Advancements in the nature-inspired swarm intelligence algorithms continue to be useful in solving complicated problems in nonlinear, non-differentiable, and un-continuous functions as well as being applied to solve real-world applications. Recent Algorithms and Applications in Swarm Intelligence Research highlights the current research on swarm intelligence algorithms and its applications. Including research and survey and application papers, this book serves as a platform for students and scholars interested in achieving their studies on swarm intelligence algorithms and their applications.

Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learning Key Features A no-math, code-driven programmer's guide to text processing and NLP Get state of the art results with modern tooling across linguistics, text vectors and machine learning Fundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorch Book DescriptionNLP in Python is among the most sought after skills among data scientists. With code and relevant case studies, this book will show how you can use industry-grade tools to implement NLP programs capable of learning from relevant data. We will explore many modern methods ranging from spaCy to word vectors that have reinvented NLP. The book takes you from the basics of NLP to building text processing applications. We start with an introduction to the basic vocabulary along with a workflow for building NLP applications. We use industry-grade NLP tools for cleaning and pre-processing text, automatic question and answer generation using linguistics, text embedding, text classifier, and building a chatbot. With each project, you will learn a new concept of NLP. You will learn about entity recognition, part of speech tagging and dependency parsing for Q and A. We use text embedding for both clustering documents and making chatbots, and then build classifiers using scikit-learn. We conclude by deploying these models as REST APIs with Flask. By the end, you will be confident building NLP applications, and know exactly what to look for when approaching new challenges. What you will learn Understand classical linguistics in using English grammar for automatically generating questions and answers from a free text corpus Work with text embedding models for dense number representations of words, subwords and characters in the English language for exploring document clustering Deep Learning in NLP using PyTorch with a code-driven introduction to PyTorch Using an NLP project management Framework for estimating timelines and organizing your project into stages Hack and build a simple chatbot application in 30 minutes Deploy an NLP or machine learning application using Flask as RESTFUL APIs Who this book is forProgrammers who wish to build systems that can interpret language. Exposure to Python programming is required. Familiarity with NLP or machine learning vocabulary will be helpful, but not mandatory.

Analysis and Design of Algorithms provides a structured view of algorithm design techniques in a concise, easy-to-read manner. The book was written with an express purpose of being easy - to understand, read, and carry. It presents a pioneering approach in the teaching of algorithms, based on learning algorithm design techniques, and not merely solving a collection of problems. This allows students to master one design technique at a time and apply it to a rich variety of problems. Analysis and Design of Algorithms covers the algorithmic design techniques of divide and conquer, greedy, dynamic programming, branch and bound, and graph traversal. For each of these techniques, there are templates and guidelines on when to use and not to use each technique. Many sections contain innovative mnemonics to aid the readers in remembering the templates and key takeaways. Additionally, the book covers NP-completeness and the inherent hardness of problems. The third edition includes a new section on polynomial multiplication, as well as additional exercise problems, and an updated appendix. Written with input from students and professionals, Analysis and Design of Algorithms is well suited for introductory algorithm courses at the undergraduate and graduate levels. The structured organization of the text makes it especially appropriate for online and distance learning.

Although recognized as a key to the design process, prototyping often falls victim to budget cuts, deadlines, or lack of access to sophisticated tools. This can lead to sloppy and ineffective prototypes or the abandonment of them altogether. Rather than lose this important step, people are turning to Microsoft Excel(r) to create effective, simple, and inexpensive prototypes. Conveniently, the software is available to nearly everyone, and most are proficient in its basic functionality.
Effective Prototyping with Excel offers how-to guidance on how everyone can use basic Excel skills to create prototypes ranging from narrative wire frames to hi-fidelity prototypes. A wide array of software design problems and business demands are solved via practical step-by-step examples and illustrations.
Step-by-step guide to prototyping with a simple and affordable tool nearly everyone already has on their desktop.
Quickly and easily allows web and software designers to explore usability, design alternatives, and test theories prior to starting production.
Perfect companion to Effective Prototyping for Software Makers with the same author team and full-color treatment, useful case studies, and hands-on exercises."

Philosophical paradigms, theoretical frameworks, and methodologies make up the answering and problem solving systems that define current research approaches. While there are multiple research method books, the subject lacks an update and integrated source of reference for graduate courses. Research Methodologies, Innovations and Philosophies in Software Systems Engineering and Information Systems aims to advance scientific knowledge on research approaches used in systems engineering, software engineering, and information systems and to update and integrate disperse and valuable knowledge on research approaches. This aims to be a collection of knowledge for PhD students, research-oriented faculty, and instructors of graduate courses.

Advances in web technology and the proliferation of sensors and mobile devices connected to the internet have resulted in the generation of immense data sets available on the web that need to be represented, saved, and exchanged. Massive data can be managed effectively and efficiently to support various problem-solving and decision-making techniques. Emerging Technologies and Applications in Data Modeling and Processing is a critical scholarly publication that examines the importance of data management strategies that coincide with advancements in web technologies. Highlighting topics such as geospatial coverages, data analysis, and keyword query, this book is ideal for professionals, researchers, academicians, data analysts, web developers, and web engineers.

As modern organizations migrate from older information architectures to new Web-based systems, the discipline of software engineering is changing both in terms of technologies and methodologies. There is a need to examine this new frontier from both a theoretical and pragmatic perspective, and offer not only a survey of new technologies and methodologies but discussions of the applicability and pros/cons of each.

Software Engineering for Modern Web Applications: Methodologies and Technologies presents current, effective software engineering methods for the design and development of modern Web-based applications, offering scholars, researchers, and practitioners innovative research on the theoretical frameworks, structures, management, and implications software engineering for modern Web applications.

The Definitive, Practical, Proven Guide to Architecting Modern Software--Fully Updated with New Content on Mobility, the Cloud, Energy Management, DevOps, Quantum Computing, and More Updated with eleven new chapters, Software Architecture in Practice, Fourth Edition, thoroughly explains what software architecture is, why it's important, and how to design, instantiate, analyze, evolve, and manage it in disciplined and effective ways. Three renowned software architects cover the entire lifecycle, presenting practical guidance, expert methods, and tested models for use in any project, no matter how complex. You'll learn how to use architecture to address accelerating growth in requirements, system size, and abstraction, and to manage emergent quality attributes as systems are dynamically combined in new ways. With insights for utilizing architecture to optimize key quality attributes--including performance, modifiability, security, availability, interoperability, testability, usability, deployability, and more--this guide explains how to manage and refine existing architectures, transform them to solve new problems, and build reusable architectures that become strategic business assets. Discover how architecture influences (and is influenced by) technical environments, project lifecycles, business profiles, and your own practices Leverage proven patterns, interfaces, and practices for optimizing quality through architecture Architect for mobility, the cloud, machine learning, and quantum computing Design for increasingly crucial attributes such as energy efficiency and safety Scale systems by discovering architecturally significant influences, using DevOps and deployment pipelines, and managing architecture debt Understand architecture's role in the organization, so you can deliver more value Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.

The Newnes Know It All Series takes the best of what our authors have written to create hard-working desk references that will be an engineer's first port of call for key information, design techniques and rules of thumb. Guaranteed not to gather dust on a shelf!
Embedded software is present everywhere - from a garage door opener to implanted medical devices to multicore computer systems. This book covers the development and testing of embedded software from many different angles and using different programming languages. Optimization of code, and the testing of that code, are detailed to enable readers to create the best solutions on-time and on-budget. Bringing together the work of leading experts in the field, this a comprehensive reference that every embedded developer will need!
Chapter 1: Basic Embedded Programming Concepts
Chapter 2: Device Drivers
Chapter 3: Embedded Operating Systems
Chapter 4: Networking
Chapter 5: Error Handling and Debugging
Chapter 6: Hardware/Software Co-Verification
Chapter 7: Techniques for Embedded Media Processing
Chapter 8: DSP in Embedded Systems
Chapter 9: Practical Embedded Coding Techniques
Chapter 10: Development Technologies and Trends
*Proven, real-world advice and guidance from such "name" authors as Tammy Noergard, Jen LaBrosse, and Keith Curtis
*Popular architectures and languages fully discussed
*Gives a comprehensive, detailed overview of the techniques and methodologies for developing effective, efficient embedded software