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This book presents recent research in the field of reuse and integration, and will help researchers and practitioners alike to understand how they can implement reuse in different stages of software development and in various domains, from robotics and security authentication to environmental issues. Indeed, reuse is not only confined to reusing code; it can be included in every software development step. The challenge today is more about adapting solutions from one language to another, or from one domain to another. The relative validation of the reused artifacts in their new environment is also necessary, at time even critical. The book includes high-quality research papers on these and many other aspects, written by experts in information reuse and integration, who cover the latest advances in the field. Their contributions are extended versions of the best papers presented at the IEEE International Conference on Information Reuse and Integration (IRI) and IEEE International Workshop on Formal Methods Integration (FMI), which were held in San Diego in August 2017.
Book Synopsis Theory and Application of Reuse, Integration, and Data Science by : Thouraya Bouabana-Tebibel
Download or read book Theory and Application of Reuse, Integration, and Data Science written by Thouraya Bouabana-Tebibel and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent research in the field of reuse and integration, and will help researchers and practitioners alike to understand how they can implement reuse in different stages of software development and in various domains, from robotics and security authentication to environmental issues. Indeed, reuse is not only confined to reusing code; it can be included in every software development step. The challenge today is more about adapting solutions from one language to another, or from one domain to another. The relative validation of the reused artifacts in their new environment is also necessary, at time even critical. The book includes high-quality research papers on these and many other aspects, written by experts in information reuse and integration, who cover the latest advances in the field. Their contributions are extended versions of the best papers presented at the IEEE International Conference on Information Reuse and Integration (IRI) and IEEE International Workshop on Formal Methods Integration (FMI), which were held in San Diego in August 2017.
This book presents recent research in the field of reuse and integration, and will help researchers and practitioners alike to understand how they can implement reuse in different stages of software development and in various domains, from robotics and security authentication to environmental issues. Indeed, reuse is not only confined to reusing code; it can be included in every software development step. The challenge today is more about adapting solutions from one language to another, or from one domain to another. The relative validation of the reused artifacts in their new environment is also necessary, at time even critical. The book includes high-quality research papers on these and many other aspects, written by experts in information reuse and integration, who cover the latest advances in the field. Their contributions are extended versions of the best papers presented at the IEEE International Conference on Information Reuse and Integration (IRI) and IEEE International Workshop on Formal Methods Integration (FMI), which were held in San Diego in August 2017.
Book Synopsis Theory and Application of Reuse, Integration, and Data Science by : Thouraya Bouabana-Tebibel
Download or read book Theory and Application of Reuse, Integration, and Data Science written by Thouraya Bouabana-Tebibel and published by Springer. This book was released on 2019-05-07 with total page 189 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents recent research in the field of reuse and integration, and will help researchers and practitioners alike to understand how they can implement reuse in different stages of software development and in various domains, from robotics and security authentication to environmental issues. Indeed, reuse is not only confined to reusing code; it can be included in every software development step. The challenge today is more about adapting solutions from one language to another, or from one domain to another. The relative validation of the reused artifacts in their new environment is also necessary, at time even critical. The book includes high-quality research papers on these and many other aspects, written by experts in information reuse and integration, who cover the latest advances in the field. Their contributions are extended versions of the best papers presented at the IEEE International Conference on Information Reuse and Integration (IRI) and IEEE International Workshop on Formal Methods Integration (FMI), which were held in San Diego in August 2017.
This book will focus on utilizing statistical modelling of the software source code, in order to resolve issues associated with the software development processes. Writing and maintaining software source code is a costly business; software developers need to constantly rely on large existing code bases. Statistical modelling identifies the patterns in software artifacts and utilize them for predicting the possible issues.
Book Synopsis Software Source Code by : Raghavendra Rao Althar
Download or read book Software Source Code written by Raghavendra Rao Althar and published by Walter de Gruyter GmbH & Co KG. This book was released on 2021-07-19 with total page 358 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book will focus on utilizing statistical modelling of the software source code, in order to resolve issues associated with the software development processes. Writing and maintaining software source code is a costly business; software developers need to constantly rely on large existing code bases. Statistical modelling identifies the patterns in software artifacts and utilize them for predicting the possible issues.
From artificial neural net / game theory / semantic applications, to modeling tools, smart manufacturing systems, and data science research – this book offers a broad overview of modern intelligent methods and applications of machine learning, evolutionary computation, Industry 4.0 technologies, and autonomous agents leading to the Internet of Things and potentially a new technological revolution. Though chiefly intended for IT professionals, it will also help a broad range of users of future emerging technologies adapt to the new smart / intelligent wave. In separate chapters, the book highlights fourteen successful examples of recent advances in the rapidly evolving area of intelligent systems. Covering major European projects paving the way to a serious smart / intelligent collaboration, the chapters explore e.g. cyber-security issues, 3D digitization, aerial robots, and SMEs that have introduced cyber-physical production systems. Taken together, they offer unique insights into contemporary artificial intelligence and its potential for innovation.
Book Synopsis Intelligent Systems: Theory, Research and Innovation in Applications by : Ricardo Jardim-Goncalves
Download or read book Intelligent Systems: Theory, Research and Innovation in Applications written by Ricardo Jardim-Goncalves and published by Springer Nature. This book was released on 2020-03-03 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: From artificial neural net / game theory / semantic applications, to modeling tools, smart manufacturing systems, and data science research – this book offers a broad overview of modern intelligent methods and applications of machine learning, evolutionary computation, Industry 4.0 technologies, and autonomous agents leading to the Internet of Things and potentially a new technological revolution. Though chiefly intended for IT professionals, it will also help a broad range of users of future emerging technologies adapt to the new smart / intelligent wave. In separate chapters, the book highlights fourteen successful examples of recent advances in the rapidly evolving area of intelligent systems. Covering major European projects paving the way to a serious smart / intelligent collaboration, the chapters explore e.g. cyber-security issues, 3D digitization, aerial robots, and SMEs that have introduced cyber-physical production systems. Taken together, they offer unique insights into contemporary artificial intelligence and its potential for innovation.
This two-volume set of LNCS 11643 and LNCS 11644 constitutes - in conjunction with the volume LNAI 11645 - the refereed proceedings of the 15th International Conference on Intelligent Computing, ICIC 2019, held in Nanchang, China, in August 2019. The 217 full papers of the three proceedings volumes were carefully reviewed and selected from 609 submissions. The ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. The theme for this conference is “Advanced Intelligent Computing Methodologies and Applications.” Papers related to this theme are especially solicited, including theories, methodologies, and applications in science and technology.
Book Synopsis Intelligent Computing Theories and Application by : De-Shuang Huang
Download or read book Intelligent Computing Theories and Application written by De-Shuang Huang and published by Springer. This book was released on 2019-09-22 with total page 790 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set of LNCS 11643 and LNCS 11644 constitutes - in conjunction with the volume LNAI 11645 - the refereed proceedings of the 15th International Conference on Intelligent Computing, ICIC 2019, held in Nanchang, China, in August 2019. The 217 full papers of the three proceedings volumes were carefully reviewed and selected from 609 submissions. The ICIC theme unifies the picture of contemporary intelligent computing techniques as an integral concept that highlights the trends in advanced computational intelligence and bridges theoretical research with applications. The theme for this conference is “Advanced Intelligent Computing Methodologies and Applications.” Papers related to this theme are especially solicited, including theories, methodologies, and applications in science and technology.
Digital evolution has become increasingly present in our lives, whether on cellphones, computers, watches, or other appliances. As a result of the wide access we have to the digital world, the amount of data generated daily is vast. This density of information generated at every moment can be the insight needed for the success of an organization. Much is said about data-based decision-making to generate the best results. The new capabilities of data intelligence unleashed by the emergence of cloud computing and artificial intelligence make it one of the most promising areas of digital transformation change management. Enhancing Business Communications and Collaboration Through Data Science Applications provides relevant theoretical frameworks and the latest empirical research findings in the area. It is written for professionals who wish to improve their understanding of the strategic role of trust at different levels of the information and knowledge society. Covering topics such as data science, online business communication, and user-centered design, this premier reference source is an ideal resource for business managers and leaders, entrepreneurs, data scientists, data analysts, sociologists, students and educators of higher education, librarians, researchers, and academicians.
Book Synopsis Enhancing Business Communications and Collaboration Through Data Science Applications by : Geada, Nuno
Download or read book Enhancing Business Communications and Collaboration Through Data Science Applications written by Geada, Nuno and published by IGI Global. This book was released on 2023-03-21 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: Digital evolution has become increasingly present in our lives, whether on cellphones, computers, watches, or other appliances. As a result of the wide access we have to the digital world, the amount of data generated daily is vast. This density of information generated at every moment can be the insight needed for the success of an organization. Much is said about data-based decision-making to generate the best results. The new capabilities of data intelligence unleashed by the emergence of cloud computing and artificial intelligence make it one of the most promising areas of digital transformation change management. Enhancing Business Communications and Collaboration Through Data Science Applications provides relevant theoretical frameworks and the latest empirical research findings in the area. It is written for professionals who wish to improve their understanding of the strategic role of trust at different levels of the information and knowledge society. Covering topics such as data science, online business communication, and user-centered design, this premier reference source is an ideal resource for business managers and leaders, entrepreneurs, data scientists, data analysts, sociologists, students and educators of higher education, librarians, researchers, and academicians.
Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.
Book Synopsis Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications by : Management Association, Information Resources
Download or read book Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2019-10-11 with total page 1671 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the growing use of web applications and communication devices, the use of data has increased throughout various industries. It is necessary to develop new techniques for managing data in order to ensure adequate usage. Deep learning, a subset of artificial intelligence and machine learning, has been recognized in various real-world applications such as computer vision, image processing, and pattern recognition. The deep learning approach has opened new opportunities that can make such real-life applications and tasks easier and more efficient. Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications is a vital reference source that trends in data analytics and potential technologies that will facilitate insight in various domains of science, industry, business, and consumer applications. It also explores the latest concepts, algorithms, and techniques of deep learning and data mining and analysis. Highlighting a range of topics such as natural language processing, predictive analytics, and deep neural networks, this multi-volume book is ideally designed for computer engineers, software developers, IT professionals, academicians, researchers, and upper-level students seeking current research on the latest trends in the field of deep learning.
This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics.
Book Synopsis Financial Data Analytics by : Sinem Derindere Köseoğlu
Download or read book Financial Data Analytics written by Sinem Derindere Köseoğlu and published by Springer Nature. This book was released on 2022-04-25 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents both theory of financial data analytics, as well as comprehensive insights into the application of financial data analytics techniques in real financial world situations. It offers solutions on how to logically analyze the enormous amount of structured and unstructured data generated every moment in the finance sector. This data can be used by companies, organizations, and investors to create strategies, as the finance sector rapidly moves towards data-driven optimization. This book provides an efficient resource, addressing all applications of data analytics in the finance sector. International experts from around the globe cover the most important subjects in finance, including data processing, knowledge management, machine learning models, data modeling, visualization, optimization for financial problems, financial econometrics, financial time series analysis, project management, and decision making. The authors provide empirical evidence as examples of specific topics. By combining both applications and theory, the book offers a holistic approach. Therefore, it is a must-read for researchers and scholars of financial economics and finance, as well as practitioners interested in a better understanding of financial data analytics.
Transformation of the Earth's social and ecological systems is occurring at a rate and magnitude unparalleled in human experience. Data science is a revolutionary new way to understand human-environment relationships at the heart of pressing challenges like climate change and sustainable development. However, data science faces serious shortcomings when it comes to human-environment research. There are challenges with social and environmental data, the methods that manipulate and analyze the information, and the theory underlying the data science itself; as well as significant legal, ethical and policy concerns. This timely book offers a comprehensive, balanced, and accessible account of the promise and problems of this work in terms of data, methods, theory, and policy. It demonstrates the need for data scientists to work with human-environment scholars to tackle pressing real-world problems, making it ideal for researchers and graduate students in Earth and environmental science, data science and the environmental social sciences.
Book Synopsis Data Science and Human-Environment Systems by : Steven M. Manson
Download or read book Data Science and Human-Environment Systems written by Steven M. Manson and published by Cambridge University Press. This book was released on 2023-01-31 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transformation of the Earth's social and ecological systems is occurring at a rate and magnitude unparalleled in human experience. Data science is a revolutionary new way to understand human-environment relationships at the heart of pressing challenges like climate change and sustainable development. However, data science faces serious shortcomings when it comes to human-environment research. There are challenges with social and environmental data, the methods that manipulate and analyze the information, and the theory underlying the data science itself; as well as significant legal, ethical and policy concerns. This timely book offers a comprehensive, balanced, and accessible account of the promise and problems of this work in terms of data, methods, theory, and policy. It demonstrates the need for data scientists to work with human-environment scholars to tackle pressing real-world problems, making it ideal for researchers and graduate students in Earth and environmental science, data science and the environmental social sciences.
Machine Learning Theory and Applications Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Additional topics covered in Machine Learning Theory and Applications include: Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs) Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.
Book Synopsis Machine Learning Theory and Applications by : Xavier Vasques
Download or read book Machine Learning Theory and Applications written by Xavier Vasques and published by John Wiley & Sons. This book was released on 2024-01-11 with total page 516 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning Theory and Applications Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Additional topics covered in Machine Learning Theory and Applications include: Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs) Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.
