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Apple Pay Essentials. Harness the power of Apple Pay in your iOS apps and integrate it with global payment gateways

Ernest Bruce

Apple Pay, one of the most talked about offerings of the latest iOS 9 release, is a digital wallet and electronic payment system developed by Apple Inc. Paying in stores or within apps has never been easier or safer. Gone are the days of searching for your wallet, and the wasted moments finding the right card! Now you can use your credit cards and rewards cards with just a touch. It allows payment to merchants, using Near field Communication (NFC), and within iOS apps. Implementing Apple Pay within apps for payment is a bit tricky, but our book solves this problem for you.Whether you are a brand new iOS app developer or a seasoned expert, this book arms you with necessary skills to successfully implement Apple Pay in your online-payment workflow.Whether you are a brand new iOS app developer or a seasoned expert, this book arms you with the necessary skills to successfully implement Apple Pay. We start off by teaching you how to obtain the certificates necessary to encrypt customers’ payment information. We will use Xcode and Objective C for the interface and Node.js for server side code. You will then learn how to determine whether the customer can use Apple Pay, and how to create payment requests. You will come to grips with designing a payment-processor program to interact with the payment gateway. Finally, we take a look at a business-focused view of Apple Pay protocols and classes. By the end of this book, you will be able to build a fully functional Apple Pay-integrated iOS app

Application Development for IBM WebSphere Process Server 7 and Enterprise Service Bus 7. A Service Oriented Architecture approach has many benefits for your applications, including flexibility, reusability, and increased revenue. You can exploit those benefits to the fullest by following this step-by-step tutorial for WPS and WESB

Salil Ahuja, Swami Chandrasekaran

By adopting an SOA approach in Business Process Management (BPM), you can make your application flexible, reusable, and adaptable to new developments. The SOA approach also gives you the potential to lower costs (from reuse), and increase revenue (from adaptability and flexibility). However, integrating basic SOA constructs (such as Process, Business Services, and Components) and core building blocks of BPM (such as Process Modeling and Enterprise Service Bus) in a real-world application can be challenging.This book introduces basic concepts of Business Integration, SOA Fundamentals, and SOA Programming Model and implements them in numerous examples. It guides you to building an Order Management application from scratch using the principles of Business Process Management and Service Oriented Architecture and using WebSphere Process Server (WPS) and WebSphere Enterprise Service Bus (WESB). The various detailed aspects, features, and capabilities of the product are conveyed through examplesWe begin with essential concepts on Business Integration, SOA Fundamentals and SOA Programming Model. Then we set up the development environment to build your first Hello Process and Hello Mediation applications.Gradually, we build an SOA-based Order Management Application. We cover important aspects and functions of WPS and WESB with numerous practical examples. We show how to analyze your application's business requirements and check if an SOA approach is appropriate for your project. Then you do a top-down decomposition of your application and identify its use cases, business processes, and services. Having built the SOA Application, we introduce you to various non-functional topics, including: Administration, Governance, Management, Monitoring, and Security. We also discuss deployment topologies for WPS and WESB, performance tuning, and recommended practices.

Application Development in iOS 7

Kyle Begeman

A hybrid between informative overview and step-by-step instruction, this book is the ultimate guide to iOS 7 application development. If you are an iOS developer who is looking to understand the new features within iOS 7 and Xcode 5, then this book is for you. In order to fully understand the concepts in this book, it is recommended you have at least some experience with previous versions of iOS.

Application Development with Swift. Develop highly efficient and appealing iOS applications by using the Swift language

Hossam Ghareeb

After years of using Objective-C for developing apps for iOS/Mac OS, Apple now offers a new, creative, easy, and innovative programming language for application development, called Swift. Swift makes iOS application development a breeze by offering speed, security and power to your application development process. Swift is easy to learn and has awesome features such as being open source, debugging,interactive playgrounds, error handling model, and so on. Swift has simplified its memory management with Automatic Reference Counting (ARC) and it is compatible with Objective-C.This book has been created to provide you with the information and skills you need to use the new programming language Swift. The book starts with an introduction to Swift and code structure. Following this, you will use playgrounds to become familiar with the language in no time. Then the book takes you through the advanced features offered by Swift and how to use them with your old Objective-C code or projects. You will then learn to use Swift in real projects by covering APIs such as HealthKit, Metal, WatchKit, and Touch ID in each chapter. The book's easy to follow structure ensures you get the best start to developing applications with Swift.

Applied Architecture Patterns on the Microsoft Platform. An in-depth scenario-driven approach to architecting systems using Microsoft technologies

Richard Seroter, Ewan Fairweather

Every day, architects and developers are asked to solve specific business problems in the most efficient way possible using a broad range of technologies. Packed with real-world examples of how to use the latest Microsoft technologies, this book tackles over a dozen specific use case patterns and provides an applied implementation with supporting code downloads for every chapter.In this book, we guide you through thirteen architectural patterns and provide detailed code samples for the following technologies: Windows Server AppFabric, Windows Azure Platform AppFabric, SQL Server (including Integration Services, Service Broker, and StreamInsight), BizTalk Server, Windows Communication Foundation (WCF), and Windows Workflow Foundation (WF). This book brings together – and simplifies – the information and methodology you need to make the right architectural decisions and use a broad range of the Microsoft platform to meet your requirements. Throughout the book, we will follow a consistent architectural decision framework which considers key business, organizational, and technology factors.The book is broken up into four sections. First, we define the techniques and methodologies used to make architectural decisions throughout the book. In Part I, we provide a set of primers designed to get you up to speed with each of the technologies demonstrated in the book. Part II looks at messaging patterns and includes use cases which highlight content-based routing, workflow, publish/subscribe, and distributed messaging. Part III digs into data processing patterns and looks at bulk data processing, complex events, multi-master synchronization, and more. Finally, Part IV covers performance-related patterns including low latency, failover to the cloud, and reference data caching.

Applied Computational Thinking with Python. Algorithm design for complex real-world problems - Second Edition

Sofía De Jesús, Dayrene Martinez

Computational thinking helps you to develop logical processing and algorithmic thinking while solving real-world problems across a wide range of domains. It's an essential skill that you should possess to keep ahead of the curve in this modern era of information technology. Developers can apply their knowledge of computational thinking to solve problems in multiple areas, including economics, mathematics, and artificial intelligence.This book begins by helping you get to grips with decomposition, pattern recognition, pattern generalization and abstraction, and algorithm design, along with teaching you how to apply these elements practically while designing solutions for challenging problems. You’ll then learn about various techniques involved in problem analysis, logical reasoning, algorithm design, clusters and classification, data analysis, and modeling, and understand how computational thinking elements can be used together with these aspects to design solutions. Toward the end, you will discover how to identify pitfalls in the solution design process and how to choose the right functionalities to create the best possible algorithmic solutions.By the end of this algorithm book, you will have gained the confidence to successfully apply computational thinking techniques to software development.

Applied Deep Learning with Python. Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions

Alex Galea, Luis Capelo

Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before you train your first predictive model. You’ll then explore a variety of approaches to classification such as support vector networks, random decision forests and k-nearest neighbors to build on your knowledge before moving on to advanced topics.After covering classification, you’ll go on to discover ethical web scraping and interactive visualizations, which will help you professionally gather and present your analysis. Next, you’ll start building your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. You’ll then be guided through a trained neural network, which will help you explore common deep learning network architectures (convolutional, recurrent, and generative adversarial networks) and deep reinforcement learning. Later, you’ll delve into model optimization and evaluation. You’ll do all this while working on a production-ready web application that combines TensorFlow and Keras to produce meaningful user-friendly results.By the end of this book, you’ll be equipped with the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.

Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems

David S. Jordan

Data scientists, when presented with a myriad of data, can often lose sight of how to present geospatial analyses in a meaningful way so that it makes sense to everyone. Using Python to visualize data helps stakeholders in less technical roles to understand the problem and seek solutions. The goal of this book is to help data scientists and GIS professionals learn and implement geospatial data science workflows using Python.Throughout this book, you’ll uncover numerous geospatial Python libraries with which you can develop end-to-end spatial data science workflows. You’ll learn how to read, process, and manipulate spatial data effectively. With data in hand, you’ll move on to crafting spatial data visualizations to better understand and tell the story of your data through static and dynamic mapping applications. As you progress through the book, you’ll find yourself developing geospatial AI and ML models focused on clustering, regression, and optimization. The use cases can be leveraged as building blocks for more advanced work in a variety of industries.By the end of the book, you’ll be able to tackle random data, find meaningful correlations, and make geospatial data models.

Applied Machine Learning and High-Performance Computing on AWS. Accelerate the development of machine learning applications following architectural best practices

Mani Khanuja, Farooq Sabir , Shreyas Subramanian,...

Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles.This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you’ll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases.By the end of this book, you’ll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle.

Applied Machine Learning Explainability Techniques. Make ML models explainable and trustworthy for practical applications using LIME, SHAP, and more

Aditya Bhattacharya

Explainable AI (XAI) is an emerging field that brings artificial intelligence (AI) closer to non-technical end users. XAI makes machine learning (ML) models transparent and trustworthy along with promoting AI adoption for industrial and research use cases.Applied Machine Learning Explainability Techniques comes with a unique blend of industrial and academic research perspectives to help you acquire practical XAI skills. You'll begin by gaining a conceptual understanding of XAI and why it's so important in AI. Next, you'll get the practical experience needed to utilize XAI in AI/ML problem-solving processes using state-of-the-art methods and frameworks. Finally, you'll get the essential guidelines needed to take your XAI journey to the next level and bridge the existing gaps between AI and end users.By the end of this ML book, you'll be equipped with best practices in the AI/ML life cycle and will be able to implement XAI methods and approaches using Python to solve industrial problems, successfully addressing key pain points encountered.

Applied Machine Learning for Healthcare and Life Sciences using AWS. Transformational AI implementations for biotech, clinical, and healthcare organizations

Ujjwal Ratan

While machine learning is not new, it's only now that we are beginning to uncover its true potential in the healthcare and life sciences industry. The availability of real-world datasets and access to better compute resources have helped researchers invent applications that utilize known AI techniques in every segment of this industry, such as providers, payers, drug discovery, and genomics.This book starts by summarizing the introductory concepts of machine learning and AWS machine learning services. You’ll then go through chapters dedicated to each segment of the healthcare and life sciences industry. Each of these chapters has three key purposes -- First, to introduce each segment of the industry, its challenges, and the applications of machine learning relevant to that segment. Second, to help you get to grips with the features of the services available in the AWS machine learning stack like Amazon SageMaker and Amazon Comprehend Medical. Third, to enable you to apply your new skills to create an ML-driven solution to solve problems particular to that segment. The concluding chapters outline future industry trends and applications.By the end of this book, you’ll be aware of key challenges faced in applying AI to healthcare and life sciences industry and learn how to address those challenges with confidence.

Applied Network Security. Proven tactics to detect and defend against all kinds of network attack

Warun Levesque, Arthur Salmon, Michael McLafferty

Computer networks are increasing at an exponential rate and the most challenging factor organisations are currently facing is network security. Breaching a network is not considered an ingenious effort anymore, so it is very important to gain expertise in securing your network.The book begins by showing you how to identify malicious network behaviour and improve your wireless security. We will teach you what network sniffing is, the various tools associated with it, and how to scan for vulnerable wireless networks. Then we’ll show you how attackers hide the payloads and bypass the victim’s antivirus. Furthermore, we’ll teach you how to spoof IP / MAC address and perform an SQL injection attack and prevent it on your website. We will create an evil twin and demonstrate how to intercept network traffic. Later, you will get familiar with Shodan and Intrusion Detection and will explore the features and tools associated with it. Toward the end, we cover tools such as Yardstick, Ubertooth, Wifi Pineapple, and Alfa used for wireless penetration testing and auditing. This book will show the tools and platform to ethically hack your own network whether it is for your business or for your personal home Wi-Fi.

Applied Unsupervised Learning with R. Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA

Alok Malik, Bradford Tuckfield

Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and features of R that enable you to understand your data better and get answers to your most pressing business questions. This book begins with the most important and commonly used method for unsupervised learning - clustering - and explains the three main clustering algorithms - k-means, divisive, and agglomerative. Following this, you'll study market basket analysis, kernel density estimation, principal component analysis, and anomaly detection. You'll be introduced to these methods using code written in R, with further instructions on how to work with, edit, and improve R code. To help you gain a practical understanding, the book also features useful tips on applying these methods to real business problems, including market segmentation and fraud detection. By working through interesting activities, you'll explore data encoders and latent variable models. By the end of this book, you will have a better understanding of different anomaly detection methods, such as outlier detection, Mahalanobis distances, and contextual and collective anomaly detection.

Applying and Extending Oracle Spatial. This guide takes you straight into the attributes of Oracle Spatial and teaches you to extend, apply, and combine them with other Oracle and open source technologies. A vital manual for solving everyday problems

Simon Greener, Siva Ravada

Spatial applications should be developed in the same way that users develop other database applications: by starting with an integrated data model in which the SDO_GEOMETRY objects are just another attribute describing entities and by using as many of the database features as possible for managing the data. If a task can be done using a database feature like replication, then it should be done using the standard replication technology instead of inventing a new procedure for replicating spatial data. Sometimes solving a business problem using a PL/SQL function can be more powerful, accessible, and easier to use than trying to use external software. Because Oracle Spatial's offerings are standards compliant, this book shows you how Oracle Spatial technology can be used to build cross-vendor database solutions. Applying and Extending Oracle Spatial shows you the clever things that can be done not just with Oracle Spatial on its own, but in combination with other database technologies. This is a great resource book that will convince you to purchase other Oracle technology books on non-spatial specialist technologies because you will finally see that spatial is not special: it is a small, fun, and clever part of a much larger whole.

Applying Artificial Intelligence in Project Management. Harness the power of AI to transform project management practices

Mercury Learning and Information, Paul Boudreau

Artificial intelligence is reshaping the way projects are managed, offering unprecedented opportunities to improve efficiency, accuracy, and outcomes. This course begins with an introduction to AI’s role in project management, exploring how machine learning, natural language processing, and predictive algorithms can transform traditional approaches. You’ll learn about the key components of AI-driven projects and how to develop a strong business case for adopting these innovations.As you progress, the course delves into practical applications of AI in automating project tasks, analyzing data, and predicting results. Participants will gain hands-on experience with tools that leverage machine learning to forecast project success, improve productivity, and resolve potential failures. Additionally, you’ll discover how generative AI and large language models can enhance communication, planning, and decision-making throughout the project lifecycle.Finally, the course examines the broader implications of integrating AI into project management. You’ll explore strategies for acquiring AI solutions, implementing them within teams, and navigating the ethical challenges they present. By the end of the course, participants will have a clear understanding of how to leverage AI to optimize projects and stay competitive in a rapidly evolving technological landscape.

Applying Math with Python. Over 70 practical recipes for solving real-world computational math problems - Second Edition

Sam Morley

The updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX.You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you’ve developed a solid base in these topics, you’ll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code.By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.