scholarly journals Automatic Labeled Dialogue Generation for Nursing Record Systems

2020 ◽  
Vol 10 (3) ◽  
pp. 62
Author(s):  
Tittaya Mairittha ◽  
Nattaya Mairittha ◽  
Sozo Inoue
Keyword(s):  
Data Augmentation ◽  
Short Term Memory ◽  
Generative Models ◽  
Abstract Knowledge ◽  
Augmentation Techniques ◽  
Nursing Record ◽  
Long Short Term Memory ◽  
The Individual ◽  
High Level ◽  
Embedding Methods

The integration of digital voice assistants in nursing residences is becoming increasingly important to facilitate nursing productivity with documentation. A key idea behind this system is training natural language understanding (NLU) modules that enable the machine to classify the purpose of the user utterance (intent) and extract pieces of valuable information present in the utterance (entity). One of the main obstacles when creating robust NLU is the lack of sufficient labeled data, which generally relies on human labeling. This process is cost-intensive and time-consuming, particularly in the high-level nursing care domain, which requires abstract knowledge. In this paper, we propose an automatic dialogue labeling framework of NLU tasks, specifically for nursing record systems. First, we apply data augmentation techniques to create a collection of variant sample utterances. The individual evaluation result strongly shows a stratification rate, with regard to both fluency and accuracy in utterances. We also investigate the possibility of applying deep generative models for our augmented dataset. The preliminary character-based model based on long short-term memory (LSTM) obtains an accuracy of 90% and generates various reasonable texts with BLEU scores of 0.76. Secondly, we introduce an idea for intent and entity labeling by using feature embeddings and semantic similarity-based clustering. We also empirically evaluate different embedding methods for learning good representations that are most suitable to use with our data and clustering tasks. Experimental results show that fastText embeddings produce strong performances both for intent labeling and on entity labeling, which achieves an accuracy level of 0.79 and 0.78 f1-scores and 0.67 and 0.61 silhouette scores, respectively.

scholarly journals Insufficient Data Can Also Rock! Learning to Converse Using Smaller Data with Augmentation

2019 ◽  
Vol 33 ◽  
pp. 6698-6705
Author(s):  
Juntao Li ◽  
Lisong Qiu ◽  
Bo Tang ◽  
Dongmin Chen ◽  
Dongyan Zhao ◽  
...  
Keyword(s):  
Data Augmentation ◽  
Network Models ◽  
Generative Models ◽  
Training Data ◽  
Insufficient Data ◽  
Dialogue System ◽  
Neural Network Models ◽  
Augmentation Techniques ◽  
Training Pair ◽  
Existing Data

Recent successes of open-domain dialogue generation mainly rely on the advances of deep neural networks. The effectiveness of deep neural network models depends on the amount of training data. As it is laboursome and expensive to acquire a huge amount of data in most scenarios, how to effectively utilize existing data is the crux of this issue. In this paper, we use data augmentation techniques to improve the performance of neural dialogue models on the condition of insufficient data. Specifically, we propose a novel generative model to augment existing data, where the conditional variational autoencoder (CVAE) is employed as the generator to output more training data with diversified expressions. To improve the correlation of each augmented training pair, we design a discriminator with adversarial training to supervise the augmentation process. Moreover, we thoroughly investigate various data augmentation schemes for neural dialogue system with generative models, both GAN and CVAE. Experimental results on two open corpora, Weibo and Twitter, demonstrate the superiority of our proposed data augmentation model.

scholarly journals DNA6mA-MINT: DNA-6mA Modification Identification Neural Tool

Genes ◽  
2020 ◽  
Vol 11 (8) ◽  
pp. 898 ◽  
Author(s):  
Mobeen Ur Rehman ◽  
Kil To Chong
Keyword(s):  
Computational Models ◽  
Short Term Memory ◽  
Binary Sequence ◽  
The Neural Network ◽  
Proposed Model ◽  
Time Parameters ◽  
Long Short Term Memory ◽  
High Level ◽  
User Friendly ◽  
Fold Cross Validation

DNA N6-methyladenine (6mA) is part of numerous biological processes including DNA repair, DNA replication, and DNA transcription. The 6mA modification sites hold a great impact when their biological function is under consideration. Research in biochemical experiments for this purpose is carried out and they have demonstrated good results. However, they proved not to be a practical solution when accessed under cost and time parameters. This led researchers to develop computational models to fulfill the requirement of modification identification. In consensus, we have developed a computational model recommended by Chou’s 5-steps rule. The Neural Network (NN) model uses convolution layers to extract the high-level features from the encoded binary sequence. These extracted features were given an optimal interpretation by using a Long Short-Term Memory (LSTM) layer. The proposed architecture showed higher performance compared to state-of-the-art techniques. The proposed model is evaluated on Mus musculus, Rice, and “Combined-species” genomes with 5- and 10-fold cross-validation. Further, with access to a user-friendly web server, publicly available can be accessed freely.

Synchronization of chaotic systems and long short-term memory networks by sharing a single variable

2020 ◽  
pp. 2150106
Author(s):  
Kai Zhang ◽  
Xiaolu Chen ◽  
Tongfeng Weng ◽  
Hao Wang ◽  
Huijie Yang ◽  
...  
Keyword(s):  
Chaotic System ◽  
Chaotic Systems ◽  
Short Term Memory ◽  
Single Variable ◽  
Short Term ◽  
Dynamical Equations ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Common Signal ◽  
High Level

We adopt long short-term memory (LSTM) networks to model and characterize chaotic systems rather than conventional dynamical equations. We find that a well-trained LSTM system can synchronize with its learned chaotic system via transmitting a common signal. In the same fashion, we show that when learning an identical chaotic system, the trained LSTM systems can also be synchronized. Remarkably, we find that a cascading synchronization will be achieved among chaotic systems and their trained LSTM systems in the same manner. We further validate that this synchronization behavior is robust even the transmitting signal is contaminated with relatively a high level of white noise. Our work reveals that synchronization is a common behavior linking chaotic systems and their learned LSTM networks.

scholarly journals A Comparison of Power Quality Disturbance Detection and Classification Methods Using CNN, LSTM and CNN-LSTM

2020 ◽  
Vol 10 (19) ◽  
pp. 6755
Author(s):  
Carlos Iturrino Garcia ◽  
Francesco Grasso ◽  
Antonio Luchetta ◽  
Maria Cristina Piccirilli ◽  
Libero Paolucci ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Validation Dataset ◽  
Short Term ◽  
Current Time ◽  
Term Memory ◽  
Long Short Term Memory

The use of electronic loads has improved many aspects of everyday life, permitting more efficient, precise and automated process. As a drawback, the nonlinear behavior of these systems entails the injection of electrical disturbances on the power grid that can cause distortion of voltage and current. In order to adopt countermeasures, it is important to detect and classify these disturbances. To do this, several Machine Learning Algorithms are currently exploited. Among them, for the present work, the Long Short Term Memory (LSTM), the Convolutional Neural Networks (CNN), the Convolutional Neural Networks Long Short Term Memory (CNN-LSTM) and the CNN-LSTM with adjusted hyperparameters are compared. As a preliminary stage of the research, the voltage and current time signals are simulated using MATLAB Simulink. Thanks to the simulation results, it is possible to acquire a current and voltage dataset with which the identification algorithms are trained, validated and tested. These datasets include simulations of several disturbances such as Sag, Swell, Harmonics, Transient, Notch and Interruption. Data Augmentation techniques are used in order to increase the variability of the training and validation dataset in order to obtain a generalized result. After that, the networks are fed with an experimental dataset of voltage and current field measurements containing the disturbances mentioned above. The networks have been compared, resulting in a 79.14% correct classification rate with the LSTM network versus a 84.58% for the CNN, 84.76% for the CNN-LSTM and a 83.66% for the CNN-LSTM with adjusted hyperparameters. All of these networks are tested using real measurements.

scholarly journals Native Language Recognition using Bidirectional Long Short-Term Memory Network

2019 ◽  
Vol 9 (2) ◽  
pp. 2373-2377
Keyword(s):  
Short Term Memory ◽  
Native Language ◽  
Neural Systems ◽  
Language Recognition ◽  
Short Term ◽  
Combined System ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Cepstral Coefficients ◽  
The Individual

Speech Recognition of native language is the process of recognizing the language of a client dependent on the speech or content writing in another language. This article proposes the utilization of spectrogram as well as on cochleagram-oriented concepts separated from extremely short speech expressions (0.8 s by and large) to deduce the local language of the speaking person. The bidirectional long short-term memory (BLSTM) neural systems are received to classify the expressions between the local dialects. A lot of analyses is completed for the system engineering look and the framework's precision is assessed on the approval informational index. By and large precision is accomplished utilizing the Mel-recurrence Cepstral coefficients (MRCC) and Gammatone Recurrence Cepstral Coefficients (GRCC), separately. In addition, the advanced MFCC oriented BLSTM system and GFCC based BLSTM systems are combined to make use of their features. The examinations demonstrate that the execution of the combined system outperforms the individual BLSTM systems and precision of 75.69% is accomplished on the assessment information.

An End-to-End Model Based on Multiple Neural Networks with Data Augmentation for Keyword Spotting

2020 ◽  
pp. 2050006
Author(s):  
Shuzhou Chai ◽  
Wei-Qiang Zhang ◽  
Changsheng Lv ◽  
Zhenye Yang
Keyword(s):  
Neural Network ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Attention Mechanism ◽  
Keyword Spotting ◽  
Data Set ◽  
Positive Rate ◽  
Memory Network ◽  
Hidden Layer ◽  
High Level

In this paper, we propose a network for small footprint keyword spotting. It includes four parts, data augmentation, Time-Delay Neural Network (TDNN) and Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and attention mechanism. Data augmentation is Google SpecAugment with time warping and frequency mask and time mask to the spectrum on clean data set and noisy data set. TDNN and CNN model the spectrogram features from the time and space dimensions. RNN-type networks include RNN, Long Short-Term Memory network (LSTM), Gated Recurrent Unit (GRU), Bidirectional Long Short-Term Memory network (BiLSTM), and Bidirectional Gated Recurrent Unit (BiGRU). The RNN extracts hidden layer features and transforms them into high-level representations. The attention mechanism is selected to generate different weights and multiplied by the high-level representation generated by the RNN to obtain a fixed-length vector. Finally, we use a linear transformation and softmax function to generate scores. We also explored the size of attention mechanism, two attention mechanisms, rectified linear unit and hidden layer of RNN. Our model has achieved a true positive rate of 99.81% at a 5% false positive rate.

scholarly journals Radar Sensing for Activity Classification in Elderly People Exploiting Micro-Doppler Signatures Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3881
Author(s):  
William Taylor ◽  
Kia Dashtipour ◽  
Syed Aziz Shah ◽  
Amir Hussain ◽  
Qammer H. Abbasi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Elderly People ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Principal Component ◽  
Fall Detection ◽  
Component Analysis ◽  
Detection Systems ◽  
Long Short Term Memory

The health status of an elderly person can be identified by examining the additive effects of aging along with disease linked to it and can lead to ‘unstable incapacity’. This health status is determined by the apparent decline of independence in activities of daily living (ADLs). Detecting ADLs provides possibilities of improving the home life of elderly people as it can be applied to fall detection systems. This paper presents fall detection in elderly people based on radar image classification by examining their daily routine activities, using radar data that were previously collected for 99 volunteers. Machine learning techniques are used classify six human activities, namely walking, sitting, standing, picking up objects, drinking water and fall events. Different machine learning algorithms, such as random forest, K-nearest neighbours, support vector machine, long short-term memory, bi-directional long short-term memory and convolutional neural networks, were used for data classification. To obtain optimum results, we applied data processing techniques, such as principal component analysis and data augmentation, to the available radar images. The aim of this paper is to improve upon the results achieved using a publicly available dataset to further improve upon research of fall detection systems. It was found out that the best results were obtained using the CNN algorithm with principal component analysis and data augmentation together to obtain a result of 95.30% accuracy. The results also demonstrated that principal component analysis was most beneficial when the training data were expanded by augmentation of the available data. The results of our proposed approach, in comparison to the state of the art, have shown the highest accuracy.

scholarly journals Data Augmentation Using Generative Adversarial Networks (GANs) For GAN-Based Detection Of Pneumonia And COVID-19 In Chest X-Ray Images

2021 ◽  
Author(s):  
Saman Motamed ◽  
Patrik Rogalla ◽  
Farzad Khalvati
Keyword(s):  
Neural Networks ◽  
Data Augmentation ◽  
Generative Models ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
X Rays ◽  
X Ray ◽  
Standard Data ◽  
Adversarial Networks ◽  
Augmentation Techniques

Abstract Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets networks generalize poorly. Data Augmentation techniques improve the generalizability of neural networks by using existing training data more effectively. Standard data augmentation methods, however, produce limited plausible alternative data. Generative Adversarial Networks (GANs) have been utilized to generate new data and improve the performance of CNNs. Nevertheless, data augmentation techniques for training GANs are under-explored compared to CNNs. In this work, we propose a new GAN architecture for augmentation of chest X-rays for semi-supervised detection of pneumonia and COVID-19 using generative models. We show that the proposed GAN can be used to effectively augment data and improve classification accuracy of disease in chest X-rays for pneumonia and COVID-19. We compare our augmentation GAN model with Deep Convolutional GAN and traditional augmentation methods (rotate, zoom, etc) on two different X-ray datasets and show our GAN-based augmentation method surpasses other augmentation methods for training a GAN in detecting anomalies in X-ray images.

scholarly journals Automatic Structuring of Ontology Terms Based on Lexical Granularity and Machine Learning: Algorithm Development and Validation (Preprint)

2020 ◽  
Author(s):  
Lingyun Luo ◽  
Jingtao Feng ◽  
Huijun Yu ◽  
Jiaolong Wang
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Learning Algorithm ◽  
Ontology Development ◽  
Biomedical Ontologies ◽  
Long Short Term Memory ◽  
Development And Validation ◽  
And Training ◽  
Embedding Methods ◽  
Shed Light

BACKGROUND As the manual creation and maintenance of biomedical ontologies are labor-intensive, automatic aids are desirable in the lifecycle of ontology development. OBJECTIVE Provided with a set of concept names in the Foundational Model of Anatomy (FMA), we propose an innovative method for automatically generating the taxonomy and the partonomy structures among them, respectively. METHODS Our approach comprises 2 main tasks: The first task is predicting the direct relation between 2 given concept names by utilizing word embedding methods and training 2 machine learning models, Convolutional Neural Networks (CNN) and Bidirectional Long Short-term Memory Networks (Bi-LSTM). The second task is the introduction of an original granularity-based method to identify the semantic structures among a group of given concept names by leveraging these trained models. RESULTS Results show that both CNN and Bi-LSTM perform well on the first task, with F1 measures above 0.91. For the second task, our approach achieves an average F1 measure of 0.79 on 100 case studies in the FMA using Bi-LSTM, which outperforms the primitive pairwise-based method. CONCLUSIONS We have investigated an automatic way of predicting a hierarchical relationship between 2 concept names; based on this, we have further invented a methodology to structure a group of concept names automatically. This study is an initial investigation that will shed light on further work on the automatic creation and enrichment of biomedical ontologies.

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