Neural Information Processing [E-Book] : 29th International Conference, ICONIP 2022, Virtual Event, November 22-26, 2022, Proceedings, Part VII / edited by Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatowt.
The four-volume set CCIS 1791, 1792, 1793 and 1794 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22-26, 2022. The 213 papers presented in the proceedings set were carefully reviewed and selec...
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| Personal Name(s): | Agarwal, Sonali, editor |
| Ekbal, Asif, editor / Jatowt, Adam, editor / Ozawa, Seiichi, editor / Tanveer, Mohammad, editor | |
| Edition: |
1st edition 2023. |
| Imprint: |
Singapore :
Springer,
2023
|
| Physical Description: |
XXXV, 569 pages 193 illustrations, 169 illustrations in color (online resource) |
| Note: |
englisch |
| ISBN: |
9789819916481 |
| DOI: |
10.1007/978-981-99-1648-1 |
| Series Title: |
Communications in Computer and Information Science ;
1794 |
| Subject (LOC): |
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| 245 | 1 | 0 | |a Neural Information Processing |h [E-Book] : |b 29th International Conference, ICONIP 2022, Virtual Event, November 22-26, 2022, Proceedings, Part VII / |c edited by Mohammad Tanveer, Sonali Agarwal, Seiichi Ozawa, Asif Ekbal, Adam Jatowt. |
| 250 | |a 1st edition 2023. | ||
| 264 | 1 | |a Singapore : |b Springer, |c 2023 |e (Springer LINK) |f SpringerComputerScience20230712 | |
| 300 | |a XXXV, 569 pages 193 illustrations, 169 illustrations in color (online resource) | ||
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| 505 | 0 | |a Applications II -- An Interpretable Multi-target Regression Method for Hierarchical Load Forecasting -- Automating Patient-Level Lung Cancer Diagnosis in Different Data Regimes -- Multi-level 3DCNN with Min-Max Ranking Loss for Weakly-supervised Video Anomaly Detection -- Automatically Generating Storylines from Microblogging Platforms -- Improving Document Image Understanding with Reinforcement Finetuning -- MSK-Net: Multi-source Knowledge Base Enhanced Networks for Script Event Prediction -- Vision Transformer-based Federated Learning for COVID-19 Detection using Chest X-ray -- HYCEDIS: HYbrid Confidence Engine for Deep Document Intelligence System -- Multi-level Network Based on Text Attention and Pose-guided for Person Re-ID -- Sketch Image Style Transfer based on Sketch Density Controlling -- VAE-AD: Unsupervised Variational Autoencoder for Anomaly Detection in Hyperspectral Images -- DSE-Net: Deep Semantic Enhanced Network for Mobile Tongue Image Segmentation -- Efficient-Nets and their Fuzzy Ensemble: An Approach for Skin Cancer Classification -- A Framework for Software Defect Prediction Using Optimal Hyper-parameters of Deep Neural Network -- Improved Feature Fusion by Branched 1-D CNN for Speech Emotion Recognition -- A Multi-modal Graph Convolutional Network for Predicting Human Breast Cancer Prognosis -- Anomaly detection in surveillance videos using transformer based attention model -- Change Detection in Hyperspectral Images using Deep Feature Extraction and Active Learning -- TeethU2Net: A Deep Learning-Based Approach for Tooth Saliency Detection in Dental Panoramic Radiographs -- The EsnTorch Library: Efficient Implementation of Transformer-Based Echo State Networks -- Wine Characterisation with Spectral Information and Predictive Artificial Intelligence -- MRCE: A Multi-Representation Collaborative Enhancement Model for Aspect-Opinion Pair Extraction -- Diverse and High-Quality Data Augmentation Using GPT for Named Entity Recognition -- Transformer-based Original Content Recovery from Obfuscated PowerShell Scripts -- A Generic Enhancer for Backdoor Attacks on Deep Neural Networks -- Attention Based Twin Convolutional Neural Network with Inception Blocks for Plant Disease Detection using Wavelet Transform -- A Medical Image Steganography Scheme with High Embedding Capacity to Solve Falling-Off Boundary Problem using Pixel Value Difference Method -- Deep Ensemble Architecture: A Region Mapping for Chest Abnormalities -- Privacy-Preserving Federated Learning for Pneumonia Diagnosis -- Towards Automated Segmentation of Human Abdominal Aorta and Its Branches Using a Hybrid Feature Extraction Module with LSTM -- p-LSTM: An explainable LSTM architecture for Glucose Level Prediction -- A Wide Ensemble of Interpretable TSK Fuzzy Classifiers with Application to Smartphone Sensor-based Human Activity Recognition -- Prediction of the Facial Growth Direction: Regression Perspective -- A Methodology for the Prediction of Drug Target Interaction using CDK Descriptors -- PSSM2Vec: A Compact Alignment-Free Embedding Approach for Coronavirus Spike Sequence Classification -- An optimized hybrid solution for IoT based lifestyle disease classification using stress data -- A Deep Concatenated Convolutional Neural Network-based Method to Classify Autism -- Deep Learning-based Human Action Recognition Framework to Assess Children on the Risk of Autism or Developmental Delays -- Dynamic Convolutional Network for Generalizable Face Anti-Spoofing -- Challenges Of Facial Micro-expression Detection and Recognition : A Survey -- Biometric Iris Identifier Recognition With Privacy Preserving Phenomenon: A Federated Learning Approach -- Traffic Flow Forecasting using Attention Enabled Bi-LSTM and GRU Hybrid Model -- Commissioning Random Matrix Theory and Synthetic Minority Oversampling Technique for Power System Faults Detection and Classification -- Deep reinforcement learning with comprehensive reward for stock trading -- Deep Learning based automobile identification application -- Automatic Firearm Detection in Images and Videos Using YOLO-based Model. | |
| 520 | |a The four-volume set CCIS 1791, 1792, 1793 and 1794 constitutes the refereed proceedings of the 29th International Conference on Neural Information Processing, ICONIP 2022, held as a virtual event, November 22-26, 2022. The 213 papers presented in the proceedings set were carefully reviewed and selected from 810 submissions. They were organized in topical sections as follows: Theory and Algorithms; Cognitive Neurosciences; Human Centered Computing; and Applications. The ICONIP conference aims to provide a leading international forum for researchers, scientists, and industry professionals who are working in neuroscience, neural networks, deep learning, and related fields to share their new ideas, progress, and achievements. | ||
| 650 | 0 | |a Artificial intelligence. | |
| 650 | 0 | |a Computer networks . | |
| 650 | 0 | |a Computer vision. | |
| 650 | 0 | |a Education-Data processing. | |
| 650 | 0 | |a Pattern recognition systems. | |
| 700 | 1 | |a Agarwal, Sonali, |e editor | |
| 700 | 1 | |a Ekbal, Asif, |e editor | |
| 700 | 1 | |a Jatowt, Adam, |e editor | |
| 700 | 1 | |a Ozawa, Seiichi, |e editor | |
| 700 | 1 | |a Tanveer, Mohammad, |e editor | |
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