98 results on 'LN cat08778a'
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2. Big data, IoT, and machine learning : tools and applications.
- Author
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Agrawal, Rashmi, Paprzycki, Marcin, and Gupta, Neha
- Subjects
Big data ,Internet of things ,Machine learning - Abstract
Summary: The idea behind this book is to simplify the journey of aspiring readers and researchers to understand Big Data, IoT and Machine Learning. It also includes various real-time/offline applications and case studies in the fields of engineering, computer science, information security and cloud computing using modern tools. This book consists of two sections: Section I contains the topics related to Applications of Machine Learning, and Section II addresses issues about Big Data, the Cloud and the Internet of Things. This brings all the related technologies into a single source so that undergraduate and postgraduate students, researchers, academicians and people in industry can easily understand them. Features Addresses the complete data science technologies workflow Explores basic and high-level concepts and services as a manual for those in the industry and at the same time can help beginners to understand both basic and advanced aspects of machine learning Covers data processing and security solutions in IoT and Big Data applications Offers adaptive, robust, scalable and reliable applications to develop solutions for day-to-day problems Presents security issues and data migration techniques of NoSQL databases
- Published
- 2021
3. Machine Learning Design Patterns: solutions to common challenges in data preparation, model building, and MLOps.
- Author
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Lakshmanan, Valliappa, Robinson, Sara, and Munn, Michael
- Subjects
Machine learning ,Big data ,Design patterns - Abstract
Summary: The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors Valliappa Lakshmanan, Sara Robinson, and Michael Munn catalog the first tried-and-proven methods to help engineers tackle problems that frequently crop up during the ML process. These design patterns codify the experience of hundreds of experts into advice you can easily follow. The authors, three Google Cloud engineers, describe 30 patterns for data and problem representation, operationalization, repeatability, reproducibility, flexibility, explainability, and fairness. Each pattern includes a description of the problem, a variety of potential solutions, and recommendations for choosing the most appropriate remedy for your situation. You'll learn how to: Identify and mitigate common challenges when training, evaluating, and deploying ML models Represent data for different ML model types, including embeddings, feature crosses, and more Choose the right model type for specific problems Build a robust training loop that uses checkpoints, distribution strategy, and hyperparameter tuning Deploy scalable ML systems that you can retrain and update to reflect new data Interpret model predictions for stakeholders and ensure that models are treating users fairly.
- Published
- 2021
4. Demystifying big data, machine learning, and deep learning for healthcare analytics / edited by Pradeep Nijalingappa, Sandeep Kautish, Sheng Lung Peng.
- Author
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Nijalingappa, Pradeep, Kautish, Sandeep, and Peng, Sheng Lung
- Subjects
Medical informatics ,Machine learning ,Big data - Abstract
Summary: "Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics presents the changing world of data utilization, especially in clinical healthcare. Various techniques, methodologies, and algorithms are presented in this book to organize data in a structured manner that will assist physicians in the care of patients and help biomedical engineers and computer scientists understand the impact of these techniques on healthcare analytics. The book is divided into two parts: Part 1 covers big data aspects such as healthcare decision support systems and analytics-related topics. Part 2 focuses on the current frameworks and applications of deep learning and machine learning, and provides an outlook on future directions of research and development. The entire book takes a case study approach, providing a wealth of real-world case studies in the application chapters to act as a foundational reference for biomedical engineers, computer scientists, healthcare researchers, and clinicians"-- Provided by publisher.
- Published
- 2021
5. Big data and social science : data science methods and tools for research and practice.
- Author
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Foster, Ian
- Subjects
Social sciences -- Data processing ,Social sciences -- Statistical methods ,Data mining ,Big data - Abstract
Summary: "This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner"-- Provided by publisher.
- Published
- 2021
6. Smarter data science : succeeding with enterprise-grade data and ai projects.
- Author
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Fishman, Neal and Stryker, Cole
- Subjects
Big data ,Data Mining ,Artificial intelligence - Abstract
Summary: Organizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how.' Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive. Smarter Data Science helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments. When an organization manages its data effectively, its data science program becomes a fully scalable function that's both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise. By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements: -Improving time-to-value with infused AI models for common use cases -Optimizing knowledge work and business processes -Utilizing AI-based business intelligence and data visualization -Establishing a data topology to support general or highly specialized needs -Successfully completing AI projects in a predictable manner -Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.
- Published
- 2020
7. Big Data in Education.
- Author
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Wagan, Emelyn Cereno
- Subjects
Big Data ,Data Processing ,Psychology ,Eduucation - Abstract
Summary: This book discusses how Big Data could be implemented in educational settings and research, using empirical data and suggesting both best practices and areas in which to invest future research and development. It also explores: 1) the use of learning analytics to improve learning and teaching; 2) the opportunities and challenges of learning analytics in education. As Big Data becomes a common part of the fabric of our world, education and research are challenged to use this data to improve educational and research systems, and also are tasked with teaching coming generations to deal with Big Data both effectively and ethically. The Big Data era is changing the data landscape for statistical analysis, the ways in which data is captured and presented, and the necessary level of statistical literacy to analyse and interpret data for future decision making. The advent of Big Data accentuates the need to enable citizens to develop statistical skills, thinking and reasoning needed for representing, integrating and exploring complex information. This book offers guidance to researchers who are seeking suitable topics to explore. It presents research into the skills needed by data practitioners (data analysts, data managers, statisticians, and data consumers, academics), and provides insights into the statistical skills, thinking and reasoning needed by educators and researchers in the future to work with Big Data. This book serves as a concise reference for policymakers, who must make critical decisions regarding funding and applications. .
- Published
- 2020
8. Managing Your Data Science Projects: Learn Salesmanship, Presentation, and Maintenance of Completed Models
- Author
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de Graaf, Robert
- Subjects
Database management ,Big data - Abstract
Summary: At first glance, the skills required to work in the data science field appear to be self-explanatory. Do not be fooled. Impactful data science demands an interdisciplinary knowledge of business philosophy, project management, salesmanship, presentation, and more. In Managing Your Data Science Projects, author Robert de Graaf explores important concepts that are frequently overlooked in much of the instructional literature that is available to data scientists new to the field. If your completed models are to be used and maintained most effectively, you must be able to present and sell them within your organization in a compelling way. The value of data science within an organization cannot be overstated. Thus, it is vital that strategies and communication between teams are dexterously managed. Three main ways that data science strategy is used in a company is to research its customers, assess risk analytics, and log operational measurements. These all require different managerial instincts, backgrounds, and experiences, and de Graaf cogently breaks down the unique reasons behind each. They must align seamlessly to eventually be adopted as dynamic models. Data science is a relatively new discipline, and as such, internal processes for it are not as well-developed within an operational business as others. With Managing Your Data Science Projects, you will learn how to create products that solve important problems for your customers and ensure that the initial success is sustained throughout the products intended life. Your users will trust you and your models, and most importantly, you will be a more well-rounded and effectual data scientist throughout your career.
- Published
- 2020
9. Codeless Data Structures and Algorithms: Learn DSA Without Writing a Single Line of Code.
- Author
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Subero, Armstrong
- Subjects
Coding and algorithm theory ,Algorithm analyis and Problem complexity ,Big data ,Machine learning - Abstract
Summary: In the era of self-taught developers and programmers, essential topics in the industry are frequently learned without a formal academic foundation. A solid grasp of data structures and algorithms (DSA) is imperative for anyone looking to do professional software development and engineering, but classes in the subject can be dry or spend too much time on theory and unnecessary readings. Regardless of your programming language background, Codeless Data Structures and Algorithms has you covered. In this book, author Armstrong Subero will help you learn DSAs without writing a single line of code. Straightforward explanations and diagrams give you a confident handle on the topic while ensuring you never have to open your code editor, use a compiler, or look at an integrated development environment. Subero introduces you to linear, tree, and hash data structures and gives you important insights behind the most common algorithms that you can directly apply to your own programs. Codeless Data Structures and Algorithms provides you with the knowledge about DSAs that you will need in the professional programming world, without using any complex mathematics or irrelevant information. Whether you are a new developer seeking a basic understanding of the subject or a decision-maker wanting a grasp of algorithms to apply to your projects, this book belongs on your shelf. Quite often, a new, refreshing, and unpretentious approach to a topic is all you need to get inspired.
- Published
- 2020
10. Machine Learning and AI for Healthcare : Big Data for Improved Health Outcomes
- Author
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Panesar, Arjun
- Subjects
Artificial intelligence ,Big data ,Computer programming ,Open source software - Abstract
Summary: Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You'll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You'll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things.
- Published
- 2019
11. Data analytics for smart cities.
- Author
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Alavi, Amir H. and Buttlar, William G.
- Subjects
Smart cities ,Big data ,Data analytics ,Quantitative research - Abstract
Summary: The development of smart cities is one of the most important challenges over the next few decades. Governments and companies are leveraging billions of dollars in public and private funds for smart cities. Next generation smart cities are heavily dependent on distributed smart sensing systems and devices to monitor the urban infrastructure. The smart sensor networks serve as autonomous intelligent nodes to measure a variety of physical or environmental parameters. They should react in time, establish automated control, and collect information for intelligent decision-making. In this context, one of the major tasks is to develop advanced frameworks for the interpretation of the huge amount of information provided by the emerging testing and monitoring systems. Data Analytics for Smart Cities brings together some of the most exciting new developments in the area of integrating advanced data analytics systems into smart cities along with complementary technological paradigms such as cloud computing and Internet of Things (IoT). The book serves as a reference for researchers and engineers in domains of advanced computation, optimization, and data mining for smart civil infrastructure condition assessment, dynamic visualization, intelligent transportation systems (ITS), cyber-physical systems, and smart construction technologies. The chapters are presented in a hands-on manner to facilitate researchers in tackling applications. Arguably, data analytics technologies play a key role in tackling the challenge of creating smart cities. Data analytics applications involve collecting, integrating, and preparing time- and space-dependent data produced by sensors, complex engineered systems, and physical assets, followed by developing and testing analytical models to verify the accuracy of results. This book covers this multidisciplinary field and examines multiple paradigms such as machine learning, pattern recognition, statistics, intelligent databases, knowledge acquisition, data visualization, high performance computing, and expert systems. The book explores new territory by discussing the cutting-edge concept of Big Data analytics for interpreting massive amounts of data in smart city applications.
- Published
- 2019
12. Data science strategy.
- Author
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Jagare, Ulrika and Pierson, Lillian
- Subjects
Big Data ,Data Mining ,Data Structure ,Database Management - Abstract
Summary: Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the "what" and the "why" of data science and covering what it takes to lead and nurture a top-notch team of data scientists. With this book, you'll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data. This book outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value. This book will enable you to: Learn exactly what data science is and why it's important ; Adopt a data-driven mindset as the foundation to success ; Understand the processes and common roadblocks behind data science ; Keep your data science program focused on generating business value ; Nurture a top-quality data science team.
- Published
- 2019
13. Spatial planning in the big data revolution.
- Author
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Voghera, Angioletta and La Riccia, Luigi
- Subjects
Public spaces -- Planning -- Data processing ,City planning -- Data processing ,Geographic information systems ,Big data - Abstract
Summary: "This book explores in a systematic way the themes of big data and the spatial analysis, with theoretical and operative recommendations for urban planning. It also brings together different work methodologies that combine the potential of large data analysis with GIS applications in dedicated tools specifically for sectoral, territorial, environmental, transport, energy, real estate and landscape assessment"-- Provided by publisher.
- Published
- 2019
14. Multimedia Big Data Computing for IoT Applications : Concepts, Paradigms and Solutions.
- Author
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Tanwar, Sudeep, Tyagi, Sudhanshu, and Kumar, Neeraj
- Subjects
Big data ,Multimedia systems - Abstract
Summary: This book considers all aspects of managing the complexity of Multimedia Big Data Computing (MMBD) for IoT applications and develops a comprehensive taxonomy. It also discusses a process model that addresses a number of research challenges associated with MMBD, such as scalability, accessibility, reliability, heterogeneity, and Quality of Service (QoS) requirements, presenting case studies to demonstrate its application. Further, the book examines the layered architecture of MMBD computing and compares the life cycle of both big data and MMBD. Written by leading experts, it also includes numerous solved examples, technical descriptions, scenarios, procedures, and algorithms.
- Published
- 2019
15. Advances in deep learning.
- Author
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Wani, Arif M.
- Subjects
Education--Data processing ,Machine learning ,Big data - Abstract
Summary: This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.
- Published
- 2019
16. Deep learning through sparse and low-rank modeling.
- Author
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Wang, Zhangyang, Fu, Yun, and Huang, Thomas S.
- Subjects
Machine learning ,Big data ,Data mining - Abstract
Summary: Deep Learning through Sparse Representation and Low-Rank Modeling bridges classical sparse and low rank models-those that emphasize problem-specific Interpretability-with recent deep network models that have enabled a larger learning capacity and better utilization of Big Data. It shows how the toolkit of deep learning is closely tied with the sparse/low rank methods and algorithms, providing a rich variety of theoretical and analytic tools to guide the design and interpretation of deep learning models. The development of the theory and models is supported by a wide variety of applications in computer vision, machine learning, signal processing, and data mining. This book will be highly useful for researchers, graduate students and practitioners working in the fields of computer vision, machine learning, signal processing, optimization and statistics.
- Published
- 2019
17. Text Analytics with Python : A Practitioner's Guide to Natural Language Processing.
- Author
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Sarkar, Dipanjan
- Subjects
Artificial intelligence ,Python (Computer program language) ,Big data - Abstract
Summary: Leverage Natural Language Processing (NLP) in Python and learn how to set up your own robust environment for performing text analytics. The second edition of this book will show you how to use the latest state-of-the-art frameworks in NLP, coupled with Machine Learning and Deep Learning to solve real-world case studies leveraging the power of Python. This edition has gone through a major revamp introducing several major changes and new topics based on the recent trends in NLP. We have a dedicated chapter around Python for NLP covering fundamentals on how to work with strings and text data along with introducing the current state-of-the-art open-source frameworks in NLP. We have a dedicated chapter on feature engineering representation methods for text data including both traditional statistical models and newer deep learning based embedding models. Techniques around parsing and processing text data have also been improved with some new methods. Considering popular NLP applications, for text classification, we also cover methods for tuning and improving our models. Text Summarization has gone through a major overhaul in the context of topic models where we showcase how to build, tune and interpret topic models in the context of an interest dataset on NIPS conference papers. Similarly, we cover text similarity techniques with a real-world example of movie recommenders. Sentiment Analysis is covered in-depth with both supervised and unsupervised techniques. We also cover both machine learning and deep learning models for supervised sentiment analysis. Semantic Analysis gets its own dedicated chapter where we also showcase how you can build your own Named Entity Recognition (NER) system from scratch. To conclude things, we also have a completely new chapter on the promised of Deep Learning for NLP where we also showcase a hands-on example on deep transfer learning. While the overall structure of the book remains the same, the entire code base, modules, and chapters will be updated to the latest Python 3.x release. ---------------------------------- Also the key selling points ? Implementations are based on Python 3.x and state-of-the-art popular open source libraries in NLP ? Covers Machine Learning and Deep Learning for Advanced Text Analytics and NLP ? Showcases diverse NLP applications including Classification, Clustering, Similarity Recommenders, Topic Models, Sentiment and Semantic Analysis.
- Published
- 2019
18. Development and Analysis of Deep Learning Architectures.
- Author
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Pedrycz, Witold and Chen, Shyi-Ming
- Subjects
Machine learning ,Deep learning ,Big data - Abstract
Summary: This book offers a timely reflection on the remarkable range of algorithms and applications that have made the area of deep learning so attractive and heavily researched today. Introducing the diversity of learning mechanisms in the environment of big data, and presenting authoritative studies in fields such as sensor design, health care, autonomous driving, industrial control and wireless communication, it enables readers to gain a practical understanding of design. The book also discusses systematic design procedures, optimization techniques, and validation processes.
- Published
- 2019
19. Innovations in big data mining and embedded knowledge.
- Author
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Esposito, Anna, Esposito, Antonietta M., and Jain, L. C.
- Subjects
Big data ,Data mining - Abstract
Summary: This book addresses the usefulness of knowledge discovery through data mining. With this aim, contributors from different fields propose concrete problems and applications showing how data mining and discovering embedded knowledge from raw data can be beneficial to social organizations, domestic spheres, and ICT markets. Data mining or knowledge discovery in databases (KDD) has received increasing interest due to its focus on transforming large amounts of data into novel, valid, useful, and structured knowledge by detecting concealed patterns and relationships. The concept of knowledge is broad and speculative and has promoted epistemological debates in western philosophies. The intensified interest in knowledge management and data mining stems from the difficulty in identifying computational models able to approximate human behaviors and abilities in resolving organizational, social, and physical problems. Current ICT interfaces are not yet adequately advanced to support and simulate the abilities of physicians, teachers, assistants or housekeepers in domestic spheres. And unlike in industrial contexts where abilities are routinely applied, the domestic world is continuously changing and unpredictable. There are challenging questions in this field: Can knowledge locked in conventions, rules of conduct, common sense, ethics, emotions, laws, cultures, and experiences be mined from data? Is it acceptable for automatic systems displaying emotional behaviors to govern complex interactions based solely on the mining of large volumes of data? Discussing multidisciplinary themes, the book proposes computational models able to approximate, to a certain degree, human behaviors and abilities in resolving organizational, social, and physical problems. The innovations presented are of primary importance for: a. The academic research community b. The ICT market c. Ph. D. students and early stage researchers d. Schools, hospitals, rehabilitation and assisted-living centers e. Representatives from multimedia industries and standardization bodies.
- Published
- 2019
20. Collecting experiments : making big data biology.
- Author
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Strasser, Bruno J.
- Subjects
Biology, Experimental -- Data processing ,Biology, Experimental -- Databases ,Biological models -- Data processing ,Biological specimens -- Collection and preservation -- Technological innovations ,Big data - Published
- 2019
21. Applications of machine learning in wireless communications.
- Author
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He, Ruisi and Zhiguo Ding
- Subjects
Wireless communication systems ,Analysis ,Data mining ,Data processing ,Machine learning ,Radio ,Telecommunication ,Big Data ,data analysis ,data mining ,learning (artificial intelligence) ,radiocommunication ,telecommunication computing - Abstract
Summary: In such an era of big data where data mining and data analysis technologies are effective approaches for wireless system evaluation and design, the applications of machine learning in wireless communications have received a lot of attention recently. Machine learning provides feasible and new solutions for the complex wireless communication system design. It has been a powerful tool and popular research topic with many potential applications to enhance wireless communications, e.g. radio channel modelling, channel estimation and signal detection, network management and performance improvement, access control, resource allocation. However, most of the current researches are separated into different fields and have not been well organized and presented yet. It is therefore difficult for academic and industrial groups to see the potentialities of using machine learning in wireless communications. It is now appropriate to present a detailed guidance of how to combine the disciplines of wireless communications and machine learning.
- Published
- 2019
22. Artificial Intelligence for Big Data.
- Author
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Deshpande, Anand and Kumar, Manish
- Subjects
Artificial Intelligence ,Big data ,Ontology for Big Data - Abstract
Summary: Build next-generation Artificial Intelligence systems with Java About This Book Implement AI techniques to build smart applications using Deeplearning4j Perform big data analytics to derive quality insights using Spark MLlib Create self-learning systems using neural networks, NLP, and reinforcement learning Who This Book Is For This book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus. What You Will Learn Manage Artificial Intelligence techniques for big data with Java Build smart systems to analyze data for enhanced customer experience Learn to use Artificial Intelligence frameworks for big data Understand complex problems with algorithms and Neuro-Fuzzy systems Design stratagems to leverage data using Machine Learning process Apply Deep Learning techniques to prepare data for modeling Construct models that learn from data using open source tools Analyze big data problems using scalable Machine Learning algorithms In Detail In this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data. With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems. By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems. Style and approach An easy-to-follow, step-by-step guide to help you get to grips with real-world applications of Artificial Intelligence for big data using Java
- Published
- 2018
23. Practical Enterprise Data Lake Insights : Handle Data-Driven Challenges in an Enterprise Big Data Lake.
- Author
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Gupta, Saurabh and Giri, Venkata
- Subjects
Application software ,Big data ,Big Data ,Big Data/Analytics ,Computer Applications - Abstract
Summary: Use this practical guide to successfully handle the challenges encountered when designing an enterprise data lake and learn industry best practices to resolve issues. When designing an enterprise data lake you often hit a roadblock when you must leave the comfort of the relational world and learn the nuances of handling non-relational data. Starting from sourcing data into the Hadoop ecosystem, you will go through stages that can bring up tough questions such as data processing, data querying, and security. Concepts such as change data capture and data streaming are covered. The book takes an end-to-end solution approach in a data lake environment that includes data security, high availability, data processing, data streaming, and more. Each chapter includes application of a concept, code snippets, and use case demonstrations to provide you with a practical approach. You will learn the concept, scope, application, and starting point. What You'll Learn: Get to know data lake architecture and design principles Implement data capture and streaming strategies Implement data processing strategies in Hadoop Understand the data lake security framework and availability model.
- Published
- 2018
24. Data science.
- Author
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Kelleher, John D. and Tierney, Brendan
- Subjects
Big data ,Machine learning ,Data mining ,Quantitative research - Abstract
Summary: "The goal of data science is to improve decision making through the analysis of data. Today data science determines the ads we see online, the books and movies that are recommended to us online, which emails are filtered into our spam folders, and even how much we pay for health insurance. This volume in the MIT Press Essential Knowledge series offers a concise introduction to the emerging field of data science, explaining its evolution, current uses, data infrastructure issues, and ethical challenges."--Provided by publisher.
- Published
- 2018
25. Applied Natural Language Processing with Python : Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing
- Author
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Beysolow II, Taweh
- Subjects
Artificial intelligence ,Big data ,Computer programming ,Open source software ,Python (Computer program language) - Abstract
Summary: Learn to harness the power of AI for natural language processing, performing tasks such as spell check, text summarization, document classification, and natural language generation. Along the way, you will learn the skills to implement these methods in larger infrastructures to replace existing code or create new algorithms. Applied Natural Language Processing with Python starts with reviewing the necessary machine learning concepts before moving onto discussing various NLP problems. After reading this book, you will have the skills to apply these concepts in your own professional environment. You will: Utilize various machine learning and natural language processing libraries such as TensorFlow, Keras, NLTK, and Gensim Manipulate and preprocess raw text data in formats such as .txt and .pdf Strengthen your skills in data science by learning both the theory and the application of various algorithms .
- Published
- 2018
26. Journalism in an era of big data : cases, concepts, and critiques.
- Author
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Lewis, Seth C.
- Subjects
Journalism ,Computer network resources ,Big data - Abstract
Summary: This volume thus explores a range of phenomena, from the use of algorithms in the newsroom to the emergence of automated news stories, at the intersection between journalism and the social, computer, and information sciences.
- Published
- 2018
27. Big data in engineering applications.
- Author
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Roy, Sanjiban Sekhar, Samui, Pijush, Deo, Ravinesh, and Ntalampiras, Stavros
- Subjects
Big data ,Engineering ,Computer science -- Mathematics - Abstract
Summary: This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.
- Published
- 2018
28. Next-Generation Big Data : A Practical Guide to Apache Kudu, Impala, and Spark.
- Author
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Quinto, Butch
- Subjects
Big data ,Big Data Fundamentals ,Introduction to Apache Kudu ,Impala ,Spark - Abstract
Summary: Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You'll Learn: Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing Turbocharge Spark with Alluxio, a distributed in-memory storage platform Deploy big data in the cloud using Cloudera Director Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modeling Study real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard.
- Published
- 2018
29. Big Data Demystified : How to use big data, data science and AI to make better business decisions and gain competitive advantag.
- Author
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Stephenson, David
- Subjects
Big data ,Data science ,Artificial intelligence - Abstract
Summary: Big Data' refers to a new class of data, to which 'big' doesn't quite do it justice. Much like an ocean is more than simply a deeper swimming pool, big data is fundamentally different to traditional data and needs a whole new approach. Packed with examples and case studies, this clear, comprehensive book will show you how to accumulate and utilise 'big data' in order to develop your business strategy. Big Data Demystified is your practical guide to help you draw deeper insights from the vast information at your fingertips; you will be able to understand customer motivations, speed up production lines, and even offer personalised experiences to each and every customer. With 20 years of industry experience, David Stephenson shows how big data can give you the best competitive edge, and why it is integral to the future of your business. "--Publisher's description.
- Published
- 2018
30. The AI delusion.
- Author
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Smith, Gary
- Subjects
Computers -- Social aspects ,Data mining ,Artificial intelligence ,Big data - Abstract
Summary: "The AI delusion demonstrates why we should not be intimidated into thinking that computers are infallible, that data-mining is knowledge discovery, or that black boxes should be trusted"--Back dust jacket.
- Published
- 2018
31. Big data and computational intelligence in networking.
- Author
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Jonathon, Riley N.
- Subjects
Big data ,Cloud computing ,Computer networks -- Management ,Computational intelligence - Published
- 2018
32. The efficiency paradox : what big data can't do.
- Author
-
Tenner, Edward
- Subjects
Industrial efficiency ,Serendipity ,Artificial intelligence ,Big data ,BUSINESS & ECONOMICS / Knowledge Capital ,SOCIAL SCIENCE / Media Studies ,SELF-HELP / History of Technology - Abstract
Summary: "A bold challenge to our obsession with efficiency--and a new understanding of how to benefit from the powerful potential of serendipity Algorithms, multitasking, sharing economy, life hacks: our culture can't get enough of efficiency. One of the great promises of the Internet and big data revolutions is the idea that we can improve the processes and routines of our work and personal lives to get more done in less time than ever before. There is no doubt that we're performing at higher scales and going faster than ever, but what if we're headed in the wrong direction? The Efficiency Paradox questions our ingrained assumptions about efficiency, persuasively showing how relying on the algorithms of platforms can in fact lead to wasted efforts, missed opportunities, and above all an inability to break out of established patterns. Edward Tenner offers a smarter way to think about efficiency, showing how we can combine artificial intelligence and our own intuition, leaving ourselves and our institutions open to learning from the random and unexpected"-- Provided by publisher.
- Published
- 2018
33. Practical Data Science : A Guide to Building the Technology Stack for Turning Data Lakes into Business Assets.
- Author
-
Vermeulen, Andreas François
- Subjects
Big data ,Data mining ,Data structures (Computer science) ,Data Mining and Knowledge Discovery ,Big Data ,Big Data/Analytics ,Data Storage Representation - Abstract
Summary: Learn how to build a data science technology stack and perform good data science with repeatable methods. You will learn how to turn data lakes into business assets. The data science technology stack demonstrated in Practical Data Science is built from components in general use in the industry. Data scientist Andreas Vermeulen demonstrates in detail how to build and provision a technology stack to yield repeatable results. He shows you how to apply practical methods to extract actionable business knowledge from data lakes consisting of data from a polyglot of data types and dimensions. What You'll Learn: Become fluent in the essential concepts and terminology of data science and data engineering Build and use a technology stack that meets industry criteria Master the methods for retrieving actionable business knowledge Coordinate the handling of polyglot data types in a data lake for repeatable results.
- Published
- 2018
34. Next-Generation Big Data : A Practical Guide to Apache Kudu, Impala, and Spark.
- Author
-
Quinto, Butch
- Subjects
Big data ,Data Mining ,Computer science - Abstract
Summary: Utilize this practical and easy-to-follow guide to modernize traditional enterprise data warehouse and business intelligence environments with next-generation big data technologies. Next-Generation Big Data takes a holistic approach, covering the most important aspects of modern enterprise big data. The book covers not only the main technology stack but also the next-generation tools and applications used for big data warehousing, data warehouse optimization, real-time and batch data ingestion and processing, real-time data visualization, big data governance, data wrangling, big data cloud deployments, and distributed in-memory big data computing. Finally, the book has an extensive and detailed coverage of big data case studies from Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard. What You'll Learn: Install Apache Kudu, Impala, and Spark to modernize enterprise data warehouse and business intelligence environments, complete with real-world, easy-to-follow examples, and practical advice Integrate HBase, Solr, Oracle, SQL Server, MySQL, Flume, Kafka, HDFS, and Amazon S3 with Apache Kudu, Impala, and Spark Use StreamSets, Talend, Pentaho, and CDAP for real-time and batch data ingestion and processing Utilize Trifacta, Alteryx, and Datameer for data wrangling and interactive data processing Turbocharge Spark with Alluxio, a distributed in-memory storage platform Deploy big data in the cloud using Cloudera Director Perform real-time data visualization and time series analysis using Zoomdata, Apache Kudu, Impala, and Spark Understand enterprise big data topics such as big data governance, metadata management, data lineage, impact analysis, and policy enforcement, and how to use Cloudera Navigator to perform common data governance tasks Implement big data use cases such as big data warehousing, data warehouse optimization, Internet of Things, real-time data ingestion and analytics, complex event processing, and scalable predictive modeling Study real-world big data case studies from innovative companies, including Navistar, Cerner, British Telecom, Shopzilla, Thomson Reuters, and Mastercard.
- Published
- 2018
35. Research analytics : boosting university productivity and competitiveness through scientometrics.
- Author
-
Cantu-Ortiz, Francisco J.
- Subjects
Science indicators ,Big data ,Universities and colleges ,Bibliometrics - Abstract
Summary: "Scientific knowledge is doubling every 10 to 15 years worldwide. Who generates this knowledge? Where is all this information stored? How is it being utilized? What are the main metrics for its use? What kind of impact is this knowledge having aside from economic considerations? These are some issues addressed in this book on data analytics and scientometrics, a field that treats themes of scientific production, research evaluation, and reputation of world-class universities as measured by university rankings and big-league tables. In this book, the main players in bibliometrics databases, research metrics, and university rankings present their methodologies and tools on these topics."--Provided by publisher
- Published
- 2018
36. The deep learning revolution.
- Author
-
Sejnowski, Terrence J.
- Subjects
Machine learning ,Big data ,Artificial intelligence - Abstract
Summary: How deep learning-from Google Translate to driverless cars to personal cognitive assistants-is changing our lives and transforming every sector of the economy. The deep learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. Learning algorithms extract information from raw data; information can be used to create knowledge; knowledge underlies understanding; understanding leads to wisdom. Someday a driverless car will know the road better than you do and drive with more skill; a deep learning network will diagnose your illness; a personal cognitive assistant will augment your puny human brain. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future.
- Published
- 2018
37. Veracity of Big Data : Machine Learning and Other Approaches to Verifying Truthfulness.
- Author
-
Pendyala, Vishnu
- Subjects
Artificial intelligence ,Big data ,Big Data ,Artificial Intelligence - Abstract
Summary: Examine the problem of maintaining the quality of big data and discover novel solutions. You will learn the four V's of big data, including veracity, and study the problem from various angles. The solutions discussed are drawn from diverse areas of engineering and math, including machine learning, statistics, formal methods, and the Blockchain technology. Veracity of Big Data serves as an introduction to machine learning algorithms and diverse techniques such as the Kalman filter, SPRT, CUSUM, fuzzy logic, and Blockchain, showing how they can be used to solve problems in the veracity domain. Using examples, the math behind the techniques is explained in easy-to-understand language. Determining the truth of big data in real-world applications involves using various tools to analyze the available information. This book delves into some of the techniques that can be used. Microblogging websites such as Twitter have played a major role in public life, including during presidential elections. The book uses examples of microblogs posted on a particular topic to demonstrate how veracity can be examined and established. Some of the techniques are described in the context of detecting veiled attacks on microblogging websites to influence public opinion. What You'll Learn: Understand the problem concerning data veracity and its ramifications Develop the mathematical foundation needed to help minimize the impact of the problem using easy-to-understand language and examples Use diverse tools and techniques such as machine learning algorithms, Blockchain, and the Kalman filter to address veracity issues.
- Published
- 2018
38. Big data analytics for satellite image processing and remote sensing.
- Author
-
Swarnalatha, P. and Sevugan, Prabu
- Subjects
Geospatial data ,Big data ,Artificial satellites in remote sensing ,Image processing ,Artificial satellites - Abstract
Summary: "This book explores the difficulties and challenges that various fields have faced in implementing the technologies and applications. It addresses different aspects of using big data upon image processing for remote sensing and related topics and it explores the impact of such technologies on the applications in which this advanced technology is being implemented"-- Provided by publisher.
- Published
- 2018
39. Mathematics of big data : spreadsheets, databases, matrices, and graphs.
- Author
-
Kepner, Jeremy V. and Jananthan, Hayden
- Subjects
Big data ,Graphic methods - Abstract
Summary: Today, the volume, velocity, and variety of data are increasing rapidly across a range of fields, including Internet search, healthcare, finance, social media, wireless devices, and cybersecurity. Indeed, these data are growing at a rate beyond our capacity to analyze them. The tools-including spreadsheets, databases, matrices, and graphs-developed to address this challenge all reflect the need to store and operate on data as whole sets rather than as individual elements. This book presents the common mathematical foundations of these data sets that apply across many applications and technologies. Associative arrays unify and simplify data, allowing readers to look past the differences among the various tools and leverage their mathematical similarities in order to solve the hardest big data challenges.
- Published
- 2018
40. Pro Deep Learning with TensorFlow : A Mathematical Approach to Advanced Artificial Intelligence in Python
- Author
-
Pattanayak, Santanu
- Subjects
Artificial intelligence ,Big data ,Python (Computer program language) ,Artificial Intelligence ,Big Data ,Python - Abstract
Summary: Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures. All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways. You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community. What You'll Learn: Understand full stack deep learning using TensorFlow and gain a solid mathematical foundation for deep learning Deploy complex deep learning solutions in production using TensorFlow Carry out research on deep learning and perform experiments using TensorFlow.
- Published
- 2017
41. Beginning data science in R : data analysis, visualization, and modelling for the data scientist.
- Author
-
Mailund, Thomas
- Subjects
Quantitative research ,Computer Science ,Data Mining and Knowledge Discovery ,Big Data ,Programming Languages, Compilers, Interpreters ,Programming Techniques ,Databases - Abstract
Summary: Discover best practices for data analysis and software development in R and start on the path to becoming a fully-fledged data scientist. This book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Data Science in R details how data science is a combination of statistics, computational science, and machine learning. You'll see how to efficiently structure and mine data to extract useful patterns and build mathematical models. This requires computational methods and programming, and R is an ideal programming language for this. This book is based on a number of lecture notes for classes the author has taught on data science and statistical programming using the R programming language. Modern data analysis requires computational skills and usually a minimum of programming. You will: Perform data science and analytics using statistics and the R programming language Visualize and explore data, including working with large data sets found in big data Build an R package Test and check your code Practice version control Profile and optimize your code.
- Published
- 2017
42. Social Media Analytics Strategy : Using Data to Optimize Business Performance.
- Author
-
Goncalves, Alex
- Subjects
Big data ,Internet marketing ,Big Data/Analytics ,Online Marketing/Social Media - Abstract
Summary: This book shows you how to use social media analytics to optimize your business performance. The tools discussed will prepare you to create and implement an effective digital marketing strategy. From understanding the data and its sources to detailed metrics, dashboards, and reports, this book is a robust tool for anyone seeking a tangible return on investment from social media and digital marketing. Social Media Analytics Strategy speaks to marketers who do not have a technical background and creates a bridge into the digital world. Comparable books are either too technical for marketers (aimed at software developers) or too basic and do not take strategy into account. They also lack an overview of the entire process around using analytics within a company project. They don't go into the everyday details and also don't touch upon common mistakes made by marketers. This book highlights patterns of common challenges experienced by marketers from entry level to directors and C-level executives. Social media analytics are explored and explained using real-world examples and interviews with experienced professionals and founders of social media analytics companies. What You'll Learn: Get a clear view of the available data for social media marketing and how to access all of it Make use of data and information behind social media networks to your favor Know the details of social media analytics tools and platforms so you can use any tool in the market Apply social media analytics to many different real-world use cases Obtain tips from interviews with professional marketers and founders of social media analytics platforms Understand where social media is heading, and what to expect in the future.
- Published
- 2017
43. Pro Hadoop data analytics : designing and building big data systems using the Hadoop ecosystem.
- Author
-
Koitzsch, Kerry
- Subjects
Database management ,Computer Science ,Big Data ,Programming Techniques ,Programming Languages, Compilers, Interpreters ,Data Mining and Knowledge Discovery - Abstract
Summary: Learn advanced analytical techniques and leverage existing toolkits to make your analytic applications more powerful, precise, and efficient. This book provides the right combination of architecture, design, and implementation information to create analytical systems which go beyond the basics of classification, clustering, and recommendation. In Pro Hadoop Data Analytics best practices are emphasized to ensure coherent, efficient development. A complete example system will be developed using standard third-party components which will consist of the toolkits, libraries, visualization and reporting code, as well as support glue to provide a working and extensible end-to-end system. The book emphasizes four important topics: The importance of end-to-end, flexible, configurable, high-performance data pipeline systems with analytical components as well as appropriate visualization results. Deep-dive topics will include Spark, H20, Vopal Wabbit (NLP), Stanford NLP, and other appropriate toolkits and plugins. Best practices and structured design principles. This will include strategic topics as well as the how to example portions. The importance of mix-and-match or hybrid systems, using different analytical components in one application to accomplish application goals. The hybrid approach will be prominent in the examples. Use of existing third-party libraries is key to effective development. Deep dive examples of the functionality of some of these toolkits will be showcased as you develop the example system.
- Published
- 2017
44. Cloud computing for machine learning and cognitive applications.
- Author
-
Hwang, Kai
- Subjects
Cloud computing ,Machine learning ,Data mining ,Big data - Abstract
Summary: The first textbook to teach students how to build data analytic solutions on large data sets using cloud-based technologies. This is the first textbook to teach students how to build data analytic solutions on large data sets (specifically in Internet of Things applications) using cloud-based technologies for data storage, transmission and mashup, and AI techniques to analyze this data. This textbook is designed to train college students to master modern cloud computing systems in operating principles, architecture design, machine learning algorithms, programming models and software tools for big data mining, analytics, and cognitive applications. The book will be suitable for use in one-semester computer science or electrical engineering courses on cloud computing, machine learning, cloud programming, cognitive computing, or big data science. The book will also be very useful as a reference for professionals who want to work in cloud computing and data science. Cloud and Cognitive Computing begins with two introductory chapters on fundamentals of cloud computing, data science, and adaptive computing that lay the foundation for the rest of the book. Subsequent chapters cover topics including cloud architecture, mashup services, virtual machines, Docker containers, mobile clouds, IoT and AI, inter-cloud mashups, and cloud performance and benchmarks, with a focus on Google’s Brain Project, DeepMind, and X-Lab programs, IBKai HwangM SyNapse, Bluemix programs, cognitive initiatives, and neurocomputers. The book then covers machine learning algorithms and cloud programming software tools and application development, applying the tools in machine learning, social media, deep learning, and cognitive applications. All cloud systems are illustrated with big data and cognitive application examples.
- Published
- 2017
45. The Human element of big data : issues, analytics, and performance.
- Author
-
Tomar, Geetam, Chaudhari, Narendra S., Bhadoria, Robin Singh, and Deka, Ganesh Chandra
- Subjects
Big data ,Data mining -- Social aspects ,Quantitative research -- Social aspects - Published
- 2017
46. Data visualization and statistical literacy for open and big data.
- Author
-
Prodromou, Theodosia
- Subjects
Information visualization ,Big data ,Statistical literacy - Abstract
Summary: "This book highlights methodological developments in the way that data analytics is both learned and taught. Featuring extensive coverage on emerging relevant topics such as data complexity, statistics education, and curriculum development"-- Provided by publisher.
- Published
- 2017
47. Big data and social science : a practical guide to methods and tools.
- Author
-
Foster, Ian
- Subjects
Social sciences -- Data processing ,Social sciences -- Statistical methods ,Data mining ,Big data - Abstract
Summary: Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation. The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations. -- Provided by Publisher.
- Published
- 2017
48. Big data in cognitive science.
- Author
-
Jones, Michael N.
- Subjects
Cognitive science ,Data mining ,Big data ,PSYCHOLOGY - Abstract
While laboratory research is the backbone of collecting experimental data in cognitive science, a rapidly increasing amount of research is now capitalizing on large-scale and real-world digital data. Each piece of data is a trace of human behavior and offers us a potential clue to understanding basic cognitive principles. However, we have to be able to put the pieces together in a reasonable way, which necessitates both advances in our theoretical models and development of new methodological techniques. The primary goal of this volume is to present cutting-edge examples of mining large-scale and naturalistic data to discover important principles of cognition and evaluate theories that would not be possible without such a scale. This book also has a mission to stimulate cognitive scientists to consider new ways to harness big data in order to enhance our understanding of fundamental cognitive processes. Finally, this book aims to warn of the potential pitfalls of using, or being over-reliant on, big data and to show how big data can work alongside traditional, rigorously gathered experimental data rather than simply supersede it.
- Published
- 2016
49. Streaming, sharing, stealing : big data and the future of entertainment.
- Author
-
Smith, Michael D. and Telang, Rahul
- Subjects
Streaming technology (Telecommunications) ,Data transmission systems ,Big data ,Motion pictures - Published
- 2016
50. Managing and processing big data in cloud computing.
- Author
-
Kannan, Rajkumar, Rasool, Raihan Ur, Jin, Hai, and Balasundaram, S. R.
- Subjects
Database management ,Big data ,Cloud computing - Abstract
Summary: "This book explores the challenges of supporting big data processing and cloud-based platforms as a proposed solution, emphasizing a number of crucial topics such as data analytics, wireless networks, mobile clouds, and machine learning"-- Provided by publisher.
- Published
- 2016
51. Information fusion and analytics for big data and IoT.
- Author
-
Bossé, Éloi and Solaiman, Basel
- Subjects
Multisensor data fusion ,Internet of things ,Big data ,Cooperating objects (Computer systems) ,Integration (Theory of knowledge) ,Data mining - Published
- 2016
52. Big data fundamentals : concepts, drivers & techniques.
- Author
-
Erl, Thomas, Khattak, Wajid, and Buhler, Paul
- Subjects
Big data ,Data mining ,Decision making -- Data processing - Published
- 2016
53. R for data science : import, tidy, transform, visualize, and model data.
- Author
-
Wickham, Hadley and Grolemund, Garrett
- Subjects
R (Computer program language) ,Data mining -- Computer programs ,Information visualization -- Computer programs ,Big data ,Databases ,Electronic data processing ,Statistics - Abstract
Summary: "This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"-- Provided by publisher.
- Published
- 2016
54. Big Digital Humanities : imagining a meeting place for the humanities and the digital.
- Author
-
Svensson, Patrik
- Subjects
Digital humanities ,Big data - Published
- 2016
55. R for data science : import, tidy, transform, visualize, and model data.
- Author
-
Wickham, Hadley and Grolemund, Garrett
- Subjects
Data mining -- Computer programs ,Information visualization -- Computer programs ,R (Computer program language) ,Big data ,Databases - Abstract
Summary: "This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience"--Page 4 of cover.
- Published
- 2016
56. Mastering parallel programming with R : master the robust features of R parallel programming to accelerate your data science computations.
- Author
-
Chapple, Simon R., Troup, Eilidh, Forster, Thorsten, and Sloan, Terence
- Subjects
Parallel programming (Computer science) ,R (Computer program language) ,Big Data - Published
- 2016
57. Application of big data for national security : a practitioner's guide to emerging technologies.
- Author
-
Akhgar, Babak, Saathoff, Gregory B., Arabnia, Hamid R., Hill, Richard, Staniforth, Andrew, and Bayerl, Petra Saskia
- Subjects
Terrorism -- Prevention -- Technological innovations ,Data mining in law enforcement ,Big data ,National security -- Technological innovations ,Terrorism -- Prevention -- Data processing - Abstract
Summary: Application of Big Data for National Security provides users with state-of-the-art concepts, methods, and technologies for Big Data analytics in the fight against terrorism and crime, including a wide range of case studies and application scenarios. This book combines expertise from an international team of experts in law enforcement, national security, and law, as well as computer sciences, criminology, linguistics, and psychology, creating a unique cross-disciplinary collection of knowledge and insights into this increasingly global issue. The strategic frameworks and critical factors presented in Application of Big Data for National Security consider technical, legal, ethical, and societal impacts, but also practical considerations of Big Data system design and deployment, illustrating how data and security concerns intersect. In identifying current and future technical and operational challenges it supports law enforcement and government agencies in their operational, tactical and strategic decisions when employing Big Data for national security
- Published
- 2015
58. Real-world data mining : applied business analytics and decision making.
- Author
-
Delen, Dursun
- Subjects
Data mining ,Big data - Published
- 2015
59. Compromised data : from social media to big data.
- Author
-
Langlois, Ganaele, Redden, Joanna, and Elmer, Greg
- Subjects
Data mining -- Social aspects ,Social media ,Online social networks ,Big data - Abstract
Summary: There has been a data rush in the past decade brought about by online communication and, in particular, social media (Facebook, Twitter, Youtube, among others), which promises a new age of digital enlightenment. But social data is compromised: it is being seized by specific economic interests, it leads to a fundamental shift in the relationship between research and the public good, and it fosters new forms of control and surveillance. Compromised Data: From Social Media to Big Data explores how we perform critical research within a compromised social data framework. The expert, international lineup of contributors explores the limits and challenges of social data research in order to invent and develop new modes of doing public research. At its core, this collection argues that we are witnessing a fundamental reshaping of the social through social data mining.
- Published
- 2015
60. Big Data and the Internet of Things : enterprise information architecture for a new age.
- Author
-
Stackowiak, Robert, Licht, Art, Mantha, Venu, and Nagode, Louis
- Subjects
Big data ,Internet of things ,Management information systems - Abstract
Summary: "Your guide to defining an information architecture for emerging trends like Big Data and the Internet of Things"--Page 1 of cover.
- Published
- 2015
61. From big data to big profits : success with data and analytics.
- Author
-
Walker, Russell
- Subjects
Big data ,Business -- Data processing ,Management -- Data processing - Abstract
Summary: In From Big Data to Big Profits, Russell Walker investigates the use of Big Data to stimulate innovations in operational effectiveness and business growth. Walker examines the nature of Big Data and how businesses can use it to create new monetization opportunities. Using case studies of Apple, Netflix, Google, LinkedIn, Zillow, Amazon, and other leaders in the use of Big Data, Walker explores how digital platforms such as mobile apps and social networks are changing the nature of customer interactions and the way Big Data is created and used by companies. Such changes, as Walker points out, will require careful consideration of legal and unspoken business practices as they affect consumer privacy. Companies looking to develop a Big Data strategy will find great value in the SIGMA framework, which he has developed to assess companies for Big Data readiness and provide direction on the steps necessary to get the most from Big Data.
- Published
- 2015
62. Learning Spark : Lightning-Fast Data Analytics.
- Author
-
Damji, Jules S., Wenig, Brooke, Das, Tathagata, and Lee, Denny Yeu
- Subjects
Spark ,Big data ,Data mining - Abstract
Summary: This book introduces Apache Spark, the open source cluster computing system that makes data analytics fast to write and fast to run. You'll learn how to express parallel jobs with just a few lines of code, and cover applications from simple batch jobs to stream processing and machine learning.-- Source other than Library of Congress.
- Published
- 2015
63. Big data, mining, and analytics : components of strategic decision making.
- Author
-
Kudyba, Stephan
- Subjects
Strategic planning -- Data processing ,Data mining ,Big data ,Business planning -- Data processing ,Webometrics ,Data loggers ,COMPUTERS / Database Management / General ,COMPUTERS / Database Management / Data Mining ,COMPUTERS / Information Technology - Abstract
Summary: "Foreword Big data and analytics promise to change virtually every industry and business function over the next decade. Any organization that gets started early with big data can gain a significant competitive edge. Just as early analytical competitors in the "small data" era (including Capital One bank, Progressive Insurance, and Marriott hotels) moved out ahead of their competitors and built a sizable competitive edge, the time is now for firms to seize the big data opportunity. As this book describes, the potential of big data is enabled by ubiquitous computing and data gathering devices; sensors and microprocessors will soon be everywhere. Virtually every mechanical or electronic device can leave a trail that describes its performance, location, or state. These devices, and the people who use them, communicate through the Internet--which leads to another vast data source. When all these bits are combined with those from other media--wireless and wired telephony, cable, satellite, and so forth--the future of data appears even bigger. The availability of all this data means that virtually every business or organizational activity can be viewed as a big data problem or initiative. Manufacturing, in which most machines already have one or more microprocessors, is increasingly a big data environment. Consumer marketing, with myriad customer touchpoints and clickstreams, is already a big data problem. Google has even described its self-driving car as a big data project. Big data is undeniably a big deal, but it needs to be put in context"-- Provided by publisher.
- Published
- 2014
64. Analysis of multivariate and high-dimensional data.
- Author
-
Koch, Inge
- Subjects
Multivariate analysis ,Big data ,Multidimensional data ,Machine learning - Abstract
Summary: "'Big data' poses challenges that require both classical multivariate methods and contemporary techniques from machine learning and engineering. This modern text integrates the two strands into a coherent treatment, drawing together theory, data, computation and recent research. The theoretical framework includes formal definitions, theorems and proofs, which clearly set out the guaranteed 'safe operating zone' for the methods and allow users to assess whether data is in or near the zone. Extensive examples showcase the strengths and limitations of different methods in a range of cases: small classical data; data from medicine, biology, marketing and finance; high-dimensional data from bioinformatics; functional data from proteomics; and simulated data. High-dimension, low-sample-size data gets special attention. Several data sets are revisited repeatedly to allow comparison of methods. Generous use of colour, algorithms, Matlab code and problem sets complete the package. The text is suitable for graduate students in statistics and researchers in data-rich disciplines"-- Provided by publisher.
- Published
- 2014
65. Open data now : the secret to hot startups, smart investing, savvy marketing, and fast innovation.
- Author
-
Gurin, Joel
- Subjects
Statistical services ,Surveys -- Data processing ,Social surveys -- Data processing ,Marketing research -- Data processing ,Big data ,Freedom of information ,Technological innovations ,New products ,Entrepreneurship - Abstract
Summary: Get unprecedented access to thousands of databases. It's called Open Data, and it's revolutionizing business. The business leader's guide to using Open Data to analyze patterns and trends, manage risk, solve problems-and seize the competitive edge Two major trends-the exponential growth of digital data and an emerging culture of disclosure and transparency-have converged to create a world where voluminous information about businesses, government, and the population is becoming visible, accessible, and usable. It's called Open Data, and this book helps leaders harness its power to market and grow their companies. Open Data Now gives you the knowledge and tools to take advantage of this phenomenon in its early stages-and beat the competition to leveraging its many benefits. Joel Gurin is an expert on making complex data sets useful in solving consumer problems, analyzing corporate information, and addressing social issues. He has collaborated with leaders in data, technology, and policy in the U.S. and UK governments, including officials in the White House and 10 Downing Street and at more than 20 U.S. federal agencies.
- Published
- 2014
66. Big data bootcamp : what managers need to know to profit from the big data revolution.
- Author
-
Feinleib, David
- Subjects
Big data ,Information system - Published
- 2014
67. Reality mining : using big data to engineer a better world.
- Author
-
Eagle, Nathan and Greene, Kate
- Subjects
Data mining ,Big data ,Computer networks ,Information science - Abstract
Summary: Big Data is made up of lots of little data: numbers entered into cell phones,addresses entered into GPS devices, visits to websites, online purchases, ATM transactions, and anyother activity that leaves a digital trail.
- Published
- 2014
68. Big data, big innovation : enabling competitive differentiation through business analytics.
- Author
-
Stubbs, Evan
- Subjects
Business planning ,Strategic planning ,Big data - Abstract
Summary: "A practical guide to leveraging your data to spur innovation and growth. Your business generates reams of data, but what do you do with it? Reporting is only the beginning. Your data holds the key to innovation and growth - you just need the proper analytics. In Big Data, Big Innovation
- Published
- 2014
69. Big data analytics : from strategic planning to enterprise integration with tools, techniques, NoSQL, and graph.
- Author
-
Loshin, David
- Subjects
Big data ,Database Management ,Data Warehousing - Published
- 2013
70. Machine learning in action.
- Author
-
Harrington, Peter
- Subjects
Machine learning ,Machine learning -- Handbooks, manuals, etc ,Big data ,Apriori algorithm - Abstract
Summary: Machine Learning in Action is a unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About this Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interesting or useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos About the Author Peter Harrington is a professional developer and data scientist. He holds five US patents and his work has been published in numerous academic journals.
- Published
- 2012
71. Pattern recognition and machine learning.
- Author
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Bishop, Christopher M.
- Subjects
Pattern perception ,Machine learning ,Programming language ,Big Data - Published
- 2006
72. Symmetric and Asymmetric Data in Solution Models.
- Author
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Kazimieras Zavadskas, Edmundas, Turskis, Zenonas, Antuchevičienė, Jurgita, and Kazimieras Zavadskas, Edmundas
- Subjects
Information technology industries ,COVID-19 ,criteria importance through inter-criteria correlation (CRITIC) ,combined compromise solution (CoCoSo) ,gray values ,temporary hospital ,location selection ,ARAS ,interval-valued triangular fuzzy numbers ,e-learning courses ,MCDM ,neutrosophic sets ,quadripartitioned bipolar neutrosophic sets ,similarity measure ,decision making ,multiple-criteria decision-making ,neutrosophic ,single-valued neutrosophic sets ,TOPSIS ,Hamming distance ,Euclidean distance ,e-commerce development strategies ,Symmetry ,bibliometric analysis ,Web of Science ,co-citation ,burst detection analysis ,supply chain ,DANP-mV model ,performance analysis ,asymmetric underactuated ,rehabilitation ,robotic exoskeleton ,symmetric and asymmetric trajectory ,Bowden cable ,video processing data ,EOQ ,Wilson's formulation ,lot size ,reordering time ,visual analogue scales (VAS) ,criteria weighting ,matrix question ,survey ,WASPAS-SVNS ,entropy ,direct rating ,rehabilitation device ,electromyogram ,symmetry ,window parameters ,feature extraction ,pattern recognition ,sensitivity analysis ,reliability ,failure probability ,quantile ,civil engineering ,limit states ,mathematical model ,uncertainty ,cost overrun ,construction project ,fuzzy sets ,earned value management (EVM) ,artificial neural networks (ANNs) ,multiple regression analysis ,road industry ,buckling ,safety ,superquantile ,subquantile ,aerial imagery ,lossy compression ,qualitative evaluation ,WASPAS ,neutrosophic set ,Analytic Hierarchy Process ,fuzzy Analytic Hierarchy Process ,symmetric and asymmetric fuzzy numbers ,stability ,landscape ,micro factor ,macro factor ,real estate market ,Big Data analysis ,Big Data ,land price ,R and Python ,land Big Data ,symmetric data ,asymmetric data ,solution models - Abstract
Summary: This book is a Printed Edition of the Special Issue that covers research on symmetric and asymmetric data that occur in real-life problems. We invited authors to submit their theoretical or experimental research to present engineering and economic problem solution models that deal with symmetry or asymmetry of different data types. The Special Issue gained interest in the research community and received many submissions. After rigorous scientific evaluation by editors and reviewers, seventeen papers were accepted and published. The authors proposed different solution models, mainly covering uncertain data in multicriteria decision-making (MCDM) problems as complex tools to balance the symmetry between goals, risks, and constraints to cope with the complicated problems in engineering or management. Therefore, we invite researchers interested in the topics to read the papers provided in the book.
73. Software Engineering and Data Science.
- Author
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Tosi, Davide and Tosi, Davide
- Subjects
Information technology industries ,COVID-19 ,SARS-CoV-2 ,data analytics ,schools' impact ,Google mobility impact ,feature selection ,ontology ,text classification ,machine-learning ,SARS-COV-2 ,Bayesian regression ,changepoint detection ,European football championship ,big data ,delay-tolerant network (DTN) ,multi-attribute decision making ,public transport ,energy consumption ,software development process ,operations ,software engineering ,information system development ,team structure ,Software Library Recommendation ,graph filters ,dependency graphs ,link prediction ,n/a - Abstract
Summary: This reprint focuses on data-driven software solutions and their impact on research and development at the academic, industry, business, and government levels to exploit the hidden knowledge and big data that can be offered to cities and citizens in the future. Data-driven software solutions are different from "traditional" software development projects, as the focus of the main development core is on managing the data (e.g., data store and data quality) and designing behavioral models with the aid of artificial intelligence and machine learning techniques. To this end, new life cycles, algorithms, methods, processes, and tools are required. This reprint is centered on the recent trends and advancements in the field of engineering data-intensive software solutions to address the challenges in developing, testing, and maintaining such data-driven systems, with a focus on the application of data-driven solutions to real-life problems and techniques and algorithms addressing the different challenges of data-driven software engineering.
74. Inaugural Section Special Issue. Key Topics and Future Perspectives in Natural Hazards Research.
- Author
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Tapete, Deodato and Tapete, Deodato
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Research & information: general ,big data ,disaster management ,review ,natural hazards ,disaster ,scientometrics ,bibliometrics ,citation analysis ,NatCatSERVICE ,Sigma Explorer ,Oroville Dam ,spillway ,incident ,flood control ,flood-frequency analysis ,dam operation ,drought ,impacts ,exposure ,vulnerability ,risk ,policy ,risk assessment ,earthquake risk ,energy security ,reliability of power supply ,Eurasian Economic Union (EAEU) ,integration process ,common electricity market ,masonry aggregates ,vulnerability assessment ,vulnerability curves ,damage scenarios ,local hazard effect ,psychological representation of earthquakes ,open-ended and closed-questions surveys ,children ,seismic hazard assessment ,emotions ,emotional prevention ,African easterly wave ,attractor coexistence ,chaos ,hurricane ,limit cycle ,Lorenz model ,predictability ,recurrence ,extended range weather prediction ,Jakarta basin ,site effects ,shear-wave velocity ,urban fabrics ,seismic vulnerability ,critic analysis ,cost modelling ,urban preservation programming ,building works programming ,natural hazard ,earthquake ,dam spillway ,psychology ,cyber-infrastructure - Abstract
Summary: This book collects selected high-quality papers published in 2018-2020 to inaugurate the "Natural Hazards" Section of the Geosciences journal. The topics encompass: trends in publications at international level in the field of natural hazards research; the role of Big Data in natural disaster management; assessment of seismic risk through the understanding and quantification of its different components; climatic/hydro-meteorological hazards; and finally, the scientific analysis and disaster forensics of recent natural hazard events. The target audience includes not only specialists, but also graduate students who wish to approach the challenging, but also fascinating
75. Open Data and Energy Analytics.
- Author
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Nastasi, Benedetto, Manfren, Massimiliano, Noussan, Michel, and Nastasi, Benedetto
- Subjects
Research & information: general ,data envelopment analysis ,Kohonen self-organizing maps ,factor analysis ,multiple regression ,energy efficiency ,social media ,energy-consuming activities ,energy consumption ,machine learning ,ontology ,energy performance certificate ,heating energy demand ,buildings ,data mining ,classification ,regression ,decision tree ,support vector machine ,random forest ,artificial neural network ,open data ,electrification modelling ,Malawi ,OnSSET ,MESSAGEix ,reproducibility ,collaborative work ,open modelling and data ,data-handling ,integrated assessment modelling ,data pre- and post-processing ,space heating ,domestic hot water ,market assessment ,EU28 ,district heating ,data analytics ,big data ,forecasting ,energy ,polygeneration ,clustering ,kNN ,pattern recognition ,heating ,building stock ,heat map ,spatial analysis ,heat density map ,building performance simulation ,parametric modelling ,energy management ,model calibration ,Passive House ,energy planning ,energy potential mapping ,urban energy atlas ,urban energy transition ,energy data ,data-aware planning ,spatial planning ,open data analytics ,smart cities ,open energy governance ,urban database ,energy mapping ,building dataset ,energy modelling - Abstract
Summary: Open data and policy implications coming from data-aware planning entail collection and pre- and postprocessing as operations of primary interest. Before these steps, making data available to people and their decision-makers is a crucial point. Referring to the relationship between data and energy, public administrations, governments, and research bodies are promoting the construction of reliable and robust datasets to pursue policies coherent with the Sustainable Development Goals, as well as to allow citizens to make informed choices. Energy engineers and planners must provide the simplest and most robust tools to collect, process, and analyze data in order to offer solid data-based evidence for future projections in building, district, and regional systems planning. This Special Issue aims at providing the state-of-the-art on open-energy data analytics; its availability in the different contexts, i.e., country peculiarities; and its availability at different scales, i.e., building, district, and regional for data-aware planning and policy-making. For all the aforementioned reasons, we encourage researchers to share their original works on the field of open data and energy analytics. Topics of primary interest include but are not limited to the following: 1. Open data and energy sustainability; 2. Open data science and energy planning; 3. Open science and open governance for sustainable development goals; 4. Key performance indicators of data-aware energy modelling, planning, and policy; 5. Energy, water, and sustainability database for building, district, and regional systems; 6. Best practices and case studies.
76. Comprehensive Systems Biomedicine.
- Author
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Pietro Lio and Enrico Capobianco
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inference ,systems biomedicine ,big data ,translational science ,paradigm shift - Abstract
Summary: Systems Biomedicine is a field in perpetual development. By definition a translational discipline, it emphasizes the role of quantitative systems approaches in biomedicine and aims to offer solutions to many emerging problems characterized by levels and types of complexity and uncertainty unmet before. Many factors, including technological and societal ones, need to be considered. In particular, new technologies are providing researchers with the data deluge whose management and exploitation requires a reinvention of cross-disciplinary team efforts. The advent of "omics" and high-content imaging are examples of advances de facto establishing the necessity of systems approaches. Hypothesis-driven models and in silico validation tools in support to all the varieties of experimental applications call for a profound revision. The focus on phases like mining and assimilating the data has substantially increased so to allow for interpretable knowledge to be inferred. Notably, to be able to tackle the newly generated data dimensionality, heterogeneity and complexity, model-free and data-driven intensive applications are increasingly shaping the computational pipelines and architectures that quant specialists set aside of the high-throughput genomics, transcriptomics, proteomics platforms. As for the societal aspects, in many advanced societies health care needs now more than in the past to address the problem of managing ageing populations and their complex morbidity patterns. In parallel, there is a growing research interest on the impact that cross-disciplinary clinical, epidemiological and quantitative modelling studies can have in relation to outcomes potentially affecting the quality of life of many people. Complex systems, including those characterizing biomedicine, are assessed in both their functionality and stability, and also relatively to the capacity of generating information from diversity, variation, and complexity. Due to the combined interactions and effects, such systems embed prediction power available for instance in both target identification or marker discovery, or more generally for conducting inference about patients' pathological states, i.e. normal versus disease, diagnostic or prognostic analysis, and preventive assessment (e.g., risk evaluation). The ultimate goal, personalized medicine, will be achieved based on the confluence of the system's predictive power to patient-specific profiling.
77. Digital innovation in Multiple Sclerosis Management.
- Author
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Ziemssen, Tjalf, Haase, Rocco, and Ziemssen, Tjalf
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Medicine ,multiple sclerosis ,digital health ,eHealth ,intervention ,patient management ,chronic disease ,disease management ,Patient Reported Outcomes ,e-health ,app ,communication ,digital tools ,patient empowerment ,health information seeking ,user-centered design ,patient portal ,master's program ,education ,multiple sclerosis management ,Dresden International University ,digitization ,icompanion ,icobrain ,digital health technology ,mobile application ,patient reported outcomes ,magnetic resonance imaging ,mHealth ,telemonitoring ,longitudinal assessment ,rehabilitation ,fatigue ,walking ,cognition ,software as a medical device ,participatory health ,monitoring ,smartphone-based assessments ,clinical validation ,technical validation ,MS apps ,digital health solution development ,digital biomarkers ,AI ,(early) Health Technology Assessment ,home monitoring ,MS disease activity ,MS disease progression ,early detection ,disease modelling ,digital therapeutics ,gait analysis ,mobility ,digital tools and applications ,precision medicine ,personalized therapy ,big data ,digital twin ,relapsing-remitting multiple sclerosis (RRMS) ,magnetic resonance imaging (MRI) ,brain MRI analysis software ,non-evidence of disease activity (NEDA) ,Markov model ,n/a - Abstract
Summary: Due to innovation in technology, a new type of patient has been created, the e-patient, characterized by the use of electronic communication tools and commitment to participate in their own care. The extent to which the world of digital health has changed during the COVID-19 pandemic has been widely recognized. Remote medicine has become part of the new normal for patients and clinicians, introducing innovative care delivery models that are likely to endure even if the pendulum swings back to some degree in a post-COVID age. The development of digital applications and remote communication technologies for patients with multiple sclerosis has increased rapidly in recent years. For patients, eHealth apps have been shown to improve outcomes and increase access to care, disease information, and support. For HCPs, eHealth technology may facilitate the assessment of clinical disability, analysis of lab and imaging data, and remote monitoring of patient symptoms, adverse events, and outcomes. It may allow time optimization and more timely intervention than is possible with scheduled face-to-face visits. The way we measure the impact of MS on daily life has remained relatively unchanged for decades, and is heavily reliant on clinic visits that may only occur once or twice each year.These benefits are important because multiple sclerosis requires ongoing monitoring, assessment, and management.The aim of this Special Issue is to cover the state of knowledge and expertise in the field of eHealth technology applied to multiple sclerosis, from clinical evaluation to patient education.
78. Algorithms in Decision Support Systems.
- Author
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García-Díaz, Vicente and García-Díaz, Vicente
- Subjects
History of engineering & technology ,semi-supervised learning ,transfer learning ,radar emitter ,decision support systems ,population health management ,big data ,machine learning ,deep learning ,personalized patient care ,Nonlinear regression ,interactive platform ,component-based approach ,software architecture ,Eclipse-RCP (Rich Client Platform) ,spatial prediction ,rule-based expert systems ,tennis hitting technique ,computer algebra systems ,Groebner bases ,Boolean logic ,data envelopment analysis ,dimensionality reduction ,ensembles ,exhaustive state space search ,entropy ,associative classification ,class association rule ,vertical data representation ,classification ,algorithm evaluation ,parallel algorithms ,multi-objective optimization ,train rescheduling ,very large-scale decision support systems ,very large-scale data and program cores of information systems ,meta-database ,teleological meta-database ,thematic list ,indicators list ,computational methods list ,geographically dispersed systems ,external sources - Abstract
Summary: This book aims to provide a new vision of how algorithms are the core of decision support systems (DSSs), which are increasingly important information systems that help to make decisions related to unstructured and semi-unstructured decision problems that do not have a simple solution from a human point of view. It begins with a discussion of how DSSs will be vital to improving the health of the population. The following article deals with how DSSs can be applied to improve the performance of people doing a specific task, like playing tennis. It continues with a work in which authors apply DSSs to insect pest management, together with an interactive platform for fitting data and carrying out spatial visualization. The next article improves how to reschedule trains whenever disturbances occur, together with an evaluation framework. The final works focus on different relevant areas of DSSs: 1) a comparison of ensemble and dimensionality reduction models based on an entropy criterion; 2) a radar emitter identification method based on semi-supervised and transfer learning; 3) design limitations, errors, and hazards in creating very large-scale DSSs; and 4) efficient rule generation for associative classification. We hope you enjoy all the contents in the book.
79. The Economics of Big Science. Essays by Leading Scientists and Policymakers.
- Author
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Beck, Hans Peter, Charitos, Panagiotis, and Beck, Hans Peter
- Subjects
Particle & high-energy physics ,Research & development management ,Astronautics ,Databases ,Investment & securities ,Particle and Nuclear Physics ,R & D/Technology Policy ,Space Sciences (including Extraterrestrial Physics, Space Exploration and Astronautics) ,Big Data ,Investment Appraisal ,Nuclear and Particle Physics ,Economics ,Space Physics ,Finance ,Investing in fundamental science ,Societal benefits / value of science ,Measuring socio-economic impact of science ,Benefits from fundamental research ,Big science projects finance/costs ,Cost of large-scale scientific projects ,Societal value of fundamental science ,Open Access - Abstract
Summary: The essays in this open access volume identify the key ingredients for success in capitalizing on public investments in scientific projects and the development of large-scale research infrastructures. Investment in science - whether in education and training or through public funding for developing new research tools and technologies - is a crucial priority. Authors from big research laboratories/organizations, funding agencies and academia discuss how investing in science can produce societal benefits as well as identifying future challenges for scientists and policy makers. The volume cites different ways to assess the socio-economic impact of Research Infrastructures and their role as hubs of global collaboration, creativity and innovation. It highlights the different benefits stemming from fundamental research at the local, national and global level, while also inviting us to rethink the notion of "benefit" in the 21st century. Public investment is required to maintain the pace of technological and scientific advancements over the next decades. Far from advocating a radical transformation and massive expansion in funding, the authors suggest ways for maintaining a strong foundation of science and research to ensure that we continue to benefit from the outputs. The volume draws inspiration from the first "Economics of Big Science" workshop, held in Brussels in 2019 with the aim of creating a new space for dialogue and interaction between representatives of Big Science organizations, policy makers and academia. It aspires to provide useful reading for policy makers, scientists and students of science, who are increasingly called upon to explain the value of fundamental research and adopt the language and logic of economics when engaging in policy discussions.
80. Industry 4.0 for SMEs. Challenges, Opportunities and Requirements.
- Author
-
Matt, Dominik T., Modrák, Vladimír, Zsifkovits, Helmut, and Matt, Dominik T.
- Subjects
Research & development management ,Business mathematics & systems ,Small businesses & self-employed ,Business ,Management science ,Management ,Industrial management ,Small business ,Big data - Abstract
Summary: This open access book explores the concept of Industry 4.0, which presents a considerable challenge for the production and service sectors. While digitization initiatives are usually integrated into the central corporate strategy of larger companies, smaller firms often have problems putting Industry 4.0 paradigms into practice. Small and medium-sized enterprises (SMEs) possess neither the human nor financial resources to systematically investigate the potential and risks of introducing Industry 4.0. Addressing this obstacle, the international team of authors focuses on the development of smart manufacturing concepts, logistics solutions and managerial models specifically for SMEs. Aiming to provide methodological frameworks and pilot solutions for SMEs during their digital transformation, this innovative and timely book will be of great use to scholars researching technology management, digitization and small business, as well as practitioners within manufacturing companies.
81. Applications in Electronics Pervading Industry, Environment and Society. Sensing Systems and Pervasive Intelligence.
- Author
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Saponara, Sergio, De Gloria, Alessandro, Bellotti, Francesco, and Saponara, Sergio
- Subjects
Technology: general issues ,model-based design ,FPGA ,HDL code generation ,wearable sensors ,embedded devices ,face recognition ,face verification ,biometric sensors ,deep learning ,distillation ,convolutional neural networks ,spatial transformer network ,video coding ,discrete cosine transform ,directional transform ,VLSI ,alternative representations to float numbers ,posit arithmetic ,Deep Neural Networks (DNNs) ,neural network activation functions ,surface electromyography ,event-driven ,functional electrical stimulation ,embedded system ,resampling ,interpolating polynomial ,polyphase filter ,digital circuit design ,ASIC ,bitmap indexing ,processing in memory ,memory wall ,big data ,internet of things ,intelligent sensors ,autonomous driving ,cyber security ,HW accelerator ,on-chip random number generator (RNG) ,SHA2 ,ASIC standard-cell ,machine learning ,edge computing ,edge analytics ,ANN ,k-NN ,SVM ,decision trees ,ARM ,X-Cube-AI ,STM32 Nucleo ,rad-hard ,PLL (phase-locked loop) ,SEE (single event effects) ,Spacefibre ,TID (total ionization dose) ,charge pump ,phase/frequency detector ,frequency divider ,ring oscillator ,LC-tank oscillator ,SpaceFibre ,rad-hard circuits ,radiation effects ,high-speed data transfer ,support attitude ,inertial measurement unit ,coal mining ,unscented Kalman filter ,quaternion ,gradient descent ,research data collection and sharing ,connected and automated driving ,deployment and field testing ,vehicular sensors ,impact assessment ,knowledge management ,collaborative project methodology ,n/a - Abstract
Summary: This book features the manuscripts accepted for the Special Issue "Applications in Electronics Pervading Industry, Environment and Society-Sensing Systems and Pervasive Intelligence" of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the "Applications in Electronics Pervading Industry, Environment and Society" (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs.
82. The Application of Computer Techniques to ECG Interpretation.
- Author
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Macfarlane, Peter and Macfarlane, Peter
- Subjects
Medicine ,electrocardiographic imaging (ECGI) ,heart failure (HF) ,cardiac resynchronization therapy (CRT) ,ultrasound ,strain ,speckle tracking echocardiography ,in silico ,electrophysiology ,electrocardiogram ,ECG ,cardiac disease ,arrhythmia ,ischemia ,standardization ,computerized ECG ,personalized medicine ,telemedicine ,digital ECG data interchange protocol ,eHealth ,ECG equipment ,computerized electrocardiograph ,ECG analysis algorithms ,computerized ECG interpretation ,interatrial block ,partial interatrial block ,advanced interatrial block ,atypical patterns ,electrocardiogram (ECG) ,automated ECG analysis ,CSE study ,age ,sex ,race ,historical aspects ,electronic cohort ,mortality ,big data ,telehealth ,alarm fatigue ,annotation of ECG data ,arrhythmia alarms ,intensive care unit ,patient monitoring ,ambulatory ECG ,machine learning ,deep learning ,pattern recognition ,noise reduction ,Holter ECG ,ECG interpretation ,artificial intelligence ,body surface mapping ,electrocardiographic imaging ,image processing ,clinical applications ,n/a - Abstract
Summary: This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field.
83. Review Papers for Journal of Risk and Financial Management (JRFM)
- Author
-
McAleer, Michael and McAleer, Michael
- Subjects
Technology: general issues ,big data ,computational science ,economics ,finance ,management ,theoretical models ,econometric and statistical models ,applications ,n/a ,bank regulation ,capital adequacy standards ,regulatory complexity ,US banking crises ,supply chain management ,supply chain finance ,working capital ,factors ,outcomes ,solutions ,optimisation ,portfolio selection ,risk measure ,fat tail ,Copula ,shrinkage ,semi-variance ,CVaR ,excess returns ,efficient market hypothesis ,data snooping ,investment and capital markets ,market efficiency ,price-volume ,adaptive market hypothesis ,time-varying or adaptive market efficiency ,cross section of country equity returns ,country-level stock market anomalies ,empirical asset pricing ,international equity markets ,return predictability ,bank regulatory capital requirements ,marketing ,psychology ,price-volume relationship ,adaptive market efficiency ,covariance matrix estimation ,portfolio risk measurement ,stock investment ,country equity returns - Abstract
Summary: The Journal of Risk and Financial Management (JRFM) was inaugurated in 2008 and has successfully continued publishing, with Volume 13 in 2020. Since the journal was established, JRFM has published in excess of 580 topical and interesting theoretical and empirical papers in financial economics, financial econometrics, banking, finance, mathematical finance, statistical finance, accounting, decision sciences, information management, tourism economics and finance, international rankings of journals in financial economics, and bibliometric rankings of journals in cognate disciplines. Papers published in the journal range from novel technical and theoretical papers to innovative empirical contributions. The journal wishes to encourage critical review papers on topical subjects in any of the topics mentioned above in financial economics and in cognate disciplines.
84. Big Data in Dental Research and Oral Healthcare.
- Author
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Joda, Tim and Joda, Tim
- Subjects
Medicine ,digital transformation ,rapid prototyping ,augmented and virtual reality (AR/VR) ,artificial intelligence (AI) ,machine learning (ML) ,personalized dental medicine ,tele-health ,patient-centered outcomes ,integrated care, medical-dental integration, simulation model, dental research ,oral medicine ,oral healthcare ,dentistry ,gerodontology ,elderly patient ,big data ,Big Data ,digital dentistry ,oral health ,ethical issues ,dental education ,augmented reality (AR) ,virtual reality (VR) ,artificial intelligence ,AI ,machine learning ,ML ,cone beam computed tomography (CBCT) ,intraoral scanning ,facial scanning ,healthcare cost ,medical healthcare cost ,dental healthcare cost ,zero-inflated model ,neural network - Abstract
Summary: Progress in information technology has fostered a global explosion of data generation. Accumulated big data are now estimated to be 4.4 zettabytes in the digital universe; and trends predict an exponential increase in the future. Health data are gathered from professional routine care and other expanded sources including the social determinants of health, such as Internet of Things. Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of stored health data, dental medicine is edging into its fourth stage of digitization using new technologies including augmented and virtual reality, artificial intelligence, and blockchain. Big data collaborations involve interactions between a diverse range of stakeholders with analytical, technical and political focus. In oral healthcare, data technology has many areas of application: prognostic analysis and predictive modeling, the identification of unknown correlations of diseases, clinical decision support for novel treatment concepts, public health surveys and population-based clinical research, as well as the evaluation of healthcare systems. The objective of this Special Issue is to provide an update on the current knowledge with state-of-the-art theory and practical information on human and social perspectives that determine the uptake of technological innovations in big data science in the field of dental medicine. Moreover, it will focus on the identification of future research needs to manage the continuous increase in health data and to accomplish its clinical translation for patient-centered research and personalized dentistry. This Special Issue welcomes all types of studies and reviews considering the perspectives of different stakeholders on technological innovations for big data science in all dental disciplines. Kind regards,
85. Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation.
- Author
-
Cheungpasitporn, Wisit, Thongprayoon, Charat, Kaewput, Wisit, and Cheungpasitporn, Wisit
- Subjects
Medicine ,tacrolimus ,C/D ratio ,tacrolimus metabolism ,everolimus ,conversion ,kidney transplantation ,gut microbiome ,renal transplant recipient ,diarrhea ,immunosuppressive medication ,gut microbiota ,16S rRNA sequencing ,butyrate-producing bacteria ,Proteobacteria ,torquetenovirus ,immunosuppression ,transplantation ,immunosuppressed host ,outcome ,renal transplantation ,Goodpasture syndrome ,anti-GBM disease ,epidemiology ,hospitalization ,outcomes ,acute kidney injury ,risk prediction ,artificial intelligence ,patent ductus arteriosus ,conservative management ,blood pressure ,eradication ,interferon-free regimen ,hepatitis C infection ,kidney transplant ,allograft steatosis ,lipopeliosis ,transplant numbers ,live donors ,public awareness ,Google TrendsTM ,machine learning ,big data ,nephrology ,chronic kidney disease ,NLR ,PLR ,RPGN ,predictive value ,hemodialysis ,withdrawal ,cellular crescent ,global sclerosis ,procurement kidney biopsy ,glomerulosclerosis ,minimally-invasive donor nephrectomy ,robot-assisted surgery ,laparoscopic surgery ,organ donation ,living kidney donation ,MeltDose® ,LCPT ,renal function ,liver transplantation ,metabolism ,erythropoietin ,fibroblast growth factor 23 ,death ,weekend effect ,in-hospital mortality ,comorbidity ,dialysis ,elderly ,klotho ,α-Klotho ,FGF-23 ,kidney donor ,Nephrology ,CKD-MBD ,CKD-Mineral and Bone Disorder ,deceased donor ,Eurotransplant Senior Program ,risk stratification ,intensive care ,kidney transplant recipients ,long-term outcomes ,graft failure ,cardiovascular mortality ,lifestyle ,inflammation ,vascular calcification ,bone mineral density ,dual-energy X-ray absorptiometry ,living donation ,repeated kidney transplantation ,graft survival ,prolonged ischaemic time ,patient survival ,pre-emptive transplantation ,metabolomics ,urine ,acute rejection ,allograft ,cystatin C ,hyperfiltration ,kidney injury molecule (KIM)-1 ,tubular damage ,genetic polymorphisms ,(cardiac) surgery ,inflammatory cytokines ,clinical studies ,chronic kidney disease (CKD) ,no known kidney disease (NKD) ,ICD-10 billing codes ,phenotyping ,electronic health record (EHR) ,estimated glomerular filtration rate (eGFR) ,machine learning (ML) ,generalized linear model network (GLMnet) ,random forest (RF) ,artificial neural network (ANN), clinical natural language processing (clinical NLP) ,discharge summaries ,laboratory values ,area under the receiver operating characteristic (AUROC) ,area under the precision-recall curve (AUCPR) ,fibrosis ,extracellular matrix ,collagen type VI ,living-donor kidney transplantation ,ethnic disparity - Abstract
Summary: In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, assisting clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as through hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions. For this Special Issue, we made a call to action to stimulate researchers and clinicians to submit their invaluable works and present, here, a collection of articles covering original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid-base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation toward improving patient outcomes.
86. Cultural Heritage Storytelling, Engagement and Management in the Era of Big Data and the Semantic Web.
- Author
-
Dimoulas, Charalampos and Dimoulas, Charalampos
- Subjects
Film, TV & radio ,3D modeling ,3D reconstruction ,event detection ,Twitter ,spectral clustering ,cultural heritage ,social media ,news ,journalism ,semantic analysis ,big data ,data center ,digital marketing ,eco-friendly ,environmental communication ,green websites ,green culture ,green hosting ,sustainability ,software sustainability ,multimedia tools ,static analysis ,evolution analytics ,interactive documentary ,audience engagement ,digital storytelling ,intangible heritage ,media users' engagement ,marine heritage ,biocultural heritage ,heritage management ,heritage communication ,digital narrative ,Instagram ,UNESCO ,marine protected areas of outstanding universal value ,soundscapes ,audiovisual heritage ,semantic audio ,data-driven storytelling ,content crowdsourcing ,requirements engineering ,authoring tools ,3D content ,IEEE 830 standard ,semantic indexing ,text classification ,Greek literature ,TextRank ,BERT ,smart cities ,energy transition ,Évora ,POCITYF ,relation extraction ,distant supervision ,deep neural networks ,Transformers ,Greek NLP ,literary fiction ,metadata extraction ,Katharevousa ,n/a - Abstract
Summary: The current Special Issue launched with the aim of further enlightening important CH areas, inviting researchers to submit original/featured multidisciplinary research works related to heritage crowdsourcing, documentation, management, authoring, storytelling, and dissemination. Audience engagement is considered very important at both sites of the CH production-consumption chain (i.e., push and pull ends). At the same time, sustainability factors are placed at the center of the envisioned analysis. A total of eleven (11) contributions were finally published within this Special Issue, enlightening various aspects of contemporary heritage strategies placed in today's ubiquitous society. The finally published papers are related but not limited to the following multidisciplinary topics:Digital storytelling for cultural heritage;Audience engagement in cultural heritage;Sustainability impact indicators of cultural heritage;Cultural heritage digitization, organization, and management;Collaborative cultural heritage archiving, dissemination, and management;Cultural heritage communication and education for sustainable development;Semantic services of cultural heritage;Big data of cultural heritage;Smart systems for Historical cities - smart cities;Smart systems for cultural heritage sustainability.
87. New Horizons for a Data-Driven Economy. A Roadmap for Usage and Exploitation of Big Data in Europe.
- Author
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Cavanillas, José María, Curry, Edward, Wahlster, Wolfgang, and Cavanillas, José María
- Subjects
Library & information sciences ,Information technology industries ,Engineering: general ,Computer science ,Coins, banknotes, medals, seals (numismatics) ,Information Storage and Retrieval ,Innovation/Technology Management ,Computer Applications ,Computers and Society ,Big data - Abstract
Summary: Information Storage and Retrieval; Innovation/Technology Management; Computer Applications; Computers and Society; Big data
88. Document sobre bioètica i Big Data de salut: explotació i comercialització de les dades dels usuaris de la sanitat pública. Document on bioethics and Big Data: exploitation and commercialisation of user data in public health care.
- Author
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María Casado, Observatori de Bioètica i Dret, M. Rosa Llácer Matacás, and Lídia Buisan Espeleta
- Subjects
Bioètica ,Dades massives ,Bioethics ,Big data ,Salut pública ,Public health - Abstract
Summary: En aquest volum del Grup d'Opinió de l'Observatori de Bioètica i Dret, coordinat per les doctores María Casado, Maria Rosa Llàcer i Lídia Buisan, s'analitzen, des de la perspectiva bioètica, els inconvenients de l'explotació i la comercialització de dades dels usuaris de la sanitat pública arran dels problemes detectats en el projecte VISC+ de la Generalitat de Catalunya, tant en relació amb possibles vulneracions dels drets dels ciutadans com amb la manca de transparència i de debat públic informat en una qüestió de tanta importància com és el tràfic de dades personals. El document posa de manifest que la implementació de les tecnologies Big Data en l'àmbit sanitari, associada a una eventual comercialització d'aquestes dades, impacta directament en el nostre sistema sanitari i investigador i afecta de ple a l'àmbit privat dels ciutadans.
89. The Financial Industry 4.0.
- Author
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Ngo, Thanh, Guegan, Dominique, Vo, Dinh-Tri, and Ngo, Thanh
- Subjects
Coins, banknotes, medals, seals (numismatics) ,stochastic volatility with co-jumps ,threshold GARCH ,RiskMetrics ,validation ,cryptocurrency market ,technology ,banking 4.0 ,industry 4.0 ,roadmap ,digitalization ,big data ,blockchain ,disruptive technology ,corporate governance ,corporate voting ,tokenisation ,smart contracts ,artificial intelligence ,digital financial inclusion ,finance ,digital financial services ,digital credit ,betting ,financial distress ,coping strategies ,welfare outcomes - Abstract
Summary: We invite you to read the Special Issue on The Financial Industry 4.0. It is a collection of 13 articles published in a Special Issue of International Journal of Financial Studies (MDPI) in 2020-2022. The main emphasis of this reprint is on The Financial Industry 4.0 to provide insightful understanding about the benefits as well as the challenges that financial institutions are facing under the Industry 4.0 era. The articles in this Special Issue discussed the potential of blockchain technology, the impact of fintech on financial inclusion in developing countries, the role of fintech in the insurance industry, and so on. It highlights the benefits of fintech, such as improved efficiency, accuracy, and customer experience, but also notes the challenges and risks involved, such as data privacy and security concerns. Collaboration between financial institutions, regulators, and technology firms is seen as necessary to promote innovation and ensure the stability and security of the financial system while addressing the challenges and risks associated with fintech.
90. Sustainable Real Estate: Management, Assessment and Innovations.
- Author
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De Paola, Pierfrancesco, Tajani, Francesco, Locurcio, Marco, and De Paola, Pierfrancesco
- Subjects
Information technology industries ,big data ,decision-making ,feasibility study ,fuzzy theory ,high-rise building ,mixed-use development ,urban tree canopy (UTC) ,hedonic price model ,two-stage spatial model ,multi-level mixed model ,varying effect ,customer gender ,women ,tenure choice ,sustainable housing ,housing market ,mass appraisal techniques ,evaluation model ,hedonic price method ,geographically weighted regression ,evolutionary polynomial regression ,market value ,smart building ,smart energy system ,renewable energy resources ,energy storage ,reserve power system ,investor motives ,investment profitability ,smart readiness indicator ,discounted cash flow analysis ,natural landscape ,views ,visual perception ,housing price ,quantile regression ,marginal impact ,wealth inequality ,growth management ,sustainable development ,transit-oriented development ,contingent valuation method ,retirement ,housing downsizing ,housing consumption ,housing tenure choice ,consumption ,housing wealth effect ,financial wealth effect ,multi-step causality ,ESG ,real estate companies ,ratings ,sustainability ,energy efficiency ,sustainable decision-making ,sustainable social housing management ,multi-criteria decision-making (MCDM) ,AHP ,WASPAS ,COPRAS ,social cohesion ,uncertainty ,U.S. housing markets ,local projection method ,impulse response functions ,n/a - Abstract
Summary: Production and consumption activities have determined a weakness of the sustainable real estate economy. The main problems are the subordination of public decision making, which is subjected to pressure from big companies; inefficient appraisal procedures; excessive use of financial leverage in investment projects; the atypical nature of markets; income positions in urban transformations; and the financialization of real estate markets, with widespread negative effects. A delicate role in these complex problems is assigned to real estate appraisal activities, called to make value judgments on real estate goods and investment projects, the prices of which are often formed in atypical real estate markets, giving ever greater importance to sustainable development and transformation issues. This Special Issue is dedicated to developing and disseminating knowledge and innovations related to most recent real estate evaluation methodologies applied in the fields of architecture and civil, building, environmental, and territorial engineering. Suitable works include studies on econometric models, sustainable building management, building costs, risk management and real estate appraisal, mass appraisal methods applied to real estate properties, urban and land economics, transport economics, the application of economics and financial techniques to real estate markets, the economic valuation of real estate investment projects, the economic effects of building transformations or projects on the environment, and sustainable real estate.
91. Data Science for Economics and Finance. Methodologies and Applications.
- Author
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Consoli, Sergio, Reforgiato Recupero, Diego, Saisana, Michaela, and Consoli, Sergio
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Data mining ,Machine learning ,Business mathematics & systems ,Public administration ,Information retrieval ,Data Mining and Knowledge Discovery ,Machine Learning ,Business Information Systems ,Big Data/Analytics ,Computer Appl. in Administrative Data Processing ,Information Storage and Retrieval ,IT in Business ,Computer and Information Systems Applications ,Open Access ,Data Mining ,Big Data ,Data Analytics ,Decision Support Systems ,Semantics and Reasoning ,Expert systems / knowledge-based systems ,Information technology: general issues ,Data warehousing - Abstract
Summary: This open access book covers the use of data science, including advanced machine learning, big data analytics, Semantic Web technologies, natural language processing, social media analysis, time series analysis, among others, for applications in economics and finance. In addition, it shows some successful applications of advanced data science solutions used to extract new knowledge from data in order to improve economic forecasting models. The book starts with an introduction on the use of data science technologies in economics and finance and is followed by thirteen chapters showing success stories of the application of specific data science methodologies, touching on particular topics related to novel big data sources and technologies for economic analysis (e.g. social media and news); big data models leveraging on supervised/unsupervised (deep) machine learning; natural language processing to build economic and financial indicators; and forecasting and nowcasting of economic variables through time series analysis. This book is relevant to all stakeholders involved in digital and data-intensive research in economics and finance, helping them to understand the main opportunities and challenges, become familiar with the latest methodological findings, and learn how to use and evaluate the performances of novel tools and frameworks. It primarily targets data scientists and business analysts exploiting data science technologies, and it will also be a useful resource to research students in disciplines and courses related to these topics. Overall, readers will learn modern and effective data science solutions to create tangible innovations for economic and financial applications.
92. Data Science in Healthcare.
- Author
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Hulsen, Tim and Hulsen, Tim
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Medicine ,Pharmacology ,data sharing ,data management ,data science ,big data ,healthcare ,depression ,psychological treatment ,task sharing ,primary care ,pilot study ,non-specialist health worker ,training ,digital technology ,mental health ,COVID-19 ,SARS-CoV-2 ,pneumonia ,computed tomography ,case fatality rate ,social distancing ,smoking ,metabolically healthy obese phenotype ,metabolic syndrome ,obesity ,coronavirus ,machine learning ,social media ,apache spark ,Twitter ,Arabic language ,distributed computing ,smart cities ,smart healthcare ,smart governance ,Triple Bottom Line (TBL) ,thoracic pain ,tree classification ,cross-validation ,hand-foot-and-mouth disease ,early-warning model ,neural network ,genetic algorithm ,sentinel surveillance system ,outbreak prediction ,artificial intelligence ,vascular access surveillance ,arteriovenous fistula ,end stage kidney disease ,dialysis ,kidney failure ,chronic kidney disease (CKD) ,end-stage kidney disease (ESKD) ,kidney replacement therapy (KRT) ,risk prediction ,naïve Bayes classifiers ,precision medicine ,machine learning models ,data exploratory techniques ,breast cancer diagnosis ,tumors classification ,n/a - Abstract
Summary: Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management.
93. Sustainable Marketing, Branding and CSR in the Digital Economy.
- Author
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Ozuem, Wilson, Ranfagni, Silvia, and Ozuem, Wilson
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Information technology industries ,advertising copy ,smartphone consumer ,consumer typology ,sustainable marketing ,unobserved heterogeneity ,business analytics ,social media ,CSR ,strategy formulation ,strategic planning ,governance ,celebrity-brand association ,real-life setting on social media ,para-social interaction ,self-brand connection ,brand quality ,advertising ,emotions ,emotional states ,regions ,emotional appeal ,adolescents ,SNS ,emojis ,self-presentation ,symbolic value ,playfulness ,need for uniqueness ,Internet of Things ,business models ,smart cities ,big data ,consumer data ,n/a - Abstract
Summary: Sustainable marketing practice is essential for developing a more comprehensive understanding of consumers' purchase decisions in dynamic digital marketing environments. Scholars and practitioners conceive sustainable marketing practices as episodic, predicated on temporal practices in response to emerging digital environments. Consumers are increasingly becoming aware of the ecological issues that their consumption creates in the marketplace. Despite the importance of sustainable practices, when and how sustainability occurs regarding the consumer's purchase decision remains largely unexplored. In part, this is because the practices of sustainability in the emerging computer-mediated marketing environments (CMMEs) are difficult to anticipate and study. Much of what we know about sustainable marketing practice is mainly focused on customer-brand relationships. Prior literature examining sustainable marketing practice through CMMEs remains sparse, despite consistent emphasis on the benefits of sustainable marketing practices in the emerging digital world.
94. Introducing data science : big data, machine learning, and more, using Python tools.
- Author
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Cielen, Davy, Meysman, Arno, and Ali, Mohamed
- Subjects
Data mining ,Big data ,Machine learning ,Python (Computer program language) ,Python ,Massendaten - Abstract
Summary: "Introducing Data Science explains vital data science concepts and teaches you how to accomplish the fundamental tasks that occupy data scientists. You'll explore data visualization, graph databases, the use of NoSQL, and the data science process. You'll use the Python language and common Python libraries as you experience firsthand the challenges of dealing with data at scale. Discover how Python allows you to gain insights from data sets so big that they need to be stored on multiple machines, or from data moving so quickly that no single machine can handle it. This book gives you hands-on experience with the most popular Python data science libraries, Scikit-learn and StatsModels"--Back cover.
95. Swarm intelligence methods for statistical regression.
- Author
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Mohanty, Soumya D
- Subjects
Swarm intelligence ,Regression analysis ,Big data ,Computational intelligence - Abstract
Summary: A core task in statistical analysis, especially in the era of Big Data, is the fitting of flexible, high-dimensional, and non-linear models to noisy data in order to capture meaningful patterns. This can often result in challenging non-linear and non-convex global optimization problems. The large data volume that must be handled in Big Data applications further increases the difficulty of these problems. Swarm Intelligence Methods for Statistical Regression describes methods from the field of computational swarm intelligence (SI), and how they can be used to overcome the optimization bottleneck encountered in statistical analysis. Features Provides a short, self-contained overview of statistical data analysis and key results in stochastic optimization theory Focuses on methodology and results rather than formal proofs Reviews SI methods with a deeper focus on Particle Swarm Optimization (PSO) Uses concrete and realistic data analysis examples to guide the reader Includes practical tips and tricks for tuning PSO to extract good performance in real world data analysis challenges https://www.crcpress.com/Swarm-Intelligence-Methods-for-Statistical-Regression/Mohanty/p/book/9781138558182
96. Big Data-Enabled Internet of Things.
- Author
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Khan, Muhammad Usman Shahid, Khan, Samee U., and Zomaya, Albert Y.
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Big data ,Internet of things - Abstract
Summary: Since both IoT and Big Data have a lot of overlap, it is an ideal time to present the recent advances which are taking place at the intersections of both these fields to identify future trends. The book covers the important aspects of Big Data-enabled IoT. The main focus of the book is on the analytical techniques for handling the huge amount of data generated by the IoT. The book is oriented toward those professionals and researchers interested in both of these booming fields. The topics covered in the book will be of interest to computing researchers, practitioners, engineers, and Information Technology professionals working in the highly dynamic field of Big Data-enabled IoT. The book can be viewed as an introduction to the area, as it cover the most important issues, presenting applied research works. The book identifies and shows the research challenges that are yet to be solved. Thus, it can also be used by researchers starting their work in the area
97. BIG DATA: Management and Analytics / Nitin Upadhyay.
- Author
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Upadhyay, Nitin
- Subjects
Big data ,Big data analytics ,Data engineering - Abstract
Summary: If you still have a doubt in contemplating the value of big data management and analytics, then this book is for you. It is intended to fill the current gaps in the thinking shift of the data stakeholders by providing elaborative discussions on big data ecosystems, fundamentals, management and analytics in a readable and straight-forward fashion. Business leaders, analysts, data engineers and scholars will be the primary beneficiaries of this pristine book which aims at enriching the journey of big data management and analytics through a very pragmatic approach. Attaining big data business leadership is a challenging task. Nonetheless, it is not only achievable but also potentially the most rewarding. More and more value is demanded from businesses, both internally and externally, to attain competitive edge. The recent surge in big data ecosystem and landscape has left them wondering how to develop the insight, best practices and data leadership. Some industries are acquiring technical skill-set at their workplace to harness big data analytics projects, while some other sectors are focusing on building technological and infrastructural capability by including a portfolio of big data ecosystems. This book proffers big data business leadership model that will enable industries to develop their own big data journey pathways, competencies and competitiveness.
98. Mastering machine learning with python in six steps: a practical implementation guide to predictive data analytics using Python.
- Author
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Swamynathan, Manohar
- Subjects
Computer science ,Computing methodologies ,Big data ,Open source ,Machine learning ,Computers - Machine theory ,Python - Programming language ,Data mining - Abstract
Summary: Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book's approach is based on the "Six degrees of separation" theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. You'll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you'll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation. https://www.apress.com/in/book/9781484228654
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