Unlock the potential of your BIM workflows with artificial intelligence and data visualization tools. This book provides guided instruction on using software like Revit, Dynamo, Python, and Power BI to automate processes, derive insights, and craft tailored dashboards that empower data-driven decisions in AEC projects. What this Book will help me do Effectively preprocess and manage BIM data for analysis and visualization. Design interactive and insightful dashboards in Power BI for project stakeholders. Integrate real-time IoT data and advanced analytics into BIM projects. Automate repetitive tasks in Revit using Dynamo and Python scripting. Understand the ethical considerations and emerging trends in AI for BIM. Author(s) Bruno Martorelli, a seasoned BIM manager, specializes in integrating technology and data analytics into construction workflows. With a background in architecture and programming, he bridges the gap between traditional methods and modern innovations. Bruno is dedicated to sharing practical strategies for data automation and visualization. Who is it for? This book is tailored for architects, engineers, and construction managers interested in elevating their BIM practices. If you're familiar with Revit and possess a basic understanding of data management, you'll find this resource invaluable. Beginners in Python or Power BI will also find accessible guidance to start applying advanced techniques in their workflows.

Secure your expertise in the next wave of computing with this essential book, which provides a comprehensive guide to Edge AI, detailing its foundational concepts, deployment strategies, and real-world applications for revolutionizing performance and privacy across various industries. Edge AI has the potential to bring the computational power of AI algorithms closer to where data is generated, processed, and utilized. Traditionally, AI models are deployed in centralized cloud environments, leading to latency issues, bandwidth constraints, and privacy concerns. Edge AI addresses these limitations by enabling AI inference and decision-making directly on edge devices, such as smartphones, IoT sensors, and edge servers. Despite its challenges, edge AI presents numerous opportunities across various domains. From real-time health monitoring and predictive maintenance in industrial IoT to personalized recommendations in retail and immersive experiences in augmented reality, edge AI has the potential to revolutionize how we interact with technology. This book aims to provide a comprehensive exploration of edge AI, covering its foundational concepts, development frameworks, deployment strategies, security considerations, ethical implications, emerging trends, and real-world applications. This guide is essential for anyone pushing the boundaries to leverage edge computing for enhanced performance and efficiency. Readers will find this volume: Dives deep into the world of edge AI with a comprehensive exploration covering foundational concepts, development frameworks, deployment strategies, security considerations, ethical implications, governance frameworks, optimization techniques, and real-world applications; Offers practical guidance on implementing edge AI solutions effectively in various domains, including architecture design, development frameworks, deployment strategies, and optimization techniques; Explores concrete examples of edge AI applications across diverse domains such as healthcare, industrial IoT, smart cities, and autonomous systems, providing insights into how edge AI is revolutionizing industries and everyday life; Provides insights into emerging trends and technologies in the field of edge AI, including convergence with blockchain, augmented reality, virtual reality, autonomous systems, personalized experiences, and cybersecurity. Audience Researchers, AI experts, and industry professionals in the field of computer science, IT, and business management.

Presents a structured approach to privacy management, an indispensable resource for safeguarding data in an ever-evolving digital landscape In today’s data-driven world, protecting personal information has become a critical priority for organizations of all sizes. Building Effective Privacy Programs: Cybersecurity from Principles to Practice equips professionals with the tools and knowledge to design, implement, and sustain robust privacy programs. Seamlessly integrating foundational principles, advanced privacy concepts, and actionable strategies, this practical guide serves as a detailed roadmap for navigating the complex landscape of data privacy. Bridging the gap between theoretical concepts and practical implementation, Building Effective Privacy Programs combines in-depth analysis with practical insights, offering step-by-step instructions on building privacy-by-design frameworks, conducting privacy impact assessments, and managing compliance with global regulations. In-depth chapters feature real-world case studies and examples that illustrate the application of privacy practices in a variety of scenarios, complemented by discussions of emerging trends such as artificial intelligence, blockchain, IoT, and more. Providing timely and comprehensive coverage of privacy principles, regulatory compliance, and actionable strategies, Building Effective Privacy Programs: Addresses all essential areas of cyberprivacy, from foundational principles to advanced topics Presents detailed analysis of major laws, such as GDPR, CCPA, and HIPAA, and their practical implications Offers strategies to integrate privacy principles into business processes and IT systems Covers industry-specific applications for healthcare, finance, and technology sectors Highlights successful privacy program implementations and lessons learned from enforcement actions Includes glossaries, comparison charts, sample policies, and additional resources for quick reference Written by seasoned professionals with deep expertise in privacy law, cybersecurity, and data protection, Building Effective Privacy Programs: Cybersecurity from Principles to Practice is a vital reference for privacy officers, legal advisors, IT professionals, and business executives responsible for data governance and regulatory compliance. It is also an excellent textbook for advanced courses in cybersecurity, information systems, business law, and business management.

The book explores the fundamental principles and transformative advancements in cutting-edge algorithmic technologies, detailing their application and impact on revolutionizing healthcare. This book provides an in-depth account of how technologies such as artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) are reshaping healthcare, transitioning from traditional diagnostic and treatment approaches to data-driven solutions that improve predictive accuracy and patient outcomes. The text also addresses the challenges and considerations associated with adopting these technologies, including ethical implications, data security concerns, and the need for human-centered approaches in algorithmic medicine. After introducing digital twin technology and its potential to enhance healthcare delivery, the book examines the broader effects of digital technology on the healthcare system. Subsequent chapters explore topics such as innovations in medical imaging, predictive analytics for improved patient outcomes, and deep learning algorithms for brain tumor detection. Other topics include generative adversarial networks (GANs), convolutional neural networks (CNNs), smart wearables for remote patient monitoring, effective IoT solutions, telemedicine advancements, and blockchain security for healthcare systems. The integration of biometric systems driven by AI, securing cyber-physical systems in healthcare, and digitizing wellness through electronic health records (EHRs) and electronic medical records (EMRs) are also discussed. The book concludes with an extensive case study comparing the impacts of various healthcare applications, offering insights and encouraging further research and innovation in this dynamic field. Audience This book is suitable for academicians and professionals in health informatics, bioinformatics, biomedical science and engineering, artificial intelligence, as well as clinicians, IT specialists, and policymakers in healthcare.

The world of SAP is undergoing a major transformation, with many customers either planning or actively modernizing their SAP landscapes as part of the S/4HANA digital transformation. Given the extensive SAP transformation efforts adopted by nearly all SAP customers in recent years and the profound impact these digital changes have had on their business models and IT organizations, the authors decided to write this book. As customers embark on their SAP on AWS journey, they face three main challenges: deciding on the overall strategy, selecting the right business use cases and implementing them effectively. This book aims to address these challenges by guiding readers through the process of identifying and executing the appropriate use cases. It will highlight how customers can harness AWS services beyond merely hosting their SAP systems on AWS, demonstrating the potential of these services to drive innovation. This book covers the entire journey, from defining strategy and identifying business use cases to their implementation, providing practical tips, strategies, and insights. It serves as an essential guide for customers planning to migrate or those who have already migrated their SAP workloads to AWS, helping them explore beyond just the infrastructure aspects of their journey. You Will : Discover how to go beyond just hosting SAP systems on AWS, using the full range of AWS services to innovate and extend your SAP applications. Learn how to identify the right business use cases and implement them effectively, with practical examples and real-world scenarios. Develop the mindset and skills needed to architect modern, cloud-native, event-driven architectures, balancing trade-offs between simplicity, efficiency, and cost. This book is for: Business leaders, IT professionals, and SAP specialists who are looking to modernize their SAP landscapes by leveraging AWS services

Computational Intelligence in Sustainable Computing and Optimization: Trends and Applications focuses on developing and evolving advanced computational intelligence algorithms for the analysis of data involved in applications, such as agriculture, biomedical systems, bioinformatics, business intelligence, economics, disaster management, e-learning, education management, financial management, and environmental policies. The book presents research in sustainable computing and optimization, combining methods from engineering, mathematics, artificial intelligence, and computer science to optimize environmental resources Computational intelligence in the field of sustainable computing combines computer science and engineering in applications ranging from Internet of Things (IoT), information security systems, smart storage, cloud computing, intelligent transport management, cognitive and bio-inspired computing, and management science. In addition, data intelligence techniques play a critical role in sustainable computing. Recent advances in data management, data modeling, data analysis, and artificial intelligence are finding applications in energy networks and thus making our environment more sustainable. Presents computational, intelligence–based data analysis for sustainable computing applications such as pattern recognition, biomedical imaging, sustainable cities, sustainable transport, sustainable agriculture, and sustainable financial management Develops research in sustainable computing and optimization, combining methods from engineering, mathematics, and computer science to optimize environmental resources Includes three foundational chapters dedicated to providing an overview of computational intelligence and optimization techniques and their applications for sustainable computing

The convergence of Artif icial Intelligence (AI) and Internet of Things (IoT) is reshaping the way industries, businesses, and economies function; the 34 chapters in this collection show how the full potential of these technologies is being enabled to create intelligent machines that simulate smart behavior and support decision-making with little or no human interference, thereby providing startling organizational efficiencies. Readers will discover that in Reshaping Intelligent Business and Industry: The book unpacks the two superpowers of innovation, AI and IoT, and explains how they connect to better communicate and exchange information about online activities; How the center and the network's edge generate predictive analytics or anomaly alerts; The meaning of AI at the edge and IoT networks. How bandwidth is reduced and privacy and security are enhanced; How AI applications increase operating efficiency, spawn new products and services, and enhance risk management; How AI and IoT create 'intelligent' devices and how new AI technology enables IoT to reach its full potential; Analyzes AIOT platforms and the handling of personal information for shared frameworks that remain sensitive to customers’ privacy while effectively utilizing data. Audience This book will appeal to all business and organization leaders, entrepreneurs, policymakers, and economists, as well as scientists, engineers, and students working in artificial intelligence, software engineering, and information technology.

The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies. If you're a data professional, you need to be aligned with your company's business activities more than ever before. This practical book provides the background, tools, and best practices necessary to help you design, implement, and operationalize predictive analytics on-premises or in the cloud. Explore ways that predictive analytics can provide direct input back to your business Understand mathematical tools commonly used in predictive analytics Learn the development frameworks used in predictive analytics applications Appreciate the role of predictive analytics in the machine learning process Examine industry implementations of predictive analytics Build, train, and retrain predictive models using Python and TensorFlow

This book highlights how optimized big data applications can be used for patient monitoring and clinical diagnosis. In fact, IoT-based applications are data-driven and mostly employ modern optimization techniques. The book also explores challenges, opportunities, and future research directions, discussing the stages of data collection and pre-processing, as well as the associated challenges and issues in data handling and setup.

Dive into the capabilities of the MongoDB aggregation framework with this official guide, "Practical MongoDB Aggregations". You'll learn how to design and optimize efficient aggregation pipelines for MongoDB 7.0, empowering you to handle complex data analysis and processing tasks directly within the database. What this Book will help me do Gain expertise in crafting advanced MongoDB aggregation pipelines for custom data workflows. Learn to perform time series analysis for financial datasets and IoT applications. Discover optimization techniques for working with sharded clusters and large datasets. Master array manipulation and other specific operations essential for MongoDB data models. Build pipelines that ensure data security and distribution while maintaining performance. Author(s) Paul Done, a recognized expert in MongoDB, brings his extensive experience in database technologies to this book. With years of practice in helping companies leverage MongoDB for big data solutions, Paul shares his deep knowledge in an accessible and logical manner. His approach to writing is hands-on, focusing on practical insights and clear explanations. Who is it for? This book is tailored for intermediate-level developers, database architects, data analysts, engineers, and scientists who use MongoDB. If you are familiar with MongoDB and looking to expand your understanding specifically around its aggregation capabilities, this guide is for you. Whether you're analyzing time series data or need to optimize pipelines for performance, you'll find actionable tips and examples here to suit your needs.

Your company relies on data to succeed—data that traditionally comes from a business's transactional processes, pulled from the transaction systems through an extract-transform-load (ETL) process into a warehouse for reporting purposes. But this data flow is no longer sufficient given the growth of the internet of things (IOT), web commerce, and cybersecurity. How can your company keep up with today's increasing magnitude of data and insights? Organizations that can no longer rely on data generated by business processes are looking outside their workflow for information on customer behavior, retail patterns, and industry trends. In this report, author Christopher Gardner examines the challenges of building a framework that provides universal access to data. You will: Learn the advantages and challenges of universal data access, including data diversity, data volume, and the speed of analytic operations Discover how to build a framework for data diversity and universal access Learn common methods for improving database and performance SLAs Examine the organizational requirements that a fast universal data access platform must meet Explore a case study that demonstrates how components work together to form a multiaccess, high-volume, high-performance interface About the author: Christopher Gardner is the campus Tableau application administrator at the University of Michigan, controlling security, updates, and performance maintenance.

BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics. The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data. The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT). New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches. Audience Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.

Make log processing a real asset to your organization with powerful and free open source tools. In Logging in Action you will learn how to: Deploy Fluentd and Fluent Bit into traditional on-premises, IoT, hybrid, cloud, and multi-cloud environments, both small and hyperscaled Configure Fluentd and Fluent Bit to solve common log management problems Use Fluentd within Kubernetes and Docker services Connect a custom log source or destination with Fluentd’s extensible plugin framework Logging best practices and common pitfalls Logging in Action is a guide to optimize and organize logging using the CNCF Fluentd and Fluent Bit projects. You’ll use the powerful log management tool Fluentd to solve common log management, and learn how proper log management can improve performance and make management of software and infrastructure solutions easier. Through useful examples like sending log-driven events to Slack, you’ll get hands-on experience applying structure to your unstructured data. About the Technology Don’t fly blind! An effective logging system can help you see and correct problems before they cripple your software. With the Fluentd log management tool, it’s a snap to monitor the behavior and health of your software and infrastructure in real time. Designed to collect and process log data from multiple sources using the industry-standard JSON format, Fluentd delivers a truly unified logging layer across all your systems. About the Book Logging in Action teaches you to record and analyze application and infrastructure data using Fluentd. Using clear, relevant examples, it shows you exactly how to transform raw system data into a unified stream of actionable information. You’ll discover how logging configuration impacts the way your system functions and set up Fluentd to handle data from legacy IT environments, local data centers, and massive Kubernetes-driven distributed systems. You’ll even learn how to implement complex log parsing with RegEx and output events to MongoDB and Slack. What's Inside Capture log events from a wide range of systems and software, including Kubernetes and Docker Connect to custom log sources and destinations Employ Fluentd’s extensible plugin framework Create a custom plugin for niche problems About the Reader For developers, architects, and operations professionals familiar with the basics of monitoring and logging. About the Author Phil Wilkins has spent over 30 years in the software industry. Has worked for small startups through to international brands. Quotes I highly recommend using Logging in Action as a getting-started guide, a refresher, or as a way to optimize your logging journey. - From the Foreword by Anurag Gupta, Fluent maintainer and Cofounder, Calyptia Covers everything you need if you want to implement a logging system using open source technology such as Fluentd and Kubernetes. - Alex Saez, Naranja X A great exploration of the features and capabilities of Fluentd, along with very useful hands-on exercises. - George Thomas, Manhattan Associates A practical holistic guide to integrating logging into your enterprise architecture. - Satej Sahu, Honeywell

A friendly, framework-agnostic tutorial that will help you grok how streaming systems work—and how to build your own! In Grokking Streaming Systems you will learn how to: Implement and troubleshoot streaming systems Design streaming systems for complex functionalities Assess parallelization requirements Spot networking bottlenecks and resolve back pressure Group data for high-performance systems Handle delayed events in real-time systems Grokking Streaming Systems is a simple guide to the complex concepts behind streaming systems. This friendly and framework-agnostic tutorial teaches you how to handle real-time events, and even design and build your own streaming job that’s a perfect fit for your needs. Each new idea is carefully explained with diagrams, clear examples, and fun dialogue between perplexed personalities! About the Technology Streaming systems minimize the time between receiving and processing event data, so they can deliver responses in real time. For applications in finance, security, and IoT where milliseconds matter, streaming systems are a requirement. And streaming is hot! Skills on platforms like Spark, Heron, and Kafka are in high demand. About the Book Grokking Streaming Systems introduces real-time event streaming applications in clear, reader-friendly language. This engaging book illuminates core concepts like data parallelization, event windows, and backpressure without getting bogged down in framework-specific details. As you go, you’ll build your own simple streaming tool from the ground up to make sure all the ideas and techniques stick. The helpful and entertaining illustrations make streaming systems come alive as you tackle relevant examples like real-time credit card fraud detection and monitoring IoT services. What's Inside Implement and troubleshoot streaming systems Design streaming systems for complex functionalities Spot networking bottlenecks and resolve backpressure Group data for high-performance systems About the Reader No prior experience with streaming systems is assumed. Examples in Java. About the Authors Josh Fischer and Ning Wang are Apache Committers, and part of the committee for the Apache Heron distributed stream processing engine. Quotes Very well-written and enjoyable. I recommend this book to all software engineers working on data processing. - Apoorv Gupta, Facebook Finally, a much-needed introduction to streaming systems—a must-read for anyone interested in this technology. - Anupam Sengupta, Red Hat Tackles complex topics in a very approachable manner. - Marc Roulleau, GIRO A superb resource for helping you grasp the fundamentals of open-source streaming systems. - Simon Verhoeven, Cronos Explains all the main streaming concepts in a friendly way. Start with this one! - Cicero Zandona, Calypso Technologies

IoT-Enabled Smart Healthcare Systems, Services and Applications Explore the latest healthcare applications of cutting-edge technologies In IoT-Enabled Smart Healthcare Systems, Services and Applications, an accomplished team of researchers delivers an insightful and comprehensive exploration of the roles played by cutting-edge technologies in modern healthcare delivery. The distinguished editors have included resources from a diverse array of learned experts in the field that combine to create a broad examination of a rapidly developing field. With a particular focus on Internet of Things (IoT) technologies, readers will discover how new technologies are impacting healthcare applications from remote monitoring systems to entire healthcare delivery methodologies. After an introduction to the role of emerging technologies in smart health care, this volume includes treatments of ICN-Fog computing, edge computing, security and privacy, IoT architecture, vehicular ad-hoc networks (VANETs), and patient surveillance systems, all in the context of healthcare delivery. Readers will also find: A thorough introduction to ICN-Fog computing for IoT based healthcare, including its architecture and challenges Comprehensive explorations of Internet of Things enabled software defined networking for edge computing in healthcare Practical discussions of a review of e-healthcare systems in India and Thailand, as well as the security and privacy issues that arise through the use of smart healthcare systems using Internet of Things devices In-depth examinations of the architecture and applications of an Internet of Things based healthcare system Perfect for healthcare practitioners and allied health professionals, hospital administrators, and technology professionals, IoT-Enabled Smart Healthcare Systems, Services and Applications is an indispensable addition to the libraries of healthcare regulators and policymakers seeking a one-stop resource that explains cutting-edge technologies in modern healthcare.

Deliver lightning fast and reliable messaging for your distributed applications with the flexible and resilient Apache Pulsar platform. In Apache Pulsar in Action you will learn how to: Publish from Apache Pulsar into third-party data repositories and platforms Design and develop Apache Pulsar functions Perform interactive SQL queries against data stored in Apache Pulsar Apache Pulsar in Action is a comprehensive and practical guide to building high-traffic applications with Pulsar. You’ll learn to use this mature and battle-tested platform to deliver extreme levels of speed and durability to your messaging. Apache Pulsar committer David Kjerrumgaard teaches you to apply Pulsar’s seamless scalability through hands-on case studies, including IOT analytics applications and a microservices app based on Pulsar functions. About the Technology Reliable server-to-server messaging is the heart of a distributed application. Apache Pulsar is a flexible real-time messaging platform built to run on Kubernetes and deliver the scalability and resilience required for cloud-based systems. Pulsar supports both streaming and message queuing, and unlike other solutions, it can communicate over multiple protocols including MQTT, AMQP, and Kafka’s binary protocol. About the Book Apache Pulsar in Action teaches you to build scalable streaming messaging systems using Pulsar. You’ll start with a rapid introduction to enterprise messaging and discover the unique benefits of Pulsar. Following crystal-clear explanations and engaging examples, you’ll use the Pulsar Functions framework to develop a microservices-based application. Real-world case studies illustrate how to implement the most important messaging design patterns. What's Inside Publish from Pulsar into third-party data repositories and platforms Design and develop Apache Pulsar functions Create an event-driven food delivery application About the Reader Written for experienced Java developers. No prior knowledge of Pulsar required. About the Author David Kjerrumgaard is a committer on the Apache Pulsar project. He currently serves as a Developer Advocate for StreamNative, where he develops Pulsar best practices and solutions. Quotes Apache Pulsar in Action is able to seamlessly mix the theory and abstract concepts with the clarity of practical step-by-step examples. I’d recommend to anyone! - Matteo Merli, co-creator of Apache Pulsar Gives readers insights into how the ‘magic’ works… Definitely recommended. - Henry Saputra, Splunk A complete, practical, fun-filled book. - Satej Kumar Sahu, Honeywell A definitive guide that will help you scale your applications. - Alessandro Campeis, Vimar The best book to start working with Pulsar. - Emanuele Piccinelli, Empirix

Single-purpose databases were designed to address specific problems and use cases. Given this narrow focus, there are inherent tradeoffs required when trying to accommodate multiple datatypes or workloads in your enterprise environment. The result is data fragmentation that spills over into application development, IT operations, data security, system scalability, and availability. In this report, author Alice LaPlante explains why developing modern, data-driven applications may be easier and more synergistic when using a converged database. Senior developers, architects, and technical decision-makers will learn cloud-native application development techniques for working with both structured and unstructured data. You'll discover ways to run transactional and analytical workloads on a single, unified data platform. This report covers: Benefits and challenges of using a converged database to develop data-driven applications How to use one platform to work with both structured and unstructured data that includes JSON, XML, text and files, spatial and graph, Blockchain, IoT, time series, and relational data Modern development practices on a converged database, including API-driven development, containers, microservices, and event streaming Use case examples including online food delivery, real-time fraud detection, and marketing based on real-time analytics and geospatial targeting

Through explosive growth in the past decade, data now drives significant portions of our lives, from crowdsourced restaurant recommendations to AI systems identifying effective medical treatments. Software developers have unprecedented opportunity to build data applications that generate value from massive datasets across use cases such as customer 360, application health and security analytics, the IoT, machine learning, and embedded analytics. With this report, product managers, architects, and engineering teams will learn how to make key technical decisions when building data-intensive applications, including how to implement extensible data pipelines and share data securely. The report includes design considerations for making these decisions and uses the Snowflake Data Cloud to illustrate best practices. This report explores: Why data applications matter: Get an introduction to data applications and some of the most common use cases Evaluating platforms for building data apps: Evaluate modern data platforms to confidently consider the merits of potential solutions Building scalable data applications: Learn design patterns and best practices for storage, compute, and security Handling and processing data: Explore techniques and real-world examples for building data pipelines to support data applications Designing for data sharing: Learn best practices for sharing data in modern data applications

IoT Based Data Analytics for the Healthcare Industry: Techniques and Applications explores recent advances in the analysis of healthcare industry data through IoT data analytics. The book covers the analysis of ubiquitous data generated by the healthcare industry, from a wide range of sources, including patients, doctors, hospitals, and health insurance companies. The book provides AI solutions and support for healthcare industry end-users who need to analyze and manipulate this vast amount of data. These solutions feature deep learning and a wide range of intelligent methods, including simulated annealing, tabu search, genetic algorithm, ant colony optimization, and particle swarm optimization. The book also explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages, challenges and issues in data collection, data handling, and data collection set-up. Healthcare industry data or streaming data generated by ubiquitous sensors cocooned into the IoT requires advanced analytics to transform data into information. With advances in computing power, communications, and techniques for data acquisition, the need for advanced data analytics is in high demand. Provides state-of-art methods and current trends in data analytics for the healthcare industry Addresses the top concerns in the healthcare industry using IoT and data analytics, and machine learning and deep learning techniques Discusses several potential AI techniques developed using IoT for the healthcare industry Explores challenges, opportunities, and future research directions, and discusses the data collection and pre-processing stages

Security and Privacy Issues in IoT Devices and Sensor Networks investigates security breach issues in IoT and sensor networks, exploring various solutions. The book follows a two-fold approach, first focusing on the fundamentals and theory surrounding sensor networks and IoT security. It then explores practical solutions that can be implemented to develop security for these elements, providing case studies to enhance understanding. Machine learning techniques are covered, as well as other security paradigms, such as cloud security and cryptocurrency technologies. The book highlights how these techniques can be applied to identify attacks and vulnerabilities, preserve privacy, and enhance data security. This in-depth reference is ideal for industry professionals dealing with WSN and IoT systems who want to enhance the security of these systems. Additionally, researchers, material developers and technology specialists dealing with the multifarious aspects of data privacy and security enhancement will benefit from the book's comprehensive information. Provides insights into the latest research trends and theory in the field of sensor networks and IoT security Presents machine learning-based solutions for data security enhancement Discusses the challenges to implement various security techniques Informs on how analytics can be used in security and privacy