The rapid advancements in telecommunications, computing hardware and software, and data encryption, and the widespread use of electronic data processing and electronic business conducted through the Internet have led to a strong increase in information security threats. The latest advances in information security have increased practical deployments and scalability across a wide range of applications to better secure and protect our information systems and the information stored, processed and transmitted. This book outlines key emerging trends in information security from the foundations and technologies in biometrics, cybersecurity, and big data security to applications in hardware and embedded systems security, computer forensics, the Internet of Things security, and network security. Information Security: Foundations, technologies and applications is a comprehensive review of cutting-edge algorithms, technologies, and applications, and provides new insights into a range of fundamentally important topics in the field. This up-to-date body of knowledge is essential reading for researchers and advanced students in information security, and for professionals in sectors where information security is required.

This book paves the road for researchers from various areas of engineering working in the realm of smart cities to discuss the intersections in these areas when it comes to infrastructure and its flexibility. The authors lay out models, algorithms and frameworks related to the 'smartness' in the future smart cities. In particular, manufacturing firms, electric generation, transmission and distribution utilities, hardware and software computer companies, automation and control manufacturing firms, and other industries will be able to use this book to enhance their energy operations, improve their comfort and privacy, as well as to increase the benefit from the electrical system. The book pertains to researchers, professionals, and R&D in an array of industries.

Increasingly, human beings are sensors engaging directly with the mobile Internet. Individuals can now share real-time experiences at an unprecedented scale. Social Sensing: Building Reliable Systems on Unreliable Data looks at recent advances in the emerging field of social sensing, emphasizing the key problem faced by application designers: how to extract reliable information from data collected from largely unknown and possibly unreliable sources. The book explains how a myriad of societal applications can be derived from this massive amount of data collected and shared by average individuals. The title offers theoretical foundations to support emerging data-driven cyber-physical applications and touches on key issues such as privacy. The authors present solutions based on recent research and novel ideas that leverage techniques from cyber-physical systems, sensor networks, machine learning, data mining, and information fusion.

Due to the tremendous amount of data generated daily from fields such as business, research, and sciences, big data is everywhere. Therefore, alternative management and processing methods have to be created to handle this complex and unstructured data size. Big Data Management, Technologies, and Applications discusses the exponential growth of information size and the innovative methods for data capture, storage, sharing, and analysis for big data. With its prevalence, this collection of articles on big data methodologies and technologies are beneficial for IT workers, researchers, students, and practitioners in this timely field.

Fuzzy cognitive maps (FCMs) have gained popularity in the scientific community due to their capabilities in modeling and decision making for complex problems.This book presents a novel algorithm called glassoFCM to enable automatic learning of FCM models from data. Specifically, glassoFCM is a combination of two methods, glasso (a technique originated from machine learning) for data modeling and FCM simulation for decision making. The book outlines that glassoFCM elaborates simple, accurate, and more stable models that are easy to interpret and offer meaningful decisions. The research results presented are based on an investigation related to a real-world business intelligence problem to evaluate characteristics that influence employee work readiness.Finally, this book provides readers with a step-by-step guide of the 'fcm' package to execute and visualize their policies and decisions through the FCM simulation process.

"Big data" has become a commonly used term to describe large-scale and complex data sets which are difficult to manage and analyze using standard data management methodologies. With applications across sectors and fields of study, the implementation and possible uses of big data are limitless. The Handbook of Research on Big Data Management and Applications explores emerging research on the ever-growing field of big data and facilitates further knowledge development on methods for handling and interpreting large data sets. Providing multi-disciplinary perspectives fueled by international research, this publication is designed for use by data analysts, IT professionals, researchers, and graduate-level students interested in learning about the latest trends and concepts in big data.

Online survey research suites offer a vast array of capabilities, supporting the presentation of virtually every type of digital data - text, imagery, audio, video, and multimedia forms. With some researcher sophistication, these online survey research suites can enable a wide range of quantitative, qualitative, and mixed methods research. Online Survey Design and Data Analytics: Emerging Research and Opportunities is a critical scholarly resource that explores the utilization of online platforms for setting up surveys to achieve a specific result, eliciting data in in-depth ways and applying creative analytics methods to online survey data. Highlighting topics such as coding, education-based analysis, and online Delphi studies, this publication is ideal for researchers, professionals, academicians, data analysts, IT consultants, and students.

This book summarizes recent inventions, provides guidelines and recommendations, and demonstrates many practical applications of homomorphic encryption. This collection of papers represents the combined wisdom of the community of leading experts on Homomorphic Encryption. In the past 3 years, a global community consisting of researchers in academia, industry, and government, has been working closely to standardize homomorphic encryption. This is the first publication of whitepapers created by these experts that comprehensively describes the scientific inventions, presents a concrete security analysis, and broadly discusses applicable use scenarios and markets. This book also features a collection of privacy-preserving machine learning applications powered by homomorphic encryption designed by groups of top graduate students worldwide at the Private AI Bootcamp hosted by Microsoft Research. The volume aims to connect non-expert readers with this important new cryptographic technology in an accessible and actionable way. Readers who have heard good things about homomorphic encryption but are not familiar with the details will find this book full of inspiration. Readers who have preconceived biases based on out-of-date knowledge will see the recent progress made by industrial and academic pioneers on optimizing and standardizing this technology. A clear picture of how homomorphic encryption works, how to use it to solve real-world problems, and how to efficiently strengthen privacy protection, will naturally become clear.

With at least 40% new or updated content since the last edition, "Clinical Decision Support," 2nd Edition explores the crucial new motivating factors poised to accelerate Clinical Decision Support (CDS) adoption. This book is mostly focused on the US perspective because of initiatives driving EHR adoption, the articulation of 'meaningful use', and new policy attention in process including the Office of the National Coordinator for Health Information Technology (ONC) and the Center for Medicare and Medicaid Services (CMS). A few chapters focus on the broader international perspective. "Clinical Decision Support," 2nd Edition explores the technology, sources of knowledge, evolution of successful forms of CDS, and organizational and policy perspectives surrounding CDS.

Exploring a roadmap for CDS, with all its efficacy benefits including reduced errors, improved quality, and cost savings, as well as the still substantial roadblocks needed to be overcome by policy-makers, clinicians, and clinical informatics experts, the field is poised anew on the brink of broad adoption. "Clinical Decision Support," 2nd Edition provides an updated and pragmatic view of the methodological processes and implementation considerations. This book also considers advanced technologies and architectures, standards, and cooperative activities needed on a societal basis for truly large-scale adoption.
At least 40% updated, and seven new chapters since the previous edition, with the new and revised content focused on new opportunities and challenges for clinical decision support at point of care, given changes in science, technology, regulatory policy, and healthcare financeInforms healthcare leaders and planners, health IT system developers, healthcare IT organization leaders and staff, clinical informatics professionals and researchers, and clinicians with an interest in the role of technology in shaping healthcare of the future

Websites are a central part of today's business world; however, with the vast amount of information that constantly changes and the frequency of required updates, this can come at a high cost to modern businesses. Web Data Mining and the Development of Knowledge-Based Decision Support Systems is a key reference source on decision support systems in view of end user accessibility and identifies methods for extraction and analysis of useful information from web documents. Featuring extensive coverage across a range of relevant perspectives and topics, such as semantic web, machine learning, and expert systems, this book is ideally designed for web developers, internet users, online application developers, researchers, and faculty.

"Information Management: Gaining a Competitive Advantage with Data" is about making smart decisions to make the most of company information. Expert author William McKnight develops the value proposition for information in the enterprise and succinctly outlines the numerous forms of data storage. "Information Management" will enlighten you, challenge your preconceived notions, and help activate information in the enterprise. Get the big picture on managing data so that your team can make smart decisions by understanding how everything from workload allocation to data stores fits together.

The practical, hands-on guidance in this book includes:

Part 1: The importance of information management and analytics to business, and how data warehouses are used Part 2: The technologies and data that advance an organization, and extend data warehouses and related functionality Part 3: Big Data and NoSQL, and how technologies like Hadoop enable management of new forms of data Part 4: Pulls it all together, while addressing topics of agile development, modern business intelligence, and organizational change management

Read the book cover-to-cover, or keep it within reach for a quick and useful resource. Either way, this book will enable you to master all of the possibilities for data or the broadest view across the enterprise.
Balances business and technology, with non-product-specific technical detailShows how to leverage data to deliver ROI for a businessEngaging and approachable, with practical advice on the pros and cons of each domain, so that you learn how information fits together into a complete architecture Provides a path for the data warehouse professional into the new normal of heterogeneity, including NoSQL solutions

Method engineering is a very young field. Generally, method engineering can be considered from engineering of an entire methodology for information systems development to engineering of modeling techniques according to project requirements. Computer aided method engineering is about generation and use of information systems design techniques according to user needs. Some times such environments are called generic tools or MetaCASE. Computer-Aided Method Engineering: Designing Case Repositories for the 21st Century presents a contribution on a methodology and architecture of a CASE repository, forwarding a theory that will bring about the component based development into CASE tool design and development covering a repository construction principle for the 21st century.

Churn prediction, recognition, and mitigation have become essential topics in various industries. As a means for forecasting and manageing risk, further research in this field can greatly assist companies in making informed decisions based on future possible scenarios. Developing Churn Models Using Data Mining Techniques and Social Network Analysis provides an in-depth analysis of attrition modeling relevant to business planning and management. Through its insightful and detailed explanation of best practices, tools, and theory surrounding churn prediction and the integration of analytics tools, this publication is especially relevant to managers, data specialists, business analysts, academicians, and upper-level students.

"Implementing Analytics" demystifies the concept, technology and application of analytics and breaks its implementation down to repeatable and manageable steps, making it possible for widespread adoption across all functions of an organization. "Implementing Analytics "simplifies and helps democratize a very specialized discipline to foster business efficiency and innovation without investing in multi-million dollar technology and manpower. A technology agnostic methodology that breaks down complex tasks like model design and tuning and emphasizes business decisions rather than the technology behind analytics.

Simplifies the understanding of analytics from a technical and functional perspective and shows a wide array of problems that can be tackled using existing technology

Provides a detailed step by step approach to identify opportunities, extract requirements, design variables and build and test models. It further explains the business decision strategies to use analytics models and provides an overview for governance and tuning

Helps formalize analytics projects from staffing, technology and implementation perspectives

Emphasizes machine learning and data mining over statistics and shows how the role of a Data Scientist can be broken down and still deliver the value by building a robust development process

This book highlights new trends and challenges in research on agents and the new digital and knowledge economy. It includes papers on business process management, agent-based modeling and simulation, and anthropic-oriented computing that were originally presented at the 15th International KES Conference on Agents and Multi-Agent Systems: Technologies and Applications (KES-AMSTA 2021), being held as a Virtual Conference in June 14-16, 2021. The respective papers cover topics such as software agents, multi-agent systems, agent modeling, mobile and cloud computing, big data analysis, business intelligence, artificial intelligence, social systems, computer embedded systems, and nature-inspired manufacturing, all of which contribute to the modern digital economy.

Everyone wants privacy and security online, something that most computer users have more or less given up on as far as their personal data is concerned. There is no shortage of good encryption software, and no shortage of books, articles and essays that purport to be about how to use it. Yet there is precious little for ordinary users who want just enough information about encryption to use it safely and securely and appropriately--WITHOUT having to become experts in cryptography.

Data encryption is a powerful tool, if used properly. Encryption turns ordinary, readable data into what looks like gibberish, but gibberish that only the end user can turn back into readable data again. The difficulty of encryption has much to do with deciding what kinds of threats one needs to protect against and then using the proper tool in the correct way. It's kind of like a manual transmission in a car: learning to drive with one is easy; learning to build one is hard.

The goal of this title is to present just enough for an average reader to begin protecting his or her data, immediately. Books and articles currently available about encryption start out with statistics and reports on the costs of data loss, and quickly get bogged down in cryptographic theory and jargon followed by attempts to comprehensively list all the latest and greatest tools and techniques. After step-by-step walkthroughs of the download and install process, there's precious little room left for what most readers really want: how to encrypt a thumb drive or email message, or digitally sign a data file.

There are terabytes of content that explain how cryptography works, why it's important, and all the different pieces of software that can be used to do it; there is precious little content available that couples concrete threats to data with explicit responses to those threats. This title fills that niche.

By reading this title readers will be provided with a step by step hands-on guide that includes:

Simple descriptions of actual threat scenarios

Simple, step-by-step instructions for securing data

How to use open source, time-proven and peer-reviewed cryptographic software

Easy to follow tips for safer computing

Unbiased and platform-independent coverage of encryption tools and techniques
Simple descriptions of actual threat scenarios Simple, step-by-step instructions for securing dataHow to use open source, time-proven and peer-reviewed cryptographic software Easy-to-follow tips for safer computing Unbiased and platform-independent coverage of encryption tools and techniques

As enterprise access networks evolve with a larger number of mobile users, a wide range of devices and new cloud-based applications, managing user performance on an end-to-end basis has become rather challenging. Recent advances in big data network analytics combined with AI and cloud computing are being leveraged to tackle this growing problem. AI is becoming further integrated with software that manage networks, storage, and can compute. This edited book focuses on how new network analytics, IoTs and Cloud Computing platforms are being used to ingest, analyse and correlate a myriad of big data across the entire network stack in order to increase quality of service and quality of experience (QoS/QoE) and to improve network performance. From big data and AI analytical techniques for handling the huge amount of data generated by IoT devices, the authors cover cloud storage optimization, the design of next generation access protocols and internet architecture, fault tolerance and reliability in intelligent networks, and discuss a range of emerging applications. This book will be useful to researchers, scientists, engineers, professionals, advanced students and faculty members in ICTs, data science, networking, AI, machine learning and sensing. It will also be of interest to professionals in data science, AI, cloud and IoT start-up companies, as well as developers and designers.

This book presents Proceedings of the International Conference on Intelligent Systems and Networks (ICISN 2021), held at Hanoi in Vietnam. It includes peer-reviewed high-quality articles on intelligent system and networks. It brings together professionals and researchers in the area and presents a platform for exchange of ideas and to foster future collaboration. The topics covered in this book include-foundations of computer science; computational intelligence language and speech processing; software engineering software development methods; wireless communications signal processing for communications; electronics track IoT and sensor systems embedded systems; etc.