A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series

Karasu, Seçkin; Altan, Aytaç; Bekiros, Stelios; Ahmad, Wasim

doi:10.1016/j.energy.2020.118750

Cited by 385 publications

(115 citation statements)

References 35 publications

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“…The coefficients in the feature matrix were optimized using the CSSA and COVID-19 disease was diagnosed by the EfficientNet-B0 model, which is one of the deep learning methods. For more details on other new chaotic methods see [7] , [8] . Further studies considering the forecast of the pandemic can be found in [9] , [10] , [11] , [12] , [13] , [14] , [15] , [16] .…”

Section: Introductionmentioning

confidence: 99%

Forecasting COVID-19 pandemic using optimal singular spectrum analysis

Kalantari

2021

Chaos, Solitons & Fractals

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Highlights Potential advantages of Singular Spectrum Analysis (SSA) for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19 until 29 October 2020. Proposing an algorithm to calculate the optimal parameters of SSA. The results of V-SSA and R-SSA are compared to those from ARIMA, ARFIMA, Exponential Smoothing, TBATS, and NNAR. SSA technique is found to be viable option for forecasting the number of daily confirmed cases, deaths, and recoveries caused by COVID-19 based on the number of times that it outperforms the competing models. Providing 40 days ahead point forecasts for top ten countries affected by COVID-19.

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Section: Introductionmentioning

confidence: 99%

Forecasting COVID-19 pandemic using optimal singular spectrum analysis

Kalantari

2021

Chaos, Solitons & Fractals

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“…. Hereafter, the term "deterministic SEIHR model" or "dSEIHR model" refers to the one described by system (2). A diagram showing the transitions of the dSEIHR model ( 2) is given in Fig.…”

Section: Reasons Includementioning

confidence: 99%

A stochastic SEIHR model for COVID-19 data fluctuations

Niu

Chan

Wong

et al. 2021

Preprint

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Although deterministic compartmental models are useful for predicting the general trend of a disease's spread, they are unable to describe the random daily fluctuations in the number of new infections and hospitalizations, which is crucial in determining the necessary healthcare capacity for a specified level of risk. In this paper, we propose a stochastic SEIHR (sSEIHR) model to describe such random fluctuations and provide sufficient conditions for stochastic stability of the disease-free equilibrium, based on the basic reproduction number that we estimated. Our extensive numerical results demonstrate strong threshold behavior near the estimated basic reproduction number, suggesting that the necessary conditions for stochastic stability are close to the sufficient conditions derived. Furthermore, we found that increasing the noise level slightly reduces the final proportion of infected individuals. In addition, we analyze COVID-19 data from various regions worldwide and demonstrate that by changing only a few parameter values, our sSEIHR model can accurately describe both the general trend and the random fluctuations in the number of daily new cases in each region, allowing governments and hospitals to make more accurate caseload predictions using fewer compartments and parameters than other comparable stochastic compartmental models.

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“…Thus, recently, chest X-ray images have been used for early and improved detection of COVID-19 using machine learning techniques [4] , [5] . Apart from medical diagnostics, machine learning techniques have found applications in diverse domains such as speech recognition, weather forecasting, and forecasting market trends [6] , [7] , [8] , [9] . For example, Altan et al.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, Karasu et al. [8] applied support vector regression (SVR) for forecasting of crude oil prices in a multiobjective setting using particle swarm optimization (PSO). However, in this paper, we will focus on applying machine learning techniques for the detection of COVID-19.…”

Section: Introductionmentioning

confidence: 99%

Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images

Rajpal

Lakhyani²,

Singh

et al. 2021

Chaos, Solitons & Fractals

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Coronaviruses are a family of viruses that majorly cause respiratory disorders in humans. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus that causes the coronavirus disease 2019 (COVID-19). WHO has identified COVID-19 as a pandemic as it has spread across the globe due to its highly contagious nature. For early diagnosis of COVID-19, the reverse transcription-polymerase chain reaction (RT-PCR) test is commonly done. However, it suffers from a high false-negative rate of up to 67% if the test is done during the first five days of exposure. As an alternative, research on the efficacy of deep learning techniques employed in the identification of COVID-19 disease using chest X-ray images is intensely pursued. As pneumonia and COVID-19 exhibit similar/ overlapping symptoms and affect the human lungs, a distinction between the chest X-ray images of pneumonia patients and COVID-19 patients becomes challenging. In this work, we have modeled the COVID-19 classification problem as a multiclass classification problem involving three classes, namely COVID-19, pneumonia, and normal. We have proposed a novel classification framework which combines a set of handpicked features with those obtained from a deep convolutional neural network. The proposed framework comprises of three modules. In the first module, we exploit the strength of transfer learning using ResNet-50 for training the network on a set of preprocessed images and obtain a vector of 2048 features. In the second module, we construct a pool of frequency and texture based 252 handpicked features that are further reduced to a set of 64 features using PCA. Subsequently, these are passed to a feed forward neural network to obtain a set of 16 features. The third module concatenates the features obtained from first and second modules, and passes them to a dense layer followed by the softmax layer to yield the desired classification model. We have used chest X-ray images of COVID-19 patients from four independent publicly available repositories, in addition to images from the Mendeley and Kaggle Chest X-Ray Datasets for pneumonia and normal cases. To establish the efficacy of the proposed model, 10-fold cross-validation is carried out. The model generated an overall classification accuracy of 0.974 0.02 and a sensitivity of 0.987 0.05, 0.963 0.05, and 0.973 0.04 at 95% confidence interval for COVID-19, normal, and pneumonia classes, respectively. To ensure the effectiveness of the proposed model, it was validated using an independent Chest X-ray cohort and an overall classification accuracy of 0.979 was achieved. Comparison of the proposed framework with state-of-the-art methods reveal that the proposed framework outperforms others in terms of accuracy and sensitivity. Since interpretability of results is crucial in the medical domain, the gradient-based localizations are captured using Gradient-weighted Class Activation Mapping (Grad-CAM). In su...

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A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series

Cited by 385 publications

References 35 publications

Forecasting COVID-19 pandemic using optimal singular spectrum analysis

Forecasting COVID-19 pandemic using optimal singular spectrum analysis

A stochastic SEIHR model for COVID-19 data fluctuations

Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images

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