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Application Of Big Data Deep Learning Machine Learning And Other Advanced Analytical Techniques In Environmental Economics And Policy Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Application Of Big Data Deep Learning Machine Learning And Other Advanced Analytical Techniques In Environmental Economics And Policy book. This book definitely worth reading, it is an incredibly well-written.
Author : Tsun Se Cheong,Xunpeng (Roc) Shi,Yanfei Li,Yongping Sun
Publisher : Frontiers Media SA
Page : 485 pages
File Size : 40,9 Mb
Release : 2022-07-25
Category : Technology & Engineering
ISBN : 9782889765966
Author : Pushpa Singh
Publisher : Springer Nature
Page : 357 pages
File Size : 44,8 Mb
Release : 2024-06-25
Category : Electronic
ISBN : 9789819704484
Author : Dewan Masud Karim
Publisher : CRC Press
Page : 410 pages
File Size : 42,5 Mb
Release : 2023-12-01
Category : Computers
ISBN : 9781003822820
In the face of resource depletion, environmental changes, lifestyle changes, demographic and digital adaptation, old ideologies of city building and expensive and complex automobility solutions are in freefall. These changes are creating severe friction between the old and new paradigms. This book provides new perspectives through the process of ideological disassociation and concepts of human mobility code. The basic premise of the book, human mobility is an essential component of our creativity that comes from our unconscious desire to become a part of a community. Several new concepts in the book starts with the hallmark of new discovery of human mobility code and its implications of urban mobility boundary systems to stay within safe planetary zone. A new discovery of human mobility code from comprehensive research finding prove that each individual develops a unique mobility footprint and become our mobility identity. Beyond individual hallmarks, human develops collective mobility codes through interaction with the third space on which entire mobility systems lie and are created by the fundamentals of city planning and the design process. Readers are introduced to an innovative mobility planning process and reinvention of multimodal mobility approaches based on new mobility code while formulating new concepts, practical solutions and implementation techniques, tools, policies, and processes to reinforce low-carbon mobility options while addressing social equity, environmental, and health benefits. Finally, the book arms us with knowledge to prevent the disaster of full technological enlightenment against our natural human mobility code.
Author : Pushpa Singh,Asha Rani Mishra,Payal Garg
Publisher : Springer
Page : 0 pages
File Size : 53,7 Mb
Release : 2024-04-22
Category : Mathematics
ISBN : 9819704472
This book presents an in-depth analysis of successful data-driven initiatives, highlighting how organizations have leveraged data to drive decision-making processes, optimize operations, and achieve remarkable outcomes. Through case studies, readers gain valuable insights and learn practical strategies for implementing data analytics, big data, and machine learning solutions in their own organizations. The book discusses the transformative power of data analytics and big data in various industries and sectors and how machine learning applications have revolutionized exploration by enabling advanced data analysis techniques for mapping, geospatial analysis, and environmental monitoring, enhancing our understanding of the world and its dynamic processes. This book explores how big data explosion, the power of analytics and machine learning revolution can bring new prospects and opportunities in the dynamic and data-rich landscape. It highlights the future research directions in data analytics, big data, and machine learning that explores the emerging trends, challenges, and opportunities in these fields by covering interdisciplinary approaches such as handling and analyzing real-time and streaming data.
Author : Thi Thi Zin,Jerry Chun-Wei Lin
Publisher : Springer
Page : 386 pages
File Size : 42,8 Mb
Release : 2018-06-06
Category : Technology & Engineering
ISBN : 9789811308697
This book presents a compilation of selected papers from the first International Conference on Big Data Analysis and Deep Learning Applications (ICBDL 2018), and focuses on novel techniques in the fields of big data analysis, machine learning, system monitoring, image processing, conventional neural networks, communication, industrial information, and their applications. Readers will find insights to help them realize more efficient algorithms and systems used in real-life applications and contexts, making the book an essential reference guide for academic researchers, professionals, software engineers in the industry, and regulators of aviation authorities.
Author : Sergio Consoli,Diego Reforgiato Recupero,Michaela Saisana
Publisher : Springer Nature
Page : 357 pages
File Size : 48,7 Mb
Release : 2021
Category : Application software
ISBN : 9783030668914
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.
Author : Rohit Sharma,Dilip Kumar Sharma,Dhowmya Bhatt,Binh Thai Pham
Publisher : CRC Press
Page : 175 pages
File Size : 42,9 Mb
Release : 2021-10-28
Category : Computers
ISBN : 9781000481785
This book focuses on big data in business intelligence, data management, machine learning, cloud computing, and smart cities. It also provides an interdisciplinary platform to present and discuss recent innovations, trends, and concerns in the fields of big data and analytics. Big Data Analysis for Green Computing: Concepts and Applications presents the latest technologies and covers the major challenges, issues, and advances of big data and data analytics in green computing. It explores basic as well as high-level concepts. It also includes the use of machine learning using big data and discusses advanced system implementation for smart cities. The book is intended for business and management educators, management researchers, doctoral scholars, university professors, policymakers, and higher academic research organizations.
Author : Peter Ghavami
Publisher : de Gruyter
Page : 254 pages
File Size : 49,6 Mb
Release : 2019-12-16
Category : Business & Economics
ISBN : 1547417951
Big Data Analytics Methods unveils secrets to advanced analytics techniques ranging from machine learning, random forest classifiers, predictive modeling, cluster analysis, natural language processing (NLP), Kalman filtering and ensembles of models for optimal accuracy of analysis and prediction. More than 100 analytics techniques and methods provide big data professionals, business intelligence professionals and citizen data scientists insight on how to overcome challenges and avoid common pitfalls and traps in data analytics. The book offers solutions and tips on handling missing data, noisy and dirty data, error reduction and boosting signal to reduce noise. It discusses data visualization, prediction, optimization, artificial intelligence, regression analysis, the Cox hazard model and many analytics using case examples with applications in the healthcare, transportation, retail, telecommunication, consulting, manufacturing, energy and financial services industries. This book's state of the art treatment of advanced data analytics methods and important best practices will help readers succeed in data analytics.
Author : Murad Khan,Bilal Jan,Haleem Farman
Publisher : Springer
Page : 0 pages
File Size : 41,9 Mb
Release : 2019-01-10
Category : Computers
ISBN : 9811334587
This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.
Author : Ting Yu,Nitesh Chawla,Simeon Simoff
Publisher : CRC Press
Page : 443 pages
File Size : 55,8 Mb
Release : 2016-04-19
Category : Business & Economics
ISBN : 9781439895955
Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development present
Author : El Bachir Boukherouaa,Mr. Ghiath Shabsigh,Khaled AlAjmi,Jose Deodoro,Aquiles Farias,Ebru S Iskender,Mr. Alin T Mirestean,Rangachary Ravikumar
Publisher : International Monetary Fund
Page : 35 pages
File Size : 47,8 Mb
Release : 2021-10-22
Category : Business & Economics
ISBN : 9781589063952
This paper discusses the impact of the rapid adoption of artificial intelligence (AI) and machine learning (ML) in the financial sector. It highlights the benefits these technologies bring in terms of financial deepening and efficiency, while raising concerns about its potential in widening the digital divide between advanced and developing economies. The paper advances the discussion on the impact of this technology by distilling and categorizing the unique risks that it could pose to the integrity and stability of the financial system, policy challenges, and potential regulatory approaches. The evolving nature of this technology and its application in finance means that the full extent of its strengths and weaknesses is yet to be fully understood. Given the risk of unexpected pitfalls, countries will need to strengthen prudential oversight.
Author : Haldorai, Anandakumar,Ramu, Arulmurugan
Publisher : IGI Global
Page : 263 pages
File Size : 53,9 Mb
Release : 2019-09-20
Category : Computers
ISBN : 9781522597520
Big data consists of data sets that are too large and complex for traditional data processing and data management applications. Therefore, to obtain the valuable information within the data, one must use a variety of innovative analytical methods, such as web analytics, machine learning, and network analytics. As the study of big data becomes more popular, there is an urgent demand for studies on high-level computational intelligence and computing services for analyzing this significant area of information science. Big Data Analytics for Sustainable Computing is a collection of innovative research that focuses on new computing and system development issues in emerging sustainable applications. Featuring coverage on a wide range of topics such as data filtering, knowledge engineering, and cognitive analytics, this publication is ideally designed for data scientists, IT specialists, computer science practitioners, computer engineers, academicians, professionals, and students seeking current research on emerging analytical techniques and data processing software.
Author : CPA John Kimani ,Dr. James Scott
Publisher : Finstock Evarsity Publishers
Page : 77 pages
File Size : 51,9 Mb
Release : 2023-08-28
Category : Computers
ISBN : 9789914753882
BOOK SUMMARY The main topics in this book are; • Machine Learning Algorithms for Predictive Analysis • Natural Language Processing in Text Analytics • Graph Analytics and Network Analysis • Time Series Analysis and Forecasting • Deep Learning in Image and Video Analytics • Streaming Data Analytics • Spatial Data Analysis and Geospatial Analytics • Big Data Ethics and Privacy Considerations “Advanced Big Data Analytics” offers a comprehensive exploration of cutting-edge techniques and methodologies in the realm of big data analysis. Through a blend of theoretical insights, practical examples and real-world case studies, the book guides readers in harnessing the power of vast datasets to uncover valuable insights, make informed decisions and address contemporary data-driven challenges.
Author : John A
Publisher : Springer Nature
Page : 253 pages
File Size : 43,7 Mb
Release : 2024-06-25
Category : Electronic
ISBN : 9789819996513
Author : Ansgar Steland,Kwok-Leung Tsui
Publisher : Springer Nature
Page : 378 pages
File Size : 44,8 Mb
Release : 2022-11-15
Category : Mathematics
ISBN : 9783031071553
This book discusses the interplay between statistics, data science, machine learning and artificial intelligence, with a focus on environmental science, the natural sciences, and technology. It covers the state of the art from both a theoretical and a practical viewpoint and describes how to successfully apply machine learning methods, demonstrating the benefits of statistics for modeling and analyzing high-dimensional and big data. The book’s expert contributions include theoretical studies of machine learning methods, expositions of general methodologies for sound statistical analyses of data as well as novel approaches to modeling and analyzing data for specific problems and areas. In terms of applications, the contributions deal with data as arising in industrial quality control, autonomous driving, transportation and traffic, chip manufacturing, photovoltaics, football, transmission of infectious diseases, Covid-19 and public health. The book will appeal to statisticians and data scientists, as well as engineers and computer scientists working in related fields or applications.
