다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Artificial neural networks (ANNs) constitute a class of flexible nonlinear models designed to mimic biological neural systems. In this entry, we introduce ANN using familiar econometric terminology and provide an overview of ANN modeling approach and its implementation methods. † Correspondence: Chung-Ming Kuan, Institute of Economics, Academia Sinica, 128 Academia Road, Sec. 2, Taipei 115, Taiwan; ckuan@econ.sinica.edu.tw. †† I would like to express my sincere gratitude to the editor, Professor Steven Durlauf, for his patience and constructive comments on early drafts of this entry. I also thank Shih-Hsun Hsu and Yu-Lieh Huang for very helpful suggestions. The remaining errors are all mine.

Artificial neural networks

: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

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Nonlinear Time Series Analysis.

Many ``real-world'' networks are clearly defined while most ``social'' networks are to some extent subjective. Indeed, the accuracy of empirically-determined social networks is a question of some concern because individuals may have distinct perceptions of what constitutes a social link. One unambiguous type of connection is sexual contact. Here we analyze data on the sexual behavior of a random sample of individuals, and find that the cumulative distributions of the number of sexual partners during the twelve months prior to the survey decays as a power law with similar exponents $\alpha \approx 2.4$ for females and males. The scale-free nature of the web of human sexual contacts suggests that strategic interventions aimed at preventing the spread of sexually-transmitted diseases may be the most efficient approach.

The Web of Human Sexual Contacts

Background and objective During the recent global urgency, scientists, clinicians, and healthcare experts around the globe keep on searching for a new technology to support in tackling the Covid-19 pandemic The evidence of Machine Learning (ML) and Artificial Intelligence (AI) application on the previous epidemic encourage researchers by giving a new angle to fight against the novel Coronavirus outbreak This paper aims to comprehensively review the role of AI and ML as one significant method in the arena of screening, predicting, forecasting, contact tracing, and drug development for SARS-CoV-2 and its related epidemic Method A selective assessment of information on the research article was executed on the databases related to the application of ML and AI technology on Covid-19 Rapid and critical analysis of the three crucial parameters, ie, abstract, methodology, and the conclusion was done to relate to the model's possibilities for tackling the SARS-CoV-2 epidemic Result This paper addresses on recent studies that apply ML and AI technology towards augmenting the researchers on multiple angles It also addresses a few errors and challenges while using such algorithms in real-world problems The paper also discusses suggestions conveying researchers on model design, medical experts, and policymakers in the current situation while tackling the Covid-19 pandemic and ahead Conclusion The ongoing development in AI and ML has significantly improved treatment, medication, screening, prediction, forecasting, contact tracing, and drug/vaccine development process for the Covid-19 pandemic and reduce the human intervention in medical practice However, most of the models are not deployed enough to show their real-world operation, but they are still up to the mark to tackle the SARS-CoV-2 epidemic

Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review

The coronavirus disease 2019 (COVID-19) has become a public health emergency of international concern affecting more than 200 countries and territories worldwide. As of September 30, 2020, it has caused a pandemic outbreak with more than 33 million confirmed infections, and more than 1 million reported deaths worldwide. Several statistical, machine learning, and hybrid models have previously been applied to forecast COVID-19 confirmed cases for profoundly affected countries. Future predictions of daily COVID-19 cases are useful for the effective allocation of healthcare resources and will act as an early-warning system for government policymakers. However, due to the presence of extreme uncertainty in these time series datasets, forecasting of COVID-19 confirmed cases has become a very challenging job. For univariate time series forecasting, there are various statistical and machine learning models available in the literature. Still, nowcasting and forecasting of COVID-19 cases are difficult due to insufficient input data, flaw in modeling assumptions, lack of epidemiological features, inadequate past evidence on effects of available interventions, and lack of transparency. This chapter focuses on assessing different short-term forecasting models that are popularly used to forecast the daily COVID-19 cases for various countries. This chapter provides strong empirical evidence that there is no universal method available that can accurately forecast pandemic data. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Nowcasting of COVID-19 Confirmed Cases: Foundations, Trends, and Challenges

The ongoing coronavirus disease 2019 (COVID-19) pandemic is one of the major health emergencies in decades that affected almost every country in the world. As of June 30, 2020, it has caused an out...

An integrated deterministic-stochastic approach for forecasting the long-term trajectories of COVID-19

The ongoing COVID-19 pandemic is one of the major health emergencies in decades that affected almost every country in the world. As of May 10, 2020, it has caused an outbreak with more than 41,78,000 confirmed infections and more than 2,83,000 reported deaths globally. Due to the unavailability of an effective treatment (or vaccine) and insufficient evidence regarding the transmission mechanism of the epidemic, the world population is currently in a vulnerable position. The daily cases data sets of COVID-19 for profoundly affected countries represent a stochastic process comprised of deterministic and stochastic components. This study proposes an integrated deterministic-stochastic approach to predict the long-term trajectories of the COVID-19 cases for Italy and Spain. The deterministic component of the daily-cases univariate time-series is assessed by an extended SIR (SIRCX) model whereas its stochastic component is modeled using an autoregressive (AR) time series model. The proposed integrated SIRCX-AR (ISA) approach based on two operationally distinct modeling paradigms utilizes the superiority of both the deterministic SIRCX and stochastic AR models to find the long-term trajectories of the epidemic curves. Experimental analysis based on the proposed ISA model suggest that the estimated numbers of cases in Italy and Spain between 11 May, 2020 – 09 June, 2020 will be 10982 (6383–15582) and 13731 (3395–29013), respectively. Additionally, the expected number daily cases on 09 July, 2020 for Italy and Spain are estimated to be 30 (0–183) and 92 (0–602), respectively. These log-term forecasts for Italy and Spain of the coming outbreaks will be very useful for the effective allocation of health care resources to mitigate COVID-19.

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An integrated deterministic-stochastic approach for predicting the long-term trajectories of COVID-19

Real-world heavy-tailed networks are claimed to be scale-free, meaning that the degree distributions follow the classical power-law. But it is evident from a closer observation that there exists a clearly identifiable non-linear pattern in the entire degree distribution in a log-log scale. Thus, the classical power-law distribution is often inadequate to fit the large-scale complex network data sets. The presence of this non-linearity can also be linked to the recent debate on scarcity versus the universality of scale-free networks. The search, therefore, continues to develop probabilistic models that can efficiently capture the crucial aspect of heavy-tailed and long-tailed behavior of the entire degree distribution of real-world complex networks. This paper proposes a new variant of the popular Lomax distribution, termed as modified Lomax (MLM) distribution, which can efficiently fit the entire degree distribution of real-world networks. The newly introduced MLM distribution arises from a hierarchical family of Lomax distributions and belongs to the basin of attraction of Frechet distribution. Some interesting statistical properties of MLM including characteristics of the maximum likelihood estimates have been studied. Finally, the proposed MLM model is applied over several real-world complex networks to showcase its excellent performance in uncovering the patterns of these heavy-tailed networks.

Uncovering patterns in heavy-tailed networks : A journey beyond scale-free

Infectious diseases remain among the top contributors to human illness and death worldwide, among which many diseases produce epidemic waves of infection. The unavailability of specific drugs and ready-to-use vaccines to prevent most of these epidemics makes the situation worse. These force public health officials and policymakers to rely on early warning systems generated by reliable and accurate forecasts of epidemics. Accurate forecasts of epidemics can assist stakeholders in tailoring countermeasures, such as vaccination campaigns, staff scheduling, and resource allocation, to the situation at hand, which could translate to reductions in the impact of a disease. Unfortunately, most of these past epidemics exhibit nonlinear and non-stationary characteristics due to their spreading fluctuations based on seasonal-dependent variability and the nature of these epidemics. We analyse a wide variety of epidemic time series datasets using a maximal overlap discrete wavelet transform (MODWT) based autoregressive neural network and call it EWNet model. MODWT techniques effectively characterize non-stationary behavior and seasonal dependencies in the epidemic time series and improve the nonlinear forecasting scheme of the autoregressive neural network in the proposed ensemble wavelet network framework. From a nonlinear time series viewpoint, we explore the asymptotic stationarity of the proposed EWNet model to show the asymptotic behavior of the associated Markov Chain. We also theoretically investigate the effect of learning stability and the choice of hidden neurons in the proposal. From a practical perspective, we compare our proposed EWNet framework with several statistical, machine learning, and deep learning models. Experimental results show that the proposed EWNet is highly competitive compared to the state-of-the-art epidemic forecasting methods.

Tanujit Chakraborty

Papers

Nowcasting of COVID-19 Confirmed Cases: Foundations, Trends, and Challenges

An integrated deterministic-stochastic approach for forecasting the long-term trajectories of COVID-19

An integrated deterministic-stochastic approach for predicting the long-term trajectories of COVID-19

Uncovering patterns in heavy-tailed networks : A journey beyond scale-free

Epicasting: An Ensemble Wavelet Neural Network (EWNet) for Forecasting Epidemics