Intractable 发表于 2025-3-25 04:43:02
http://reply.papertrans.cn/16/1593/159256/159256_21.pngethnology 发表于 2025-3-25 09:52:02
http://reply.papertrans.cn/16/1593/159256/159256_22.png致敬 发表于 2025-3-25 12:48:18
http://reply.papertrans.cn/16/1593/159256/159256_23.png杀子女者 发表于 2025-3-25 19:18:50
Fieldwork with Vulnerable Young People,tter social network is a source of valuable information in simple text and appropriated to use this technology. In this paper is described the process used to select the most suitable algorithms to analyze tweets for particular words written in Spanish, also the results obtained by every algorithm are reported.典型 发表于 2025-3-25 23:04:42
http://reply.papertrans.cn/16/1593/159256/159256_25.pngGlossy 发表于 2025-3-26 00:09:04
Exploiting Data of the Twitter Social Network Using Sentiment Analysistter social network is a source of valuable information in simple text and appropriated to use this technology. In this paper is described the process used to select the most suitable algorithms to analyze tweets for particular words written in Spanish, also the results obtained by every algorithm are reported.lavish 发表于 2025-3-26 04:25:53
http://reply.papertrans.cn/16/1593/159256/159256_27.png商业上 发表于 2025-3-26 08:39:08
Towards a Generic Ontology for Video Surveillancet complex behaviors (fights, thefts, attacks). To solve these challenges, the use of ontologies has been proposed as a tool to reduce this gap between low and high level information. In this work, we present the foundations of an ontology to be used in an intelligent video surveillance system.挖掘 发表于 2025-3-26 15:00:13
http://reply.papertrans.cn/16/1593/159256/159256_29.pngILEUM 发表于 2025-3-26 16:58:18
Using Intermediate Models and Knowledge Learning to Improve Stress Predictionl-world setting, from 29 employees in two different organisations over 5 weeks. To improve classification performance we propose to use .. These intermediate models represent the mood state of a person which is used to build the final stress prediction model. In particular, we obtained an accuracy of 78.2 % to classify stress levels.