Based on improved deep convolutional neural network model pneumonia image classification

Pneumonia remains the leading infectious cause of death in children under the age of five, killing about 700,000 children each year and affecting 7% of the world’s population. X-ray images of lung become the key to the diagnosis of this disease, skilled doctors in the diagnosis of a certain degree of subjectivity, if the use of computer-aided medical diagnosis to automatically detect lung abnormalities, will improve the accuracy of diagnosis. This research aims to introduce a deep learning technology based on the combination of Xception neural network and long-term short-term memory (LSTM), which can realize automatic diagnosis of patients with pneumonia in X-ray images. First, the model uses the Xception network to extract the deep features of the data, passes the extracted features to the LSTM, and then the LSTM detects the extracted features, and finally selects the most needed features. Secondly, in the training set samples, the traditional cross-entropy loss cannot more balance the mismatch between categories. Therefore, this research combines Pearson’s feature selection ideas, fusion of the correlation between the two loss functions, and optimizes the problem. The experimental results show that the accuracy rate of this paper is 96%, the receiver operator characteristic curve accuracy rate is 99%, the precision rate is 98%, the recall rate is 91%, and the F1 score accuracy rate is 94%. Compared with the existing technical methods, the research has achieved expected results on the currently available datasets. And assist doctors to provide higher reliability in the classification task of childhood pneumonia.

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Prediction of cold rolling gas based on EEMD-LSTM deep learning technology

Assembly Automation ◽

10.1108/aa-02-2021-0018 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Hui Zhai ◽

Wei Xiong ◽

Fujin Li ◽

Jie Yang ◽

Dongyan Su ◽

...

Keyword(s):

Neural Network ◽

Short Term Memory ◽

Back Propagation ◽

Prediction Method ◽

Absolute Error ◽

Learning Technology ◽

Intrinsic Mode Functions ◽

Content Type ◽

Gas Consumption

Purpose The prediction of by-product gas is an important guarantee for the full utilization of resources. The purpose of this research is to predict gas consumption to provide a basis for gas dispatch and reduce the production cost of enterprises. Design/methodology/approach In this paper, a new method using the ensemble empirical mode decomposition (EEMD) and the back propagation neural network is proposed. Unfortunately, this method does not achieve the ideal prediction. Further, using the advantages of long short-term memory (LSTM) neural network for long-term dependence, a prediction method based on EEMD and LSTM is proposed. In this model, the gas consumption series is decomposed into several intrinsic mode functions and a residual term (r(t)) by EEMD. Second, each component is predicted by LSTM. The predicted values of all components are added together to get the final prediction result. Findings The results show that the root mean square error is reduced to 0.35%, the average absolute error is reduced to 1.852 and the R-squared is reached to 0.963. Originality/value A new gas consumption prediction method is proposed in this paper. The production data collected in the actual production process is non-linear, unstable and contains a lot of noise. But the EEMD method has the unique superiority in the analysis data aspect and may solve these questions well. The prediction of gas consumption is the result of long-term training and needs a lot of prior knowledge. Relying on LSTM can solve the problem of long-term dependence.

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Recurrent neural network-based acute concussion classifier using raw resting state EEG data

Scientific Reports ◽

10.1038/s41598-021-91614-4 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Karun Thanjavur ◽

Arif Babul ◽

Brandon Foran ◽

Maya Bielecki ◽

Adam Gilchrist ◽

...

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Resting State ◽

Short Term Memory ◽

Characteristic Curve ◽

Health Concern ◽

Adolescent Athletes ◽

Individual Level ◽

Diffuse Brain Injury ◽

Eeg Data

AbstractConcussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and that individuals suffering from a concussion are more prone to experience additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, the most significant challenge in concussion management is the lack of objective, clinically- accepted, brain-based approaches for determining whether an athlete has suffered a concussion. Here, we report on our efforts to address this challenge. Specifically, we introduce a deep learning long short-term memory (LSTM)-based recurrent neural network that is able to distinguish between non-concussed and acute post-concussed adolescent athletes using only short (i.e. 90 s long) samples of resting state EEG data as input. The athletes were neither required to perform a specific task nor expected to respond to a stimulus during data collection. The acquired EEG data were neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and validated using data from 27 male, adolescent athletes with sports related concussion, benchmarked against 35 non-concussed adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of > 90% and achieved an ensemble median Area Under the Receiver Operating Characteristic Curve (ROC/AUC) equal to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state, raw EEG data. Our concussion classifier represents a promising first step towards the development of an easy-to-use, objective, brain-based, automatic classification of concussion at an individual level.

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Flutter speed prediction by using deep learning

Advances in Mechanical Engineering ◽

10.1177/16878140211062275 ◽

2021 ◽

Vol 13 (11) ◽

pp. 168781402110622

Author(s):

Yi-Ren Wang ◽

Yi-Jyun Wang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Short Term Memory ◽

Learning Technology ◽

Memory Space ◽

Flutter Speed ◽

Aeroelastic Flutter ◽

Speed Prediction ◽

Computational Fluid Dynamics Cfd ◽

Cfd Method

Deep learning technology has been widely used in various field in recent years. This study intends to use deep learning algorithms to analyze the aeroelastic phenomenon and compare the differences between Deep Neural Network (DNN) and Long Short-term Memory (LSTM) applied on the flutter speed prediction. In this present work, DNN and LSTM are used to address complex aeroelastic systems by superimposing multi-layer Artificial Neural Network. Under such an architecture, the neurons in neural network can extract features from various flight data. Instead of time-consuming high-fidelity computational fluid dynamics (CFD) method, this study uses the K method to build the aeroelastic flutter speed big data for different flight conditions. The flutter speeds for various flight conditions are predicted by the deep learning methods and verified by the K method. The detailed physical meaning of aerodynamics and aeroelasticity of the prediction results are studied. The LSTM model has a cyclic architecture, which enables it to store information and update it with the latest information at the same time. Although the training of the model is more time-consuming than DNN, this method can increase the memory space. The results of this work show that the LSTM model established in this study can provide more accurate flutter speed prediction than the DNN algorithm.

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Long Short Term Memory Recurrent Network for Standard and Poor’s 500 Index Modelling

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.15.21365 ◽

2018 ◽

Vol 7 (4.15) ◽

pp. 25 ◽

Cited By ~ 3

Author(s):

Said Jadid Abdulkadir ◽

Hitham Alhussian ◽

Muhammad Nazmi ◽

Asim A Elsheikh

Keyword(s):

Neural Network ◽

Time Series ◽

Recurrent Neural Network ◽

Time Series Data ◽

Short Term Memory ◽

Series Data ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

Forecasting time-series data are imperative especially when planning is required through modelling using uncertain knowledge of future events. Recurrent neural network models have been applied in the industry and outperform standard artificial neural networks in forecasting, but fail in long term time-series forecasting due to the vanishing gradient problem. This study offers a robust solution that can be implemented for long-term forecasting using a special architecture of recurrent neural network known as Long Short Term Memory (LSTM) model to overcome the vanishing gradient problem. LSTM is specially designed to avoid the long-term dependency problem as their default behavior. Empirical analysis is performed using quantitative forecasting metrics and comparative model performance on the forecasted outputs. An evaluation analysis is performed to validate that the LSTM model provides better forecasted outputs on Standard & Poor’s 500 Index (S&P 500) in terms of error metrics as compared to other forecasting models.

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Enhancing Short-Term Plasticity by Inserting a Thin TiO2 Layer in WOx-Based Resistive Switching Memory

Coatings ◽

10.3390/coatings10090908 ◽

2020 ◽

Vol 10 (9) ◽

pp. 908 ◽

Cited By ~ 2

Author(s):

Hyojong Cho ◽

Sungjun Kim

Keyword(s):

Photoelectron Spectroscopy ◽

Oxygen Vacancies ◽

Short Term Memory ◽

Long Term Memory ◽

Short Term ◽

Term Memory ◽

X Ray ◽

Short Term Plasticity ◽

Good Retention

In this work, we emulate biological synaptic properties such as long-term plasticity (LTP) and short-term plasticity (STP) in an artificial synaptic device with a TiN/TiO2/WOx/Pt structure. The graded WOx layer with oxygen vacancies is confirmed via X-ray photoelectron spectroscopy (XPS) analysis. The control TiN/WOx/Pt device shows filamentary switching with abrupt set and gradual reset processes in DC sweep mode. The TiN/WOx/Pt device is vulnerable to set stuck because of negative set behavior, as verified by both DC sweep and pulse modes. The TiN/WOx/Pt device has good retention and can mimic long-term memory (LTM), including potentiation and depression, given repeated pulses. On the other hand, TiN/TiO2/WOx/Pt devices show non-filamentary type switching that is suitable for fine conductance modulation. Potentiation and depression are demonstrated in the TiN/TiO2 (2 nm)/WOx/Pt device with moderate conductance decay by application of identical repeated pulses. Short-term memory (STM) is demonstrated by varying the interval time of pulse inputs for the TiN/TiO2 (6 nm)/WOx/Pt device with a quick decay in conductance.

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Fault diagnosis of high power grid wind turbine based on particle swarm optimization BP neural network during COVID-19 epidemic period

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-189301 ◽

2020 ◽

Vol 39 (6) ◽

pp. 9027-9035

Author(s):

Xi Chen

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Wind Turbine ◽

Bp Neural Network ◽

Short Term Memory ◽

Rolling Bearing ◽

Short Term ◽

Swarm Optimization ◽

Term Memory

During the COVID-19 pandemic, the maintenance of the wind turbine is unable to be processed due to the problem of personnel. This paper presents two neural network models: BP neural network and LSTM neural network combined with Particle Swarm Optimization (PSO) algorithm to realize obstacle maintenance detection for wind turbine. Aiming at the problem of gradient vanishing existing in the traditional regression neural network, a fault diagnosis model of wind turbine rolling bearing is proposed by using long-term and short-term memory neural network. Through the analysis of an example, it is verified that the diagnosis results of this method are consistent with the actual fault diagnosis results of wind turbine rolling bearing and the diagnosis accuracy is high. The results show that the proposed method can effectively diagnose the rolling bearing of wind turbine, and the long-term and short-term memory neural network still has good fault diagnosis performance when the difference of fault characteristics is not obvious, which shows the feasibility and effectiveness of the method.

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A Novel Fault Diagnosis Approach for Chillers Based on 1-D Convolutional Neural Network and Gated Recurrent Unit

Sensors ◽

10.3390/s20092458 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2458 ◽

Cited By ~ 4

Author(s):

Zhuozheng Wang ◽

Yingjie Dong ◽

Wei Liu ◽

Zhuo Ma

Keyword(s):

Neural Network ◽

Fault Diagnosis ◽

Convolutional Neural Network ◽

Short Term Memory ◽

Sequence Data ◽

Experimental Results ◽

Learning Technology ◽

Global Features ◽

Gated Recurrent Unit ◽

Diagnosis Approach

The safety of an Internet Data Center (IDC) is directly determined by the reliability and stability of its chiller system. Thus, combined with deep learning technology, an innovative hybrid fault diagnosis approach (1D-CNN_GRU) based on the time-series sequences is proposed in this study for the chiller system using 1-Dimensional Convolutional Neural Network (1D-CNN) and Gated Recurrent Unit (GRU). Firstly, 1D-CNN is applied to automatically extract the local abstract features of the sensor sequence data. Secondly, GRU with long and short term memory characteristics is applied to capture the global features, as well as the dynamic information of the sequence. Moreover, batch normalization and dropout are introduced to accelerate network training and address the overfitting issue. The effectiveness and reliability of the proposed hybrid algorithm are assessed on the RP-1043 dataset; based on the experimental results, 1D-CNN_GRU displays the best performance compared with the other state-of-the-art algorithms. Further, the experimental results reveal that 1D-CNN_GRU has a superior identification rate for minor faults.

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A Novel Stock Index Intelligent Prediction Algorithm Based on Attention-Guided Deep Neural Network

Wireless Communications and Mobile Computing ◽

10.1155/2021/6210627 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Yangzi Zhao

Keyword(s):

Neural Network ◽

Stock Market ◽

Deep Neural Network ◽

Short Term Memory ◽

Stock Index ◽

Prediction Algorithm ◽

Short Term ◽

Term Memory ◽

Model Based

The stock market is affected by economic market, policy, and other factors, and its internal change law is extremely complex. With the rapid development of the stock market and the expansion of the scale of investors, the stock market has produced a large number of transaction data, which makes it more difficult to obtain valuable information. Because deep neural network is good at dealing with the prediction problems with large amount of data and complex nonlinear mapping relationship, this paper proposes an attention-guided deep neural network stock prediction algorithm. This paper synthesizes the daily stock social media text emotion index and stock technology index as the data source and applies them to the long-term and short-term memory neural network (LSTM) model to predict the stock market. The stock emotion index is extracted by constructing a social text classification emotion model of bidirectional long-term and short-term memory neural network (Bi-LSTM) based on attention mechanism and glove word vector representation algorithm. In addition, a dimensionality reduction model based on decision tree (DT) and principal component analysis (PCA) is constructed to reduce the dimensionality of stock technical indicators and extract the main data information. Furthermore, this paper proposes a model based on nasNet for pattern recognition. The recognition results can be used to automatically identify short-term K-line patterns, predict reliable trading signals, and help investors customize short-term high-efficiency investment strategies. The experimental results show that the prediction accuracy of the proposed algorithm can reach 98.6%, which has high application value.

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Predicting Sentiment Polarity of Microblogs using an LSTM – CNN Deep Learning Model

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f8933.088619 ◽

2019 ◽

Vol 8 (6) ◽

pp. 4368-4373

Keyword(s):

Neural Network ◽

Machine Learning ◽

Prediction Accuracy ◽

Short Term Memory ◽

Learning Model ◽

Supervised Machine Learning ◽

Machine Learning Model ◽

Proposed Model ◽

Network Approaches

In this paper we propose a novel supervised machine learning model to predict the polarity of sentiments expressed in microblogs. The proposed model has a stacked neural network structure consisting of Long Short Term Memory (LSTM) and Convolutional Neural Network (CNN) layers. In order to capture the long-term dependencies of sentiments in the text ordering of a microblog, the proposed model employs an LSTM layer. The encodings produced by the LSTM layer are then fed to a CNN layer, which generates localized patterns of higher accuracy. These patterns are capable of capturing both local and global long-term dependences in the text of the microblogs. It was observed that the proposed model performs better and gives improved prediction accuracy when compared to semantic, machine learning and deep neural network approaches such as SVM, CNN, LSTM, CNN-LSTM, etc. This paper utilizes the benchmark Stanford Large Movie Review dataset to show the significance of the new approach. The prediction accuracy of the proposed approach is comparable to other state-of-art approaches.

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Latent representation of the human pan-celltype epigenome through a deep recurrent neural network

10.1101/2021.03.08.434446 ◽

2021 ◽

Author(s):

Kevin B. Dsouza ◽

Adam Y. Li ◽

Vijay K. Bhargava ◽

Maxwell W. Libbrecht

Keyword(s):

Neural Network ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Replication Timing ◽

Cell Types ◽

Data Sets ◽

Deep Recurrent Neural Network ◽

Long Short Term Memory ◽

Latent Representations

AbstractThe availability of thousands of assays of epigenetic activity necessitates compressed representations of these data sets that summarize the epigenetic landscape of the genome. Until recently, most such representations were celltype specific, applying to a single tissue or cell state. Recently, neural networks have made it possible to summarize data across tissues to produce a pan-celltype representation. In this work, we propose Epi-LSTM, a deep long short-term memory (LSTM) recurrent neural network autoencoder to capture the long-term dependencies in the epigenomic data. The latent representations from Epi-LSTM capture a variety of genomic phenomena, including gene-expression, promoter-enhancer interactions, replication timing, frequently interacting regions and evolutionary conservation. These representations outperform existing methods in a majority of cell-types, while yielding smoother representations along the genomic axis due to their sequential nature.

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