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Recently, there has been a rapid increase in interest regarding social network analysis in the data mining community. Cognitive radios are expected to play a major role in meeting this exploding traffic demand on social networks due to their ability to sense the environment, analyze outdoor parameters, and then make decisions for dynamic time, frequency, space, resource allocation, and management to improve the utilization of mining the social data. Cognitive Social Mining Applications in Data Analytics and Forensics is an essential reference source that reviews cognitive radio concepts and examines their applications to social mining using a machine learning approach so that an adaptive and intelligent mining is achieved. Featuring research on topics such as data mining, real-time ubiquitous social mining services, and cognitive computing, this book is ideally designed for social network analysts, researchers, academicians, and industry professionals.
Society is now completely driven by data with many industries relying on data to conduct business or basic functions within the organization. With the efficiencies that big data bring to all institutions, data is continuously being collected and analyzed. However, data sets may be too complex for traditional data-processing, and therefore, different strategies must evolve to solve the issue. The field of big data works as a valuable tool for many different industries. The Research Anthology on Big Data Analytics, Architectures, and Applications is a complete reference source on big data analytics that offers the latest, innovative architectures and frameworks and explores a variety of applications within various industries. Offering an international perspective, the applications discussed within this anthology feature global representation. Covering topics such as advertising curricula, driven supply chain, and smart cities, this research anthology is ideal for data scientists, data analysts, computer engineers, software engineers, technologists, government officials, managers, CEOs, professors, graduate students, researchers, and academicians.
Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science. Big Data Analytics for Sustainable Computing is a collection of innovative research that focuses on new computing and system development issues in emerging sustainable applications. Featuring coverage on a wide range of topics such as data filtering, knowledge engineering, and cognitive analytics, this publication is ideally designed for data scientists, IT specialists, computer science practitioners, computer engineers, academicians, professionals, and students seeking current research on emerging analytical techniques and data processing software.
As social robots and the artificial intelligence (AI) that powers them become more advanced, they will likely take on more social and work roles. There is a variety of ways social robots can be engaged in human life, and they can leave an impact in terms of ease of use, productivity, and human support. The interactivity and receptivity of social robots can encourage humans to form social relationships with them. But now robots are intended to perform socially intelligent and interactive services like reception, guidance, emotional companionship, and more, which makes social human-robot interaction essential to help improve aspects of quality of life as well as to improve the efficiency of human care services. AI-Enabled Social Robotics in Human Care Services addresses recent advances in the latest technologies, new research results, and developments in the area of social robotics and AI and the latest developments in the field and future directions that can be beneficial to human society and human care services. Covering topics such as agriculture waste management systems, elder care, and facial emotion recognition, this premier reference source is an essential resource for AI professionals, computer scientists, robotics engineers, human care professionals, students and educators of higher education, librarians, researchers, and academicians.
With the immense amount of data that is now available online, security concerns have been an issue from the start, and have grown as new technologies are increasingly integrated in data collection, storage, and transmission. Online cyber threats, cyber terrorism, hacking, and other cybercrimes have begun to take advantage of this information that can be easily accessed if not properly handled. New privacy and security measures have been developed to address this cause for concern and have become an essential area of research within the past few years and into the foreseeable future. The ways in which data is secured and privatized should be discussed in terms of the technologies being used, the methods and models for security that have been developed, and the ways in which risks can be detected, analyzed, and mitigated. The Research Anthology on Privatizing and Securing Data reveals the latest tools and technologies for privatizing and securing data across different technologies and industries. It takes a deeper dive into both risk detection and mitigation, including an analysis of cybercrimes and cyber threats, along with a sharper focus on the technologies and methods being actively implemented and utilized to secure data online. Highlighted topics include information governance and privacy, cybersecurity, data protection, challenges in big data, security threats, and more. This book is essential for data analysts, cybersecurity professionals, data scientists, security analysts, IT specialists, practitioners, researchers, academicians, and students interested in the latest trends and technologies for privatizing and securing data.
This book highlights the need for an efficient Handover Decision (HD) mechanism to perform switches from one network to another and to provide unified and continuous mobile services that include seamless connectivity and ubiquitous service access. The author shows how the HD involves efficiently combining handover initiation and network selection process. The author describes how the network selection decision is a challenging task that is a central component to making HD for any mobile user in a heterogeneous environment that involves a number of static and dynamic parameters. The author also discusses prevailing technical challenges like Dynamic Spectrum Allocation (DSA) methods, spectrum sensing, cooperative communications, cognitive network architecture protocol design, cognitive network security challenges and dynamic adaptation algorithms for cognitive system and the evolving behavior of systems in general. The book allows the reader to optimize the sensing time for maximizing the spectrum utilization, improve the lifetime of the cognitive radio network (CRN) using active scan spectrum sensing techniques, analyze energy efficiency of CRN, find a secondary user spectrum allocation, perform dynamic handovers, and use efficient data communication in the cognitive networks. Identifies energy efficient spectrum sensing techniques for Cooperative Cognitive Radio Networks (CRN); Shows how to maximize the energy capacity by minimizing the outage probability; Features end-of-chapter summaries, performance measures, and case studies.
The internet of things (IoT) has emerged to address the need for connectivity and seamless integration with other devices as well as big data platforms for analytics. However, there are challenges that IoT-based applications face including design and implementation issues; connectivity problems; data gathering, storing, and analyzing in cloud-based environments; and IoT security and privacy issues. Emerging Trends in IoT and Integration with Data Science, Cloud Computing, and Big Data Analytics is a critical reference source that provides theoretical frameworks and research findings on IoT and big data integration. Highlighting topics that include wearable sensors, machine learning, machine intelligence, and mobile computing, this book serves professionals who want to improve their understanding of the strategic role of trust at different levels of the information and knowledge society. It is therefore of most value to data scientists, computer scientists, data analysts, IT specialists, academicians, professionals, researchers, and students working in the field of information and knowledge management in various disciplines that include but are not limited to information and communication sciences, administrative sciences and management, education, sociology, computer science, etc. Moreover, the book provides insights and supports executives concerned with the management of expertise, knowledge, information, and organizational development in different types of work communities and environments.
This book discusses Internet of Things (IoT) as it relates to enterprise applications, systems, and infrastructures. The authors discuss IoT and how it’s disrupting industries such as enterprise manufacturing, enterprise transportation, enterprise smart market, enterprise utilities, and enterprise healthcare. They cover how IoT in the enterprise will have a major impact on the lives of consumers and professionals around the world and how it will change the way we think about professional and consumer networks. The book's topics include IoT enterprise system architecture, IoT enabling enterprise technologies, and IoT enterprise services and applications. Examples include enterprise on demand, market impacts, and implications on smart technologies, big data enterprise management, and future enterprise Internet design for various IoT use cases, such as share markets, healthcare, smart cities, smart environments, smart communications and smart homes.
Our world is increasingly driven by sophisticated networks of advanced computing technology, and the basic operation of everyday society is becoming increasingly vulnerable to these networks’ shortcomings. The implementation and upkeep of a strong network defense is a substantial challenge, beset not only by economic disincentives but also by an inherent logistical bias that grants advantage to attackers. Research Anthology on Combating Denial-of-Service Attacks examines the latest research on the development of intrusion detection systems and best practices for preventing and combatting cyber-attacks intended to disrupt business and user experience. Highlighting a range of topics such as network administration, application-layer protocols, and malware detection, this publication is an ideal reference source for cybersecurity professionals, IT specialists, policymakers, forensic analysts, technology developers, security administrators, academicians, researchers, and students.