Search

The fusion process considers the boundary between correct and conflict records. It has been a fundamental component in ensuring the accuracy of many mathematical algorithms that utilize multiple input sources. Fusion techniques give priority and high weight to reliable and qualified sources since their information is most likely to be trustworthy. This study stochastically investigates the three most common fusion techniques: Kalman filtering, particle filtering and Bayesian probability (which is the basis of other techniques). The paper focuses on using fusion techniques in the context of state estimation for dynamic systems to improve reliability and accuracy. The fusion methods are investigated using different types of datasets to find out their performance and accuracy in state estimation. [ABSTRACT FROM AUTHOR]

An important concern with regard to the ensembles of algorithms is that using the individually optimal parameter settings of the members does not necessarily maximize the performance of the ensemble itself. In this paper, we propose a novel evaluation method for simulated annealing that combines dataset sampling and image downscaling to accelerate the parameter optimization of medical image segmentation ensembles. The scaling levels and sample sizes required to maintain the convergence of the search are theoretically determined by adapting previous results for simulated annealing with imprecise energy measurements. To demonstrate the efficiency of the proposed method, we optimize the parameters of an ensemble for lung segmentation in CT scans. Our experimental results show that the proposed method can maintain the solution quality of the base method with significantly lower runtime. In our problem, optimization with simulated annealing yielded an F 1 score of 0.9397 and an associated M C C of 0.7757. Our proposed method maintained the solution quality with an F 1 score of 0.9395 and M C C of 0.7755 while exhibiting a 42.01% reduction in runtime. It was also shown that the proposed method is more efficient than simulated annealing with only sampling-based evaluation when the dataset size is below a problem-specific threshold. [ABSTRACT FROM AUTHOR]