Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic

Castillo, Oscar; Melín, Patricia

doi:10.1016/j.chaos.2020.110242

Cited by 118 publications

(82 citation statements)

References 35 publications

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“…for building the prediction models based on various risk factors. These type of approaches were provided the effective results in the previous researches (Yi et al 2018 ; Castillo and Melin 2020 ; Dansana et al 2020 ; Kannan et al 2020 ; Melin et al 2020a , b ). The compulsory input (on the linguistic scale) from all decision-makers may be gathered independently for optimum weight computation of the risk factors using BWM.…”

Section: Conclusion Limitations and Future Scopementioning

confidence: 94%

“…Castillo and Melin ( 2020 ) proposed a hybrid intelligent approach for forecasting the future trends of pandemic situations based on the COVID-19 time series of confirmed cases and deaths. Dansana et al ( 2020 ) used deep learning algorithms to map the computed tomography and X-rays reports of COVID-19 patients for providing better and faster treatment.…”

Section: Relevant Literaturementioning

confidence: 99%

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RETRACTED ARTICLE: An integrated multi-criteria decision-making approach for identifying the risk level of musculoskeletal disorders among handheld device users

2021

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In work-from-home (WFH) situation due to coronavirus (COVID-19) pandemic, the handheld device (HHD) users work in awkward postures for longer hours because of unavailability of ergonomically designed workstations. This problem results in different type of musculoskeletal disorders (MSDs) among the HHD users. An integrated multi-criteria decision-making approach was offered for identifying the risk level of MSDs among HHD users. A case example implemented the proposed approach in which, firstly, the best–worst method (BWM) technique was used to prioritize and determine the relative importance (weightage) of the risk factors. The weightages of the risk factors further used to rank the seven alternatives (HHD users) using Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) technique. The outcomes of the BWM investigation showed that the three most significant risk factors responsible for MSDs are duration of working, poor working posture and un-ergonomic design. The outcome of the VIKOR technique exhibited that computer professionals were at the highest risk among all users. The risk factor priority must be used for designing a working strategy for the WFH situation which will help to mitigate the risks of MSDs.

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Section: Conclusion Limitations and Future Scopementioning

confidence: 94%

Section: Relevant Literaturementioning

confidence: 99%

RETRACTED ARTICLE: An integrated multi-criteria decision-making approach for identifying the risk level of musculoskeletal disorders among handheld device users

2021

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“… Castillo and Melin (2020) presented a hybrid intelligent method for forecasting COVID-19-time series combining fuzzy logic and fractal theory that was the main contribution of their paper. Fuzzy logic employed to show the uncertainty in the process of making a forecast and the fractal dimension utilized to measure the complexity of the dynamics in the time series of the countries in the world.…”

Section: Literature Reviewmentioning

confidence: 99%

An integrated sustainable medical supply chain network during COVID-19

Goodarzian

Taleizadeh

Ghasemi

et al. 2021

Engineering Applications of Artificial Intelligence

190

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Nowadays, in the pharmaceutical industry, a growing concern with sustainability has become a strict consideration during the COVID-19 pandemic. There is a lack of good mathematical models in the field. In this research, a production–distribution–inventory–allocation–location problem in the sustainable medical supply chain network is designed to fill this gap. Also, the distribution of medicines related to COVID-19 patients and the periods of production and delivery of medicine according to the perishability of some medicines are considered. In the model, a multi-objective, multi-level, multi-product, and multi-period problem for a sustainable medical supply chain network is designed. Three hybrid meta-heuristic algorithms, namely, ant colony optimization, fish swarm algorithm, and firefly algorithm are suggested, hybridized with variable neighborhood search to solve the sustainable medical supply chain network model. Response surface method is used to tune the parameters since meta-heuristic algorithms are sensitive to input parameters. Six assessment metrics were used to assess the quality of the obtained Pareto frontier by the meta-heuristic algorithms on the considered problems. A real case study is used and empirical results indicate the superiority of the hybrid fish swarm algorithm with variable neighborhood search.

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“…In [3] mathematical and computational models such as Logistic, Gompertz, and ANN were applied to model the number of cases in Mexico. Castillo and Melin [8] proposed a new combined approach with fuzzy fractal and fuzzy logic to predict the number of confirmed cases of COVID-19 in 10 countries. Also, in [9] , a new ensemble approach based on ANNs and fuzzy aggregation was proposed and its performance was evaluated on COVID-19 time series of Mexico and its 12 states which showed significant improvement than single ANN.…”

Section: Introductionmentioning

confidence: 99%

“…To evaluate the performance of the proposed models, we perform two sets of experiments. The first set of experiments explores the effectiveness of the proposed models in short-term forecasting and compares their performance with the results of the fuzzy fractal model presented in [8] . The results indicated the deep models achieve better performance than the fuzzy fractal across all countries.…”

Section: Introductionmentioning

confidence: 99%

Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization

Abbasimehr

Paki

2021

Chaos, Solitons & Fractals

141

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Highlights Three methods combining deep learning and Bayesian optimization are proposed. Bayesian optimization efficiently selects the optimized values for hyperparameters. The design of methods is based on the multiple-output forecasting strategy. The proposed methods outperform the benchmark model on COVID-19 time series data.

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Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic

Cited by 118 publications

References 35 publications

RETRACTED ARTICLE: An integrated multi-criteria decision-making approach for identifying the risk level of musculoskeletal disorders among handheld device users

RETRACTED ARTICLE: An integrated multi-criteria decision-making approach for identifying the risk level of musculoskeletal disorders among handheld device users

An integrated sustainable medical supply chain network during COVID-19

Prediction of COVID-19 confirmed cases combining deep learning methods and Bayesian optimization

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