Deep learning applications. Volume 3 / M. Arif Wani, Bhiksha Raj, Feng Luo, Dejing Dou, editors.
International Conference on Machine Learning and Applications (19th : 2020 : Miami, FL); Wani, M. A. (M. Arif), editor.; Raj, Bhiksha, editor.; Luo, Feng, editor.; Dou, Dejing, editor.; IEEE International Conference on Machine Learning and Applications (19th : 2020)
2022
Q325.5 .I58 19th 2020eb
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Title Deep learning applications. Volume 3 / M. Arif Wani, Bhiksha Raj, Feng Luo, Dejing Dou, editors.
Meeting Name International Conference on Machine Learning and Applications (19th : 2020 : Miami, FL)
ISBN 9789811633577 (electronic bk.)
9811633576 (electronic bk.)
9789811633560 (print)
9811633568
DOI https://doi.org/10.1007/978-981-16-3357-7
Published Singapore : Springer, [2022]
Language English
Description 1 online resource (xii, 322 pages : illustrations (some color))
Other Standard Identifiers 10.1007/978-981-16-3357-7 doi
Call Number Q325.5 .I58 19th 2020eb
Dewey Decimal Classification 006.3/1
Summary This book presents a compilation of extended version of selected papers from the 19th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2020) and focuses on deep learning networks in applications such as pneumonia detection in chest X-ray images, object detection and classification, RGB and depth image fusion, NLP tasks, dimensionality estimation, time series forecasting, building electric power grid for controllable energy resources, guiding charities in maximizing donations, and robotic control in industrial environments. Novel ways of using convolutional neural networks, recurrent neural network, autoencoder, deep evidential active learning, deep rapid class augmentation techniques, BERT models, multi-task learning networks, model compression and acceleration techniques, and conditional Feature Augmented and Transformed GAN (cFAT-GAN) for the above applications are covered in this book. Readers will find insights to help them realize novel ways of using deep learning architectures and algorithms in real-world applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and innovative product developers.
Note Includes author index.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed November 18, 2021).
Added Author Wani, M. A. (M. Arif), editor.
Raj, Bhiksha, editor.
Luo, Feng, editor.
Dou, Dejing, editor.
Added Meeting Name IEEE International Conference on Machine Learning and Applications (19th : 2020)
Series Advances in intelligent systems and computing ; 1395. 2194-5365
Available in Other Form Deep learning applications. Volume 3.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Deep Rapid Class Augmentation; a New Progressive Learning Approach that Eliminates the Issue of Catastrophic Forgetting
A Comprehensive Analysis of Subword Contextual Embeddings for Languages with Rich Morphology
RGB and Depth Image Fusion for Object Detection using Deep Learning
Dimension Estimation Using Autoencoders with Applications to Financial Market Analysis
A New Clustering-Based Technique for the Acceleration of Deep Convolutional Networks
Deep Learning based Time Series Forecasting
DEAL: Deep Evidential Active Learning for Image Classification
LB-CNN: Convolutional Neural Network with Latent Binarization for Large Scale Multi[1]class Classification
Efficient Deployment of Deep Learning Models on Autonomous Robots in the ROS Environment
Building Power Grid 2.0: Deep Learning and Federated Computations for Energy Decarbonization and Edge Resilience
Improving the Donor Journey with Convolutional and Recurrent Neural Networks.

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