36,254 research outputs found

    Advanced deep learning approaches for biosingnals applications

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    University of Technology Sydney. Faculty of Engineering and Information Technology.A wide gap exists between clinical application results and those from laboratory observations concerning hand rehabilitation devices. In most instances, laboratory observations show superior outcomes the real-time applications demonstrate poor consequences. The robust nature of the electromyography signal and limited laboratory applications are the principal reasons for the gap. This thesis aims to introduce and develop a deep learning model that is capable of learning features from biosignals. The deep learning model is expected to tame the variable nature of the electromyography signal which will lead to the best available outcomes. Furthermore, the suggested deep learning scheme will be trained to be skilled in learning the best features that match the biosignal application regardless of the number of classes. Moreover, traditional feature extraction is time consuming and extremely reliant on the user’s experience and the application. The objective of this research is accomplished via the following four implemented models. 1. Developing a deep learning model via implementing a two-stage autoencoder along with applying different signal representations like spectrogram, wavelet and wavelet packet to tame variations of the electromyography signal. Support vector machine, extreme learning machine with two activation functions (sigmoid and radial basis function) and softmax layer were used for classifications. Moreover, the classifier fusion layer achieved testing accuracy of more than 92% and training attained more than 98%. The same dataset was implemented for superimposed signal representations for two stages autoencoder and softmax layer, support vector machine, k-nearest neighbor and discriminant analysis for classification besides the classifier fusion which led to testing accuracy of more than 90%. 2. Presenting principal component analysis and independent component analysis for feature learning purposes after applying different signal representations algorithms such as spectrogram, wavelet and wavelet packet. Discriminant analysis, extreme learning machine and support vector machine were used for classification. Furthermore, the two proposed models showed acceptable accuracy along with shorter simulation time. The testing accuracy achieved more than 90% by implementing a classifier fusion layer. Manhattan index was estimated for all features and only the top 50 Manhattan index features were included to decrease the simulation time while attaining acceptable accuracy values. 3. Introducing a self-organising map for deep learning whereby the biosignal was represented by spectrograms, wavelet and wavelet packet. The presented biosignal was introduced to a layer of self- organising map then the suggested system performance was evaluated by extreme learning machine, self-adaptive evolutionally extreme learning machine, discriminant analysis and support vector machine for classification. Adding a classifier fusion layer increased the testing accuracy to 96.60% for ten-finger movements and 99.73% for training. The proposed system showed superior behavior regarding accuracy and simulation time. 4. Presenting a deep learning model where 1) the data was augmented after representing the biosignal by a spectrogram, 2) the augmented signal was represented by a tensor, and finally 3) The signal was introduced to the two-stage autoencoder. The same dataset was used with traditional pattern recognition for comparison purposes. Classifier fusion layer was executed in deep learning scheme whereby the ten-finger movements achieved 90.25% and 87.11% attained by pattern recognition. Besides, the six finger movement dataset was acquired from amputee participants and accomplished 91.85% for deep learning and reached 89.64% for traditional pattern recognition. Furthermore, different datasets for different applications were tested using the recommended deep learning model. Eventually, feeding the deep learning model with various datasets for different applications afforded the model with higher fidelity, combined with real outcomes and generalization

    Robust Retinal Vessel Segmentation using ELM and SVM Classifier

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    The diagnosis of retinal blood vessels is of much clinical importance, as they are generally examined to evaluate and monitor both the ophthalmological diseases and the non-retinal diseases. The vascular nature of retinal is very complex and the manual segmentation process is tedious. It requires more time and skill. In this paper, a novel supervised approach using Extreme Learning Machine (ELM) classifier and Support Vector Machine (SVM) classifier is proposed to segment the retinal blood vessel. This approach calculates 7-D feature vector comprises of green channel intensity, Median-Local Binary Pattern (M-LBP), Stroke Width Transform (SWT) response, Weber�s Local Descriptor (WLD) measure, Frangi�s vesselness measure, Laplacian Of Gaussian (LOG) filter response and morphological bottom-hat transform. This 7-D vector is given as input to the ELM classifier to classify each pixel as vessel or non-vessel. The primary vessel map from the ELM classifier is combined with the ridges detected from the enhanced bottom-hat transformed image. Then the high-level features computed from the combined image are used for final classification using SVM. The performance of this technique was evaluated on the publically available databases like DRIVE, STARE and CHASE-DB1. The result demonstrates that the proposed approach is very fast and achieves high accuracy about 96.1% , 94.4% and 94.5% for DRIVE, STARE and CHASE-DB1 respectively

    Detection of atrial fibrillation episodes in long-term heart rhythm signals using a support vector machine

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    Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.Web of Science203art. no. 76

    Augmenting Data with Generative Adversarial Networks to Improve Machine Learning-Based Fraud Detection

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    While current machine learning methods can detect financial fraud more effectively, they suffer from a common problem: dataset imbalance, i.e. there are substantially more non-fraud than fraud cases. In this paper, we propose the application of generative adversarial networks (GANs) to generate synthetic fraud cases on a dataset of public firms convicted by the United States Securities and Exchange Commission for accounting malpractice. This approach aims to increase the prediction accuracy of a downstream logit, support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) classifier by training on a more well-balanced dataset. While the results indicate that a state-of-the-art machine learning model like XGBoost can outperform previous fraud detection models on the same data, generating synthetic fraud cases before applying a machine learning model does not improve performance

    Comparison of Different Machine Learning and Self-Learning Methods for Predicting Obesity on Generalized and Gender-Segregated Data

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    Obesity is a global health concern with long-term implications. Our research applies numerous Machine Learning models consisting of  Random Forest model, XGBT(Extreme Gradient Boosting) model, Decision Tree model, k-Nearest Neighbors technique, Support Vector Machine model, Linear Regression model, Naïve Bayes classifier  and a neural network named Multilayer Perceptron on an obesity dataset so that we can predict obesity and reduce it. The models are evaluated on recall, accuracy, F1-score, and precision. The findings reveal the performance of the algorithms on generalised and gender-segregated data providing insights concerning feature selection and early obesity identification. This research aims to demonstrate the comparative study of obesity prediction for gender-neutral and gender-specific datasets

    Land use/land cover classification using machine learning models

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    An ensemble model has been proposed in this work by combining the extreme gradient boosting classification (XGBoost) model with support vector machine (SVM) for land use and land cover classification (LULCC). We have used the multispectral Landsat-8 operational land imager sensor (OLI) data with six spectral bands in the electromagnetic spectrum (EM). The area of study is the administrative boundary of the twin cities of Odisha. Data collected in 2020 is classified into seven land use classes/labels: river, canal, pond, forest, urban, agricultural land, and sand. Comparative assessments of the results of ten machine learning models are accomplished by computing the overall accuracy, kappa coefficient, producer accuracy and user accuracy. An ensemble classifier model makes the classification more precise than the other state-of-the-art machine learning classifiers

    Complex extreme learning machine applications in terahertz pulsed signals feature sets

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    This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets
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