The process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results.

This book constitutes the refereed proceedings of the 22nd International TRIZ Future Conference on Automated Invention for Smart Industries, TFC 2022, which took place in Warsaw, Poland, in September 2022; the event was sponsored by IFIP WG 5.4.The 39 full papers presented were carefully reviewed and selected from 43 submissions. They are organized in the following thematic sections: New perspectives of TRIZ; AI in systematic innovation; systematic innovations supporting IT and AI; TRIZ applications; TRIZ education and ecosystem.

Modern information systems differ in essence from their predecessors. They support operations at multiple locations and different time zones, are distributed and network-based, and use multidimensional data analysis, data warehousing, knowledge discovery, knowledge management, mobile computing, and other modern information processing methods. This book considers fundamental issues of modern information systems. It discusses query processing, data quality, data mining, knowledge management, mobile computing, software engineering for information systems construction, and other topics. The book presents research results that are not available elsewhere. With more than 40 contributors, it is a solid source of information about the state of the art in the field of databases and information systems. It is intended for researchers, advanced students, and practitioners who are concerned with the development of advanced information systems.

This book provides a thorough overview of cutting-edge research on electronics applications relevant to industry, the environment, and society at large. It covers a broad spectrum of application domains, from automotive to space and from health to security, while devoting special attention to the use of embedded devices and sensors for imaging, communication and control. The volume is based on the 2021 ApplePies Conference, held online in September 2021, which brought together researchers and stakeholders to consider the most significant current trends in the field of applied electronics and to debate visions for the future. Areas addressed by the conference included information communication technology; biotechnology and biomedical imaging; space; secure, clean and efficient energy; the environment; and smart, green and integrated transport. As electronics technology continues to develop apace, constantly meeting previously unthinkable targets, further attention needs to be directed toward the electronics applications and the development of systems that facilitate human activities. This book, written by industrial and academic professionals, represents a valuable contribution in this endeavor.

Intelligent Integration of Information presents a collection of chapters bringing the science of intelligent integration forward. The focus on integration defines tasks that increase the value of information when information from multiple sources is accessed, related, and combined. This contributed volume has also been published as a special double issue of the Journal of Intelligent Information Systems (JIIS), Volume 6:2/3.

This book presents recent advances in Knowledge discovery in databases (KDD) with a focus on the areas of market basket database, time-stamped databases and multiple related databases. Various interesting and intelligent algorithms are reported on data mining tasks. A large number of association measures are presented, which play significant roles in decision support applications. This book presents, discusses and contrasts new developments in mining time-stamped data, time-based data analyses, the identification of temporal patterns, the mining of multiple related databases, as well as local patterns analysis.

The textbook at hand aims to provide an introduction to the use of automated methods for gathering strategic competitiveintelligence. Hereby, the text does not describe a singleton research discipline in its own right, such as machine learning or Web mining. It rather contemplates an "application scenario," namely the gathering of knowledge that appears of paramount importance to organizations, e.g., companies and corporations.

To this end, the book first summarizes the range of research disciplines that contribute to addressing the issue, extracting from each those grains that are of utmost relevance to the depicted application scope. Moreover, the book presents systems that put these techniques to practical use (e.g., reputation monitoring platforms) and takes an inductive approach to define the "gestalt" of mining for competitive strategic intelligence by selecting major use cases that are laid out and explained in detail. These pieces form the first part of the book.

Each of those use cases is backed by a number of research papers, some of which are contained in its largely original version in the second part of the monograph. "

The book equips students with the end-to-end skills needed to do data science. That means gathering, cleaning, preparing, and sharing data, then using statistical models to analyse data, writing about the results of those models, drawing conclusions from them, and finally, using the cloud to put a model into production, all done in a reproducible way. At the moment, there are a lot of books that teach data science, but most of them assume that you already have the data. This book fills that gap by detailing how to go about gathering datasets, cleaning and preparing them, before analysing them. There are also a lot of books that teach statistical modelling, but few of them teach how to communicate the results of the models and how they help us learn about the world. Very few data science textbooks cover ethics, and most of those that do, have a token ethics chapter. Finally, reproducibility is not often emphasised in data science books. This book is based around a straight-forward workflow conducted in an ethical and reproducible way: gather data, prepare data, analyse data, and communicate those findings. This book will achieve the goals by working through extensive case studies in terms of gathering and preparing data, and integrating ethics throughout. It is specifically designed around teaching how to write about the data and models, so aspects such as writing are explicitly covered. And finally, the use of GitHub and the open-source statistical language R are built in throughout the book. Key Features: Extensive code examples. Ethics integrated throughout. Reproducibility integrated throughout. Focus on data gathering, messy data, and cleaning data. Extensive formative assessment throughout.

Big Data technologies have the potential to revolutionize the agriculture sector, in particular food safety and quality practices. This book is designed to provide a foundational understanding of various applications of Big Data in Food Safety. Big Data requires the use of sophisticated approaches for cleaning, processing and extracting useful information to improve decision-making. The contributed volume reviews some of these approaches and algorithms in the context of real-world food safety studies. Food safety and quality related data are being generated in large volumes and from a variety of sources such as farms, processors, retailers, government organizations, and other industries. The editors have included examples of how big data can be used in the fields of bacteriology, virology and mycology to improve food safety. Additional chapters detail how the big data sources are aggregated and used in food safety and quality areas such as food spoilage and quality deterioration along the supply chain, food supply chain traceability, as well as policy and regulations. The volume also contains solutions to address standardization, data interoperability, and other data governance and data related technical challenges. Furthermore, this volume discusses how the application of machine-learning has successfully improved the speed and/or accuracy of many processes in the food supply chain, and also discusses some of the inherent challenges. Included in this volume as well is a practical example of the digital transformation that happened in Dubai, with a particular emphasis on how data is enabling better decision-making in food safety. To complete this volume, researchers discuss how although big data is and will continue to be a major disruptor in the area of food safety, it also raises some important questions with regards to issues such as security/privacy, data control and data governance, all of which must be carefully considered by governments and law makers.

Error Coding for Engineers provides a useful tool for practicing engineers, students, and researchers, focusing on the applied rather than the theoretical. It describes the processes involved in coding messages in such a way that, if errors occur during transmission or storage, they are detected and, if necessary, corrected. Very little knowledge beyond a basic understanding of binary manipulation and Boolean algebra is assumed, making the subject accessible to a broad readership including non-specialists. The approach is tutorial: numerous examples, illustrations, and tables are included, along with over 30 pages of hands-on exercises and solutions. Error coding is essential in many modern engineering applications. Engineers involved in communications design, DSP-based applications, IC design, protocol design, storage solutions, and memory product design are among those who will find the book to be a valuable reference. Error Coding for Engineers is also suitable as a text for basic and advanced university courses in communications and engineering.

This book provides an overview of the history of integrative bioinformatics and the actual situation and the relevant tools. Subjects cover the essential topics, basic introductions, and latest developments; biological data integration and manipulation; modeling and simulation of networks; as well as a number of applications of integrative bioinformatics. It aims to provide basic introduction of biological information systems and guidance for the computational analysis of systems biology. This book covers a range of issues and methods that unveil a multitude of omics data integration and relevance that integrative bioinformatics has today. It contains a unique compilation of invited and selected articles from the Journal of Integrative Bioinformatics (JIB) and annual meetings of the International Symposium on Integrative Bioinformatics.

Artificial Intelligence for Capital Market throws light on application of AI/ML techniques in the financial capital markets. This book discusses the challenges posed by the AI/ML techniques as these are prone to "black box" syndrome. The complexity of understanding the underlying dynamics for results generated by these methods is one of the major concerns which is highlighted in this book: Features: Showcases artificial intelligence in finance service industry Explains Credit and Risk Analysis Elaborates on cryptocurrencies and blockchain technology Focuses on optimal choice of asset pricing model Introduces Testing of market efficiency and Forecasting in Indian Stock Market This book serves as a reference book for Academicians, Industry Professional, Traders, Finance Mangers and Stock Brokers. It may also be used as textbook for graduate level courses in financial services and financial Analytics.

Locating empirical information on specific service industry characteristics is not an easy task, even for an individual familiar with various sources of data. This book is a quick source of information on service industry statistics across many nations of the world. The reader is introduced to finding key sources of data, building analytical ratios from diverse sources, and understanding the advantages and disadvantages of data selection methods in the service sector. The global nature of the data compiled in this book, especially an extensive coverage of the United States, makes it an invaluable resource to active researchers and stakeholders in the service industry as well as those who seek to enter it.

For the first time in history, the International Federation for Information Processing (IFIP) and the International Medical Informatics Association (IMIA) held the joint "E-Health" Symposium as part of "Treat IT" stream of the IFIP World Congress 2010 at Brisbane, Australia during September 22-23, 2010. IMIA is an independent organization established under Swiss law in 1989. The organization originated in 1967 from Technical Committee 4 of IFIP that is a n- governmental, non-profit umbrella organization for national societies working in the field of information processing. It was established in 1960 under the auspices of UNESCO following the First World Computer Congress held in Paris in 1959. Today, IFIP has several types of members and maintains friendly connections to specialized agencies of the UN system and non-governmental organizations. Technical work, which is the heart of IFIP's activity, is managed by a series of Technical Committees. Due to strong needs for promoting informatics in healthcare and the rapid progress of information and communication technology, IMIA President Reinhold Haux p- posed to strengthen the collaboration with IFIP. The IMIA General Assembly (GA) approved the move and an IMIA Vice President (VP) for special services (Hiroshi Takeda) was assigned as a liaison to IFIP at Brisbane during MEDINFO2007 where th the 40 birthday of IMIA was celebrated.

This book addresses a range of aging intensity functions, which make it possible to measure and compare aging trends for lifetime random variables. Moreover, they can be used for the characterization of lifetime distributions, also with bounded support. Stochastic orders based on the aging intensities, and their connections with some other orders, are also discussed. To demonstrate the applicability of aging intensity in reliability practice, the book analyzes both real and generated data. The estimated, properly chosen, aging intensity function is mainly recommended to identify data's lifetime distribution, and secondly, to estimate some of the parameters of the identified distribution. Both reliability researchers and practitioners will find the book a valuable guide and source of inspiration.

Intelligent information and database systems are two closely related subfields of modern computer science which have been known for over thirty years. They focus on the integration of artificial intelligence and classic database technologies to create the class of next generation information systems. The book focuses on new trends in intelligent information and database systems and discusses topics addressed to the foundations and principles of data, information, and knowledge models, methodologies for intelligent information and database systems analysis, design, and implementation, their validation, maintenance and evolution. They cover a broad spectrum of research topics discussed both from the practical and theoretical points of view such as: intelligent information retrieval, natural language processing, semantic web, social networks, machine learning, knowledge discovery, data mining, uncertainty management and reasoning under uncertainty, intelligent optimization techniques in information systems, security in databases systems, and multimedia data analysis. Intelligent information systems and their applications in business, medicine and industry, database systems applications, and intelligent internet systems are also presented and discussed in the book. The book consists of 38 chapters based on original works presented during the 7th Asian Conference on Intelligent Information and Database Systems (ACIIDS 2015) held on 23-25 March 2015 in Bali, Indonesia. The book is divided into six parts related to Advanced Machine Learning and Data Mining, Intelligent Computational Methods in Information Systems, Semantic Web, Social Networks and Recommendation Systems, Cloud Computing and Intelligent Internet Systems, Knowledge and Language Processing, and Intelligent Information and Database Systems: Applications.

The book explores a new general approach to selecting-and designing-data processing techniques. Symmetry and invariance ideas behind this algebraic approach have been successful in physics, where many new theories are formulated in symmetry terms. The book explains this approach and expands it to new application areas ranging from engineering, medicine, education to social sciences. In many cases, this approach leads to optimal techniques and optimal solutions. That the same data processing techniques help us better analyze wooden structures, lung dysfunctions, and deep learning algorithms is a good indication that these techniques can be used in many other applications as well. The book is recommended to researchers and practitioners who need to select a data processing technique-or who want to design a new technique when the existing techniques do not work. It is also recommended to students who want to learn the state-of-the-art data processing.

This textbook integrates important mathematical foundations, efficient computational algorithms, applied statistical inference techniques, and cutting-edge machine learning approaches to address a wide range of crucial biomedical informatics, health analytics applications, and decision science challenges. Each concept in the book includes a rigorous symbolic formulation coupled with computational algorithms and complete end-to-end pipeline protocols implemented as functional R electronic markdown notebooks. These workflows support active learning and demonstrate comprehensive data manipulations, interactive visualizations, and sophisticated analytics. The content includes open problems, state-of-the-art scientific knowledge, ethical integration of heterogeneous scientific tools, and procedures for systematic validation and dissemination of reproducible research findings.Complementary to the enormous challenges related to handling, interrogating, and understanding massive amounts of complex structured and unstructured data, there are unique opportunities that come with access to a wealth of feature-rich, high-dimensional, and time-varying information. The topics covered in Data Science and Predictive Analytics address specific knowledge gaps, resolve educational barriers, and mitigate workforce information-readiness and data science deficiencies. Specifically, it provides a transdisciplinary curriculum integrating core mathematical principles, modern computational methods, advanced data science techniques, model-based machine learning, model-free artificial intelligence, and innovative biomedical applications. The book's fourteen chapters start with an introduction and progressively build foundational skills from visualization to linear modeling, dimensionality reduction, supervised classification, black-box machine learning techniques, qualitative learning methods, unsupervised clustering, model performance assessment, feature selection strategies, longitudinal data analytics, optimization, neural networks, and deep learning. The second edition of the book includes additional learning-based strategies utilizing generative adversarial networks, transfer learning, and synthetic data generation, as well as eight complementary electronic appendices. This textbook is suitable for formal didactic instructor-guided course education, as well as for individual or team-supported self-learning. The material is presented at the upper-division and graduate-level college courses and covers applied and interdisciplinary mathematics, contemporary learning-based data science techniques, computational algorithm development, optimization theory, statistical computing, and biomedical sciences. The analytical techniques and predictive scientific methods described in the book may be useful to a wide range of readers, formal and informal learners, college instructors, researchers, and engineers throughout the academy, industry, government, regulatory, funding, and policy agencies. The supporting book website provides many examples, datasets, functional scripts, complete electronic notebooks, extensive appendices, and additional materials.

Value-Driven Data explains how data and business leaders can co-create and deploy data-driven solutions for their organizations. Value-Driven Data explores how organizations can understand their problems and come up with better solutions, aligning data storytelling with business needs. The book reviews the main challenges that plague most data-to-business interactions and offers actionable strategies for effective data value implementation, including methods for tackling obstacles and incentivizing change. Value-Driven Data is supported by tried-and-tested frameworks that can be applied to different contexts and organizations. It features cutting-edge examples relating to digital transformation, data strategy, resolving conflicts of interests, building a data P&L and AI value prediction methodology. Recognizing different types of data value, this book presents tangible methodologies for identifying, capturing, communicating, measuring and deploying data-enabled opportunities. This is essential reading for data specialists, business stakeholders and leaders involved in capturing and executing data value opportunities for organizations and for informing data value strategies.

As organizations continue to develop, there is an increasing need for technological methods that can keep up with the rising amount of data and information that is being generated. Machine learning is a tool that has become powerful due to its ability to analyze large amounts of data quickly. Machine learning is one of many technological advancements that is being implemented into a multitude of specialized fields. An extensive study on the execution of these advancements within professional industries is necessary. Advanced Multi-Industry Applications of Big Data Clustering and Machine Learning is an essential reference source that synthesizes the analytic principles of clustering and machine learning to big data and provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of management. Featuring research on topics such as project management, contextual data modeling, and business information systems, this book is ideally designed for engineers, economists, finance officers, marketers, decision makers, business professionals, industry practitioners, academicians, students, and researchers seeking coverage on the implementation of big data and machine learning within specific professional fields.

Public key cryptography was introduced by Diffie and Hellman in 1976, and it was soon followed by concrete instantiations of public-key encryption and signatures; these led to an entirely new field of research with formal definitions and security models. Since then, impressive tools have been developed with seemingly magical properties, including those that exploit the rich structure of pairings on elliptic curves. Asymmetric Cryptography starts by presenting encryption and signatures, the basic primitives in public-key cryptography. It goes on to explain the notion of provable security, which formally defines what "secure" means in terms of a cryptographic scheme. A selection of famous families of protocols are then described, including zero-knowledge proofs, multi-party computation and key exchange. After a general introduction to pairing-based cryptography, this book presents advanced cryptographic schemes for confidentiality and authentication with additional properties such as anonymous signatures and multi-recipient encryption schemes. Finally, it details the more recent topic of verifiable computation.

This book constitutes the refereed post-conference proceedings of the Fourth IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2021, held in Chennai, India, in March 2021. The 20 revised full papers presented were carefully reviewed and selected from 75 submissions. The papers cover topics such as computational intelligence for text analysis; computational intelligence for image and video analysis; blockchain and data science.

The preservation of private data is a main concern of governments, organizations, and individuals alike. For individuals, a breach in personal information can mean dire consequences for an individual's finances, medical information, and personal property. Identity Theft: Breakthroughs in Research and Practice highlights emerging perspectives and critical insights into the preservation of personal data and the complications that can arise when one's identity is compromised. This critical volume features key research on methods and technologies for protection, the problems associated with identity theft, and outlooks for the future. This publication is an essential resource for information security professionals, researchers, and graduate-level students in the fields of criminal science, business, and computer science.

Geographic Information Systems (GIS) have been experiencing a steady and unprecedented growth in terms of general interest, theory development, and new applications in the last decade or so. GIS is an inter-disciplinary field that brings together many diverse areas such as computer science, geography, cartography, engineering, and urban planning. Database Issues in Geographic Information Systems approaches several important topics in GIS from a database perspective. Database management has a central role to play in most computer-based information systems, and is expected to have an equally important role to play in managing information in GIS as well. Existing database technology, however, focuses on the alphanumeric data that are required in business applications. GIS, like many other application areas, requires the ability to handle spatial as well as alphanumeric data. This requires new innovations in data management, which is the central theme of this monograph. The monograph begins with an overview of different application areas and their data and functional requirements. Next it addresses the following topics in the context of GIS: representation and manipulation of spatial data, data modeling, indexing, and query processing. Future research directions are outlined in each of the above topics. The last chapter discusses issues that are emerging as important areas of technological innovations in GIS. Database Issues in Geographic Information Systems is suitable as a secondary text for a graduate level course on Geographic Information Systems, Database Systems or Cartography, and as a reference for researchers and practitioners in industry.