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Expert Systems and Applied Artificial Intelligence
Author: Efraim Turban
Publisher: Macmillan College
Published: 1992
Total Pages: 840
ISBN-13:
DOWNLOAD EBOOK →"This book is devoted mainly to applied expert systems. It does cover four additional applied AI Topics: natural language processing, computer vision, speech understanding and intelligent robotics"--Preface
Rule Based Systems for Big Data
Author: Han Liu
Publisher: Springer
Published: 2015-09-09
Total Pages: 121
ISBN-13: 3319236962
DOWNLOAD EBOOK →The ideas introduced in this book explore the relationships among rule based systems, machine learning and big data. Rule based systems are seen as a special type of expert systems, which can be built by using expert knowledge or learning from real data. The book focuses on the development and evaluation of rule based systems in terms of accuracy, efficiency and interpretability. In particular, a unified framework for building rule based systems, which consists of the operations of rule generation, rule simplification and rule representation, is presented. Each of these operations is detailed using specific methods or techniques. In addition, this book also presents some ensemble learning frameworks for building ensemble rule based systems.
Intelligent Systems
Author: Crina Grosan
Publisher: Springer Science & Business Media
Published: 2011-07-29
Total Pages: 456
ISBN-13: 364221004X
DOWNLOAD EBOOK →Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains in other words) and are beginning to be built to perform a variety of tasks that are difficult or impossible to do with conventional computers. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. This book comprising of 17 chapters offers a step-by-step introduction (in a chronological order) to the various modern computational intelligence tools used in practical problem solving. Staring with different search techniques including informed and uninformed search, heuristic search, minmax, alpha-beta pruning methods, evolutionary algorithms and swarm intelligent techniques; the authors illustrate the design of knowledge-based systems and advanced expert systems, which incorporate uncertainty and fuzziness. Machine learning algorithms including decision trees and artificial neural networks are presented and finally the fundamentals of hybrid intelligent systems are also depicted. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques, machine learning and data mining would find the comprehensive coverage of this book invaluable.
Verification and Validation of Rule-Based Expert Systems
Author: Suzanne Smith
Publisher: CRC Press
Published: 1993-08-16
Total Pages: 224
ISBN-13: 9780849389023
DOWNLOAD EBOOK →This book presents an innovative approach to verifying and validating rule-based expert systems. It features a complete set of techniques and tools that provide a more formal, objective, and automated means of carrying out verification and validation procedures. Many of the concepts behind these procedures have been adapted from conventional software, while others have required that new techniques or tools be created because of the uniqueness of rule-based expert systems. Verification and Validation of Rule-Based Expert Systems is a valuable reference for electrical engineers, software engineers, artificial intelligence experts, and computer scientists involved with object-oriented development, expert systems, and programming languages.
Fuzzy Rule-Based Expert Systems and Genetic Machine Learning
Author: Andreas Geyer-Schulz
Publisher: Physica
Published: 1997
Total Pages: 460
ISBN-13:
DOWNLOAD EBOOK →This book integrates fuzzy rule-languages with genetic algorithms, genetic programming, and classifier systems with the goal of obtaining fuzzy rule-based expert systems with learning capabilities. The main topics are first introduced by solving small problems, then a prototype implementation of the algorithm is explained, and last but not least the theoretical foundations are given. The second edition takes into account the rapid progress in the application of fuzzy genetic algorithms with a survey of recent developments in the field. The chapter on genetic programming has been revised. An exact uniform initialization algorithm replaces the heuristic presented in the first edition. A new method of abstraction, compound derivations, is introduced.
Programming Expert Systems in OPS5
Author: Lee Brownston
Publisher: Addison Wesley Publishing Company
Published: 1985
Total Pages: 502
ISBN-13:
DOWNLOAD EBOOK →Software -- Programming Techniques.
Expert Systems and Probabilistic Network Models
Author: Enrique Castillo
Publisher: Springer Science & Business Media
Published: 2012-12-06
Total Pages: 612
ISBN-13: 1461222702
DOWNLOAD EBOOK →Artificial intelligence and expert systems have seen a great deal of research in recent years, much of which has been devoted to methods for incorporating uncertainty into models. This book is devoted to providing a thorough and up-to-date survey of this field for researchers and students.
Contemporary Issues in Audit Management and Forensic Accounting
Author: Simon Grima
Publisher: Emerald Group Publishing
Published: 2020-02-10
Total Pages: 487
ISBN-13: 183867635X
DOWNLOAD EBOOK →In the 18 chapters in this volume of Contemporary Studies in Economic and Financial Analysis, expert contributors gather together to examine the extent and characteristics of forensic accounting, a field which has been practiced for many years, but is still not internationally regulated yet.
Python Natural Language Processing
Author: Jalaj Thanaki
Publisher: Packt Publishing Ltd
Published: 2017-07-31
Total Pages: 486
ISBN-13: 1787285529
DOWNLOAD EBOOK →Leverage the power of machine learning and deep learning to extract information from text data About This Book Implement Machine Learning and Deep Learning techniques for efficient natural language processing Get started with NLTK and implement NLP in your applications with ease Understand and interpret human languages with the power of text analysis via Python Who This Book Is For This book is intended for Python developers who wish to start with natural language processing and want to make their applications smarter by implementing NLP in them. What You Will Learn Focus on Python programming paradigms, which are used to develop NLP applications Understand corpus analysis and different types of data attribute. Learn NLP using Python libraries such as NLTK, Polyglot, SpaCy, Standford CoreNLP and so on Learn about Features Extraction and Feature selection as part of Features Engineering. Explore the advantages of vectorization in Deep Learning. Get a better understanding of the architecture of a rule-based system. Optimize and fine-tune Supervised and Unsupervised Machine Learning algorithms for NLP problems. Identify Deep Learning techniques for Natural Language Processing and Natural Language Generation problems. In Detail This book starts off by laying the foundation for Natural Language Processing and why Python is one of the best options to build an NLP-based expert system with advantages such as Community support, availability of frameworks and so on. Later it gives you a better understanding of available free forms of corpus and different types of dataset. After this, you will know how to choose a dataset for natural language processing applications and find the right NLP techniques to process sentences in datasets and understand their structure. You will also learn how to tokenize different parts of sentences and ways to analyze them. During the course of the book, you will explore the semantic as well as syntactic analysis of text. You will understand how to solve various ambiguities in processing human language and will come across various scenarios while performing text analysis. You will learn the very basics of getting the environment ready for natural language processing, move on to the initial setup, and then quickly understand sentences and language parts. You will learn the power of Machine Learning and Deep Learning to extract information from text data. By the end of the book, you will have a clear understanding of natural language processing and will have worked on multiple examples that implement NLP in the real world. Style and approach This book teaches the readers various aspects of natural language Processing using NLTK. It takes the reader from the basic to advance level in a smooth way.
