Titlebook: Knowledge Science, Engineering and Management; 16th International C Zhi Jin,Yuncheng Jiang,Wenjun Ma Conference proceedings 2023 The Editor

scoliosis

Joint Feature Selection and Classifier Parameter Optimization: A Bio-Inspired Approachal HBA. Finally, a binary mechanism is adopted to make it suitable for the feature selection problem. Experiments conducted in 27 public datasets have demonstrated that the proposed approach can outperform some well-known swarm-based algorithms.

健忘症

Boosting LightWeight Depth Estimation via Knowledge Distillation guide KD, enabling the student to better learn from the teacher’s predictions. This approach helps fill the gap between the teacher and the student, resulting in improved data-driven learning. The experiments show that our method achieves comparable performance to state-of-the-art methods while usi

Peculate

攀登

Multitask-Based Cluster Transmission for Few-Shot Text Classificationntation of basic classification features as an auxiliary task to further enhance the diversity of spatial vectors and alleviate the over-fitting problem. Experiments show that our approach further improves the performance of the few-shot text classification task.

白杨

含糊其辞

RTAD-TP: Real-Time Anomaly Detection Algorithm for Univariate Time Series Data Based on Two-Parameteo determine the threshold for the residual. In addition, we use two-parameter estimation to improve the speed and accuracy of parameter estimation in automatic thresholding, addressing the limitations of SPOT, DSPOT, and FluxEV. Experimental results show that the RTAD-TP algorithm has better detecti

Terrace

Multi-Sampling Item Response Ranking Neural Cognitive Diagnosis with Bilinear Feature Interaction element product feature interaction with bilinear feature interaction in the multi-sampling item response ranking neural cognitive diagnosis to enhance interaction in the deep learning process. Specifically, our model is stable and can be easily applied to cognitive diagnosis. We observed improveme

顾客

Confidential

Dual-Dimensional Refinement of Knowledge Graph Embedding Representationd UMLS. Through validation experiments, we substantiate our assumptions and analyses regarding datasets and model capabilities, thereby addressing the interpretability shortcomings of existing embedding models on underperforming datasets.

Laconic

An Enhanced Fitness-Distance Balance Slime Mould Algorithm and Its Application in Feature Selectionsition update mechanism. Secondly, an elite opposition-based learning strategy is adopted in the population initialization for increasing population diversity. Then chaotic tent sequence, with traversal property, is integrated into the position updating of SMA to perturb the position and jump out of