Titlebook: Data Management Technologies and Applications; 10th International C Alfredo Cuzzocrea,Oleg Gusikhin,Christoph Quix Conference proceedings 2

小卒

懲罰

,Automatic Detection of?Facial Landmarks for?Denture Models, as a reference for planning facial or dental reconstruction treatments. However, the vertical canon assumption that the face is divided vertically into three equal thirds, which was adopted a long time ago, has not been verified yet. We used 2D photos freely available online and annotated them with

漸強

,Combining Image and?Text Matching for?Product Classification in?Retail, single product can have several hundred attributes which are often entered manually. In addition, products have to be classified by hand in many cases by grouping them into categories based on their properties and their relationships to other products. This is a very labor-intensive, time-consuming

Largess

,Automatic Sentiment Labelling of?Multimodal Data,mage and textual information for supervised machine learning. Because both the image and text components, individually and collectively, convey sentiment, assessing the sentiment of multimodal data typically requires both image and text information. Consequently, the majority of studies classify sen

KIN

DUST

Data Mining and Machine Learning to Predict the Sulphur Content in the Hot Metal of a Coke-Fired Blding, in this sense, there has been increased interest in data mining and machine learning in the steel industry. Reducing energy consumption and neutralizing greenhouse gas emissions have become challenges for the steel industry. Nowadays, it is normal to use artificial intelligence to integrate In

打包

,Automatic Instructional Feedback, and?a?Lecture Hub System: A Strategy Towards Nurturing the?Acquisisition. However, most of these interventions are mainly structured around a battery of assessments and mostly overlooking the participants’ engagement patterns. Ergo, large amounts of easily avoidable errors, most of which are syntactic, are generated in the course of a student’s exercise task enga

aggrieve

Data Management Technologies and Applications978-3-031-37890-4Series ISSN 1865-0929 Series E-ISSN 1865-0937

實施生效

https://doi.org/10.1007/978-3-031-37890-4artificial intelligence; Business Process Management (BPM); computer systems; correlation analysis; data

束縛