In order to build high-quality concrete, it is imperative to know the raw materials in advance. It is possible to accurately predict the quality of concrete and the amount of raw materials used using machine learning-enhanced methods. An automated process based on machine learning strategies is proposed in this paper for predicting the compressive strength of concrete. Fusion-learning-based optimization is used in the proposed approach to generate a strong learner by pooling support vector regression models. The SVR technique proposes an (...) optimization method for finding the kernel radial basis function parameters based on improving the innovative gunner algorithm. As a result of AIG's diverse solutions, local optima are effectively avoided. Therefore, the novelty of our research is that, in solving the uncertainty of predicted outputs based on integrated models, we use fusion-learning-based optimization to improve regression discrimination. We also collected a standard dataset to analyze the proposed algorithm, and subsequently, the dataset was designed from concrete laboratory tests on 244 samples, seven features, and three outputs. Different regression intensities are determined by correlation analysis of responses. Regression fusion is sufficiently accurate to estimate the number of desired outcomes examined based on the appropriate input data sample. The best quality concrete can be achieved with an error rate of less than 5%. (shrink)

Studies have supported the effectiveness of the From Timid to a Tiger and Supportive Parenting for Anxious Childhood Emotions program in reducing childhood anxiety. This study is the first to compare the effectiveness of the two programs in the treatment of childhood anxiety disorder and reducing family accommodations levels. Parents of children aged 6 to 9 were randomly allocated to either FTTT or SPACE groups, and each attended ten online sessions following the manuals of the interventions. Throughout the study, 9 (...) participants dropped out, resulting in a total of 49 participants, and we performed statistical analyses based on data from these 49 participants. The assessment took place pre- and post-treatment and at a ten-week follow-up on parent-rated child anxiety and family accommodation measures. Analyses of variance and gain scores were conducted to examine the data. The results indicated that both treatments approaches produced significant reductions in outcome measures, and the post-treatment gains of both treatments were maintained at a ten-week follow-up, though when considering the mean differences and effect sizes across the assessment scores and between groups, overall, the FTTT was significantly more effective in reducing child anxiety scores and family accommodation levels. Our results demonstrated that FTTT significantly outperformed the SPACE program in reducing childhood anxiety problems and family accommodation levels. (shrink)