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Artificial intelligence
\"Artificial intelligence is just as artificial as it always was, but it's gotten considerably more intelligent lately. This book helps you stay in the know about how artificial intelligence is changing, and changing the world. You'll learn about the latest generative AI tools as well as the expert systems that are changing industries. This beginner-friendly guide also delves into the role of data in AI, so you can understand where AI is getting the information it gives us. Hungry for more? Explore the fascinating, behind-the-scenes AI systems that are transforming just about everything you (yes, you) do\"-- Back cover.
BOOK
Principles of Data Fabric
Apply Data Fabric solutions to automate Data Integration, Data Sharing, and Data Protection across disparate data sources using different data management styles. Purchase of the print or Kindle book includes a free PDF eBook
Key Features
Learn to design Data Fabric architecture effectively with your choice of toolBuild and use a Data Fabric solution using DataOps and Data Mesh frameworksFind out how to build Data Integration, Data Governance, and Self-Service analytics architecture
Book Description
Data can be found everywhere, from cloud environments and relational and non-relational databases to data lakes, data warehouses, and data lakehouses. Data management practices can be standardized across the cloud, on-premises, and edge devices with Data Fabric, a powerful architecture that creates a unified view of data. This book will enable you to design a Data Fabric solution by addressing all the key aspects that need to be considered. The book begins by introducing you to Data Fabric architecture, why you need them, and how they relate to other strategic data management frameworks. You’ll then quickly progress to grasping the principles of DataOps, an operational model for Data Fabric architecture. The next set of chapters will show you how to combine Data Fabric with DataOps and Data Mesh and how they work together by making the most out of it. After that, you’ll discover how to design Data Integration, Data Governance, and Self-Service analytics architecture. The book ends with technical architecture to implement distributed data management and regulatory compliance, followed by industry best practices and principles. By the end of this data book, you will have a clear understanding of what Data Fabric is and what the architecture looks like, along with the level of effort that goes into designing a Data Fabric solution.
What you will learn
Understand the core components of Data Fabric solutionsCombine Data Fabric with Data Mesh and DataOps frameworksImplement distributed data management and regulatory compliance using Data FabricManage and enforce Data Governance with active metadata using Data FabricExplore industry best practices for effectively implementing a Data Fabric solution
Who this book is for
If you are a data engineer, data architect, or business analyst who wants to learn all about implementing Data Fabric architecture, then this is the book for you. This book will also benefit senior data professionals such as chief data officers looking to integrate Data Fabric architecture into the broader ecosystem.
eBook
Information systems : intelligent information processing systems, natural language processing, affective computing and artificial intelligence, and an attempt to build a conversational nursing robot
This text deals with intelligent information processing systems related to natural language processing, text mining, web information processing, and nursing and caring robot technologies. It introduces the latest trends and past research results of researchers in a wide range of fields related to knowledge information processing, which is one of the ultimate goals of information processing technology and is necessary for making artificial brains useful in our society.
Book
Artificial Intelligence and Expert Systems
This book is designed to identify some of the current applications and techniques of artificial intelligence as an aid to solving problems and accomplishing tasks. It provides a general introduction to the various branches of AI which include formal logic, reasoning, knowledge engineering, expert systems, neural networks, and fuzzy logic, etc. The book has been structured into five parts with an emphasis on expert systems: problems and state space search, knowledge engineering, neural networks, fuzzy logic, and Prolog.
eBook
The Emerging Role of AI-Based Expert Systems in Cyber Defense and Security
The world, especially developing countries, is going through digital transformation. Digital transformation of businesses, cities, manufacturing sector, service sector, government offices and procedures, vehicles (Internet enabled vehicles), and Industry 4.0 are taking place at a distinguished pace and have many rewards. Digital transformation helps to address many social, techno-financial, and governance issues, but it also carries many challenges like increased volume of cyber-attacks, privacy concerns, security threats, digital identity threat, phishing frauds, operational security, and many other vulnerabilities. Aforesaid challenges have become a common phenomenon in today's interconnected world. It is the time when cyber-security must be stationed as a central issue and be established as a management issue besides a technological issue. The COVID-19 pandemic has accelerated the digital transformation. Work from home (remote working) and learn from home (online learning) are the new normal of life. The repercussions set by the COVID-19 pandemic will outlast the post-pandemic era. AI can help human society to resolve modern issues of cyber security. AI can be used by organizations to mitigate risks and increase revenue by detecting cyber threats and fraud at an early stage. Although keeping up with new viruses and malware updates is becoming more difficult, cyber security using artificial intelligence technologies will facilitate the detection and response to threats and malware by using previous cyber-attack data to determine the best course of action. AI may often be better and more effective than humans in detecting malicious malware. The faster the data breach was identified and contained, the lower the costs. This book will fill the gaps between artificial intelligence and its usage in cyber security and defense.
eBook
Vision based hand gesture recognition for human computer interaction: a survey
As computers become more pervasive in society, facilitating natural human-computer interaction (HCI) will have a positive impact on their use. Hence, there has been growing interest in the development of new approaches and technologies for bridging the human-computer barrier. The ultimate aim is to bring HCI to a regime where interactions with computers will be as natural as an interaction between humans, and to this end, incorporating gestures in HCI is an important research area. Gestures have long been considered as an interaction technique that can potentially deliver more natural, creative and intuitive methods for communicating with our computers. This paper provides an analysis of comparative surveys done in this area. The use of hand gestures as a natural interface serves as a motivating force for research in gesture taxonomies, its representations and recognition techniques, software platforms and frameworks which is discussed briefly in this paper. It focuses on the three main phases of hand gesture recognition i.e. detection, tracking and recognition. Different application which employs hand gestures for efficient interaction has been discussed under core and advanced application domains. This paper also provides an analysis of existing literature related to gesture recognition systems for human computer interaction by categorizing it under different key parameters. It further discusses the advances that are needed to further improvise the present hand gesture recognition systems for future perspective that can be widely used for efficient human computer interaction. The main goal of this survey is to provide researchers in the field of gesture based HCI with a summary of progress achieved to date and to help identify areas where further research is needed.[PUBLICATION ABSTRACT]
Journal Article
Artificial Intelligence for Developers in Easy Steps
Artificial Intelligence for Developers in easy steps is for coders who want to enhance their skillset quickly and easily. Artificial Intelligence (AI) is here to stay, and this guide reveals how AI works and illustrates how to build AI applications. It even covers no-code AI tools. This primer comes with free downloadable source code to get you started straightaway. Topics covered include:Creating a chatbot.Building an expert system. Understanding the flatworld, fuzzy logic, and subsumption architecture. Genetic algorithms, neural networks, generative AI, and low code. Aimed at aspiring developers and students who are familiar with Python and now want to master AI concepts and build intelligent AI solutions. AI programming is mainstream now. Update your coding skills and stay on top!
eBook
A systematic review and taxonomy of explanations in decision support and recommender systems
With the recent advances in the field of artificial intelligence, an increasing number of decision-making tasks are delegated to software systems. A key requirement for the success and adoption of such systems is that users must trust system choices or even fully automated decisions. To achieve this, explanation facilities have been widely investigated as a means of establishing trust in these systems since the early years of expert systems. With today’s increasingly sophisticated machine learning algorithms, new challenges in the context of explanations, accountability, and trust towards such systems constantly arise. In this work, we systematically review the literature on explanations in advice-giving systems. This is a family of systems that includes recommender systems, which is one of the most successful classes of advice-giving software in practice. We investigate the purposes of explanations as well as how they are generated, presented to users, and evaluated. As a result, we derive a novel comprehensive taxonomy of aspects to be considered when designing explanation facilities for current and future decision support systems. The taxonomy includes a variety of different facets, such as explanation objective, responsiveness, content and presentation. Moreover, we identified several challenges that remain unaddressed so far, for example related to fine-grained issues associated with the presentation of explanations and how explanation facilities are evaluated.
Journal Article
On the assessment of generative AI in modeling tasks: an experience report with ChatGPT and UML
Most experts agree that large language models (LLMs), such as those used by Copilot and ChatGPT, are expected to revolutionize the way in which software is developed. Many papers are currently devoted to analyzing the potential advantages and limitations of these generative AI models for writing code. However, the analysis of the current state of LLMs with respect to software modeling has received little attention. In this paper, we investigate the current capabilities of ChatGPT to perform modeling tasks and to assist modelers, while also trying to identify its main shortcomings. Our findings show that, in contrast to code generation, the performance of the current version of ChatGPT for software modeling is limited, with various syntactic and semantic deficiencies, lack of consistency in responses and scalability issues. We also outline our views on how we perceive the role that LLMs can play in the software modeling discipline in the short term, and how the modeling community can help to improve the current capabilities of ChatGPT and the coming LLMs for software modeling.
Journal Article
A survey on indexing techniques for big data: taxonomy and performance evaluation
The explosive growth in volume, velocity, and diversity of data produced by mobile devices and cloud applications has contributed to the abundance of data or ‘big data.’ Available solutions for efficient data storage and management cannot fulfill the needs of such heterogeneous data where the amount of data is continuously increasing. For efficient retrieval and management, existing indexing solutions become inefficient with the rapidly growing index size and seek time and an optimized index scheme is required for big data. Regarding real-world applications, the indexing issue with big data in cloud computing is widespread in healthcare, enterprises, scientific experiments, and social networks. To date, diverse soft computing, machine learning, and other techniques in terms of artificial intelligence have been utilized to satisfy the indexing requirements, yet in the literature, there is no reported state-of-the-art survey investigating the performance and consequences of techniques for solving indexing in big data issues as they enter cloud computing. The objective of this paper is to investigate and examine the existing indexing techniques for big data. Taxonomy of indexing techniques is developed to provide insight to enable researchers understand and select a technique as a basis to design an indexing mechanism with reduced time and space consumption for BD-MCC. In this study, 48 indexing techniques have been studied and compared based on 60 articles related to the topic. The indexing techniques’ performance is analyzed based on their characteristics and big data indexing requirements. The main contribution of this study is taxonomy of categorized indexing techniques based on their method. The categories are non-artificial intelligence, artificial intelligence, and collaborative artificial intelligence indexing methods. In addition, the significance of different procedures and performance is analyzed, besides limitations of each technique. In conclusion, several key future research topics with potential to accelerate the progress and deployment of artificial intelligence-based cooperative indexing in BD-MCC are elaborated on.
Journal Article