Artificial intelligence and its various components are rapidly engulfing almost every professional industry. Specific features of AI that have proven to be vital solutions to numerous real-world issues are machine learning and deep learning. These intelligent agents unlock higher levels of performance and efficiency, creating a wide span of industrial applications. However, there is a lack of research on the specific uses of machine/deep learning in the professional realm. Machine Learning and Deep Learning in Real-Time Applications provides emerging research exploring the theoretical and practical aspects of machine learning and deep learning and their implementations as well as their ability to solve real-world problems within several professional disciplines including healthcare, business, and computer science. Featuring coverage on a broad range of topics such as image processing, medical improvements, and smart grids, this book is ideally designed for researchers, academicians, scientists, industry experts, scholars, IT professionals, engineers, and students seeking current research on the multifaceted uses and implementations of machine learning and deep learning across the globe.

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there's a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

A new kind of manifesto for the working woman, with practical guidance on building wealth as well as inspiration for harnessing the freedom and power that comes from a breadwinning mindset. Women are now the main breadwinner in one-in-four households in the UK. Yet the majority of women still aren't being brought up to think like breadwinners. In fact, they're actively discouraged - by institutional bias and subconscious beliefs - from building their own wealth, pursuing their full earning potential, and providing for themselves and others financially. The result is that women earn less, owe more, and have significantly less money saved and invested for the future than men do. And if women do end up as the main breadwinner, they've been conditioned to feel reluctant and unprepared to manage the role. In Think Like a Breadwinner, financial expert Jennifer Barrett reframes what it really means to be a breadwinner by dismantling the narrative that women don't - and shouldn't - take full financial responsibility to create the lives they want. Featuring a wide variety of case studies from women at all stages of their careers and financial lives, Barrett shares the secrets of women who already think like breadwinners. Barrett reveals not only the importance of women building their own wealth, but also the freedom and power that comes with it. 'Barrett's manifesto is a must read for any woman at any stage of her career.' - Eve Rodsky, author of Fair Play

Cell phone apps share location information; software companies store user data in the cloud; biometric scanners read fingerprints; employees of some businesses have microchips implanted in their hands. In each of these instances we trade a share of privacy or an aspect of identity for greater convenience or improved security. What Robert M. Pallitto asks in Bargaining with the Machine is whether we are truly making such bargains freely - whether, in fact, such a transaction can be conducted freely or advisedly in our ever more technologically sophisticated world. Pallitto uses the social theory of bargaining to look at the daily compromises we make with technology. Specifically, he explores whether resisting these 'bargains' is still possible when the technologies in question are backed by persuasive, even coercive, corporate and state power. Who, he asks, is proposing the bargain? What is the balance of bargaining power? What is surrendered and what is gained? And are the perceived and the actual gains and losses the same - that is, what is hidden? At the center of Pallitto's work is the paradox of bargaining in a world of limited agency. Assurances that we are in control are abundant whether we are consumers, voters, or party to the social contract. But when purchasing goods from a technological behemoth like Amazon, or when choosing a candidate whose image is crafted and shaped by campaign strategists and media outlets, how truly free, let alone informed, are our choices? The tension between claims of agency and awareness of its limits is the site where we experience our social lives - and nowhere is this tension more pronounced than in the surveillance society. This book offers a cogent analysis of how that complex, contested, and even paradoxical experience arises as well as an unusually clear and troubling view of the consequential compromises we may be making.

The internet of things (IoT) has drawn great attention from both academia and industry, since it offers a challenging notion of creating a world where all things around us are connected to the internet and communicate with each other with minimal human intervention. Another component for helping IoT to succeed is cloud computing. The combination of cloud computing and IoT will enable new monitoring services and powerful processing of sensory data streams. These applications, alongside implementation details and challenges, should also be explored for successful mainstream adoption. IoT is also fueled by the advancement of digital technologies, and the next generation era will be cloud-based IoT systems. Integration and Implementation of the Internet of Things Through Cloud Computing studies, analyzes, and presents cloud-based IoT-related technologies, protocols, and standards along with recent research and development in cloud-based IoT. It also presents recent emerging trends and technological advances of cloud-based IoT, innovative applications, and the challenges and implications for society. The chapters included take a strong look at the societal and social aspects of this technology along with its implementations and technological analyses. This book is intended for IT specialists, technologists, practitioners, researchers, academicians, and students who are interested in the next era of IoT through cloud computing.

Wireless sensor networks have a range of applications, including military uses and in environmental monitoring. When an area of interest is inaccessible by conventional means, such a network can be deployed in ways resulting in a random distribution of the sensors. Randomly Deployed Wireless Sensor Networks offers a probabilistic method to model and analyze these networks. The book considers the network design, coverage, target detection, localization and tracking of sensors in randomly deployed wireless networks, and proposes a stochastic model. It quantifies the relationship between parameters of the network and its performance, and puts forward a communication protocol. The title provides analyses and formulas, giving engineering insight into randomly deployed wireless sensor networks. Five chapters consider the analysis of coverage performance; working modes and scheduling mechanisms; the relationship between sensor behavior and network performance properties; probabilistic forwarding routing protocols; localization methods for multiple targets and target number estimation; and experiments on target localization and tracking with a Mica sensor system.

FINANCIAL FREEDOM IS JUST A DISTANT DREAM FOR MANY OF US. BUT WHAT IF I TELL YOU THAT THERE IS AN EASY WAY FOR YOU TO EARN A PASSIVE INCOME WITH LITTLE TO NO INVESTMENT? Yes, you read that right! Many people think that the only way to earn more and to save more is to stick it out with the 9-to-5 grind. However, they could not be more mistaken. This book will give you important information on how to earn PASSIVE income, and not just that! This supplemental income can eventually become your key to financial freedom if you do things right, and this book will tell you how! In this book, you will find all the critical information you need to get started with affiliate marketing. PLUS included is a BONUS training course - FREE 4 Day blueprint on how to build a profitable online business with affiliate marketing The key to your financial freedom is between the pages of this book, and the time to get started is NOW!

The transition towards exascale computing has resulted in major transformations in computing paradigms. The need to analyze and respond to such large amounts of data sets has led to the adoption of machine learning (ML) and deep learning (DL) methods in a wide range of applications. One of the major challenges is the fetching of data from computing memory and writing it back without experiencing a memory-wall bottleneck. To address such concerns, in-memory computing (IMC) and supporting frameworks have been introduced. In-memory computing methods have ultra-low power and high-density embedded storage. Resistive Random-Access Memory (ReRAM) technology seems the most promising IMC solution due to its minimized leakage power, reduced power consumption and smaller hardware footprint, as well as its compatibility with CMOS technology, which is widely used in industry. In this book, the authors introduce ReRAM techniques for performing distributed computing using IMC accelerators, present ReRAM-based IMC architectures that can perform computations of ML and data-intensive applications, as well as strategies to map ML designs onto hardware accelerators. The book serves as a bridge between researchers in the computing domain (algorithm designers for ML and DL) and computing hardware designers.

The field of healthcare is seeing a rapid expansion of technological advancement within current medical practices. The implementation of technologies including neural networks, multi-model imaging, genetic algorithms, and soft computing are assisting in predicting and identifying diseases, diagnosing cancer, and the examination of cells. Implementing these biomedical technologies remains a challenge for hospitals worldwide, creating a need for research on the specific applications of these computational techniques. Deep Neural Networks for Multimodal Imaging and Biomedical Applications provides research exploring the theoretical and practical aspects of emerging data computing methods and imaging techniques within healthcare and biomedicine. The publication provides a complete set of information in a single module starting from developing deep neural networks to predicting disease by employing multi-modal imaging. Featuring coverage on a broad range of topics such as prediction models, edge computing, and quantitative measurements, this book is ideally designed for researchers, academicians, physicians, IT consultants, medical software developers, practitioners, policymakers, scholars, and students seeking current research on biomedical advancements and developing computational methods in healthcare.

A Wall Street Journal Bestseller 'IT SHOULD BE READ BY ANYONE TRYING TO MAKE SENSE OF GEOPOLITICS TODAY' FINANCIAL TIMES Three of our most accomplished and deep thinkers come together to explore Artificial Intelligence (AI) and the way it is transforming human society - and what it means for us all. An AI learned to win chess by making moves human grand masters had never conceived. Another AI discovered a new antibiotic by analysing molecular properties human scientists did not understand. Now, AI-powered jets are defeating experienced human pilots in simulated dogfights. AI is coming online in searching, streaming, medicine, education, and many other fields and, in so doing, transforming how humans are experiencing reality. In The Age of AI, three leading thinkers have come together to consider how AI will change our relationships with knowledge, politics, and the societies in which we live. The Age of AI is an essential roadmap to our present and our future, an era unlike any that has come before.

Explore everything you need to know to set up secure remote access, harden your firewall deployment, and protect against phishing Key Features Learn the ins and outs of log forwarding and troubleshooting issues Set up GlobalProtect satellite connections, configure site-to-site VPNs, and troubleshoot LSVPN issues Gain an in-depth understanding of user credential detection to prevent data leaks Book DescriptionThis book builds on the content found in Mastering Palo Alto Networks, focusing on the different methods of establishing remote connectivity, automating log actions, and protecting against phishing attacks through user credential detection. Complete with step-by-step instructions, practical examples, and troubleshooting tips, you will gain a solid understanding of how to configure and deploy Palo Alto Networks remote access products. As you advance, you will learn how to design, deploy, and troubleshoot large-scale end-to-end user VPNs. Later, you will explore new features and discover how to incorporate them into your environment. By the end of this Palo Alto Networks book, you will have mastered the skills needed to design and configure SASE-compliant remote connectivity and prevent credential theft with credential detection. What you will learn Understand how log forwarding is configured on the firewall Focus on effectively enabling remote access Explore alternative ways for connecting users and remote networks Protect against phishing with credential detection Understand how to troubleshoot complex issues confidently Strengthen the security posture of your firewalls Who this book is forThis book is for anyone who wants to learn more about remote access for users and remote locations by using GlobalProtect and Prisma access and by deploying Large Scale VPN. Basic knowledge of Palo Alto Networks, network protocols, and network design will be helpful, which is why reading Mastering Palo Alto Networks is recommended first to help you make the most of this book.