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The mushroomed Deepfake synthetic materials circulated on the internet have raised a profound social impact on politicians, celebrities, and individuals worldwide. In this survey, we provide a thorough review of the existing Deepfake detection studies ...

Thailand is dealing with air pollution, particularly from small particulate matter (PM), significantly impacting public health. Wind speed is pivotal in the dispersion of these particles. Due to its unpredictability, we are interested in estimating the ...

Over the past years, numerous studies demonstrated the vulnerability of deep neural networks (DNNs) to make correct classifications in the presence of small noise. This motivated the formal analysis of DNNs to ensure that they delineate acceptable ...

Many traditional approaches to constructing confidence intervals for randomized experiments with binary outcomes are based on a binomial model for the outcome distribution. However, the assumptions underlying the binomial model are highly problematic ...

For exploratory data analysis, it is often desirable to know what answers you are likely to get before actually obtaining those answers. This can potentially be achieved by designing systems to offer the estimates of a data operation result-say op(data)-...

 Fuzzy sets provide an effective method for dealing with uncertain and imprecise problems. For data of intermediate fuzzy distribution, membership degrees of objects whose attribute values are larger or smaller than the normal value would be the same and ...

As the Internet of Things (IoT) becomes more and more popular and full-grown, diverse technologies for measurement and collection of business data continually improve as well. Effective data analysis of and applications can be helpful to stores to make ...

Uncertain time series analysis has been developed for studying the imprecise observations. In this paper, we propose a nonlinear model called uncertain max-autoregressive (UMAR) model. The unknown parameters in model are estimated by the least squares ...

In this paper, Burr-X distribution with Type-II hybrid censored data is considered. The E-Bayesian estimation (the expectation of the Bayesian estimate) and the corresponding Bayesian and maximum likelihood estimation methods are studied for the ...

To overcome the problems of large errors in data feature acquisition and long detection delays in traditional detection methods, this paper proposes an anomaly detection method based on feature mining for wireless sensor networks (WSNs). In our method, ...

In recent years, reinforcement learning has played an important role in the study of decision problem in computer games. To solve the problem of how to better estimate the value function with limited computational resources, this paper proposes a dynamic ...

Risk-based evaluation is a failure analysis tool that can be combined with traditional effectiveness metrics to ensure that the improvements observed are consistent across topics when comparing systems. Here we explore the stability of confidence ...

Regression model is a powerful analytical tool for estimating the relationships between explanatory variables and the response variable. Traditionally, it is often assumed that the data are observed precisely and characterized by crisp values. However, in ...

Study of fluctuation in genetic algorithm has been a sub-objective in genetic algorithm implementations. The reliability of genetic algorithm may vary based on implementation case, hence it is necessary to investigate its performance pattern for ...

We discuss a general approach to building non-asymptotic confidence bounds for Stochastic Optimization problems. Our principal contribution is the observation that a Sample Average Approximation of a problem supplies upper and lower bounds for the ...

Evaluating the quality of workers is very important in crowdsourcing system and impactful methods are required in order to obtain the most appropriate quality. Previous work have introduced confidence intervals to estimate the quality of workers. ...

Basic topics from probability and statistics -- such as probability distributions, parameter estimation, confidence intervals and statistical hypothesis testing -- are often included in computing curricula and used as tools for experimental performance ...

When we use simulations to estimate the performance of stochastic systems, the simulation is often driven by input models estimated from finite real-world data. A complete statistical characterization of system performance estimates requires quantifying ...

Teaching statistics in an undergraduate or a graduate business administration course, business analytics course, or engineering program remains a challenging task for the instructors. Often students fail to comprehend the real-life application of ...