Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational ap...
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One effort to maintain documents or records is to make changes in the form of digital images. The drawings further processing needs to be done so that the text or sentences therein can be operated as do the search, an...
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ISBN:
(纸本)9781509016419
One effort to maintain documents or records is to make changes in the form of digital images. The drawings further processing needs to be done so that the text or sentences therein can be operated as do the search, analysis, or manipulation of the contents of the text. The treatment process is known as optical character recognition (OCR) and continues to develop. OCR is generally divided into three main stages, namely preprocessing, feature extraction and classification. Feature extraction is one of the essential or fundamental processes in character recognition. The purpose of feature extraction is to obtain the characteristics of each character. The results at this stage can affect the quality of character recognition. Generally, feature extraction on character is done by a complex calculation so as to cause the necessary time computing is not a little, especially in real time recognition case. In this paper, feature extraction can be done simply proposed as an alternative, called Shape Energy. This method uses the approach of how humans are able to distinguish between characters or numbers in a simple. It results in three elements which are elasticity, curvature, and texture. The elasticity is first derivative and the curvature is second derivative of each pixel in the frame of the character, which is obtained from thinning. While the texture value is 4-direction chain-codes. This method testing has been done on some type of character by using back propagation neural network as a method on classification stage. This testing resulted in average value accuracy rate of success in identifying these characters by 90.3%.
Development of microarray technology makes the number of research about Bioinformatics will increase as well. Microarray dataset contains genetic information and can be used to analyze thousands of samples and feature...
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ISBN:
(纸本)9781509016419
Development of microarray technology makes the number of research about Bioinformatics will increase as well. Microarray dataset contains genetic information and can be used to analyze thousands of samples and features. Especially in cancer research, the cancer data is generated by microarray technology, and will be a primary data for training and testing in the machine learning process. The main difficulties lie in the nature of microarray gene expression data which usually are noisy and high-dimensional. Microarray dataset usually has a large number of attributes or features, but it has a small number of samples. This condition makes the learning process microarray dataset has become harder because of the curse of dimensionality, where the machine will be difficult to handle a number of data with a very high-dimensional. The solution to handle a high-dimensional dataset and improve the accuracy of microarray dataset is using feature selection. The method of feature selection is followed the principle of natural selection, called Evolutionary Algorithms. We proposed to implement some Evolutionary Algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO), which ACO improved F-measure of 9 datasets and ROC Area of 7 datasets from 11 datasets existing in the cancer research.
Recently, a modified data hiding scheme based on pixel value differencing and improving exploiting modification directions is proposed by Shen and Huang. There are two major contributions in this scheme. One is to enh...
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The growth of large area single-layer graphene (1-LG) is studied using ambient pressure chemical vapor deposition on single-crystal Ni(111), Ni(110), and Ni(100). By varying both the furnace temperature in the range o...
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The growth of large area single-layer graphene (1-LG) is studied using ambient pressure chemical vapor deposition on single-crystal Ni(111), Ni(110), and Ni(100). By varying both the furnace temperature in the range of 800–1100 °C and the gas flow through the growth chamber, uniform, high-quality 1-LG is obtained for Ni(111) and Ni(110) single crystals and for Ni(100) thin films. Surprisingly, only multilayer graphene growth could be obtained for single-crystal Ni(100). The experimental results are analyzed to determine the optimum combination of temperature and gas flow. Characterization with optical microscopy, Raman spectroscopy, and optical transmission support our findings. Density-functional theory calculations are performed to determine the energy barriers for diffusion, segregation, and adsorption, and model the kinetic pathways for formation of different carbon structures on the low-index surfaces of Ni.
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