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Sheikhi F, Kowsari Z. Time series forecasting of COVID-19 infections and deaths in Alpha and Delta variants using LSTM networks. PLoS One 2023; 18:e0282624. [PMID: 37862318 PMCID: PMC10588884 DOI: 10.1371/journal.pone.0282624] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Accepted: 02/21/2023] [Indexed: 10/22/2023] Open
Abstract
Since the beginning of the rapidly spreading COVID-19 pandemic, several mutations have occurred in the genetic sequence of the virus, resulting in emerging different variants of concern. These variants vary in transmissibility, severity of infections, and mortality rate. Designing models that are capable of predicting the future behavior of these variants in the societies can help decision makers and the healthcare system to design efficient health policies, and to be prepared with the sufficient medical devices and an adequate number of personnel to fight against this virus and the similar ones. Among variants of COVID-19, Alpha and Delta variants differ noticeably in the virus structures. In this paper, we study these variants in the geographical regions with different size, population densities, and social life styles. These regions include the country of Iran, the continent of Asia, and the whole world. We propose four deep learning models based on Long Short-Term Memory (LSTM), and examine their predictive power in forecasting the number of infections and deaths for the next three, next five, and next seven days in each variant. These models include Encoder Decoder LSTM (ED-LSTM), Bidirectional LSTM (Bi-LSTM), Convolutional LSTM (Conv-LSTM), and Gated Recurrent Unit (GRU). Performance of these models in predictions are evaluated using the root mean square error, mean absolute error, and mean absolute percentage error. Then, the Friedman test is applied to find the leading model for predictions in all conditions. The results show that ED-LSTM is generally the leading model for predicting the number of infections and deaths for both variants of Alpha and Delta, with the ability to forecast long time intervals ahead.
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Affiliation(s)
- Farnaz Sheikhi
- Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
| | - Zahra Kowsari
- Faculty of Computer Engineering, K. N. Toosi University of Technology, Tehran, Iran
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Al-qaness MAA, Ewees AA, Abualigah L, AlRassas AM, Thanh HV, Abd Elaziz M. Evaluating the Applications of Dendritic Neuron Model with Metaheuristic Optimization Algorithms for Crude-Oil-Production Forecasting. ENTROPY (BASEL, SWITZERLAND) 2022; 24:1674. [PMID: 36421530 PMCID: PMC9689334 DOI: 10.3390/e24111674] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 11/14/2022] [Accepted: 11/15/2022] [Indexed: 06/16/2023]
Abstract
The forecasting and prediction of crude oil are necessary in enabling governments to compile their economic plans. Artificial neural networks (ANN) have been widely used in different forecasting and prediction applications, including in the oil industry. The dendritic neural regression (DNR) model is an ANNs that has showed promising performance in time-series prediction. The DNR has the capability to deal with the nonlinear characteristics of historical data for time-series forecasting applications. However, it faces certain limitations in training and configuring its parameters. To this end, we utilized the power of metaheuristic optimization algorithms to boost the training process and optimize its parameters. A comprehensive evaluation is presented in this study with six MH optimization algorithms used for this purpose: whale optimization algorithm (WOA), particle swarm optimization algorithm (PSO), genetic algorithm (GA), sine-cosine algorithm (SCA), differential evolution (DE), and harmony search algorithm (HS). We used oil-production datasets for historical records of crude oil production from seven real-world oilfields (from Tahe oilfields, in China), provided by a local partner. Extensive evaluation experiments were carried out using several performance measures to study the validity of the DNR with MH optimization methods in time-series applications. The findings of this study have confirmed the applicability of MH with DNR. The applications of MH methods improved the performance of the original DNR. We also concluded that the PSO and WOA achieved the best performance compared with other methods.
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Affiliation(s)
- Mohammed A. A. Al-qaness
- College of Physics and Electronic Information Engineering, Zhejiang Normal University, Jinhua 321004, China
| | - Ahmed A. Ewees
- Department of Computer, Damietta University, Damietta 34517, Egypt
| | - Laith Abualigah
- Faculty of Information Technology, Al-Ahliyya Amman University, Amman 19328, Jordan
- Faculty of Information Technology, Middle East University, Amman 11831, Jordan
- Faculty of Information Technology, Applied Science Private University, Amman 11931, Jordan
- School of Computer Sciences, Universiti Sains Malaysia, Pulau Pinang 11800, Malaysia
| | - Ayman Mutahar AlRassas
- School of Petroleum Engineering, China University of Petroleum (East China), Qingdao 266580, China
| | - Hung Vo Thanh
- Laboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City 700000, Vietnam
- Faculty of Mechanical-Electrical and Computer Engineering, Van Lang University, Ho Chi Minh City 700000, Vietnam
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig 44519, Egypt
- Faculty of Computer Science & Engineering, Galala University, Suze 435611, Egypt
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 4307, Lebanon
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Mathematical Modeling to Study Optimal Allocation of Vaccines against COVID-19 Using an Age-Structured Population. AXIOMS 2022. [DOI: 10.3390/axioms11030109] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Vaccination against the coronavirus disease 2019 (COVID-19) started in early December of 2020 in the USA. The efficacy of the vaccines vary depending on the SARS-CoV-2 variant. Some countries have been able to deploy strong vaccination programs, and large proportions of their populations have been fully vaccinated. In other countries, low proportions of their populations have been vaccinated, due to different factors. For instance, countries such as Afghanistan, Cameroon, Ghana, Haiti and Syria have less than 10% of their populations fully vaccinated at this time. Implementing an optimal vaccination program is a very complex process due to a variety of variables that affect the programs. Besides, science, policy and ethics are all involved in the determination of the main objectives of the vaccination program. We present two nonlinear mathematical models that allow us to gain insight into the optimal vaccination strategy under different situations, taking into account the case fatality rate and age-structure of the population. We study scenarios with different availabilities and efficacies of the vaccines. The results of this study show that for most scenarios, the optimal allocation of vaccines is to first give the doses to people in the 55+ age group. However, in some situations the optimal strategy is to first allocate vaccines to the 15–54 age group. This situation occurs whenever the SARS-CoV-2 transmission rate is relatively high and the people in the 55+ age group have a transmission rate 50% or less that of those in the 15–54 age group. This study and similar ones can provide scientific recommendations for countries where the proportion of vaccinated individuals is relatively small or for future pandemics.
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Boosting arithmetic optimization algorithm by sine cosine algorithm and levy flight distribution for solving engineering optimization problems. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-06906-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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AlRassas AM, Al-qaness MAA, Ewees AA, Ren S, Sun R, Pan L, Abd Elaziz M. Advance artificial time series forecasting model for oil production using neuro fuzzy-based slime mould algorithm. JOURNAL OF PETROLEUM EXPLORATION AND PRODUCTION TECHNOLOGY 2022; 12:383-395. [PMID: 34926107 PMCID: PMC8664677 DOI: 10.1007/s13202-021-01405-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 11/26/2021] [Indexed: 05/06/2023]
Abstract
Oil production forecasting is an important task to manage petroleum reservoirs operations. In this study, a developed time series forecasting model is proposed for oil production using a new improved version of the adaptive neuro-fuzzy inference system (ANFIS). This model is improved by using an optimization algorithm, the slime mould algorithm (SMA). The SMA is a new algorithm that is applied for solving different optimization tasks. However, its search mechanism suffers from some limitations, for example, trapping at local optima. Thus, we modify the SMA using an intelligence search technique called opposition-based learning (OLB). The developed model, ANFIS-SMAOLB, is evaluated with different real-world oil production data collected from two oilfields in two different countries, Masila oilfield (Yemen) and Tahe oilfield (China). Furthermore, the evaluation of this model is considered with extensive comparisons to several methods, using several evaluation measures. The outcomes assessed the high ability of the developed ANFIS-SMAOLB as an efficient time series forecasting model that showed significant performance.
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Affiliation(s)
- Ayman Mutahar AlRassas
- School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, China
| | - Mohammed A. A. Al-qaness
- State Key Laboratory for Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079 China
| | - Ahmed A. Ewees
- Department of Computer, Damietta University, Damietta, Egypt
| | - Shaoran Ren
- School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, China
| | - Renyuan Sun
- School of Petroleum Engineering, China University of Petroleum (East China), Qingdao, China
| | - Lin Pan
- Faculty of Earth Resources, China University of Geosciences, Wuhan, China
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519 Egypt
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman, 346 United Arab Emirates
- Department of Artificial Intelligence Science & Engineering, Galala University, Suze, 435611 Egypt
- School of Computer Science and Robotics, Tomsk Polytechnic University, Tomsk, 634050 Russia
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Abstract
The first round of vaccination against coronavirus disease 2019 (COVID-19) began in early December of 2020 in a few countries. There are several vaccines, and each has a different efficacy and mechanism of action. Several countries, for example, the United Kingdom and the USA, have been able to develop consistent vaccination programs where a great percentage of the population has been vaccinated (May 2021). However, in other countries, a low percentage of the population has been vaccinated due to constraints related to vaccine supply and distribution capacity. Countries such as the USA and the UK have implemented different vaccination strategies, and some scholars have been debating the optimal strategy for vaccine campaigns. This problem is complex due to the great number of variables that affect the relevant outcomes. In this article, we study the impact of different vaccination regimens on main health outcomes such as deaths, hospitalizations, and the number of infected. We develop a mathematical model of COVID-19 transmission to focus on this important health policy issue. Thus, we are able to identify the optimal strategy regarding vaccination campaigns. We find that for vaccines with high efficacy (>70%) after the first dose, the optimal strategy is to delay inoculation with the second dose. On the other hand, for a low first dose vaccine efficacy, it is better to use the standard vaccination regimen of 4 weeks between doses. Thus, under the delayed second dose option, a campaign focus on generating a certain immunity in as great a number of people as fast as possible is preferable to having an almost perfect immunity in fewer people first. Therefore, based on these results, we suggest that the UK implemented a better vaccination campaign than that in the USA with regard to time between doses. The results presented here provide scientific guidelines for other countries where vaccination campaigns are just starting, or the percentage of vaccinated people is small.
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Affiliation(s)
- Gilberto Gonzalez-Parra
- Department of Mathematics, New Mexico Tech, New Mexico Institute of Mining and Technology, Socorro, NM 87801, USA
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Al-Qaness MAA, Fan H, Ewees AA, Yousri D, Abd Elaziz M. Improved ANFIS model for forecasting Wuhan City Air Quality and analysis COVID-19 lockdown impacts on air quality. ENVIRONMENTAL RESEARCH 2021; 194:110607. [PMID: 33345891 DOI: 10.1016/j.envres.2020.110607] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/03/2020] [Accepted: 12/05/2020] [Indexed: 06/12/2023]
Abstract
In this study, we propose an improved version of the adaptive neuro-fuzzy inference system (ANFIS) for forecasting the air quality index in Wuhan City, China. We propose a hybrid optimization method to improve ANFIS performance, called PSOSMA, using a new modified meta-heuristics (MH) algorithm, Slime mould algorithm (SMA), which is improved by using the particle swarm optimizer (PSO). The proposed PSOSMA-ANFIS has been trained with air quality index time series data of three years and has been applied to forecast the fine particulate matter (PM2.5), sulfur dioxide (SO2), carbon dioxide (CO2), and nitrogen dioxide (NO2) for one year. We also compared the proposed PSOSMA to other MH algorithms used to train ANFIS. We found that the modified ANFIS using PSOSMA achieved better performance than compared algorithms. Moreover, we analyzed the impacts of the lockdown of Wuhan City on the concentrations of PM2.5, NO2, CO2, and SO2. We compared the correspondence period with previous years, and we concluded that there are significant decreases in the concentrations of PM2.5, CO2, SO2, and NO2.
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Affiliation(s)
- Mohammed A A Al-Qaness
- State Key Laboratory for Information Engineering in Surveying,Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China.
| | - Hong Fan
- State Key Laboratory for Information Engineering in Surveying,Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China.
| | - Ahmed A Ewees
- Department of e-Systems, University of Bisha, Bisha, 61922, Saudi Arabia; Department of Computer, Damietta University, Damietta, 34511, Egypt.
| | - Dalia Yousri
- Department of Electrical Engineering, Faculty of Engineering, Fayoum University, Fayoum, Egypt.
| | - Mohamed Abd Elaziz
- Department of Mathematics, Faculty of Science, Zagazig University, Zagazig, 44519, Egypt.
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Mehrabi P, Honarbari S, Rafiei S, Jahandari S, Alizadeh Bidgoli M. Seismic response prediction of FRC rectangular columns using intelligent fuzzy-based hybrid metaheuristic techniques. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2021; 12:10105-10123. [PMID: 33425053 PMCID: PMC7778570 DOI: 10.1007/s12652-020-02776-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2020] [Accepted: 11/28/2020] [Indexed: 06/12/2023]
Abstract
This research study focused on the dynamic response and mechanical performance of fiber-reinforced concrete columns using hybrid numerical algorithms. Whereas test data has non-linearity, an artificial intelligence (AI) algorithm has been incorporated with different metaheuristic algorithms. About 317 datasets have been applied from the real test results to detect the promising factor of strength subjected to the seismic loads. Adaptive neuro-fuzzy inference system (ANFIS) was carried out as an AI beside the combination of particle swarm optimization (PSO) and genetic algorithm (GA). Extreme Machine Learning (ELM) was also performed in order to approve the obtained results. According to the findings, it is demonstrated that ANFIS-PSO predicts the lateral load with promising evaluation indexes [R2 (test) = 0.86, R2 (train) = 0.90]. Mechanical performance prediction was also carried out in this study, and the results showed that ELM predicts the compressive strength with promising evaluation indexes [R2 (test) = 0.66, R2 (train) = 0.86]. Finally, both ANFIS-GA and ANFIS-PSO techniques illustrated a reliable performance for prediction, which encourage scholars to replace costly and time-consuming experimental tests with predicting utilities.
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Affiliation(s)
- Peyman Mehrabi
- Department of Civil Engineering, K.N. Toosi University of Technology, Tehran, Iran
| | - Soheil Honarbari
- Department of Electrical Engineering, Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran
| | - Shervin Rafiei
- Department of Construction Engineering and Management, Amirkabir University of Technology, Tehran, Iran
| | - Soheil Jahandari
- Centre for Infrastructure Engineering, Western Sydney University, Penrith, NSW 2751 Australia
| | - Mohsen Alizadeh Bidgoli
- Department of Electrical Engineering, Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran
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Forecasting Weekly Influenza Outpatient Visits Using a Two-Dimensional Hierarchical Decision Tree Scheme. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17134743. [PMID: 32630311 PMCID: PMC7369891 DOI: 10.3390/ijerph17134743] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 06/24/2020] [Accepted: 06/27/2020] [Indexed: 01/20/2023]
Abstract
Influenza is a serious public health issue, as it can cause acute suffering and even death, social disruption, and economic loss. Effective forecasting of influenza outpatient visits is beneficial to anticipate and prevent medical resource shortages. This study uses regional data on influenza outpatient visits to propose a two-dimensional hierarchical decision tree scheme for forecasting influenza outpatient visits. The Taiwan weekly influenza outpatient visit data were collected from the national infectious disease statistics system and used for an empirical example. The 788 data points start in the first week of 2005 and end in the second week of 2020. The empirical results revealed that the proposed forecasting scheme outperformed five competing models and was able to forecast one to four weeks of anticipated influenza outpatient visits. The scheme may be an effective and promising alternative for forecasting one to four steps (weeks) ahead of nationwide influenza outpatient visits in Taiwan. Our results also suggest that, for forecasting nationwide influenza outpatient visits in Taiwan, one- and two-time lag information and regional information from the Taipei, North, and South regions are significant.
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