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This paper proposes a fusion model to enhance classification accuracy of support vector machines (SVMs) for fault detection. The proposed method consists of two different phases, where in the first phase, different SVMs are constructed based on training datasets, and these trained SVMs are evaluated with respect to test datasets by calculating distances between test samples and trained hyperplanes...
Recognizing secondary structures in proteins can be a highly computationally expensive task that may not always yield good results. Using Restricted Boltzmann Machines (RBM) we were able to train a simple neural network to recognize an alpha-helix with a good degree of accuracy. Modifying the RBM implementation to be much simpler and more efficient than the standard implementation we are able to see...
Two GF-1 WFV images on August 3, 2015 and October 2, 2015 were selected to extract the cultivated area of paddy rice in Jianhu county of Jiangsu Province. Vegetation indexes were extracted from the original spectrum data in order to extract paddy rice area with Maximum Likelihood Classifier (MLC), Support Vector Machine (SVM) and Classification and Regression Trees (CART). The extraction accuracy...
This paper proposes a method for construction of classifiers for discharge summaries. First, morphological analysis is applied to a set of summaries and a term matrix is generated. Second, correspond analysis is applied to the classification labels and the term matrixand generates two dimensional coordinates. By measuring thedistance between categories and the assigned points, ranking of key wordswill...
Neuroscience researchers have a keen interest in finding the connection between various brain regions of an organism. Researchers all across the globe are finding new connections everyday and it is very difficult to keep track of all those, so it is important to create a centralized system which is able to give the relation between brain entities. Databases like PubMed contains abstracts and references...
Youtube is one of the most popular video sharing platform in Indonesia. A person can react to a video by commenting on the video. A comment may contain an emotion that can be identified automatically. In this study, we conducted experiments on emotion classification on Indonesian Youtube comments. A corpus containing 8,115 Youtube comments is collected and manually labelled using 6 basic emotion label...
This paper discusses about the process of classifying odor using Support Vector Machine. The training data was taken using a robot that ran in indoor room. The odor was sensed by 3 gas sensors, namely: TGS 2600, TGS 2602, and TGS 2620. The experimental environment was controlled and conditioned. The temperature was kept between 27.5 0C to 30.5 0C and humidity was in the range of 65%–75 %. After simulation...
Based on the spectral data from SDSS, Kernel Support Vector Machines (K-SVM) is applied to classify quasars from other celestial body. Firstly, the basic theory of the SVM(Support Vector Machine) with relaxation factor and kernel function is introduced. Then, the main parameters are designed and selected. Finally, the method is applied to the classification and identification of the quasars. The classification...
Epileptic seizure is one of the most common neurological diseases around the world. It is clinical symptoms and/or signs due to abnormal excessive or synchronous neuronal activity in the human brain. Electroencephalogram (EEG) that measures the electrical activity of the brain generated by the cerebral cortex nerve cells, is the most utilized test to detect the seizure activities by visual scanning...
Development of smart cities has grasped much attention in research community and industry as well. Smart healthcare, communication, infrastructure are required for the development of smart cities. Security is one of the major concern in the development of smart cities. Automatic surveillance helps in boosting security in multiple areas like traffic, hospitals, schools, and industries etc. Video camera...
Patten recognition techniques are widely used for image processing in medical imaging. It provides assistance to physicians and scientists in large scale diagnosis. In this paper, we have proposed an automated system for detecting melanoma from dermoscopic images. We detected melanoma by extracting information from region of interest (ROI) rather than the whole image composed of lesion and background...
Fault diagnosis is a major concern of the prognostics and health management of rotating machinery. Current practice in fault diagnosis is often challenged by the non-normality, multimodality, and nonlinearity of machinery health monitoring signals and their extracted features. A single classifier used in fault diagnosis fails when all these challenges exist. Thus, in this paper a hybrid ensemble learning...
The medical datasets have many features if the features have a tendency of mutation then the risk of disease increases which makes difficult to provide a diagnosis of disease. In the dataset, every feature is a contributor for prediction accuracy, the selection of significant features from the dataset is a challenging task. The feature selection technique based on metaheuristic algorithms is used...
Sparse representation (SR) based hyperspectral image (HSI) classification is a rapidly evolving research topic. How to construct an optimized dictionary to better characterize spectral-spatial features of HSI is an important problem. In this paper, a novel spectral-spatial online dictionary learning (SSODL) method for HSI classification is proposed. The main idea is to learn a complete and discriminative...
Polarimetric Synthetic Aperture Radar (PolSAR) images are an important source of information. Speckle noise gives SAR images a granular appearance that makes interpretation and analysis hard tasks. A major issue is the assessment of information content in these kind of images, and how it is affected by usual processing techniques. We study this problem from the classification accuracy viewpoint. Our...
In this work, we develop a new framework to combine ensemble learning and composite kernel learning for hyperspectral image classification. We refer it as the multiple composite kernel learning, which is based on an iterative architecture. More specifically, in each iteration, we use the rotation-based ensemble to create rotation matrix, which is used to generate rotated features for both spectral...
Extinction profile (EP) is an effective feature extraction method which can well preserve the geometrical characteristics of a hyperspectral image (HSI) and by extracting the EP from first three independent components (ICs) of an HSI, three correlated and complementary groups of EP features can be constructed. In this paper, an EPs fusion (EPs-F) strategy is proposed for HSI classification by exploring...
This study aims at evaluating two classes of methods to discriminate 13 peatland vegetation types using reflectance data from hyperspectral in situ measurements. These vegetation types were empirically defined according to their composition, strata and biodiversity richness. We suppose that specific biophysical properties/components may help discriminating vegetation types applying supervised classification...
In the tropical and the subtropical countries, malaria has been a challenge, which really needs a quick and precise diagnosis to put a stop or control the disease. The conventional microscopy method has some shortcomings which includes time consumption and reproducibility. Many of the alternative methods are expensive and it's not readily accessible to the developing countries that need them. In this...
Polarimetric SAR classification is an effective approach in image understanding. This paper proposes a novel semantic method for classification of Polarimetric SAR data. The method combines superpixels and semantic model to benefit from both the object-oriented classification and the high-level semantic information. Firstly, pixels was grouped into superpixels via Simple Linear Iterative Clustering...
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