Special Issue on Deep Learning

Deep learning is the application of artificial neural networks (ANNs) to learning tasks that contain more than one hidden layer. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, partially supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks and recurrent neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation and bioinformatics where they produced results comparable to and in some cases superior to human experts.

In this special issue, we intend to invite front-line researchers and authors to submit original research and review articles on deep learning. Potential topics include, but are not limited to:

• Convolutional neural networks 
• Artificial neural networks
• Audio, speech, and language processing
• Speech recognition and image recognition
• Acoustic modeling and language modeling
• Machine learning and machine intelligence
• Computer vision
• Augmented reality

Authors should read over the journal’s For Authors carefully before submission. Prospective authors should submit an electronic copy of their complete manuscript through the journal’s Paper Submission System.

Please kindly notice that the “Special Issue” under your manuscript title is supposed to be specified and the research field “Special Issue – Deep Learning” should be chosen during your submission.

According to the following timetable:

JSEA Editorial Office