AIMC Topic: Pattern Recognition, Automated

Clear Filters Showing 1331 to 1340 of 1689 articles

Automated classification of neurological disorders of gait using spatio-temporal gait parameters.

Journal of electromyography and kinesiology : official journal of the International Society of Electrophysiological Kinesiology
OBJECTIVE: Automated pattern recognition systems have been used for accurate identification of neurological conditions as well as the evaluation of the treatment outcomes. This study aims to determine the accuracy of diagnoses of (oto-)neurological g...

Pervasive Sound Sensing: A Weakly Supervised Training Approach.

IEEE transactions on cybernetics
Modern smartphones present an ideal device for pervasive sensing of human behavior. Microphones have the potential to reveal key information about a person's behavior. However, they have been utilized to a significantly lesser extent than other smart...

A framework for final drive simultaneous failure diagnosis based on fuzzy entropy and sparse bayesian extreme learning machine.

Computational intelligence and neuroscience
This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction ...

Adaptive semi-supervised recursive tree partitioning: The ART towards large scale patient indexing in personalized healthcare.

Journal of biomedical informatics
With the rapid development of information technologies, tremendous amount of data became readily available in various application domains. This big data era presents challenges to many conventional data analytics research directions including data ca...

A novel multiple instance learning method based on extreme learning machine.

Computational intelligence and neuroscience
Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm. As a learning paradigm, MIL is different from traditional supervised learning that handles the...

Spatiotemporal System Identification With Continuous Spatial Maps and Sparse Estimation.

IEEE transactions on neural networks and learning systems
We present a framework for the identification of spatiotemporal linear dynamical systems. We use a state-space model representation that has the following attributes: 1) the number of spatial observation locations are decoupled from the model order; ...

Solving nonlinear equality constrained multiobjective optimization problems using neural networks.

IEEE transactions on neural networks and learning systems
This paper develops a neural network architecture and a new processing method for solving in real time, the nonlinear equality constrained multiobjective optimization problem (NECMOP), where several nonlinear objective functions must be optimized in ...

Structured patch model for a unified automatic and interactive segmentation framework.

Medical image analysis
We present a novel interactive segmentation framework incorporating a priori knowledge learned from training data. The knowledge is learned as a structured patch model (StPM) comprising sets of corresponding local patch priors and their pairwise spat...

Feedback error learning control of magnetic satellites using type-2 fuzzy neural networks with elliptic membership functions.

IEEE transactions on cybernetics
A novel type-2 fuzzy membership function (MF) in the form of an ellipse has recently been proposed in literature, the parameters of which that represent uncertainties are de-coupled from its parameters that determine the center and the support. This ...

Automatic epileptic seizure detection using scalp EEG and advanced artificial intelligence techniques.

BioMed research international
The epilepsies are a heterogeneous group of neurological disorders and syndromes characterised by recurrent, involuntary, paroxysmal seizure activity, which is often associated with a clinicoelectrical correlate on the electroencephalogram. The diagn...