Performance analysis of machine learning algorithms on automated sleep staging feature sets

Satapathy, Santosh Kumar; Loganathan, D.; Kondaveeti, Hari Kishan; Rath, Ramakrushna

doi:10.1049/cit2.12042

Cited by 31 publications

(11 citation statements)

References 94 publications

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“…The Area Under the Curve (AUC) is a popular metric used in the industry [15], which represents the area under the Receiver Operating Characteristic (ROC) or the Precision Recall (PR) curve. The PR curve illustrates the trade‐off between precision and recall.…”

Section: Results and Comparisonsmentioning

confidence: 99%

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Feature‐based augmentation and classification for tabular data

Sathianarayanan

Samant

Guruprasad

et al. 2022

CAAI Trans on Intel Tech

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Generating synthetic samples for a tabular data is a strenuous task. Most of the time, the columns (features) in the dataset may not follow an ideal distribution function. The objective of the proposed algorithm, Histogram Augmentation Technique (HAT), is to generate a dataset whose distribution is similar to that of the original dataset. This augmentation is achieved based on individual columns, where separate algorithms are designed for continuous and discrete columns. Humans also use features of an object for interpretation. When humans make a judgement, they notice prominent features and characterise the perceived object. However, conventional Machine Learning classifiers are designed and trained on the basis of samples. Taking the features as the basis for classification, Feature Importance Classifier (FIC) has been attempted in this work. FIC treats every feature independent of each other, and ranks the features based on its dependence with the classified label. It has been found that the FIC has the highest accuracy and has improved the accuracy by 5.54% on average, when it's compared to other classifiers. The suggested algorithms have been experimented on five datasets and compared with two augmentation algorithms and four state‐of‐the‐art ML classification algorithms.

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Section: Results and Comparisonsmentioning

confidence: 99%

“…Currently, a lot of technology has leaped light years ahead because of improvements in ML [13, 14]. As the world moves towards an intelligent tomorrow, it is important to make constant experimentation [15] for new algorithms.…”

Section: Introductionmentioning

confidence: 99%

Feature‐based augmentation and classification for tabular data

Sathianarayanan

Samant

Guruprasad

et al. 2022

CAAI Trans on Intel Tech

View full text Add to dashboard Cite

show abstract

“…In the first approach, DNN [ 36 , 37 ] is used along with dropout layers. The architecture for the DNN is shown in Figure 2 .…”

Section: Methodsmentioning

confidence: 99%

Two-Way Feature Extraction for Speech Emotion Recognition Using Deep Learning

Aggarwal

Srivastava

Agarwal³

et al. 2022

Sensors

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Recognizing human emotions by machines is a complex task. Deep learning models attempt to automate this process by rendering machines to exhibit learning capabilities. However, identifying human emotions from speech with good performance is still challenging. With the advent of deep learning algorithms, this problem has been addressed recently. However, most research work in the past focused on feature extraction as only one method for training. In this research, we have explored two different methods of extracting features to address effective speech emotion recognition. Initially, two-way feature extraction is proposed by utilizing super convergence to extract two sets of potential features from the speech data. For the first set of features, principal component analysis (PCA) is applied to obtain the first feature set. Thereafter, a deep neural network (DNN) with dense and dropout layers is implemented. In the second approach, mel-spectrogram images are extracted from audio files, and the 2D images are given as input to the pre-trained VGG-16 model. Extensive experiments and an in-depth comparative analysis over both the feature extraction methods with multiple algorithms and over two datasets are performed in this work. The RAVDESS dataset provided significantly better accuracy than using numeric features on a DNN.

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“…Features in X-ray or CT images are extracted for COVID-19 diagnosis by segmenting regions of interest and capturing fine structures. One of the important subfields of AI is machine learning (ML) [ 18 ]. It is already widely applied to medical images [ 19 , 20 , 21 ].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Deep Learning in COVID-19 Diagnosis

Han

Wang

et al. 2022

J. Imaging

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According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.

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Performance analysis of machine learning algorithms on automated sleep staging feature sets

Cited by 31 publications

References 94 publications

Feature‐based augmentation and classification for tabular data

Feature‐based augmentation and classification for tabular data

Two-Way Feature Extraction for Speech Emotion Recognition Using Deep Learning

A Survey on Deep Learning in COVID-19 Diagnosis

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