Artificial Intelligence and Mobile Services - AIMS 2020 (eBook, PDF)

9th International Conference, Held as Part of the Services Conference Federation, SCF 2020, Honolulu, HI, USA, September 18-20, 2020, Proceedings
Redaktion: Xu, Ruifeng; Zhang, Liang-Jie; Bai, Yongsheng; Tian, Ling; Zhong, Wei; de, Wang

• Format: PDF

Produktbeschreibung

This book constitutes the proceedings of the 9th International Conference on Artificial Intelligence and Mobile Services, AIMS 2020, held as part of SCF 2020, during September 18-20, 2020. The conference was planned to take place in Honolulu, HI, USA and was changed to a virtual format due to the COVID-19 pandemic.

The 11 full and 2 short papers presented were carefully reviewed and selected from 42 submissions. They cover topics in AI Modeling, AI Analysis, AI and Mobile Applications, AI Architecture, AI Management, AI Engineering, Mobile backend as a service (MBaaS), User experience of AI and mobile services.

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Produktdetails

• Verlag: Springer Nature Switzerland
• Seitenzahl: 177
• Erscheinungstermin: 17. September 2020
• Englisch
• ISBN-13: 9783030596057
• Artikelnr.: 62300412

Inhaltsangabe

Research Track.- Infant Sound Classification on Multi-stage CNNs with Hybrid Features and Prior Knowledge.- Building Vector Representations for Candidates and Projects in a CV Recommender System.- Candidate Classification and Skill Recommendation in a CV Recommender System.- A Novel Method to Estimate Students' Knowledge Assessment.- Answer Selection Based on Mixed Embedding and Composite Features.- A Neural Framework for Chinese Medical Named Entity Recognition.- An Annotated Chinese Corpus for Rumor Veracity Detection.- Attention-based Asymmetric Fusion Network for Saliency Prediction in 3D Images.- Review Spam Detection Based on Multidimensional.- Application Track.- Rehabilitation XAI to Predict Outcome with Optimal Therapies.- A Mobile Application using Deep Learning to Automatically Classify Adult-only Images.- Short Paper Track.- OSAF_e: One-Stage Anchor Free Object Detection Method Considering Effective Area.- Attention-based Interaction Trajectory Prediction.