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Hyperspectral face images present productive information captured using a Hyperspectral camera compared to normal RGB camera capturing face images. Hyper spectral imaging is the collecting and processing of information from across the visible electromagnetic spectrum. Hyper spectral imaging deals with the imaging of narrow spectral bands over a continuous visible spectral range, and produces the spectra...
The word-to-vector (W2V) technique represents words as low-dimensional continuous vectors in such a way that semantic related words are close to each other. This produces a semantic space where a word or a word collection (e.g., a document) can be well represented, and thus lends itself to a multitude of applications including document classification. Our previous study demonstrated that representations...
In order to solve the small sample problems and the linear inseparable problems caused by some nonlinear factors, this paper proposed a method to generate multiple virtual samples similar to the original images by its class, then all virtual samples were combined as a new database for training. The method not only helps to increase more samples, but strengthens the reliance of virtual samples on the...
Text categorization is an important research in nature language process and content analysis. In this paper, we present latent factor SVM (LF-SVM) for text categorization which use latent factor vectors for category representation on text categorization. We prove that latent factors extracted by PLSA (probability latent semantic analysis) can span convex structure to express text category. Based on...
Compared to single classifiers, ensemble learning offers significant and stable performance improvement, while ensemble pruning can improve efficiency and performance of the ensembles further, so both of them are hot topics, not only in traditional machine learning but also in recent data stream mining scopes. Aiming to provide a uniform platform and framework for developing and evaluating ensemble...
This paper presents the application of FATHOM, a computerised non-verbal comprehension detection system, to distinguish participant comprehension levels in an interactive tutorial. FATHOM detects high and low levels of human comprehension by concurrently tracking multiple non-verbal behaviours using artificial neural networks. Presently, human comprehension is predominantly monitored from written...
In this paper, we study channel-based authentication, where the receiver can identify and authenticate the senders through channel vectors estimated from their frames. The authentication process is formulated as a sequence of hypothesis test problems. In order to improve the detection probability and reduce the false alarm probability, two schemes are proposed based on different classification algorithms...
The realization of robotic systems that understands human intentions and produces accordingly complex behaviors is needed particularly for disabled persons, and would consequently benefit the aged. For this purpose, a control technique that recognizes human intentions from neural responses called brain machine interface (BMI) have been suggested. The unique ability to communicate with machines by...
Software-Defined Networking (SDN) is an emerging concept that intends to replace traditional networks by breaking vertical integration. It does so by separating the control logic of network from the underlying switches and routers, suggesting logical centralization of network control, and allowing to program the network. Although SDN promises more flexible network management, there are numerous security...
Face recognition, one of the most explored themes in biometry, is used in a wide range of applications: access control, forensic detection, surveillance and monitoring system, robotic and human machine interaction. In this paper, a new classifier is proposed for face recognition. The performance of this new classifier is compared with the performance of the KNN classifier. The face image database...
Indirect immunofluorescence (IIF) with HEp-2 cells is considered as a powerful, sensitive and comprehensive technique for analyzing antinuclear autoantibodies (ANAs). Fractal dimension can be used on the analysis of image representing and also on the property quantification like texture complexity and spatial occupation. In this study, we apply the fractal theory in the application of HEp-2 cell staining...
Sparse representation for classification (SRC) has attracted much attention in recent years. It usually performs well under the following assumptions. The first assumption is that each class has sufficient training samples. In other words, SRC is not good at dealing with the undersampled problem, i.e., each class has few training samples, even single sample. The second one is that the sample vectors...
The high spectral and spatial resolution of hyperspectral images increases the capability to distinguish physical materials and objects, presenting new challenges to image analysis and classification. In fact, many studies have been conducted to extract and integrate spectral and contextual information in the classification process. However, the availability of various spatial features (e.g. morphological...
In modern times, it has become very essential for e-commerce businesses to empower their end customers to write reviews about the services that they have utilized. Such reviews provide vital sources of information on these products or services. This information is utilized by the future potential customers before deciding on purchase of new products or services. These opinions or reviews are also...
Online social media is used to show the sentiments of different individuals about various subjects. Sentiment analysis or opinion mining has recently been considered as one of the highly dynamic research fields in natural language processing, web mining, and machine learning. There has been a very limited amount of research that focuses on sentiment analysis in the Malay language. This study investigates...
In this paper, we propose a new image annotation method by combining content-based image annotation and tag-based image annotation techniques. Content-based image annotation technique is adopted to extract "loosely defined concepts" by analyzing pre-given images' features such as color moment (CM), edge orientation histogram (EOH), and local binary pattern (LBP), followed by constructing...
Traditional classifiers in steganalysis are no longer applicable when faced with massive feature set and high-dimensional sample set. We proposed a kind of selective ensemble classifier for universal steganalysis based on virus-evolutionary genetic algorithm. After generating some base learners, we selected some of them according to genetic optimization with an additonal virus population. The final...
In this paper we report on the performance of a coupled oscillator based implementation of the HMAX image-processing pipeline. Within this pipeline we have used coupled oscillator arrays to replace traditional Boolean logic with a Degree-of-Match (DoM) function that measures the L2 distance squared between two vectors in an n-dimensional space. We show that this operation can be used in three stages...
On-line learning for fuzzy classifiers are investigated in this paper. In the on-learning problems, it is assumed that there are only a single training patterns available at a time. This paper extends the assumption by allowing multiple training patterns available for incrementally update the fuzzy classifiers. Confidence-weighted learning is used for the on-line learning of the fuzzy classifiers...
This paper proposes a novel approach for automatic estimation of four important traits of speakers, namely age, height, weight and smoking habit, from speech signals. In this method, each utterance is modeled using the i-vector framework which is based on the factor analysis on Gaussian Mixture Model (GMM) mean supervectors, and the Non-negative Factor Analysis (NFA) framework which is based on a...
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