Prediction of COVID-19 Using a WOA-BILSTM Model

Yang, Xinyue; Li, Shuangyin

doi:10.3390/bioengineering10080883

Cited by 3 publications

(2 citation statements)

References 40 publications

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“…( ) The basic parameters of the LSTM model proposed in this study are shown in Table 1. The employed loss function is the mean square error function, as displayed in Formula (27).…”

Section: Emd-iwoa-lstm Based Prediction Model For Water Quality Param...mentioning

confidence: 99%

“…The findings indicate that optimizing LSTM parameters can enhance model performance. Yang et al [27] proposed a whale optimization algorithm-bidirectional long and short-term memory (WOA-BILSTM) model using WOA to optimize the hyperparameters of the BILSTM model. Their study found that WOA performed better than Bayesian optimization and lattice search algorithms, as it converged faster and was better at finding the optimal hyperparameters.…”

Section: Introductionmentioning

confidence: 99%

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An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters

Li,

Xu,

Lian

et al. 2023

Sustainability

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Prediction of water quality parameters is a significant aspect of contemporary green development and ecological restoration. However, the conventional water quality prediction models have limited accuracy and poor generalization capability. This study aims to develop a dependable prediction model for ammonia nitrogen concentration in water quality parameters. Based on the characteristics of the long-term dependence of water quality parameters, the unique memory ability of the Long Short-Term Memory (LSTM) neural network was utilized to predict water quality parameters. To improve the accuracy of the LSTM prediction model, the ammonia nitrogen data were decomposed using Empirical Modal Decomposition (EMD), and then the parameters of the LSTM model were optimized using the Improved Whale Optimization Algorithm (IWOA), and a combined prediction model based on EMD-IWOA-LSTM was proposed. The study outcomes demonstrate that EMD-IWOA-LSTM displays improved prediction accuracy with reduced RootMean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) in comparison to the LSTM and IWOA-LSTM approaches. These research findings better enable the monitoring and prediction of water quality parameters, offering a novel approach to preventing water pollution rather than merely treating it afterwards.

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“…( ) The basic parameters of the LSTM model proposed in this study are shown in Table 1. The employed loss function is the mean square error function, as displayed in Formula (27).…”

Section: Emd-iwoa-lstm Based Prediction Model For Water Quality Param...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters

Li,

Xu,

Lian

et al. 2023

Sustainability

View full text Add to dashboard Cite

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Study of flow stress in Mg-Gd-Y-Nd-Zr alloys based on IWOA-BPNN model

Qin,

Kang,

Tang

et al. 2024

Journal of Materials Research and Technology

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Inverse chi-square-based flamingo search optimization with machine learning-based security solution for Internet of Things edge devices

Alotaibi,

Deepa,

Shankar

et al. 2024

MATH

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<abstract> <p>Internet of Things (IoT) edge devices are becoming extremely popular because of their ability to process data locally, conserve bandwidth, and reduce latency. However, with the developing count of IoT devices, threat detection, and security are becoming major concerns. IoT edge devices must avoid cyber threats and protect user data. These devices frequently take limited resources and can run on lightweight operating systems, which makes them vulnerable to security attacks. Intrusion detection systems (IDS) can be run on edge devices to recognize suspicious actions and possible risks. These systems monitor traffic patterns, and behavior, and identify attack signatures to detect and report on possible attacks. This study presents a design for an inverse chi square-based flamingo search optimization algorithm with machine learning (ICSFSO-ML) as a security solution for Internet of Things edge devices. The goal of the ICSFSO-ML technique is to apply ML and metaheuristics for threat recognition in IoT edge devices. To reduce the high dimensionality problem, the ICSFSO-ML technique uses the ICSFSO algorithm for feature selection purposes. Further, the ICSFSO-ML technique exploits the stacked bidirectional long short-term memory (SBiLSTM) model for the threat detection process. To enhance the efficacy of the SBiLSTM model, an arithmetic optimization algorithm (AOA) is applied for the hyperparameter selection process. The simulation performance of the ICSFSO-ML technique can be tested on a benchmark threat database. The performance analysis showed the benefits of the ICSFSO-ML methodology compared to existing methodologies with a maximum accuracy of 98.22%.</p> </abstract>

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Prediction of COVID-19 Using a WOA-BILSTM Model

Cited by 3 publications

References 40 publications

An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters

An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters

Study of flow stress in Mg-Gd-Y-Nd-Zr alloys based on IWOA-BPNN model

Inverse chi-square-based flamingo search optimization with machine learning-based security solution for Internet of Things edge devices

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