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This textbook provides a compact but comprehensive treatment that provides analytical and design steps to recurrent neural networks from scratch. It provides a treatment of the general recurrent neural networks with principled methods for training that render the (generalized) backpropagation through time (BPTT). This author focuses on the basics and nuances of recurrent neural networks, providing technical and principled treatment of the subject, with a view toward using coding and deep learning computational frameworks, e.g., Python and Tensorflow-Keras. Recurrent neural networks are treated holistically from simple to gated architectures, adopting the technical machinery of adaptive non-convex optimization with dynamic constraints to leverage its systematic power in organizing the learning and training processes. This permits the flow of concepts and techniques that provide grounded support for design and training choices. The author's approach enables strategic co-trainingof output layers, using supervised learning, and hidden layers, using unsupervised learning, to generate more efficient internal representations and accuracy performance. As a result, readers will be enabled to create designs tailoring proficient procedures for recurrent neural networks in their targeted applications.

The integration of technology into the aviation system planning has allowed for more stable, yet increasingly complex, models that enable better analysis techniques and new approaches to decision-making. These modern advances ensure higher productivity in addressing various planning problems. Socio-Technical Decision Support in Air Navigation Systems: Emerging Research and Opportunities is a critical scholarly resource that contains a systematic analysis of formalized factors affecting socio-technical systems operators and how these factors influence decision-making process of professional and non-professional activities in air navigation systems. Featuring coverage on a broad range of topics, such as dimensional modeling, applications of decision support systems, and semantic analysis, this book is geared towards academicians, future pilots, aviation dispatchers, engineers, managers, and students.

This book constitutes the refereed proceedings of the First International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2012, held in Cairo, Egypt, in December 2012. The 58 full papers presented were carefully reviewed and selected from 99 intial submissions. The papers are organized in topical sections on rough sets and applications, machine learning in pattern recognition and image processing, machine learning in multimedia computing, bioinformatics and cheminformatics, data classification and clustering, cloud computing and recommender systems.