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We live in the era of large data analysis, where processing vast datasets has become essential for uncovering valuable insights across various domains of our lives. Machine learning (ML) algorithms offer powerful tools for processing and analyzing this abundance of information. However, the considerable time and computational resources needed for training ML models pose significant challenges, especially within cascade schemes, due to the iterative nature of training algorithms, the complexity of feature extraction and transformation processes, and the large sizes of the datasets involved. This paper proposes a modification to the existing ML-based cascade scheme for analyzing large biomedical datasets by incorporating principal component analysis (PCA) at each level of the cascade. We selected the number of principal components to replace the initial inputs so that it ensured 95% variance retention. Furthermore, we enhanced the training and application algorithms and demonstrated the effectiveness of the modified cascade scheme through comparative analysis, which showcased a significant reduction in training time while improving the generalization properties of the method and the accuracy of the large data analysis. The improved enhanced generalization properties of the scheme stemmed from the reduction in nonsignificant independent attributes in the dataset, which further enhanced its performance in intelligent large data analysis. [ABSTRACT FROM AUTHOR]

Amidst rapid technological and healthcare advancements, biomedical data classification using machine learning (ML) is pivotal for revolutionizing medical diagnosis, treatment, and research by organizing vast healthcarerelated data. Despite efforts to apply single ML models on clean datasets, satisfactory classification accuracy can still be elusive. In such cases, MLbased ensembles offer a promising solution. This paper explores cascaded ensembles as highly accurate methods. Existing cascade classifiers often partition large datasets into equal unique parts, limiting accuracy due to insufficient amount of useful information processed by weak classifiers of all levels of the cascade ensemble. To address this, we propose an improved cascaded ensemble scheme using a different data sampling approach. Our method forms larger subsamples at each cascade level, enhancing accuracy, and generalization properties during biomedical data analysis. Experimental comparisons demonstrate substantial increases in classification accuracy and generalization properties of the improved cascade ensemble. [ABSTRACT FROM AUTHOR]

The article focuses on the broader impact of bridge failures on transport networks and regional resilience. Topics include the role of soil-structure interactions in seismic bridge design, the development of new strengthening systems for existing bridges, and the use of analytical methods to assess bridge performance under multiple earthquake events.

A personal watercraft (PWC) is a vessel that uses an inboard motor powering a water jet pump as a source of power and is operated by a person sitting, standing, or kneeling. Maneuvering a PWC is different from operating a motor vehicle or boat. An obstacle cannot be avoided by slowing down and turning the watercraft; throttle power is required to turn or maneuver the PWC. The watercraft stops only by drifting or turning sharply. The study examined sixty court decisions published in LexisNexis databases of the United States over the last decade. Cases included individuals injured while operating a PWC as a driver, passenger, or as a result of contact with a watercraft. A content analysis identified items to be used in the study. Crosstab and logistic regression analyses were used to identify demographic information and the characteristics of those who succeeded in a court of law. One-third of the cases were successful; adults, males, and the party who sustained a severe injury were more successful in a court of law with the exception of the statistically significant factors (high risk maneuvers and sharp turns). Among the additional results, we should be aware that insurance companies may not pay; additionally, it is unwise to loan a PWC to a female who has no experience. [ABSTRACT FROM AUTHOR]