Artificial Intelligence in Data Mining

Artificial Intelligence in Data Mining
Author: D. Binu,B.R. Rajakumar
Publsiher: Academic Press
Total Pages: 270
Release: 2021-02-17
ISBN: 0128206160
Category: Science
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Data Mining Book Excerpt:

Artificial Intelligence in Data Mining: Theories and Applications offers a comprehensive introduction to data mining theories, relevant AI techniques, and their many real-world applications. This book is written by experienced engineers for engineers, biomedical engineers, and researchers in neural networks, as well as computer scientists with an interest in the area. Provides coverage of the fundamentals of Artificial Intelligence as applied to data mining, including computational intelligence and unsupervised learning methods for data clustering Presents coverage of key topics such as heuristic methods for data clustering, deep learning methods for data classification, and neural networks Includes case studies and real-world applications of AI techniques in data mining, for improved outcomes in clinical diagnosis, satellite data extraction, agriculture, security and defense

Artificial Intelligence and Data Mining in Healthcare

Artificial Intelligence and Data Mining in Healthcare
Author: Malek Masmoudi,Bassem Jarboui,Patrick Siarry
Publsiher: Springer Nature
Total Pages: 195
Release: 2021
ISBN: 3030452409
Category: Artificial intelligence
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Data Mining in Healthcare Book Excerpt:

This book presents recent work on healthcare management and engineering using artificial intelligence and data mining techniques. Specific topics covered in the contributed chapters include predictive mining, decision support, capacity management, patient flow optimization, image compression, data clustering, and feature selection. The content will be valuable for researchers and postgraduate students in computer science, information technology, industrial engineering, and applied mathematics.

Machine Learning and Data Mining

Machine Learning and Data Mining
Author: Igor Kononenko,Matjaz Kukar
Publsiher: Elsevier
Total Pages: 480
Release: 2007-04-30
ISBN: 0857099442
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning and Data Mining Book Excerpt:

Data mining is often referred to by real-time users and software solutions providers as knowledge discovery in databases (KDD). Good data mining practice for business intelligence (the art of turning raw software into meaningful information) is demonstrated by the many new techniques and developments in the conversion of fresh scientific discovery into widely accessible software solutions. This book has been written as an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining. Suitable for advanced undergraduates and their tutors at postgraduate level in a wide area of computer science and technology topics as well as researchers looking to adapt various algorithms for particular data mining tasks. A valuable addition to the libraries and bookshelves of the many companies who are using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions. Provides an introduction to the main issues associated with the basics of machine learning and the algorithms used in data mining A valuable addition to the libraries and bookshelves of companies using the principles of data mining (or KDD) to effectively deliver solid business and industry solutions

Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
Author: Petra Perner
Publsiher: Springer Science & Business Media
Total Pages: 824
Release: 2009-07-21
ISBN: 364203070X
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning and Data Mining in Pattern Recognition Book Excerpt:

There is no royal road to science, and only those who do not dread the fatiguing climb of its steep paths have a chance of gaining its luminous summits. Karl Marx A Universial Genius of the 19th Century Many scientists from all over the world during the past two years since the MLDM 2007 have come along on the stony way to the sunny summit of science and have worked hard on new ideas and applications in the area of data mining in pattern r- ognition. Our thanks go to all those who took part in this year's MLDM. We appre- ate their submissions and the ideas shared with the Program Committee. We received over 205 submissions from all over the world to the International Conference on - chine Learning and Data Mining, MLDM 2009. The Program Committee carefully selected the best papers for this year’s program and gave detailed comments on each submitted paper. There were 63 papers selected for oral presentation and 17 papers for poster presentation. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data-mining methods for the different multimedia data types such as image mining, text mining, video mining and Web mining. Among these topics this year were special contributions to subtopics such as attribute discre- zation and data preparation, novelty and outlier detection, and distances and simila- ties.

Artificial Intelligence and Data Mining for Mergers and Acquisitions

Artificial Intelligence and Data Mining for Mergers and Acquisitions
Author: Debasis Chanda
Publsiher: CRC Press
Total Pages: 188
Release: 2021-03-18
ISBN: 0429755414
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Data Mining for Mergers and Acquisitions Book Excerpt:

The goal of this book is to present a modeling framework for the Virtual Organization that is focused on process composition. This framework uses Predicate Calculus Knowledge Bases. Petri Net-based modeling is also discussed. In this context, a Data Mining model is proposed, using a fuzzy mathematical approach, aiming to discover knowledge. A Knowledge-Based framework has been proposed in order to present an all-inclusive knowledge store for static and dynamic properties. Toward this direction, a Knowledge Base is created, and inferences are arrived at. This book features an advisory tool for Mergers and Acquisitions of Organizations using the Fuzzy Data Mining Framework and highlights the novelty of a Knowledge-Based Service-Oriented Architecture approach and development of an Enterprise Architectural model using AI that serves a wide audience. Students of Strategic Management in business schools and postgraduate programs in technology institutes seeking application areas of AI and Data Mining, as well as business/technology professionals in organizations aiming to create value through Mergers and Acquisitions and elsewhere, will benefit from the reading of this book.

Artificial Intelligence and Data Mining Approaches in Security Frameworks

Artificial Intelligence and Data Mining Approaches in Security Frameworks
Author: Neeraj Bhargava,Ritu Bhargava,Pramod Singh Rathore,Rashmi Agrawal
Publsiher: John Wiley & Sons
Total Pages: 320
Release: 2021-08-11
ISBN: 1119760437
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Data Mining Approaches in Security Frameworks Book Excerpt:

Artificial intelligence (AI) and data mining is the fastest growing field in computer science. AI and data mining algorithms and techniques are found to be useful in different areas like pattern recognition, automatic threat detection, automatic problem solving, visual recognition, fraud detection, detecting developmental delay in children, and many other applications. However, applying AI and data mining techniques or algorithms successfully in these areas needs a concerted effort, fostering integrative research between experts ranging from diverse disciplines from data science to Artificial Intelligence. Successful application of security frameworks to enable meaningful, cost effective, personalize security service is a primary aim of engineers and researchers today. However realizing this goal requires effective understanding, application and amalgamation of AI and Data Mining and several other computing technologies to deploy such system in an effective manner. This book provides state of the art approaches of artificial intelligence and data mining in these areas. It includes areas of detection, prediction, as well as future framework identification, development, building service systems and analytical aspects. In all these topics, applications of AI and data mining, such as artificial neural networks, fuzzy logic, genetic algorithm and hybrid mechanisms, are explained and explored. This book is aimed at the modeling and performance prediction of efficient security framework systems, bringing to light a new dimension in the theory and practice. This groundbreaking new volume presents these topics and trends, bridging the research gap on AI and data mining to enable wide-scale implementation. Whether for the veteran engineer or the student, this is a must-have for any library.

Machine Learning and Data Mining in Pattern Recognition

Machine Learning and Data Mining in Pattern Recognition
Author: Petra Perner
Publsiher: Springer Science & Business Media
Total Pages: 614
Release: 2011-08-12
ISBN: 3642231985
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning and Data Mining in Pattern Recognition Book Excerpt:

This book constitutes the refereed proceedings of the 7th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2011, held in New York, NY, USA. The 44 revised full papers presented were carefully reviewed and selected from 170 submissions. The papers are organized in topical sections on classification and decision theory, theory of learning, clustering, application in medicine, webmining and information mining; and machine learning and image mining.

Data Mining and Machine Learning Applications

Data Mining and Machine Learning Applications
Author: Rohit Raja,Kapil Kumar Nagwanshi,Sandeep Kumar,K. Ramya Laxmi
Publsiher: John Wiley & Sons
Total Pages: 496
Release: 2022-01-26
ISBN: 1119792509
Category: Computers
Language: EN, FR, DE, ES & NL

Data Mining and Machine Learning Applications Book Excerpt:

DATA MINING AND MACHINE LEARNING APPLICATIONS The book elaborates in detail on the current needs of data mining and machine learning and promotes mutual understanding among research in different disciplines, thus facilitating research development and collaboration. Data, the latest currency of today’s world, is the new gold. In this new form of gold, the most beautiful jewels are data analytics and machine learning. Data mining and machine learning are considered interdisciplinary fields. Data mining is a subset of data analytics and machine learning involves the use of algorithms that automatically improve through experience based on data. Massive datasets can be classified and clustered to obtain accurate results. The most common technologies used include classification and clustering methods. Accuracy and error rates are calculated for regression and classification and clustering to find actual results through algorithms like support vector machines and neural networks with forward and backward propagation. Applications include fraud detection, image processing, medical diagnosis, weather prediction, e-commerce and so forth. The book features: A review of the state-of-the-art in data mining and machine learning, A review and description of the learning methods in human-computer interaction, Implementation strategies and future research directions used to meet the design and application requirements of several modern and real-time applications for a long time, The scope and implementation of a majority of data mining and machine learning strategies. A discussion of real-time problems. Audience Industry and academic researchers, scientists, and engineers in information technology, data science and machine and deep learning, as well as artificial intelligence more broadly.

Data Mining Practical Machine Learning Tools and Techniques

Data Mining  Practical Machine Learning Tools and Techniques
Author: Ian H. Witten,Eibe Frank,Mark A. Hall
Publsiher: Elsevier
Total Pages: 664
Release: 2011-02-03
ISBN: 0080890369
Category: Computers
Language: EN, FR, DE, ES & NL

Data Mining Practical Machine Learning Tools and Techniques Book Excerpt:

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect the technical changes and modernizations that have taken place in the field since the last edition, including new material on Data Transformations, Ensemble Learning, Massive Data Sets, Multi-instance Learning, plus a new version of the popular Weka machine learning software developed by the authors. Witten, Frank, and Hall include both tried-and-true techniques of today as well as methods at the leading edge of contemporary research. The book is targeted at information systems practitioners, programmers, consultants, developers, information technology managers, specification writers, data analysts, data modelers, database R&D professionals, data warehouse engineers, data mining professionals. The book will also be useful for professors and students of upper-level undergraduate and graduate-level data mining and machine learning courses who want to incorporate data mining as part of their data management knowledge base and expertise. Provides a thorough grounding in machine learning concepts as well as practical advice on applying the tools and techniques to your data mining projects Offers concrete tips and techniques for performance improvement that work by transforming the input or output in machine learning methods Includes downloadable Weka software toolkit, a collection of machine learning algorithms for data mining tasks—in an updated, interactive interface. Algorithms in toolkit cover: data pre-processing, classification, regression, clustering, association rules, visualization

Data Science

Data Science
Author: Richard Hurley
Publsiher: Unknown
Total Pages: 180
Release: 2019-11-02
ISBN: 9781704636030
Category: Big data
Language: EN, FR, DE, ES & NL

Data Science Book Excerpt:

If you want to learn about data science and big data, then keep reading... Two manuscripts in one book: Data Science: What You Need to Know About Data Analytics, Data Mining, Regression Analysis, Artificial Intelligence, Big Data for Business, Data Visualization, Database Querying, and Machine Learning Big Data: A Guide to Big Data Trends, Artificial Intelligence, Machine Learning, Predictive Analytics, Internet of Things, Data Science, Data Analytics, Business Intelligence, and Data Mining This book will discuss everything that you need to know when it comes to working in the field of data science. This world has changed, and with the modern technology that we have, it is easier than ever for companies to amass a large amount of data on the industry, on their competition, on their products, and their customers. Gathering the data is the easy part, though. Being able to sort through this data and understand what it is saying is going to be a unique challenge all on its own. This is where the process and field of data science can come in. There is so much that we can explore and learn about when it comes to the world of data science, and this ultimate guide is here to help you navigate through these specialties. You will see just how important the ideas of data mining, data analytics, and even artificial intelligence are to our world as a whole today. Some of the topics covered in part 1 of this book include: What is Data Science? What Exactly Does a Data Scientist Do? A Look at What Data Analytics Is All About What is Data Mining and How Does It Fit in with Data Science? Regression Analysis Why is Data Visualization So Important When It Comes to Understanding Your Data? How to work with Database Querying A Look at Artificial Intelligence What is Machine Learning and How Is It Different from Artificial Intelligence? What is the Future of Artificial Intelligence and Machine Learning? And much more! Some of the topics covered in part 2 of this book include: What is big data, and why is it important? The five V's behind big data How big data is already impacting your life, and where big data may be headed How big data and your everyday devices and appliances will come together in unexpected ways via the Internet of Things How companies and governments are using predictive analytics to get ahead of the competition or improve service How big data is used for fraud detection How big data can train intelligent computer systems The many ways large corporations are benefiting from big data and the tools that use it like machine learning, AI, and predictive analytics Upcoming trends in big data that are sure to have a large impact on your future Artificial intelligence, and how big data drives its development What machine learning is and how it is tied to big data The relationship between big data, data analytics, and business intelligence Insights into how big data impacts privacy issues The pros and cons regarding big data And much, much more! So if you want to learn more about data science and big data, click the "add to cart" button!

MACHINE LEARNING

MACHINE LEARNING
Author: Mulayam Singh
Publsiher: BookRix
Total Pages: 36
Release: 2020-05-29
ISBN: 3748743572
Category: Education
Language: EN, FR, DE, ES & NL

MACHINE LEARNING Book Excerpt:

The Evolution of Data Science and the Information Age. Data science is a large vast time period that encompasses a variety of disciplines and standards which includes big data, Artificial Intelligence (AI), data mining and machine learning. The self-discipline of analyzing giant volumes of data recognized as 'data science', is relatively new and has grown hand-in-hand with the improvement and widespread adoption of computers. Prior to computers, records used to be calculated and processed by hand underneath the umbrella of 'statistics' or what we would possibly now refer to as 'classical statistics'. Baseball batting averages, for example, existed properly earlier than the creation of computers. Anyone with a pencil, notepad and primary arithmetic abilities could calculate Babe Ruth's batting common over a season with the useful resource of classical statistics. The method of calculating a batting common concerned the dedication of time to accumulate and assessment batting sheets, and the software of addition and division. The key factor to make about classical data is that you don't strictly need a laptop to work the statistics and draw new insight. As you're working with small facts units it is feasible to even for pre-university college students to conduct statistics. Indeed data is nevertheless taught in colleges today, and as they have been for centuries. In this book you will learn all about machine learning and data mining. Hope you will love this book.

Intelligent Data Mining and Fusion Systems in Agriculture

Intelligent Data Mining and Fusion Systems in Agriculture
Author: Xanthoula Eirini Pantazi,Dimitrios Moshou,Dionysis Bochtis
Publsiher: Academic Press
Total Pages: 330
Release: 2019-10-08
ISBN: 0128143924
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Intelligent Data Mining and Fusion Systems in Agriculture Book Excerpt:

Intelligent Data Mining and Fusion Systems in Agriculture presents methods of computational intelligence and data fusion that have applications in agriculture for the non-destructive testing of agricultural products and crop condition monitoring. Sections cover the combination of sensors with artificial intelligence architectures in precision agriculture, including algorithms, bio-inspired hierarchical neural maps, and novelty detection algorithms capable of detecting sudden changes in different conditions. This book offers advanced students and entry-level professionals in agricultural science and engineering, geography and geoinformation science an in-depth overview of the connection between decision-making in agricultural operations and the decision support features offered by advanced computational intelligence algorithms. Covers crop protection, automation in agriculture, artificial intelligence in agriculture, sensing and Internet of Things (IoTs) in agriculture Addresses AI use in weed management, disease detection, yield prediction and crop production Utilizes case studies to provide real-world insights and direction

Data Mining for Business Applications

Data Mining for Business Applications
Author: Carlos A. Mota Soares,Rayid Ghani
Publsiher: IOS Press
Total Pages: 181
Release: 2010-01-01
ISBN: 1607506327
Category: Computers
Language: EN, FR, DE, ES & NL

Data Mining for Business Applications Book Excerpt:

Data mining is already incorporated into the business processes in sectors such as health, retail, automotive, finance, telecom and insurance as well as in government. This book contains extended versions of a selection of papers presented at a series of workshops held between 2005 and 2008 on the subject of data mining for business applications.

Artificial Intelligence and Machine Learning for Business

Artificial Intelligence and Machine Learning for Business
Author: Oliver Tensor
Publsiher: Unknown
Total Pages: 122
Release: 2020-12-23
ISBN: 9781914284045
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Artificial Intelligence and Machine Learning for Business Book Excerpt:

Are you a new business owner? Or an entrepreneur looking to catch up to the big companies in your industrial sector? If you want to understand and master the fundamentals and importance of data science technologies to kick start your business or take it to the next level, then keep reading. Thanks to the smart and savvy customer of today, the competition to gain new customers while retaining the existing customers is fierce. As a result, companies are increasingly relying upon cutting edge technologies such as big data analytics, data mining technology, machine learning, and artificial intelligence technology to gain an edge over the competition.Today, machine learning and artificial intelligence have given rise to sophisticated machines that can study human behavior and activity to identify underlying human behavioral patterns and precisely predict what products and services consumers are interested in. Businesses with an eye on the future are gradually turning into technology companies under the façade of their intended business model. It is getting increasingly challenging for traditional businesses to retain their customers without adopting one or more of the cutting-edge technology explained in this book. Those entrepreneurs and business executives who have a sound understanding of the current challenges and status of their business will be primed to make informed decisions to meet the challenges head-on and improve their bottom line. Receive overarching guidance on how you can adopt any and all of the Data Science technologies in your business model to accelerate your growth rate. Learn how researchers are breaking the boundaries of data science to mimic human intelligence in machines. Learn the data science lifecycle in such extensive detail that you will be fully prepared to initiate and complete a data science implementation project in your business. Learn all about the historical development to the current explosion in this field of Big Data Analytics and how it differs data visualization techniques. Dig deep into the data mining process, the benefits of using data mining technology, the challenges facing the data mining technology and learn about some data mining tools that you can leverage for your business. Gain an in-depth understanding of various machine learning algorithms do assess the best Machine learning algorithm applicable to your business model. Learn the very important concept of data science and machine learning Decision Trees, applicable to small and large businesses across the industrial spectrum, explained thoroughly using real-life examples for ease of understanding. Master the concept of sales and marketing funnel along with the tools available for sales funnel analytics in the market today. Deep dive into the concept of personalized marketing, predictive analytics, customer analytics, and exploratory data analysis presented with details on how you can make sense out of all your customer behavioral data. This book is filled with real-life examples to help you understand the nitty-gritty of all the concepts as well as names and description of multiple tools that you can further explore and selectively implement in your business to reap the benefits of these cutting-edge technologies. Would You Like to Know More? Get This Book Today to get access to Artificial Intelligence and Machine Learning power.

Metalearning

Metalearning
Author: Pavel B. Brazdil,Jan N. van Rijn,Carlos Soares,Joaquin Vanschoren
Publsiher: Springer Nature
Total Pages: 346
Release: 2021
ISBN: 3030670244
Category: Data mining
Language: EN, FR, DE, ES & NL

Metalearning Book Excerpt:

This open access book as one of the fastest-growing areas of research in machine learning, metalearning studies principled methods to obtain efficient models and solutions by adapting machine learning and data mining processes. This adaptation usually exploits information from past experience on other tasks and the adaptive processes can involve machine learning approaches. As a related area to metalearning and a hot topic currently, automated machine learning (AutoML) is concerned with automating the machine learning processes. Metalearning and AutoML can help AI learn to control the application of different learning methods and acquire new solutions faster without unnecessary interventions from the user. This book offers a comprehensive and thorough introduction to almost all aspects of metalearning and AutoML, covering the basic concepts and architecture, evaluation, datasets, hyperparameter optimization, ensembles and workflows, and also how this knowledge can be used to select, combine, compose, adapt and configure both algorithms and models to yield faster and better solutions to data mining and data science problems. It can thus help developers to develop systems that can improve themselves through experience. This book is a substantial update of the first edition published in 2009. It includes 18 chapters, more than twice as much as the previous version. This enabled the authors to cover the most relevant topics in more depth and incorporate the overview of recent research in the respective area. The book will be of interest to researchers and graduate students in the areas of machine learning, data mining, data science and artificial intelligence.

Progress in Artificial Intelligence

Progress in Artificial Intelligence
Author: Fernando Moura Pires,Salvador Abreu
Publsiher: Springer
Total Pages: 508
Release: 2003-11-06
ISBN: 3540245804
Category: Computers
Language: EN, FR, DE, ES & NL

Progress in Artificial Intelligence Book Excerpt:

When we set about organizing EPIA 2003 in Porto during the APPIA meeting at the previous edition of the conference, EPIA 2001, it was decided that it would be organized by Fernando Moura Pires (Fajþ e) and myself. We chose Beja as the venue to host the conference, as it provided a good support infrastructure and Fernando had a good working relationship with several people at the Beja Polytechnic Institute. Shortly thereafter, Fernando came to know that he was ailing from a disease thatwastotakehislifeinMay2003. Aswithmanyotherprojectsinwhichhegot involved, Fernando clung to the organization of this conference with dedication and perseverance, even while knowing that he might not see the results of his work. EPIA 2003 is a tribute to his work. Taking up on the successful experience gained from EPIA 2001, we decided to structure EPIA 2003 as a set of?ve distinct workshops, roughly re?ecting the panorama of AI research in Portugal. Special thanks are due to the organizers of each workshop, for the quality and timeliness of the work they carried out. The conference was all the more interesting because of the eight invited p- sentations and tutorials, by Alexander Bockmayr, Amþ?lcar Cardoso, Dario F- reano, HaroldBoley, PedroDomingos, PieterAdriaans, VeronicaDahlandVitor Santos Costa. There are short one-page abstracts included in these proceedings for some of these presentations.

Intelligent Data Engineering and Automated Learning IDEAL 2000 Data Mining Financial Engineering and Intelligent Agents

Intelligent Data Engineering and Automated Learning   IDEAL 2000  Data Mining  Financial Engineering  and Intelligent Agents
Author: China) IDEAL 2000 (2000 : Hong Kong,Kwong S. Leung,Lai-wan Chan
Publsiher: Springer Science & Business Media
Total Pages: 573
Release: 2000-11-29
ISBN: 3540414509
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Intelligent Data Engineering and Automated Learning IDEAL 2000 Data Mining Financial Engineering and Intelligent Agents Book Excerpt:

This book constitutes the refereed proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2000, held in Shatin, N.T., Hong Kong, China in December 2000. The 81 revised papers presented were carefully reviewed and selected from numerous submissions. The book is divided in topical sections on data mining and automated learning, financial engineering, intelligent agents, Internet applications, multimedia processing, and genetic programming.

Data Mining Concepts Methodologies Tools and Applications

Data Mining  Concepts  Methodologies  Tools  and Applications
Author: Management Association, Information Resources
Publsiher: IGI Global
Total Pages: 2120
Release: 2012-11-30
ISBN: 1466624566
Category: Computers
Language: EN, FR, DE, ES & NL

Data Mining Concepts Methodologies Tools and Applications Book Excerpt:

Data mining continues to be an emerging interdisciplinary field that offers the ability to extract information from an existing data set and translate that knowledge for end-users into an understandable way. Data Mining: Concepts, Methodologies, Tools, and Applications is a comprehensive collection of research on the latest advancements and developments of data mining and how it fits into the current technological world.

The Impact of Artificial Intelligence on Governance Economics and Finance Volume I

The Impact of Artificial Intelligence on Governance  Economics and Finance  Volume I
Author: Sezer Bozkuş Kahyaoğlu
Publsiher: Springer Nature
Total Pages: 328
Release: 2021-04-26
ISBN: 981336811X
Category: Business & Economics
Language: EN, FR, DE, ES & NL

The Impact of Artificial Intelligence on Governance Economics and Finance Volume I Book Excerpt:

The book discusses the effects of artificial intelligence in terms of economics and finance. In particular, the book focuses on the effects of the change in the structure of financial markets, institutions and central banks, along with digitalization analyzed based on fintech ecosystems. In addition to finance sectors, other sectors, such as health, logistics, and industry 4.0, all of which are undergoing an artificial intelligence induced rapid transformation, are addressed in this book. Readers will receive an understanding of an integrated approach towards the use of artificial intelligence across various industries and disciplines with a vision to address the strategic issues and priorities in the dynamic business environment in order to facilitate decision-making processes. Economists, board members of central banks, bankers, financial analysts, regulatory authorities, accounting and finance professionals, chief executive officers, chief audit officers and chief financial officers, chief financial officers, as well as business and management academic researchers, will benefit from reading this book.

Predictive Analytics

Predictive Analytics
Author: Dursun Delen
Publsiher: FT Press Analytics
Total Pages: 350
Release: 2020-10-30
ISBN: 9780136738510
Category: Business & Economics
Language: EN, FR, DE, ES & NL

Predictive Analytics Book Excerpt:

In Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, Dr. Dursun Delen illuminates state-of-the-art best practices for predictive analytics for students. Using predictive analytics techniques, students can uncover hidden patterns and correlations in their data, and leverage this insight to improve a wide range of business decisions. Delen's holistic approach covers all this, and more: Data mining processes, methods, and techniques The role and management of data Predictive analytics tools and metrics Techniques for text and web mining, and for sentiment analysis Integration with cutting-edge Big Data approaches Throughout, Delen promotes understanding by presenting numerous conceptual illustrations, motivational success stories, failed projects that teach important lessons, and simple, hands-on tutorials that set this guide apart from competitors.