Applied intelligence (Dordrecht, Netherlands)最新文献

The coronavirus disease 2019 (COVID-19) is rapidly becoming one of the leading causes for mortality worldwide. Various models have been built in previous works to study the spread characteristics and trends of the COVID-19 pandemic. Nevertheless, due to the limited information and data source, the understanding of the spread and impact of the COVID-19 pandemic is still restricted. Therefore, within this paper not only daily historical time-series data of COVID-19 have been taken into account during the modeling, but also regional attributes, e.g., geographic and local factors, which may have played an important role on the confirmed COVID-19 cases in certain regions. In this regard, this study then conducts a comprehensive cross-sectional analysis and data-driven forecasting on this pandemic. The critical features, which has the significant influence on the infection rate of COVID-19, is determined by employing XGB (eXtreme Gradient Boosting) algorithm and SHAP (SHapley Additive exPlanation) and the comparison is carried out by utilizing the RF (Random Forest) and LGB (Light Gradient Boosting) models. To forecast the number of confirmed COVID-19 cases more accurately, a Dual-Stage Attention-Based Recurrent Neural Network (DA-RNN) is applied in this paper. This model has better performance than SVR (Support Vector Regression) and the encoder-decoder network on the experimental dataset. And the model performance is evaluated in the light of three statistic metrics, i.e. MAE, RMSE and R ². Furthermore, this study is expected to serve as meaningful references for the control and prevention of the COVID-19 pandemic.

Due to the explosive growth of short text on various social media platforms, short text stream clustering has become an increasingly prominent issue. Unlike traditional text streams, short text stream data present the following characteristics: short length, weak signal, high volume, high velocity, topic drift, etc. Existing methods cannot simultaneously address two major problems very well: inferring the number of topics and topic drift. Therefore, we propose a dynamic clustering algorithm for short text streams based on the Dirichlet process (DCSS), which can automatically learn the number of topics in documents and solve the topic drift problem of short text streams. To solve the sparsity problem of short texts, DCSS considers the correlation of the topic distribution at neighbouring time points and uses the inferred topic distribution of past documents as a prior of the topic distribution at the current moment while simultaneously allowing newly streamed documents to change the posterior distribution of topics. We conduct experiments on two widely used datasets, and the results show that DCSS outperforms existing methods and has better stability.

The rise of high-quality cloud services has made service recommendation a crucial research question. Quality of Service (QoS) is widely adopted to characterize the performance of services invoked by users. For this purpose, the QoS prediction of services constitutes a decisive tool to allow end-users to optimally choose high-quality cloud services aligned with their needs. The fact is that users only consume a few of the broad range of existing services. Thereby, perform a high-accurate service recommendation becomes a challenging task. To tackle the aforementioned challenges, we propose a data sparsity resilient service recommendation approach that aims to predict relevant services in a sustainable manner for end-users. Indeed, our method performs both a QoS prediction of the current time interval using a flexible matrix factorization technique and a QoS prediction of the future time interval using a time series forecasting method based on an AutoRegressive Integrated Moving Average (ARIMA) model. The service recommendation in our approach is based on a couple of criteria ensuring in a lasting way, the appropriateness of the services returned to the active user. The experiments are conducted on a real-world dataset and demonstrate the effectiveness of our method compared to the competing recommendation methods.

Interactive group evaluation is a decision-making method to obtain group consensus by constantly modifying the initial weight of experts. Probabilistic hesitant Pythagorean fuzzy set (PrHPFS) is to be added the corresponding probability values for each membership degree and non-membership degree on the hesitant Pythagorean fuzzy set (HPFS). It is not only a generalization of HPFS and the Pythagorean fuzzy set (PFS), but also a more comprehensive and accurate reflection of the initial decision information given by experts. Especially, it can deal with the decision-making problem of multi-attribute fuzzy information in a wider area. In this paper, some basic definitions and related operations of the probabilistic hesitant Pythagorean fuzzy numbers (PrHPFNs) are first reviewed, and propose score function and accuracy function in PrHPFNs environment. Secondly, the concepts of Hamming distance measure, weighted distance measure and degree of similarity are put forward in PrHPFNs space, and the degree of similarity of two probabilistic hesitant Pythagorean fuzzy matrices (PrHPFMs) is suggested through the aggregation operator formula of PFNs. Finally, an interactive group decision-making method is designed based on the PrHPFM and the degree of similarity under the PrHPFNs environment, the effectiveness of the method is verified by an example, so as to overcome the hesitant psychological state of experts and achieve the consistent consensus evaluation of group preference.

One of the promising methods for early detection of Coronavirus Disease 2019 (COVID-19) among symptomatic patients is to analyze chest Computed Tomography (CT) scans or chest x-rays images of individuals using Deep Learning (DL) techniques. This paper proposes a novel stacked ensemble to detect COVID-19 either from chest CT scans or chest x-ray images of an individual. The proposed model is a stacked ensemble of heterogenous pre-trained computer vision models. Four pre-trained DL models were considered: Visual Geometry Group (VGG 19), Residual Network (ResNet 101), Densely Connected Convolutional Networks (DenseNet 169) and Wide Residual Network (WideResNet 50 2). From each pre-trained model, the potential candidates for base classifiers were obtained by varying the number of additional fully-connected layers. After an exhaustive search, three best-performing diverse models were selected to design a weighted average-based heterogeneous stacked ensemble. Five different chest CT scans and chest x-ray images were used to train and evaluate the proposed model. The performance of the proposed model was compared with two other ensemble models, baseline pre-trained computer vision models and existing models for COVID-19 detection. The proposed model achieved uniformly good performance on five different datasets, consisting of chest CT scans and chest x-rays images. In relevance to COVID-19, as the recall is more important than precision, the trade-offs between recall and precision at different thresholds were explored. Recommended threshold values which yielded a high recall and accuracy were obtained for each dataset.

COVID-19 is one of the largest spreading pandemic diseases faced in the documented history of mankind. Human to human interaction is the most prolific method of transmission of this virus. Nations all across the globe started to issue stay at home orders and mandating to wear masks or a form of face-covering in public to minimize the transmission by reducing contact between majority of the populace. The epidemiological models used in the literature have considerable drawbacks in the assumption of homogeneous mixing among the populace. Moreover, the effect of mitigation strategies such as mask mandate and stay at home orders cannot be efficiently accounted for in these models. In this work, we propose a novel data driven approach using LSTM (Long Short Term Memory) neural network model to form a functional mapping of daily new confirmed cases with mobility data which has been quantified from cell phone traffic information and mask mandate information. With this approach no pre-defined equations are used to predict the spread, no homogeneous mixing assumption is made, and the effect of mitigation strategies can be accounted for. The model learns the spread of the virus based on factual data from verified resources. A study of the number of cases for the state of New York (NY) and state of Florida (FL) in the USA are performed using the model. The model correctly predicts that with higher mobility the cases would increase and vice-versa. It further predicts the rate of new cases would see a decline if a mask mandate is administered. Both these predictions are in agreement with the opinions of leading medical and immunological experts. The model also predicts that with the mask mandate option even a higher mobility would reduce the daily cases than lower mobility without masks. We additionally generate results and provide RMSE (Root Mean Square Error) comparison with ARIMA based model of other published work for Italy, Turkey, Australia, Brazil, Canada, Egypt, Japan, and the UK. Our model reports lower RMSE than the ARIMA based work for all eight countries which were tested. The proposed model would provide administrations with a quantifiable basis of how mobility, mask mandates are related to new confirmed cases; so far no epidemiological models provide that information. It gives fast and relatively accurate prediction of the number of cases and would enable the administrations to make informed decisions and make plans for mitigation strategies and changes in hospital resources.

Realizing the accurate prediction of data flow is an important and challenging problem in industrial automation. However, due to the diversity of data types, it is difficult for traditional time series prediction models to have good prediction effects on different types of data. To improve the versatility and accuracy of the model, this paper proposes a novel hybrid time-series prediction model based on recursive empirical mode decomposition (REMD) and long short-term memory (LSTM). In REMD-LSTM, we first propose a new REMD to overcome the marginal effects and mode confusion problems in traditional decomposition methods. Then use REMD to decompose the data stream into multiple in intrinsic modal functions (IMF). After that, LSTM is used to predict each IMF subsequence separately and obtain the corresponding prediction results. Finally, the true prediction value of the input data is obtained by accumulating the prediction results of all IMF subsequences. The final experimental results show that the prediction accuracy of our proposed model is improved by more than 20% compared with the LSTM algorithm. In addition, the model has the highest prediction accuracy on all different types of data sets. This fully shows the model proposed in this paper has a greater advantage in prediction accuracy and versatility than the state-of-the-art models. The data used in the experiment can be downloaded from this website: https://github.com/Yang-Yun726/REMD-LSTM.

In addition to the almost five million lives lost and millions more than that in hospitalisations, efforts to mitigate the spread of the COVID-19 pandemic, which that has disrupted every aspect of human life deserves the contributions of all and sundry. Education is one of the areas most affected by the COVID-imposed abhorrence to physical (i.e., face-to-face (F2F)) communication. Consequently, schools, colleges, and universities worldwide have been forced to transition to different forms of online and virtual learning. Unlike F2F classes where the instructors could monitor and adjust lessons and content in tandem with the learners' perceived emotions and engagement, in online learning environments (OLE), such tasks are daunting to undertake. In our modest contribution to ameliorate disruptions to education caused by the pandemic, this study presents an intuitive model to monitor the concentration, understanding, and engagement expected of a productive classroom environment. The proposed apposite OLE (i.e., AOLE) provides an intelligent 3D visualisation of the classroom atmosphere (CA), which could assist instructors adjust and tailor both content and instruction for maximum delivery. Furthermore, individual learner status could be tracked via visualisation of his/her emotion curve at any stage of the lesson or learning cycle. Considering the enormous emotional and psychological toll caused by COVID and the attendant shift to OLE, the emotion curves could be progressively compared through the duration of the learning cycle and the semester to track learners' performance through to the final examinations. In terms of learning within the CA, our proposed AOLE is assessed within a class of 15 students and three instructors. Correlation of the outcomes reported with those from administered questionnaires validate the potential of our proposed model as a support for learning and counselling during these unprecedentedtimes that we find ourselves.