scholarly journals Evaluation of Hyperparameter Optimization in Machine and Deep Learning Methods for Decoding Imagined Speech EEG

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4629 ◽  
Author(s):  
Ciaran Cooney ◽  
Attila Korik ◽  
Raffaella Folli ◽  
Damien Coyle
Keyword(s):  
Deep Learning ◽  
Support Vector ◽  
Great Success ◽  
Hyperparameter Optimization ◽  
Linear Discriminant ◽  
Classifier Performance ◽  
Open Question ◽  
The Impact ◽  
Regularized Linear Discriminant Analysis

Classification of electroencephalography (EEG) signals corresponding to imagined speech production is important for the development of a direct-speech brain–computer interface (DS-BCI). Deep learning (DL) has been utilized with great success across several domains. However, it remains an open question whether DL methods provide significant advances over traditional machine learning (ML) approaches for classification of imagined speech. Furthermore, hyperparameter (HP) optimization has been neglected in DL-EEG studies, resulting in the significance of its effects remaining uncertain. In this study, we aim to improve classification of imagined speech EEG by employing DL methods while also statistically evaluating the impact of HP optimization on classifier performance. We trained three distinct convolutional neural networks (CNN) on imagined speech EEG using a nested cross-validation approach to HP optimization. Each of the CNNs evaluated was designed specifically for EEG decoding. An imagined speech EEG dataset consisting of both words and vowels facilitated training on both sets independently. CNN results were compared with three benchmark ML methods: Support Vector Machine, Random Forest and regularized Linear Discriminant Analysis. Intra- and inter-subject methods of HP optimization were tested and the effects of HPs statistically analyzed. Accuracies obtained by the CNNs were significantly greater than the benchmark methods when trained on both datasets (words: 24.97%, p < 1 × 10–7, chance: 16.67%; vowels: 30.00%, p < 1 × 10–7, chance: 20%). The effects of varying HP values, and interactions between HPs and the CNNs were both statistically significant. The results of HP optimization demonstrate how critical it is for training CNNs to decode imagined speech.

A Study on the Auxiliary Diagnosis of Thyroid Disease Images Based on Multiple Dimensional Deep Learning Algorithms

2020 ◽  
Vol 16 (3) ◽  
pp. 199-205
Author(s):  
Yuejun Liu ◽  
Yifei Xu ◽  
Xiangzheng Meng ◽  
Xuguang Wang ◽  
Tianxu Bai
Keyword(s):  
Deep Learning ◽  
Learning Algorithms ◽  
Region Of Interest ◽  
Classification Performance ◽  
Thyroid Diseases ◽  
Great Success ◽  
Learning Models ◽  
Good Classification Performance ◽  
Spect Images

Background: Medical imaging plays an important role in the diagnosis of thyroid diseases. In the field of machine learning, multiple dimensional deep learning algorithms are widely used in image classification and recognition, and have achieved great success. Objective: The method based on multiple dimensional deep learning is employed for the auxiliary diagnosis of thyroid diseases based on SPECT images. The performances of different deep learning models are evaluated and compared. Methods: Thyroid SPECT images are collected with three types, they are hyperthyroidism, normal and hypothyroidism. In the pre-processing, the region of interest of thyroid is segmented and the amount of data sample is expanded. Four CNN models, including CNN, Inception, VGG16 and RNN, are used to evaluate deep learning methods. Results: Deep learning based methods have good classification performance, the accuracy is 92.9%-96.2%, AUC is 97.8%-99.6%. VGG16 model has the best performance, the accuracy is 96.2% and AUC is 99.6%. Especially, the VGG16 model with a changing learning rate works best. Conclusion: The standard CNN, Inception, VGG16, and RNN four deep learning models are efficient for the classification of thyroid diseases with SPECT images. The accuracy of the assisted diagnostic method based on deep learning is higher than that of other methods reported in the literature.

scholarly journals Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 495
Author(s):  
Imayanmosha Wahlang ◽  
Arnab Kumar Maji ◽  
Goutam Saha ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Support Vector ◽  
Variational Autoencoder ◽  
Different Types ◽  
Static Images ◽  
Long Short Term Memory ◽  
2D And 3D ◽  
Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

Identification and Classification of Fungal Colonies in Moldy Paddy Based on Computer Vision

2018 ◽  
Vol 61 (5) ◽  
pp. 1497-1504
Author(s):  
Zhenjie Wang ◽  
Ke Sun ◽  
Lihui Du ◽  
Jian Yuan ◽  
Kang Tu ◽  
...  
Keyword(s):  
Computer Vision ◽  
Discriminant Analysis ◽  
Texture Features ◽  
Fungal Species ◽  
Projection Algorithm ◽  
Support Vector ◽  
Linear Discriminant ◽  
Sequential Selection ◽  
The Individual

Abstract. In this study, computer vision was used for the identification and classification of fungi on moldy paddy. To develop a rapid and efficient method for the classification of common fungal species found in stored paddy, computer vision was used to acquire images of individual colonies of growing fungi for three consecutive days. After image processing, the color, shape, and texture features were acquired and used in a subsequent discriminant analysis. Both linear (i.e., linear discriminant analysis and partial least squares discriminant analysis) and nonlinear (i.e., random forest and support vector machine [SVM]) pattern recognition models were employed for the classification of fungal colonies, and the results were compared. The results indicate that when using all of the features for three consecutive days, the performance of the nonlinear tools was superior to that of the linear tools, especially in the case of the SVM models, which achieved an accuracy of 100% on the calibration sets and an accuracy of 93.2% to 97.6% on the prediction sets. After sequential selection of projection algorithm, ten common features were selected for building the classification models. The results showed that the SVM model achieved an overall accuracy of 95.6%, 98.3%, and 99.0% on the prediction sets on days 2, 3, and 4, respectively. This work demonstrated that computer vision with several features is suitable for the identification and classification of fungi on moldy paddy based on the form of the individual colonies at an early growth stage during paddy storage. Keywords: Classification, Computer vision, Fungal colony, Feature selection, SVM.

scholarly journals Statistical and Visual Analysis of Audio, Text, and Image Features for Multi-Modal Music Genre Recognition

Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1502
Author(s):  
Ben Wilkes ◽  
Igor Vatolkin ◽  
Heinrich Müller
Keyword(s):  
Visual Analysis ◽  
Binary Classification ◽  
Statistical Significance ◽  
Image Features ◽  
Support Vector ◽  
Audio Signals ◽  
Text And Image ◽  
The Impact ◽  
Music Track

We present a multi-modal genre recognition framework that considers the modalities audio, text, and image by features extracted from audio signals, album cover images, and lyrics of music tracks. In contrast to pure learning of features by a neural network as done in the related work, handcrafted features designed for a respective modality are also integrated, allowing for higher interpretability of created models and further theoretical analysis of the impact of individual features on genre prediction. Genre recognition is performed by binary classification of a music track with respect to each genre based on combinations of elementary features. For feature combination a two-level technique is used, which combines aggregation into fixed-length feature vectors with confidence-based fusion of classification results. Extensive experiments have been conducted for three classifier models (Naïve Bayes, Support Vector Machine, and Random Forest) and numerous feature combinations. The results are presented visually, with data reduction for improved perceptibility achieved by multi-objective analysis and restriction to non-dominated data. Feature- and classifier-related hypotheses are formulated based on the data, and their statistical significance is formally analyzed. The statistical analysis shows that the combination of two modalities almost always leads to a significant increase of performance and the combination of three modalities in several cases.

The impact of different parameter sets on the classification of asteroid types

2021 ◽  
Author(s):  
Hanna Klimczak ◽  
Wojciech Kotłowski ◽  
Dagmara Oszkiewicz ◽  
Francesca DeMeo ◽  
Agnieszka Kryszczyńska ◽  
...  
Keyword(s):  
Gradient Boosting ◽  
Support Vector ◽  
Multilayer Perceptrons ◽  
Machine Learning Methods ◽  
Vector Machines ◽  
Science Centre ◽  
The Difference ◽  
The Impact

&lt;p&gt;The aim of the project is the classification of asteroids according to the most commonly used asteroid taxonomy (Bus-Demeo et al. 2009) with the use of various machine learning methods like Logistic Regression, Naive Bayes, Support Vector Machines, Gradient Boosting and Multilayer Perceptrons. Different parameter sets are used for classification in order to compare the quality of prediction with limited amount of data, namely the difference in performance between using the 0.45mu to 2.45mu spectral range and multiple spectral features, as well as performing the Prinicpal Component Analysis to reduce the dimensions of the spectral data.&lt;/p&gt; &lt;p&gt;&amp;#160;&lt;/p&gt; &lt;p&gt;This work has been supported by grant&amp;#160;No. 2017/25/B/ST9/00740 from the National Science Centre, Poland.&lt;/p&gt;

scholarly journals The Impact of Emotional Music on Active ROI in Patients with Depression Based on Deep Learning: A Task-State fMRI Study

2019 ◽  
Vol 2019 ◽  
pp. 1-14
Author(s):  
Renzhou Gui ◽  
Tongjie Chen ◽  
Han Nie
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Correlation Matrix ◽  
Nearest Neighbor ◽  
Learning Model ◽  
Fmri Data ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
The Impact ◽  
Deep Learning Model

With the continuous development of science, more and more research results have proved that machine learning is capable of diagnosing and studying the major depressive disorder (MDD) in the brain. We propose a deep learning network with multibranch and local residual feedback, for four different types of functional magnetic resonance imaging (fMRI) data produced by depressed patients and control people under the condition of listening to positive- and negative-emotions music. We use the large convolution kernel of the same size as the correlation matrix to match the features and obtain the results of feature matching of 264 regions of interest (ROIs). Firstly, four-dimensional fMRI data are used to generate the two-dimensional correlation matrix of one person’s brain based on ROIs and then processed by the threshold value which is selected according to the characteristics of complex network and small-world network. After that, the deep learning model in this paper is compared with support vector machine (SVM), logistic regression (LR), k-nearest neighbor (kNN), a common deep neural network (DNN), and a deep convolutional neural network (CNN) for classification. Finally, we further calculate the matched ROIs from the intermediate results of our deep learning model which can help related fields further explore the pathogeny of depression patients.

scholarly journals Discriminating Quasars from Stars Based on SDSS and UKIDSS Databases

2012 ◽  
Vol 8 (S295) ◽  
pp. 180-180
Author(s):  
He Ma ◽  
Yanxia Zhang ◽  
Yongheng Zhao ◽  
Bo Zhang
Keyword(s):  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Linear Discriminant Analysis ◽  
Experimental Result ◽  
Support Vector ◽  
Linear Discriminant ◽  
Vector Machines ◽  
Joint Approach

AbstractIn this work, two different algorithms: Linear Discriminant Analysis (LDA) and Support Vector Machines (SVMs) are combined for the classification of unresolved sources from SDSS DR8 and UKIDSS DR8. The experimental result shows that this joint approach is effective for our case.

scholarly journals A Deep Learning Approach for Classification and Diagnosis of Parkinson’s Disease

2021 ◽  
Author(s):  
Monika Jyotiyana ◽  
Nishtha Kesswani ◽  
Munish Kumar
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Nearest Neighbor ◽  
Health Sector ◽  
Classification Model ◽  
Support Vector ◽  
Learning Approaches ◽  
K Nearest Neighbor ◽  
Linear Discriminant

Abstract Deep learning techniques are playing an important role in the classification and prediction of diseases. Undoubtedly deep learning has a promising future in the health sector, especially in medical imaging. The popularity of deep learning approaches is because of their ability to handle a large amount of data related to the patients with accuracy, reliability in a short span of time. However, the practitioners may take time in analyzing and generating reports. In this paper, we have proposed a Deep Neural Network-based classification model for Parkinson’s disease. Our proposed method is one such good example giving faster and more accurate results for the classification of Parkinson’s disease patients with excellent accuracy of 94.87%. Based on the attributes of the dataset of the patient, the model can be used for the identification of Parkinsonism's. We have also compared the results with other existing approaches like Linear Discriminant Analysis, Support Vector Machine, K-Nearest Neighbor, Decision Tree, Classification and Regression Trees, Random Forest, Linear Regression, Logistic Regression, Multi-Layer Perceptron, and Naive Bayes.

scholarly journals Multivariate characterisation of Italian monovarietal red wines using MIR spectroscopy

OENO One ◽  
2019 ◽  
Vol 53 (4) ◽  
Author(s):  
Giuseppina P. Parpinello ◽  
Arianna Ricci ◽  
Panagiotis Arapitsas ◽  
Andrea Curioni ◽  
Luigi Moio ◽  
...  
Keyword(s):  
Principal Component ◽  
Rapid Screening ◽  
Support Vector ◽  
Red Wines ◽  
Grape Variety ◽  
Absorption Bands ◽  
Linear Discriminant ◽  
Mir Spectroscopy ◽  
Grape Varieties

Aim: The aim of this study was to investigate the application of mid-infrared (MIR) spectroscopy combined with multivariate analysis, to provide a rapid screening tool for discriminating among different Italian monovarietal red wines based on the relationship between grape variety and wine composition in particular phenolic compounds.Methods and results: The MIR spectra (from 4000 to 700 cm‒1) of 110 monovarietal Italian red wines, vintage 2016, were collected and evaluated by selected multivariate data analyses, including principal component analysis (PCA), linear discriminant analysis (DA), support vector machine (SVM), and soft intelligent modelling of class analogy (SIMCA). Samples were collected directly from companies across different regions of Italy and included 11 grape varieties: Sangiovese, Nebbiolo, Aglianico, Nerello Mascalese, Primitivo, Raboso, Cannonau, Teroldego, Sagrantino, Montepulciano and Corvina. PCA showed five wavelengths that mainly contributed to the PC1, including a much-closed peak at 1043 cm‒1, which correspond to the C–O stretch absorption bands that are important regions for glycerol, whereas the ethanol peaks at around 1085 cm‒1. The band at 877 cm‒1 are related to the C–C stretching vibration of organic molecules, whereas the asymmetric stretching for C–O in the aromatic –OH group of polyphenols is within spectral regions from 1050 to 1165 cm‒1. In particular, the (1175)–1100–1060 cm‒1 vibrational bands are combination bands, involving C–O stretching and O–H deformation of phenolic rings. The 1166–1168 cm‒1 peak is attributable to in-plane bending deformations of C–H and C–O groups of polyphenols, respectively, for which polymerisation may cause a slight peak shift due to the formation of H-bridges.The best result was obtained with the SVM, which achieved an overall correct classification for up to 72.2% of the training set, and 44.4% for the validation set of wines, respectively. The Sangiovese wines (n=19) were split into two sub-groups (Sang-Romagna, n=12 and Sang-Tuscany, n=7) considering the indeterminacy of its origins, which is disputed between Romagna and Tuscany. Although the classification of three grape varieties was problematic (Nerello Mascalese, Raboso and Primitivo), the remaining wines were almost correctly assigned to their actual classes.Conclusions: MIR spectroscopy coupled with chemometrics represents an interesting approach for the classification of monovarietal Italian red wines, which is important in quality control and authenticity monitoring.Significance and impact of the study: Authenticity is a main issue in winemaking in terms of quality evaluation and adulteration, in particular for origin certified/protected wines, for which the added marketing value is related to the link of grape variety with the area of origin. This study is part of the D-wine project “The diversity of tannins in Italian red wines”.

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