Intelligent Information Processing XII
13th IFIP TC 12 International Conference, IIP 2024, Shenzhen, China, May 3–6, 2024, Proceedings, Part II
Series: IFIP Advances in Information and Communication Technology; 704;
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Product details:
- Publisher Springer Nature Switzerland
- Date of Publication 19 April 2025
- Number of Volumes 1 pieces, Book
- ISBN 9783031579219
- Binding Paperback
- See also 9783031579189
- No. of pages207 pages
- Size 235x155 mm
- Language English
- Illustrations XXX, 207 p. 73 illus., 65 illus. in color. Illustrations, black & white 646
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Long description:
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.
Table of Contents:
.- 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.
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