9,194 research outputs found
A systematic review of data quality issues in knowledge discovery tasks
Hay un gran crecimiento en el volumen de datos porque las organizaciones capturan permanentemente la cantidad colectiva de datos para lograr un mejor proceso de toma de decisiones. El desafĂo mas fundamental es la exploraciĂłn de los grandes volĂşmenes de datos y la extracciĂłn de conocimiento Ăştil para futuras acciones por medio de tareas para el descubrimiento del conocimiento; sin embargo, muchos datos presentan mala calidad. Presentamos una revisiĂłn sistemática de los asuntos de calidad de datos en las áreas del descubrimiento de conocimiento y un estudio de caso aplicado a la enfermedad agrĂcola conocida como la roya del cafĂ©.Large volume of data is growing because the organizations are continuously capturing the collective amount of data for better decision-making process. The most fundamental challenge is to explore the large volumes of data and extract useful knowledge for future actions through knowledge discovery tasks, nevertheless many data has poor quality. We presented a systematic review of the data quality issues in knowledge discovery tasks and a case study applied to agricultural disease named coffee rust
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
A supervised clustering approach for fMRI-based inference of brain states
We propose a method that combines signals from many brain regions observed in
functional Magnetic Resonance Imaging (fMRI) to predict the subject's behavior
during a scanning session. Such predictions suffer from the huge number of
brain regions sampled on the voxel grid of standard fMRI data sets: the curse
of dimensionality. Dimensionality reduction is thus needed, but it is often
performed using a univariate feature selection procedure, that handles neither
the spatial structure of the images, nor the multivariate nature of the signal.
By introducing a hierarchical clustering of the brain volume that incorporates
connectivity constraints, we reduce the span of the possible spatial
configurations to a single tree of nested regions tailored to the signal. We
then prune the tree in a supervised setting, hence the name supervised
clustering, in order to extract a parcellation (division of the volume) such
that parcel-based signal averages best predict the target information.
Dimensionality reduction is thus achieved by feature agglomeration, and the
constructed features now provide a multi-scale representation of the signal.
Comparisons with reference methods on both simulated and real data show that
our approach yields higher prediction accuracy than standard voxel-based
approaches. Moreover, the method infers an explicit weighting of the regions
involved in the regression or classification task
Medical imaging analysis with artificial neural networks
Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging
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