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Author: Sergio Consoli
Publisher: Springer Nature
ISBN: 3030668916
Category : Application software
Languages : en
Pages : 357
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Book Description
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: Sergio Consoli
Publisher: Springer Nature
ISBN: 3030668916
Category : Application software
Languages : en
Pages : 357
Get Book Here
Book Description
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: Tohid Atashbar
Publisher: International Monetary Fund
ISBN:
Category : Business & Economics
Languages : en
Pages : 32
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Book Description
The application of Deep Reinforcement Learning (DRL) in economics has been an area of active research in recent years. A number of recent works have shown how deep reinforcement learning can be used to study a variety of economic problems, including optimal policy-making, game theory, and bounded rationality. In this paper, after a theoretical introduction to deep reinforcement learning and various DRL algorithms, we provide an overview of the literature on deep reinforcement learning in economics, with a focus on the main applications of deep reinforcement learning in macromodeling. Then, we analyze the potentials and limitations of deep reinforcement learning in macroeconomics and identify a number of issues that need to be addressed in order for deep reinforcement learning to be more widely used in macro modeling.
Author: Subhash C. Ray
Publisher: Springer Nature
ISBN: 9811034559
Category : Business & Economics
Languages : en
Pages : 1797
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Book Description
This three-volume handbook includes state-of-the-art surveys in different areas of neoclassical production economics. Volumes 1 and 2 cover theoretical and methodological issues only. Volume 3 includes surveys of empirical applications in different areas like manufacturing, agriculture, banking, energy and environment, and so forth.
Author: Mohamed Fakir
Publisher: Springer Nature
ISBN: 3031064585
Category : Computers
Languages : en
Pages : 301
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Book Description
This book constitutes the proceedings of the 7th International Conference on Business Intelligence, CBI 2022, which took place in Khouribga, Morocco, during May 26-28, 2022. The 23 full papers included in this book were carefully reviewed and selected from a total of 68 submissions. They were organized in topical sections as follows: decision support and artificial intelligence; business intelligence and database; and optimization and dynamic programming.
Author: R. Kanthavel
Publisher: John Wiley & Sons
ISBN: 1119821789
Category : Computers
Languages : en
Pages : 388
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Book Description
ARTIFICIAL INTELLIGENT TECHNIQUES FOR WIRELESS COMMUNICATION AND NETWORKING The 20 chapters address AI principles and techniques used in wireless communication and networking and outline their benefit, function, and future role in the field. Wireless communication and networking based on AI concepts and techniques are explored in this book, specifically focusing on the current research in the field by highlighting empirical results along with theoretical concepts. The possibility of applying AI mechanisms towards security aspects in the communication domain is elaborated; also explored is the application side of integrated technologies that enhance AI-based innovations, insights, intelligent predictions, cost optimization, inventory management, identification processes, classification mechanisms, cooperative spectrum sensing techniques, ad-hoc network architecture, and protocol and simulation-based environments. Audience Researchers, industry IT engineers, and graduate students working on and implementing AI-based wireless sensor networks, 5G, IoT, deep learning, reinforcement learning, and robotics in WSN, and related technologies.
Author: Siba K. Udgata
Publisher: Springer Nature
ISBN: 9811909016
Category : Technology & Engineering
Languages : en
Pages : 691
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Book Description
This book features best selected research papers presented at the International Conference on Machine Learning, Internet of Things, and Big Data (ICMIB 2021) held at Indira Gandhi Institute of Technology, Sarang, India, during December 2021. It comprises high-quality research work by academicians and industrial experts in the field of machine learning, mobile computing, natural language processing, fuzzy computing, green computing, human–computer interaction, information retrieval, intelligent control, data mining and knowledge discovery, evolutionary computing, IoT and applications in smart environments, smart health, smart city, wireless networks, big data, cloud computing, business intelligence, Internet security, pattern recognition, predictive analytics applications in healthcare, sensor networks and social sensing, and statistical analysis of search techniques.
Author: Nga Thi Hong Nguyen
Publisher: CRC Press
ISBN: 1003808581
Category : Technology & Engineering
Languages : en
Pages : 397
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Book Description
This book presents contemporary issues and challenges in finance and risk management in a time of rapid transformation due to technological advancements. It includes research articles based on financial and economic data and intends to cover the emerging role of analytics in financial management, asset management, and risk management. Analytics in Finance and Risk Management covers statistical techniques for data analysis in finance It explores applications in finance and risk management, covering empirical properties of financial systems. It addresses data science involving the study of statistical and computational models and includes basic and advanced concepts. The chapters incorporate the latest methodologies and challenges facing financial and risk management and illustrate related issues and their implications in the real world. The primary users of this book will include researchers, academicians, postgraduate students, professionals in engineering and business analytics, managers, consultants, and advisors in IT firms, financial markets, and services domains.
Author: Raman Kumar
Publisher: CRC Press
ISBN: 1000986241
Category : Technology & Engineering
Languages : en
Pages : 261
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Book Description
The book focuses on the recent developments in the areas of error reduction, resource optimization, and revenue growth in sustainable manufacturing using machine learning. It presents the integration of smart technologies such as machine learning in the field of Industry 4.0 for better quality products and efficient manufacturing methods. Focusses on machine learning applications in Industry 4.0 ecosystem, such as resource optimization, data analysis, and predictions. Highlights the importance of the explainable machine learning model in the manufacturing processes. Presents the integration of machine learning and big data analytics from an industry 4.0 perspective. Discusses advanced computational techniques for sustainable manufacturing. Examines environmental impacts of operations and supply chain from an industry 4.0 perspective. This book provides scientific and technological insight into sustainable manufacturing by covering a wide range of machine learning applications fault detection, cyber-attack prediction, and inventory management. It further discusses resource optimization using machine learning in industry 4.0, and explainable machine learning models for industry 4.0. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the fields including mechanical engineering, manufacturing engineering, production engineering, aerospace engineering, and computer engineering.
Author: Francesco Paolo Appio
Publisher: Taylor & Francis
ISBN: 1000912639
Category : Business & Economics
Languages : en
Pages : 280
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Book Description
Belonging to the realm of intelligent technologies, it is increasingly accepted that artificial intelligence (AI) has evolved from being merely a development standpoint in computer science. Indeed, recent reports and academic publications show that we are clearly on the path toward pervasive AI in both business and society. Organizations must adopt AI to maintain a competitive advantage and explore opportunities for unprecedented innovation. This book focuses on understanding the wide range of opportunities as well as the spectrum of challenges AI brings in different business contexts and society at large. The book highlights novel and high-quality research in data science and business analytics and examines the current and future impact of AI in business and society. The authors bridge the gap between business and technical perspectives and demonstrate the potential (and actual) impact on society. Embracing applied, qualitative, and quantitative research as well as field experiments and data analysis, the book covers a broad range of topics including but not limited to human-centered AI, product and process innovation, corporate governance, AI and ethics, organizational performance, and entrepreneurship. This comprehensive book will be a valuable resource for researchers, academics, and postgraduate students across AI, technology and innovation management, and a wide range of business disciplines.
Author: Naida Ademović
Publisher: Springer Nature
ISBN: 3031716949
Category :
Languages : en
Pages : 932
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Book Description
