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Trends in Deep Learning Methodologies: Algorithms, Applications, and Systems covers deep learning approaches such as neural networks, deep belief networks, recurrent neural networks, convolutional neural networks, deep auto-encoder, and deep generative networks, which have emerged as powerful computational models. Chapters elaborate on these models which have shown significant success in dealing with massive data for a large number of applications, given their capacity to extract complex hidden features and learn efficient representation in unsupervised settings. Chapters investigate deep learning-based algorithms in a variety of application, including biomedical and health informatics, computer vision, image processing, and more. In recent years, many powerful algorithms have been developed for matching patterns in data and making predictions about future events. The major advantage of deep learning is to process big data analytics for better analysis and self-adaptive algorithms to handle more data. Deep learning methods can deal with multiple levels of representation in which the system learns to abstract higher level representations of raw data. Earlier, it was a common requirement to have a domain expert to develop a specific model for each specific application, however, recent advancements in representation learning algorithms allow researchers across various subject domains to automatically learn the patterns and representation of the given data for the development of specific models. - Provides insights into the theory, algorithms, implementation and the application of deep learning techniques - Covers a wide range of applications of deep learning across smart healthcare and smart engineering - Investigates the development of new models and how they can be exploited to find appropriate solutions

This book examines the context, motivation and current status of biometric systems based on the palmprint, with a specific focus on touchless and less-constrained systems. It covers new technologies in this rapidly evolving field and is one of the first comprehensive books on palmprint recognition systems. It discusses the research literature and the most relevant industrial applications of palmprint biometrics, including the low-cost solutions based on webcams. The steps of biometric recognition are described in detail, including acquisition setups, algorithms, and evaluation procedures. Constraints and limitations of current palmprint recognition systems are analyzed and discussed. The authors also introduce innovative methods for touchless and less-constrained palmprint recognition, with the aim to make palmprint biometrics easier to use in practical, daily-life applications, and overcome the typical constraints and limitations described. Touchless Palmprint Recognition Systems targets professionals and researchers working in biometrics, image processing and three-dimensional reconstruction. Advanced-level students studying biometrics and computer science will also find this material valuable as a secondary text book or reference.