الصفحة 3
الصفحة 3
E-Learning Methodologies : Fundamentals, technologies and applications
Covers state of the art topics including user modeling for e-learning systems and cloud, IOT, and mobile-based frameworks. It also considers security challenges and ethical conduct using Blockchain technology. E-learning has become an important part of our educational life with the development of e-learning systems and platforms and the need for online and remote learning. ICT and computational intelligence techniques are being used to design more intelligent and adaptive systems. However, the art of designing good real-time e-learning systems is difficult as different aspects of learning need to be considered including challenges such as learning rates, involvement, knowledge, qualifications, as well as networking and security issues. The earlier concepts of standalone integrated virtual e-learning systems have been greatly enhanced with emerging technologies such as cloud computing, mobile computing, big data, Internet of Things (IoT), AI and machine learning, and AR/VT technologies.
Earth Observation Open Science and Innovation
The digital transformation is revolutionizing our ability to monitor our planet and transforming the way we access, process and exploit Earth Observation data from satellites.This book reviews these megatrends and their implications for the Earth Observation community as well as the wider data economy. It provides insight into new paradigms of Open Science and Innovation applied to space data, which are characterized by openness, access to large volume of complex data, wide availability of new community tools, new techniques for big data analytics such as Artificial Intelligence, unprecedented level of computing power, and new types of collaboration among researchers, innovators, entrepreneurs and citizen scientists. In addition, this book aims to provide readers with some reflections on the future of Earth Observation, highlighting through a series of use cases not just the new opportunities created by the New Space revolution, but also the new challenges that must be addressed in order to make the most of the large volume of complex and diverse data delivered by the new generation of satellites.
Disruptive trends in automation technology
The industrial sector is being transformed by the convergence of information technology and operational technology. The latter is another name for automation technology and covers established systems such as supervisory control and data acquisition (SCADA), programmable logic controllers (PLC), fieldbuses, and automation and control systems. As this technology is connected to the Internet and 5G networks, some monitoring, control, and analytic functionalities are deployed to the edge or cloud, and researchers are challenged to ensure the security, dependability, real-time performance, and maintainability of the resulting systems. The big data that is accessible from these systems create opportunities for artificial intelligence applications that can further disrupt the established practices in the automation domain.
Discovering Knowledge in Data : An Introduction to Data Mining
Provides the tools needed to thrive in today’s big data world. Demonstrates how to leverage a company’s existing databases to increase profits and market share, and carefully explains the most current data science methods and techniques. The reader will “learn data mining by doing data mining”.
Digitization of healthcare data using blockchain
Gives a detailed description of the integration of blockchain technology for Electronic Health Records and provides the research challenges to consider in various disciplines such as supply chain, drug discovery, and data management. he aim of the book is to investigate the concepts of blockchain technology and its association with the recent development and advancements in the medical field. Moreover, it focuses on the integration of workflow strategies like NLP, and AI which could be adopted for boosting the clinical documentation and electronic healthcare records (EHR) usage by bringing down the physician EHR data entry. Also, the book covers the usage of smart contracts for securing patient records. Digitization of Healthcare Data Using Blockchain presents the practical implementations that deal with developing a web framework for building highly usable healthcare applications, a simple blockchain-powered EHR system.
Digital Libraries : The Era of Big Data and Data Science; 16th Italian Research Conference on Digital Libraries, IRCDL 2020, Bari, Italy, January 30–31, 2020, Proceedings
The chapter "Identifying, Classifying and Searching Graphic Symbols in the NOTAE System" is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.
Digital Fluency : Understanding the Basics of Artificial Intelligence, Blockchain Technology, Quantum Computing, and Their Applications for Digital Transformation
If you are curious about the basics of artificial intelligence, blockchain technology, and quantum computing as key enablers for digital transformation, Digital Fluency is your handy guide. The real-world applications of these cutting-edge technologies are expanding rapidly, and your daily life will continue to be affected by each of them. There is no better time than now to get started and become digitally fluent.
Designing big data platforms : How to use, deploy, and maintain big data systems
Provides expert guidance and valuable insights on getting the most out of Big Data systems. Helps readers understand how to process large amounts of data with well-known Linux tools and database solutions, use effective techniques to collect and manage data from multiple sources, transform data into meaningful business insights, and much more. Author Yusuf Aytas, a software engineer with a vast amount of big data experience, discusses the design of the ideal Big Data platform: one that meets the needs of data analysts, data engineers, data scientists, software engineers, and a spectrum of other stakeholders across an organization. Detailed yet accessible chapters cover key topics such as stream data processing, data analytics, data science, data discovery, and data security. This real-world manual for Big Data technologies: Provides up-to-date coverage of the tools currently used in Big Data processing and management / Offers step-by-step guidance on building a data pipeline, from basic scripting to distributed systems / Highlights and explains how data is processed at scale / Includes an introduction to the foundation of a modern data platform
Deep fake detection
Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learning-powered applications recently emerged is “deepfake”. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable.
Decoding the city urbanism in the Age of Big Data
Shows how Big Data change reality and, hence, the way we deal with the city. They demonstrate how the Lab interprets digital data as material that can be used for the formulation of a different urban future. The publication also looks at the negative aspects of the city-related data acquisition and control.
Data science, AI, and machine learning in drug development
The confluence of big data, AI, and machine learning has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R&D, emerging applications of big data, AI and machine learning in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations
Data science in theory and practice : Techniques for big data analytics and complex data sets
Delivers a comprehensive treatment of the mathematical and statistical models useful for analyzing data sets arising in various disciplines, like banking, finance, health care, bioinformatics, security, education, and social services. Written in five parts, the book examines some of the most commonly used and fundamental mathematical and statistical concepts that form the basis of data science. The authors go on to analyze various data transformation techniques useful for extracting information from raw data, long memory behavior, and predictive modeling. Readers will also learn from topics like: Analyses of foundational theoretical subjects, including the history of data science, matrix algebra and random vectors, and multivariate analysis A comprehensive examination of time series forecasting, including the different components of time series and transformations to achieve stationarity Introductions to both the R and Python programming languages, including basic data types and sample manipulations for both languages An exploration of algorithms, including how to write one and how to perform an asymptotic analysis A comprehensive discussion of several techniques for analyzing and predicting complex data sets
Data science for economics and finance : Methodologies and applications
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.
Data science and data analytics : Opportunities and challenges
Gives the concept of data science, tools, and algorithms that exist for many useful applications / Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems / Identifies many areas and uses of data science in the smart era / Applies data science to agriculture, healthcare, graph mining, education, security, etc.
Data science and analytics ; 5th International conference on recent developments in science, engineering and technology, REDSET 2019, Gurugram, India, November 15–16, 2019, Revised Selected Papers, Part II
This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics
Data science and analytics ; 5th International conference on recent developments in science, engineering and technology, REDSET 2019, Gurugram, India, November 15–16, 2019, Revised Selected Papers, Part I
This two-volume set (CCIS 1229 and CCIS 1230) constitutes the refereed proceedings of the 5th International Conference on Recent Developments in Science, Engineering and Technology, REDSET 2019, held in Gurugram, India, in November 2019. The 74 revised full papers presented were carefully reviewed and selected from total 353 submissions. The papers are organized in topical sections on data centric programming; next generation computing; social and web analytics; security in data science analytics; big data analytics.
Data mining : Concepts, models, methods, and algorithms ; 3rd ed.
Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces. Explores big data and cloud computing Examines deep learning Includes information on convolutional neural networks (CNN) Offers reinforcement learning Contains semi-supervised learning and S3VM Reviews model evaluation for unbalanced data
Data Management Technologies and Applications ; 8th International Conference, DATA 2019, Prague, Czech Republic, July 26–28, 2019, Revised Selected Papers
This book constitutes the thoroughly refereed proceedings of the 8th International Conference on Data Management Technologies and Applications, DATA 2019, held in Prague, Czech Republic, in July 2019. The 8 revised full papers were carefully reviewed and selected from 90 submissions. The papers deal with the following topics: decision support systems, data analytics, data and information quality, digital rights management, big data, knowledge management, ontology engineering, digital libraries, mobile databases, object-oriented database systems, and data integrity.
Data center networking : Network topologies and traffic management in large-scale data centers
Provides a comprehensive reference in large data center networking. It first summarizes the developing trend of DCNs, and reports four novel DCNs, including a switch-centric DCN, a modular DCN, a wireless DCN, and a hybrid DCN. Furthermore another important factor in DCN targets at managing and optimizing the network activity at the level of transfers to aggregate correlated data flows and thus directly to lower down the network traffic resulting from such data transfers. In particular, the book reports the in-network aggregation of incast transfer, shuffle transfer, uncertain incast transfer, and the cooperative scheduling of uncertain multicast transfer.
Data augmented design : Embracing new data for sustainable urban planning and design
This book offers an essential introduction to a new urban planning and design methodology called Data Augmented Design (DAD) and its evolution and progresses, highlighting data driven methods, urban planning and design applications and related theories. The authors draw on many kinds of data, including big, open, and conventional data, and discuss cutting-edge technologies that illustrate DAD as a future-oriented design framework in terms of its focus on multi-data, multi-method, multi-stage and multi-scale sustainable urban planning. In four sections and ten chapters, the book presents case studies to address the core concepts of DAD, the first type of applications of DAD that emerged in redevelopment-oriented planning and design, the second type committed to the planning and design for urban expansion, and the future-oriented applications of DAD to advance sustainable technologies and the future structural form of the built environment. The book is geared towards a broad readership, ranging from researchers and students of urban planning, urban design, urban geography, urban economics, and urban sociology, to practitioners in the areas of urban planning and design.
