Daniel Way

Francisco Herrera

Venom's back and has popped up near the Arctic Circle, leaving a trail of dead bodies in his wake in this big-budget, sci-fi/horror roller coaster ride. Now able to change human hosts in the blink of an eye, he's on the run from mysterious clandestine forces, each with their own agenda for the deadly, elusive alien.

Shiver

Mikel Galar

Salvador García

Ronaldo C. Prati

Alberto Fernández

Bartosz Krawczyk

This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.

Learning from Imbalanced Data Sets

Julián Luengo

Diego García-Gil

Sergio Ramírez-Gallego

This book offers a comprehensible overview of Big Data Preprocessing, which includes a formal description of each problem. It also focuses on the most relevant proposed solutions. This book illustrates actual implementations of algorithms that helps the reader deal with these problems. This book stresses the gap that exists between big, raw data and the requirements of quality data that businesses are demanding. This is called Smart Data, and to achieve Smart Data the preprocessing is a key step, where the imperfections, integration tasks and other processes are carried out to eliminate superfluous information. The authors present the concept of Smart Data through data preprocessing in Big Data scenarios and connect it with the emerging paradigms of IoT and edge computing, where the end points generate Smart Data without completely relying on the cloud. Finally, this book provides some novel areas of study that are gathering a deeper attention on the Big Data preprocessing. Specifically, it considers the relation with Deep Learning (as of a technique that also relies in large volumes of data), the difficulty of finding the appropriate selection and concatenation of preprocessing techniques applied and some other open problems. Practitioners and data scientists who work in this field, and want to introduce themselves to preprocessing in large data volume scenarios will want to purchase this book. Researchers that work in this field, who want to know which algorithms are currently implemented to help their investigations, may also be interested in this book.

Enabling Smart Data

Big Data Preprocessing

Luis Martínez

Rosa M. Rodriguez

This book examines one of the more common and wide-spread methodologies to deal with uncertainty in real-world decision making problems, the computing with words paradigm, and the fuzzy linguistic approach. The 2-tuple linguistic model is the most popular methodology for computing with words (CWW), because it improves the accuracy of the linguistic computations and keeps the interpretability of the results. The authors provide a thorough review of the specialized literature in CWW and highlight the rapid growth and applicability of the 2-tuple linguistic model. They explore the foundations and methodologies for CWW in complex frameworks and extensions. The book introduces the software FLINTSTONES that provides tools for solving linguistic decision problems based on the 2-tuple linguistic model. Professionals and researchers working in the field of classification or fuzzy sets and systems will find The 2-tuple Linguistic Model: Computing with Words in Decision Making a valuable resource. Undergraduate and postdoctoral students studying computer science and statistics will also find this book a useful study guide.

Computing with Words in Decision Making

The 2-tuple Linguistic Model

Lorna Uden

Juan Manuel Corchado Rodríguez

Javier Bajo Pérez

The seventh International Conference on Knowledge Management in Organizations (KMO) brings together researchers and developers from industry and the academic world to report on the latest scientific and technical advances on knowledge management in organisations. KMO 2012 provides an international forum for authors to present and discuss research focused on the role of knowledge management for innovative services in industries, to shed light on recent advances in cloud computing for KM as well as to identify future directions for researching the role of knowledge management in service innovation and how cloud computing can be used to address many of the issues currently facing KM in academia and industrial sectors. The conference took place at Salamanca in Spain on the 11th-13th July in 2012.

7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing

Kenji Matsui

Sara Rodríguez-González

This book features the outcomes of the 16th International Conference on Distributed Computing and Artificial Intelligence 2019 (DCAI 2019), which is a forum to present applications of innovative techniques for studying and solving complex problems in artificial intelligence and computing. The exchange of ideas between scientists and technicians from both the academic and industrial sectors is essential to facilitate the development of systems that can meet the ever-increasing demands of today’s society. This book brings together lessons learned, current work and promising future trends associated with distributed computing, artificial intelligence and their application to provide efficient solutions to real-world problems. The book includes 29 high-quality and diverse contributions in established and emerging areas of research presented at the symposium organized by the Osaka Institute of Technology, Hiroshima University, University of Granada and University of Salamanca, which was held in Ávila, Spain, from 26th–28th June 2019

Distributed Computing and Artificial Intelligence, 16th International Conference

ENSEÑAR LÉXICO EN EL AULA DE ESPAÑOL. EL PODER DE LAS PALABRAS

Moonis Ali

Colin Fyfe

Nicolás García-Pedrajas

José Manuel Benítez Sánchez

The presentations of theinvitedspeakersandauthorsmainlyfocusedondevelopingandstudyingnew methods to cope with the problems posed by real-life applications of arti?cial intelligence.Paperspresentedinthetwentythirdconferenceintheseriescovered theories as well as applications of intelligent systems in solving complex real-life problems. We received 297 papers for the main track, selecting 119 of them with the highest quality standards. Each paper was revised by at least three members of the Program Committee.

23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Cordoba, Spain, June 1-4, 2010, Proceedings, Part I

Trends in Applied Intelligent Systems

Yasmin B. Kafai

William A. Sandoval

Noel Enyedy

Althea Scott Nixon

More than a decade has passed since the First International Conference of the Learning Sciences (ICLS) was held at Northwestern University in 1991. The conference has now become an established place for researchers to gather. The 2004 meeting is the first under the official sponsorship of the International Society of the Learning Sciences (ISLS). The theme of this conference is "Embracing Diversity in the Learning Sciences." As a field, the learning sciences have always drawn from a diverse set of disciplines to study learning in an array of settings. Psychology, cognitive science, anthropology, and artificial intelligence have all contributed to the development of methodologies to study learning in schools, museums, and organizations. As the field grows, however, it increasingly recognizes the challenges to studying and changing learning environments across levels in complex social systems. This demands attention to new kinds of diversity in who, what, and how we study; and to the issues raised to develop coherent accounts of how learning occurs. Ranging from schools to families, and across all levels of formal schooling from pre-school through higher education, this ideology can be supported in a multitude of social contexts. The papers in these conference proceedings respond to the call.

Proceedings of the Sixth International Conference of the Learning Sciences

Embracing Diversity in the Learning Sciences

Paulo Novais

Pablo Chamoso

Gianni Vercelli

Josep L. Larriba-Pey

This book includes the outcomes of the 11th International Symposium on Ambient Intelligence (ISAmI 2020). The 11th International Symposium on Ambient Intelligence is hosted by the University of L’Aquila and is going to be held in L’Aquila (Italy). Initially planned on the 17th to the 19th of June 2020, it was postponed to the 7th to the 9th of October 2020, due to the COVID-19 outbreak.

11th International Symposium on Ambient Intelligence

Ambient Intelligence – Software and Applications

Ajith Abraham

Aboul-Ella Hassanien

Foundations of Computational Intelligence Volume 2: Approximation Reasoning: Theoretical Foundations and Applications Human reasoning usually is very approximate and involves various types of - certainties. Approximate reasoning is the computational modelling of any part of the process used by humans to reason about natural phenomena or to solve real world problems. The scope of this book includes fuzzy sets, Dempster-Shafer theory, multi-valued logic, probability, random sets, and rough set, near set and hybrid intelligent systems. Besides research articles and expository papers on t- ory and algorithms of approximation reasoning, papers on numerical experiments and real world applications were also encouraged. This Volume comprises of 12 chapters including an overview chapter providing an up-to-date and state-of-the research on the applications of Computational Intelligence techniques for - proximation reasoning. The Volume is divided into 2 parts: Part-I: Approximate Reasoning – Theoretical Foundations Part-II: Approximate Reasoning – Success Stories and Real World Applications Part I on Approximate Reasoning – Theoretical Foundations contains four ch- ters that describe several approaches of fuzzy and Para consistent annotated logic approximation reasoning. In Chapter 1, “Fuzzy Sets, Near Sets, and Rough Sets for Your Computational Intelligence Toolbox” by Peters considers how a user might utilize fuzzy sets, near sets, and rough sets, taken separately or taken together in hybridizations as part of a computational intelligence toolbox. In multi-criteria decision making, it is necessary to aggregate (combine) utility values corresponding to several criteria (parameters).

Approximate Reasoning

Foundations of Computational Intelligence Volume 2

Chris Cornelis

Sebastián Ventura

Rafael Bello

Amelia Zafra

Dánel Sánchez-Tarragó

Sarah Vluymans

This book provides a general overview of multiple instance learning (MIL), defining the framework and covering the central paradigms. The authors discuss the most important algorithms for MIL such as classification, regression and clustering. With a focus on classification, a taxonomy is set and the most relevant proposals are specified. Efficient algorithms are developed to discover relevant information when working with uncertainty. Key representative applications are included. This book carries out a study of the key related fields of distance metrics and alternative hypothesis. Chapters examine new and developing aspects of MIL such as data reduction for multi-instance problems and imbalanced MIL data. Class imbalance for multi-instance problems is defined at the bag level, a type of representation that utilizes ambiguity due to the fact that bag labels are available, but the labels of the individual instances are not defined. Additionally, multiple instance multiple label learning is explored. This learning framework introduces flexibility and ambiguity in the object representation providing a natural formulation for representing complicated objects. Thus, an object is represented by a bag of instances and is allowed to have associated multiple class labels simultaneously. This book is suitable for developers and engineers working to apply MIL techniques to solve a variety of real-world problems. It is also useful for researchers or students seeking a thorough overview of MIL literature, methods, and tools.

Foundations and Algorithms

Multiple Instance Learning

Humberto Bustince

Javier Montero

This carefully edited book presents an up-to-date state of current research in the use of fuzzy sets and their extensions. It pays particular attention to foundation issues and to their application to four important areas where fuzzy sets are seen to be an important tool for modeling and solving problems. The book’s 34 chapters deal with the subject with clarity and effectiveness. They include four review papers introducing some non-standard representations

Intelligent Systems from Decision Making to Data Mining, Web Intelligence and Computer Vision

Fuzzy Sets and Their Extensions: Representation, Aggregation and Models

Annotation The three volume set LNAI 6096, LNAI 6097, and LNAI 6098 constitutes the thoroughly refereed conference proceedings of the 23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligend Systems, IEA/AIE 2010, held in Cordoba, Spain, in June 2010. The total of 119 papers selected for the proceedings were carefully reviewed and selected from 297 submissions.

23rd International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2010, Cordoba, Spain, June 1-4, 2010, Proceedings, Part II

Alicia Troncoso

Sergio Damas

Rosana Montes

Sergio Alonso

Óscar Cordón

Antonio González

​This book constitutes the refereed proceedings of the 18th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2018, held in Granada, Spain, in October 2018. The 36 full papers presented were carefully selected from 240 submissions. The Conference of the Spanish Association of Artificial Intelligence (CAEPIA) is a biennial forum open to researchers from all over the world to present and discuss their latest scientific and technological advances in Antificial Intelligence (AI). Authors are kindly requested to submit unpublished original papers describing relevant research on AI issues from all points of view: formal, methodological, technical or applied.

18th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2018, Granada, Spain, October 23–26, 2018, Proceedings

Advances in Artificial Intelligence

Data Preprocessing for Data Mining addresses one of the most important issues within the well-known Knowledge Discovery from Data process. Data directly taken from the source will likely have inconsistencies, errors or most importantly, it is not ready to be considered for a data mining process. Furthermore, the increasing amount of data in recent science, industry and business applications, calls to the requirement of more complex tools to analyze it. Thanks to data preprocessing, it is possible to convert the impossible into possible, adapting the data to fulfill the input demands of each data mining algorithm. Data preprocessing includes the data reduction techniques, which aim at reducing the complexity of the data, detecting or removing irrelevant and noisy elements from the data. This book is intended to review the tasks that fill the gap between the data acquisition from the source and the data mining process. A comprehensive look from a practical point of view, including basic concepts and surveying the techniques proposed in the specialized literature, is given.Each chapter is a stand-alone guide to a particular data preprocessing topic, from basic concepts and detailed descriptions of classical algorithms, to an incursion of an exhaustive catalog of recent developments. The in-depth technical descriptions make this book suitable for technical professionals, researchers, senior undergraduate and graduate students in data science, computer science and engineering.

Data Preprocessing in Data Mining

Take a glimpse on how Francisco Herrera, renowned comic book (Marvel, Soleil, Aspen), movie concept (DreamWorks) and brand (Warner, Disney) artist, takes you through his process in creating interesting and peculiar characters. Including basic tips for character design and a sketchbook showing his cartoon love.

Francisco Herrera's the Magic Box

Francisco Herrera

About

Books

Join the club