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In the clinical diagnosis of facial dysmorphology, geneticists attempt to identify the underlying syndromes by associating facial features before cyto or molecular techniques are explored. Specifying genotype-phenotype correlations correctly among many syndromes is labor intensive especially for very rare diseases. The use of a computer based prediagnosis system can offer effective decision support...
Identification of condition-specific protein interaction subnetworks has emerged as an attractive research field to reveal molecular mechanisms of diseases and provide reliable network biomarkers for disease diagnosis. Several methods have been proposed, which integrate gene expression and protein-protein interaction (PPI) data to identify subnetworks. However, existing methods treat differential...
The goal of our data-mining multi-agent system is to facilitate data-mining experiments without the necessary knowledge of the most suitable machine learning method and its parameters to the data. In order to replace the expertâs knowledge, the meta-learning subsystems are proposed including the parameter-space search and method recommendation based on previous experiments. In this paper...
An error-driven adaptive model-based control system, for optimizing machine or assembly plant performance and operation under normal and fault conditions, is proposed. In such complex system it is imperative to differentiate between a system failure and a sensor failure or between process noise and measurement noise. In this paper, we present a comprehensive approach based on a hierarchical, multilevel...
In many unsupervised learning applications both spatial and temporal regularities in the data need to be represented. Traditional clustering algorithms, which are commonly employed by unsupervised learning engines, lack the ability to naturally capture temporal dependencies. In supervised learning methods, temporal features are often learned through the use of a feedback (or recurrent) signal. Drawing...
This paper introduces a sequentially motivated approach to processing streams of images from datasets with low memory demands. We utilize fuzzy clustering as an incremental dictionary learning scheme and explain how the corresponding membership functions can be subsequently used in encoding features for image patches. We focus on replicating the codebook learning and classification stages from an...
This paper presents a new approach to the problem of semantic segmentation of digital images. We aim to improve the performance of some state-of-the-art approaches for the task. We exploit a new version of texton feature [28], which can encode image texture and object layout for learning a robust classifier. We propose to use a genetic algorithm for the learning parameters of weak classifiers in a...
Ground Penetrating Radar systems have been successfully used to access concrete structures conditions. Moreover, inclusions in concrete can be discriminated by simple models based on traces obtained by GPR. In this work, concrete blocks with different inclusions were probed in controlled conditions. Some features were extracted from Ascans of this experimental data set. To get efficient models, raw...
Mobile phones have become one of the primary tools for individuals to communicate, to access data networks, and to share information. Service providers collect data about the calls placed on their network, and these calls exhibit a large degree of variability. Providers model the structure of the relationships between network subscribers as a mobile call graph. In this paper, we apply a new measure...
The problem of robust sparse coding is considered. It is defined as finding linear reconstruction coefficients that minimize the sum of absolute values of the errors, instead of the more typically used sum of squares of the errors. This change lowers the influence of large errors and enhances the robustness of the solution to noise in the data. Sparsity is enforced by limiting the sum of absolute...
In this paper, we develop an automated and adaptive framework that aims to move active data to high performance storage tiers and inactive data to low cost/high capacity storage tiers by learning patterns of the storage workloads. The framework proposed is designed using efficient Markov chain correlation based clustering method (MCC), which can quickly predict or detect any changes in the current...
Users need to discern how the soil characteristics at locations of their interest are, but soil properties can be determined only in a small number of sampling points. Therefore, it is necessary to predict how the soil is at points that have not been sampled. This study proposes a system for predicting soil property values, based on Generalized Regression Neural Networks and Genetic Algorithms. The...
Continuous, automated, electronic patient vital signs data are important to physicians in evaluating traumatic brain injury (TBI) patients' physiological status and reaching timely decisions for therapeutic interventions. However, missing values in the medical data streams hinder applying many standard statistical or machine learning algorithms and result in losing some episodes of clinical importance...
We propose a case-based reasoning (CBR) approach to answer validation/answer scoring and reranking in question answering (QA) systems, where annotated answer candidates for known questions provide evidence for validating answer candidates for new questions. The use of CBR promises a continuous increase in answer quality, given user feedback that extends the case base. In the paper, we present the...
Facial expression recognition is an interesting research topic. Considerable methods have been proposed in order to achieve high accuracy in facial expression recognition, however, only a few of these methods have considered factors like memory consumption and computational complexity. In this paper, we focus on smile detection which belongs to facial expression recognition. Compare with the proposed...
Ordinal data classification (ODC) has a wide range of applications in areas where human evaluation plays an important role, ranging from psychology and medicine to information retrieval. In ODC the output variable has a natural order, however, there is not a precise notion of the distance between classes. The recently proposed method for ordinal data, Kernel Discriminant Learning Ordinal Regression...
Real-time 3D sensing has many important applications in areas such as robotic navigation, virtual reality and human-computer interaction. A variety of techniques have been developed for the determination of 3D geometry information such as binocular vision, structured light and their combination. However, existing non-contact optical 3D sensing approaches have their own limitations in the process of...
Healthcare is particularly rich in semantic information and background knowledge describing data. This paper discusses a number of semantic data types that can be found in healthcare data, presents how the semantics can be extracted from existing sources including the Unified Medical Language System (UMLS), discusses how the semantics can be used in both supervised and unsupervised learning, and presents...
This paper concerns the resource management problem arising in public cycle sharing schemes, when some docking stations become empty and remain so while others fill to capacity. To alleviate this, managing companies move bicycles between docking stations in order to maximise the number of satisfied customers while minimising the movement cost. We identify Reinforcement learning (RL) as the most promising...
Boarding or holding in the Emergency Department (ED) reduces capacity of the ED and delays patients from receiving specialized care. Estimating accurately the number of admissions from the ED can help determine appropriate level of staffing to reduce holding. We propose a randomized non-linear regression algorithm, RT-KGERS, to estimate the number of admissions a week in advance. We also devise features...
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