scholarly journals Comparison between two PCR-based bacterial identification methods through artificial neural network data analysis

2008 ◽  
Vol 22 (1) ◽  
pp. 14-20 ◽  
Author(s):  
Jie Wen ◽  
Xiaohui Zhang ◽  
Peng Gao ◽  
Qiuhong Jiang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Network Data ◽  
Bacterial Identification ◽  
Identification Methods ◽  
Artificial Neural

Model of artificial neural network for complex data analysis in slag glass-ceramic

2004 ◽  
Vol 95 (2) ◽  
pp. 97-101 ◽  
Author(s):  
Hongyuan Sun ◽  
Qiye Wen ◽  
Peixin Zhang ◽  
Jianhong Liu ◽  
Qianling Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Glass Ceramic ◽  
Complex Data ◽  
Slag Glass ◽  
Artificial Neural

scholarly journals A New Analyzer Based on Pellistor Sensor with Neural Network Data Postprocessing for Measurement of Hydrocarbons in Lower Explosive Limit Range

2005 ◽  
Vol 2005 (2) ◽  
pp. 55-57
Author(s):  
Radoslaw Bandomir ◽  
Mariusz Krawczyk ◽  
Jacek Namiesnik
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Development Work ◽  
Network Data ◽  
Stage Of Development ◽  
New Type ◽  
Artificial Neural ◽  
Concentration Measurements ◽  
Explosive Limit ◽  
Lower Explosive Limit

We present the results of a first stage of development work on a new type of analyzer for hydrogen and C1–C3hydrocarbons concentration measurements in the lower explosive limit range, based on single pellistor sensor with artificial neural network data postprocessing.

scholarly journals Sample Reduction for Physiological Data Analysis Using Principal Component Analysis in Artificial Neural Network

2021 ◽  
Vol 11 (17) ◽  
pp. 8240
Author(s):  
Cid Mathew Santiago Adolfo ◽  
Hassan Chizari ◽  
Thu Yein Win ◽  
Salah Al-Majeed
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Biomedical Applications ◽  
Reduction Process ◽  
Principal Component ◽  
Classification Problem ◽  
Multidimensional Datasets ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

With its potential, extensive data analysis is a vital part of biomedical applications and of medical practitioner interpretations, as data analysis ensures the integrity of multidimensional datasets and improves classification accuracy; however, with machine learning, the integrity of the sources is compromised when the acquired data pose a significant threat in diagnosing and analysing such information, such as by including noisy and biased samples in the multidimensional datasets. Removing noisy samples in dirty datasets is integral to and crucial in biomedical applications, such as the classification and prediction problems using artificial neural networks (ANNs) in the body’s physiological signal analysis. In this study, we developed a methodology to identify and remove noisy data from a dataset before addressing the classification problem of an artificial neural network (ANN) by proposing the use of the principal component analysis–sample reduction process (PCA–SRP) to improve its performance as a data-cleaning agent. We first discuss the theoretical background to this data-cleansing methodology in the classification problem of an artificial neural network (ANN). Then, we discuss how the PCA is used in data-cleansing techniques through a sample reduction process (SRP) using various publicly available biomedical datasets with different samples and feature sizes. Lastly, the cleaned datasets were tested through the following: PCA–SRP in ANN accuracy comparison testing, sensitivity vs. specificity testing, receiver operating characteristic (ROC) curve testing, and accuracy vs. additional random sample testing. The results show a significant improvement in the classification of ANNs using the developed methodology and suggested a recommended range of selectivity (Sc) factors for typical cleaning and ANN applications. Our approach successfully cleaned the noisy biomedical multidimensional datasets and yielded up to an 8% increase in accuracy with the aid of the Python language.

scholarly journals Arduino data-logger and artificial neural network to data analysis

2019 ◽  
Vol 1386 ◽  
pp. 012070 ◽  
Author(s):  
G F Contreras Contreras ◽  
H J Dulcé-Moreno ◽  
R Ardila Melo
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Data Analysis ◽  
Data Logger ◽  
Artificial Neural
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