scholarly journals Assessing Visual Science Literacy Using Functional Near-Infrared Spectroscopy with an Artificial Neural Network

2021 ◽  
Author(s):  
Amanda Kavner ◽  
Richard Lamb ◽  
Pavlo Antonenko ◽  
Do Hyong Koh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Visual Literacy ◽  
Near Infrared ◽  
Science Literacy ◽  
Infrared Spectrometry ◽  
Functional Near Infrared Spectroscopy ◽  
Representational Competence ◽  
Visual Science ◽  
Artificial Neural

The primary barrier to understanding visual and abstract information in STEM fields is representational competence the ability to generate, transform, analyze and explain representations. The relationship is known between the foundational visual literacy and the domain specific science literacy, however how science literacy is a function of science learning is still not well understood despite investigation across many fields. To support the improvement of students’ representational competence and promote learning in science, identification of visualization skills is necessary. This project details the development of an artificial neural network (ANN) capable of measuring and modeling visual science literacy (VSL) via neurological measurements using functional near infrared spectrometry (fNIRS). The developed model has the capacity to classify levels of scientific visual literacy allowing educators and curriculum designers the ability to create more targeted and immersive classroom resources such as virtual reality, to enhance the fundamental visual tools in science.

Artificial and convolutional neural networks for assessing functional connectivity in resting-state functional near infrared spectroscopy

2019 ◽  
Vol 27 (3) ◽  
pp. 191-205
Author(s):  
Bahareh Behboodi ◽  
Sung-Ho Lim ◽  
Miguel Luna ◽  
Hyeon-Ae Jeon ◽  
Ji-Woong Choi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Infrared Spectroscopy ◽  
Functional Connectivity ◽  
Independent Component Analysis ◽  
Convolutional Neural Network ◽  
Resting State ◽  
Near Infrared ◽  
Functional Near Infrared Spectroscopy ◽  
Artificial Neural

Functional connectivity derived from resting-state functional near infrared spectroscopy has gained attention of recent scholars because of its capability in providing valuable insight into intrinsic networks and various neurological disorders in a human brain. Several progressive methodologies in detecting resting-state functional connectivity patterns in functional near infrared spectroscopy, such as seed-based correlation analysis and independent component analysis as the most widely used methods, were adopted in previous studies. Although these two methods provide complementary information each other, the conventional seed-based method shows degraded performance compared to the independent component analysis-based scheme in terms of the sensitivity and specificity. In this study, artificial neural network and convolutional neural network were utilized in order to overcome the performance degradation of the conventional seed-based method. First of all, the results of artificial neural network- and convolutional neural network-based method illustrated the superior performance in terms of specificity and sensitivity compared to both conventional approaches. Second, artificial neural network, convolutional neural network, and independent component analysis methods showed more robustness compared to seed-based method. Moreover, resting-state functional connectivity patterns derived from artificial neural network- and convolutional neural network-based methods in sensorimotor and motor areas were consistent with the previous findings. The main contribution of the present work is to emphasize that artificial neural network as well as convolutional neural network can be exploited for a high-performance seed-based method to estimate the temporal relation among brain networks during resting state.

scholarly journals Simultaneous Determination of Some Components in Detergent Washing Powder by Mid-Infrared Spectrometry and Artificial Neural Network

2011 ◽  
Vol 94 (1) ◽  
pp. 322-326
Author(s):  
Mohammadreza Khanmohammadi ◽  
Amir Bagheri Garmarudi ◽  
Mohammad Babaei Rouchi ◽  
Nafiseh Khoddami
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Simultaneous Determination ◽  
Sodium Tripolyphosphate ◽  
Back Propagation ◽  
Infrared Spectrometry ◽  
Feed Forward Back Propagation ◽  
Powder Samples ◽  
Artificial Neural

Abstract A method has been established for simultaneous determination of sodium sulfate, sodium carbonate, and sodium tripolyphosphate in detergent washing powder samples based on attenuated total reflectance Fourier transform IR spectrometry in the mid-IR spectral region (800–1550 cm−1). Genetic algorithm (GA) wavelength selection followed by feed forward back-propagation artificial neural network (BP-ANN) was the chemometric approach. Root mean square error of prediction for BP-ANN and GA-BP-ANN was 0.0051 and 0.0048, respectively. The proposed method is simple, with no tedious pretreatment step, for simultaneous determination of the above-mentioned components in commercial washing powder samples.

Near Infrared Spectroscopy and Artificial Neural Network to Characterise Olive Fruit and Oil Online for Process Optimisation

2015 ◽  
Vol 23 (2) ◽  
pp. 111-121 ◽  
Author(s):  
Yosra Allouche ◽  
Estrella Funes López ◽  
Gabriel Beltrán Maza ◽  
Antonio Jiménez Márquez
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wavelet Transform ◽  
Near Infrared ◽  
Moving Average ◽  
Coefficient Of Determination ◽  
Hammer Mill ◽  
Predictive Capacity ◽  
Olive Fruit ◽  
Artificial Neural

A sensor-software based on an artificial neural network (SS-ANN) was designed for real-time characterisation of olive fruit (pulp/stone ratio, extractability index, moisture and oil contents) and the potential characteristics of the extracted oil (free acidity, peroxide index, K232 and K270, pigments and polyphenols) in olive paste prior to the kneading step. These predictions were achieved by measuring variables related to olive fruit at the crushing stage, including the type of hammer mill (single grid, double grid and Listello), sieve diameter (4 mm, 5 mm, 6 mm and 7 mm), hammer rotation speed (from 2000 rpm to 3000 rpm), temperature before crushing and mill room temperature. These were related to the near infrared (NIR) spectra from online scanned freshly milled olive paste in the malaxer with data pretreated by either the moving average or wavelet transform technique. The networks obtained showed good predictive capacity for all the parameters examined. Based on the root mean square error of prediction ( RMSEP), residual predictive deviation ( RPD) and coefficient of determination of validation ( r2), the models that used the wavelet preprocessing procedure were more accurate than those that used the moving average. As examples, for moisture and polyphenols, RMSEP values were 1.79% and 87.80 mg kg−1, and 1.46% and 61.50 mg kg−1, respectively for the moving average and wavelet transform. Similar results were found for the other parameters. In conclusion, these results confirm the feasibility of SS-ANN as a tool for optimising the olive oil elaboration process.

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