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The growing interests in multi-way data analysis have made the tensor factorization and classification a crucial issue in machine learning for signal processing. Conventional neural network (NN) classifier is estimated from a set of input vectors. The multi-way data are unfolded as high-dimensional vectors for model training. The classification performance is constrained because the neighboring temporal...
In hyerspectral remote sensing community, sparse representation based classification (SRC) is a novel concept — a testing pixel is linearly represented by labeled data, and weight coefficients are often solved by an ℓ1-norm minimization. In this work, an extension of SRC is proposed by imposing an adaptive similarity measurement between the testing pixel and labeled data on the ℓ1-norm penalty, named...
We present PharmaGuard, a novel system for the automatic discovery of illegal online pharmacies, aimed at assisting law-enforcement toward their early identification, blacklisting and shutdown. Given a previously labelled set of examples, the system is able to learn a profile of (illegal) pharmacies, and then exploit it to discover never-before-seen instances indexed by popular web search engines...
Big data consists of large multidimensional datasets that would often be difficult to analyze if working with the original tensor. There is a rising interest in the use of tensor decompositions for feature extraction due to the ability to extract necessary features from a large dimensional feature space. In this paper the matrix product state (MPS) decomposition is used for feature extraction of large...
This paper provides a novel and unified framework of representation based classification technique. The proposed atomic representation based classification (ARC) framework includes, but not limited to, sparse representation based classification (SRC), low-rank representation based classification (LRRC) as special cases. Despite good performance, most existing classification methods are heavily reliant...
This paper presents the process of Quranic Accent Automatic Identification. Recent feature extraction technique that is used for Quranic verse rule identification/Tajweed include Mel Frequency Cepstral Coefficients (MFCC) which prone to additive noise and may reduce the classification result. Therefore, to improve the performance of MFCC with addition of Spectral Centroid features and is proposed...
In this paper, we propose classifiers based on Tensor Voting (TV) framework for supervised binary and multiclass problems. Traditional classification approaches classify a test sample or point based on its proximity to classes of a training set, where proximity is generally taken as some variant of the Euclidean distance in the original or some transformed higher dimensional space. However, we may...
Autoimmune diseases occur when an inappropriate immune response takes place and produces autoantibodies to fight against human antigens. In order to detect autoimmune disease, a test, called indirect immunofluorescence (IIF) is carried out to identify antinuclear autoantibodies (ANA) in the HEp-2 cell. Current method of analyzing the results is inconsistent as it is limited to subjective factors such...
The classical front end analysis in speech recognition is a spectral analysis which parameterizes the speech signal into feature vectors. This paper proposes a voice recognition model that is able to automatically classify and recognize a voice signal with background noise. The model uses the concept of spectrogram, pitch period, short time energy, zero crossing rate, mel frequency scale and cepestral...
In hyperspectral imagery, there exist homogeneous regions where neighboring pixels tend to belong to the same class with high probability. However, even though neighboring pixels are from the same material, their spectral characteristics may be different due to various factors, such as internal instrument noise or atmospheric scattering, which results in misclassification. In this work, the proposed...
Due to the high dimensionality of hyperspectral data, dimension reduction is becoming an important problem in hyperspectral image classification. Band selection can retain the information which is capable of keeping the original meaning of the data, and thus has attracted more attention. This paper tackles the band selection problem from the perspective of multiple classifiers combination, which can...
A novel multiple classifiers fusion approach based on SFLS (Shortest Feature Line Segment) is proposed in this paper. SFLS is a kind of simple yet effective classification method depending on the shortest feature line. The original form of SFLS's output is just class label. To use SFLS as the member classifier in the multiple classifier system, the form of SFLS's output is modeled using the membership...
The diagnostic process for schizophrenia is mainly clinical and has to be performed by an experienced psychiatrist, relying mainly on clinical signs and symptoms. Current neurophysiological measurements can distinguish groups of healthy controls and groups of schizophrenia patients. Individual classification based on neurophysiological measurements only shows moderate accuracy. In this study, we wanted...
To date, there are no reliable markers for making an early diagnosis of schizophrenia before clinical diagnostic criteria are fully met. Neuroimaging and pattern classification techniques are promising tools towards predicting transition to schizophrenia. Here, we investigated the diagnostic performance of a combination of neuroanatomical and clinical data in predicting transition to schizophrenia...
Quality and reliability are extremely important for Hard Disk Drive (HDD) manufactory, which increasing upon the expansion capacity of HDD. The production process contains many tests in order to evaluate quality and efficiency which resulting high amount of complex data. Some HDDs will be randomly selected from each lot to test with various environments simulation. Due to complexity of tests and higher...
Digital circuits are preferred over its analog counterpart with the invention of microprocessors, microcontrollers, digital signal processors and Field Programmable Gate Arrays (FPGA). Digital circuits in the form of digital arithmetic and digital logics are employed for various applications. On the other side, Support Vector Machine (SVM) is considered as a state-of-the-art tool for pattern recognition...
This paper proposes the use of a deep neural network for the recognition of isolated acoustic events such as footsteps, baby crying, motorcycle, rain etc. For an acoustic event classification task containing 61 distinct classes, classification accuracy of the neural network classifier (60.3%) excels that of the conventional Gaussian mixture model based hidden Markov model classifier (54.8%). In addition,...
In this paper, we study neural network ensembles (NNE) classifier with regularized negative correlation learning (RNCL) and its application to pattern classification. In RNCL algorithm, the regularization parameter is used to control the trade off between mean square error and regularization, and to improve the ensemble's generalization ability. We propose an automatic RNCL algorithm based on gradient...
Influenza poses a significant risk to public health, as evident by the 2009 H1N1 pandemic. Hospital emergency departments monitor infectious diseases such as influenza with surveillance systems based on arriving chief complaints. However, existing systems are too reliant on the completeness of data and are not acceptably accurate in a practical setting. To improve prediction accuracy, we propose a...
Indirect immunofluorescence imaging is employed as a standard method to detect antinuclear antibodies in HEp-2 cells which is important for diagnosing autoimmune diseases and other important pathological conditions involving the immune system. HEp-2 cells are generally categorised into six groups: homogeneous, fine speckled, coarse speckled, nucleolar, cytoplasmic, and centromere cells, which give...
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