Deep Learning And Missing Data In Engineering Systems

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Deep Learning and Missing Data in Engineering Systems

Author : Collins Achepsah Leke
Publisher : Springer
ISBN 13 : 3030011801
Total Pages : 179 pages
Book Rating : 4.0/5 (3 download)

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Book Synopsis Deep Learning and Missing Data in Engineering Systems by : Collins Achepsah Leke

Download or read book Deep Learning and Missing Data in Engineering Systems written by Collins Achepsah Leke and published by Springer. This book was released on 2018-12-13 with total page 179 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including: deep autoencoder neural networks; deep denoising autoencoder networks; the bat algorithm; the cuckoo search algorithm; and the firefly algorithm. The hybrid models proposed are used to estimate the missing data in high-dimensional data settings more accurately. Swarm intelligence algorithms are applied to address critical questions such as model selection and model parameter estimation. The authors address feature extraction for the purpose of reconstructing the input data from reduced dimensions by the use of deep autoencoder neural networks. They illustrate new models diagrammatically, report their findings in tables, so as to put their methods on a sound statistical basis. The methods proposed speed up the process of data estimation while preserving known features of the data matrix. This book is a valuable source of information for researchers and practitioners in data science. Advanced undergraduate and postgraduate students studying topics in computational intelligence and big data, can also use the book as a reference for identifying and introducing new research thrusts in missing data estimation.

Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques

Author : Marwala, Tshilidzi
Publisher : IGI Global
ISBN 13 : 1605663379
Total Pages : 326 pages
Book Rating : 4.6/5 (56 download)

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Book Synopsis Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques by : Marwala, Tshilidzi

Download or read book Computational Intelligence for Missing Data Imputation, Estimation, and Management: Knowledge Optimization Techniques written by Marwala, Tshilidzi and published by IGI Global. This book was released on 2009-04-30 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This book is for those who use data analysis to build decision support systems, particularly engineers, scientists and statisticians"--Provided by publisher.

Machine Learning and Knowledge Discovery for Engineering Systems Health Management

Author : Ashok N. Srivastava
Publisher : CRC Press
ISBN 13 : 1000755711
Total Pages : 505 pages
Book Rating : 4.0/5 (7 download)

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Book Synopsis Machine Learning and Knowledge Discovery for Engineering Systems Health Management by : Ashok N. Srivastava

Download or read book Machine Learning and Knowledge Discovery for Engineering Systems Health Management written by Ashok N. Srivastava and published by CRC Press. This book was released on 2016-04-19 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents state-of-the-art tools and techniques for automatically detecting, diagnosing, and predicting the effects of adverse events in an engineered system. It emphasizes the importance of these techniques in managing the intricate interactions within and between engineering systems to maintain a high degree of reliability. Reflecting the interdisciplinary nature of the field, the book explains how the fundamental algorithms and methods of both physics-based and data-driven approaches effectively address systems health management in application areas such as data centers, aircraft, and software systems.

Handbook Of Machine Learning - Volume 2: Optimization And Decision Making

Author : Tshilidzi Marwala
Publisher : World Scientific
ISBN 13 : 981120568X
Total Pages : 321 pages
Book Rating : 4.8/5 (112 download)

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Book Synopsis Handbook Of Machine Learning - Volume 2: Optimization And Decision Making by : Tshilidzi Marwala

Download or read book Handbook Of Machine Learning - Volume 2: Optimization And Decision Making written by Tshilidzi Marwala and published by World Scientific. This book was released on 2019-11-21 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Building on , this volume on Optimization and Decision Making covers a range of algorithms and their applications. Like the first volume, it provides a starting point for machine learning enthusiasts as a comprehensive guide on classical optimization methods. It also provides an in-depth overview on how artificial intelligence can be used to define, disprove or validate economic modeling and decision making concepts.

Artificial Intelligence, Game Theory and Mechanism Design in Politics

Author : Tshilidzi Marwala
Publisher : Springer Nature
ISBN 13 : 9819951038
Total Pages : 221 pages
Book Rating : 4.8/5 (199 download)

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Book Synopsis Artificial Intelligence, Game Theory and Mechanism Design in Politics by : Tshilidzi Marwala

Download or read book Artificial Intelligence, Game Theory and Mechanism Design in Politics written by Tshilidzi Marwala and published by Springer Nature. This book was released on 2023-08-04 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explores how AI and mechanism design can provide a new framework for international politics. The international political system is all manners in which countries, governments and people relate. Mechanism design in international politics relates to identifying rules that define relationships between people and countries that achieve a particular outcome, e.g., peace or more trade or democracy or economic development. Artificial intelligence is technique of making machines intelligent. This book explores mechanism design and artificial intelligence in international politics and applies these technologies to politics, economy and society. This book will be of interest to scholars of international relations, politics, sustainable development, and artificial intelligence.

Rational Machines and Artificial Intelligence

Author : Tshilidzi Marwala
Publisher : Academic Press
ISBN 13 : 0128209445
Total Pages : 272 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Rational Machines and Artificial Intelligence by : Tshilidzi Marwala

Download or read book Rational Machines and Artificial Intelligence written by Tshilidzi Marwala and published by Academic Press. This book was released on 2021-03-31 with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial Intelligence technologies such as deep learning. They are used in decision making. One element of decision making is the issue of rationality. Regulations such as the General Data Protection Regulation (GDPR) require that decisions that are made by these intelligent machines are explainable. Rational Machines and Artificial Intelligence proposes that explainable decisions are good but the explanation must be rational to prevent these decisions from being challenged. Noted author Tshilidzi Marwala studies the concept of machine rationality and compares this to the rationality bounds prescribed by Nobel Laureate Herbert Simon and rationality bounds derived from the work of Nobel Laureates Richard Thaler and Daniel Kahneman. Rational Machines and Artificial Intelligence describes why machine rationality is flexibly bounded due to advances in technology. This effectively means that optimally designed machines are more rational than human beings. Readers will also learn whether machine rationality can be quantified and identify how this can be achieved. Furthermore, the author discusses whether machine rationality is subjective. Finally, the author examines whether a population of intelligent machines collectively make more rational decisions than individual machines. Examples in biomedical engineering, social sciences and the financial sectors are used to illustrate these concepts. Provides an introduction to the key questions and challenges surrounding Rational Machines, including, When do we rely on decisions made by intelligent machines? What do decisions made by intelligent machines mean? Are these decisions rational or fair? Can we quantify these decisions? and Is rationality subjective? Introduces for the first time the concept of rational opportunity costs and the concept of flexibly bounded rationality as a rationality of intelligent machines and the implications of these issues on the reliability of machine decisions Includes coverage of Rational Counterfactuals, group versus individual rationality, and rational markets Discusses the application of Moore’s Law and advancements in Artificial Intelligence, as well as developments in the area of data acquisition and analysis technologies and how they affect the boundaries of intelligent machine rationality

The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care

Author : Patrick Siarry
Publisher : Springer Nature
ISBN 13 : 3030752208
Total Pages : 270 pages
Book Rating : 4.0/5 (37 download)

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Book Synopsis The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care by : Patrick Siarry

Download or read book The Fusion of Internet of Things, Artificial Intelligence, and Cloud Computing in Health Care written by Patrick Siarry and published by Springer Nature. This book was released on 2021-08-11 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book reviews the convergence technologies like cloud computing, artificial intelligence (AI) and Internet of Things (IoT) in healthcare and how they can help all stakeholders in the healthcare sector. The book is a proficient guide on the relationship between AI, IoT and healthcare and gives examples into how IoT is changing all aspects of the healthcare industry. Topics include remote patient monitoring, the telemedicine ecosystem, pattern imaging analytics using AI, disease identification and diagnosis using AI, robotic surgery, prediction of epidemic outbreaks, and more. The contributors include applications and case studies across all areas of computational intelligence in healthcare data. The authors also include workflow in IoT-enabled healthcare technologies and explore privacy and security issues in healthcare-based IoT.

Data-Driven Science and Engineering

Author : Steven L. Brunton
Publisher : Cambridge University Press
ISBN 13 : 1009098489
Total Pages : 615 pages
Book Rating : 4.0/5 (9 download)

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Book Synopsis Data-Driven Science and Engineering by : Steven L. Brunton

Download or read book Data-Driven Science and Engineering written by Steven L. Brunton and published by Cambridge University Press. This book was released on 2022-05-05 with total page 615 pages. Available in PDF, EPUB and Kindle. Book excerpt: A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.

Artificial Intelligence in Economics and Finance Theories

Author : Tankiso Moloi
Publisher : Springer Nature
ISBN 13 : 3030429628
Total Pages : 131 pages
Book Rating : 4.0/5 (34 download)

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Book Synopsis Artificial Intelligence in Economics and Finance Theories by : Tankiso Moloi

Download or read book Artificial Intelligence in Economics and Finance Theories written by Tankiso Moloi and published by Springer Nature. This book was released on 2020-05-07 with total page 131 pages. Available in PDF, EPUB and Kindle. Book excerpt: As Artificial Intelligence (AI) seizes all aspects of human life, there is a fundamental shift in the way in which humans are thinking of and doing things. Ordinarily, humans have relied on economics and finance theories to make sense of, and predict concepts such as comparative advantage, long run economic growth, lack or distortion of information and failures, role of labour as a factor of production and the decision making process for the purpose of allocating resources among other theories. Of interest though is that literature has not attempted to utilize these advances in technology in order to modernize economic and finance theories that are fundamental in the decision making process for the purpose of allocating scarce resources among other things. With the simulated intelligence in machines, which allows machines to act like humans and to some extent even anticipate events better than humans, thanks to their ability to handle massive data sets, this book will use artificial intelligence to explain what these economic and finance theories mean in the context of the agent wanting to make a decision. The main feature of finance and economic theories is that they try to eliminate the effects of uncertainties by attempting to bring the future to the present. The fundamentals of this statement is deeply rooted in risk and risk management. In behavioural sciences, economics as a discipline has always provided a well-established foundation for understanding uncertainties and what this means for decision making. Finance and economics have done this through different models which attempt to predict the future. On its part, risk management attempts to hedge or mitigate these uncertainties in order for “the planner” to reach the favourable outcome. This book focuses on how AI is to redefine certain important economic and financial theories that are specifically used for the purpose of eliminating uncertainties so as to allow agents to make informed decisions. In effect, certain aspects of finance and economic theories cannot be understood in their entirety without the incorporation of AI.

Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics

Author : R. Sujatha
Publisher : CRC Press
ISBN 13 : 1000454533
Total Pages : 217 pages
Book Rating : 4.0/5 (4 download)

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Book Synopsis Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics by : R. Sujatha

Download or read book Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics written by R. Sujatha and published by CRC Press. This book was released on 2021-09-22 with total page 217 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval. FEATURES Provides insight into the skill set that leverages one’s strength to act as a good data analyst Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making Covers numerous potential applications in healthcare, education, communication, media, and entertainment Offers innovative platforms for integrating Big Data and Deep Learning Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big Data This book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts.

Heal our World

Author : Tshilidzi Marwala
Publisher : Jonathan Ball Publishers
ISBN 13 : 1998958604
Total Pages : 270 pages
Book Rating : 4.9/5 (989 download)

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Book Synopsis Heal our World by : Tshilidzi Marwala

Download or read book Heal our World written by Tshilidzi Marwala and published by Jonathan Ball Publishers. This book was released on 2022-09-19 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: The world is emerging from the COVID-19 pandemic, more fragmented and further away from the more equal and equitable iteration imagined in 2015 when the Sustainable Development Goals (SDGs) were conceptualised. As we hurtle at seemingly lightning speed towards the 2030 deadline to achieve these goals, the urgency is palpable. Although we have certainly strayed further away from the targets, there is still time to act in order to ensure that we inch closer to this vision. Professor Tshilidzi Marwala paints a stark, and often grim, picture of our current context, one defined by monumental setbacks in the SDGs. Yet, as he carves out each developmental goal and its implications, it is apparent that there are tangible solutions that can be implemented now. Tshilidzi's assertion that now is the time to act is backed by intricate and actionable data with a simple mission statement: we must heal the future. He offers a new narrative that addresses how we can translate the latent potential that exists through technology, innovation and Fourth Industrial Revolution approaches to leadership and policy making to deal with, among others, corruption, poverty eradication, joblessness, an education system in crisis, declining economies and food insecurity. Heal our World is a deep dive into the SDGs, particularly in the African context, and it looks toward securing a future in which our divisions are blurred, and our goals seem almost in reach again. Tshilidzi Marwala, the author of Heal our World, Leading in the 21st Century and Leadership Lessons from Books I Have Read is the Vice-Chancellor and Principal of the University of Johannesburg. From 1 March 2023, he will be the Rector of the United Nations University based in Tokyo, Japan. He was previously Deputy Vice-Chancellor for Research and Executive Dean of the Faculty of Engineering at the University of Johannesburg and Full Professor at the Carl & Emily Fuchs Chair of Systems and Control Engineering at the University of the Witwatersrand. Tshilidzi holds a Bachelor of Science in Mechanical Engineering (magna cum laude) from Case Western Reserve University, a PhD in Artificial Intelligence from the University of Cambridge and a Post-Doc at Imperial College (London). He is a member of the American Academy of Arts and Sciences, The World Academy of Sciences (TWAS), the Academy of Science of South Africa (ASSAf), the African Academy of Sciences (AAS) and the South African Academy of Engineering (SAAE). He is a distinguished member of the Association for Computing Machinery (ACM). His research interests are multidisciplinary and include the theory and application of artificial intelligence to engineering, computer science, finance, social science and medicine. He has supervised 37 doctoral students. He has also published 23 books on artificial intelligence (one translated into Chinese) and over 300 papers in journals, proceedings, book chapters and magazines. He holds five international patents.

Missing Data Problems in Machine Learning

Author : Benjamin M. Marlin
Publisher :
ISBN 13 :
Total Pages : 312 pages
Book Rating : 4.:/5 (272 download)

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Book Synopsis Missing Data Problems in Machine Learning by : Benjamin M. Marlin

Download or read book Missing Data Problems in Machine Learning written by Benjamin M. Marlin and published by . This book was released on 2008 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Multiple Imputation of Missing Data Using SAS

Author : Patricia Berglund
Publisher : SAS Institute
ISBN 13 : 162959203X
Total Pages : 164 pages
Book Rating : 4.6/5 (295 download)

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Book Synopsis Multiple Imputation of Missing Data Using SAS by : Patricia Berglund

Download or read book Multiple Imputation of Missing Data Using SAS written by Patricia Berglund and published by SAS Institute. This book was released on 2014-07-01 with total page 164 pages. Available in PDF, EPUB and Kindle. Book excerpt: Find guidance on using SAS for multiple imputation and solving common missing data issues. Multiple Imputation of Missing Data Using SAS provides both theoretical background and constructive solutions for those working with incomplete data sets in an engaging example-driven format. It offers practical instruction on the use of SAS for multiple imputation and provides numerous examples that use a variety of public release data sets with applications to survey data. Written for users with an intermediate background in SAS programming and statistics, this book is an excellent resource for anyone seeking guidance on multiple imputation. The authors cover the MI and MIANALYZE procedures in detail, along with other procedures used for analysis of complete data sets. They guide analysts through the multiple imputation process, including evaluation of missing data patterns, choice of an imputation method, execution of the process, and interpretation of results. Topics discussed include how to deal with missing data problems in a statistically appropriate manner, how to intelligently select an imputation method, how to incorporate the uncertainty introduced by the imputation process, and how to incorporate the complex sample design (if appropriate) through use of the SAS SURVEY procedures. Discover the theoretical background and see extensive applications of the multiple imputation process in action. This book is part of the SAS Press program.

Big Data Analysis and Deep Learning Applications

Author : Thi Thi Zin
Publisher : Springer
ISBN 13 : 9811308691
Total Pages : 388 pages
Book Rating : 4.8/5 (113 download)

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Book Synopsis Big Data Analysis and Deep Learning Applications by : Thi Thi Zin

Download or read book Big Data Analysis and Deep Learning Applications written by Thi Thi Zin and published by Springer. This book was released on 2018-06-06 with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a compilation of selected papers from the first International Conference on Big Data Analysis and Deep Learning Applications (ICBDL 2018), and focuses on novel techniques in the fields of big data analysis, machine learning, system monitoring, image processing, conventional neural networks, communication, industrial information, and their applications. Readers will find insights to help them realize more efficient algorithms and systems used in real-life applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and regulators of aviation authorities.

Building Machine Learning Systems Using Python

Author : Dr Deepti Chopra
Publisher : BPB Publications
ISBN 13 : 9389423619
Total Pages : 134 pages
Book Rating : 4.3/5 (894 download)

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Book Synopsis Building Machine Learning Systems Using Python by : Dr Deepti Chopra

Download or read book Building Machine Learning Systems Using Python written by Dr Deepti Chopra and published by BPB Publications. This book was released on 2021-05-07 with total page 134 pages. Available in PDF, EPUB and Kindle. Book excerpt: Explore Machine Learning Techniques, Different Predictive Models, and its Applications Ê KEY FEATURESÊÊ _ Extensive coverage of real examples on implementation and working of ML models. _ Includes different strategies used in Machine Learning by leading data scientists. _ Focuses on Machine Learning concepts and their evolution to algorithms. DESCRIPTIONÊ This book covers basic concepts of Machine Learning, various learning paradigms, different architectures and algorithms used in these paradigms. You will learn the power of ML models by exploring different predictive modeling techniques such as Regression, Clustering, and Classification. You will also get hands-on experience on methods and techniques such as Overfitting, Underfitting, Random Forest, Decision Trees, PCA, and Support Vector Machines. In this book real life examples with fully working of Python implementations are discussed in detail. At the end of the book you will learn about the unsupervised learning covering Hierarchical Clustering, K-means Clustering, Dimensionality Reduction, Anomaly detection, Principal Component Analysis.Ê WHAT YOU WILL LEARN _ Learn to perform data engineering and analysis. _ Build prototype ML models and production ML models from scratch. _ Develop strong proficiency in using scikit-learn and Python. _ Get hands-on experience with Random Forest, Logistic Regression, SVM, PCA, and Neural Networks. WHO THIS BOOK IS FORÊÊ This book is meant for beginners who want to gain knowledge about Machine Learning in detail. This book can also be used by Machine Learning users for a quick reference for fundamentals in Machine Learning. Readers should have basic knowledge of Python and Scikit-Learn before reading the book. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Linear Regression 3. Classification Using Logistic Regression 4. Overfitting and Regularization 5. Feasibility of Learning 6. Support Vector Machine 7. Neural Network 8. Decision Trees 9. Unsupervised Learning 10. Theory of Generalization 11. Bias and Fairness in ML

Handbook of Statistical Data Editing and Imputation

Author : Ton de Waal
Publisher : John Wiley & Sons
ISBN 13 : 0470904836
Total Pages : 453 pages
Book Rating : 4.4/5 (79 download)

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Book Synopsis Handbook of Statistical Data Editing and Imputation by : Ton de Waal

Download or read book Handbook of Statistical Data Editing and Imputation written by Ton de Waal and published by John Wiley & Sons. This book was released on 2011-03-04 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: A practical, one-stop reference on the theory and applications of statistical data editing and imputation techniques Collected survey data are vulnerable to error. In particular, the data collection stage is a potential source of errors and missing values. As a result, the important role of statistical data editing, and the amount of resources involved, has motivated considerable research efforts to enhance the efficiency and effectiveness of this process. Handbook of Statistical Data Editing and Imputation equips readers with the essential statistical procedures for detecting and correcting inconsistencies and filling in missing values with estimates. The authors supply an easily accessible treatment of the existing methodology in this field, featuring an overview of common errors encountered in practice and techniques for resolving these issues. The book begins with an overview of methods and strategies for statistical data editing and imputation. Subsequent chapters provide detailed treatment of the central theoretical methods and modern applications, with topics of coverage including: Localization of errors in continuous data, with an outline of selective editing strategies, automatic editing for systematic and random errors, and other relevant state-of-the-art methods Extensions of automatic editing to categorical data and integer data The basic framework for imputation, with a breakdown of key methods and models and a comparison of imputation with the weighting approach to correct for missing values More advanced imputation methods, including imputation under edit restraints Throughout the book, the treatment of each topic is presented in a uniform fashion. Following an introduction, each chapter presents the key theories and formulas underlying the topic and then illustrates common applications. The discussion concludes with a summary of the main concepts and a real-world example that incorporates realistic data along with professional insight into common challenges and best practices. Handbook of Statistical Data Editing and Imputation is an essential reference for survey researchers working in the fields of business, economics, government, and the social sciences who gather, analyze, and draw results from data. It is also a suitable supplement for courses on survey methods at the upper-undergraduate and graduate levels.

Soft Computing for Sustainability Science

Author : Carlos Cruz Corona
Publisher : Springer
ISBN 13 : 3319623591
Total Pages : 360 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Soft Computing for Sustainability Science by : Carlos Cruz Corona

Download or read book Soft Computing for Sustainability Science written by Carlos Cruz Corona and published by Springer. This book was released on 2017-07-12 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a timely snapshot of soft computing methodologies and their applications to various problems related to sustainability, including electric energy consumption; fault diagnosis; vessel fuel consumption; determining the best sites for new malls; maritime port projects; and ad-hoc vehicular networks. Further, it demonstrates how metaheuristics and machine learning methods, fuzzy linear programming, neural networks, computing with words, linguistic models and other soft computing methods can be efficiently used to solve real-world problems. Intended as a practice-oriented guide for students, researchers, and professionals working at the interface between computer science, industrial engineering, naval engineering, agriculture, and sustainable development / climate change research, it provides readers with a set of intelligent solutions, helping them answer a range of emerging questions related to sustainability.