A Survey of Adaptive Resonance Theory Neural Network Models for Engineering Applications

This survey samples from the ever-growing family of adaptive resonance theory (ART) neural network models used to perform the three primary machine learning modalities, namely, unsupervised, supervised and reinforcement learning. It comprises a representative list from classic to modern ART models, thereby painting a general picture of the architectures developed by researchers over the past 30 years. The learning dynamics of these ART models are briefly described, and their distinctive characteristics such as code representation, long-term memory and corresponding geometric interpretation are discussed. Useful engineering properties of ART (speed, configurability, explainability, parallelization and hardware implementation) are examined along with current challenges. Finally, a compilation of online software libraries is provided. It is expected that this overview will be helpful to new and seasoned ART researchers

Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Uncertainty Management of Intelligent Feature Selection in Wireless Sensor Networks

Wireless sensor networks (WSN) are envisioned to revolutionize the paradigm of monitoring complex real-world systems at a very high resolution. However, the deployment of a large number of unattended sensor nodes in hostile environments, frequent changes of environment dynamics, and severe resource constraints pose uncertainties and limit the potential use of WSN in complex real-world applications. Although uncertainty management in Artificial Intelligence (AI) is well developed and well investigated, its implications in wireless sensor environments are inadequately addressed. This dissertation addresses uncertainty management issues of spatio-temporal patterns generated from sensor data. It provides a framework for characterizing spatio-temporal pattern in WSN. Using rough set theory and temporal reasoning a novel formalism has been developed to characterize and quantify the uncertainties in predicting spatio-temporal patterns from sensor data. This research also uncovers the trade-off among the uncertainty measures, which can be used to develop a multi-objective optimization model for real-time decision making in sensor data aggregation and samplin

Data mining by means of generalized patterns

The thesis is mainly focused on the study and the application of pattern discovery algorithms that aggregate database knowledge to discover and exploit valuable correlations, hidden in the analyzed data, at different abstraction levels. The aim of the research effort described in this work is two-fold: the discovery of associations, in the form of generalized patterns, from large data collections and the inference of semantic models, i.e., taxonomies and ontologies, suitable for driving the mining proces

Técnica de mineración de datos: una revisión de la literatura

The purpose of this study was to conduct a literature review on data mining (DM) technique in the LILACS and SciELO databases and specialized books. A broad data literature search using the words data mining (in English) and/or mineração de dados (in Portuguese) and limited to publications between 1999 and 2008, was conducted. The exclusion criteria were the keywords mining industry, mines, mineralogy, and publications that did not describe the methods and the tasks related to data mining. Of 123 publications retrieved, 38 were selected to review. Findings suggest that the existent amount of stored data is titanic and it continue to increase considerably. Thus, the process of knowledge discovery in databases and DM have developed tasks and methods for the retrieval of useful knowledge that may be of interest and necessary for just-in-time decision making in different areas of knowledge.En este artículo se tuvo como objetivo realizar una revisión de la literatura sobre la técnica de mineración de datos (Data Mining - DM) en las bases de datos que abarcaban la Literatura Latino-Americana y del Caribe en Ciencias de la Salud (LILACS), Scientific Eletronic Library Online (SCIELO) y algunos libros sobre el tema. Se buscó una recolección amplia utilizando las palabras data mining y mineración de datos, en el período comprendido entre 1999 a 2008. Como criterios de exclusión fueron utilizados los descriptores: industria minera, minas, mineralogía; se excluyeron artículos que no aclaraban el método y las tareas relacionadas a la mineración de dados. De los 123 artículos encontrados, 32 fueron seleccionados. Se observó que el volumen de datos almacenados es gigantesco y continúa creciendo exponencialmente. Con eso el proceso de Descubrimiento del Conocimiento en Bases de Datos y DM incluye tareas y métodos para la extracción del conocimiento útil, interesante e indispensable para la toma de decisiones rápidas en las más diversas áreas del conocimiento.Este artigo teve como objetivo realizar uma revisão da literatura sobre a técnica de mineração de dados (Data Mining - DM) nas bases de dados abrangendo o Literatura Latino-Americana e do Caribe em Ciências da Saúde (LILACS), Scientific Eletronic Library Online (SCIELO) e alguns livros sobre o tema. Buscou-se uma coleta ampla utilizando as palavras data mining e mineração de dados, abrangendo o período de 1999 a 2008. Como critérios de exclusão foram utilizados os descritores: indústria mineira, minas, mineralogia; foram excluídos artigos que não esclareciam o método e as tarefas relacionadas à mineração de dados. Dos 123 artigos encontrados, 32 foram selecionados. Observou-se que o volume de dados armazenados é gigantesco e continua crescendo exponencialmente. Com isso o processo de Descoberta do Conhecimento em Bases de Dados e DM inclui tarefas e métodos para extração de conhecimento útil, interessante e indispensável na tomada de decisões rápidas nas mais diversas áreas de conhecimento.Universidade de São Paulo Departamento de EnfermagemSecretaria de Estado de Saúde de Mato GrossoUniversidade Federal de São Paulo (UNIFESP)UNIFESPSciEL