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Soft Computing in Wireless Sensor Networks
This book focuses on the suitable methods to solve optimization problems in wireless network system utilizing digital sensors like Wireless Sensor Network. This kind of system has been emerging as the cornerstone technology for all new smart devices and its direct application in many fields in life.
Empowering India Through Digital Literacy (Vol. 1)
Traditionally education is centered on sources such as schools, teachers and print media. The learners reached the information sources by enrolling with schools, teachers and libraries. Prior to the digital era, information was not accessible by the majority of people, and even those accessed were unable to obtain current information with respect to today's context. The modern society wants to know the information as it happens and when it happens, and the world is moving from an information society to a knowledge society. Thus education is given the highest priority and brainpower is becoming the most valuable asset of an organisation. Advances in digital technology have opened up many avenues of learning. Technology has made information accessible / transmittable from anywhere and by / to all groups of people. The higher education landscape is changing rapidly, challenging academic professionals to think critically about their roles in the field.
Industrial Internet of Things
Industrial Internet of Things: Technologies, Design, and Applications addresses the complete functional framework workflow in IoT technology. It explores basic and high-level concepts, thus serving as a manual for those in the industry while also helping beginners. The book incorporates the working methodology of Industrial IoT works, is based on the latest technologies, and will cover the major challenges, issues, and advances while exploring data-based intelligent and automated systems and their implications to the real world. The book discusses data acquisition, security, learning, intelligent data analysis, and case studies related to Industrial IoT-based applications.
Implementing Data Analytics and Architectures for Next Generation Wireless Communications
Wireless communication is continuously evolving to improve and be a part of our daily communication. This leads to improved quality of services and applications supported by networking technologies. We are now able to use LTE, LTE-Advanced, and other emerging technologies due to the enormous efforts that are made to improve the quality of service in cellular networks. As the future of networking is uncertain, the use of deep learning and big data analytics is a point of focus as it can work in many capacities at a variety of levels for wireless communications. Implementing Data Analytics and Architectures for Next Generation Wireless Communications addresses the existing and emerging theoret...
Forensic Investigations and Risk Management in Mobile and Wireless Communications
Mobile forensics has grown from a relatively obscure tradecraft to a crucial part of many criminal investigations, and is now used daily by examiners and analysts within local, state, and federal law enforcement as well as within the military, US government organizations, and the private “e-Discovery” industry. Developments in forensic research, tools, and processes over the past decade have been very successful and continue to change at a rapid pace. Forensic Investigations and Risk Management in Mobile and Wireless Communications is a collection of innovative research on the methods and applications of analyzing mobile devices and data for collection of information pertaining to the legal evidence related to various security breaches and intrusion detection. While highlighting topics including cybercrime, neural networks, and smartphone security, this book is ideally designed for security analysts, IT professionals, researchers, practitioners, academicians, and students currently investigating the up-and-coming aspects surrounding network security, computer science, and security engineering.
Evolution of Machine Learning and Internet of Things Applications in Biomedical Engineering
This book provides a platform for presenting machine learning (ML)-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes ML techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers around the world. Evolution of Machine Learning and Internet of Things Applications in Biomedical Engineering discusses the Internet of Things (IoT) and ML devices that are deployed for enabling patient health tracking, various emergency issues, and the smart administration of patients. It looks at the problems of cardiac analysis in e-healthcare, explores the employment of smart devices ai...
Social Network Analytics
Social Network Analytics: Computational Research Methods and Techniques focuses on various technical concepts and aspects of social network analysis. The book features the latest developments and findings in this emerging area of research. In addition, it includes a variety of applications from several domains, such as scientific research, and the business and industrial sectors. The technical aspects of analysis are covered in detail, including visualizing and modeling, network theory, mathematical models, the big data analytics of social networks, multidimensional scaling, and more. As analyzing social network data is rapidly gaining interest in the scientific research community because of...
Research Anthology on Securing Mobile Technologies and Applications
Mobile technologies have become a staple in society for their accessibility and diverse range of applications that are continually growing and advancing. Users are increasingly using these devices for activities beyond simple communication including gaming and e-commerce and to access confidential information including banking accounts and medical records. While mobile devices are being so widely used and accepted in daily life, and subsequently housing more and more personal data, it is evident that the security of these devices is paramount. As mobile applications now create easy access to personal information, they can incorporate location tracking services, and data collection can happen...
Handbook of Research on Big Data and the IoT
The increase in connected devices in the internet of things (IoT) is leading to an exponential increase in the data that an organization is required to manage. To successfully utilize IoT in businesses, big data analytics are necessary in order to efficiently sort through the increased data. The combination of big data and IoT can thus enable new monitoring services and powerful processing of sensory data streams. The Handbook of Research on Big Data and the IoT is a pivotal reference source that provides vital research on emerging trends and recent innovative applications of big data and IoT, challenges facing organizations and the implications of these technologies on society, and best practices for their implementation. While highlighting topics such as bootstrapping, data fusion, and graph mining, this publication is ideally designed for IT specialists, managers, policymakers, analysts, software engineers, academicians, and researchers.
Machine Learning and Cognitive Science Applications in Cyber Security
In the past few years, with the evolution of advanced persistent threats and mutation techniques, sensitive and damaging information from a variety of sources have been exposed to possible corruption and hacking. Machine learning, artificial intelligence, predictive analytics, and similar disciplines of cognitive science applications have been found to have significant applications in the domain of cyber security. Machine Learning and Cognitive Science Applications in Cyber Security examines different applications of cognition that can be used to detect threats and analyze data to capture malware. Highlighting such topics as anomaly detection, intelligent platforms, and triangle scheme, this publication is designed for IT specialists, computer engineers, researchers, academicians, and industry professionals interested in the impact of machine learning in cyber security and the methodologies that can help improve the performance and reliability of machine learning applications.
