Estimation of COVID-19 epidemic curves using genetic programming algorithm

Anđelić, Nikola; Šegota, Sandi Baressi; Lorencin, Ivan; Mrzljak, Vedran; Car, Zlatan

doi:10.1177/1460458220976728

Cited by 23 publications

(13 citation statements)

References 78 publications

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“…Moreover, the system behavior after government applied policies regarding COVID-19 is analyzed [59]. The evolutionary algorithms also used by various studies to estimate the COVID-19 [60], such as in [60] where optimization algorithms were also used to optimize the proposed methods of COVID-19 identification [61].…”

Section: Background and Literature Reviewmentioning

confidence: 99%

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

et al. 2021

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Chest diseases can be dangerous and deadly. They include many chest infections such as pneumonia, asthma, edema, and, lately, COVID-19. COVID-19 has many similar symptoms compared to pneumonia, such as breathing hardness and chest burden. However, it is a challenging task to differentiate COVID-19 from other chest diseases. Several related studies proposed a computer-aided COVID-19 detection system for the single-class COVID-19 detection, which may be misleading due to similar symptoms of other chest diseases. This paper proposes a framework for the detection of 15 types of chest diseases, including the COVID-19 disease, via a chest X-ray modality. Two-way classification is performed in proposed Framework. First, a deep learning-based convolutional neural network (CNN) architecture with a soft-max classifier is proposed. Second, transfer learning is applied using fully-connected layer of proposed CNN that extracted deep features. The deep features are fed to the classical Machine Learning (ML) classification methods. However, the proposed framework improves the accuracy for COVID-19 detection and increases the predictability rates for other chest diseases. The experimental results show that the proposed framework, when compared to other state-of-the-art models for diagnosing COVID-19 and other chest diseases, is more robust, and the results are promising.

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Section: Background and Literature Reviewmentioning

confidence: 99%

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

et al. 2021

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“…Another common discontinuity is division by zero. In the case of division by zero, or near-zero values, a protected division is used, which returns the value of 1.0 [39][40][41]. These modified operations vary between GP implementations and must be taken into account when the generated models are implemented.…”

Section: Confirmed Patients Analysis Deceased Patients Analysis Recovmentioning

confidence: 99%

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm

Anđelić

Šegota

Lorencin

et al. 2021

IJERPH

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Estimation of the epidemiology curve for the COVID-19 pandemic can be a very computationally challenging task. Thus far, there have been some implementations of artificial intelligence (AI) methods applied to develop epidemiology curve for a specific country. However, most applied AI methods generated models that are almost impossible to translate into a mathematical equation. In this paper, the AI method called genetic programming (GP) algorithm is utilized to develop a symbolic expression (mathematical equation) which can be used for the estimation of the epidemiology curve for the entire U.S. with high accuracy. The GP algorithm is utilized on the publicly available dataset that contains the number of confirmed, deceased and recovered patients for each U.S. state to obtain the symbolic expression for the estimation of the number of the aforementioned patient groups. The dataset consists of the latitude and longitude of the central location for each state and the number of patients in each of the goal groups for each day in the period of 22 January 2020–3 December 2020. The obtained symbolic expressions for each state are summed up to obtain symbolic expressions for estimation of each of the patient groups (confirmed, deceased and recovered). These symbolic expressions are combined to obtain the symbolic expression for the estimation of the epidemiology curve for the entire U.S. The obtained symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for each state achieved R2 score in the ranges 0.9406–0.9992, 0.9404–0.9998 and 0.9797–0.99955, respectively. These equations are summed up to formulate symbolic expressions for the estimation of the number of confirmed, deceased and recovered patients for the entire U.S. with achieved R2 score of 0.9992, 0.9997 and 0.9996, respectively. Using these symbolic expressions, the equation for the estimation of the epidemiology curve for the entire U.S. is formulated which achieved R2 score of 0.9933. Investigation showed that GP algorithm can produce symbolic expressions for the estimation of the number of confirmed, recovered and deceased patients as well as the epidemiology curve not only for the states but for the entire U.S. with very high accuracy.

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“…Further research and analysis of the whole observed steam turbine, turbine cylinders and cylinder parts will be performed by using other optimization algorithms and various artificial intelligence methods which proved its applicability and good performance not only in power systems, but also in other engineering fields [106][107][108][109]. It will be investigated can some other optimization algorithm or method show better performance related to this turbine exergy destruction and efficiency.…”

Section: Exergy Analysis During the Ambient Temperature Changementioning

confidence: 99%

The influence of various optimization algorithms on nuclear power plant steam turbine exergy efficiency and destruction

et al. 2021

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This paper presents an exergy analysis of the whole turbine, turbine cylinders and cylinder parts in four different operating regimes. Analyzed turbine operates in nuclear power plant while three of four operating regimes are obtained by using optimization algorithms – SA (Simplex Algorithm), GA (Genetic Algorithm) and IGSA (Improved Genetic-Simplex Algorithm). IGSA operating regime gives the highest developed mechanical power of the whole turbine equal to 1022.48 MW, followed by GA (1020.06 MW) and SA (1017.16 MW), while in Original operating regime whole turbine develop mechanical power equal to 996.29 MW. In addition, IGSA causes the highest increase in developed mechanical power of almost all cylinders and cylinder parts in comparison to the Original operating regime. All observed optimization algorithms increases the exergy destruction of the whole turbine in comparison to Original operating regime - the lowest increase causes IGSA, followed by GA and finally SA. The highest exergy efficiency of the whole turbine, equal to 85.92% is obtained by IGSA, followed by GA (85.89%) and SA (85.82%), while the lowest exergy efficiency is obtained in Original operating regime (85.70%). Analyzed turbine, which operates by using wet steam is low influenced by the ambient temperature change. IGSA, which shows dominant performance in exergy analysis parameters of the analyzed turbine, in certain situations is overpowered by GA. Therefore, in optimization of steam turbine performance, IGSA and GA can be recommended.

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Estimation of COVID-19 epidemic curves using genetic programming algorithm

Cited by 23 publications

References 78 publications

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

A Self-Activated CNN Approach for Multi-Class Chest-Related COVID-19 Detection

Estimation of COVID-19 Epidemiology Curve of the United States Using Genetic Programming Algorithm

The influence of various optimization algorithms on nuclear power plant steam turbine exergy efficiency and destruction

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