Jin | Artificial Intelligence and Machine Learning | Buch | 978-981-967190-8 | www.sack.de

Buch, Englisch, 218 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 365 g

Reihe: Communications in Computer and Information Science

Jin

Artificial Intelligence and Machine Learning

Second International Artificial Intelligence Conference, IAIC 2024, Jinyun, China, November 8-10, 2024, Revised Selected Papers, Part II
Erscheinungsjahr 2025
ISBN: 978-981-967190-8
Verlag: Springer

Second International Artificial Intelligence Conference, IAIC 2024, Jinyun, China, November 8-10, 2024, Revised Selected Papers, Part II

Buch, Englisch, 218 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 365 g

Reihe: Communications in Computer and Information Science

ISBN: 978-981-967190-8
Verlag: Springer


This CCIS volume constitutes the refereed proceedings of Second International Artificial Intelligence Conference on Artificial Intelligence and Machine Learning, IAIC 2024, held in Jinyun, China, November 2024.

The 38 full papers presented were carefully reviewed and selected from 100 submissions.They were organized in following topical sections as follows:

Part I : Artificial Intelligence in Real-World Applications.
Part II : Artificial Intelligence in Network and Security systems.

Jin Artificial Intelligence and Machine Learning jetzt bestellen!

Zielgruppe


Research


Autoren/Hrsg.


Weitere Infos & Material


.- Artificial Intelligence in Network and Security Systems.
.- Noise-Resilient Compressive Sensing via Orthogonality Constraints.
.- Joint Sensing of Computing Power and Network for Smart Grid: A Deep Matrix Completion Approach.
.- Rethinking RSSI-based Key Extraction for UAVs and Ground Stations.
.- Campus Card Security Depository System based on State Secret Algorithm.
.- Network Traffic Classification via Deep Hashing Network.
.- A Trust Management Integrated Bayesian Network Prediction in Vehicular Networks.
.- Research and Development of Distribution Line Defects and Violations Detection Method and Edge 
Computing System Based on Improved PPYOLOe.
.- A Joint Optimization Algorithm for Computing Resource Allocation, UAV Trajectory, and Task 
Offloading in Remote Regional VANETs.
.- LSTMR: Spatio-Temporal 3D Multiscale ResNet Model for Cellular Network Traffic Prediction.
.- An Efficient Name Lookup Approach Based on Character Indexing.
.- A Multi-Channel Target Direction Speech Separation Model Based on Efficient Convolutional Networks.
.- Secure Cloud-Edge Collaborative Task Scheduling Framework Across Data Centers.
.- Research on Security Sharing Mechanism of Cyber Threat Intelligence Based on Consortium Blockchain.
.- Towards Automated Decoding of Vehicle CAN Data using Deep Learning.
.- Transfer Learning-driven Surrogate-assisted Differential Evolution Algorithm with User Generated 
Content.



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.