Smart education and smart e-Learning are emerging and rapidly growing areas. They represent the innovative integration of smart systems, technologies and objects, smart environments, smart pedagogy, smart learning and academic analytics, various branches of computer science and computer engineering, and state-of-the-art smart educational software and/or hardware systems.
It contains a total of 48 peer-reviewed book chapters that are grouped into several parts: Part 1 – Smart Pedagogy, Part 2 – Smart e-Learning, Part 3 – Systems and Technologies for Smart Education, Part 4 – Smart Teaching, and Part 5 – Smart Education: National Initiatives and Approaches.
The book offers a valuable source of research data, information on best practices, and case studies for educators, researchers, Ph.D. students, administrators, and practitioners—and all those who are interested in innovative areas of smart education and smart e-Learning.
Image data hiding scheme based on vector quantization and image graph coloring
The copyright protection system for Android platform
Reversible data hiding
ICA-based image and video watermarking
Content-based invariant image watermarking
Single bitmap block truncation coding of color images using cat swarm optimization
Genetic-based wavelet packet watermarking for copyright protection
Lossless text steganography in compression coding
Fast and low-distortion capacity acoustic synchronized acoustic-to-acoustic steganography scheme
Video watermarking with shot detection.
In this book at hand, we examine recent Recommendation Services. Recommendation services appear in the mobile environment, medicine/biology, tourism, education, and so on. The book includes ten chapters, which present various recently developed recommendation services.
This research book is directed to professors, researchers, application engineers and students of all disciplines who are interested in learning more about recommendation services, advancing the corresponding state of the art and developing innovative recommendation services.
In this book at hand, we examine recent Advances in Recommender Systems. Recommender systems are crucial in multimedia services, as they aim at protecting the service users from information overload. The book includes nine chapters, which present various recent research results in recommender systems.
This research book is directed to professors, researchers, application engineers and students of all disciplines who are interested in learning more about recommender systems, advancing the corresponding state of the art and developing recommender systems for specific applications.
The field of feature selection is evolving constantly, providing numerous new algorithms, new solutions, and new applications. Some of the advances presented focus on theoretical approaches, introducing novel propositions highlighting and discussing properties of objects, and analysing the intricacies of processes and bounds on computational complexity, while others are dedicated to the specific requirements of application domains or the particularities of tasks waiting to be solved or improved.
Divided into four parts – nature and representation of data; ranking and exploration of features; image, shape, motion, and audio detection and recognition; decision support systems, it is of great interest to a large section of researchers including students, professors and practitioners.
This volume highlights new trends and challenges in agent, new digital and knowledge economy research and includes 28 papers classified in the following specific topics: business process management, agent-based modeling and simulation, anthropic-oriented computing, learning paradigms, business informatics and gaming, digital economy, and advances in networked virtual enterprises. Published papers were selected for presentation at the 10th KES Conference on Agent and Multi-Agent Systems: Technologies and Applications (KES-AMSTA 2016) held in Puerto de la Cruz, Tenerife, Spain.
Presented results would be of theoretical and practical value to researchers and industrial practitioners working in the fields of artificial intelligence, collective computational intelligence, innovative business models, new digital and knowledge economy and, in particular, agent and multi-agent systems, technologies, tools and applications.
Coming as the third volume under the general title MACHINE LEARNING PARADIGMS, the book includes an editorial note (Chapter 1) and an additional 12 chapters, and is divided into five parts: (1) Data Analytics in the Medical, Biological and Signal Sciences, (2) Data Analytics in Social Studies and Social Interactions, (3) Data Analytics in Traffic, Computer and Power Networks, (4) Data Analytics for Digital Forensics, and (5) Theoretical Advances and Tools for Data Analytics.
This research book is intended for both experts/researchers in the field of data analytics, and readers working in the fields of artificial and computational intelligence as well as computer science in general who wish to learn more about the field of data analytics and its applications. An extensive list of bibliographic references at the end of each chapter guides readers to probe further into the application areas of interest to them.