Intelligent Information Processing XII

13th IFIP TC 12 International Conference, IIP 2024, Shenzhen, China, May 3-6, 2024, Proceedings, Part II
2024
ISBN: 978-3-031-57918-9
Verlag: Springer Nature Switzerland

Buch, Englisch, 207 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 530 g

Reihe: IFIP Advances in Information and Communication Technology

The two-volume set IFIP AICT 703 and 704 constitutes the refereed conference proceedings of the 13th IFIP TC 12 International Conference on Intelligent Information Processing XII, IIP 2024, held in Shenzhen, China, during May 3–6, 2024. 
The 49 full papers and 5 short papers presented in these proceedings were carefully reviewed and selected from 58 submissions. The papers are organized in the following topical sections: 
Volume I: Machine Learning; Natural Language Processing; Neural and Evolutionary Computing; Recommendation and Social Computing; Business Intelligence and Risk Control; and Pattern Recognition.
Volume II: Image Understanding.

Zielgruppe

Research

Autoren/Hrsg.

Shi, Zhongzhi

Yang, Shengxiang

Torresen, Jim

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

.- Early Anomaly Detection in Hydraulic Pumps Based on LSTM Traffic Prediction Model.

.- Dynamic Parameter Estimation for Mixtures of Plackett-Luce Models.

.- Recognition of Signal Modulation Pattern Based on Multi-Task Self-Supervised Learning.

.- Dependency-Type Weighted Graph Convolutional Network on End-to-End Aspect-Based Sentiment Analysis.

.- Utilizing Attention for Continuous Human Action Recognition Based on Multimodal Fusion of Visual and Inertial.

.- HARFMR: Human Activity Recognition with Feature Masking and Reconstruction.

.- CAPPIMU: A Composite Activities Dataset for Human Activity Recognition Utilizing Plantar Pressure and IMU Sensors.

.- Open-Set Sensor Human Activity Recognition Based on Reciprocal Time Series.

.- Image Understanding.

.- A Concept-Based Local Interpretable Model-agnostic Explanation Approach for Deep Neural Networks in Image Classification.

.- A Deep Neural Network-based Segmentation Method for Multimodal Brain Tumor Images.

.- Graph Convolutional Networks for Predicting Mechanical Characteristics of 3D Lattice Structures.

.- 3D Object Reconstruction with Deep Learning.

.- Adaptive Prototype Triplet Loss for Cross-Resolution Face Recognition.

.- Hand Gesture Recognition Using a Multi-modal Deep Neural Network.