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In this paper a new approach from the combination of band ratioing function and MLP Neural Networks technique is proposed to differentiate between clouds and background in Landsat ETM+ and MSG SEVIRI data. First, in order to increase the contrast of the clouds and background, a band ratioing function is applied to each sub-image. Second, the sub-images are segmented by MLP Neural Networks technique...
This paper addresses the problem of parameter optimization for Markov random field (MRF) models for supervised classification of remote sensing images. MRF model parameters generally impact on classification accuracy, and their automatic optimization is still an open issue especially in the supervised case. The proposed approach combines a mean square error (MSE) formulation with Platt's sequential...
Recently a few works of semi-supervised learning methods based on graph have been proposed for remote sensing. The common idea of these methods are that they build a graph using the samples of the image. Most of their time complexity is relatively large, and they ignore the spatial information of the image, which leads to unsatisfactory classification results. this paper proposes a novel semi-supervised...
In this paper, we propose a novel spectral-spatial conditional random field classification algorithm with location cues (CRFSS) for high spatial resolution remote sensing imagery. In the CRFSS algorithm, the spectral and spatial location cues are integrated to provide the complementary information from spectral and spatial location perspectives. The spectral cues of different land-cover types are...
A novel polarimetric synthetic aperture radar (PolSAR) image classification method based on Deep Belief Networks (DBNs) is proposed in this paper. First, the coherency matrix data are converted to a 9-dimentional data. Second, many patches are randomly selected from each dimension in the 9-dimentional data, and many filters can be obtained from a Restricted Boltzmann Machine (RBM) trained by using...
This paper presents a novel feature selection and fuzzy-neural classification scheme to decode motor imagery signals during driving. To perform this, we would consider the fuzziness involved in sudden left bent, where the driver is supposed to take sudden 90º left turn during acceleration. This requires classification of motor imagery signals during acceleration and steering left control. The fuzzy-recurrent...
Currently, various perspectives of neural networks are proposed for solving classification problems. Some of them are based on two types of mapping functions, namely, linear and nonlinear, for mapping an input space into a feature space. In addition, some neural networks are proposed based on probability theory. Since some models are appropriated for some kinds of data, depending on a distribution...
The bank direct marketing campaign for offering products that meet the customers' needs is the challenge problems. The bank direct marketing data analysis is important work that helps the banks predict whether customers will sign a long term deposits with the banks. The method that can predict such customers' needs can be profitable to the banks for improving their marketing campaign strategies. Unfortunately,...
Data Streams are instances that arrive at a very rapid rate with changes in underlying conceptual distributions. Many ensemble learning approaches were developed to handle these changes in the dataset, which proved to be better than a single classifier system. In our work, we will discuss the framework of our new approach, Double Weighted Methodology and empirically prove it to be better than the...
A way of combining SVM(Support Vector Machine) with Supervised Subset Density Clustering is proposed in this paper. How to minimize the training set of SVM by means of clustering is researched. Original center positions are of great importance to clustering accuracy. However the traditional clustering center choosing algorithm doesn't work properly when the same kind of samples aren't closely-spaced...
Hand gestures are used widely in communication. An important example is using in the sign languages. Many hand gesture silhouettes are the part of other hand gesture silhouettes. For example, V sign gesture is a part of the high five gesture, because we can create high five gesture silhouettes from the V sign gesture silhouettes by extending the other three fingers. Here we propose the partial contour...
We propose a bootstrap-based iterative method for generating classifier ensembles called Iterative Classifier Selection Bagging (ICS-Bagging). Each iteration of ICS-Bagging has two phases: i) bootstrap sampling to generate a pool of classifiers; and, ii) selection of the best classifier of the pool using a fitness function based on the ensemble accuracy and diversity. The selected classifier is added...
In the proposed approach, an attempt was made to disambiguate Bengali ambiguous words using Naïve Bayes Classification algorithm. The whole task was divided into two modules. Each module executes a specific task. In the first module, the algorithm was applied on a regular text, collected from the Bengali text corpus developed in the TDIL project of the Govt. of India and the accuracy of disambiguation...
As the ageing population increases, more and more people require care and support. To date there has been limited understanding of older people's needs and the general well-being when they are in care. In this paper we investigated the relationships between the indices of independence in daily activities of the people who are in care and the behaviour rating scale [2] defined in the nursing assessment...
Data mining has recently drawn a lot of interests as an effective way of generating a concept map in an adaptive learning system that provides students with the personalized learning guidance. Even with significant progresses witnessed in this field, the data mining-based concept map generation needs further improvement both in accuracy and complexity before it can be employed in actual education...
The research using computational intelligence methods to improve bad debt recovery is imperative due to the rapid increase in the cost of healthcare in the U.S. This study explores effectiveness of using cost-sensitive learning methods to classify the unknown cases in imbalanced bad debt datasets and compares the results with those of two other methods: undersampling and oversampling, often used in...
The identification of network application is important for network management such as traffic controls and anomaly traffic detection. To apply application-based traffic control promptly, the technique to progressive application identification is important. We previously proposed a method of timeliness identification of application, which can achieve to use first several packets of flow to identify...
Text Categorization plays an important role in the fields of information retrieval, machine learning, natural language processing, data mining and others. With the development of computer and information technology, there have been many classification algorithms. Each text classification algorithms will get result at differing speeds and efficiency due to the various feature of test text. It has been...
Text classification is the foundation and core of text mining. Naive Bayes is an effective method for text classification. This paper improves the accuracy of Naive Bayes classification using improved information gain, one of methods of feature extraction, by reducing the impact of low-frequency words. In this paper, we use a widely corpus of NLTK. According to the test results, The accuracy of the...
Context aware systems like smart homes and offices will benefit from determining human-object and human-human interactions. In this paper, we explore interaction detection methods using only wearable Inertial Measurement Units (IMUs). The interactions we explore involve two actors — the primary person and a secondary object or person. We explore how several commonly used time domain signal processing...
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