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. The papers were organized in topical sections named: modeling with neural networks; modeling with statistical learning; modeling language and graphs; and modeling special data formats.

Identifying Emerging Trends in Technological Innovation Doctoral programs in science and engineering are important sources of innovative ideas and techniques that might lead to new products and technological innovation. Certainly most PhD students are not experienced researchers and are in the process of learning how to do research. Nevertheless, a number of empiric studies also show that a high number of technological innovation ideas are produced in the early careers of researchers. The combination of the eagerness to try new approaches and directions of young doctoral students with the experience and broad knowledge of their supervisors is likely to result in an important pool of innovation potential. The DoCEIS doctoral conference on Computing, Electrical and Industrial En- neering aims at creating a space for sharing and discussing ideas and results from doctoral research in these inter-related areas of engineering. Innovative ideas and hypotheses can be better enhanced when presented and discussed in an encouraging and open environment. DoCEIS aims to provide such an environment, releasing PhD students from the pressure of presenting their propositions in more formal contexts.

Identifying Emerging Trends in Technological Innovation Doctoral programs in science and engineering are important sources of innovative ideas and techniques that might lead to new products and technological innovation. Certainly most PhD students are not experienced researchers and are in the process of learning how to do research. Nevertheless, a number of empiric studies also show that a high number of technological innovation ideas are produced in the early careers of researchers. The combination of the eagerness to try new approaches and directions of young doctoral students with the experience and broad knowledge of their supervisors is likely to result in an important pool of innovation potential. The DoCEIS doctoral conference on Computing, Electrical and Industrial En- neering aims at creating a space for sharing and discussing ideas and results from doctoral research in these inter-related areas of engineering. Innovative ideas and hypotheses can be better enhanced when presented and discussed in an encouraging and open environment. DoCEIS aims to provide such an environment, releasing PhD students from the pressure of presenting their propositions in more formal contexts.

This book constitutes the refereed proceedings of the 19th International Conference on Artificial Intelligence in Medicine, AIME 2021, held as a virtual event, in June 2021. The 28 full papers presented together with 30 short papers were selected from 138 submissions. The papers are grouped in topical sections on image analysis; predictive modelling; temporal data analysis; unsupervised learning; planning and decision support; deep learning; natural language processing; and knowledge representation and rule mining.

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

The three volume set LNAI 9284, 9285, and 9286 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2015, held in Porto, Portugal, in September 2015. The 131 papers presented in these proceedings were carefully reviewed and selected from a total of 483 submissions. These include 89 research papers, 11 industrial papers, 14 nectar papers, and 17 demo papers. They were organized in topical sections named: classification, regression and supervised learning; clustering and unsupervised learning; data preprocessing; data streams and online learning; deep learning; distance and metric learning; large scale learning and big data; matrix and tensor analysis; pattern and sequence mining; preference learning and label ranking; probabilistic, statistical, and graphical approaches; rich data; and social and graphs. Part III is structured in industrial track, nectar track, and demo track.

Instructional technologies used to be optional and supplemental pedagogical tools until the global health crisis of 2020 compelled education systems to rely on digital devices and services to guarantee academic continuity. Suddenly, the contemporary principles and practices utilized in delivering health education curricula were insufficient and ineffective. Acknowledging the vital role of technology in shaping the future of education, there is now a greater demand to foster innovative interventions and continuous improvement in strategies, methodologies, and systems to empower learners, educators, and leaders in the digital age. Instructional Technologies in Health Education and Allied Disciplines provides comprehensive coverage of innovative methods and strategies to produce the next generation of health professionals. The book lays the groundwork for an implementable teaching and learning model that facilitates basic knowledge acquisition, enhances perceptual variation, improves skill coordination, and develops a scientific and technological mindset. Covering key topics such as gamification, telehealth, and robotics, this reference work is ideal for healthcare professionals, nurses, administrators, researchers, academicians, scholars, practitioners, instructors, and students.

Swarm intelligence techniques are among the most talented and successful approaches that gained a lot of popularity over the past two decades. They are inspired by animal behaviour (such as ants, termites and bees) and insect conduct (by swarm, herd, flock and shoal phenomena) in order to develop these techniques in terms of mimicking their problem/solution abilities. These techniques provide good approximate solutions in a reasonable time for solving hard and complex problems in many engineering fields. This book is intended for researchers, engineers and graduate students with interests in swarm intelligence algorithms and their applications. It discusses and describes the various swarm intelligence techniques as useful tools for solving practical problems, such as urban traffic optimisation, electrical engineering problems and the design of integrated analogue circuits.

Due to the current paradigm shift from traditional teaching to a mixed model with the inclusion of e-learning strategies, reforms in clinical education models are necessary and must carefully consider the socio-professional changes needed to support such efforts. Further study of the implementation of clinical and virtual reality education simulators in education, the irreplaceable role of teaching in the design of advanced roles for health professionals, and the role of education in the continuing professional development are all necessary for the future of successful allied health professional education. The Handbook of Research on Improving Allied Health Professions Education: Advancing Clinical Training and Interdisciplinary Translational Research discusses a range of important topics related to medical and health professions education and clarifies purposes, processes, and future priorities in introducing changes in the educational system. Covering topics such as new technologies and patient safety, this major reference work is ideal for researchers, practitioners, academicians, industry professionals, instructors, and students.

O autor elaborou poesia em oitava rima ao modo camoniano contando a Historia de Portugal desde D. Sebastiao aos nossos tempos.