AIMC Topic: Artificial Intelligence

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Improved Fault Classification in Series Compensated Transmission Line: Comparative Evaluation of Chebyshev Neural Network Training Algorithms.

IEEE transactions on neural networks and learning systems
This paper presents the Chebyshev neural network (ChNN) as an improved artificial intelligence technique for power system protection studies and examines the performances of two ChNN learning algorithms for fault classification of series compensated ...

Deep learning in neural networks: an overview.

Neural networks : the official journal of the International Neural Network Society
In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Sh...

Multivariate decoding of cerebral blood flow measures in a clinical model of on-going postsurgical pain.

Human brain mapping
Recent reports of multivariate machine learning (ML) techniques have highlighted their potential use to detect prognostic and diagnostic markers of pain. However, applications to date have focussed on acute experimental nociceptive stimuli rather tha...

Manifold regularized multitask feature learning for multimodality disease classification.

Human brain mapping
Multimodality based methods have shown great advantages in classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Recently, multitask feature selection methods are typically used for joint select...

Learning Multiscale Active Facial Patches for Expression Analysis.

IEEE transactions on cybernetics
In this paper, we present a new idea to analyze facial expression by exploring some common and specific information among different expressions. Inspired by the observation that only a few facial parts are active in expression disclosure (e.g., aroun...

Two-layer contractive encodings for learning stable nonlinear features.

Neural networks : the official journal of the International Neural Network Society
Unsupervised learning of feature hierarchies is often a good strategy to initialize deep architectures for supervised learning. Most existing deep learning methods build these feature hierarchies layer by layer in a greedy fashion using either auto-e...

Development of early-warning protocol for predicting chlorophyll-a concentration using machine learning models in freshwater and estuarine reservoirs, Korea.

The Science of the total environment
Chlorophyll-a (Chl-a) is a direct indicator used to evaluate the ecological state of a waterbody, such as algal blooms that degrade the water quality in lakes, reservoirs and estuaries. In this study, artificial neural network (ANN) and support vecto...

Neural coordination can be enhanced by occasional interruption of normal firing patterns: a self-optimizing spiking neural network model.

Neural networks : the official journal of the International Neural Network Society
The state space of a conventional Hopfield network typically exhibits many different attractors of which only a small subset satisfies constraints between neurons in a globally optimal fashion. It has recently been demonstrated that combining Hebbian...

Estimation of teaching-learning-based optimization primer design using regression analysis for different melting temperature calculations.

IEEE transactions on nanobioscience
Primers plays important role in polymerase chain reaction (PCR) experiments, thus it is necessary to select characteristic primers. Unfortunately, manual primer design manners are time-consuming and easy to get human negligence because many PCR const...

Two-Stage Orthogonal Least Squares Methods for Neural Network Construction.

IEEE transactions on neural networks and learning systems
A number of neural networks can be formulated as the linear-in-the-parameters models. Training such networks can be transformed to a model selection problem where a compact model is selected from all the candidates using subset selection algorithms. ...