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Multi-instance learning concerns about building learning models from a number of labeled instance bags, where each bag consists of instances with unknown labels. A bag is labeled positive if one or more multiple instances inside the bag is positive, and negative otherwise. For all existing multi-instance learning algorithms, they are only applicable to the setting where instances in each bag are represented...
An intelligent classification technique for MR brain images are extremely important for medical analysis and treatment selection. Manual interpretation of these images by physicians may lead to wrong diagnosis when a large number of MRIs are analyzed. In this paper an automated decision support system for classification is proposed. It consists of computing the uniform LBP with mapping. Haar wavelet...
Environmental monitoring is one of the key approaches to safeguard the global ecosystem. Classifications of different water levels facilitate in preserving water reserves and maintain the equilibrium in the ecosystem. In this paper we shall inspect the classification of drainage water levels in Canada. A powerful statistical tool called support vector machines is used to classify the said drainage...
Network performance metrics such as available bandwidth and latency are essential to achieve good Quality of Service (QoS) in multimedia streaming. There are unique requirements in network performance metrics for media applications, such as audio conferencing, video streaming, video conferencing, and high-definition (HD) video conferencing. In this paper, we focus on conference call type suggestion...
The textural content of FDG-PET brain images has been shown to be useful for the diagnosis of Alzheimer's disease (AD) and Mild Cognitive Impairment (MCI). In this paper, we investigate the use of the textons method [1], a powerful texture extraction procedure that uses a full statistical representation of the response of the image to a set of filters. We also extend the MR8 filter bank used in [1]...
This paper presents an FPGA implementation of a Support Vector Machine (SVM) classification using the DSP slices and block RAMs in the Xilinx Virtex-6 family FPGA. In our approach, the SVM classification is performed by the multiple DSPs. Our implementation supports 3 types of kernel functions, the sigmoid kernel, the polynomial kernel, and the RBF kernel. We connect DSPs with the built-in cascade...
This paper presents an iterative classification algorithm called Ridge-adjusted Slack Variable Optimization (RiSVO). RiSVO is an iterative procedure with two steps: (1) A working subset of the training data is selected so as to reject “extreme” patterns. (2) the decision vector and threshold value are obtained by minimizing the energy function associated with the slack variables. From a computational...
Remote sensing techniques are widely used for land cover classification and related analyses; however the availability of high resolution images have limited the accuracy of pixel based approaches. In this paper, we have analyzed the feasibility of incorporating contextual information to a support machine and have evaluated its performances with reference to the traditional approaches. Accuracy improvement...
Drivetrain gearboxes play an important role in many modern industrial applications. This paper presents a novel method consisting of adaptive feature extraction and support vector machine (SVM)-based classification for condition monitoring and fault diagnosis of drivetrain gearboxes operating in variable-speed conditions. An adaptive signal resampling algorithm, a frequency tracker, and a feature...
In the last few years there has been growing interest in the use of functional Magnetic Resonance Imaging (fMRI) for brain mapping. To decode brain patterns in fMRI data, we need reliable and accurate classifiers. Towards this goal, we compared performance of eleven popular pattern recognition methods. Before performing pattern recognition, applying the dimensionality reduction methods can improve...
Jamu is made from natural materials such as roots, leaves, timber and fruits. Jamu has many variations of formula. The composition of Jamu formula is usually based on empirical data or personal experiences. Thus, the classification for the efficacy of Jamu based on its compositions of plants still remains an interesting task. The purpose of this research is to develop a classification system for Jamu...
Alzheimer disease (AD) is known as the most common form of dementia, which imposes a considerable burden on society. In this paper, we focus on the automated diagnosis of Alzheimer disease. Based on the researches on neuropathology, we adopt the thickness of cortex regions from the magnetic resonance imaging (MRI) to characterize the pathology of AD. 3D reconstruction technique is utilized to extract...
Accurate head pose estimation is significant for many applications such as face recognition and human-computer interaction. In this paper, we treat the head pose estimation as a classification problem and employ the Lie Algebrized Gaussians (LAG) feature as the representation approach for head image. The LAG feature, which is built on Gausssian Mixture Model (GMM), has the capability to preserve the...
Emotions are mental states that can be expressed by motion, speech and other physiological reactions. In human-to-human interaction emotion perception is the perception on the emotion of the other people, which, due to the nature of emotions is not so precise. On the other hand, perception on emotions in human-computer interaction is still an open problem. A lot of work is done in direction of finding...
In this paper, we used echo state networks — a class of recurrent neural networks — for prediction of drought based on remote sensing data. To this end, the drought index was obtained for a number of stations in different clime zones of Iran. For each station, we also extracted the corresponding vegetation indices based on satellite imagery. Our model takes the satellite-based features as input and...
In this paper, we propose a new sparse multiple-kernel learning (MKL) method for classification of hyperspectral images. The proposed method adopts two-step strategy to carry out model learning from multiple basis kernels rather than simultaneously optimizing the kernel combination and learning performance. In the first step, we firstly reformulate the multiple-kernel learning so as to making the...
Wireless Capsule Endoscopy (WCE) allows physicians to examine the entire digestive system without any surgical operation. Although it provides a noninvasive imaging approach to access the gastrointestinal (GI) tract, the biggest drawback of this technology is its incapability of localizing the capsule when an abnormality is found by the video source. Existing localization methods based on radio frequency...
Network Intrusion Detection System (NIDS) plays an important role in providing network security. Efficient NIDS can be developed by defining a proper rule set for classifying network audit data into normal or attack patterns. Generally, each dataset is characterized by a large set of features, but not all features will be relevant or fully contribute identifying an attack. Since different attacks...
Revised algorithm for online learning with kernels (OLK) in classification and regression is proposed in a reproducing kernel hilbert space (RKHS). Compared with the original OLK, the revised algorithm allows that the new data points arrive either one by one or two by two.
Inter- and intra-rater agreement in assessing Interstitial Pulmonary Fibrosis (IPF) on CT scans is poor. Lack of approaches to understand the nuances of CT reconstruction parameters and their effect on patient scans hampers objective IPF quantification. In this paper, we propose an image analytic methodology to characterize the relationship between CT reconstruction parameters and IPF manifestation...
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