This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem-oriented chapters. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.

This book gathers outstanding research papers presented in the International Conference on Computational Intelligence and Emerging Power System (ICCIPS 2021), held on March 9-10, 2021, at Engineering College Ajmer. ICCIPS 2021 is jointly organized by the Department of CSE and Department of EE, Engineering College Ajmer, Rajasthan, India. The topics covered in the book are collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, natural language processing, renewable energy, signal processing, optimization methods for power system, smart grid, micro-grid, energy management, power system, monitoring system, load management, and distributed generation.

This book covers a large set of methods in the field of Artificial Intelligence - Deep Learning applied to real-world problems. The fundamentals of the Deep Learning approach and different types of Deep Neural Networks (DNNs) are first summarized in this book, which offers a comprehensive preamble for further problem-oriented chapters. The most interesting and open problems of machine learning in the framework of Deep Learning are discussed in this book and solutions are proposed. This book illustrates how to implement the zero-shot learning with Deep Neural Network Classifiers, which require a large amount of training data. The lack of annotated training data naturally pushes the researchers to implement low supervision algorithms. Metric learning is a long-term research but in the framework of Deep Learning approaches, it gets freshness and originality. Fine-grained classification with a low inter-class variability is a difficult problem for any classification tasks. This book presents how it is solved, by using different modalities and attention mechanisms in 3D convolutional networks. Researchers focused on Machine Learning, Deep learning, Multimedia and Computer Vision will want to buy this book. Advanced level students studying computer science within these topic areas will also find this book useful.

This book gathers outstanding research papers presented in the International Conference on Computational Intelligence and Emerging Power System (ICCIPS 2021), held on March 9-10, 2021, at Engineering College Ajmer. ICCIPS 2021 is jointly organized by the Department of CSE and Department of EE, Engineering College Ajmer, Rajasthan, India. The topics covered in the book are collective intelligence, soft computing, optimization, cloud computing, machine learning, intelligent software, robotics, data science, data security, big data analytics, natural language processing, renewable energy, signal processing, optimization methods for power system, smart grid, micro-grid, energy management, power system, monitoring system, load management, and distributed generation.

This book provides the reader with the fundamental knowledge in the area of deep learning with application to visual content mining. The authors give a fresh view on Deep learning approaches both from the point of view of image understanding and supervised machine learning. It contains chapters which introduce theoretical and mathematical foundations of neural networks and related optimization methods. Then it discusses some particular very popular architectures used in the domain: convolutional neural networks and recurrent neural networks. Deep Learning is currently at the heart of most cutting edge technologies. It is in the core of the recent advances in Artificial Intelligence. Visual information in Digital form is constantly growing in volume. In such active domains as Computer Vision and Robotics visual information understanding is based on the use of deep learning. Other chapters present applications of deep learning for visual content mining. These include attention mechanisms in deep neural networks and application to digital cultural content mining. An additional application field is also discussed, and illustrates how deep learning can be of very high interest to computer-aided diagnostics of Alzheimer's disease on multimodal imaging. This book targets advanced-level students studying computer science including computer vision, data analytics and multimedia. Researchers and professionals working in computer science, signal and image processing may also be interested in this book.

This book constitutes the revised selected papers from the 13th International Conference on Risks and Security of Internet and Systems, CRiSIS 2018, held in Arcachon, France, in October 2018. The 12 full papers and 6 short papers presented in this volume were carefully reviewed and selected from 34 submissions. They cover diverse research themes that range from classic topics, such as vulnerability analysis and classification; apps security; access control and filtering; cloud security; cyber-insurance and cyber threat intelligence; human-centric security and trust; and risk analysis.