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 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

scholarly journals Artificial Neural Network Modelling for Optimizing the Optical Parameters of Plasmonic Paired Nanostructures

Nanomaterials ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 170
Author(s):  
Sneha Verma ◽  
Sunny Chugh ◽  
Souvik Ghosh ◽  
B. M. Azizur Rahman
Keyword(s):  
Neural Network ◽  
Finite Element Method ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Plasmonic Nanostructures ◽  
Optical Parameters ◽  
Complex Data ◽  
The Neural Network ◽  
Artificial Neural ◽  
Element Method

The Artificial Neural Network (ANN) has become an attractive approach in Machine Learning (ML) to analyze a complex data-driven problem. Due to its time efficient findings, it has became popular in many scientific fields such as physics, optics, and material science. This paper presents a new approach to design and optimize the electromagnetic plasmonic nanostructures using a computationally efficient method based on the ANN. In this work, the nanostructures have been simulated by using a Finite Element Method (FEM), then Artificial Intelligence (AI) is used for making predictions of associated sensitivity (S), Full Width Half Maximum (FWHM), Figure of Merit (FOM), and Plasmonic Wavelength (PW) for different paired nanostructures. At first, the computational model is developed by using a Finite Element Method (FEM) to prepare the dataset. The input parameters were considered as the Major axis, a, the Minor axis, b, and the separation gap, g, which have been used to calculate the corresponding sensitivity (nm/RIU), FWHM (nm), FOM, and plasmonic wavelength (nm) to prepare the dataset. Secondly, the neural network has been designed where the number of hidden layers and neurons were optimized as part of a comprehensive analysis to improve the efficiency of ML model. After successfully optimizing the neural network, this model is used to make predictions for specific inputs and its corresponding outputs. This article also compares the error between the predicted and simulated results. This approach outperforms the direct numerical simulation methods for predicting output for various input device parameters.

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