Titlebook: Advances in Artificial Intelligence; 20th Conference of t Amparo Alonso-Betanzos,Bertha Guijarro-Berdiñas,Al Conference proceedings 2024 Th

毛发

书目名称Advances in Artificial Intelligence影响因子(影响力)




书目名称Advances in Artificial Intelligence影响因子(影响力)学科排名




书目名称Advances in Artificial Intelligence网络公开度




书目名称Advances in Artificial Intelligence网络公开度学科排名




书目名称Advances in Artificial Intelligence被引频次




书目名称Advances in Artificial Intelligence被引频次学科排名




书目名称Advances in Artificial Intelligence年度引用




书目名称Advances in Artificial Intelligence年度引用学科排名




书目名称Advances in Artificial Intelligence读者反馈




书目名称Advances in Artificial Intelligence读者反馈学科排名

ACRID

,An Architecture Towards Building a Reliable Suicide Information Chatbot,dition, all the components of the architecture have been automatically tested to prove their suitability to be incorporated to the chatbot. The developed system is a step towards helping in one of the greatest global public health concerns.

灰心丧气

Age Estimation Using Soft Labelling Ordinal Classification Approaches,g, soft labelling more effectively acknowledges the continuity and uncertainty inherent in age estimation. The experiments conducted in this study facilitate the comparison of soft labelling approaches against the nominal baseline. Results demonstrate superior performance of soft labelling approache

极端的正确性

,O-Hydra: A Hybrid Convolutional and Dictionary-Based Approach to Time Series Ordinal Classification categories, to TSOC, resulting in O-Hydra. For the experiments, we have included a collection of 21 ordinal problems sourced from two well-known archives. O-Hydra has been benchmarked against its nominal counterpart, Hydra, as well as against two state-of-the-art approaches in the two previous cate

增减字母法

周兴旺

不如屎壳郎

,Semi-supervised Learning Methods for Semantic Segmentation of Polyps,ated data, most models obtained better results, but two of them performed worse in the baseline case. Furthermore, we can conclude that the Data Distillation method increases the performance of the models a 48.6% and 30.6% on average using 60 and 120 annotated images respectively. Finally, using onl

表两个

paragon

0302-9743 le AI, fuzzy logic, natural language processing, knowledge representation, reasoning and logic, constraints, search and planning, multi-agent systems, computer vision and robotics, and intelligent web and information retrieval..978-3-031-62798-9978-3-031-62799-6Series ISSN 0302-9743 Series E-ISSN 1611-3349

爱得痛了

https://doi.org/10.1007/978-3-322-85683-8of training techniques for SNNs, including supervised, unsupervised, and reinforcement learning approaches. This paper culminates by highlighting imperative future research directions in SNNs. It underscores the pressing need for developing sophisticated training algorithms and customizing models to