Though traditionally information systems have been centralized, these systems are now distributed over the web. This requires a re-investigation into the way information systems are modeled and designed. Because of this new function, critical problems, including security, never-fail systems, and quality of service have begun to emerge. Novel Approaches to Information Systems Design is an essential publication that explores the most recent, cutting-edge research in information systems and exposes the reader to emerging but relatively mature models and techniques in the area. Highlighting a wide range of topics such as big data, business intelligence, and energy efficiency, this publication is ideally designed for managers, administrators, system developers, information system engineers, researchers, academicians, and graduate-level students seeking coverage on critical components of information systems.

Information Security and Ethics: Social and Organizational Issues brings together examples of the latest research from a number of international scholars addressing a wide range of issues significant to this important and growing field of study. These issues are relevant to the wider society, as well as to the individual, citizen, educator, student and industry professional. With individual chapters focusing on areas including web accessibility; the digital divide; youth protection and surveillance; Information security; education; ethics in the Information professions and Internet voting; this book provides an invaluable resource for students, scholars and professionals currently working in information Technology related areas.

The clinical use of Artificial Intelligence (AI) in radiation oncology is in its infancy. However, it is certain that AI is capable of making radiation oncology more precise and personalized with improved outcomes. Radiation oncology deploys an array of state-of-the-art technologies for imaging, treatment, planning, simulation, targeting, and quality assurance while managing the massive amount of data involving therapists, dosimetrists, physicists, nurses, technologists, and managers. AI consists of many powerful tools which can process a huge amount of inter-related data to improve accuracy, productivity, and automation in complex operations such as radiation oncology.This book offers an array of AI scientific concepts, and AI technology tools with selected examples of current applications to serve as a one-stop AI resource for the radiation oncology community. The clinical adoption, beyond research, will require ethical considerations and a framework for an overall assessment of AI as a set of powerful tools.30 renowned experts contributed to sixteen chapters organized into six sections: Define the Future, Strategy, AI Tools, AI Applications, and Assessment and Outcomes. The future is defined from a clinical and a technical perspective and the strategy discusses lessons learned from radiology experience in AI and the role of open access data to enhance the performance of AI tools. The AI tools include radiomics, segmentation, knowledge representation, and natural language processing. The AI applications discuss knowledge-based treatment planning and automation, AI-based treatment planning, prediction of radiotherapy toxicity, radiomics in cancer prognostication and treatment response, and the use of AI for mitigation of error propagation. The sixth section elucidates two critical issues in the clinical adoption: ethical issues and the evaluation of AI as a transformative technology.

With exponentially increasing amounts of data accumulating in real-time, there is no reason why one should not turn data into a competitive advantage. While machine learning, driven by advancements in artificial intelligence, has made great strides, it has not been able to surpass a number of challenges that still prevail in the way of better success. Such limitations as the lack of better methods, deeper understanding of problems, and advanced tools are hindering progress. Challenges and Applications of Data Analytics in Social Perspectives provides innovative insights into the prevailing challenges in data analytics and its application on social media and focuses on various machine learning and deep learning techniques in improving practice and research. The content within this publication examines topics that include collaborative filtering, data visualization, and edge computing. It provides research ideal for data scientists, data analysts, IT specialists, website designers, e-commerce professionals, government officials, software engineers, social media analysts, industry professionals, academicians, researchers, and students.

In recent years, artificial intelligence (AI) has drawn significant attention with respect to its applications in several scientific fields, varying from big data handling to medical diagnosis. A tremendous transformation has taken place with the emerging application of AI. AI can provide a wide range of solutions to address many challenges in civil engineering. Artificial Intelligence and Machine Learning Techniques for Civil Engineering highlights the latest technologies and applications of AI in structural engineering, transportation engineering, geotechnical engineering, and more. It features a collection of innovative research on the methods and implementation of AI and machine learning in multiple facets of civil engineering. Covering topics such as damage inspection, safety risk management, and information modeling, this premier reference source is an essential resource for engineers, government officials, business leaders and executives, construction managers, students and faculty of higher education, librarians, researchers, and academicians.

Applications of Computer Vision in Fashion and Textiles provides a systematic and comprehensive discussion of three key areas that are taking advantage of developments in computer vision technology, namely textile defect detection and quality control, fashion recognition and 3D modeling, and 2D and 3D human body modeling for improving clothing fit. It introduces the fundamentals of computer vision techniques for fashion and textile applications, also reviewing computer vision techniques for textile quality control, including chapters on wavelet transforms, Gibor filters, Fourier transforms, and neural network techniques. Final sections cover recognition, modeling, retrieval technologies and advanced human shape modeling techniques. The book is essential reading for scientists and researchers working in the field of fashion production, quality assurance, product development, textiles, fashion supply chain managers, R&D professionals and managers in the textile industry.

Ongoing advancements in modern technology have led to significant developments with smart technologies. With the numerous applications available, it becomes imperative to conduct research and make further progress in this field. Smart Technologies: Breakthroughs in Research and Practice provides comprehensive and interdisciplinary research on the most emerging areas of information science and technology. Including innovative studies on image and speech recognition, human-computer interface, and wireless technologies, this multi-volume book is an ideal source for researchers, academicians, practitioners, and students interested in advanced technological applications and developments.

This organizational history relates the role of the National Science Foundation (NSF) in the development of modern computing. Drawing upon new and existing oral histories, extensive use of NSF documents, and the experience of two of the authors as senior managers, this book describes how NSF's programmatic activities originated and evolved to become the primary source of funding for fundamental research in computing and information technologies. The book traces how NSF's support has provided facilities and education for computing usage by all scientific disciplines, aided in institution and professional community building, supported fundamental research in computer science and allied disciplines, and led the efforts to broaden participation in computing by all segments of society. Today, the research and infrastructure facilitated by NSF computing programs are significant economic drivers of American society and industry. For example, NSF supported work that led to the first widely-used web browser, Netscape; sponsored the creation of algorithms at the core of the Google search engine; facilitated the growth of the public Internet; and funded research on the scientific basis for countless other applications and technologies. NSF has advanced the development of human capital and ideas for future advances in computing and its applications. This account is the first comprehensive coverage of NSF's role in the extraordinary growth and expansion of modern computing and its use. It will appeal to historians of computing, policy makers and leaders in government and academia, and individuals interested in the history and development of computing and the NSF.

FLINS, an acronym originally for Fuzzy Logic and Intelligent Technologies in Nuclear Science, was inaugurated by Prof. Da Ruan of the Belgian Nuclear Research Center (SCK*CEN) in 1994 with the purpose of providing PhD and Postdoc researchers with a platform to present their research ideas in fuzzy logic and artificial intelligence. For more than 28 years, FLINS has been expanded to include research in both theoretical and practical development of computational intelligent systems.With this successful conference series: FLINS1994 and FLINS1996 in Mol, FLINS1998 in Antwerp, FLINS2000 in Bruges, FLINS2002 in Gent, FLINS2004 in Blankenberge, FLINS2006 in Genova, FLINS2008 in Marid, FLINS2010 in Chengdu, FLINS2012 in Istanbul, FLINS2014 in Juan Pesoa, FLINS2016 in Roubaix, FLINS2018 in Belfast and FLINS2020 in Cologne, FLINS2022 was organized by Nankai University, and co-organized by Southwest Jiaotong University, University of Technology Sydney and Ecole Nationale Superieure des Arts et Industries Textiles of University of Lille. This unique international research collaboration has provided researchers with a platform to share and exchange ideas on state-of-art development in machine learning, multi agent and cyber physical systems.Following the wishes of Prof. Da Ruan, FLINS2022 offered an international platform that brought together mathematicians, computer scientists, and engineers who are actively involved in machine learning, intelligent systems, data analysis, knowledge engineering and their applications, to share their latest innovations and developments, exchange notes on the state-of-the-art research ideas, especially in the areas of industrial microgrids, intelligent wearable systems, sustainable development, logistics, supply chain and production optimization, evaluation systems and performance analysis, as well as risk and security management, that have now become part and parcel of Fuzzy Logic and Intelligent Technologies in Nuclear Science.This FLINS2022 Proceedings has selected 78 conference papers that cover the following seven areas of interests:

Wearable technology can range anywhere between activity trackers to prosthetics. These new advancements are continuously progressing and becoming a part of daily life. Examining Developments and Applications of Wearable Devices in Modern Society is a pivotal reference source for the most innovative research on the expansion of wearable computing and technology. Featuring coverage on a broad range of topics such as stroke monitoring, augmented reality, and cancer detection, this publication is ideally designed for academicians, researchers, and students seeking current research on the challenges and benefits of the latest wearable devices.

Change Detection and Image Time Series Analysis 2 presents supervised machine-learning-based methods for temporal evolution analysis by using image time series associated with Earth observation data. Chapter 1 addresses the fusion of multisensor, multiresolution and multitemporal data. It proposes two supervised solutions that are based on a Markov random field: the first relies on a quad-tree and the second is specifically designed to deal with multimission, multifrequency and multiresolution time series. Chapter 2 provides an overview of pixel based methods for time series classification, from the earliest shallow learning methods to the most recent deep-learning-based approaches. Chapter 3 focuses on very high spatial resolution data time series and on the use of semantic information for modeling spatio-temporal evolution patterns. Chapter 4 centers on the challenges of dense time series analysis, including pre processing aspects and a taxonomy of existing methodologies. Finally, since the evaluation of a learning system can be subject to multiple considerations, Chapters 5 and 6 offer extensive evaluations of the methodologies and learning frameworks used to produce change maps, in the context of multiclass and/or multilabel change classification issues.

Artificial intelligence has been utilized in a diverse range of industries as more people and businesses discover its many uses and applications. A current field of study that requires more attention, as there is much opportunity for improvement, is the use of artificial intelligence within literary works and social media analysis. Artificial Intelligence Applications in Literary Works and Social Media presents contemporary developments in the adoption of artificial intelligence in textual analysis of literary works and social media and introduces current approaches, techniques, and practices in data science that are implemented to scrap and analyze text data. This book initiates a new multidisciplinary field that is the combination of artificial intelligence, data science, social science, literature, and social media study. Covering key topics such as opinion mining, sentiment analysis, and machine learning, this reference work is ideal for computer scientists, industry professionals, researchers, scholars, practitioners, academicians, instructors, and students.

Machine Learning in Bioinformatics of Protein Sequences guides readers around the rapidly advancing world of cutting-edge machine learning applications in the protein bioinformatics field. Edited by bioinformatics expert, Dr Lukasz Kurgan, and with contributions by a dozen of accomplished researchers, this book provides a holistic view of the structural bioinformatics by covering a broad spectrum of algorithms, databases and software resources for the efficient and accurate prediction and characterization of functional and structural aspects of proteins. It spotlights key advances which include deep neural networks, natural language processing-based sequence embedding and covers a wide range of predictions which comprise of tertiary structure, secondary structure, residue contacts, intrinsic disorder, protein, peptide and nucleic acids-binding sites, hotspots, post-translational modification sites, and protein function. This volume is loaded with practical information that identifies and describes leading predictive tools, useful databases, webservers, and modern software platforms for the development of novel predictive tools.

Relational databases have been predominant for many years and are used throughout various industries. The current system faces challenges related to size and variety of data thus the NoSQL databases emerged. By joining these two database models, there is room for crucial developments in the field of computer science. Bridging Relational and NoSQL Databases is an innovative source of academic content on the convergence process between databases and describes key features of the next database generation. Featuring coverage on a wide variety of topics and perspectives such as BASE approach, CAP theorem, and hybrid and native solutions, this publication is ideally designed for professionals and researchers interested in the features and collaboration of relational and NoSQL databases.

There is a tremendous need for computer scientists, data scientists, and software developers to learn how to develop Socratic problem-solving applications. While the amount of data and information processing has been accelerating, our ability to learn and problem-solve with that data has fallen behind. Meanwhile, problems have become too complex to solve in the workplace without a concerted effort to follow a problem-solving process. This problem-solving process must be able to deal with big and disparate data. Furthermore, it must solve problems that do not have a "rule" to apply in solving them. Moreover, it must deal with ambiguity and help humans use informed judgment to build on previous steps and create new understanding. Computer-based Socratic problem-solving systems answer this need for a problem-solving process using big and disparate data. Furthermore, computer scientists, data scientists, and software developers need the knowledge to develop these systems. Socrates Digital (TM) for Learning and Problem Solving presents the rationale for developing a Socratic problem-solving application. It describes how a computer-based Socratic problem-solving system called Socrates DigitalTM can keep problem-solvers on track, document the outcome of a problem-solving session, and share those results with problem-solvers and larger audiences. In addition, Socrates DigitalTM assists problem-solvers to combine evidence about their quality of reasoning for individual problem-solving steps and their overall confidence in the solution. Socrates DigitalTM also captures, manages, and distributes this knowledge across organizations to improve problem-solving. This book also presents how to build a Socrates DigitalTM system by detailing the four phases of design and development: Understand, Explore, Materialize, and Realize. The details include flow charts and pseudo-code for readers to implement Socrates DigitalTM in a general-purpose programming language. The completion of the design and development process results in a Socrates DigitalTM system that leverages artificial intelligence services from providers that include Apple, Microsoft, Google, IBM, and Amazon. In addition, an appendix provides a demonstration of a no-code implementation of Socrates DigitalTM in Microsoft Power Virtual Agent.

The considerable growth of RFID is currently accompanied by the development of numerous identification technologies that complement those already available while seeking to answer new problems. Chipless RFID is one example. The goal is to both significantly reduce the price of the tag and increase the amount of information it contains, in order to compete with the barcode while retaining the benefits of a flexible reading approach based on radio communication. To solve the problem of the number of bits, this book describes the possibility of coding the information at the level of the overall shape of the RCS of the tag, which would facilitate reaching very large quantities. The design of the tags then returns to the resolution of the inverse problem of the electromagnetic signature. The proposed design methodology regularizes the problem by decomposing the signature on a basis of elementary patterns whose signature is chosen in advance.

Automatic identification has evolved to use techniques that can identify an object or subject without direct human intervention. Such devices include the bar code, magnetic-stripe, integrated circuit, and biometric and radio-frequency identification. Innovative Automatic Identification & Location-Based Services: From Bar Codes to Chip Implants emphasizes the convergence and trajectory of automatic identification and location-based services toward chip implants and real-time positioning capabilities. Recording the history of automatic identification, this book also discusses the social, cultural, and ethical implications of the technological possibilities with respect to national security initiatives.

As environmental issues remain at the forefront of energy research, renewable energy is now an all-important field of study. And as smart technology continues to grow and be refined, its applications broaden and increase in their potential to revolutionize sustainability studies. This potential can only be fully realized with a thorough understanding of the most recent breakthroughs in the field. Research Advancements in Smart Technology, Optimization, and Renewable Energy is a collection of innovative research that explores the recent steps forward for smart applications in sustainability. Featuring coverage on a wide range of topics including energy assessment, neural fuzzy control, and biogeography, this book is ideally designed for advocates, policymakers, engineers, software developers, academicians, researchers, and students.

Predictive Modeling of Drug Sensitivity gives an overview of drug sensitivity modeling for personalized medicine that includes data characterizations, modeling techniques, applications, and research challenges. It covers the major mathematical techniques used for modeling drug sensitivity, and includes the requisite biological knowledge to guide a user to apply the mathematical tools in different biological scenarios. This book is an ideal reference for computer scientists, engineers, computational biologists, and mathematicians who want to understand and apply multiple approaches and methods to drug sensitivity modeling. The reader will learn a broad range of mathematical and computational techniques applied to the modeling of drug sensitivity, biological concepts, and measurement techniques crucial to drug sensitivity modeling, how to design a combination of drugs under different constraints, and the applications of drug sensitivity prediction methodologies.

Digital Signal Processing 101: Everything You Need to Know to Get Started provides a basic tutorial on digital signal processing (DSP). Beginning with discussions of numerical representation and complex numbers and exponentials, it goes on to explain difficult concepts such as sampling, aliasing, imaginary numbers, and frequency response. It does so using easy-to-understand examples with minimum mathematics. In addition, there is an overview of the DSP functions and implementation used in several DSP-intensive fields or applications, from error correction to CDMA mobile communication to airborne radar systems. This book has been updated to include the latest developments in Digital Signal Processing, and has eight new chapters on: Automotive Radar Signal Processing Space-Time Adaptive Processing Radar Field Orientated Motor Control Matrix Inversion algorithms GPUs for computing Machine Learning Entropy and Predictive Coding Video compression