Application of artificial intelligence in cognitive load analysis using functional near-infrared spectroscopy: A systematic review

Khan, Mehshan Ahmed; Asadi, Houshyar; Zhang, Li; Qazani, Mohammad Reza Chalak; Oladazimi, Sam; Loo, Chu Kiong; Lim, Chee Peng; Nahavandi, Saeid

doi:10.1016/j.eswa.2024.123717

Cited by 4 publications

(1 citation statement)

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“…At present, AI and related topics (e.g., Machine Learning, Artificial Neural Networks, and Deep Learning) are receiving significant attention in the science, in engineering and in business. For instance, there are many scientific papers dealing with applications of AI in various branches of spectrometry [1][2][3][4][5][6][7][8][9][10][11] (e.g., atomic, molecular, Raman, SERS). The role of AI in generating original research has also been discussed [10].…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence (and related topics, e.g., machine learning, deep learning, artificial neural networks or ANNs) as applied to the teaching and to the practice of analytical spectrochemistry

Tat,

Karanassios

2024

Next-Generation Spectroscopic Technologies XVI

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In this paper, the application of Artificial Intelligence (AI) and related topics (e.g., Machine Learning, Artificial Neural Networks (ANNs), deep learning) as they apply to analytic spectrometry (e.g., either using Inductively Coupled Plasma -Optical Emission Spectrometry (ICP-OES), or a portable, battery-operated microplasma-OES) using a fiber-optic spectrometer) will be described, and the application of AI to teaching analytical atomic spectrometry will be outlined.

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