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Histopathology image classification can provide automated support towards cancer diagnosis. In this paper, we present a transfer learning-based approach for histopathology image classification. We first represent the image feature by Fisher Vector (FV) encoding of local features that are extracted using the Convolutional Neural Network (CNN) model pretrained on ImageNet. Next, to better transfer the...
We analyze the theoretical vulnerability of maximum a posteriori(MAP) speaker adaptation, which is widely used in practical speaker recognition systems. First, we proved that there exist a set of feature vectors, what are called wolves, which can impersonate almost all the registered speakers with probability asymptotically close to 1 with at most two trials. Second, our experiment shows that the...
Biometric is a pattern recognition system that automatically identifies people according to their physiologic and behavioral properties. Among the physiologic properties, hand has a special place so that all features of hand like palm lines, inner knuckles, external knuckles and geometry could be used. More recently, the usage of blood vessels pattern in the palm, in addition to the high acceptability,...
The electrical energy consumption associated with sanitary water heating makes up a large part of the total load associated with residential energy consumption, and therefore load models thereof could find use in various Energy Management (EM) applications. This paper presents the results of an investigation to model the electrical load associated with the combined sanitary hot water heating systems...
The worldwide increase in the integration of photovoltaic generation has necessitated improvements in the forecasting approaches. Two models are proposed to cater for PV generation forecasts for few minutes to several hours look-ahead times. A very fast and accurate prediction model based on extreme learning machine is deployed for day-ahead prediction. Moreover, an adaptive and sequential model is...
This paper proposes a neurobiology-based extension of integrate-and-fire models of Radial Basis Function Neural Networks (RBFNN) that adapts to novel stimuli by means of dynamic restructuring of the network's structural parameters. The new architecture automatically balances synapses modulation, re-centers hidden Radial Basis Functions (RBFs), and stochastically shifts parameter-space decision planes...
For applications of face recognition (FR) in video surveillance, it is often costly or unfeasible to collect several high quality reference samples a priori to design representative facial models. Moreover, changes in capture conditions and human physiology create divergence between facial models and input captures. Multiple classifier systems (MCS) have been successfully applied to video-to-video...
The modeling for fermentation process has important significance in achieving control and optimal control of the fermentation process. Generalization capability of the model based on global learning support vector machine was not strong, so according to local learning theory the method of establishing the fermentation process dynamic model was proposed in this paper. The dynamic of the fermentation...
Numerous sophisticated algorithms exist for discovering reoccurring patterns in financial time series. However, the most accurate techniques available produce opaque models, from which it is impossible to discern the rationale behind trading decisions. It is therefore desirable to sacrifice some degree of accuracy for transparency. One fairly recent evolutionary computational technology that creates...
Online handwritten signature is a behavioral biometric trait with several practical applications. Examples of these applications include access control to personal devices and validation of online transactions. Several research work have been done to improve the performance of online signature verification systems. This paper presents an improvement of a recently proposed online signature verification...
Multiple-Instance learning (MIL), which relaxes training annotation granularity from instance level to instance collection (bag) level by applying bag concept, obtains increasing attentions from computer vision community. Due to its flexible annotation mechanism, MIL has been naturally utilized on a variety of computer vision problems. And numerous models have been proposed, each of which is ingeniously...
We present an efficient and accurate algorithm for face tracking using a set of Active Appearance Models (AAMs). We observe that a single AAM, trained at a particular model resolution and a particular range of displacements, has a “sweet spot” - a range of displacements for which it is most accurate. A common approach to increasing the range of convergence is to use a multi-resolution model, or a...
Visual attention is the cognitive process of selectively focusing on certain areas of a visual scene while ignoring the others. It is a desirable capability for intelligent video surveillance systems, as it allows them to control the aim of mobile cameras or to selectively process the most relevant parts of the captured images. This paper proposes an adaptation of a well-known biologically-inspired...
We create a spiking neural network of Integrate and Fire neurons with spike frequency adaption based on parameters adjusted for our e-nose device, and investigate the use of this model for odor classification. Addition of spike frequency adaptation term brings the model closer to the response of the olfactory system. Data from Cyranose 320, a polymer based 32-sensor array, is used to test the system...
In this article, for the purpose of improving neural network models applied in face recognition using single image per person, a bidirectional neural network inspired of neocortex functional model is presented. In the proposed model, recognition is not performed in a single stage, but via two bottom-up and top-down phases and the recognition results of first stage is used for model adaptation. We...
A new BP neural network is introduced and at the same time, its' structure, feature and principium are also expatiated. In order to approach compensate the effects of improves non-linearity, a BP neural network model is set up and trained in this paper. The test result indicates that: this method is practical and dependable in the field of salinity modeling, has a good applied foreground.
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