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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.
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R398
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