Technology enhanced learning takes place in many different forms and contexts, including formal and informal settings, individual and collaborative learning, learning in the classroom, at home, at work, and outdoor in real life situations, as well as desktop-based learning and learning by using mobile devices. Environments range from desktop-based learning systems such as learning management systems, which present learners with learning material and activities, to mobile, pervasive, and ubiquitous learning environments which are used in real life settings and enable learners to learn from real learning objects. In each of these forms and contexts, adaptive and intelligent support has potential to contribute in making such learning environments more personalized, user-friendly, and effective in supporting learners in learning. Intelligent and Adaptive Learning Systems: Technology Enhanced Support for Learners and Teachers focuses on how intelligent support and adaptive features can be integrated in currently used learning systems and discusses how intelligent and adaptive learning systems can be improved in order to provide a better learning environment for learners. This book provides academics as well as professional practitioners innovative research work for enhancing learning environments with adaptively and intelligent support in different contexts and settings, ranging from provision of courses and assessment in formal desktop-based learning systems to learning environments that support collaborative, informal, ubiquitous learning.

With the ever-increasing usage of distance learning programs in academia, the need for well-designed automated systems has become vital to the success of open and distance education. Practical solutions should be discovered and disseminated to meet the software needs of instructors, academic researchers, and software engineers.System and Technology Advancements in Distance Learning meets this need, outlining computational methods, algorithms, implemented prototype systems, and applications of open and distance learning. It is targeted toward academic researchers and engineers who work with distance learning programs and software systems, as well as general participants of distance education.

In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book: Provides broad background on various image thresholding and segmentation techniques Discusses information on various assessment metrics and the confusion matrix Proposes integration of the thresholding technique with the bio-inspired algorithms Explores case studies including MRI, CT, dermoscopy, and ultrasound images Includes separate chapters on machine learning and deep learning for medical image processing

Designing Distributed Learning Environments with Intelligent Software Agents reports on the most recent, important advances in agent technologies for distributed learning. Several chapters will be devoted to various aspects of intelligent software agents in distributed learning, including the methodological and technical issues on where and how intelligent agents can contribute to meeting distributed learning needs today and tomorrow. It will benefit the Al (artificial intelligence) community and educational community in their research and development. It will propose some new and interesting research issues about developing distributed learning environments in the semantic Web age. In addition, the ideas presented in the book may also be applicable to other domains such as agent-supported Web services, distributed business process and resource integration, computer-supported collaborative work (CSCW) and e-commerce.