Książki

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.

Deep Learning Applications, Volume 3

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.