Affective state prediction of E-learner using SS-ROA based deep LSTM

Rathi, Snehal; Hiran, Kamal Kant; Sakhare, Sachin R.

doi:10.1016/j.array.2023.100315

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Artificial Intelligence and Biosensors1

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2024

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Cited by 14 publications

(2 citation statements)

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“…Learners enrol in courses of different levels of difficulty and generate log files based on their learning patterns. Subsequently, feature extraction is applied to this data, which is then utilised to forecast emotional states via LSTM [98].…”

Section: Artificial Intelligence and Biosensorsmentioning

confidence: 99%

See 1 more Smart Citation

Chemometrics‐based signal processing methods for biosensors in health and environment: A review

Wu,

Yang,

Zhou

et al. 2023

Electroanalysis

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Section: Artificial Intelligence and Biosensorsmentioning

confidence: 99%

“…Esmaeili et al proposed a data augmentation method in their study of predicting analyte concentrations from electrochemical aptamer sensor signals using LSTM recurrent networks. The method aimed to overcome the problem of insufficient raw data and long short-term memory [97] and [98].…”

Section: Recurrentmentioning

confidence: 99%

Chemometrics‐based signal processing methods for biosensors in health and environment: A review

Wu,

Yang,

Zhou

et al. 2023

Electroanalysis

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show abstract

Special issue “Towards a higher education of the future: Transformational roles of edge intelligence”

Doshi,

Hu,

Garg

et al. 2024

Array

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The Study of Cardiology Through an Augmented Reality-Based System

Ruiz

2024

Advances in Medical Technologies and Clinical Practice

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This chapter presents an augmented reality project applied to the study of cardiology, which is crystallized through a system known as service-oriented architecture (SOA). This system serves as an innovative and efficient learning platform for students interested in cardiology because it helps them to understand abstract concepts in cardiology, which require visual and manipulable objects that are difficult to obtain, due to the large space they occupy in magnetic media or because of the difficulty of obtaining their models in physical form. This system strengthened the process of anatomical identification of the human heart and allowed a better interaction with the student, i.e., the system enhanced the use of sight, hearing, and kinesthetic, which, together, allowed a better assimilation of knowledge. The effectiveness of this system was validated using a survey of 389 students from four public universities where the following aspects were verified: 1) Significant learning; 2) Motivation; 3) Ease of use, and; 4) Performance.

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Affective state prediction of E-learner using SS-ROA based deep LSTM

Cited by 14 publications

References 19 publications

Chemometrics‐based signal processing methods for biosensors in health and environment: A review

Chemometrics‐based signal processing methods for biosensors in health and environment: A review

Special issue “Towards a higher education of the future: Transformational roles of edge intelligence”

The Study of Cardiology Through an Augmented Reality-Based System

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