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This paper investigates various emotion recognition techniques from the facial expression of human subjects. To describe human facial expressions, a number of characteristic points are extracted from input face images using active shape models (ASMs), and translated 49 scalar features so that they are invariant to scale and position changes. The scalar feature values then construct a 49-dimensional...
Connectivity-network-based techniques have been recently developed for the diagnosis of Alzheimer's disease (AD) as well as its prodromal stage, i.e., mild cognitive impairment (MCI). However, most existing methods focus on using only a single property of connectivity networks (e.g., The correlation between paired brain regions), which can not fully reflect the topological information among multiple...
In this paper, we propose a robust proximal classifier via absolute value inequalities (AVIPC) for pattern classification. AVIPC determines K proximal planes by solving K optimization problems with absolute value inequalities. In AVIPC, each proximal plane is closer to one class and far away from the others. By using the absolute value inequalities, AVIPC is more robust and sparse than traditional...
The current trend of growth of information reveals that it is inevitable that large-scale learning problems become the norm. In this paper, we propose and analyze a novel Low-density Cut based tree Decomposition method for large-scale SVM problems, called LCD-SVM. The basic idea here is divide and conquer: use a decision tree to decompose the data space and train SVMs on the decomposed regions. Specifically,...
Labeled data, in real world, is quite scarce compared with unlabeled data. Manual annotation is usually expensive and inefficient. Active learning paradigm is used to handle this problem by identifying the most informative instances to annotate. In this paper, we proposed a new active learning algorithm based on nonparallel support vector machine. Numeric experiment shows the effective performance...
A land cover map that represents the land surface of the earth is based primarily on analysis of remotely sensed images. However, the rate of concordance of existing land cover maps is not high. This lack of concordance results from a difference in classification methods and observation conditions of remotely sensed images. Also, conducting field surveys around the world is unrealistic. Therefore,...
The paper presents Echo State Network (ESN) as classifier to diagnose the abnormalities in mammogram images. Abnormalities in mammograms can be of different types. An efficient system which can handle these abnormalities and draw correct diagnosis is vital. We experimented with wavelet and Local Energy based Shape Histogram (LESH) features combined with Echo State Network classifier. The suggested...
In this preliminary research we examine the suitability of hierarchical strategies of multi-class support vector machines for classification of induced pluripotent stem cell (iPSC) colony images. The iPSC technology gives incredible possibilities for safe and patient specific drug therapy without any ethical problems. However, growing of iPSCs is a sensitive process and abnormalities may occur during...
People take regular medical examinations mostly not for discovering diseases but for having a peace of mind regarding their health status. Therefore, it is important to give them an overall feedback with respect to all the health indicators that have been ranked against the whole population. In this paper, we propose a framework of mining Personal Health Index (PHI) from a large and comprehensive...
Linking multiple data collections to create longitudinal data is an important research problem with multiple applications. Longitudinal data allows analysts to perform studies that would be unfeasible otherwise. In our research we are interested in linking historical census collections to create longitudinal data that would allow tracking people overtime. The goal of the linking is to identify the...
The traditional financial time series forecasting methods use accurate input data for prediction, and then make single-step or multi-step prediction based on the established regression model. So its prediction result is one or more specific values. But because of the complexity of financial markets, the traditional forecasting methods are less reliable. In this paper, we transform the financial time...
Because of great volume of web information, information retrieval process of a search engine is of great importance. For each query of user, the number of queries can reach hundred thousands, whereas a few number of the first results have the chance of being checked by user; therefore, a search engine pays attention to putting relevance results in the first ranks as a necessity. This paper introduces...
In the past, fingerprinting algorithms have been suggested as a practical and cost-effective means for deploying localisation services. Previous research, however, often assumes an (idealised) laboratory environment rather than a realistic set-up. In our work we analyse challenges occurring from a university environment which is characterised by hundreds of access points deployed and by heterogeneous...
Surface Enhanced Raman Spectroscopy (SERS) is a trace amount substance detecting technique developing quickly in recent years. In this paper, the saliva SERS spectrum of 59 lung cancer patients and 18 normal people were measured, and analyzed with data mining technology and the traditional statistical classification methods. The data were established by the Support Vector Machine (SVM), Random Forests...
A method is proposed to distinguish patients with depression from healthy persons using data measured by Functional Near Infrared Spectroscopy (FNIRS) during a cognitive task. Firstly, General Linear Model (GLM) is used to extract features from 52-channel FNIRS data of patients with depression and normal healthy persons. Then a Support Vector Machine (SVM) classifier is designed for classification...
A fully instrumented surface vessel involving underactuated structure was designed. The problem of straight line course tracking of the underactuated surface vessel at a constant forward speed is addressed. The Serret-Frenet frame is used to define the tracking error, therefore the position tracking errors could be stabilized by stabilizing the single distance error. Least Squares Support Vector Machines...
A new method is proposed in this paper to determine the rejection thresholds. First, the minimum risk decision rules are analyzed and the cost function is founded. After taking into account the rejection cost, the cost function is modified and the requirement that the rejection thresholds shall satisfy to minimize the cost function is given. Second, the property of ROC is analyzed based on conditional...
Recently, semiconductor counterfeiting has become a serious problem. Physical Unclonable Function (PUF) prevents counterfeiting. It utilizes random characteristic patterns which are difficult to control artificially. PUF can identify genuineness semiconductor, even if all circuit patterns are duplicated. However, the conventional PUF is weak against modeling attacks. This study proposes a PUFID generating...
This paper presents popular music estimation based on a topic model using time information and audio features. The proposed method calculates latent topic distribution using Latent Dirichlet Allocation to obtain more accurate music features. In this approach, we also use release date information of each music as time information for concerning the relationship between music trends and each age. Then,...
The paper proposes a solution for document and aspect levels sentiment analysis for unstructured documents written in the Romanian language. The opinion extraction relies on two different approaches for polarity identification. At the aspect level we propose a rule-based approach. For the document level we consider supervised learning techniques, based on features extracted and filtered in different...
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