Intelligent systems research is a multidisciplinary field that focuses on the development of systems that can perceive, reason, and act autonomously. This can include areas such as machine learning, artificial intelligence, natural language processing, and robotics. The goal of intelligent systems research is to develop systems that can understand, learn from, and adapt to their environment, to perform tasks that would typically require human intelligence. Business and innovation research is an interdisciplinary field that examines how organizations can create, develop, and implement new ideas, products, and services. This can include areas such as organizational behaviour, strategic management, and marketing. The goal of business and innovation research is to understand how organizations can foster an environment that encourages creativity and innovation, and how they can develop and implement new ideas in a way that leads to success. Intelligent Systems, Business and Innovation Research is a research area that brings together these two fields to study the use of intelligent systems and technologies in the business context to drive innovation, improve operational efficiency and effectiveness of the organization. The book aims to understand how intelligent systems can be used in business applications, how to design and implement them, how to manage the associated challenges, and how to leverage them to foster innovation, create new business models, and gain competitive advantage. It offers guidance on how to navigate potential conflicts and challenges that may arise during multidisciplinary research in areas such as Industry 4.0, Internet of Things, modern machine learning, software agent applications, and data science. The book focuses on the various fields in which intelligent systems play a critical role in enabling the development of advanced technologies that can perform tasks that would typically require human intelligence. For example, in smart/control systems, intelligent algorithms can be used to optimize the performance of machines and devices, while in cyber security, they can be used to protect networks and data from cyber-attacks. In bioinformatics, intelligent systems can be used to analyse large amounts of biological data, while in virtual reality and robotics, they can be used to create realistic and responsive simulations and automatons. Additionally, the book also highlights the rapidly advancing theoretical foundations of fuzzy sets, mathematical logic, and non-classical logic. These are important theoretical frameworks for the development of intelligent systems, as they provide the foundation for the representation and manipulation of uncertainty, complexity, and imprecision. These theoretical foundations are essential in the development of intelligent systems that can make decisions and perform tasks in uncertain, complex, and dynamic environments.

This five-volume set clearly manifests the great significance of these key technologies for the new economies of the new millennium. The discussions provide a wealth of practical ideas intended to foster innovation in thought and, consequently, in the further development of technology. Together, they comprise a significant and uniquely comprehensive reference source for research workers, practitioners, computer scientists, academics, students, and others on the international scene for years to come.

This book constitutes the proceedings of the XVI Multidisciplinary International Congress on Science and Technology (CIT 2021), held in Quito, Ecuador, on June 14-18, 2021, proudly organized by Universidad de las Fuerzas Armadas ESPE in collaboration with GDEON. CIT is an international event with a multidisciplinary approach that promotes the dissemination of advances in science and technology research through the presentation of keynote conferences. In CIT, theoretical, technical, or application works that are research products are presented to discuss and debate ideas, experiences, and challenges. Presenting high-quality, peer-reviewed papers, the book discusses the following topics: Artificial Intelligence Computational Modeling Data Communications Defense Engineering Innovation, Technology, and Society Managing Technology & Sustained Innovation, and Business Development Security and Cryptography Software Engineering.

Data science is an emerging field and innovations in it need to be explored for the success of society 5.0. This book not only focuses on the practical applications of data science to achieve computational excellence, but also digs deep into the issues and implications of intelligent systems. This book highlights innovations in data science to achieve computational excellence that can optimize performance of smart applications. The book focuses on methodologies, framework, design issues, tools, architectures, and technologies necessary to develop and understand data science and its emerging applications in the present era. Data Science and Innovations for Intelligent Systems: Computational Excellence and Society 5.0 is useful for the research community, start-up entrepreneurs, academicians, data-centered industries, and professeurs who are interested in exploring innovations in varied applications and the areas of data science.

The papers in this volume are the refereed papers presented at AI-2015, the Thirty-fifth SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, held in Cambridge in December 2015 in both the technical and the application streams. They present new and innovative developments and applications, divided into technical stream sections on Knowledge Discovery and Data Mining, Machine Learning and Knowledge Acquisition, and AI in Action, followed by application stream sections on Applications of Genetic Algorithms, Applications of Intelligent Agents and Evolutionary Techniques, and AI Applications. The volume also includes the text of short papers presented as posters at the conference. This is the thirty-second volume in the Research and Development in Intelligent Systems series, which also incorporates the twenty-third volume in the Applications and Innovations in Intelligent Systems series. These series are essential reading for those who wish to keep up to date with developments in this important field.

The most common document formalisation for text classi?cation is the vector space model founded on the bag of words/phrases representation. The main advantage of the vector space model is that it can readily be employed by classi?cation - gorithms. However, the bag of words/phrases representation is suited to capturing only word/phrase frequency; structural and semantic information is ignored. It has been established that structural information plays an important role in classi?cation accuracy [14]. An alternative to the bag of words/phrases representation is a graph based rep- sentation, which intuitively possesses much more expressive power. However, this representation introduces an additional level of complexity in that the calculation of the similarity between two graphs is signi?cantly more computationally expensive than between two vectors (see for example [16]). Some work (see for example [12]) has been done on hybrid representations to capture both structural elements (- ing the graph model) and signi?cant features using the vector model. However the computational resources required to process this hybrid model are still extensive.