scholarly journals Sentiment Polarity Classification of Corporate Review Data with a Bidirectional Long-Short Term Memory (biLSTM) Neural Network Architecture

2020 ◽  
Author(s):  
R. Loke ◽  
O. Kachaniuk
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Neural Network Architecture ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Polarity Classification

scholarly journals Application of deep learning methods to predict ionosphere parameters in real time

2020 ◽  
Vol 196 ◽  
pp. 02007
Author(s):  
Vladimir Mochalov ◽  
Anastasia Mochalova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Short Term Memory ◽  
Neural Network Architecture ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

scholarly journals Accurate and Efficient Intracranial Hemorrhage Detection and Subtype Classification in 3D CT Scans with Convolutional and Long Short-Term Memory Neural Networks

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5611 ◽  
Author(s):  
Mihail Burduja ◽  
Radu Tudor Ionescu ◽  
Nicolae Verga
Keyword(s):  
Neural Network ◽  
Intracranial Hemorrhage ◽  
Network Architecture ◽  
Short Term Memory ◽  
Ct Scans ◽  
Short Term ◽  
Term Memory ◽  
Deep Model ◽  
Long Short Term Memory ◽  
Ct Slices

In this paper, we present our system for the RSNA Intracranial Hemorrhage Detection challenge, which is based on the RSNA 2019 Brain CT Hemorrhage dataset. The proposed system is based on a lightweight deep neural network architecture composed of a convolutional neural network (CNN) that takes as input individual CT slices, and a Long Short-Term Memory (LSTM) network that takes as input multiple feature embeddings provided by the CNN. For efficient processing, we consider various feature selection methods to produce a subset of useful CNN features for the LSTM. Furthermore, we reduce the CT slices by a factor of 2×, which enables us to train the model faster. Even if our model is designed to balance speed and accuracy, we report a weighted mean log loss of 0.04989 on the final test set, which places us in the top 30 ranking (2%) from a total of 1345 participants. While our computing infrastructure does not allow it, processing CT slices at their original scale is likely to improve performance. In order to enable others to reproduce our results, we provide our code as open source. After the challenge, we conducted a subjective intracranial hemorrhage detection assessment by radiologists, indicating that the performance of our deep model is on par with that of doctors specialized in reading CT scans. Another contribution of our work is to integrate Grad-CAM visualizations in our system, providing useful explanations for its predictions. We therefore consider our system as a viable option when a fast diagnosis or a second opinion on intracranial hemorrhage detection are needed.

scholarly journals Improving the Learning Power of Artificial Intelligence Using Multimodal Deep Learning

2021 ◽  
Vol 248 ◽  
pp. 01017
Author(s):  
Eugene Yu. Shchetinin ◽  
Leonid Sevastianov
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Short Term ◽  
Security Systems ◽  
Term Memory ◽  
Human Voice ◽  
Long Short Term Memory ◽  
Effective Analysis

Computer paralinguistic analysis is widely used in security systems, biometric research, call centers and banks. Paralinguistic models estimate different physical properties of voice, such as pitch, intensity, formants and harmonics to classify emotions. The main goal is to find such features that would be robust to outliers and will retain variety of human voice properties at the same time. Moreover, the model used must be able to estimate features on a time scale for an effective analysis of voice variability. In this paper a paralinguistic model based on Bidirectional Long Short-Term Memory (BLSTM) neural network is described, which was trained for vocal-based emotion recognition. The main advantage of this network architecture is that each module of the network consists of several interconnected layers, providing the ability to recognize flexible long-term dependencies in data, which is important in context of vocal analysis. We explain the architecture of a bidirectional neural network model, its main advantages over regular neural networks and compare experimental results of BLSTM network with other models.

CCG supertagging with bidirectional long short-term memory networks

2017 ◽  
Vol 24 (1) ◽  
pp. 77-90 ◽  
Author(s):  
REKIA KADARI ◽  
YU ZHANG ◽  
WEINAN ZHANG ◽  
TING LIU
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
State Of The Art ◽  
Input Sequence ◽  
Entity Recognition ◽  
Short Term ◽  
Term Memory ◽  
Pos Tagging ◽  
Long Short Term Memory

AbstractNeural Network-based approaches have recently produced good performances in Natural language tasks, such as Supertagging. In the supertagging task, a Supertag (Lexical category) is assigned to each word in an input sequence. Combinatory Categorial Grammar Supertagging is a more challenging problem than various sequence-tagging problems, such as part-of-speech (POS) tagging and named entity recognition due to the large number of the lexical categories. Specifically, simple Recurrent Neural Network (RNN) has shown to significantly outperform the previous state-of-the-art feed-forward neural networks. On the other hand, it is well known that Recurrent Networks fail to learn long dependencies. In this paper, we introduce a new neural network architecture based on backward and Bidirectional Long Short-Term Memory (BLSTM) Networks that has the ability to memorize information for long dependencies and benefit from both past and future information. State-of-the-art methods focus on previous information, whereas BLSTM has access to information in both previous and future directions. Our main findings are that bidirectional networks outperform unidirectional ones, and Long Short-Term Memory (LSTM) networks are more precise and successful than both unidirectional and bidirectional standard RNNs. Experiment results reveal the effectiveness of our proposed method on both in-domain and out-of-domain datasets. Experiments show improvements about (1.2 per cent) over standard RNN.

scholarly journals Exploiting deep neural network and long short-term memory method-ologies in bioacoustic classification of LPC-based features

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0259140
Author(s):  
Cihun-Siyong Alex Gong ◽  
Chih-Hui Simon Su ◽  
Kuo-Wei Chao ◽  
Yi-Chu Chao ◽  
Chin-Kai Su ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Principal Component ◽  
Linear Predictive Coding ◽  
Acoustic Feature ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

The research describes the recognition and classification of the acoustic characteristics of amphibians using deep learning of deep neural network (DNN) and long short-term memory (LSTM) for biological applications. First, original data is collected from 32 species of frogs and 3 species of toads commonly found in Taiwan. Secondly, two digital filtering algorithms, linear predictive coding (LPC) and Mel-frequency cepstral coefficient (MFCC), are respectively used to collect amphibian bioacoustic features and construct the datasets. In addition, principal component analysis (PCA) algorithm is applied to achieve dimensional reduction of the training model datasets. Next, the classification of amphibian bioacoustic features is accomplished through the use of DNN and LSTM. The Pytorch platform with a GPU processor (NVIDIA GeForce GTX 1050 Ti) realizes the calculation and recognition of the acoustic feature classification results. Based on above-mentioned two algorithms, the sound feature datasets are classified and effectively summarized in several classification result tables and graphs for presentation. The results of the classification experiment of the different features of bioacoustics are verified and discussed in detail. This research seeks to extract the optimal combination of the best recognition and classification algorithms in all experimental processes.

Automatic Lip Reading Using Convolution Neural Network and Bidirectional Long Short-term Memory

2019 ◽  
Vol 34 (01) ◽  
pp. 2054003 ◽  
Author(s):  
Yuanyao Lu ◽  
Jie Yan
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Active Contour Model ◽  
Video Clip ◽  
Convolution Neural Network ◽  
Short Term ◽  
Term Memory ◽  
Lip Reading ◽  
Long Short Term Memory

Traditional automatic lip-reading systems generally consist of two stages: feature extraction and recognition, while the handcrafted features are empirical and cannot learn the relevance of lip movement sequence sufficiently. Recently, deep learning approaches have attracted increasing attention, especially the significant improvements of convolution neural network (CNN) applied to image classification and long short-term memory (LSTM) used in speech recognition, video processing and text analysis. In this paper, we propose a hybrid neural network architecture, which integrates CNN and bidirectional LSTM (BiLSTM) for lip reading. First, we extract key frames from each isolated video clip and use five key points to locate mouth region. Then, features are extracted from raw mouth images using an eight-layer CNN. The extracted features have the characteristics of stronger robustness and fault-tolerant capability. Finally, we use BiLSTM to capture the correlation of sequential information among frame features in two directions and the softmax function to predict final recognition result. The proposed method is capable of extracting local features through convolution operations and finding hidden correlation in temporal information from lip image sequences. The evaluation results of lip-reading recognition experiments demonstrate that our proposed method outperforms conventional approaches such as active contour model (ACM) and hidden Markov model (HMM).

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