Public Environment Emotion Prediction Model Using LSTM Network

Zhang, Qiang; Gao, Tianze; Liu, Xueyan; Zheng, Yun

doi:10.3390/su12041665

Cited by 25 publications

(17 citation statements)

References 33 publications

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“…The Long Short-Term Memory model (LSTM) (M. Zhang, Geng, & Chen, 2020; Q. Zhang, Gao, Liu, & Zheng, 2020) is an advancement from the recurrent neural network. However, RNN suffers from vanishing gradient problems, meaning networks cannot learn from long data sequences.…”

Section: Methods and Modelsmentioning

confidence: 99%

“…Recurrent LSTM networks can address the limitations of traditional time series forecasting techniques by adapting nonlinearities of given COVID-19 dataset and can result in a state of the art results on temporal data (Chimmula & Zhang, 2020). The Long Short-Term Memory model (LSTM) (M. Zhang, Geng, & Chen, 2020;Q. Zhang, Gao, Liu, & Zheng, 2020) is an advancement from the recurrent neural network.…”

Section: Long Short-term Memory (Lstm) Networkmentioning

confidence: 99%

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Forecasting COVID-19 Dynamics and Endpoint in Bangladesh: A Data-driven Approach

Hridoy

Naim²,

Emon

et al. 2020

Preprint

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On December 31, 2019, the World Health Organization (WHO) was informed that atypical pneumonia-like cases have emerged in Wuhan City, Hubei province, China. WHO identified it as a novel coronavirus and declared a global pandemic on March 11th, 2020. At the time of writing this, the COVID-19 claimed more than 440 thousand lives worldwide and led to the global economy and social life into an abyss edge in the living memory. As of now, the confirmed cases in Bangladesh have surpassed 100 thousand and more than 1343 deaths putting startling concern on the policymakers and health professionals; thus, prediction models are necessary to forecast a possible number of cases in the future. To shed light on it, in this paper, we presented data-driven estimation methods, the Long Short-Term Memory (LSTM) networks, and Logistic Curve methods to predict the possible number of COVID-19 cases in Bangladesh for the upcoming months. The results using Logistic Curve suggests that Bangladesh has passed the inflection point on around 28-30 May 2020, a plausible end date to be on the 2nd of January 2021 and it is expected that the total number of infected people to be between 187 thousand to 193 thousand with the assumption that stringent policies are in place. The logistic curve also suggested that Bangladesh would reach peak COVID-19 cases at the end of August with more than 185 thousand total confirmed cases, and around 6000 thousand daily new cases may observe. Our findings recommend that the containment strategies should immediately implement to reduce transmission and epidemic rate of COVID-19 in upcoming days.

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Section: Methods and Modelsmentioning

confidence: 99%

Section: Long Short-term Memory (Lstm) Networkmentioning

confidence: 99%

Forecasting COVID-19 Dynamics and Endpoint in Bangladesh: A Data-driven Approach

Hridoy

Naim²,

Emon

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…The Recurrent Neural Network(RNN)was designed in the 1980s [61].The RNN algorithm consists of hidden layers, an input layer and an output layer. The RNN algorithm has a chain like-structure for repeating cells of the RNN algorithm, used to store significant information from previous process steps.…”

Section: ) Recurrent Neural Network (Rnn)mentioning

confidence: 99%

Intelligent Hybrid Model to Enhance Time Series Models for Predicting Network Traffic

et al. 2020

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Network traffic analysis and predictions have become vital for monitoring networks. Network prediction is the process of capturing network traffic and examining it deeply to decide what is the occurrence in the network. The accuracy of analysis and estimation of network traffic are increasingly becoming significant in achieving guaranteed Quality of Service (QoS) in the network. The main aim of the presented research is to propose a new methodology to improve network traffic prediction by using sequence mining. The significance of this important topic lies in the urge to contribute to solving the research problem in network traffic prediction intelligently. We propose an integrated model that combines clustering with existing series models to enhance prediction the network traffic. Clustering granules are obtained using fuzzy c-means to analyze the network data for improving the existing time series. The novelty of the proposed research has used the clustering approach to handle the ambiguity from the entire network data for enhancing the existing time series models. Furthermore, we have suggested using the weighted exponential smoothing model as preprocessing stages for increasing the reliability of the proposed model. In this research paper, machine intelligence proposed to predict network traffic. The machine intelligence is working as pre-processing for enhancing the existing time series models. The machine intelligence combines non-crisp Fuzzy-C-Means (FCM) clustering and the weight exponential method for improving deep learning Long Short-Time Memory(LSTM)and Adaptive Neuro-Fuzzy Inference System (ANFIS)time series models. The ANFIS and LSMT time series models are applied to predict network traffic. Two real network traffic traces were conducted to test the proposed time series models. The empirical results of proposed to enhanced LSTM 97.95% and enhanced ANFIS model is R=96.78% for cellular traffic data, with respect to the correlation indicator. It is observed that the proposed model outperforms alternative time series models. A comparative prediction results between the proposed model and exis ting time series models are presented. The comparisons indicate that the presented model outperforms the opponent models; the proposed method optimises the deep learning LSTM and ANFIS time series models. The proposed methodology offers more effective approach to the prediction of network traffic.

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“…The Recurrent Neural Network (RNN) was designed in the 1980s [48].The RNN algorithm consists of hidden layers, an input layer and an output layer. The RNN algorithm has a chain like-structure for repeating cells of the RNN algorithm, used to store significant information from previous process steps.…”

Section: 31recurrent Neural Network (Rnn)mentioning

confidence: 99%

Deep Learning and Holt-Trend Algorithms for predicting COVID-19 pandemic

Aldhyani

Alrasheed

Alqarn

et al. 2020

Preprint

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According to WHO, more than one million individuals are infected with COVID-19, and around 20000 people have died because of this infectious disease around the world. In addition, COVID-19 epidemic poses serious public health threat to the world where people with little or no pre-existing human immunity can be more vulnerable to the effects of the effects of the coronavirus. Thus, developing surveillance systems for predicting COVID-19 pandemic in an early stage saves millions of lives. In this study, the deep learning algorithm and Holt-trend model is proposed to predict coronavirus. The Long-Short Term Memory (LSTM ) algorithm and Holt-trend were applied to predict confirmed numbers and death cases. The real time data have been collected from the World Health Organization (WHO) . In the proposed research, we have considered three countries to test the proposed model namely Saudi Arabia, Spain and Italy. The results suggest that the LSTM models showed better performance in predicting the cases of coronavirus patients. Standard measure performance MSE, RMSE, Mean error and correlation are employed to estimate the results of the proposed models. The empirical results of the LSTM by using correlation metric are 99.94%, 99.94% and 99.91 to predict number of confirmed cases on COVID-19 in three countries. Regarding the prediction results of LSTM model to predict the number of death on COVID-19 are 99.86%, 98.876% and 99.16 with respect to the Saudi Arabia, Italy and Spain respectively. Similarly the experimented results of Holt-Trend to predict the number of confirmed cases on COVID-19 by using correlation metrics are 99.06%, 99.96% and 99.94, whereas the results of Holt-Trend to predict the number of death cases are 99.80%, 99.96 and 99.94 with respect to the Saudi Arabia, Italy and Spain respectively. The empirical results indicate the efficient performance of the presented model in predicting the number of confirmed and death cases of COVID-19 in these countries. Such findings provide better insights about the future of COVID-19 in general. The results were obtained by applying the time series models which needs to be considered for the sake of saving the lives of many people.

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Public Environment Emotion Prediction Model Using LSTM Network

Cited by 25 publications

References 33 publications

Forecasting COVID-19 Dynamics and Endpoint in Bangladesh: A Data-driven Approach

Forecasting COVID-19 Dynamics and Endpoint in Bangladesh: A Data-driven Approach

Intelligent Hybrid Model to Enhance Time Series Models for Predicting Network Traffic

Deep Learning and Holt-Trend Algorithms for predicting COVID-19 pandemic

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