Wang / Zhang / Hu | Multimedia Technology and Enhanced Learning | Buch | 978-3-031-50573-7 | sack.de

Buch, Englisch, Band 533, 300 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 482 g

Reihe: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

Wang / Zhang / Hu

Multimedia Technology and Enhanced Learning

5th EAI International Conference, ICMTEL 2023, Leicester, UK, April 28-29, 2023, Proceedings, Part II

Buch, Englisch, Band 533, 300 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 482 g

Reihe: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

ISBN: 978-3-031-50573-7
Verlag: Springer Nature Switzerland


The four-volume set LNICST 532, 533, 534 and 535 constitutes the refereed proceedings of the 5th EAI International Conference on Multimedia Technology and Enhanced Learning, ICMTEL 2023, held in Leicester, UK, during April 28-29, 2023.
The 121 papers presented in the proceedings set were carefully reviewed and selected from 285 submissions. They were organized in topical sections as follows: AI-based education and learning systems; medical and healthcare; computer vision and image processing; data mining and machine learning; workshop 1: AI-based data processing, intelligent control and their applications; workshop 2: intelligent application in education; and workshop 3: the control and data fusion for intelligent systems.
Wang / Zhang / Hu Multimedia Technology and Enhanced Learning jetzt bestellen!

Zielgruppe


Research

Weitere Infos & Material


Computer Vision and Image Processing: Nodes deployment optimization for indoor localization using FIR filter.- Security Management Method of Power Communication Access Network Based on EPON Technology.- Image Recognition Technology of UAV Tracking Navigation Path Based on ResNet.- Intelligent Extraction of Color Features in Architectural Space Based on Machine Vision.- Stability Tracking Detection of Moving Objects in Video Images Based on Computer Vision Technology.- Virtual Display Method of Garment Design Details Based on Computer Vision.- Reliability Testing Model of Micro Grid Soc Droop Control Based on Convolutional Neural Network.- Pedestrian Detection in Surveillance Video Based on Time Series Model.- Computer Vision Based Method for Identifying Grouting Defects of Prefabricated Building Sleeves.- Stability Detection of Building Bearing Structure Based onBim and Computer Vision.- Intelligent Integration of Diversified Retirement Information Based on Feature Weighting.- Recognition Method of Abnormal Behavior in Electric Power Violation Monitoring Video Based on Computer Vision.- Damage Identification Method of Building Structure Based on Computer Vision.- Automatic Focus Fusion Method of Concrete Crack Image Based on Deep Learning.- Teaching Effect Evaluation Method of College Music Course Based on Deep Learning.- Recognition of Running Gait of Track and Field Athletes Based on Convolutional Neural Network.- Research on Action Recognition Method of Traditional National Physical Education Based on Deep Convolution Neural Network.- Personalized Recommendation Method for the Video Teaching Resources of Folk Sports Shehuo Based on Mobile Learning.- Intelligent Monitoring System of Electronic Equipment Based on Wireless Sensor.- Construction Site Inspection System Based on Panoramic Image Cloud Processing Technology.


Ihre Fragen, Wünsche oder Anmerkungen
Vorname*
Nachname*
Ihre E-Mail-Adresse*
Kundennr.
Ihre Nachricht*
Lediglich mit * gekennzeichnete Felder sind Pflichtfelder.
Wenn Sie die im Kontaktformular eingegebenen Daten durch Klick auf den nachfolgenden Button übersenden, erklären Sie sich damit einverstanden, dass wir Ihr Angaben für die Beantwortung Ihrer Anfrage verwenden. Selbstverständlich werden Ihre Daten vertraulich behandelt und nicht an Dritte weitergegeben. Sie können der Verwendung Ihrer Daten jederzeit widersprechen. Das Datenhandling bei Sack Fachmedien erklären wir Ihnen in unserer Datenschutzerklärung.