Long short-term memory (LSTM) has transformed both machine learning and
neurocomputing fields. According to several online sources, this model has improved …

Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN) are one of the most
powerful dynamic classifiers publicly known. The network itself and the related learning …

Text recognition is a long-standing research problem for document digitalization. Existing
approaches are usually built based on CNN for image understanding and RNN for char …

With the wide deployments of heterogeneous networks, huge amounts of data with
characteristics of high volume, high variety, high velocity, and high veracity are generated …

Given the ubiquity of handwritten documents in human transactions, Optical Character
Recognition (OCR) of documents have invaluable practical worth. Optical character …

“Any AI smart enough to pass a Turing test is smart enough to know to fail it.”–*** Ian
McDonald Neural networks were developed to simulate the human nervous system for …

Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and
natural language processing via recurrent neural networks (RNNs). However, success …

Modern communication systems and networks, eg, Internet of Things (IoT) and cellular
networks, generate a massive and heterogeneous amount of traffic data. In such networks …

Recurrent neural networks (RNNs) are capable of learning features and long term
dependencies from sequential and time-series data. The RNNs have a stack of non-linear …

Advances in biological and medical technologies have been providing us explosive
volumes of biological and physiological data, such as medical images …