There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.

This book brings a high level of fluidity to analytics and addresses recent trends, innovative ideas, challenges and cognitive computing solutions in big data and the Internet of Things (IoT). It explores domain knowledge, data science reasoning and cognitive methods in the context of the IoT, extending current data science approaches by incorporating insights from experts as well as a notion of artificial intelligence, and performing inferences on the knowledge The book provides a comprehensive overview of the constituent paradigms underlying cognitive computing methods, which illustrate the increased focus on big data in IoT problems as they evolve. It includes novel, in-depth fundamental research contributions from a methodological/application in data science accomplishing sustainable solution for the future perspective. Mainly focusing on the design of the best cognitive embedded data science technologies to process and analyze the large amount of data collected through the IoT, and aid better decision making, the book discusses adapting decision-making approaches under cognitive computing paradigms to demonstrate how the proposed procedures as well as big data and IoT problems can be handled in practice. This book is a valuable resource for scientists, professionals, researchers, and academicians dealing with the new challenges and advances in the specific areas of cognitive computing and data science approaches.

There are a number of books on computational intelligence (CI), but they tend to cover a broad range of CI paradigms and algorithms rather than provide an in-depth exploration in learning and adaptive mechanisms. This book sets its focus on CI based architectures, modeling, case studies and applications in big data analytics, and business intelligence. The intended audiences of this book are scientists, professionals, researchers, and academicians who deal with the new challenges and advances in the specific areas mentioned above. Designers and developers of applications in these areas can learn from other experts and colleagues through this book.

Research Paper from the year 2007 in the subject Computer Science - Internet, New Technologies, grade: PG, VIT University (Net Research Labs), 26 entries in the bibliography, language: English, comment: The major challenge faced by designers of ad hoc network is the deployment of end-to-end quality-ofservice support mechanisms for streaming media services over an adhoc group network. Group-oriented services over large ad-hoc networks has a big impact on the needs of streaming services communication in terms of mobility, quality of service (QoS) support and multicasting. In Ad hoc networks, where such features are not embedded with its architecture, it is necessary to develop QoS multica, abstract: One of the major challenges faced by MANET researchers is the deployment of end-to-end quality-of-service support mechanisms for streaming media services over a group of MANET users. Group-oriented services over large, dynamically changing MANET networks has a big impact on the needs of streaming services communication in terms of mobility, quality of service (QoS) support and multicasting. In MANET networks, where such features are not embedded with its architecture, it is necessary to develop QoS multicasting strategies. The research work focuses on the basic building blocks of an mobile ad hoc group communication scheme, which achieves multicasting optimal QoS efficiency OptiQ by tracking resource availability in a node's neighborhood based on resource reservations, which announces the required QoS before each session initiation. The primary quality of service (QoS) issues such as required bandwidth, message delay, traffic type and hop count per route improves the efficiency of streaming services over ad-hoc network. Streaming services support voice, data and video traffic by assessing and adjusting for various levels of QoS. The performance analysis is performed on functional prototype of OptiQ in mobile / wireless ad-hoc network with emphasis on service satisf