Titlebook: Computer and Information Science; Roger Lee Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer

扩大

Hierarchical Medical Classification Based on DLCF,DLCF model suitable for long text and multi-classification is designed. This model has obvious advantages in CMDD and other datasets. Compared with the baseline models, this model is superior to the baseline model in accuracy, recall and other indicators.

态度暖昧

GEST

,PK-BERT: Knowledge Enhanced Pre-trained Models with Prompt for Few-Shot Learning,r data sets and fine-tune the models in downstream tasks by transfer learning. The pre-trained models could learn the general language representation from large-scale corpora but their downstream task may be different from the pre-trained tasks in form and type. It also lacks related semantic knowle

CLEAR

Typhoon Track Prediction Based on TimeForce CNN-LSTM Hybrid Model,sasters and conducting risk assessment. With the improvement of computing power of computers and the development of deep learning technology, deep neural networks are gradually applied in the field of meteorology. This paper proposes a model mixing mechanism based on TF-CNN-LSTM and applies it to ty

澄清

,The Novel Characterizing Method of Collective Behavior Pattern in PSO,y the moving patterns of fish school, we propose a visualization method of collective behavior patterns based on the velocity field, discover various collective behavior patterns, and propose a discriminate index named swarm trend factor. In addition, this paper proposes a novel swarm states divisio

filicide

编辑才信任

Scintillations

铁砧

,Noise Detection and Classification in Chagasic ECG Signals Based on One-Dimensional Convolutional N can degrade the heart condition, leading to sudden cardiac death. Unfortunately, a common challenge for these systems is the low-quality and high level of noise in ECG signal collection. Also, generic techniques to assess the ECG quality can discard useful information in these so-called chagasic EC

NAIVE