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Gene selection is one of important research issues in analysis of gene expression data classification. Current methods try to reduce genes by means of statistical calculations and have used semantic similarity under gene ontology. In this article a technique has been presented based on which in addition to considering biological relation among genes, redundant genes by means of hierarchical clustering...
One of the most interesting and important issues in the machine learning and data mining research areas is high accuracy classification. Imbalance data is a great challenge. The imbalance data is a kind of situation when the number of one data member class is significantly smaller than the other class. In the recent years this issue has got more attention among many researchers all over the world...
Fingerprint matching is adopted by a large family of indoor localization schemes, where collecting fingerprints is inevitable but all consuming. Recent studies show that the crowdsourcing scheme could be utilized to perform fingerprints collecting with desirable performance price ratio; however, since the fingerprints are collected from various mobile devices with crowdsourcing, it is difficult to...
Astronomy surveys use powerful instruments to browse the sky and identify objects of interest within the surveyed region. Sky objects are individually characterized with spatial coordinates, identifying their position in the sky, in addition to other descriptive attributes. Composing an integrated view of the sky based on catalogues produced by different surveys faces a hard problem of matching objects...
Nonnegative matrix factorization (NMF) has been widely used to reduce dimensionality of data in image processing and various applications. Incorporating the geometric structure into NMF, graph regularized nonnegative matrix factorization (GNMF) has shown significant performance improvement in comparison to conventional NMF. However, both NMF and GNMF require the data matrix to reside in the memory,...
We propose an algorithm that uses pressure image data to detect a person's sleeping posture and identifies different body limbs. Our algorithm can be used in monitoring bed-bound patients and assessing the risk of pressure ulceration. We used a GMM-based clustering approach for concurrent posture classification and limb identification. Our proposed technique, applied on 9 healthy subjects instructed...
In this paper we propose a cluster-based approach for the delineation of management zones in precision agriculture. The proposed approach was built following the steps of data mining for the clustering task, resulting in a computer application that generates maps of management zones and yield areas, allowing to compare them using known statistical indexes. The basis for this implementation was a model...
A force fields-based multi-scale docking method is proposed in this paper. Molecular docking problem has been divided into three sub problems: rigid-rigid phase, flexible-flexible phase and flexible-rigid phase. Residue groups of protein have been adopted to describe the conformation of protein. K-mean clustering algorithm and genetic algorithm have been developed to solve the optimization problem...
Better understanding of actual customers' power consumption patterns is critical for improving load forecasting (LF) accuracy and efficient deployment of smart grid technologies to enhance operation, energy management, and planning of electric power systems. Though technical literature presented extensive methodologies and models to improve LF accuracy, most of them are based upon aggregated power...
Efficiency of general classification models in various problems is different according to the characteristics and the space of the problem. Even in a particular issue, it may not be distinguished a special privilege for a classifier method than the others. Ensemble classifier methods aim to combine the results of several classifiers to cover the deficiency of each classifier by others. This combination...
Graph-based representation has an effective and extensive usage in pattern recognition due to represent properties of entities and binary relations at the same time. But a major drawback of graphs is lack of basic and essential mathematical operations required in many algorithms of pattern recognition. To overcome this problem, graph embedding in vector space enables classical statistical learning...
Optical Character Recognition alludes to the methodology of taking images or photos of letters or typewritten content and changing over them into information that a machine can easily interpret, e.g. organizations and libraries taking physical duplicates of books, magazines, or other old printed material and utilizing OCR to put them into computers. Segmentation is the indispensable and most difficult...
Neural network is easy to fall into the minimum and over-fitting in the application. The paper proposes a novel dynamic weight neural network ensemble model (DW-NNE). The Bagging algorithm generates certain neural network individuals which then are selected by the k-means clustering algorithm. In addition, for the integrated output problems, the paper proposes a dynamic weight model which is based...
In field of autonomous and intelligent vehicles, the goal of pedestrian classification is to reduce amount of accidents. The object classification accuracy depends on the type of classifier and the extracted object features used for classification. Support Vector Machines (SVM), is considered the most effective classifier for this task. However, it depends on a number of factors that require researchers...
This paper proposes an application of Wireless Sensor Network (WSN) for indoor localization using IEEE 802.15.4 standard. Proposed algorithm applies K-means clustering and Genetic Algorithm (GA) as engine to prepare offline information which result in increasing accuracy and decreasing computational cost of fingerprint technique for indoor localization. K-means clustering will be applied to cluster...
In order to solve the training time problem of the support vector machine for a large dataset, in this paper, an alternative approach motivated by the radial basis function neural network is developed to partition the subset of SVs for the SVM. The proposed method aims at obtain an optimal decision boundary based on the RBFNN, because it has good convergence and fast training. On the other hand, the...
In the paper, the architecture of a pre-radical basis function(RBF) with deep back propagation(BP) neural network is proposed. The three-layer RBF network is altered into a two-layer RBF, the output of RBF hidden layer is processed and then connected with a multilayer perceptron network. Firstly, the input samples go through RBF hidden units and are pre-trained via unsupervised learning, after the...
In order to speed up the convergence of GrabCut algorithm and enhance the accuracy of segmentation, this paper puts forward an algorithm of GrabCut color image segmentation based on region of interest (ROI). The user selects a ROI by dragging a rectangle. The GrabCut algorithm is used in ROI only. The object is then extracted automatically. The experimental results demonstrate that the new algorithm...
Recent studies have suggested significant differences in motor performances of Parkinson's Disease (PD) patients who have L-dopa induced dyskinesias (LIDs), even when off of L-dopa medication. The pathophysiology of LIDs remains obscure, so applying data-mining techniques to the patients' motor performance may provide some heuristic insight. This paper investigated visually-guided tracking performance...
Advances in ubiquitous computing over the last decade have allowed us to inch closer to the realization of true smart homes. Many sensors are already embedded in our living environments which can monitor several environmental parameters such as temperature, humidity, brightness and appliance-level power consumption. However, in order to achieve the primary goal of the smart home, we should be able...
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