We consider six different estimators of residual heterogeneity in random-effects meta-regression, five estimators already known and implemented in the R package metafor and one estimator not yet considered in random-effects meta-regression. In a numerical study, we investigate the properties of these residual heterogeneity estimators as well as the impact of these estimators on the properties of the regression parameter estimates. It turns out that the new estimator performs quite well in terms of bias and mean squared error. The impact of the different residual heterogeneity estimators on the actual confidence coefficient of confidence intervals for regression parameters can be substantially different as shown in the numerical study.
The Internet of Medical Things (IoMT) is increasingly being used for healthcare purposes. IoMT enables many sensors to collect patient data from various locations and send it to a distributed hospital for further study. IoMT provides patients with a variety of paid programmes to help them keep track of their health problems. However, the current system services are expensive, and offloaded data in the healthcare network are insecure. The research develops a new, cost-effective and stable IoMT framework based on a blockchain-enabled fog cloud. The study aims to reduce the cost of healthcare application services as they are processing in the system. The study devises an IoMT system based on different algorithm techniques, such as Blockchain-Enable Smart-Contract Cost-Efficient Scheduling Algorithm Framework (BECSAF) schemes. Smart-Contract Blockchain schemes ensure data consistency and validation with symmetric cryptography. However, due to the different workflow tasks scheduled on other nodes, the heterogeneous, earliest finish, time-based scheduling deals with execution under their deadlines. Simulation results show that the proposed algorithm schemes outperform all existing baseline approaches in terms of the implementation of applications.
A ternary semigroup is a nonempty set equipped with an associative ternary operation. A Pythagorean fuzzy set is one of the generalizations of the fuzzy set. The aim of this paper is to study rough Pythagorean fuzzy ideals in ternary semigroups. This idea is extended to the lower and upper approximations of Pythagorean fuzzy ideals.
Complex dual hesitant fuzzy set (CDHFS) is an assortment of complex fuzzy set (CFS) and dual hesitant fuzzy set (DHFS). In this manuscript, the notion of the CDHFS is explored and its operational laws are discussed. The new methodology of the complex interval-valued dual hesitant fuzzy set (CIvDHFS) and its necessary laws are introduced and are also defensible with the help of examples. Further, the antilogarithmic and with-out exponential-based similarity measures, generalized similarity measures, and their important characteristics are also developed. These similarity measures are applied in the environment of pattern recognition and medical diagnosis to evaluate the proficiency and feasibility of the established measures. We also solved some numerical examples using the established measures to examine the reliability and validity of the proposed measures by comparing these with existing measures. To strengthen the proposed study, the comparative analysis is made and it is conferred that the proposed study is much more superior to the existing studies.
Every year, many basins in Thailand face the perennial droughts and floods that lead to the great impact on agricultural segments. In order to reduce the impact, water management would be applied to the critical basin, for instance, Yom River basin. An importing task of management is quantitative prediction of water that is stated by water level. This study proposes the hybridized forecasting models between the stochastic approaches, seasonal autoregressive integrated moving average (SARIMA) models and machine learning approach, artificial neural network (ANN). The proposed hybrid model is called seasonal autoregressive integrated moving average and artificial neural network or SARIMANN model for average monthly water level (AMWL) time series of Yom River basin. The study period is from April 2007 to March 2020, over thirteen hydrological years. The forecasting performance is the minimum values of root mean squared error (RMSE) and mean absolute percentage error (MAPE) between SARIMA models, ANN models, and SARIMANN models. Results indicated that: The three models reveal the similarity of RMSE and MAPE for both four water level measurement stations for wet and dry seasons. The forecasting performance is the minimum values of RMSE and MAPE of three models. The SARIMA model is the best approach for Y.
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