CNN-SVM Learning Approach Based Human Activity Recognition

Basly, Hend; Ouarda, Wael; Sayadi, Fatma Ezahra; Ouni, Bouraoui; Alimi, Adel M.

doi:10.1007/978-3-030-51935-3_29

Cited by 51 publications

(20 citation statements)

References 25 publications

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“…It works by finding the optimal separating hyperplane that best discriminates the data. e support vector machine is fast, accurate, and reliable for analysis prediction with a small amount of data [38,39]. is forms the justification for its use in our study.…”

Section: Identity Detection In Smart Home Environmentmentioning

confidence: 93%

Internet of Things-Based Intelligent Smart Home Control System

Taiwo

Ezugwu

2021

Security and Communication Networks

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The smart home is now an established area of interest and research that contributes to comfort in modern homes. With the Internet being an essential part of broad communication in modern life, IoT has allowed homes to go beyond building to interactive abodes. In many spheres of human life, the IoT has grown exponentially, including monitoring ecological factors, controlling the home and its appliances, and storing data generated by devices in the house in the cloud. Smart home includes multiple components, technologies, and devices that generate valuable data for predicting home and environment activities. This work presents the design and development of a ubiquitous, cloud-based intelligent home automation system. The system controls, monitors, and oversees the security of a home and its environment via an Android mobile application. One module controls and monitors electrical appliances and environmental factors, while another module oversees the home’s security by detecting motion and capturing images. Our work uses a camera to capture images of objects triggered by their motion being detected. To avoid false alarms, we used the concept of machine learning to differentiate between images of regular home occupants and those of an intruder. The support vector machine algorithm is proposed in this study to classify the features of the image captured and determine if it is that of a regular home occupant or an intruder before sending an alarm to the user. The design of the mobile application allows a graphical display of the activities in the house. Our work proves that machine learning algorithms can improve home automation system functionality and enhance home security. The work’s prototype was implemented using an ESP8266 board, an ESP32-CAM board, a 5 V four-channel relay module, and sensors.

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Section: Identity Detection In Smart Home Environmentmentioning

confidence: 93%

Internet of Things-Based Intelligent Smart Home Control System

Taiwo

Ezugwu

2021

Security and Communication Networks

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“…En este enfoque, se extraen las características aprendidas con la red CNN de un modelo preentrenado basado en ResNet y las características luego se usan para entrenar un modelo SVM en el reconocimiento de la actividad humana. El modelo CNN se utiliza como extractor de características y el modelo SVM se utiliza como predictor o clasificador [12].…”

Section: Trabajos Relacionadosunclassified

Machine learning models to characterize the cough signal of patients with COVID-19

Salamea¹,

Sánchez²,

Calderón³

et al. 2022

Proceedings of the 20th LACCEI International Multi-Conference for Engineering, Education and Technology: “Education, Research A

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Automatic recognition of audio signals is a challenging signal task due to the difficulty of extracting important attributes from such signals, which relies heavily on discriminating acoustic features to determine the type of cough audio coming from COVID-19 patients. In this work, the use of state-of-the-art pre-trained models and a convolutional neural network for the extraction of characteristics of a cough signal from patients with COVID-19 is analyzed. A comparison of three machine learning models has been proposed to extract the features containing relevant information, leading to the recognition of the COVID-19 cough signal. The first model is based on a basic convolutional neural network, the second is based on a YAMNet pre-treatment model, and the third is a VGGish pre-trained model. The experimental results carried out with a ComPare 2021 CCS database show that models, of the three, used , VGGish to provide better performance when extracting the characteristics of the audio signals of the COVID-19 cough signal, having as results the performance metrics f1 score and accuracy with values of 30.76% and 80.51% , representing an improvement of 6.06% and 3.61% compared to the YANMet model, and the confusion matrices, which validate the mentioned model.

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“…Many researchers have demonstrated deep learning efficiency as a feature extraction method in recent years (Kraus, Grys, Ba, Chong, Frey, Boone and Andrews, 2017). Moreover, many works in different tasks (Govindaswamy, Montague, Raicu and Furst, 2020), (Wang, Zhang and Hao, 2019), (Basly, Ouarda, Sayadi, Ouni and Alimi, 2020), (Alzubaidi, Fadhel, Al-Shamma, Zhang and Duan, 2020), (Aurelia, Rustam, Wibowo and Setiawan, 2020), (Mu and Qiao, 2019), (Suganthi and Sathiaseelan, 2020), (Oltu, Güney, Dengiz and Ağıldere, 2021), (Bodapati and Veeranjaneyulu, 2019), (Karungaru, Dongyang and Terada, 2021), (Öznur Özaltın and Özgür Yeniay, 2021) show the effectiveness of using ML classifier to classify the data based on features extracted through deep CNN compared to end-to-end deep learning.…”

Section: Introductionmentioning

confidence: 99%

“…The authors in (Basly et al, 2020) used a pre-trained CNN ResNet architecture as feature extractor combined with SVM classifier for human activity recognition task where they achieved a good recognition performance compared to state-of-art methods.…”

Section: Introductionmentioning

confidence: 99%

Multi-features extraction based on deep learning for skin lesion classification

2022

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For various forms of skin lesion, many different feature extraction methods have been investigated so far. Indeed, feature extraction is a crucial step in machine learning processes. In general, we can distinct handcrafted and deep learning features. In this paper, we investigate the efficiency of using 17 commonly pre-trained convolutional neural networks (CNN) architectures as feature extractors and of 24 machine learning classifiers to evaluate the classification of skin lesions from two different datasets: ISIC 2019 and PH2. In this research, we find out that a DenseNet201 combined with Fine KNN or Cubic SVM achieved the best results in accuracy (92.34% and 91.71% ) for the ISIC 2019 dataset. The results also show that the suggested method outperforms others approaches with an accuracy of 99% on the PH2 dataset.

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CNN-SVM Learning Approach Based Human Activity Recognition

Cited by 51 publications

References 25 publications

Internet of Things-Based Intelligent Smart Home Control System

Internet of Things-Based Intelligent Smart Home Control System

Machine learning models to characterize the cough signal of patients with COVID-19

Multi-features extraction based on deep learning for skin lesion classification

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