Long Short-term Memory Deep Learning Research Articles

Early prediction and analysis of corona pandemic outbreak using deep learning technique

PurposeThe purpose of this paper is to analyze and build a deep learning model that can furnish statistics of COVID-19 and is able to forecast pandemic outbreak using Kaggle open research COVID-19 data set. As COVID-19 has an up-to-date data collection from the government, deep learning techniques can be used to predict future outbreak of coronavirus. The existing long short-term memory (LSTM) model is fine-tuned to forecast the outbreak of COVID-19 with better accuracy, and an empirical data exploration with advanced picturing has been made to comprehend the outbreak of coronavirus.Design/methodology/approachThis research work presents a fine-tuned LSTM deep learning model using three hidden layers, 200 LSTM unit cells, one activation function ReLu, Adam optimizer, loss function is mean square error, the number of epochs 200 and finally one dense layer to predict one value each time.FindingsLSTM is found to be more effective in forecasting future predictions. Hence, fine-tuned LSTM model predicts accurate results when applied to COVID-19 data set.Originality/valueThe fine-tuned LSTM model is developed and tested for the first time on COVID-19 data set to forecast outbreak of pandemic according to the authors’ knowledge.

The Deep Learning LSTM and MTD Models Best Predict Acute Respiratory Infection among Under-Five-Year Old Children in Somaliland

The most effective techniques for predicting time series patterns include machine learning and classical time series methods. The aim of this study is to search for the best artificial intelligence and classical forecasting techniques that can predict the spread of acute respiratory infection (ARI) and pneumonia among under-five-year old children in Somaliland. The techniques used in the study include seasonal autoregressive integrated moving averages (SARIMA), mixture transitions distribution (MTD), and long short term memory (LSTM) deep learning. The data used in the study were monthly observations collected from five regions in Somaliland from 2011–2014. Prediction results from the three best competing models are compared by using root mean square error (RMSE) and absolute mean deviation (MAD) accuracy measures. Results have shown that the deep learning LSTM and MTD models slightly outperformed the classical SARIMA model in predicting ARI values.

Developing a Novel Water Quality Prediction Model for a South African Aquaculture Farm

Providing an accurate prediction of water quality parameters for improved water quality management is a topical issue in the aquaculture industry. Conventional prediction methods have shown different challenges like a poor generalization, poor prediction accuracy, and high time complexity. Aiming at these challenges, a novel hybrid prediction model with ensemble empirical mode decomposition (EEMD) and deep learning (DL) long-short term memory (LSTM) neural network is proposed in this paper. In this innovative hybrid EEMD-DL-LSTM model, firstly, the integrity of the datasets is enhanced by applying moving average filtering and linear interpolation techniques of water quality parameter datasets pre-treatment. Secondly, the measured real sensor water quality parameters dataset is decomposed with the aid of the EEMD algorithm into disparate IMFs and a corresponding residual item. Thirdly, a multi-feature selection process is applied to make a careful selection of a strongly correlated group of IMFs with the measured real water quality parameter datasets and integrate them as inputs to the DL-LSTM neural network. The presented model is built on water quality sensor data collected from an Abalone farm in South Africa. The performance of the novel hybrid prediction model is validated by comparing the results against the real datasets. To measure the overall accuracy of the novel hybrid prediction model, different statistical indices, namely the Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE), are used.

Spindle Thermal Error Prediction Based on LSTM Deep Learning for a CNC Machine Tool

In the precision processing industry, maintaining the accuracy of machine tools for an extensive period is crucial. Machining accuracy is affected by numerous factors, among which spindle thermal elongation caused by an increase in machine temperature is the most common. This paper proposed a key temperature point selection algorithm and thermal error estimation method for spindle displacement in a machine tool. First, highly correlated temperature points were clustered into groups, and the characteristics of small differences within groups and large differences between groups were realized. The optimal number of key temperature points was then determined using the elbow method. Meanwhile, the long short-term memory (LSTM) modeling method was proposed to establish the relationship between the spindle thermal error and changes of the key temperature points. The results show the largest root mean square errors (RMSEs) of the proposed LSTM model and the key temperature point selection algorithm were within 0.6 µm in the spindle thermal displacement experiments with different temperature changes. The results demonstrated that the combined methodology can provide improved accuracy and robustness in predicting the spindle thermal displacement.

Management of Early Failure Detection of Production Process: The Case of the Clutch Shaft Alignment using LSTM Deep Learning Algorithm

Purpose: In this paper the neural networks model based on long short-term memory (LSTM) for early failure detection of the clutch shaft alignment system is developed. This issue is of particular importance when assessing the condition of the tool and predicting its durability, which are keys to the reliability and quality of the production process. Design/Methodology/Approach: Based on real fault data of the measuring system, 500 clutch fault runs were simulated. Then, the time of failure was modelled with two neural networks, the conventional neural network of the ANN and the LSTM deep learning network. The study examined and compared the effectiveness and quality of both networks in the context of fault prediction. Practical Implications: In vibroacoustic diagnostics, we often deal with machines operating in various conditions, which makes it difficult to diagnose them using standard methods. In such cases, spectral methods require analysis of frequency bands, which may contain other components in addition to information about the diagnosed parameter. The algorithm for predicting impending failure gives the possibility to monitor the current degradation status of the device. This makes it possible to streamline planning processes in the areas of inspection, preventive replacement of parts, warranty, service, or storage of spare parts. Findings: The objective of the paper is to introduce an improved computational method for failure detection based on a deep learning algorithm. It was proven that LSTM networks are suitable for successfully solving this scope of tasks. Originality/Value: The research showed that the proposed LSTM algorithm is more effective and accurate than conventional artificial neural networks (ANN) based on the multilayer perceptron model.

LSTMCNNsucc: A Bidirectional LSTM and CNN-Based Deep Learning Method for Predicting Lysine Succinylation Sites.

Lysine succinylation is a typical protein post-translational modification and plays a crucial role of regulation in the cellular process. Identifying succinylation sites is fundamental to explore its functions. Although many computational methods were developed to deal with this challenge, few considered semantic relationship between residues. We combined long short-term memory (LSTM) and convolutional neural network (CNN) into a deep learning method for predicting succinylation site. The proposed method obtained a Matthews correlation coefficient of 0.2508 on the independent test, outperforming state of the art methods. We also performed the enrichment analysis of succinylation proteins. The results showed that functions of succinylation were conserved across species but differed to a certain extent with species. On basis of the proposed method, we developed a user-friendly web server for predicting succinylation sites.

Prediction of the Hypertension Risk of the Elderly in Built Environments Based on the LSTM Deep Learning and Bayesian Fitting Method

Hypertension has become the greatest risk factor for death in elderly populations. As factors influencing cardiovascular disease, indoor environmental parameters pose potential risks for older adults. In this study, elderly residents in Dalian (Liaoning Province, China) urban dwellings were selected as the research subjects, and the environmental parameters of the dwellings’ main activity rooms and the blood pressure parameters of the older adults were measured. Based on the Long Short-Term Memory (LSTM) deep learning algorithm and Bayesian fitting method, a hypertension disease model was established using the long-term environmental parameters to predict the hypertension risk of older adults in their building’s environment. The results showed that temperature, humidity, and some air quality parameters had an impact on blood pressure under single environmental factor, and the comprehensive environmental risks of high systolic blood pressure, high diastolic blood pressure, and high blood pressure were 16.44%, 0%, and 16.44% for the male elderly and 14.11%, 7.14%, and 17.55% for the female elderly, respectively. By comparing the results for the blood pressure measurement and prediction, it can be observed that the risk error of hypertension obtained by the algorithm maintains the variables’ relationship, and the result of the algorithm is reliable in this period. This technology can provide a basis for measuring environmental parameters and will be conducive to the development of an ecological smart building environment.

HUMAN ROBOT INTERACTIVE INTENTION PREDICTION USING DEEP LEARNING TECHNIQUES

In this research, we propose a method of human robot interactive intention prediction. The proposed algorithm makes use of a OpenPose library and a Long-short term memory deep learning neural network. The neural network observes the human posture in a time series, then predicts the human interactive intention. We train the deep neural network using dataset generated by us. The experimental results show that, our proposed method is able to predict the human robot interactive intention, providing 92% the accuracy on the testing set.

An Effective Pomegranate Fruit Classification Based On CNN-LSTM Deep Learning Models

Objectives: To employ a deep learning technique that would sort the fruits into normal and abnormal based on the features such as fruit colour, number of fruit spots, and shape of the fruit. Methods: A combined CNN LSTM deep learning model is applied to classify a set of 6519 fruits into two classes namely normal and abnormal. The dataset is an excel file with each record consisting of 9 features. Convolutional Neural Networks (CNN) are applied for deep feature extraction and Long-Short Term Memory (LSTM) is used to detect the class based on extracted features. Findings: The proposed system achieved an accuracy of 98.17%, specificity of 98.65%, sensitivity of 97.77%, and an F1-score of 98.39%. Novelty: The sensitivity of disease detection was less with lesser availability of enhanced detection methods for detecting disease in earlier stages. The issue with these various existing algorithms is that the accuracy was reduced since some sources of errors were not eliminated. Deep Learning delivers methodologies, approaches, and functionalities that can help to resolve analytic and predictive analysis accurately. Keywords: Deep Learning; CNN LSTM; Classification; Hyperparameters

Deep Learning-Based Minute-Scale Digital Prediction Model of Temperature-Induced Deflection of a Cable-Stayed Bridge: Case Study

The evolution rule of temperature-induced deflection in main girders is an important index to evaluate the service performance of long-span cable-stayed bridges, which directly reflects the coupling effect between the vertical stiffness of the main girder and the tension of multiple cables. However, temperature-induced deflection is caused by the complex temperature field of the main girder, cable tower and cable, while monitoring data have documented a time-lag effect between the temperature and temperature-induced deflection. Hence, it is difficult to accurately describe and model the behavior of the temperature-induced deflection in a long-span cable-stayed bridge in service. To this end, by utilizing the advantage of long short-term memory (LSTM) network for time series prediction, a digital model in minute scale based on monitoring data and deep learning can be developed to predict temperature-induced deflection, and resolve the low precision caused by the single-point input and time-lag effect. Compared with traditional machine learning algorithm and linear regression, a deep learning LSTM network has the best performance. For the cable-stayed bridge in this paper, the mean absolute error of the LSTM model was even less than 0.5 mm, and with the combined hypothesis test, the early warning accuracy for the abnormal change of temperature-induced deflection could achieve a minimum of 0.3%.

RETRACTED ARTICLE: Development of air quality monitoring (AQM) models using different machine learning approaches

Air Quality assessment and forecasting are the essentials today and they attracted many researchers. Environmental organizations regularly monitor and predict the air contaminants to make the public awareness, provide a better environment, and suitable for human health. Physical factors like climate changes, Industrialization, Fires and Urbanization are some of the factors which directly affect and reduce the air quality. All these data are time-series and real-time data. The primary pollutant is PMx that affect the respiratory systems and cardiac activity of humans. The secondary pollutants are SO2, CO, NOx, and O3. Each has allowable range of concentration levels. In this work, meteorological elements are collected in different locations in last 5 years, with time window of 24 h and mapped to the concentration level of pollutants. The Machine Learning(ML) Methods such as Non-Linear Artificial Neural Network(ANN), Statistical Multilevel Regression, Neuro- Fuzzy and Deep Learning Long-Short-Term Memory (DL-LSTM) are used; to find the current concentration level of pollutants and will be useful for Real Time Correction (RTC) to give a feedback that can be used to reduce the contaminants in air for further days. The results are compared with the parameters such as R2, RMSE and MAPE. Using these methods, the concentration level of contaminants is predicted with the deviation of R2 in the range of 0.71–0.89. The results proved that DL-LSTM suits well when comparing to the ANN, Neuro-fuzzy and regression algorithms.

An Integrated Approach Based on Improved CEEMDAN and LSTM Deep Learning Neural Network for Fault Diagnosis of Reciprocating Pump

The reciprocating pump plays an important role in the petrochemical industry procedure, it is crucial in ensuring the systematic safety and stability. Since the useful feature information of the vibration signal from the reciprocating pump tends to be overwhelmed by the background ingredients, it is tough to realize the recognition on typical modes. Aiming at the extraction of reciprocating mechanical fault features and mode recognition, this paper proposes Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and LSTM (Long Short-Term Memory) deep neural network algorithm. Firstly, the IMF components are obtained by decomposing the vibration signals from the reciprocating pump with the Improved CEEMDAN algorithm, in which the key parameter β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">k</sub> is improved and redefined, for optimizing SNRs (Signal Noise Ratio) of the IMF (Intrinsic Mode Function) components. Then the corresponding singular spectral entropy is calculated and the feature vector is constructed. The classification modal based on LSTM deep network is developed in the data dividing-training and the final mode recognition process. The study shows that the proposed method can effectively extract the fault features of vibration signal of the reciprocating pump, and the testing modes could be accurately recognized with the developed classification model.

Abnormally high water temperature prediction using LSTM deep learning model

Recently, abnormally high water temperature (AHWT) phenomena are occurring more often due to the global warming and its impact. These phenomena have damaged extensively to the maritime economy around the southern coast of Korea and caused an illness by exacerbating the propagation of Vibrio pathogens. To mitigate damages by AHWT phenomena, it is necessary to respond as quickly as possible or predict them in advance. In this study, therefore, we proposed a deep learning-based methodology to predict the occurrences of AHWT phenomena using the long short-term memory (LSTM) model. First, a LSTM model was trained using the satellite-derived water temperature data over the past ten years. Then, the water temperatures after a few days were estimated using the trained LSTM model. In a performance evaluation, when estimating water temperatures after one-day, the model achieved results of 1.865 and 0.412 in terms of mean absolute percentage error (MAPE) and root mean square error (RMSE), respectively. Second, we developed a decision algorithm based on the Markov state transition in order to predict the AHWT occurrence probability. As a result, we obtained 0.88 of F1 score for predicting AHWT phenomena after 1 day in case of the southern coast of Korea.

Cardiac Arrhythmia Disease Classification Using LSTM Deep Learning Approach

Many approaches have been tried for the classification of arrhythmia. Due to the dynamic nature of electrocardiogram (ECG) signals, it is challenging to use traditional handcrafted techniques, making a machine learning (ML) implementation attractive. Competent monitoring of cardiac arrhythmia patients can save lives. Cardiac arrhythmia prediction and classification has improved significantly during the last few years. Arrhythmias are a group of conditions in which the electrical activity of the heart is abnormal, either faster or slower than normal. It is the most frequent cause of death for both men and women every year in the world. This paper presents a deep learning (DL) technique for the classification of arrhythmias. The proposed technique makes use of the University of California, Irvine (UCI) repository, which consists of a high-dimensional cardiac arrhythmia dataset of 279 attributes. In this research, our goal was to classify cardiac arrhythmia patients into 16 classes depending on the characteristics of the electrocardiography dataset. The DL approach in the form of long short-term memory (LSTM) is an efficient technique to deal with reduced accuracy due to vanishing and exploding gradients in traditional DL frameworks for big data analysis. The goal of this research was to categorize cardiac arrhythmia patients by developing an efficient intelligent system using the LSTM DL algorithm. This approach to arrhythmia classification includes classification algorithms along with noise removal techniques. Therefore, we utilized principal components analysis (PCA) for noise removal, and LSTM for classification. This hybrid comprehensive arrhythmia classification approach performs better than previous approaches to arrhythmia classification. We attained a highest classification accuracy of 93.5% with the DL based disease classification system, and outperformed the earlier approaches used for cardiac arrhythmia classification.

Application of LSTM approach for modelling stress–strain behaviour of soil

This paper presents a new trial to reproduce soil stress–strain behaviour by adapting a long short-term memory (LSTM) deep learning method. LSTM is an approach that employs time sequence data to predict future occurrences, and it can be used to consider the stress history of soil behaviour. The proposed LSTM method includes the following three steps: data preparation, architecture determination, and optimisation. The capacity of the adapted LSTM method is compared with that of feedforward and feedback neural networks using a new numerical benchmark dataset. The performance of the proposed LSTM method is verified through a dataset collected from laboratory tests. The results indicate that the LSTM deep-learning method outperforms the feed forward and feedback neural networks based on both accuracy and the convergence rate when reproducing the soil’s stress–strain behaviour. One new phenomenon referred to as “bias at low stress levels”, which was not noticed before, is first discovered and discussed for all neural network-based methods.

A Novel Atmosphere-Informed Data-Driven Predictive Channel Modeling for B5G/6G Satellite-Terrestrial Wireless Communication Systems at Q-Band

This paper proposes a novel atmosphere-informed predictive satellite channel model for beyond the fifth-generation (B5G)/the sixth-generation (6G) satellite-terrestrial wireless communication systems at Q-band to model/predict channel attenuation at any specific time. The proposed channel model is a data-driven model based on either of two deep learning networks, i.e., multi-layer perceptron (MLP) and long short-term memory (LSTM). The accuracy of the proposed channel model is measured by cumulative density function (CDF) of absolute error and mean square error (MSE) between modeled/predicted and measured channel attenuation. The complexity of the proposed channel model is assessedby the training time, loading time, and test time of deep learning networks. To further improve the accuracy of the proposed channel model, weather classification is developed at the stage of database construction. Based on our established channel and weather measurement campaign, the performance of the proposed data-driven channel model based on different deep learning networks, e.g., MLP and LSTM, with or without the weather classification is investigated and analyzed comprehensively. Finally, the close agreement is achieved between the channel attenuation modeled/predicted from the proposed atmosphere-informed predictive satellite channel model and the one from real channel measurements, verifying the utility of proposed channel model.

A Financial Fraud Detection Model Based on LSTM Deep Learning Technique

As the use of the internet is growing exponentially, more and more businesses such as the financial sector are operationalizing their services online. Consequently, financial frauds are increasing in number and forms around the world, which results in tremendous financial losses which make financial fraud a major problem. Unauthorized access and irregular attacks are examples of threats that should be detected by means of financial fraud detection systems. Machine learning and data mining techniques have been widely used to tackle this issue over the past few years. However, these methods still need to be improved in terms of speed computation, dealing with big data, and identify the unknown attack patterns. Therefore, in this paper, a deep learning-based method is proposed for the detection of financial fraud based on the Long Short-Term Memory (LSTM) technique. This model is aimed at enhancing the current detection techniques as well as enhancing the detection accuracy in the light of big data. To evaluate the proposed model, a real dataset of credit card frauds is utilized and the results are compared with an existing deep learning model named Auto-encoder model and some other machine learning techniques. The experimental results illustrated a prefect performance of LSTM where it achieved 99.95% of accuracy in less than a minute.

UWB Indoor Localization Using Deep Learning LSTM Networks

Localization using ultra-wide band (UWB) signals gives accurate position results for indoor localization. The penetrating characteristics of UWB pulses reduce the multipath effects and identify the user position with precise accuracy. In UWB-based localization, the localization accuracy depends on the distance estimation between anchor nodes (ANs) and the UWB tag based on the time of arrival (TOA) of UWB pulses. The TOA errors in the UWB system, reduce the distance estimation accuracy from ANs to the UWB tag and adds the localization error to the system. The position accuracy of a UWB system also depends on the line of sight (LOS) conditions between the UWB anchors and tag, and the computational complexity of localization algorithms used in the UWB system. To overcome these UWB system challenges for indoor localization, we propose a deep learning approach for UWB localization. The proposed deep learning model uses a long short-term memory (LSTM) network for predicting the user position. The proposed LSTM model receives the distance values from TOA-distance model of the UWB system and predicts the current user position. The performance of the proposed LSTM model-based UWB localization system is analyzed in terms of learning rate, optimizer, loss function, batch size, number of hidden nodes, timesteps, and we also compared the mean localization accuracy of the system with different deep learning models and conventional UWB localization approaches. The simulation results show that the proposed UWB localization approach achieved a 7 cm mean localization error as compared to conventional UWB localization approaches.

Classification and Prediction of Typhoon Levels by Satellite Cloud Pictures through GC-LSTM Deep Learning Model.

Typhoons are some of the most serious natural disasters, and the key to disaster prevention and mitigation is typhoon level classification. How to better use data of satellite cloud pictures to achieve accurate classification of typhoon levels has become one of classification the hot issues in current studies. A new framework of deep learning neural network, Graph Convolutional–Long Short-Term Memory Network (GC–LSTM), is proposed, which is based on the data of satellite cloud pictures of the Himawari-8 satellite in 2010–2019. The Graph Convolutional Network (GCN) is used to process the irregular spatial structure of satellite cloud pictures effectively, and the Long Short-Term Memory (LSTM) network is utilized to learn the characteristics of satellite cloud pictures over time. Moreover, to verify the effectiveness and accuracy of the model, the prediction effect and model stability are compared with other models. The results show that: the algorithm performance of this model is better than other prediction models; the prediction accuracy rate of typhoon level classification reaches 92.35%, and the prediction accuracy of typhoons and super typhoons reaches 95.12%. The model can accurately identify typhoon eye and spiral cloud belt, and the prediction results are always kept in the minimum range compared with the actual results, which proves that the GC–LSTM model has stronger stability. The model can accurately identify the levels of different typhoons according to the satellite cloud pictures. In summary, the results can provide a theoretical basis for the related research of typhoon level classification.

Intelligent and Reliable Deep Learning LSTM Neural Networks-Based OFDM-DCSK Demodulation Design

Chaos communications have widely been applied to provide secure, and anti-jamming transmissions by exploiting the irregular chaotic behavior. However, the real-valued chaotic sequences imposed on the information induce interferences to the user data, thereby leading to reliability performance degradations. To address this issue, in this paper, we propose to utilize the intelligent, and feature extraction capability of the deep neural network (DNN) to learn the transmission patterns to demodulate the received signals. In our design, we propose to construct the long short-term memory (LSTM) unit-aided intelligent DNN-based deep learning (DL) demodulator for orthogonal frequency division multiplexing-aided differential chaos shift keying (OFDM-DCSK) systems. After learning, and extracting features of information-bearing chaotic transmissions at the training stage, the received signals can be recovered efficiently, and reliably at the deployment stage. Thanks to the recursive LSTM-aided DL design, correlations between information-bearing chaotic modulated signals can be exploited to enhance reliability performances. Simulation results demonstrate with the proposed DL demodulation design, the intelligent OFDM-DCSK system can achieve more reliable performances over additive white Gaussian noise (AWGN) channel, and fading channels compared with benchmark systems.