Using Convolutional Neural Networks for Smart Classroom Observation

Khan, Muhammed S.; Zualkernan, Imran A.

doi:10.1109/icaiic48513.2020.9065260

Cited by 4 publications

(1 citation statement)

References 8 publications

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“…In [70], using captured audio data during class observation, deep learning models were built to classify classroom activities. The CNN classifier model outperformed the KNN and Random Forest models in this study.…”

Section: Video Image and Signal Analysis Appsmentioning

confidence: 99%

Front-end deep learning web apps development and deployment: a review

Goh

Abas

2022

Appl Intell

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Machine learning and deep learning models are commonly developed using programming languages such as Python, C++, or R and deployed as web apps delivered from a back-end server or as mobile apps installed from an app store. However, recently front-end technologies and JavaScript libraries, such as TensorFlow.js, have been introduced to make machine learning more accessible to researchers and end-users. Using JavaScript, TensorFlow.js can define, train, and run new or existing, pre-trained machine learning models entirely in the browser from the client-side, which improves the user experience through interaction while preserving privacy. Deep learning models deployed on front-end browsers must be small, have fast inference, and ideally be interactive in real-time. Therefore, the emphasis on development and deployment is different. This paper aims to review the development and deployment of these deep-learning web apps to raise awareness of the recent advancements and encourage more researchers to take advantage of this technology for their own work. First, the rationale behind the deployment stack (front-end, JavaScript, and TensorFlow.js) is discussed. Then, the development approach for obtaining deep learning models that are optimized and suitable for front-end deployment is then described. The article also provides current web applications divided into seven categories to show deep learning potential on the front end. These include web apps for deep learning playground, pose detection and gesture tracking, music and art creation, expression detection and facial recognition, video segmentation, image and signal analysis, healthcare diagnosis, recognition, and identification.

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Section: Video Image and Signal Analysis Appsmentioning

confidence: 99%

Front-end deep learning web apps development and deployment: a review

Goh

Abas

2022

Appl Intell

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IoT based smart class room monitoring system using convolutional neural network algorithm

Manthena¹,

Gottumukkala²

2023

AIP Conference Proceedings

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Novel principal component analysis‐based feature selection mechanism for classroom sound classification

Tsalera

Papadakis

Σαμαράκου

2021

Computational Intelligence

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Machine learning algorithms for sound classification can be supported by multiple temporal, spectral, and perceptual features extracted from the sound signal. The number of features affects the classification accuracy but also the computational resources requested, so the number of features has to be carefully selected. In this work, we propose a methodology for feature selection based on the principal component analysis. The case study has been the classification of classroom sounds during face‐to‐face module delivery and six sound types have been defined. The proposed method is applied upon a set of 143 sound features to produce feature ranking. The ranking results are compared with those provided by the Relief‐F. Then the selected features are used by five classification algorithms, Linear Discriminant Analysis (LDA), Quadratic Support Vector Machine (QSVM), k Nearest Neighbors, Boosted Trees, and Random Forest. The algorithms are executed with increasing number of features, from 1 to 143, considering both feature rankings, creating 1430 models. The performance of the classification algorithms increases rapidly with the number of features with LDA, QSVM, and Boosted Trees outperforming other methods and surpassing the accuracy ratio of 90% with 25 features.

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Using Convolutional Neural Networks for Smart Classroom Observation

Cited by 4 publications

References 8 publications

Front-end deep learning web apps development and deployment: a review

Front-end deep learning web apps development and deployment: a review

IoT based smart class room monitoring system using convolutional neural network algorithm

Novel principal component analysis‐based feature selection mechanism for classroom sound classification

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