical Parameters ◽  
Modified Method ◽  
Ftir Spectrometry ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Partial Least Square (PLS) and Artificial Neural Network (ANN) techniques were compared during development of an analytical method for quantitative determination of sulfamethoxazole (SMX) and trimethoprim (TMP) in Co-Trimoxazole®suspension. The procedure was based on Attenuated Total Reflectance Fourier Transform Infrared (ATR–FTIR) spectrometry. The 800–2500 cm−1spectral region was selected for quantitative analysis.R2and relative error of prediction (REP) in PLS technique were (0.989, 2.128) and (0.986, 1.381) for SMX and TMP, respectively. These statistical parameters were improved using the ANN models considering the complexity of the sample and the speediness and simplicity of the method.R2and RMSEC in modified method were (0.997, 1.064) and (0.997, 0.634) for SMX and TMP, respectively.

scholarly journals Intelligent Automatic V6 and V8 Engine Sound Detection Based on Artificial Neural Network

2019 ◽  
Vol 130 ◽  
pp. 01035 ◽  
Author(s):  
Wenny Vincent ◽  
Astuti Winda ◽  
Mahmud Iwan Solihin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Intelligent System ◽  
Recorded Sound ◽  
Sound Detection ◽  
Testing Accuracy ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The sound of V6 or V8 engines has its own cultural appeal that cannot be replaced by the modern four-cylinder naturally aspirated or turbocharged engines. The identification of the type of engine by the sound is not an easy task, even for the professionals. An intelligent system that can identify V6 to V8 engines from various cars will give an insight of the features in the engine sounds that characterized the two different engines. In this work, an Artificial Neural Network (ANN) approach is applied for identifying cylinder of the engine based on the engine sound identification is proposed. The recorded sound of the engine is then processed in order to get some features which later be used in the proposed system. The Fast Fourir Transform (FFT) is adopted as a feature and later used as input to the Artificial Neural Network (ANN) based identifier. The Experimental results confirm the effectiveness of the proposed intelligent automatic six cylinder and eight cylinder engine based on Fast Fourier Transform (FFT) and Artificial Neural Network (ANN), since it resulting the training and testing accuracy of 100 % and 100 %, respectively.

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

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.

Identification and Quantification of Industrial Grade Glycerol Adulteration in Red Wine with Fourier Transform Infrared Spectroscopy Using Chemometrics and Artificial Neural Networks

2005 ◽  
Vol 59 (12) ◽  
pp. 1553-1561 ◽  
Author(s):  
Vivechana Dixit ◽  
Jagdish C. Tewari ◽  
Byoung-Kwan Cho ◽  
Joseph M. K. Irudayaraj
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fourier Transform ◽  
Red Wine ◽  
Radial Basis Function Network ◽  
Fourier Transform Infrared ◽  
Linear Discriminant ◽  
Industrial Grade ◽  
Artificial Neural ◽  
Propagation Network

Fourier transform infrared (FT-IR) single bounce micro-attenuated total reflectance (mATR) spectroscopy, combined with multivariate and artificial neural network (ANN) data analysis, was used to determine the adulteration of industrial grade glycerol in selected red wines. Red wine samples were artificially adulterated with industrial grade glycerol over the concentration range from 0.1 to 15% and calibration models were developed and validated. Single bounce infrared spectra of glycerol adulterated wine samples were recorded in the fingerprint mid-infrared region, 900–1500 cm−1. Partial least squares (PLS) and PLS first derivatives were used for quantitative analysis ( r2 = 0.945 to 0.998), while linear discriminant analysis (LDA) and canonical variate analysis (CVA) were used for classification and discrimination. The standard error of prediction (SEP) in the validation set was between 1.44 and 2.25%. Classification of glycerol adulterants in the different brands of red wine using CVA resulted in a classification accuracy in the range between 94 and 98%. Artificial neural network analysis based on the quick back propagation network (BPN) and the radial basis function network (RBFN) algorithms had classification success rates of 93% using BPN and 100% using RBFN. The genetic algorithm network was able to predict the concentrations of glycerol in wine up to an accuracy of r2 = 0.998.

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