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Computational Artificial Intelligence and Methods for industries: A Machine-Generated Literature Overview
by Vijayarangan NatarajanThis book presents the result of an innovative challenge, to create a systematic literature overview driven by machine-generated content. Questions and related keywords were prepared for the machine to query, discover, collate and structure by Artificial Intelligence (AI) clustering. The AI-based approach seemed especially suitable to provide an innovative perspective as the topics are indeed both complex, interdisciplinary and multidisciplinary, for example, climate, planetary and evolution sciences. Springer Nature has published much on these topics in its journals over the years, so the challenge was for the machine to identify the most relevant content and present it in a structured way that the reader would find useful. The automatically generated literature summaries in this book are intended as a springboard to further discoverability. They are particularly useful to readers with limited time, looking to learn more about the subject quickly and especially if they are new to the topics. Springer Nature seeks to support anyone who needs a fast and effective start in their content discovery journey, from the undergraduate student exploring interdisciplinary content to Master- or PhD-thesis developing research questions, to the practitioner seeking support materials, this book can serve as an inspiration, to name a few examples. It is important to us as a publisher to make the advances in technology easily accessible to our authors and find new ways of AI-based author services that allow human-machine interaction to generate readable, usable, collated, research content.
Computational Bayesian Statistics: An Introduction (Institute of Mathematical Statistics Textbooks #11)
by Carlos Daniel Paulino Peter Müller M. Antónia Amaral TurkmanMeaningful use of advanced Bayesian methods requires a good understanding of the fundamentals. This engaging book explains the ideas that underpin the construction and analysis of Bayesian models, with particular focus on computational methods and schemes. The unique features of the text are the extensive discussion of available software packages combined with a brief but complete and mathematically rigorous introduction to Bayesian inference. The text introduces Monte Carlo methods, Markov chain Monte Carlo methods, and Bayesian software, with additional material on model validation and comparison, transdimensional MCMC, and conditionally Gaussian models. The inclusion of problems makes the book suitable as a textbook for a first graduate-level course in Bayesian computation with a focus on Monte Carlo methods. The extensive discussion of Bayesian software - R/R-INLA, OpenBUGS, JAGS, STAN, and BayesX - makes it useful also for researchers and graduate students from beyond statistics.
Computational Biology of Non-Coding RNA: Methods and Protocols (Methods in Molecular Biology #1912)
by Julio Vera Xin Lai Shailendra K. GuptaThis volume details a collection of state-of-art methods including identification of novel ncRNAs and their targets, functional annotation and disease association in different biological contexts. Chapters guide readers through an overview of disease-specific ncRNAs, computational methods and workflows for ncRNA discovery, annotation based on high-throughput sequencing data, bioinformatics tools and databases for ncRNA analyses, network-based methods, and kinetic modelling of ncRNA-mediated gene regulation. Written in the highly successful Methods in Molecular Biology series format, chapters include introductions to their respective topics, lists of the necessary materials and reagents, step-by-step, readily reproducible laboratory protocols, and tips on troubleshooting and avoiding known pitfalls. Authoritative and cutting-edge, Computational Biology of Non-Coding RNA: Methods and Protocols aims to provide a state-of-the-art collection of computational methods and approaches that will be of value to researchers interested in ncRNA field.
Computational Biology: A Practical Introduction to Bio Data Juggling with Worked Examples
by Röbbe WünschiersThis extensively expanded third edition offers a practical introduction to Bio Data Science. With a hands-on approach to learning, this book offers ample opportunities to practice: - Installing and utilizing Linux as a virtual machine or remotely - Processing bio data with the programming language AWK - Managing data with the relational database system MariaDB - Analyzing and visualizing data with R - Implementing good bioinformatics practices with Jupyter Notebook and GitHub This book targets both students and professionals in the life sciences. While it is aimed at beginners, it also provides valuable tips and tricks for experienced researchers dealing with large datasets. Worked examples illustrate how to utilize various bioinformatics tools such as BLAST, Clustal, PLINK, IGV, SAMtools, BCFtools, Mason2, Minimap, NCBI Datasets, Velvet, Jmol, and more for: - Identifying bacterial proteins potentially associated with pathogenicity - Querying molecular structures for redox-regulated enzymes - Mapping and assembling real or simulated sequence reads - Identifying and mapping molecular structure mutations in viruses - Conducting genome-wide association studies All software tools and datasets mentioned are freely available, and all code is accessible as Jupyter Notebooks on GitHub. Drawing from the author's experiences and knowledge gained from both academia and industry, this book provides a practical and comprehensive approach to bioinformatics.
Computational Biology: A Practical Introduction to BioData Processing and Analysis with Linux, MySQL, and R
by Röbbe WünschiersThis greatly expanded 2nd edition provides a practical introduction to - data processing with Linux tools and the programming languages AWK and Perl- data management with the relational database system MySQL, and- data analysis and visualization with the statistical computing environment R for students and practitioners in the life sciences. Although written for beginners, experienced researchers in areas involving bioinformatics and computational biology may benefit from numerous tips and tricks that help to process, filter and format large datasets. Learning by doing is the basic concept of this book. Worked examples illustrate how to employ data processing and analysis techniques, e.g. for - finding proteins potentially causing pathogenicity in bacteria, - supporting the significance of BLAST with homology modeling, or- detecting candidate proteins that may be redox-regulated, on the basis of their structure.All the software tools and datasets used are freely available. One section is devoted to explaining setup and maintenance of Linux as an operating system independent virtual machine. The author's experiences and knowledge gained from working and teaching in both academia and industry constitute the foundation for this practical approach.
Computational Biology: A Statistical Mechanics Perspective, Second Edition (Chapman & Hall/CRC Computational Biology Series)
by Ralf BlosseyComputational biology has developed rapidly during the last two decades following the genomic revolution which culminated in the sequencing of the human genome. More than ever it has developed into a field which embraces computational methods from different branches of the exact sciences: pure and applied mathematics, computer science, theoretical physics. This Second Edition provides a solid introduction to the techniques of statistical mechanics for graduate students and researchers in computational biology and biophysics. Material has been reorganized to clarify equilbrium and nonequilibrium aspects of biomolecular systems Content has been expanded, in particular in the treatment of the electrostatic interactions of biomolecules and the application of non-equilibrium statistical mechanics to biomolecules New network-based approaches for the study of proteins are presented. All treated topics are put firmly in the context of the current research literature, allowing the reader to easily follow an individual path into a specific research field. Exercises and Tasks accompany the presentations of the topics with the intention of enabling the readers to test their comprehension of the developed basic concepts.
Computational Biomechanics for Medicine: Models, Algorithms And Implementation
by Poul M.F. Nielsen Karol MillerMathematical modelling and computer simulation have proved tremendously successful in engineering. One of the greatest challenges for mechanists is to extend the success of computational mechanics to fields outside traditional engineering, in particular to biology, biomedical sciences, and medicine. The proposed workshop will provide an opportunity for computational biomechanics specialists to present and exchange opinions on the opportunities of applying their techniques to computer-integrated medicine. For example, continuum mechanics models provide a rational basis for analysing biomedical images by constraining the solution to biologically reasonable motions and processes. Biomechanical modelling can also provide clinically important information about the physical status of the underlying biology, integrating information across molecular, tissue, organ, and organism scales. The main goal of this workshop is to showcase the clinical and scientific utility of computational biomechanics in computer-integrated medicine.
Computational Biomechanics for Medicine: Personalisation, Validation and Therapy
by Karol Miller Adam Wittek Poul M. F. Nielsen Grand R. Joldes Martyn P. NashThis book contains contributions from computational biomechanics specialists who present and exchange opinions on the opportunities for applying their techniques to computer-integrated medicine, including computer-aided surgery and diagnostic systems. Computational Biomechanics for Medicine collects peer-reviewed chapters from the annual Computational Biomechanics for Medicine Workshop, in conjunction with the Medical Image Computing and Computer Assisted Intervention [MICCAI] Society conference. The works are dedicated to research in the field of methods and applications of computational biomechanics to medical image analysis, image-guided surgery, surgical simulation, surgical intervention planning, disease diagnosis and prognosis, analysis of injury mechanisms, implant and prosthesis design, artificial organ design, and medical robotics. These chapters will appeal to a wide range of researchers and students within the fields of engineering and medicine, as well as those working in computational science.
Computational Biomechanics for Medicine: Solid and Fluid Mechanics for the Benefit of Patients
by Karol Miller Adam Wittek Poul M. F. Nielsen Grand Joldes Martyn P. NashComputational Biomechanics for Medicine: Solid and fluid mechanics for the benefit of patients contributions and papers from the MICCAI Computational Biomechanics for Medicine Workshop help in conjunction with Medical Image Computing and Computer Assisted Intervention conference (MICCAI 2019) in Shenzhen, China. The content is dedicated to research in the field of methods and applications of computational biomechanics to medical image analysis, image-guided surgery, surgical simulation, surgical intervention planning, disease prognosis and diagnostics, analysis of injury mechanisms, implant and prostheses design, as well as artificial organ design and medical robotics. These proceedings appeal to researchers, students and professionals in the field.
Computational Business Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series #34)
by Subrata DasThis book presents tools and techniques for descriptive, predictive, and prescriptive analytics applicable across multiple domains. The author first covers core descriptive and inferential statistics for analytics and then enhances numerical statistical techniques with symbolic artificial intelligence and machine learning techniques for richer predictive and prescriptive analytics. Through many examples and challenging case studies from a variety of fields, practitioners easily see the connections to their own problems and can then formulate their own solution strategies.
Computational Calculus: A Numerical Companion to Elementary Calculus (Synthesis Lectures on Mathematics & Statistics)
by William C. BauldryThis book offers readers the methods that are necessary to apply the power of calculus to analyze real problems. While most calculus textbooks focus on formula-based calculus, this book explains how to do the analysis of calculus, rates of change, and accumulation from data. The author’s introductory approach prepares students with the techniques to handle numerically-based problems in more advanced classes or in real-world applications. This self-contained book uses the computer algebra system Maple for computation, and the material is easily adaptable for calculators or other computer algebra systems. The author includes historical context and example exercises throughout the book in order to provide readers with a thorough understanding of the topic. This book:Prepares students with the techniques to handle numerically-based problems in in real-world applicationsProvides historical context and example exercises to give a thorough understanding of the topicUtilizes Maple for computation and is adaptable for calculators or other computer algebra systems
Computational Cancer Biology: An Interaction Network Approach (SpringerBriefs in Electrical and Computer Engineering)
by Mathukumalli VidyasagarThis brief introduces people with a basic background in probability theory to various problems in cancer biology that are amenable to analysis using methods of probability theory and statistics. The title mentions "cancer biology" and the specific illustrative applications reference cancer data but the methods themselves are more broadly applicable to all aspects of computational biology. Aside from providing a self-contained introduction to basic biology and to cancer, the brief describes four specific problems in cancer biology that are amenable to the application of probability-based methods. The application of these methods is illustrated by applying each of them to actual data from the biology literature. After reading the brief, engineers and mathematicians should be able to collaborate fruitfully with their biologist colleagues on a wide variety of problems.
Computational Collective Intelligence: 10th International Conference, ICCCI 2018, Bristol, UK, September 5-7, 2018, Proceedings, Part I (Lecture Notes in Computer Science #11055)
by Ngoc Thanh Nguyen Elias Pimenidis Bogdan Trawiński Zaheer KhanThis two-volume set (LNAI 11055 and LNAI 11056) constitutes the refereed proceedings of the 10th International Conference on Collective Intelligence, ICCCI 2018, held in Bristol, UK, in September 2018 The 98 full papers presented were carefully reviewed and selected from 240 submissions. The conference focuses on knowledge engineering and semantic web, social network analysis, recommendation methods and recommender systems, agents and multi-agent systems, text processing and information retrieval, data mining methods and applications, decision support and control systems, sensor networks and internet of things, as well as computer vision techniques.
Computational Collective Intelligence: 10th International Conference, ICCCI 2018, Bristol, UK, September 5-7, 2018, Proceedings, Part II (Lecture Notes in Computer Science #11056)
by Ngoc Thanh Nguyen Elias Pimenidis Bogdan Trawiński Zaheer KhanThis two-volume set (LNAI 11055 and LNAI 11056) constitutes the refereed proceedings of the 10th International Conference on Collective Intelligence, ICCCI 2018, held in Bristol, UK, in September 2018 The 98 full papers presented were carefully reviewed and selected from 240 submissions. The conference focuses on knowledge engineering and semantic web, social network analysis, recommendation methods and recommender systems, agents and multi-agent systems, text processing and information retrieval, data mining methods and applications, decision support and control systems, sensor networks and internet of things, as well as computer vision techniques.
Computational Collective Intelligence: 11th International Conference, ICCCI 2019, Hendaye, France, September 4–6, 2019, Proceedings, Part II (Lecture Notes in Computer Science #11684)
by Richard Chbeir Ernesto Exposito Ngoc Thanh Nguyen Bogdan Trawiński Philippe AniortéThis two-volume set (LNAI 11683 and LNAI 11684) constitutes the refereed proceedings of the 11th International Conference on Computational Collective Intelligence, ICCCI 2019, held in Hendaye France, in September 2019.The 117 full papers presented were carefully reviewed and selected from 200 submissions. The papers are grouped in topical sections on: computational collective intelligence and natural language processing; machine learning in real-world data; distributed collective intelligence for smart manufacturing; collective intelligence for science and technology; intelligent management information systems; intelligent sustainable smart cities; new trends and challenges in education: the university 4.0; intelligent processing of multimedia in web systems; and big data streaming, applications and security.
Computational Collective Intelligence: 12th International Conference, ICCCI 2020, Da Nang, Vietnam, November 30 – December 3, 2020, Proceedings (Lecture Notes in Computer Science #12496)
by Ngoc Thanh Nguyen Gottfried Vossen Bogdan Trawiński Dosam Hwang Bao Hung Hoang Cong Phap HuynhThis volume constitutes the refereed proceedings of the 12th International Conference on Computational Collective Intelligence, ICCCI 2020, held in Da Nang, Vietnam, in November 2020.*The 70 full papers presented were carefully reviewed and selected from 314 submissions. The papers are grouped in topical sections on: knowledge engineering and semantic web; social networks and recommender systems; collective decision-making; applications of collective intelligence; data mining methods and applications; machine learning methods; deep learning and applications for industry 4.0; computer vision techniques; biosensors and biometric techniques; innovations in intelligent systems; natural language processing; low resource languages processing; computational collective intelligence and natural language processing; computational intelligence for multimedia understanding; and intelligent processing of multimedia in web systems. *The conference was held virtually due to the COVID-19 pandemic.
Computational Collective Intelligence: 13th International Conference, ICCCI 2021, Rhodes, Greece, September 29 – October 1, 2021, Proceedings (Lecture Notes in Computer Science #12876)
by Ngoc Thanh Nguyen Lazaros Iliadis Bogdan Trawiński Ilias MaglogiannisThis book constitutes the refereed proceedings of the 13th International Conference on Computational Collective Intelligence, ICCCI 2021, held in September/October 2021. The conference was held virtually due to the COVID-19 pandemic. The 58 full papers were carefully reviewed and selected from 230 submissions. The papers are grouped in topical issues on knowledge engineering and semantic web; social networks and recommender systems; collective decision-making; cooperative strategies for decision making and optimization; data mining and machine learning; computer vision techniques; natural language processing; Internet of Things: technologies and applications; Internet of Things and computational technologies for collective intelligence; computational intelligence for multimedia understanding.
Computational Collective Intelligence: 14th International Conference, ICCCI 2022, Hammamet, Tunisia, September 28–30, 2022, Proceedings (Lecture Notes in Computer Science #13501)
by Richard Chbeir Ngoc Thanh Nguyen Yannis Manolopoulos Bogdan Trawiński Adrianna KozierkiewiczThis book constitutes the refereed proceedings of the 14th International Conference on Computational Collective Intelligence, ICCCI 2022, held in Hammamet, Tunisia, in September 2022. The 56 full papers and 10 short papers were carefully reviewed and selected from 420 submissions. The papers are grouped in topical sections on collective intelligence and collective decision-making; deep learning techniques; natural language processing; data minning and machine learning; knowledge engineering and semantic web; computer vision techniques; social networks and intelligent systems; cybersecurity and internet of things; cooperative strategies for decision making and optimization; computational intelligence for digital content understanding; applications for industry 4.0.
Computational Collective Intelligence: 15th International Conference, ICCCI 2023, Budapest, Hungary, September 27–29, 2023, Proceedings (Lecture Notes in Computer Science #14162)
by Ngoc Thanh Nguyen Gottfried Vossen Manuel Núñez Jan Treur Adrianna Kozierkiewicz László Gulyás János BotzheimThis book constitutes the refereed proceedings of the 15th International Conference on Computational Collective Intelligence, ICCCI 2023, held in Budapest, Hungary, during September 27–29, 2023.The 63 full papers included in this book were carefully reviewed and selected from 218 submissions. They are organized in topical sections as follows: collective intelligence and collective decision-making; deep learning techniques; natural language processing; data mining and machine learning; social networks and intelligent systems; cybersecurity, blockchain technology and Internet of Things; cooperative strategies for decision making and optimization; computational intelligence for digital content understanding; knowledge engineering and application for Industry 4.0; computational intelligence in medical applications; and ensemble models and data fusion.
Computational Collective Intelligence: 16th International Conference, ICCCI 2024, Leipzig, Germany, September 9–11, 2024, Proceedings, Part II (Lecture Notes in Computer Science #14811)
by Ngoc Thanh Nguyen Gottfried Vossen Bogdan Franczyk Manuel Núñez Jan Treur André Ludwig Adrianna KozierkiewiczThis two-volume set LNAI 14810-14811 constitutes the refereed proceedings of the 16th International Conference on Computational Collective Intelligence, ICCCI 2024, held in Leipzig, Germany, during September 9–11, 2024. The 59 revised full papers presented in these proceedings were carefully reviewed and selected from 234 submissions. Part I: collective intelligence and collective decision-making; deep learning techniques; natural language processing; data mining and machine learning Part II: social networks and intelligent system; cybersecurity, blockchain technology, and internet of things; cooperative strategies for decision making and optimization; computational intelligence for digital content understanding; knowledge engineering and application for industry 4.0
Computational Collective Intelligence: 16th International Conference, ICCCI 2024, Leipzig, Germany, September 9–11, 2024, Proceedings, Part I (Lecture Notes in Computer Science #14810)
by Ngoc Thanh Nguyen Gottfried Vossen Bogdan Franczyk Manuel Núñez Jan Treur André Ludwig Adrianna KozierkiewiczThis two-volume set LNAI 14810-14811 constitutes the refereed proceedings of the 16th International Conference on Computational Collective Intelligence, ICCCI 2024, held in Leipzig, Germany, during September 9–11, 2024. The 59 revised full papers presented in these proceedings were carefully reviewed and selected from 234 submissions. They cover the following topics: Part I: Collective intelligence and collective decision-making; deep learning techniques; natural language processing; data mining and machine learning. Part II: Social networks and intelligent system; cybersecurity, blockchain technology, and internet of things; cooperative strategies for decision making and optimization; computational intelligence for digital content understanding; knowledge engineering and application for industry 4.0.
Computational Collective Intelligence: 8th International Conference, ICCCI 2016, Halkidiki, Greece, September 28-30, 2016. Proceedings, Part I (Lecture Notes in Computer Science #9875)
by Yannis Manolopoulos Lazaros Iliadis Bogdan Trawiński Ngoc-Thanh NguyenCollective intelligence has become one of major research issues studied by today's and future computer science. Computational collective intelligence is understood as this form of group intellectual activity that emerges from collaboration and compe- tion of many artificial individuals. Robotics, artificial intelligence, artificial cognition and group working try to create efficient models for collective intelligence in which it emerges from sets of actions carried out by more or less intelligent individuals. The major methodological, theoretical and practical aspects underlying computational collective intelligence are group decision making, collective action coordination, collective competition and knowledge description, transfer and integration. Obviously, the application of multiple computational technologies such as fuzzy systems, evo- tionary computation, neural systems, consensus theory, knowledge representation etc. is necessary to create new forms of computational collective intelligence and support existing ones. Three subfields of application of computational technologies to support forms of collective intelligence are of special attention to us. The first one is semantic web treated as an advanced tool that increases the collective intelligence in networking environments. The second one covers social networks modeling and analysis, where social networks are this area of in which various forms of computational collective intelligence emerges in a natural way. The third subfield relates us to agent and mul- agent systems understood as this computational and modeling paradigm which is especially tailored to capture the nature of computational collective intelligence in populations of autonomous individuals.
Computational Collective Intelligence: 8th International Conference, ICCCI 2016, Halkidiki, Greece, September 28-30, 2016. Proceedings, Part II (Lecture Notes in Computer Science #9876)
by Ngoc Thanh Nguyen Yannis Manolopoulos Lazaros Iliadis Bogdan TrawińskiCollective intelligence has become one of major research issues studied by today's and future computer science. Computational collective intelligence is understood as this form of group intellectual activity that emerges from collaboration and compe- tion of many artificial individuals. Robotics, artificial intelligence, artificial cognition and group working try to create efficient models for collective intelligence in which it emerges from sets of actions carried out by more or less intelligent individuals. The major methodological, theoretical and practical aspects underlying computational collective intelligence are group decision making, collective action coordination, collective competition and knowledge description, transfer and integration. Obviously, the application of multiple computational technologies such as fuzzy systems, evo- tionary computation, neural systems, consensus theory, knowledge representation etc. is necessary to create new forms of computational collective intelligence and support existing ones. Three subfields of application of computational technologies to support forms of collective intelligence are of special attention to us. The first one is semantic web treated as an advanced tool that increases the collective intelligence in networking environments. The second one covers social networks modeling and analysis, where social networks are this area of in which various forms of computational collective intelligence emerges in a natural way. The third subfield relates us to agent and mul- agent systems understood as this computational and modeling paradigm which is especially tailored to capture the nature of computational collective intelligence in populations of autonomous individuals.
Computational Collective Intelligence: 9th International Conference, ICCCI 2017, Nicosia, Cyprus, September 27-29, 2017, Proceedings, Part I (Lecture Notes in Computer Science #10448)
by Ngoc Thanh Nguyen Gottfried Vossen George A. Papadopoulos Bogdan Trawiński Piotr JędrzejowiczThis two-volume set (LNAI 10448 and LNAI 10449) constitutes the refereed proceedings of the 9th International Conference on Collective Intelligence, ICCCI 2017, held in Nicosia, Cyprus, in September 2017. The 117 full papers presented were carefully reviewed and selected from 248 submissions. The conference focuseson the methodology and applications of computational collective intelligence, included: multi-agent systems, knowledge engineering and semantic web, social networks and recommender systems, text processing and information retrieval, data mining methods and applications, sensor networks and internet of things, decision support & control systems, and computer vision techniques.
Computational Collective Intelligence: 9th International Conference, ICCCI 2017, Nicosia, Cyprus, September 27-29, 2017, Proceedings, Part II (Lecture Notes in Computer Science #10449)
by Ngoc Thanh Nguyen Gottfried Vossen George A. Papadopoulos Bogdan Trawiński Piotr JędrzejowiczThis two-volume set (LNAI 10448 and LNAI 10449) constitutes the refereed proceedings of the 9th International Conference on Collective Intelligence, ICCCI 2017, held in Nicosia, Cyprus, in September 2017. The 117 full papers presented were carefully reviewed and selected from 248 submissions. The conference focuseson the methodology and applications of computational collective intelligence, included: multi-agent systems, knowledge engineering and semantic web, social networks and recommender systems, text processing and information retrieval, data mining methods and applications, sensor networks and internet of things, decision support & control systems, and computer vision techniques.