scholarly journals Gender Classification Based on The Non-Lexical Cues Of Emergency Calls With Recurrent Neural Networks (RNN)

Symmetry ◽  
2019 ◽  
Vol 11 (4) ◽  
pp. 525 ◽  
Author(s):  
SON ◽  
KWON ◽  
PARK
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Spectral Characteristics ◽  
Behavioral Approach ◽  
Support Vector ◽  
Gender Classification ◽  
Acoustic Feature ◽  
Emergency Calls ◽  
Wide Range ◽  
Speech Features

Automatic gender classification in speech is a challenging research field with a wide range of applications in HCI (humancomputer interaction). A couple of decades of research have shown promising results, but there is still a need for improvement. Until now, gender classification has been made using differences in the spectral characteristics of males and females. We assumed that a neutral margin exists between the male and female spectral range. This margin causes misclassification of gender. To address this limitation, we studied three non-lexical speech features (fillers, overlapping, and lengthening). From the statistical analysis, we found that overlapping and lengthening are effective in gender classification. Next, we performed gender classification using overlapping, lengthening, and the baseline acoustic feature, Mel Frequency Cepstral Coefficient (MFCC). We have tried to achieve the best results by using various combinations of features at the same time or sequentially. We used two types of machine-learning methods, support vector machine (SVM) and recurrent neural networks (RNN), to classify the gender. We achieved 89.61% with RNN using a feature set including MFCC, overlapping, and lengthening at the same time. Also, we have reclassified using non-lexical features with only data belonging to the neutral margin which was empirically selected based on the result of gender classification with only MFCC. As a result, we determined that the accuracy of classification with RNN using lengthening was 1.83% better than when MFCC alone was used. We concluded that new speech features could be effective in improving gender classification through a behavioral approach, notably including emergency calls.

scholarly journals Machine-Learning-Based Muscle Control of a 3D-Printed Bionic Arm

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3144 ◽  
Author(s):  
Sherif Said ◽  
Ilyes Boulkaibet ◽  
Murtaza Sheikh ◽  
Abdullah S. Karar ◽  
Samer Alkork ◽  
...  
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machine ◽  
Experimental Test ◽  
Support Vector Machine Classifier ◽  
Low Cost ◽  
Support Vector ◽  
Wide Range ◽  
3D Printed ◽  
Semg Signals

In this paper, a customizable wearable 3D-printed bionic arm is designed, fabricated, and optimized for a right arm amputee. An experimental test has been conducted for the user, where control of the artificial bionic hand is accomplished successfully using surface electromyography (sEMG) signals acquired by a multi-channel wearable armband. The 3D-printed bionic arm was designed for the low cost of 295 USD, and was lightweight at 428 g. To facilitate a generic control of the bionic arm, sEMG data were collected for a set of gestures (fist, spread fingers, wave-in, wave-out) from a wide range of participants. The collected data were processed and features related to the gestures were extracted for the purpose of training a classifier. In this study, several classifiers based on neural networks, support vector machine, and decision trees were constructed, trained, and statistically compared. The support vector machine classifier was found to exhibit an 89.93% success rate. Real-time testing of the bionic arm with the optimum classifier is demonstrated.

scholarly journals DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy

2020 ◽  
Author(s):  
Ruopeng Xie ◽  
Jiahui Li ◽  
Jiawei Wang ◽  
Wei Dai ◽  
André Leier ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Virulence Factors ◽  
Bacterial Genome ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
Support Vector ◽  
Wide Range ◽  
Extreme Gradient Boosting ◽  
Hybrid Framework

Abstract Virulence factors (VFs) enable pathogens to infect their hosts. A wealth of individual, disease-focused studies has identified a wide variety of VFs, and the growing mass of bacterial genome sequence data provides an opportunity for computational methods aimed at predicting VFs. Despite their attractive advantages and performance improvements, the existing methods have some limitations and drawbacks. Firstly, as the characteristics and mechanisms of VFs are continually evolving with the emergence of antibiotic resistance, it is more and more difficult to identify novel VFs using existing tools that were previously developed based on the outdated data sets; secondly, few systematic feature engineering efforts have been made to examine the utility of different types of features for model performances, as the majority of tools only focused on extracting very few types of features. By addressing the aforementioned issues, the accuracy of VF predictors can likely be significantly improved. This, in turn, would be particularly useful in the context of genome wide predictions of VFs. In this work, we present a deep learning (DL)-based hybrid framework (termed DeepVF) that is utilizing the stacking strategy to achieve more accurate identification of VFs. Using an enlarged, up-to-date dataset, DeepVF comprehensively explores a wide range of heterogeneous features with popular machine learning algorithms. Specifically, four classical algorithms, including random forest, support vector machines, extreme gradient boosting and multilayer perceptron, and three DL algorithms, including convolutional neural networks, long short-term memory networks and deep neural networks are employed to train 62 baseline models using these features. In order to integrate their individual strengths, DeepVF effectively combines these baseline models to construct the final meta model using the stacking strategy. Extensive benchmarking experiments demonstrate the effectiveness of DeepVF: it achieves a more accurate and stable performance compared with baseline models on the benchmark dataset and clearly outperforms state-of-the-art VF predictors on the independent test. Using the proposed hybrid ensemble model, a user-friendly online predictor of DeepVF (http://deepvf.erc.monash.edu/) is implemented. Furthermore, its utility, from the user’s viewpoint, is compared with that of existing toolkits. We believe that DeepVF will be exploited as a useful tool for screening and identifying potential VFs from protein-coding gene sequences in bacterial genomes.

scholarly journals Automatic sleep staging using heart rate variability, body movements, and recurrent neural networks in a sleep disordered population

SLEEP ◽  
2020 ◽  
Vol 43 (9) ◽  
Author(s):  
Pedro Fonseca ◽  
Merel M van Gilst ◽  
Mustafa Radha ◽  
Marco Ross ◽  
Arnaud Moreau ◽  
...  
Keyword(s):  
Neural Networks ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Sleep Disorders ◽  
Recurrent Neural Networks ◽  
Clinical Diagnostics ◽  
Sleep Staging ◽  
Substantial Agreement ◽  
Body Movements ◽  
Wide Range

Abstract Study Objectives To validate a previously developed sleep staging algorithm using heart rate variability (HRV) and body movements in an independent broad cohort of unselected sleep disordered patients. Methods We applied a previously designed algorithm for automatic sleep staging using long short-term memory recurrent neural networks to model sleep architecture. The classifier uses 132 HRV features computed from electrocardiography and activity counts from accelerometry. We retrained our algorithm using two public datasets containing both healthy sleepers and sleep disordered patients. We then tested the performance of the algorithm on an independent hold-out validation set of sleep recordings from a wide range of sleep disorders collected in a tertiary sleep medicine center. Results The classifier achieved substantial agreement on four-class sleep staging (wake/N1–N2/N3/rapid eye movement [REM]), with an average κ of 0.60 and accuracy of 75.9%. The performance of the sleep staging algorithm was significantly higher in insomnia patients (κ = 0.62, accuracy = 77.3%). Only in REM parasomnias, the performance was significantly lower (κ = 0.47, accuracy = 70.5%). For two-class wake/sleep classification, the classifier achieved a κ of 0.65, with a sensitivity (to wake) of 72.9% and specificity of 94.0%. Conclusions This study shows that the combination of HRV, body movements, and a state-of-the-art deep neural network can reach substantial agreement in automatic sleep staging compared with polysomnography, even in patients suffering from a multitude of sleep disorders. The physiological signals required can be obtained in various ways, including non-obtrusive wrist-worn sensors, opening up new avenues for clinical diagnostics.

scholarly journals Simple framework for constructing functional spiking recurrent neural networks

2019 ◽  
Vol 116 (45) ◽  
pp. 22811-22820 ◽  
Author(s):  
Robert Kim ◽  
Yinghao Li ◽  
Terrence J. Sejnowski
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Continuous Variable ◽  
Model Parameters ◽  
Additional Parameter ◽  
Wide Range ◽  
Close Relationship ◽  
Rate Coding ◽  
One To One ◽  
The One

Cortical microcircuits exhibit complex recurrent architectures that possess dynamically rich properties. The neurons that make up these microcircuits communicate mainly via discrete spikes, and it is not clear how spikes give rise to dynamics that can be used to perform computationally challenging tasks. In contrast, continuous models of rate-coding neurons can be trained to perform complex tasks. Here, we present a simple framework to construct biologically realistic spiking recurrent neural networks (RNNs) capable of learning a wide range of tasks. Our framework involves training a continuous-variable rate RNN with important biophysical constraints and transferring the learned dynamics and constraints to a spiking RNN in a one-to-one manner. The proposed framework introduces only 1 additional parameter to establish the equivalence between rate and spiking RNN models. We also study other model parameters related to the rate and spiking networks to optimize the one-to-one mapping. By establishing a close relationship between rate and spiking models, we demonstrate that spiking RNNs could be constructed to achieve similar performance as their counterpart continuous rate networks.

scholarly journals Sea Ice Automatic Extraction in the Liaodong Bay from Sentinel-2 Imagery Using Convolutional Neural Networks

2020 ◽  
Vol 143 ◽  
pp. 02015
Author(s):  
Li Zherui ◽  
Cai Huiwen
Keyword(s):  
Neural Networks ◽  
Sea Ice ◽  
Convolutional Neural Networks ◽  
Small Sample ◽  
Likelihood Method ◽  
Support Vector ◽  
Liaodong Bay ◽  
Distance Method ◽  
Wide Range ◽  
Sentinel 2

Sea ice classification is one of the important tasks of sea ice monitoring. Accurate extraction of sea ice types is of great significance on sea ice conditions assessment, smooth navigation and safty marine operations. Sentinel-2 is an optical satellite launched by the European Space Agency. High spatial resolution and wide range imaging provide powerful support for sea ice monitoring. However, traditional supervised classification method is difficult to achieve fine results for small sample features. In order to solve the problem, this paper proposed a sea ice extraction method based on deep learning and it was applied to Liaodong Bay in Bohai Sea, China. The convolutional neural network was used to extract and classify the feature of the image from Sentinel-2. The results showed that the overall accuracy of the algorithm was 85.79% which presented a significant improvement compared with the tranditional algorithms, such as minimum distance method, maximum likelihood method, Mahalanobis distance method, and support vector machine method. The method proposed in this paper, which combines convolutional neural networks and high-resolution multispectral data, provides a new idea for remote sensing monitoring of sea ice.

scholarly journals Semi-Natural and Spontaneous Speech Recognition Using Deep Neural Networks with Hybrid Features Unification

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2286
Author(s):  
Ammar Amjad ◽  
Lal Khan ◽  
Hsien-Tsung Chang
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Feature Fusion ◽  
Spontaneous Speech ◽  
Identification Accuracy ◽  
Independent Experiment ◽  
Support Vector ◽  
Acoustic Feature ◽  
Hybrid Features ◽  
Feature Sets

Recently, identifying speech emotions in a spontaneous database has been a complex and demanding study area. This research presents an entirely new approach for recognizing semi-natural and spontaneous speech emotions with multiple feature fusion and deep neural networks (DNN). A proposed framework extracts the most discriminative features from hybrid acoustic feature sets. However, these feature sets may contain duplicate and irrelevant information, leading to inadequate emotional identification. Therefore, an support vector machine (SVM) algorithm is utilized to identify the most discriminative audio feature map after obtaining the relevant features learned by the fusion approach. We investigated our approach utilizing the eNTERFACE05 and BAUM-1s benchmark databases and observed a significant identification accuracy of 76% for a speaker-independent experiment with SVM and 59% accuracy with, respectively. Furthermore, experiments on the eNTERFACE05 and BAUM-1s dataset indicate that the suggested framework outperformed current state-of-the-art techniques on the semi-natural and spontaneous datasets.

scholarly journals Assessment the quality of apricots in the process of drying with neural networks and support vector machines

2019 ◽  
Vol 292 ◽  
pp. 03019
Author(s):  
Mаrtin Dejanov ◽  
Darinka Ilieva-Stefanova ◽  
Iva Chelik
Keyword(s):  
Neural Networks ◽  
Spectral Characteristics ◽  
Support Vector ◽  
Carotene Content ◽  
Color Features ◽  
Rbf Kernel ◽  
Linear Svm ◽  
Lab Color Model ◽  
Cie Lab

The paper presents an analysis of the assessment the quality of apricots during the drying process using two types of classifires: ANNs and SVMs. The quality of apricots is categorized in three classes according to the color and b-carotene content through the process of drying. The classification is made by using ‘CIE Lab’ color model and spectral characteristics in the VIS range. Neural networks are BPN and PNN, and classifiers are kernel and linear SVM. The spectral characteristics are pre-processed with SNV, MSC, First derivative and PCA. According to the results for color features, BPN and SVM with “rbf” kernel have the best performance while PNN has the worst performance. When using spectral characteristics the BPN network performs well: eavg = 4.1% and emax = 12.1% but the SVM linear (eavg = 3.4%, emax =5.3%) and SVM with “rbf” kernel (eavg = 2.4%, emax =5.2%) classifiers have better results. As a conclusion, it could be said that classifiers using spectral features perform well with errors at about 2-5%. Classification with color features is an alternative method, which is less complex, cheaper and with acceptable errors.

scholarly journals Recurrent Kernel Machines: Computing with Infinite Echo State Networks

2012 ◽  
Vol 24 (1) ◽  
pp. 104-133 ◽  
Author(s):  
Michiel Hermans ◽  
Benjamin Schrauwen
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Input Data ◽  
Dimensional Space ◽  
Theoretical Research ◽  
High Dimensional ◽  
Support Vector ◽  
Practical Applications ◽  
Echo State Networks ◽  
Vector Machines

Echo state networks (ESNs) are large, random recurrent neural networks with a single trained linear readout layer. Despite the untrained nature of the recurrent weights, they are capable of performing universal computations on temporal input data, which makes them interesting for both theoretical research and practical applications. The key to their success lies in the fact that the network computes a broad set of nonlinear, spatiotemporal mappings of the input data, on which linear regression or classification can easily be performed. One could consider the reservoir as a spatiotemporal kernel, in which the mapping to a high-dimensional space is computed explicitly. In this letter, we build on this idea and extend the concept of ESNs to infinite-sized recurrent neural networks, which can be considered recursive kernels that subsequently can be used to create recursive support vector machines. We present the theoretical framework, provide several practical examples of recursive kernels, and apply them to typical temporal tasks.

scholarly journals Natural language processing for cognitive therapy: Extracting schemas from thought records

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257832
Author(s):  
Franziska Burger ◽  
Mark A. Neerincx ◽  
Willem-Paul Brinkman
Keyword(s):  
Mental Health ◽  
Neural Networks ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Recurrent Neural Networks ◽  
Support Vector ◽  
Free Text ◽  
Cognitive Approach ◽  
Text Input

The cognitive approach to psychotherapy aims to change patients’ maladaptive schemas, that is, overly negative views on themselves, the world, or the future. To obtain awareness of these views, they record their thought processes in situations that caused pathogenic emotional responses. The schemas underlying such thought records have, thus far, been largely manually identified. Using recent advances in natural language processing, we take this one step further by automatically extracting schemas from thought records. To this end, we asked 320 healthy participants on Amazon Mechanical Turk to each complete five thought records consisting of several utterances reflecting cognitive processes. Agreement between two raters on manually scoring the utterances with respect to how much they reflect each schema was substantial (Cohen’s κ = 0.79). Natural language processing software pretrained on all English Wikipedia articles from 2014 (GLoVE embeddings) was used to represent words and utterances, which were then mapped to schemas using k-nearest neighbors algorithms, support vector machines, and recurrent neural networks. For the more frequently occurring schemas, all algorithms were able to leverage linguistic patterns. For example, the scores assigned to the Competence schema by the algorithms correlated with the manually assigned scores with Spearman correlations ranging between 0.64 and 0.76. For six of the nine schemas, a set of recurrent neural networks trained separately for each of the schemas outperformed the other algorithms. We present our results here as a benchmark solution, since we conducted this research to explore the possibility of automatically processing qualitative mental health data and did not aim to achieve optimal performance with any of the explored models. The dataset of 1600 thought records comprising 5747 utterances is published together with this article for researchers and machine learning enthusiasts to improve upon our outcomes. Based on our promising results, we see further opportunities for using free-text input and subsequent natural language processing in other common therapeutic tools, such as ecological momentary assessments, automated case conceptualizations, and, more generally, as an alternative to mental health scales.

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