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The coronavirus has been affecting people's everyday life since 2019. To stop the further spread of the virus, early detection of carriers of the virus and isolation are needed. Due to the limitations of nuclear accounting amplification assay (NAAT), CT imaging, which has the high sensitivity to the new corona virus, is used as a diagnostic method. In this study, a classical-quantum hybrid model using a variational quantum circuit as a classifier was designed. A pre-trained VGG16 was used for feature extraction, and the extracted features were passed to a variational quantum classifier for classification. Experiments show that using variational quantum circuits to linearly vary the classical data can improve the accuracy of the model prediction. Hybrid Model achieves good results with values of 98.78%, 97.65%, 100.00%, and 98.81% for Accuracy, Precision, Recall, and F1-score metrics, respectively. The comparison between Hybrid Model and the classical model shows that the overall performance of Hybrid Model is better than that of the classical model. Hybrid Model provides a reliable, flexible and scalable classical-quantum deep learning approach for the prediction task of COVID-19.

The COVID-19 has resulted in catastrophic situation and the deaths of millions of people all over the world. In this paper, the predictions of epidemiological propagation models, such as SIR and SEIR, are introduced to analyze the earlier COVID-19 propagation. The deep learning methods combined with transfer learning are familiar with classification-detection approaches based on chest X-ray and CT images are presented in detail. Besides, deep learning approaches have also been applied to lung ultrasound (LUS), which has been shown to be more sensitive than chest X-ray and CT images in detecting COVID-19. In the absence of a vaccine, the machine learning-related approaches are applied to analyze vaccine candidates in the realm of biology and medicine. The telehealth system played a major role in combating the pandemic from all aspects and reducing contact with patients during this period. Natural language processing-related methods are utilized to analyze tweets related to the COVID-19 epidemic on social media, and further analyze public sentiment and subject modeling, so as to arrange corresponding measures to appease public sentiment. In particular, this survey is to summarize and analyze the contributions made in various fields during the COVID-19 pandemic by considering both the contribution of deep learning in chest X-ray and CT images, as well as the application of the latest LUS during the COVID-19 pandemic. Telehealth and the importance of public sentiment analysis during a pandemic were also described in detail.

Because of the dark atmosphere created by YOLO v3's poor detection accuracy, the photographs obtained in reality usually considerably improve the difficulty of target detection tasks in extremely low light settings. Under extremely low light conditions, the YOLOv3 model is used to suggest a high-discrimination target detection approach. Target detection is an essential task in the field of computer vision, in which the image is the source of input, but the image obtained in reality may make the target detection task in extremely low light conditions because of the dark environment and poor quality imaging equipment, which increases the difficulty of target detection and affects the accuracy of detection. Therefore, based on the above reasons, this paper proposes a high-discrimination target detection method based on the YOLOv3 model under extremely low light conditions. The low light enhancement algorithm based on dehazing algorithm is improved for restraining the part of the transmission rate less than 0.5 in the pixel, to enhance the contrast of the processed images. The improved low light enhancement algorithm is 5.32% higher based on the evaluation index of target detection than the original images. Coiflet wavelet transform is applied to achieve high-discrimination feature extraction of images and decompose the low-frequency features of image information at different resolutions to obtain high-frequency features in the horizontal, vertical, and diagonal directions. The eventually constructed YOLOv3 network has reduced the negative impact of the detected target on the detection accuracy under the illumination change. The results reveal that the target detection method established in this paper has been enhanced by 40.6% on low light photos when compared to the traditional YOLOv3 model, and it still has considerable advantages in comparison to other benchmark approaches in target detection models.

Exact short-term wind control estimating modeling is of widespread significance to the steady operation of the wind control framework, particularly after wind control is associated to the framework in large-scale. Recurrent neural networks (RNNs) with numerous hidden-layers could be a common and agent strategy of profound learning approach, the estimating capacity can be utilized to construct the precise and dependable short-term wind control estimating approaches. Be that as it may, RNN’s generalization capacity is ordinarily restricted since the parameters in covered up layer ordinarily depends on human involvement. The most reason of this paper is to outline the data into the space where test is more divisible based on the factual examination, to accurately analyze the organize neurons and advance the optimization of the RNN’s demonstrate design, in this way to build the high-accuracy of the short-term wind power forecasting. Tests based on NREL dataset are connected to confirm the execution of the proposed RNN-related approaches, the short-term wind power forecasting accuracy is individually progressed by 7.01% and 14.53% refer to 1- and 2-h ahead in one year over the results that accomplished through the conventional approaches.

This paper proposes a multi-step ahead time series forecasting based on the improved process neural network. The intelligent algorithm particle swarm optimization (PSO) is used to overcome the potential disadvantages of the neural network, such as slow convergence speed and derivative local minima. Firstly, the theoretical analysis of the PSO is given to optimize the multilayer perceptron (MLP) neural network architecture. Secondly, the theoretical analysis and processing flow about the MLP architecture optimization is given. Thirdly, the performance criteria are applied to verify the performance of the proposed approach. Finally, the experimental evaluation based on a typically chaotic time series with rich spectrum information is utilized to demonstrate that the proposed approach has comparative results and superior on forecasting accuracy comparing to the traditional methods.

This paper proposes a novel 3D face recognition method using the local covariance descriptor and Riemannian kernel sparse coding in order to accurately evaluate the intrinsic correlation of the extracted features and further improve the 3D face recognition accuracy. Firstly, the keypoints are detected by the farthest point sampling method, and the corresponding keypoint neighborhood is extracted by the specified radius associated with geodesic distance. Then, different types of the efficient features are selected to construct the local covariance descriptor with inherent property. Finally, the appropriate Riemannian kernel sparse coding is used to identify the faces in probe. Experimental evaluation has been performed on two challenging 3D face datasets, FRGC v2.0 and Bosphorus, which indicates that the proposed approach can significantly improve the identification accuracy comparing with other state-of-the-art methods.

A novel adaptive feature selection based on reconstruction residual and accurately located landmarks for expression-robust 3D face recognition is proposed in this paper. Firstly, the novel facial coarse-to-fine landmarks localization method based on Active Shape Model and Gabor wavelets transformation is proposed to exactly and automatically locate facial landmarks in range image. Secondly, the multi-scale fusion of the pyramid local binary patterns (F-PLBP) based on the irregular segmentation associated with the located landmarks is proposed to extract the discriminative feature. Thirdly, a sparse representation-based classifier based on the adaptive feature selection (A-SRC) using the distribution of the reconstruction residual is presented to select the expression-robust feature and identify the faces. Finally, the experimental evaluation based on FRGC v2.0 indicates that the adaptive feature selection method using F-PLBP combined with the A-SRC can obtain the high recognition accuracy by performing the higher discriminative power to overcome the influence from the facial expression variations.

This paper proposes a novel strategy to forecast the short-term wind power using model structure selection in combined with data fusion technique. The available inputs are usually treated as an integrated one for the predictive modeling, which ignores the fusion of the different types of inputs. This results the relationship between the multi-inputs and desired outputs are not effectively reflected in forecasting procedure. Moreover, the performances of the various types of data fusion methods are susceptible to the wind power distribution and critical factors such as smoothness, overlapping, the intrinsic amount of information evaluation and the number of neighbor points. These outlined factors can result the lower generality ability of the proposed methods due to the insufficient optimization of the model structure. So this paper presents short-term wind power forecasting method using model structure selection technique in combined with four representative fusions of dimensionality reduction methods to optimize the model structure, promote the computational efficiency and improve the forecasting accuracy. Experimental evaluation based on the real data from the wind farm in Jiangsu province is given to verify the effectiveness of the proposed method by comparing the traditional techniques.

The accurate and reliable short-term wind speed forecasting can benefit the stability of the grid operation. However, it is a challenging issue to generate consistently accurate forecasts due to the complex and stochastic nature of wind speed distribution in meteorological interactions. In this study, a novel solution using AdaBoost neural network in combination with wavelet decomposition is proposed to solve the defects of the lower accuracy and enhance the model robustness. Based on the real data provided by sampling device weak wind turbine (type-FD77) in a wind farm plant of East China, the experimental evaluation demonstrates that the proposed strategy can significantly enhance model robustness and effectively improve the prediction accuracy.

Wind speed presents a potential seasonal pattern revealed by the self-similarity in wavelet periodogram with various scales. The corresponding seasonal pattern will promote the improvement of the short-term wind speed forecasting accuracy. In this study, a novel method for short-term wind speed forecasting using wavelet transformation (WT) and AdaBoost technique is proposed to analyse the wind speeds distribution features and promote the model configuration. Power spectrum and seasonal pattern analysis using the WT are presented to investigate the wind speeds feature distribution based on the scalogram percentage of energy distribution in different seasons. This procedure contributes to perfecting the investigation of wind speed seasonal pattern characteristics over time and promotes the sample division by computing the statistics measurement based on the estimated frequencies interval. The model order estimation based on the information criteria is processed to reflect the systems dynamical sustainability between the current outputs and historical data. Finally, the experiments based on the real data from Yunnan wind farm are given to verify the effectiveness of the proposed approach.

3D face shape is essentially a non-rigid free-form surface, which will produce non-rigid deformation under expression variations. In terms of that problem, a promising solution named Coherent Point Drift (CPD) non-rigid registration for the non-rigid region is applied to eliminate the influence from the facial expression while guarantees 3D surface topology. In order to take full advantage of the extracted discriminative feature of the whole face under facial expression variations, the novel expression-robust 3D face recognition method using feature-level fusion and feature-region fusion is proposed. Furthermore, the Principal Component Analysis and Linear Discriminant Analysis in combination with Rotated Sparse Regression (PL-RSR) dimensionality reduction method is presented to promote the computational efficiency and provide a solution to the curse of dimensionality problem, which benefit the performance optimization. The experimental evaluation indicates that the proposed strategy has achieved the rank-1 recognition rate of 97.91 % and 96.71 % based on Face Recognition Grand Challenge (FRGC) v2.0 and Bosphorus respectively, which means the proposed approach outperforms state-of-the-art approach.

Unsupervised learning algorithm locally linear embedding (LLE) is a typical technique which applies the preserving embedding method of high dimensional data to low dimension. The number of neighborhood nodes of LLE is a decisive parameter because the improper value will affect the manifold structure in the local neighborhood and lead to the lower computational efficiency. Based on the fact that the shortest path in low-density can be established easily, this paper proposes an improved LLE method by using the sparse matrix in combination with the weights related to each point used for the linear combination in local neighborhood. The correlation dimension between high and low dimension is used to estimate the proper number of the reduced dimension, thereby selecting the best upper bound for the non-uniform manifold. Finally, we provide the experimental evaluation to verify the effectiveness of the proposed algorithm.

Model structure selection with respect to short-term wind speed forecasting is relatively difficult due to the stochastic and intermittent nature of the wind speed distribution. In order to overcome the disadvantages in traditional approaches such as computing burden and low accuracy, a novel model structure selection technique about short-term wind speed forecasting is proposed in order to improve the computational efficiency and forecasting accuracy using the model variable selection, variable order estimation, model structure optimization techniques, and so on. The detailed and complete process flow associated to the theoretical analysis of the proposed model structure selection technique is described. Moreover, both the so-called overkill in the data filtering and so-called overfitting in the learning processing are avoided by a proper technique in the design of proposed approach. In order to verify the effectiveness of proposed strategy in a practical application, all the experimental results...

Nonlinear model structure selection (MSS) is an important step in the modeling theory of the nonlinear system. The proper model structure and model parameters estimation can be used to reduce unnecessary computations and improve the model forecast accuracy. This paper mainly investigates the short-term wind speed forecasting (STWSF) by utilizing the comprehensive MSS technique based on real data of the wind speed plant in East China. The MSS and manifold algorithm are respectively used to design proper model structure and reduce the computational complexity to improve the forecasting accuracy and promote the computational efficiency. The experimental evaluation by using support vector regression based on the real data is given to demonstrate the performance of the proposed method.

There are many features that have been taken into consideration for wind power forecasting. Since properly ranking these relevant features, often redundant, can be quite difficult, highly accurate short term wind power forcasting remains a big challenge. Another noted problem that adversely impacts the accuracy of wind forcasting stems from the weakness of the prevailing prediction models based on the feedforward neural network (FNN) in handling wind power time series. This paper thus attempts to address the aforementioned problems in short-term wind power forecasting with a novel approach that combines the infinite feature selection (Inf-FS) with the recurrent neural networks (RNN). In particular, all the possible features related to wind forecast are first clustered into multiple feature sets, after which the identified feature sets are mapped onto the paths of a graph built for Inf-FS. Traversing such a graph helps effectively determine/rank the significance of the features according to their stability and classification accuracy measured in the feature space. The proposed wind prediction model then feeds the ranked features into a deep learning prediction system enabled by RNN, whose neurons have self-feedback loops to help gather the past decisions, and thus be more effective than FNN for wind power prediction. The proposed wind power prediction approach is demonstrated through the experimental evaluations using a dataset from the National Renewable Energy Laboratory (NREL). The result shows that the accuracy of short-term wind power forecast is increased by 11%, 29%, 33%, and 19% in spring, summer, autumn and winter, respectively, over that achieved using the traditional approaches.