Enhancing teaching sufficiency is crucial because low teaching efficiency has always been a widespread issue in ideological and political theory course. Evaluating data on the course is obtained from a freshmen class of 2022 using questionnaires. The data is organised and condensed for mining and analysis. Subsequently, an intelligent artificial multi-verse optimizer (AMVO) method s developed to predict the effect of ideological and political theory course. The proposed AMVO approach was tested against various cutting-edge algorithms to demonstrate its effectiveness and stability on the benchmark functions. The experimental results indicated that AMVO ranked first among the 23 test functions. Furthermore, the binary AMVO enhanced k-nearest neighbour classifier had excellent performance in the art ideological and political theory course in terms of error rate, accuracy, specificity and sensitivity. This model can predict the overall evaluation attitude of freshmen towards the course based on the dataset. In addition, we can further analyse the potential correlations between factors that enhance the intellectual and political content of the course. This model can further refine the evaluation of ideological and political courses by teachers and students in our school, thereby achieving the fundamental goal of moral cultivation.

Chronic obstructive pulmonary disease (COPD) causes irreversible airflow limitations, increasing global morbidity and mortality. Acute exacerbations (AECOPDs) worsen symptoms and may require mechanical ventilation, leading to complications. Understanding factors affecting AECOPD prognosis during mechanical ventilation is crucial. Inspired by rime ice physics, the RIME algorithm has been proposed but it had limitations in feature selection and solution space exploration. We improve RIME by adding a dispersed foraging mechanism and differential crossover operator, creating DDRIME. Our study analyzes patient data to identify factors related to invasive mechanical ventilation in AECOPD. DDRIME’s performance is tested against RIME on 83 functions and 12 public datasets for feature selection. It outperformed most algorithms, with bDDRIME_KNN showing high accuracy in predicting AECOPD outcomes. Key indicators—chronic heart failure (CHF), D-dimer (D-D), fungal infection (FI), and pectoral muscle area (PMA)—predicted prognosis with >0.98 accuracy. bDDRIME is thus a valuable tool for predicting AECOPD patients’ outcomes on mechanical ventilation.

In recent years, many novel swarm intelligence algorithms have been proposed to apply in various fields. Kernel extreme learning machine (KELM) is combined kernel function with Extreme learning machine (ELM) algorithm. Then, KELM can reach the more robust and better generalization performance than the basic ELM. However, the performance of the method is determined by the crucial parameters in the practical cases. The key parameters of KELM are explored and the selection methods of the KELM-based swarm intelligence optimization are utilized. The FOA and GWO are intelligence computing methods which abstracts the math model from the hunting behavior of the creatures in the natural world. In order to further explore the ability of the FOA, crisscross strategy and GWO-based mechanism are introduced to FOA and called CGFOA. In this paper, a novel method of the CGFOA-KELM is proposed and successfully applied in financial bankruptcy prediction.