Alberto Fernández – författare

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.

This book constitutes the proceedings of the 19th International Symposium on Intelligent Data Analysis, IDA 2021, which was planned to take place in Porto, Portugal. Due to the COVID-19 pandemic the conference was held online during April 26-28, 2021.The 35 papers included in this book were carefully reviewed and selected from 113 submissions.

This book constitutes the proceedings of the workshops co-located with the 20th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2022, held in L'Aquila, Italy, in July 2022.The total of 25 full papers presented in this volume were carefully reviewed and selected from 39 submissions.The papers in this volume stem from the following meetings: Workshop on Artificial Intelligence for Industry (AI4Industry); Workshop on Adaptive Smart areaS and Intelligent Agents (ASSIA); Workshop on Character Computing (C2); Workshop on Deep Learning Applications (DeLA); Workshop on Decision Support, Recommendation, and Persuasion in Artificial Intelligence (DeRePAI); Workshop on Multi-agent based Applications for Modern Energy Markets, Smart Grids and Future Power Systems (MASGES).

This book constitutes the refereed proceedings of the International Workshops on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2025, held in Lille, France, during June 25–27, 2025.The 17 revised full papers presented in this book were carefully reviewed and selected from 32 submissions.PAAMS 2025 presents the following five workshops:Workshop on Adaptive Smart areaS and Intelligent Agents (ASSIA)Workshop on Deep Learning Applications (DeLA)Workshop on Digital Twinning for training purposes in Industry 4.0 (Twin4Train)

This book constitutes the refereed proceedings of the 11 workshops co-located with the 16th International Conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2018, held in Toledo, Spain, in June 2018.The 47 full papers presented were carefully reviewed and selected from 72 submissions. The volume presents the papers that have been accepted for the following workshops: Workshop on Agents and Multi-agent Systems for AAL and e-HEALTH; Workshop on Agent based Applications for Air Transport; Workshop on Agent-based Artificial Markets Computational Economics; Workshop on Agent-Based Solutions for Manufacturing and Supply Chain; Workshop on MAS for Complex Networks and Social Computation; Workshop on Intelligent Systems and Context Information Fusion; Workshop on Multi-agent based Applications for Energy Markets, Smart Grids and Sustainable Energy Systems; Workshop on Multiagent System based Learning Environments; Workshop on Smart Cities and Intelligent Agents; Workshop on Swarm Intelligence and Swarm Robotics; Workshop on Multi-Agent Systems and Simulation.

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.

This book presents the outcomes of the 15th International Conference on Distributed Computing and Artificial Intelligence, held in Toledo (Spain) from 20th to 22nd June 2018 and hosted by the UCLM, and which brought together researchers and developers from industry, education and the academic world to report on the latest scientific research, technical advances and methodologies. Highlighting multi-disciplinary and transversal aspects, the book focuses on the conferences Special Sessions, including Advances in Demand Response and Renewable Energy Sources in Smart Grids (ADRESS); AI- Driven Methods for Multimodal Networks and Processes Modeling (AIMPM); Social Modelling of Ambient Intelligence in Large Facilities (SMAILF); Communications, Electronics and Signal Processing (CESP); Complexity in Natural and Formal Languages (CNFL); and Web and Social Media Mining (WASMM).

iURBAN: Intelligent Urban Energy Tool introduces an urban energy tool integrating different ICT energy management systems (both hardware and software) in two European cities, providing useful data to a novel decision support system that makes available the necessary parameters for the generation and further operation of associated business models. The business models contribute at a global level to efficiently manage and distribute the energy produced and consumed at a local level (city or neighbourhood), incorporating behavioural aspects of the users into the software platform and in general prosumers. iURBAN integrates a smart Decision Support System (smartDSS) that collects real-time or near real-time data, aggregates, analyses and suggest actions of energy consumption and production from different buildings, renewable energy production resources, combined heat and power plants, electric vehicles (EV) charge stations, storage systems, sensors and actuators. The consumption and production data is collected via a heterogeneous data communication protocols and networks. The iURBAN smartDSS through a Local Decision Support System allows the citizens to analyse the consumptions and productions that they are generating, receive information about CO2 savings, advises in demand response and the possibility to participate actively in the energy market. Whilst, through a Centralised Decision Support System allow to utilities, ESCOs, municipalities or other authorised third parties to: Get a continuous snapshot of city energy consumption and productionManage energy consumption and productionForecasting of energy consumptionPlanning of new energy "producers" for the future needs of the cityVisualise, analyse and take decisions of all the end points that are consuming or producing energy in a city level, permitting them to forecast and planning renewable power generation available in the city.