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Non-small Cell Lung Cancer (NSCLC) is a leading death disease in many countries. Many studies are focusing on exact surgical approaches to treat the disease. The five-year overall survival rate for NSCLC patients is typically predicted by traditional regression models with small samples and data size. In this paper, we introduce machine learning tools with feature selection algorithms and random forests...
Video inpainting is to recover the distorted pixels of the image sequences. In this paper, we use Gibbs distribution and Markov Random Field (MRF) modeling to solve this problem. We build MRF on the missing region of the image sequence conditioned on its neighborhood pixels that are undistorted. We recover the missing pixels by drawing samples from the MRF by Gibbs sampling. We collect a dataset to...
Object detection is a challenging task in the field of pattern recognition. The objective of object detection is to locate the target objects in the testing images. In this paper, we use SVM trained active basis model as a sparse coding model for representing objects. The sparse coding model represents each image as the linear superposition of a small number of Gabor wavelets selected from an over-complete...
Object clustering is a very challenging unsupervised learning problem in machine learning and pattern recognition. In this paper, we will study visual object pattern clustering problem by combining the k-means clustering algorithm and the binary sketch templates, which quantify each image by a vector of indicators showing that a sketch at certain location, scale, and orientation exist or not. This...
Credit risk analysis seeks to determine whether a customer is likely to default on the financial obligation, which is a very important problem in finance. In this paper, we will present a machine learning framework to deal with this problem by formulating it as a binary classification problem. The framework consists of two parts: dictionary learning and classifier training. Firstly, we introduce a...
Credit risk assessment becomes rapidly important for credit department of the bank to determine whether to issue credit cards, and make loans to both companies and individualities. However, due to the complexity of database, it is arduous for credit managers to make decisions. In order to solve this problem, this paper proposes a framework that combines feature selection and decision tree classification...
Since the crude oil market can make an impact to global economics, it is important to develop some effective approaches to forecast crude oil price and its volatility. In this paper, the goal is to predict the tendency of crude oil future price from ten selected features that potentially affect the crude oil price. Currently, the most popular and robust prediction methods are based on machine learning,...
Aiming the problem that it is difficult for the stationary spectrum allocation method of existing HF access network to meet the requirement of intelligent frequency hopping technology, the communication requirement of intelligent frequency hopping HF access network is analyzed in this paper, to come up with the dynamic frequency spectrum allocation strategy and algorithm of maximum handling capacity...
This paper introduces an adaptive hybrid SFH/TSS/DPSK spread spectrum system with separated interference detector. The basic working principles and architecture of the system are explained. The theoretic BER (bit error rate) of the system is estimated and compared to the classic DS/BPSK system in the presence of interference. Simulation results show that the anti-jamming capability of the adaptive...
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