An Integrated Neural Network and SEIR Model to Predict COVID-19

Zisad, Sharif Noor; Hossain, Mohammad Shahadat; Hossain, Mohammed Sazzad; Andersson, Karl

doi:10.3390/a14030094

Cited by 37 publications

(27 citation statements)

References 30 publications

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“…However, the authors still required 34 iterative rounds to reach these results, and they had to change neuron values in each round. Zisad et al [83] used SEIR and SIRNN models to predict COVID-19. The model was tested and trained to employ available data from 250 days in Bangladesh.…”

Section: Discussionmentioning

confidence: 99%

A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

Aldhyani

Alkahtani

2021

Life

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Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar. The novelty of the proposed approach is that it uses an advanced prediction model—the bidirectional long short-term memory (Bi-LSTM) network deep learning model. The datasets were collected from an available repository containing updated registered cases of COVID-19 and showing the global numbers of active COVID-19 cases and deaths. Statistical analyses (e.g., mean square error, root mean square error, mean absolute error, and Spearman’s correlation coefficient) were employed to evaluate the results of the adopted Bi-LSTM model. The Bi-LSTM results based on the correlation metric gave predicted confirmed COVID-19 cases of 99.67%, 99.34%, 99.94%, 99.64%, 98.95%, and 99.91% for Saudi Arabia, Oman, the UAE, Kuwait, Bahrain, and Qatar, respectively, while testing the Bi-LSTM model for predicting COVID-19 mortality gave accuracies of 99.87%, 97.09%, 99.53%, 98.71%, 95.62%, and 99%, respectively. The Bi-LSTM model showed significant results using the correlation metric. Overall, the Bi-LSTM model demonstrated significant success in predicting COVID-19. The Bi-LSTM-based deep learning network achieves optimal prediction results and is effective and robust for predicting the numbers of active cases and deaths from COVID-19 in the studied Gulf countries.

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Section: Discussionmentioning

confidence: 99%

A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

Aldhyani

Alkahtani

2021

Life

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“…In the previous works, deep convolutional neural networks (CNN) have shown better results on speech signal features of patients with neurological disorder [30]. Though CNN is an unexplainable technique based on backpropogation, integrating [31] it with BBRES can overcome this limitation. BRBES [7,10,23] and CNN can assist each other in a symbiotic fashion.…”

Section: Discussionmentioning

confidence: 99%

A Belief Rule Base Approach to Support Comparison of Digital Speech Signal Features for Parkinson’s Disease Diagnosis

et al. 2021

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Parkinson's disease is a neurological disorder. It affects the structures of the central and peripheral nervous system that control movement. One of the symptoms of Parkinson's disease is difficulty in speaking. Hence, analysis of speech signal of patients may provide valuable features for diagnosing. Previous works on diagnosis based on speech data have employed machine learning and deep learning techniques. However, these approaches do not address the various uncertainties in data. Belief rule based expert system (BRBES) is an approach that can reason under various forms of data uncertainty. Thus, the main objective of this research is to compare the potential of BRBES on various speech signal features of patients of parkinson's disease. The research took into account various types of standard speech signal features such MFCCs, TQWTs etc. A BRBES was trained on a dataset of 188 patients of parkinson's disease and 64 healthy candidates with 5fold cross validation. It was optimized using an exploitive version of the nature inspired optimization algorithm called BRB-based adaptive differential evolution (BRBaDE). The optimized model performed better than explorative BRBaDE, genetic algorithm and MATLAB's FMIN-CON optimization on most of these features. It was also found that for speech based diagnosis of Parkinson's disease under uncertainty, the features such as Glottis Quotient, Jitter variants, MFCCs, RPDE, DFA and PPE are relatively more suitable.

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“…The proposed models are compared with respect to correlation coefficient and mean square error (MSE). Zisad et al [39] presented an integrated ANN and SEIR (Susceptible, Exposed, Infected, Removed) model to predict COVID -19.…”

Section: Literature Review On Covid-19 and Machine Learningmentioning

confidence: 99%

A predictive analytics model for COVID-19 pandemic using artificial neural networks

Kuvvetli

Deveci

Paksoy

et al. 2021

Decision Analytics Journal

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An Integrated Neural Network and SEIR Model to Predict COVID-19

Cited by 37 publications

References 30 publications

A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

A Belief Rule Base Approach to Support Comparison of Digital Speech Signal Features for Parkinson’s Disease Diagnosis

A predictive analytics model for COVID-19 pandemic using artificial neural networks

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