Optimizing Kernel Extreme Learning Machine based on a Enhanced Adaptive Whale Optimization Algorithm for classification task.

Data classification is an important research direction in machine learning. In order to effectively handle extensive datasets, researchers have introduced diverse classification algorithms. Notably, Kernel Extreme Learning Machine (KELM), as a fast and effective classification method, has received widespread attention.

Enhanced GRU-based regression analysis via a diverse strategies whale optimization algorithm.

Taking into account the whale optimization algorithm's tendency to get trapped in local optima easily and its slow convergence rate, this paper proposes a diverse strategies whale optimization algorithm (DSWOA) and uses it to optimize the parameters of GRU, thereby achieving better regression prediction effects. First, an innovative t-distribution perturbation is used to perturb the optimal whale to expand the optimization space of the optimal whale. Secondly, in the random search stage, we perform a Cauchy walk on the whale's position and then use reverse learning to enable the algorithm to effectively navigate away from the local optimum.

Comparative Study of Plasma-Enhanced-Atomic-Layer-Deposited AlO/HfO/SiO and HfO/AlO/SiO Trilayers for Ultraviolet Laser Applications.

High-performance thin films combining large optical bandgap AlO and high refractive index HfO are excellent components for constructing the next generation of laser systems with enhanced output power. However, the growth of low-defect plasma-enhanced-atomic-layer-deposited (PEALD) AlO for high-power laser applications and its combination with HfO and SiO materials commonly used in high-power laser thin films still face challenges, such as how to minimize defects, especially interface defects. In this work, substrate-layer interface defects in AlO single-layer thin films, layer-layer interface defects in AlO-based bilayer and trilayer thin films, and their effects on the laser-induced damage threshold (LIDT) were investigated via capacitance-voltage (C-V) measurements.

Environmental and Microbial Factors in Inflammatory Bowel Disease Model Establishment: A Review Partly through Mendelian Randomization.

Inflammatory bowel disease (IBD) is a complex condition resulting from environmental, microbial, immunologic, and genetic factors. With the advancement of Mendelian randomization research in IBD, we have gained new insights into the relationship between these factors and IBD. Many animal models of IBD have been developed using different methods, but few studies have attempted to model IBD by combining environmental factors and microbial factors.