This book features original research and recent advances in ICT fields related to sustainable development. Based the International Conference on Networks, Intelligent systems, Computing & Environmental Informatics for Sustainable Development, held in Marrakech in April 2020, it features peer-reviewed chapters authored by prominent researchers from around the globe. As such it is an invaluable resource for courses in computer science, electrical engineering and urban sciences for sustainable development. This book covered topics including * Green Networks * Artificial Intelligence for Sustainability* Environment Informatics* Computing Technologies

This book constitutes revised and selected papers from the Sixteenth International Conference on Information Processing, ICInPro 2021, held in Bangaluru, India in October 2021. The 33 full and 9 short papers presented in this volume were carefully reviewed and selected from a total of 177 submissions. The papers are organized in the following thematic blocks: Computing & Network Security; Data Science; Intelligence & IoT.

The book first explores the cybersecurity's landscape and the inherent susceptibility of online communication system such as e-mail, chat conversation and social media in cybercrimes. Common sources and resources of digital crimes, their causes and effects together with the emerging threats for society are illustrated in this book. This book not only explores the growing needs of cybersecurity and digital forensics but also investigates relevant technologies and methods to meet the said needs. Knowledge discovery, machine learning and data analytics are explored for collecting cyber-intelligence and forensics evidence on cybercrimes. Online communication documents, which are the main source of cybercrimes are investigated from two perspectives: the crime and the criminal. AI and machine learning methods are applied to detect illegal and criminal activities such as bot distribution, drug trafficking and child pornography. Authorship analysis is applied to identify the potential suspects and their social linguistics characteristics. Deep learning together with frequent pattern mining and link mining techniques are applied to trace the potential collaborators of the identified criminals. Finally, the aim of the book is not only to investigate the crimes and identify the potential suspects but, as well, to collect solid and precise forensics evidence to prosecute the suspects in the court of law.

This two-volume set constitutes the refereed proceedings of the 17th International Conference on Collaborative Computing: Networking, Applications, and Worksharing, CollaborateCom 2021, held in October 2021. Due to COVID-19 pandemic the conference was held virtually.The 62 full papers and 7 short papers presented were carefully reviewed and selected from 206 submissions. The papers reflect the conference sessions as follows: Optimization for Collaborate System; Optimization based on Collaborative Computing; UVA and Traffic system; Recommendation System; Recommendation System & Network and Security; Network and Security; Network and Security & IoT and Social Networks; IoT and Social Networks & Images handling and human recognition; Images handling and human recognition & Edge Computing; Edge Computing; Edge Computing & Collaborative working; Collaborative working & Deep Learning and application; Deep Learning and application; Deep Learning and application; Deep Learning and application & UVA.

This book constitutes the refereed proceedings of the 7th International IFIP WG 2.13 Conference on Open Source Systems, OSS 2010, held in Salvador, Brazil, in October 2011. The 20 revised full papers presented together with 4 industrial full papers and 8 lightning talks were carefully reviewed and selected from 56 submissions. The papers are organized in the following topical sections: OSS quality and reliability, OSS products, review of technologies of and for OSS, knowledge and research building in OSS, OSS reuse, integration, and compliance, OSS value and economics, OSS adoption in industry, and mining OSS repositories.

This is the first study of Boko Haram that brings advanced data-driven, machine learning models to both learn models capable of predicting a wide range of attacks carried out by Boko Haram, as well as develop data-driven policies to shape Boko Haram's behavior and reduce attacks by them. This book also identifies conditions that predict sexual violence, suicide bombings and attempted bombings, abduction, arson, looting, and targeting of government officials and security installations. After reducing Boko Haram's history to a spreadsheet containing monthly information about different types of attacks and different circumstances prevailing over a 9 year period, this book introduces Temporal Probabilistic (TP) rules that can be automatically learned from data and are easy to explain to policy makers and security experts. This book additionally reports on over 1 year of forecasts made using the model in order to validate predictive accuracy. It also introduces a policy computation method to rein in Boko Haram's attacks. Applied machine learning researchers, machine learning experts and predictive modeling experts agree that this book is a valuable learning asset. Counter-terrorism experts, national and international security experts, public policy experts and Africa experts will also agree this book is a valuable learning tool.

This two-volume set constitutes the refereed proceedings of the First International Conference International Conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization. The conference was held in August 2021 and due to COVID-19 pandemic virtually. The 99 revised full papers and 45 short papers have been selected from 503 submissions. The papers describe research fields such as "big data" and "information education". The aim of the conference is to provide international cooperation and exchange platforms for big data and information education experts, scholars and enterprise developers to share research results, discuss existing problems and challenges, and explore cutting-edge science and technology.

This book constitutes the refereed post-conference proceedings of the Third IFIP TC 12 International Conference on Computational Intelligence in Data Science, ICCIDS 2020, held in Chennai, India, in February 2020.The 19 revised full papers and 8 revised short papers presented were carefully reviewed and selected from 94 submissions. The papers are organized in the following topical sections: computational intelligence for text analysis; computational intelligence for image and video analysis; and data science.

A practical guide to making good decisions in a world of missing data In the era of big data, it is easy to imagine that we have all the information we need to make good decisions. But in fact the data we have are never complete, and may be only the tip of the iceberg. Just as much of the universe is composed of dark matter, invisible to us but nonetheless present, the universe of information is full of dark data that we overlook at our peril. In Dark Data, data expert David Hand takes us on a fascinating and enlightening journey into the world of the data we don't see. Dark Data explores the many ways in which we can be blind to missing data and how that can lead us to conclusions and actions that are mistaken, dangerous, or even disastrous. Examining a wealth of real-life examples, from the Challenger shuttle explosion to complex financial frauds, Hand gives us a practical taxonomy of the types of dark data that exist and the situations in which they can arise, so that we can learn to recognize and control for them. In doing so, he teaches us not only to be alert to the problems presented by the things we don't know, but also shows how dark data can be used to our advantage, leading to greater understanding and better decisions. Today, we all make decisions using data. Dark Data shows us all how to reduce the risk of making bad ones.

This proceeding features papers discussing big data innovation for sustainable cognitive computing. The papers feature details on cognitive computing and its self-learning systems that use data mining, pattern recognition and natural language processing (NLP) to mirror the way the human brain works. This international conference focuses on cognitive computing technologies, from knowledge representation techniques and natural language processing algorithms to dynamic learning approaches. Topics covered include Data Science for Cognitive Analysis, Real-Time Ubiquitous Data Science, Platform for Privacy Preserving Data Science, and Internet-Based Cognitive Platform. The 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing (BDCC 2019) took place in Coimbatore, India on December 12-13, 2019. Contains proceedings from 2nd EAI International Conference on Big Data Innovation for Sustainable Cognitive Computing (BDCC 2019), Coimbatore, India, December 12-13, 2019; Features topics ranging from Data Science for Cognitive Analysis to Internet-Based Cognitive Platforms; Includes contributions from researchers, academics, and professionals from around the world.

This book gathers a selection of peer-reviewed papers presented at the Tiangong-2 Data Utilization Conference, which was held in Beijing, China, in December 2018. As the first space laboratory in China, Tiangong-2 carries 3 new types of remote sensing payloads - the Wide-band Imaging Spectrometer (WIS), Three-dimensional Imaging Microwave Altimeter (TIMA), and Multi-band Ultraviolet Edge Imaging Spectrometer (MUEIS) - for observing the Earth. The spectrum of the WIS covers 18 bands, from visible to thermal infrared, with a swath of 300km. The TIMA is the first-ever system to use interferometric imaging radar altimeter (InIRA) technology to measure sea surface height and land topography at near-nadir angles with a wide swath. In turn, the MUEIS is the world's first large-field atmospheric detector capable of quasi-synchronously detecting the characteristics of ultraviolet limb radiation in the middle atmosphere. The Earth observation data obtained by Tiangong-2 has attracted many research groups and been applied in such diverse areas as land resources, water resources, climate change, environmental monitoring, agriculture, forestry, ecology, oceanography, meteorology and so on. The main subjects considered in this proceedings volume include: payload design, data processing, data service and application. It also provides a comprehensive introduction to the research results gleaned by engineers, researchers and scientists throughout the lifecycle of the Tiangong-2 Earth observation data, which will improve the payload development and enhance remote sensing data applications.

This book provides a comprehensive introduction to the application of artificial intelligence in social computing, from fundamental data processing to advanced social network computing. To broaden readers' understanding of the topics addressed, it includes extensive data and a large number of charts and references, covering theories, techniques and applications. It particularly focuses on data collection, data mining, artificial intelligence algorithms in social computing, and several key applications of social computing application, and also discusses network propagation mechanisms and dynamic analysis, which provide useful insights into how information is disseminated in online social networks. This book is intended for readers with a basic knowledge of advanced mathematics and computer science.

This book brings together two major trends: data science and blockchains. It is one of the first books to systematically cover the analytics aspects of blockchains, with the goal of linking traditional data mining research communities with novel data sources. Data science and big data technologies can be considered cornerstones of the data-driven digital transformation of organizations and society. The concept of blockchain is predicted to enable and spark transformation on par with that associated with the invention of the Internet. Cryptocurrencies are the first successful use case of highly distributed blockchains, like the world wide web was to the Internet. The book takes the reader through basic data exploration topics, proceeding systematically, method by method, through supervised and unsupervised learning approaches and information visualization techniques, all the way to understanding the blockchain data from the network science perspective. Chapters introduce the cryptocurrency blockchain data model and methods to explore it using structured query language, association rules, clustering, classification, visualization, and network science. Each chapter introduces basic concepts, presents examples with real cryptocurrency blockchain data and offers exercises and questions for further discussion. Such an approach intends to serve as a good starting point for undergraduate and graduate students to learn data science topics using cryptocurrency blockchain examples. It is also aimed at researchers and analysts who already possess good analytical and data skills, but who do not yet have the specific knowledge to tackle analytic questions about blockchain transactions. The readers improve their knowledge about the essential data science techniques in order to turn mere transactional information into social, economic, and business insights.

This book features research papers presented at the International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2020) held at the University of Engineering & Management, Kolkata, India, during July 2020. The book is organized in three volumes and includes high-quality research work by academicians and industrial experts in the field of computing and communication, including full-length papers, research-in-progress papers and case studies related to all the areas of data mining, machine learning, Internet of things (IoT) and information security.

This two volume set (CCIS 1451 and 1452) constitutes the refereed proceedings of the 7th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2021 held in Taiyuan, China, in September 2021.The 81 papers presented in these two volumes were carefully reviewed and selected from 256 submissions. The papers are organized in topical sections on big data management and applications; social media and recommendation systems; infrastructure for data science; basic theory and techniques for data science; machine learning for data science; multimedia data management and analysis; social media and recommendation systems; data security and privacy; applications of data science; education research, methods and materials for data science and engineering; research demo.

This book gathers a collection of high-quality peer-reviewed research papers presented at the 2nd International Conference on Data and Information Sciences (ICDIS 2019), held at Raja Balwant Singh Engineering Technical Campus, Agra, India, on March 29-30, 2019. In chapters written by leading researchers, developers, and practitioner from academia and industry, it covers virtually all aspects of computational sciences and information security, including central topics like artificial intelligence, cloud computing, and big data. Highlighting the latest developments and technical solutions, it will show readers from the computer industry how to capitalize on key advances in next-generation computer and communication technology.

This book proposes new control and protection schemes to improve the overall stability and security of future wide-area power systems. It focuses on the high penetration levels of renewable energy sources and distributed generation, particularly with the trend towards smart grids. The control methods discussed can improve the overall stability in normal and abnormal operation conditions, while the protection methods presented can be used to ensure the secure operation of systems under most severe contingencies. Presenting stability, security, and protection methods for power systems in one concise volume, this book takes the reader on a journey from concepts and fundamentals to the latest and future trends in each topic covered, making it an informative and intriguing read for researchers, graduate students, and practitioners alike.

This monograph discusses software reuse and how it can be applied at different stages of the software development process, on different types of data and at different levels of granularity. Several challenging hypotheses are analyzed and confronted using novel data-driven methodologies, in order to solve problems in requirements elicitation and specification extraction, software design and implementation, as well as software quality assurance. The book is accompanied by a number of tools, libraries and working prototypes in order to practically illustrate how the phases of the software engineering life cycle can benefit from unlocking the potential of data. Software engineering researchers, experts, and practitioners can benefit from the various methodologies presented and can better understand how knowledge extracted from software data residing in various repositories can be combined and used to enable effective decision making and save considerable time and effort through software reuse. Mining Software Engineering Data for Software Reuse can also prove handy for graduate-level students in software engineering.

In the statistical domain, certain topics have received considerable attention during the last decade or so, necessitated by the growth and evolution of data and theoretical challenges. This growth has invariably been accompanied by computational advancement, which has presented end users as well as researchers with the necessary opportunities to handle data and implement modelling solutions for statistical purposes. Showcasing the interplay among a variety of disciplines, this book offers pioneering theoretical and applied solutions to practice-oriented problems. As a carefully curated collection of prominent international thought leaders, it fosters collaboration between statisticians and biostatisticians and provides an array of thought processes and tools to its readers. The book thereby creates an understanding and appreciation of recent developments as well as an implementation of these contributions within the broader framework of both academia and industry. Computational and Methodological Statistics and Biostatistics is composed of three main themes: * Recent developments in theory and applications of statistical distributions;* Recent developments in supervised and unsupervised modelling;* Recent developments in biostatistics; and also features programming code and accompanying algorithms to enable readers to replicate and implement methodologies. Therefore, this monograph provides a concise point of reference for a variety of current trends and topics within the statistical domain. With interdisciplinary appeal, it will be useful to researchers, graduate students, and practitioners in statistics, biostatistics, clinical methodology, geology, data science, and actuarial science, amongst others.

Making use of data is not anymore a niche project but central to almost every project. With access to massive compute resources and vast amounts of data, it seems at least in principle possible to solve any problem. However, successful data science projects result from the intelligent application of: human intuition in combination with computational power; sound background knowledge with computer-aided modelling; and critical reflection of the obtained insights and results. Substantially updating the previous edition, then entitled Guide to Intelligent Data Analysis, this core textbook continues to provide a hands-on instructional approach to many data science techniques, and explains how these are used to solve real world problems. The work balances the practical aspects of applying and using data science techniques with the theoretical and algorithmic underpinnings from mathematics and statistics. Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring; includes numerous examples using the open source KNIME Analytics Platform, together with an introductory appendix; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; integrates illustrations and case-study-style examples to support pedagogical exposition; supplies further tools and information at an associated website. This practical and systematic textbook/reference is a "need-to-have" tool for graduate and advanced undergraduate students and essential reading for all professionals who face data science problems. Moreover, it is a "need to use, need to keep" resource following one's exploration of the subject.

Computational intelligence is a well-established paradigm, where new theories with a sound biological understanding have been evolving. The current experimental systems have many of the characteristics of biological computers (brains in other words) and are beginning to be built to perform a variety of tasks that are difficult or impossible to do with conventional computers. As evident, the ultimate achievement in this field would be to mimic or exceed human cognitive capabilities including reasoning, recognition, creativity, emotions, understanding, learning and so on. This book comprising of 17 chapters offers a step-by-step introduction (in a chronological order) to the various modern computational intelligence tools used in practical problem solving. Staring with different search techniques including informed and uninformed search, heuristic search, minmax, alpha-beta pruning methods, evolutionary algorithms and swarm intelligent techniques; the authors illustrate the design of knowledge-based systems and advanced expert systems, which incorporate uncertainty and fuzziness. Machine learning algorithms including decision trees and artificial neural networks are presented and finally the fundamentals of hybrid intelligent systems are also depicted. Academics, scientists as well as engineers engaged in research, development and application of computational intelligence techniques, machine learning and data mining would find the comprehensive coverage of this book invaluable.

This proceedings is focused on the emerging concept of Collaborative Innovation Networks (COINs). COINs are at the core of collaborative knowledge networks, distributed communities taking advantage of the wide connectivity and the support of communication technologies, spanning beyond the organizational perimeter of companies on a global scale. The book presents the refereed conference papers from the 7th International Conference on COINs, October 8-9, 2019, in Warsaw, Poland. It includes papers for both application areas of COINs, (1) optimizing organizational creativity and performance, and (2) discovering and predicting new trends by identifying COINs on the Web through online social media analysis. Papers at COINs19 combine a wide range of interdisciplinary fields such as social network analysis, group dynamics, design and visualization, information systems and the psychology and sociality of collaboration, and intercultural analysis through the lens of online social media. They will cover most recent advances in areas from leadership and collaboration, trend prediction and data mining, to social competence and Internet communication.

This volume LNCS 12925 constitutes the papers of the 23rd International Conference on Big Data Analytics and Knowledge Discovery, held in September 2021. Due to COVID-19 pandemic it was held virtually. The 12 full papers presented together with 15 short papers in this volume were carefully reviewed and selected from a total of 71 submissions. The papers reflect a wide range of topics in the field of data integration, data warehousing, data analytics, and recently big data analytics, in a broad sense. The main objectives of this event are to explore, disseminate, and exchange knowledge in these fields.