scholarly journals What You Say or How You Say It? Depression Detection Through Joint Modeling of Linguistic and Acoustic Aspects of Speech

2021 ◽  
Author(s):  
Nujud Aloshban ◽  
Anna Esposito ◽  
Alessandro Vinciarelli
Keyword(s):  
Short Term Memory ◽  
Joint Modeling ◽  
Joint Analysis ◽  
Health Issues ◽  
Multimodal Analysis ◽  
Sequence Modeling ◽  
Depression Detection ◽  
Long Short Term Memory ◽  
Joint Representation ◽  
Better Than

AbstractDepression is one of the most common mental health issues. (It affects more than 4% of the world’s population, according to recent estimates.) This article shows that the joint analysis of linguistic and acoustic aspects of speech allows one to discriminate between depressed and nondepressed speakers with an accuracy above 80%. The approach used in the work is based on networks designed for sequence modeling (bidirectional Long-Short Term Memory networks) and multimodal analysis methodologies (late fusion, joint representation and gated multimodal units). The experiments were performed over a corpus of 59 interviews (roughly 4 hours of material) involving 29 individuals diagnosed with depression and 30 control participants. In addition to an accuracy of 80%, the results show that multimodal approaches perform better than unimodal ones owing to people’s tendency to manifest their condition through one modality only, a source of diversity across unimodal approaches. In addition, the experiments show that it is possible to measure the “confidence” of the approach and automatically identify a subset of the test data in which the performance is above a predefined threshold. It is possible to effectively detect depression by using unobtrusive and inexpensive technologies based on the automatic analysis of speech and language.

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.

scholarly journals Implementasi Deep Learning Menggunakan Metode CNN dan LSTM untuk Menentukan Berita Palsu dalam Bahasa Indonesia

2021 ◽  
Vol 5 (4) ◽  
pp. 544
Author(s):  
Antonius Angga Kurniawan ◽  
Metty Mustikasari
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Data Input ◽  
Fake News ◽  
Accuracy Test ◽  
Learning Techniques ◽  
Data Word ◽  
Wrong Prediction ◽  
Long Short Term Memory ◽  
Better Than

This research aims to implement deep learning techniques to determine fact and fake news in Indonesian language. The methods used are Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The stages of the research consisted of collecting data, labeling data, preprocessing data, word embedding, splitting data, forming CNN and LSTM models, evaluating, testing new input data and comparing evaluations of the established CNN and LSTM models. The Data are collected from a fact and fake news provider site that is valid, namely TurnbackHoax.id. There are 1786 news used in this study, with 802 fact and 984 fake news. The results indicate that the CNN and LSTM methods were successfully applied to determine fact and fake news in Indonesian language properly. CNN has an accuracy test, precision and recall value of 0.88, while the LSTM model has an accuracy test and precision value of 0.84 and a recall of 0.83. In testing the new data input, all of the predictions obtained by CNN are correct, while the prediction results obtained by LSTM have 1 wrong prediction. Based on the evaluation results and the results of testing the new data input, the model produced by the CNN method is better than the model produced by the LSTM method.

scholarly journals Research on Premium Income Prediction Based on LSTM Neural Network

2019 ◽  
Vol 6 (11) ◽  
pp. 256-260
Author(s):  
Li Diao ◽  
Ning Wang
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Short Term Memory ◽  
Practical Significance ◽  
Monthly Data ◽  
Loss Compensation ◽  
Long Short Term Memory ◽  
Premium Income ◽  
Social Management ◽  
Better Than

As one of the four financial pillars, insurance has the functions of risk diversification, loss compensation, financing and social management. It is of great practical significance to predict the level of premium income in the new normal of economy. In this paper, long short-term memory (LSTM) neural network was innovatively applied to the study of premium income prediction. The monthly data of China's premium income from January 1999 to October 2019 was selected for prediction, and the prediction results were compared with BP neural network. The results show that LSTM model can accurately predict premium income, and its performance is better than BP neural network.

scholarly journals Extract Executable Action Sequences from Natural Language Instructions Based on DQN for Medical Service Robots

2021 ◽  
Vol 16 (2) ◽  
Author(s):  
Fengda Zhao ◽  
Zhikai Yang ◽  
Xianshan Li ◽  
Dingding Guo ◽  
Haitao Li
Keyword(s):  
Natural Language ◽  
Short Term Memory ◽  
Average Length ◽  
Service Robots ◽  
Short Term ◽  
Medical Robots ◽  
Agent Based ◽  
Action Sequences ◽  
Long Short Term Memory ◽  
Better Than

The emergence and popularization of medical robots bring great convenience to doctors in treating patients. The core of medical robots is the interaction and cooperation between doctors and robots, so it is crucial to design a simple and stable human-robots interaction system for medical robots. Language is the most convenient way for people to communicate with each other, so in this paper, a DQN agent based on long-short term memory (LSTM) and attention mechanism is proposed to enable the robots to extract executable action sequences from doctors’ natural language instructions. For this, our agent should be able to complete two related tasks: 1) extracting action names from instructions. 2) extracting action arguments according to the extracted action names. We evaluate our agent on three datasets composed of texts with an average length of 49.95, 209.34, 417.17 words respectively. The results show that our agent can perform better than similar agents. And our agent has a better ability to handle long texts than previous works.

EmailDetective: An Email Authorship Identification And Verification Model

2020 ◽  
Vol 63 (11) ◽  
pp. 1775-1787
Author(s):  
Yong Fang ◽  
Yue Yang ◽  
Cheng Huang
Keyword(s):  
Short Term Memory ◽  
Writing Style ◽  
Authorship Verification ◽  
Average Accuracy ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
The Many ◽  
Authorship Identification ◽  
Verification Model ◽  
Better Than

Abstract Emails are often used to illegal cybercrime today, so it is important to verify the identity of the email author. This paper proposes a general model for solving the problem of anonymous email author attribution, which can be used in email authorship identification and email authorship verification. The first situation is to find the author of an anonymous email among the many suspected targets. Another situation is to verify if an email was written by the sender. This paper extracts features from the email header and email body and analyzes the writing style and other behaviors of email authors. The behaviors of email authors are extracted through a statistical algorithm from email headers. Moreover, the author’s writing style in the email body is extracted by a sequence-to-sequence bidirectional long short-term memory (BiLSTM) algorithm. This model combines multiple factors to solve the problem of anonymous email author attribution. The experiments proved that the accuracy and other indicators of proposed model are better than other methods. In email authorship verification experiment, our average accuracy, average recall and average F1-score reached 89.9%. In email authorship identification experiment, our model’s accuracy rate is 98.9% for 10 authors, 92.9% for 25 authors and 89.5% for 50 authors.

scholarly journals Rotational Unit of Memory: A Novel Representation Unit for RNNs with Scalable Applications

2019 ◽  
Vol 7 ◽  
pp. 121-138 ◽  
Author(s):  
Rumen Dangovski ◽  
Li Jing ◽  
Preslav Nakov ◽  
Mićo Tatalović ◽  
Marin Soljačić
Keyword(s):  
Short Term Memory ◽  
State Of The Art ◽  
Language Modeling ◽  
Text Summarization ◽  
Short Term ◽  
Memory State ◽  
Long Short Term Memory ◽  
Gated Recurrent Units ◽  
Better Than ◽  
Real World Problems

Stacking long short-term memory (LSTM) cells or gated recurrent units (GRUs) as part of a recurrent neural network (RNN) has become a standard approach to solving a number of tasks ranging from language modeling to text summarization. Although LSTMs and GRUs were designed to model long-range dependencies more accurately than conventional RNNs, they nevertheless have problems copying or recalling information from the long distant past. Here, we derive a phase-coded representation of the memory state, Rotational Unit of Memory (RUM), that unifies the concepts of unitary learning and associative memory. We show experimentally that RNNs based on RUMs can solve basic sequential tasks such as memory copying and memory recall much better than LSTMs/GRUs. We further demonstrate that by replacing LSTM/GRU with RUM units we can apply neural networks to real-world problems such as language modeling and text summarization, yielding results comparable to the state of the art.

scholarly journals A Review Structure Based Ensemble Model for Deceptive Review Spam

Information ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 243 ◽  
Author(s):  
Zhi-Yuan Zeng ◽  
Jyun-Jie Lin ◽  
Mu-Sheng Chen ◽  
Meng-Hui Chen ◽  
Yan-Qi Lan ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Online Reviews ◽  
Attention Mechanism ◽  
Purchase Behavior ◽  
Document Representation ◽  
Short Term ◽  
Review Spam ◽  
Long Short Term Memory ◽  
Novel Model ◽  
Better Than

Consumers’ purchase behavior increasingly relies on online reviews. Accordingly, there are more and more deceptive reviews which are harmful to customers. Existing methods to detect spam reviews mainly take the problem as a general text classification task, but they ignore the important features of spam reviews. In this paper, we propose a novel model, which splits a review into three parts: first sentence, middle context, and last sentence, based on the discovery that the first and last sentence express stronger emotion than the middle context. Then, the model uses four independent bidirectional long-short term memory (LSTM) models to encode the beginning, middle, end of a review and the whole review into four document representations. After that, the four representations are integrated into one document representation by a self-attention mechanism layer and an attention mechanism layer. Based on three domain datasets, the results of in-domain and mix-domain experiments show that our proposed method performs better than the compared methods.

scholarly journals Air Pollution and Cardiorespiratory Hospitalization, Modeling and Analysis Using Artificial Intelligence Techniques

2021 ◽  
Author(s):  
Raja Sher Afgun Usmani ◽  
Thulasyammal Ramiah Pillai ◽  
Ibrahim Abaker Targio Hashem ◽  
Mohsen Marjani ◽  
Rafiza Shaharudin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Air Pollution ◽  
Short Term Memory ◽  
Short Term ◽  
Human Welfare ◽  
Term Memory ◽  
Modeling And Analysis ◽  
Klang Valley ◽  
Long Short Term Memory ◽  
Better Than

Abstract Air pollution has a serious and adverse effect on human health, and it has become a risk to human welfare and health throughout the globe. In this paper, we present the modeling and analysis of air pollution and cardiorespiratory hospitalization. This study aims to investigate the association between cardiorespiratory hospitalization and air pollution, and predict cardiorespiratory hospitalization based on air pollution using the Artificial Intelligence (AI) techniques. We propose the Enhanced Long Short-Term Memory (ELSTM) model and provide a comparison with other AI techniques, i.e., Long Short-Term Memory (LSTM), Deep Learning (DL), and Vector Autoregressive (VAR). This study was conducted at seven study locations in Klang Valley, Malaysia. The prediction results show that the ELSTM model performed significantly better than other models in all study locations, with the best RMSE scores in Klang study location (ELSTM: 0.002, LSTM: 0.013, DL: 0.006, VAR: 0.066). The results also indicated that the proposed ELSTM model was able to detect and predict the trends of monthly hospitalization significantly better than the LSTM and other models in the study. Hence, we can conclude that we can utilize AI techniques to accurately predict cardiorespiratory hospitalization based on air pollution in Klang Valley, Malaysia.

scholarly journals Leap-LSTM: Enhancing Long Short-Term Memory for Text Categorization

2019 ◽  
Author(s):  
Ting Huang ◽  
Gehui Shen ◽  
Zhi-Hong Deng
Keyword(s):  
Language Processing ◽  
Text Categorization ◽  
Question Answering ◽  
Short Term Memory ◽  
Data Sets ◽  
Trade Offs ◽  
Memory For Text ◽  
Long Short Term Memory ◽  
Current Word ◽  
Better Than

Recurrent Neural Networks (RNNs) are widely used in the field of natural language processing (NLP), ranging from text categorization to question answering and machine translation. However, RNNs generally read the whole text from beginning to end or vice versa sometimes, which makes it inefficient to process long texts. When reading a long document for a categorization task, such as topic categorization, large quantities of words are irrelevant and can be skipped. To this end, we propose Leap-LSTM, an LSTM-enhanced model which dynamically leaps between words while reading texts. At each step, we utilize several feature encoders to extract messages from preceding texts, following texts and the current word, and then determine whether to skip the current word. We evaluate Leap-LSTM on several text categorization tasks: sentiment analysis, news categorization, ontology classification and topic classification, with five benchmark data sets. The experimental results show that our model reads faster and predicts better than standard LSTM. Compared to previous models which can also skip words, our model achieves better trade-offs between performance and efficiency.

scholarly journals Network Intrusion Detection Based on an Improved Long-Short-Term Memory Model in Combination with Multiple Spatiotemporal Structures

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Xiaolong Huang
Keyword(s):  
Intrusion Detection ◽  
Short Term Memory ◽  
Network Intrusion Detection ◽  
Short Term ◽  
Detection Model ◽  
Existing Problems ◽  
Network Intrusion ◽  
The Neural Network ◽  
Long Short Term Memory ◽  
Better Than

Aimed at the existing problems in network intrusion detection, this paper proposes an improved LSTM combined with spatiotemporal structure for intrusion detection. The unsupervised spatiotemporal encoder is used to intelligently extract the spatial characteristics of network traffic data samples. It can not only retain the overall/nonlocal characteristics of the data samples but also extract the most essential deep features of the data samples. Finally, the extracted features are used as input of the LSTM model to realize classification and identification for intrusion samples. Experimental verification shows that the accuracy and false alarm rate of the intrusion detection model based on the neural network are significantly better than those of other traditional models.

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