Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning

Jogin, Manjunath; Mohana,; Madhulika, M S; Divya, Gandikota; Meghana, R K; Apoorva, SM

doi:10.1109/rteict42901.2018.9012507

Cited by 320 publications

(131 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The layers allow filter application and features extraction [ 108 ] based on the input EEG signals. The equation below presents the convolution operation.…”

Section: Methodsmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

View full text Add to dashboard Cite

Drowsiness detection (DD) has become a relevant area of active research in biomedical signal processing. Recently, various deep learning (DL) researches based on the EEG signals have been proposed to detect fatigue conditions. The research presented in this paper proposes an EEG classification system for DD based on DL networks. However, the proposed DD system is mainly realized into two procedures; (i) data acquisition and (ii) model analysis. For the data acquisition procedure, two key steps are considered, which are the signal collection using a wearable Emotiv EPOC+ headset to record 14 channels of EEG, and the signal annotation. Furthermore, a data augmentation (DA) step has been added to the proposed system to overcome the problem of over-fitting and to improve accuracy. As regards the model analysis, a comparative study is also introduced in this paper to argue the choice of DL architecture and frameworks used in our DD system. In this sense, The proposed DD protocol makes use of a convolutional neural network (CNN) architecture implemented using the Keras library. The results showed a high accuracy value (90.42%) in drowsy/awake discrimination and revealed the efficiency of the proposed DD system compared to other research works.

show abstract

“…The layers allow filter application and features extraction [ 108 ] based on the input EEG signals. The equation below presents the convolution operation.…”

Section: Methodsmentioning

confidence: 99%

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Chaabene

Bouaziz

Boudaya

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…SVM is a functioning algorithm, as shown in equation 2, where l is the label from 0 to 1, w. a − q is the output, w and q are the linear category coefficients, and a is the input vector. Equation 3will enforce the loss function that is to be reduced (Çayir et al 2018;Jogin et al 2018).…”

Section: Support Vector Machinementioning

confidence: 99%

RETRACTED ARTICLE: A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell

et al. 2020

View full text Add to dashboard Cite

Coronavirus pandemic is burdening healthcare systems around the world to the full capacity they can accommodate. There is an overwhelming need to find a treatment for this virus as early as possible. Computer algorithms and deep learning can participate positively by finding a potential treatment for SARS-CoV-2. In this paper, a deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell will be presented. The dataset selected in this work is a subset of the publicly online datasets available on RxRx.ai. The objective of this research is to automatically classify a single human cell according to the treatment type and the treatment concentration level. A DCNN model and a methodology are proposed throughout this work. The methodical idea is to convert the numerical features from the original dataset to the image domain and then fed them up into a DCNN model. The proposed DCNN model consists of three convolutional layers, three ReLU layers, three pooling layers, and two fully connected layers. The experimental results show that the proposed DCNN model for treatment classification (32 classes) achieved 98.05% in testing accuracy if it is compared with classical machine learning such as support vector machine, decision tree, and ensemble. In treatment concentration level prediction, the classical machine learning (ensemble) algorithm achieved 98.5% in testing accuracy while the proposed DCNN model achieved 98.2%. The performance metrics strengthen the obtained results from the conducted experiments for the accuracy of treatment classification and treatment concentration level prediction.

show abstract

“…The concatenated features from the AlexNet and VGG16 models achieved a detection accuracy of 88%. Jogin et al modified the AlexNet model for feature extraction on the CIFAR-10 dataset [38], and reported an image classification accuracy of 85.97%. Tranget et al classified plant diseases using a convolutional autoencoder [39] with two kernels of coder layer to extract the plant leaf images.…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Jaruenpunyasak

Duangsoithong

2021

IEEE Access

View full text Add to dashboard Cite

Deep learning algorithms are employed in many applications, especially in medical fields such as gait analysis and human pose detection for rehabilitation. However, creating the desired model with deep learning algorithms requires high memory and computing costs, which is problematic because deep learning technologies must be run on low-power devices such as edge computing equipment. To deal with these problems, feature reduction methods reduce the memory and energy costs. This paper presents an empirical analysis of deep learning with feature reduction. The method classifies foot images for knee rehabilitation using convolutional and dense autoencoders. The obtained results are compared with those of conventional methods (histograms of oriented gradients and local binary pattern algorithms). The features were classified and compared using support vector machine, k-nearest neighbor, and multilayer perceptron methods. The experimental results demonstrate that the conventional method uses fewer features than the deep learning method with higher accuracy because its algorithm projects pixels onto the histogram. In addition, using fewer features in deep learning layers maintains high accuracy, which is beneficial for edge computing implementations.

show abstract

Feature Extraction using Convolution Neural Networks (CNN) and Deep Learning

Cited by 320 publications

References 6 publications

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

Convolutional Neural Network for Drowsiness Detection Using EEG Signals

RETRACTED ARTICLE: A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell

Empirical Analysis of Feature Reduction in Deep Learning and Conventional Methods for Foot Image Classification

Contact Info

Product

Resources

About