Machine Learning and Data Science in the Power Generation Industry

Machine Learning and Data Science in the Power Generation Industry
Author: Patrick Bangert
Publsiher: Elsevier
Total Pages: 316
Release: 2021-03-01
ISBN 10: 0128226005
ISBN 13: 9780128226001
Language: EN, FR, DE, ES & NL

Machine Learning and Data Science in the Power Generation Industry Book Review:

Machine Learning and Data Science in the Power Generation Industry: Best Practices, Tools, and Case Studies explores current best practices and quantifies the value-add in developing data-oriented computational programs in the energy industry, with a focus on real-world case studies selected from modern practice. The book provides a set of realistic pathways for organizations seeking to develop machine learning methods, with discussion on data selection and curation, as well as organizational implementation in terms of staffing and continuing operationalization. The book articulates a body of case study-driven best practices, including renewable energy sources, the smart grid, and the finances around spot markets, emissions credits, and forecasting. Provides best practices on how to design and setup ML projects in power systems, including all non-technological aspects necessary to be successful Explores implementation pathways, explaining key ML algorithms and approaches, as well as the choices that must be made, how to make them, what outcomes may be expected, and how data must be prepared Determines the specific data needs for the collection, processing, and operationalization of data within machine learning algorithms for power systems Includes numerous supporting real-world case studies, providing practical guidance on best practices and potential pitfalls

Machine Learning and Data Science in the Oil and Gas Industry

Machine Learning and Data Science in the Oil and Gas Industry
Author: Patrick Bangert
Publsiher: Gulf Professional Publishing
Total Pages: 300
Release: 2021-03-01
ISBN 10: 0128209143
ISBN 13: 9780128209141
Language: EN, FR, DE, ES & NL

Machine Learning and Data Science in the Oil and Gas Industry Book Review:

Machine Learning and Data Science in the Oil and Gas Industry: Best Practices, Tools, and Case Studies explains critical facets around machine learning that are specifically tailored to oil and gas. Practical in its approach, this reference provides a chapter devoted to the early career engineer that is just starting in the industry. It then builds to a full-scale project that is supported by real-world case studies from various industry and academic contributors. Lessons learned and technology drivers are also discussed, creating a path for future engineers to apply. Rounding out with a glossary, this book delivers a reference that cuts through the hype to help today's petroleum engineers understand machine learning and where it can benefit their operations. Helps readers gain a practical understanding of machine learning used in oil and gas operations Presents change management skills that will help readers gain confidence in pursuing new technology Provides the workflow of a full-scale project and where machine learning is and isn't impactful

Big Data Application in Power Systems

Big Data Application in Power Systems
Author: Reza Arghandeh,Yuxun Zhou
Publsiher: Elsevier
Total Pages: 480
Release: 2017-11-27
ISBN 10: 0128119691
ISBN 13: 9780128119693
Language: EN, FR, DE, ES & NL

Big Data Application in Power Systems Book Review:

Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data. The book chapters discuss challenges, opportunities, success stories and pathways for utilizing big data value in smart grids. Provides expert analysis of the latest developments by global authorities Contains detailed references for further reading and extended research Provides additional cross-disciplinary lessons learned from broad disciplines such as statistics, computer science and bioinformatics Focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data

Applying Data Science

Applying Data Science
Author: Arthur K. Kordon
Publsiher: Springer Nature
Total Pages: 329
Release:
ISBN 10: 3030363759
ISBN 13: 9783030363758
Language: EN, FR, DE, ES & NL

Applying Data Science Book Review:

Proceedings of the 4th Brazilian Technology Symposium (BTSym'18)

Proceedings of the 4th Brazilian Technology Symposium (BTSym'18)
Author: Yuzo Iano,Rangel Arthur,Osamu Saotome,Vânia Vieira Estrela,Hermes José Loschi
Publsiher: Springer
Total Pages: 665
Release: 2019-05-28
ISBN 10: 303016053X
ISBN 13: 9783030160531
Language: EN, FR, DE, ES & NL

Proceedings of the 4th Brazilian Technology Symposium (BTSym'18) Book Review:

This book presents the Proceedings of The 4th Brazilian Technology Symposium (BTSym'18). Part I of the book discusses current technological issues on Systems Engineering, Mathematics and Physical Sciences, such as the Transmission Line, Protein-modified mortars, Electromagnetic Properties, Clock Domains, Chebyshev Polynomials, Satellite Control Systems, Hough Transform, Watershed Transform, Blood Smear Images, Toxoplasma Gondi, Operation System Developments, MIMO Systems, Geothermal-Photovoltaic Energy Systems, Mineral Flotation Application, CMOS Techniques, Frameworks Developments, Physiological Parameters Applications, Brain Computer Interface, Artificial Neural Networks, Computational Vision, Security Applications, FPGA Applications, IoT, Residential Automation, Data Acquisition, Industry 4.0, Cyber-Physical Systems, Digital Image Processing, Patters Recognition, Machine Learning, Photocatalytic Process, Physical-chemical analysis, Smoothing Filters, Frequency Synthesizers, Voltage Controlled Ring Oscillator, Difference Amplifier, Photocatalysis and Photodegradation. Part II of the book discusses current technological issues on Human, Smart and Sustainable Future of Cities, such as the Digital Transformation, Data Science, Hydrothermal Dispatch, Project Knowledge Transfer, Immunization Programs, Efficiency and Predictive Methods, PMBOK Applications, Logistics Process, IoT, Data Acquisition, Industry 4.0, Cyber-Physical Systems, Fingerspelling Recognition, Cognitive Ergonomics, Ecosystem services, Environmental, Ecosystem services valuation, Solid Waste and University Extension. BTSym is the brainchild of Prof. Dr. Yuzo Iano, who is responsible for the Laboratory of Visual Communications (LCV) at the Department of Communications (DECOM) of the Faculty of Electrical and Computing Engineering (FEEC), State University of Campinas (UNICAMP), Brazil.

IoT Machine Learning Applications in Telecom, Energy, and Agriculture

IoT Machine Learning Applications in Telecom, Energy, and Agriculture
Author: Puneet Mathur
Publsiher: Apress
Total Pages: 278
Release: 2020-05-09
ISBN 10: 1484255496
ISBN 13: 9781484255490
Language: EN, FR, DE, ES & NL

IoT Machine Learning Applications in Telecom, Energy, and Agriculture Book Review:

Apply machine learning using the Internet of Things (IoT) in the agriculture, telecom, and energy domains with case studies. This book begins by covering how to set up the software and hardware components including the various sensors to implement the case studies in Python. The case study section starts with an examination of call drop with IoT in the telecoms industry, followed by a case study on energy audit and predictive maintenance for an industrial machine, and finally covers techniques to predict cash crop failure in agribusiness. The last section covers pitfalls to avoid while implementing machine learning and IoT in these domains. After reading this book, you will know how IoT and machine learning are used in the example domains and have practical case studies to use and extend. You will be able to create enterprise-scale applications using Raspberry Pi 3 B+ and Arduino Mega 2560 with Python. What You Will Learn Implement machine learning with IoT and solve problems in the telecom, agriculture, and energy sectors with Python Set up and use industrial-grade IoT products, such as Modbus RS485 protocol devices, in practical scenarios Develop solutions for commercial-grade IoT or IIoT projects Implement case studies in machine learning with IoT from scratch Who This Book Is For Raspberry Pi and Arduino enthusiasts and data science and machine learning professionals.

Data Science for Wind Energy

Data Science for Wind Energy
Author: Yu Ding
Publsiher: CRC Press
Total Pages: 400
Release: 2019-06-04
ISBN 10: 0429956517
ISBN 13: 9780429956515
Language: EN, FR, DE, ES & NL

Data Science for Wind Energy Book Review:

Data Science for Wind Energy provides an in-depth discussion on how data science methods can improve decision making for wind energy applications, near-ground wind field analysis and forecast, turbine power curve fitting and performance analysis, turbine reliability assessment, and maintenance optimization for wind turbines and wind farms. A broad set of data science methods covered, including time series models, spatio-temporal analysis, kernel regression, decision trees, kNN, splines, Bayesian inference, and importance sampling. More importantly, the data science methods are described in the context of wind energy applications, with specific wind energy examples and case studies. Please also visit the author’s book site at https://aml.engr.tamu.edu/book-dswe. Features Provides an integral treatment of data science methods and wind energy applications Includes specific demonstration of particular data science methods and their use in the context of addressing wind energy needs Presents real data, case studies and computer codes from wind energy research and industrial practice Covers material based on the author's ten plus years of academic research and insights

Handbook of Research on Smart Technology Models for Business and Industry

Handbook of Research on Smart Technology Models for Business and Industry
Author: Thomas, J. Joshua,Fiore, Ugo,Lechuga, Gilberto Perez,Kharchenko, Valeriy,Vasant, Pandian
Publsiher: IGI Global
Total Pages: 491
Release: 2020-06-19
ISBN 10: 1799836460
ISBN 13: 9781799836469
Language: EN, FR, DE, ES & NL

Handbook of Research on Smart Technology Models for Business and Industry Book Review:

Advances in machine learning techniques and ever-increasing computing power has helped create a new generation of hardware and software technologies with practical applications for nearly every industry. As the progress has, in turn, excited the interest of venture investors, technology firms, and a growing number of clients, implementing intelligent automation in both physical and information systems has become a must in business. Handbook of Research on Smart Technology Models for Business and Industry is an essential reference source that discusses relevant abstract frameworks and the latest experimental research findings in theory, mathematical models, software applications, and prototypes in the area of smart technologies. Featuring research on topics such as digital security, renewable energy, and intelligence management, this book is ideally designed for machine learning specialists, industrial experts, data scientists, researchers, academicians, students, and business professionals seeking coverage on current smart technology models.

Data Analytics for Renewable Energy Integration

Data Analytics for Renewable Energy Integration
Author: Wei Lee Woon,Zeyar Aung,Stuart Madnick
Publsiher: Springer
Total Pages: 151
Release: 2014-11-20
ISBN 10: 3319132903
ISBN 13: 9783319132907
Language: EN, FR, DE, ES & NL

Data Analytics for Renewable Energy Integration Book Review:

This book constitutes revised selected papers from the second ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2014, held in Nancy, France, in September 2014. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book.

New Horizons for a Data-Driven Economy

New Horizons for a Data-Driven Economy
Author: José María Cavanillas,Edward Curry,Wolfgang Wahlster
Publsiher: Springer
Total Pages: 303
Release: 2016-04-04
ISBN 10: 3319215698
ISBN 13: 9783319215693
Language: EN, FR, DE, ES & NL

New Horizons for a Data-Driven Economy Book Review:

In this book readers will find technological discussions on the existing and emerging technologies across the different stages of the big data value chain. They will learn about legal aspects of big data, the social impact, and about education needs and requirements. And they will discover the business perspective and how big data technology can be exploited to deliver value within different sectors of the economy. The book is structured in four parts: Part I “The Big Data Opportunity” explores the value potential of big data with a particular focus on the European context. It also describes the legal, business and social dimensions that need to be addressed, and briefly introduces the European Commission’s BIG project. Part II “The Big Data Value Chain” details the complete big data lifecycle from a technical point of view, ranging from data acquisition, analysis, curation and storage, to data usage and exploitation. Next, Part III “Usage and Exploitation of Big Data” illustrates the value creation possibilities of big data applications in various sectors, including industry, healthcare, finance, energy, media and public services. Finally, Part IV “A Roadmap for Big Data Research” identifies and prioritizes the cross-sectorial requirements for big data research, and outlines the most urgent and challenging technological, economic, political and societal issues for big data in Europe. This compendium summarizes more than two years of work performed by a leading group of major European research centers and industries in the context of the BIG project. It brings together research findings, forecasts and estimates related to this challenging technological context that is becoming the major axis of the new digitally transformed business environment.

Big Data, Big Analytics

Big Data, Big Analytics
Author: Michael Minelli,Michele Chambers,Ambiga Dhiraj
Publsiher: John Wiley & Sons
Total Pages: 224
Release: 2012-12-27
ISBN 10: 1118239156
ISBN 13: 9781118239155
Language: EN, FR, DE, ES & NL

Big Data, Big Analytics Book Review:

Unique prospective on the big data analytics phenomenon for both business and IT professionals The availability of Big Data, low-cost commodity hardware and new information management and analytics software has produced a unique moment in the history of business. The convergence of these trends means that we have the capabilities required to analyze astonishing data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent a genuine leap forward and a clear opportunity to realize enormous gains in terms of efficiency, productivity, revenue and profitability. The Age of Big Data is here, and these are truly revolutionary times. This timely book looks at cutting-edge companies supporting an exciting new generation of business analytics. Learn more about the trends in big data and how they are impacting the business world (Risk, Marketing, Healthcare, Financial Services, etc.) Explains this new technology and how companies can use them effectively to gather the data that they need and glean critical insights Explores relevant topics such as data privacy, data visualization, unstructured data, crowd sourcing data scientists, cloud computing for big data, and much more.

Deep Learning Techniques and Optimization Strategies in Big Data Analytics

Deep Learning Techniques and Optimization Strategies in Big Data Analytics
Author: Thomas, J. Joshua,Karagoz, Pinar,Ahamed, B. Bazeer,Vasant, Pandian
Publsiher: IGI Global
Total Pages: 355
Release: 2019-11-29
ISBN 10: 1799811948
ISBN 13: 9781799811947
Language: EN, FR, DE, ES & NL

Deep Learning Techniques and Optimization Strategies in Big Data Analytics Book Review:

Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.

Predictive Analytics

Predictive Analytics
Author: Dursun Delen
Publsiher: FT Press Analytics
Total Pages: 350
Release: 2020-10-30
ISBN 10: 9780136738510
ISBN 13: 0136738516
Language: EN, FR, DE, ES & NL

Predictive Analytics Book Review:

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.

Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making

Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making
Author: Cengiz Kahraman,Selcuk Cebi,Sezi Cevik Onar,Basar Oztaysi,A. Cagri Tolga,Irem Ucal Sari
Publsiher: Springer
Total Pages: 1392
Release: 2019-07-05
ISBN 10: 3030237567
ISBN 13: 9783030237561
Language: EN, FR, DE, ES & NL

Intelligent and Fuzzy Techniques in Big Data Analytics and Decision Making Book Review:

This book includes the proceedings of the Intelligent and Fuzzy Techniques INFUS 2019 Conference, held in Istanbul, Turkey, on July 23–25, 2019. Big data analytics refers to the strategy of analyzing large volumes of data, or big data, gathered from a wide variety of sources, including social networks, videos, digital images, sensors, and sales transaction records. Big data analytics allows data scientists and various other users to evaluate large volumes of transaction data and other data sources that traditional business systems would be unable to tackle. Data-driven and knowledge-driven approaches and techniques have been widely used in intelligent decision-making, and they are increasingly attracting attention due to their importance and effectiveness in addressing uncertainty and incompleteness. INFUS 2019 focused on intelligent and fuzzy systems with applications in big data analytics and decision-making, providing an international forum that brought together those actively involved in areas of interest to data science and knowledge engineering. These proceeding feature about 150 peer-reviewed papers from countries such as China, Iran, Turkey, Malaysia, India, USA, Spain, France, Poland, Mexico, Bulgaria, Algeria, Pakistan, Australia, Lebanon, and Czech Republic.

Data Science and Intelligent Applications

Data Science and Intelligent Applications
Author: Ketan Kotecha,Vincenzo Piuri,Hetalkumar N. Shah,Rajan Patel
Publsiher: Springer Nature
Total Pages: 576
Release: 2020-06-17
ISBN 10: 9811544743
ISBN 13: 9789811544743
Language: EN, FR, DE, ES & NL

Data Science and Intelligent Applications Book Review:

This book includes selected papers from the International Conference on Data Science and Intelligent Applications (ICDSIA 2020), hosted by Gandhinagar Institute of Technology (GIT), Gujarat, India, on January 24–25, 2020. The proceedings present original and high-quality contributions on theory and practice concerning emerging technologies in the areas of data science and intelligent applications. The conference provides a forum for researchers from academia and industry to present and share their ideas, views and results, while also helping them approach the challenges of technological advancements from different viewpoints. The contributions cover a broad range of topics, including: collective intelligence, intelligent systems, IoT, fuzzy systems, Bayesian networks, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, speech processing, machine learning and deep learning, and intelligent applications and systems. Helping strengthen the links between academia and industry, the book offers a valuable resource for instructors, students, industry practitioners, engineers, managers, researchers, and scientists alike.

Machine Learning with TensorFlow 1.x

Machine Learning with TensorFlow 1.x
Author: Quan Hua,Shams Ul Azeem,Saif Ahmed
Publsiher: Packt Publishing Ltd
Total Pages: 304
Release: 2017-11-21
ISBN 10: 1786461986
ISBN 13: 9781786461988
Language: EN, FR, DE, ES & NL

Machine Learning with TensorFlow 1.x Book Review:

Tackle common commercial machine learning problems with Google's TensorFlow 1.x library and build deployable solutions. About This Book Enter the new era of second-generation machine learning with Python with this practical and insightful guide Set up TensorFlow 1.x for actual industrial use, including high-performance setup aspects such as multi-GPU support Create pipelines for training and using applying classifiers using raw real-world data Who This Book Is For This book is for data scientists and researchers who are looking to either migrate from an existing machine learning library or jump into a machine learning platform headfirst. The book is also for software developers who wish to learn deep learning by example. Particular focus is placed on solving commercial deep learning problems from several industries using TensorFlow's unique features. No commercial domain knowledge is required, but familiarity with Python and matrix math is expected. What You Will Learn Explore how to use different machine learning models to ask different questions of your data Learn how to build deep neural networks using TensorFlow 1.x Cover key tasks such as clustering, sentiment analysis, and regression analysis using TensorFlow 1.x Find out how to write clean and elegant Python code that will optimize the strength of your algorithms Discover how to embed your machine learning model in a web application for increased accessibility Learn how to use multiple GPUs for faster training using AWS In Detail Google's TensorFlow is a game changer in the world of machine learning. It has made machine learning faster, simpler, and more accessible than ever before. This book will teach you how to easily get started with machine learning using the power of Python and TensorFlow 1.x. Firstly, you'll cover the basic installation procedure and explore the capabilities of TensorFlow 1.x. This is followed by training and running the first classifier, and coverage of the unique features of the library including data flow graphs, training, and the visualization of performance with TensorBoard—all within an example-rich context using problems from multiple industries. You'll be able to further explore text and image analysis, and be introduced to CNN models and their setup in TensorFlow 1.x. Next, you'll implement a complete real-life production system from training to serving a deep learning model. As you advance you'll learn about Amazon Web Services (AWS) and create a deep neural network to solve a video action recognition problem. Lastly, you'll convert the Caffe model to TensorFlow and be introduced to the high-level TensorFlow library, TensorFlow-Slim. By the end of this book, you will be geared up to take on any challenges of implementing TensorFlow 1.x in your machine learning environment. Style and approach This comprehensive guide will enable you to understand the latest advances in machine learning and will empower you to implement this knowledge in your machine learning environment.

Data Analytics in the Era of the Industrial Internet of Things

Data Analytics in the Era of the Industrial Internet of Things
Author: Aldo Dagnino
Publsiher: Springer
Total Pages: 240
Release: 2021-04-11
ISBN 10: 9783030631383
ISBN 13: 3030631389
Language: EN, FR, DE, ES & NL

Data Analytics in the Era of the Industrial Internet of Things Book Review:

This book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT). These characteristics and benefits include enhanced competitiveness, increased proactive decision-making, improved creativity and innovation, augmented job creation, heightened agility to respond to continuously changing challenges, and intensified data-driven decision making. In a straightforward fashion, the book also helps readers understand complex concepts that are core to IIoT enterprises, such as Big Data, analytic architecture platforms, machine learning (ML) and data science algorithms, and the power of visualization to enrich the domains experts’ decision making. The book also guides the reader on how to think about ways to define new business paradigms that the IIoT facilitates, as well how to increase the probability of success in managing analytic projects that are the core engine of decision-making in the IIoT enterprise. The book starts by defining an IIoT enterprise and the framework used to efficiently operate. A description of the concepts of industrial analytics, which is a major engine for decision making in the IIoT enterprise, is provided. It then discusses how data and machine learning (ML) play an important role in increasing the competitiveness of industrial enterprises that operate using the IIoT technology and business concepts. Real world examples of data driven IIoT enterprises and various business models are presented and a discussion on how the use of ML and data science help address complex decision-making problems and generate new job opportunities. The book presents in an easy-to-understand manner how ML algorithms work and operate on data generated in the IIoT enterprise. Useful for any industry professional interested in advanced industrial software applications, including business managers and professionals interested in how data analytics can help industries and to develop innovative business solutions, as well as data and computer scientists who wish to bridge the analytics and computer science fields with the industrial world, and project managers interested in managing advanced analytic projects.

Applications of Artificial Intelligence Techniques in the Petroleum Industry

Applications of Artificial Intelligence Techniques in the Petroleum Industry
Author: Abdolhossein Hemmati Sarapardeh,Aydin Larestani,Nait Amar Menad,Sassan Hajirezaie
Publsiher: Gulf Professional Publishing
Total Pages: 322
Release: 2020-08-26
ISBN 10: 0128223855
ISBN 13: 9780128223857
Language: EN, FR, DE, ES & NL

Applications of Artificial Intelligence Techniques in the Petroleum Industry Book Review:

Applications of Artificial Intelligence Techniques in the Petroleum Industry gives engineers a critical resource to help them understand the machine learning that will solve specific engineering challenges. The reference begins with fundamentals, covering preprocessing of data, types of intelligent models, and training and optimization algorithms. The book moves on to methodically address artificial intelligence technology and applications by the upstream sector, covering exploration, drilling, reservoir and production engineering. Final sections cover current gaps and future challenges. Teaches how to apply machine learning algorithms that work best in exploration, drilling, reservoir or production engineering Helps readers increase their existing knowledge on intelligent data modeling, machine learning and artificial intelligence, with foundational chapters covering the preprocessing of data and training on algorithms Provides tactics on how to cover complex projects such as shale gas, tight oils, and other types of unconventional reservoirs with more advanced model input

Advances in Machine Learning and Data Science

Advances in Machine Learning and Data Science
Author: Damodar Reddy Edla,Pawan Lingras,Venkatanareshbabu K.
Publsiher: Springer
Total Pages: 380
Release: 2018-05-16
ISBN 10: 9811085692
ISBN 13: 9789811085697
Language: EN, FR, DE, ES & NL

Advances in Machine Learning and Data Science Book Review:

The Volume of “Advances in Machine Learning and Data Science - Recent Achievements and Research Directives” constitutes the proceedings of First International Conference on Latest Advances in Machine Learning and Data Science (LAMDA 2017). The 37 regular papers presented in this volume were carefully reviewed and selected from 123 submissions. These days we find many computer programs that exhibit various useful learning methods and commercial applications. Goal of machine learning is to develop computer programs that can learn from experience. Machine learning involves knowledge from various disciplines like, statistics, information theory, artificial intelligence, computational complexity, cognitive science and biology. For problems like handwriting recognition, algorithms that are based on machine learning out perform all other approaches. Both machine learning and data science are interrelated. Data science is an umbrella term to be used for techniques that clean data and extract useful information from data. In field of data science, machine learning algorithms are used frequently to identify valuable knowledge from commercial databases containing records of different industries, financial transactions, medical records, etc. The main objective of this book is to provide an overview on latest advancements in the field of machine learning and data science, with solutions to problems in field of image, video, data and graph processing, pattern recognition, data structuring, data clustering, pattern mining, association rule based approaches, feature extraction techniques, neural networks, bio inspired learning and various machine learning algorithms.

AI and Big Data’s Potential for Disruptive Innovation

AI and Big Data’s Potential for Disruptive Innovation
Author: Strydom, Moses,Buckley, Sheryl
Publsiher: IGI Global
Total Pages: 405
Release: 2019-09-27
ISBN 10: 1522596895
ISBN 13: 9781522596899
Language: EN, FR, DE, ES & NL

AI and Big Data’s Potential for Disruptive Innovation Book Review:

Big data and artificial intelligence (AI) are at the forefront of technological advances that represent a potential transformational mega-trend—a new multipolar and innovative disruption. These technologies, and their associated management paradigm, are already rapidly impacting many industries and occupations, but in some sectors, the change is just beginning. Innovating ahead of emerging technologies is the new imperative for any organization that aspires to succeed in the next decade. Faced with the power of this AI movement, it is imperative to understand the dynamics and new codes required by the disruption and to adapt accordingly. AI and Big Data’s Potential for Disruptive Innovation provides emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative technologies in a variety of sectors including business, transportation, and healthcare. Featuring coverage on a broad range of topics such as semantic mapping, ethics in AI, and big data governance, this book is ideally designed for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research on the production of new and innovative mechanization and its disruptions.