Increasingly global and online social interactions and financial transactions involve digital data, computing devices and the internet. With cloud computing, remote computing, enterprise mobility and e-commerce on the rise, network security has become a priority. Selecting an appropriate algorithm and policy is a challenge for computer security engineers, as new technologies provide malicious users with opportunities to intrude into computer networks. New Age Cyber Threat Mitigation for Cloud Computing Networks provides cloud and network engineers answers to cybersecurity challenges. It highlights new options, methodologies and feasible solutions that can be implemented in cloud architecture and IT Infrastructure, thereby securing end users. Chapters cover many topics related to cyber threats in the modern era. These topics include: · Ransomware and DDoS attacks · Security algorithms · Design and implementation solutions for resilient and fault-tolerant cloud and network services · Security policy · End user data security The book is an essential resource for anyone involved in cloud computing and network security, including learners, professionals and enthusiasts.

This book presents research focused on the design of fractal antennas using bio-inspired computing techniques. The authors present designs for fractal antennas having desirable features like size reduction characteristics, enhanced gain, and improved bandwidths. The research is summarized in six chapters which highlight the important issues related to fractal antenna design and the mentioned computing techniques. Chapters demonstrate several applied concepts and techniques used in the process such as Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), Particle Swarm Optimization (PSO) and Bacterial Foraging Optimization (BFO). The work aims to provide cost-effective and efficient solutions to the demand for compact antennas due to the increasing demand for reduced sizes of components in modern wireless communication devices. A key feature of the book includes an extensive literature survey to understand the concept of fractal antennas, their features, and design approaches. Another key feature is the systematic approach to antenna design. The book explains how the IE3D software is used to simulate various fractal antennas, and how the results can be used to select a design. This is followed by ANN model development and testing for optimization, and an exploration of ANN ensemble models for the design of fractal antennas. The bio-inspired computing techniques based on GA, PSO, and BFO are developed to find the optimal design of the proposed fractal antennas for the desired applications. The performance comparison of the given computing techniques is also explained to demonstrate how to select the best algorithm for a given bio-inspired design. Finally, the book explains how to evaluate antenna designs. This book is a valuable resource for students (from UG to PG levels) and research scholars undertaking learning modules or projects on microstrip and patch antenna design in communications or electronics engineering courses.

This book highlights the applications of deep learning algorithms in implementing big data and IoT enabled smart solutions to treat and care for terminally ill patients. It presents 5 concise chapters showing how these technologies can empower the conventional doctor patient relationship in a more dynamic, transparent, and personalized manner. The key topics covered in this book include: - The Role of Deep Learning in Healthcare Industry: Limitations - Generative Adversarial Networks for Deep Learning in Healthcare - The Role of Blockchain in the Healthcare Sector - Brain Tumor Detection Based on Different Deep Neural Networks Key features include a thorough, research-based overview of technologies that can assist deep learning models in the healthcare sector, including architecture and industrial scope. The book also presents a robust image processing model for brain tumor screening. Through this book, the editors have attempted to combine numerous compelling views, guidelines and frameworks. Healthcare industry professionals will understand how Deep Learning can improve health care service delivery.

Artificial Intelligence and Data Science in Recommendation System: Current Trends, Technologies and Applications captures the state of the art in usage of artificial intelligence in different types of recommendation systems and predictive analysis. The book provides guidelines and case studies for application of artificial intelligence in recommendation from expert researchers and practitioners. A detailed analysis of the relevant theoretical and practical aspects, current trends and future directions is presented. The book highlights many use cases for recommendation systems: - Basic application of machine learning and deep learning in recommendation process and the evaluation metrics - Machine learning techniques for text mining and spam email filtering considering the perspective of Industry 4.0 - Tensor factorization in different types of recommendation system - Ranking framework and topic modeling to recommend author specialization based on content. - Movie recommendation systems - Point of interest recommendations - Mobile tourism recommendation systems for visually disabled persons - Automation of fashion retail outlets - Human resource management (employee assessment and interview screening) This reference is essential reading for students, faculty members, researchers and industry professionals seeking insight into the working and design of recommendation systems.

This book is a detailed reference guide on deep learning and its applications. It aims to provide a basic understanding of deep learning and its different architectures that are applied to process images, speech, and natural language. It explains basic concepts and many modern use cases through fifteen chapters contributed by computer science academics and researchers. By the end of the book, the reader will become familiar with different deep learning approaches and models, and understand how to implement various deep learning algorithms using multiple frameworks and libraries. The second part is dedicated to sentiment analysis using deep learning and machine learning techniques. This book section covers the experimentation and application of deep learning techniques and architectures in real-world applications. It details the salient approaches, issues, and challenges in building ethically aligned machines. An approach inspired by traditional Eastern thought and wisdom is also presented. The final part covers artificial intelligence approaches used to explain the machine learning models that enhance transparency for the benefit of users. A review and detailed description of the use of knowledge graphs in generating explanations for black-box recommender systems and a review of ethical system design and a model for sustainable education is included in this section. An additional chapter demonstrates how a semi-supervised machine-learning technique can be used for cryptocurrency portfolio management. The book is a timely reference for academicians, professionals, researchers and students at engineering and medical institutions working on artificial intelligence applications.

Big Data is playing a vital role in HCI projects across a range of industries: healthcare, cybersecurity, forensics, education, business organizations, and scientific research. Big data analytics requires advanced tools and techniques to store, process and analyze the huge volume of data. Working on HCI projects requires specific skill sets to implement IT solutions. Big Data Analytics for Human-Computer Interactions: A New Era of Computation is a comprehensive guide that discusses the evolution of Big Data in Human Computer Interaction from promise to reality. This book provides an introduction to Big Data and HCI, followed by an overview of the state-of-the-art algorithms for processing big data, Subsequent chapters also explain the characteristics, applications, opportunities and challenges of big data systems, by describing theoretical, practical, and simulation concepts of computational intelligence and big data analytics used in designing HIC systems. The book also presents solutions for analyzing complex patterns in user data and improving productivity. Readers will be able to understand the technology that drives big data solutions in HCI projects and understand its capacity in transforming an organization.The book also helps the reader to understand HCI system design and explains how to evaluate an application portfolio that can be used when selecting pilot projects. This book is a resource for researchers, students, and professionals interested in the fields of HCI, artificial intelligence, data analytics, and computer engineering.

Numerical Machine Learning is a simple textbook on machine learning that bridges the gap between mathematics theory and practice. The book uses numerical examples with small datasets and simple Python codes to provide a complete walkthrough of the underlying mathematical steps of seven commonly used machine learning algorithms and techniques, including linear regression, regularization, logistic regression, decision trees, gradient boosting, Support Vector Machine, and K-means Clustering. Through a step-by-step exploration of concrete numerical examples, the students (primarily undergraduate and graduate students studying machine learning) can develop a well-rounded understanding of these algorithms, gain an in-depth knowledge of how the mathematics relates to the implementation and performance of the algorithms, and be better equipped to apply them to practical problems. Key features - Provides a concise introduction to numerical concepts in machine learning in simple terms - Explains the 7 basic mathematical techniques used in machine learning problems, with over 60 illustrations and tables - Focuses on numerical examples while using small datasets for easy learning - Includes simple Python codes - Includes bibliographic references for advanced reading The text is essential for college and university-level students who are required to understand the fundamentals of machine learning in their courses.

Marvels of Artificial and Computational Intelligence in Life Sciences is a primer for scholars and students who are interested in the applications of artificial intelligence (AI) and computational intelligence (CI) in life sciences and other industries. The book consists of 16 chapters (9 of which focus on AI and 7 of which showcase the benefits of CI approaches to solve specific problems). Chapters are edited by subject experts who describe the roles and applications of AI and CI in different parts of our lives in a concise and lucid manner. The book covers the following key themes: AI Revolution in Healthcare and Drug Discovery: AI's Impact on Biology and Energy Management AI and CI in Physical Sciences and Predictive Modeling Computational Biology The editors have compiled a good blend of topics in applied science and engineering to give readers a clear understanding of the multidisciplinary nature of the two facets of computing. Each chapter includes references for advanced readers.

Intelligent Computational Systems presents current and future developments in intelligent computational systems in a multi-disciplinary context. Readers will learn about the pervasive and ubiquitous roles of artificial intelligence (AI) and gain a perspective about the need for intelligent systems to behave rationally when interacting with humans in complex and realistic domains. This reference covers widespread applications of AI discussed in 11 chapters which cover topics such as AI and behavioral simulations, AI schools, automated negotiation, language analysis and learning, financial prediction, sensor management, Multi-agent systems, and much more. This reference work is will assist researchers, advanced-level students and practitioners in information technology and computer science fields interested in the broad applications of AI.

Text Analysis with Python: A Research-Oriented Guide is a quick and comprehensive reference on text mining using python code. The main objective of the book is to equip the reader with the knowledge to apply various machine learning and deep learning techniques to text data. The book is organized into eight chapters which present the topic in a structured and progressive way. Key Features - Introduces the reader to Python programming and data processing - Introduces the reader to the preliminaries of natural language processing (NLP) - Covers data analysis and visualization using predefined python libraries and datasets - Teaches how to write text mining programs in Python - Includes text classification and clustering techniques - Informs the reader about different types of neural networks for text analysis - Includes advanced analytical techniques such as fuzzy logic and deep learning techniques - Explains concepts in a simplified and structured way that is ideal for learners - Includes References for further reading Text Analysis with Python: A Research-Oriented Guide is an ideal guide for students in data science and computer science courses, and for researchers and analysts who want to work on artificial intelligence projects that require the application of text mining and NLP techniques.