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Anonymization of the medical document provides effective protection of patient privacy so as to promote the development of anonymization of electronic medical records in China. However, traditional methods which identify patient privacy manually are not only inefficient with frequent errors and omissions but also labor-consuming. To solve this problem, this paper introduces an algorithm which has...
This paper compares two different techniques for channel emulation in multiprobe anechoic chamber based setups, which is a candidate solution for the standardization of MIMO OTA performance testing of mobile devices. The comparison is performed via simulations of the field distribution, temporal correlation, and spatial correlation emulated by these methods for different number of probes. Results...
In recent power grid systems, data-driven approach has been taken to grid condition evaluation and classification after successful adoption of big data techniques in internet applications. However, the raw training data from single monitoring system, e.g. dissolved gas analysis (DGA), are rarely sufficient for training in the form of valid instances and the data quality can rarely meet the requirement...
Person re-identification is an important computer vision task with many applications in areas such as surveillance or multimedia. Approaches relying on handcrafted image features struggle with many factors (e.g. lighting, camera angle) which lead to a large variety in visual appearance for the same individual. Features based on semantic attributes of a person's appearance can help with some of these...
In this paper, a global algorithm for human action, facial and gesture recognition is presented. The proposed algorithm depends on the extraction of multiple transform domain features and Canonical Correlation Analysis (CCA) for features fusion and classification. The proposed algorithm achieved the best reported results for facial and facial expression recognition. Excellent comparable results were...
The recommender system is widely used in many areas in the age of information overload. Collaborative filtering (CF), as one of the most successful methods used for recommendation, recommends items based on the nearest neighbors of the target user. Thus, the performance of the recommender system depends largely on the similarity measure used for selecting neighbors. Most of the traditional similarity...
In this paper we investigate the use of fuzzy rule-based classifiers for multi-label classification. This classification task deals with problems where more than one label could be assigned simultaneously to a given instance. We concentrate on problem transformation methods, which use different strategies to transform a multi-label problem into a different single-label classification problems. This...
Properly addressing the performance issues presented in database systems is and has been a significant technological challenge, this due to the uncontrolled fluctuation of user requests. Being able to predict the behaviour of such systems can greatly improve their performance. Several prediction methods, such as linear regression and autoregressive moving average, among others, have extensively been...
The widely known classifier chains method for multi-label classification, which is based on the binary relevance (BR) method, overcomes the disadvantages of BR and achieves higher predictive performance, but still retains important advantages of BR, most importantly low time complexity. Nevertheless, despite its advantages, it is clear that a randomly arranged chain can be poorly ordered. We overcome...
In this paper we introduce an object-based change detection model using correlation analysis and classification. First we use eCognition to obtain an over-segmentation map. Then linear regression is used to gain three unique types of parameters — regression coefficient, offset, and correlation coefficient which can provide valuable information about the location and numeric change value derived within...
In this paper, we propose a theoretically new and effective feature for SAR image classification. The new feature combines traditional gray level co-occurrence matrix (GLCM) textural feature and the recent multilevel local pattern histogram (MLPH) feature. It can not only describe intrinsic property of land-cover/land-use surfaces, corresponding to textural information, but it also captures both local...
Spectral correlation function (SCF) is an important tool for analyzing cyclostationary signals. Only a few kinds of communication signals have analytic form for their SCF. This paper proposes a simple analytic expression for SCF of the well-known GMSK signal using super-Gaussian function. Numerical results show the validity of our approximation.
Sensor pattern noise (SPN) has been proved to be an inherent fingerprint of a camera, and it has been broadly used in the fields of image authentication and camera source identification. However, the SPN extracted using current denoising algorithm always contains image content residual, which would significatively influence the accuracy of camera source identification. In this paper, a novel patch-based...
Even though the electricity HPFC (Hourly Price Forward Curve) is still surprisingly under-researched the prediction of electricity prices is highly important in order to keep power plants profitable or in order to optimize the electricity purchases based on future customers demand. In this work two methods to model and predict HPFC based on neural networks will be proposed and compared to more common...
In developing the human-machine technology, it is essentially important to infer human mind state. A machine learning approach is promising to this need. However, the machine-learning approach essentially requires training data, ideally supervised training data, which may not be readily available. An idea is to overcome this shortcoming is to take the so-called subjective rate measure. Take the problem...
In this study, we compared several classifiers for the supervised distinction between normal elderly and Alzheimer's disease individuals, based on resting state electroencephalographic markers, age, gender and education. Three main preliminary procedures served to perform features dimensionality reduction were used and discussed: a Support Vector Machines Recursive Features Elimination, a Principal...
Learning analytics is valuable sources of understanding students' behavior and giving feedback to them so that we can improve their learning activities. Analyzing comment data written by students after each lesson helps to grasp their learning attitudes and situations. They can be a powerful source of data for all forms of assessment. In the current study, we break down student comments into different...
In this paper, we evaluate the generalization power of deep features (ConvNets) in two new scenarios: aerial and remote sensing image classification. We evaluate experimentally ConvNets trained for recognizing everyday objects for the classification of aerial and remote sensing images. ConvNets obtained the best results for aerial images, while for remote sensing, they performed well but were outperformed...
In multi-label classification, labels often have correlations with each other. Exploiting label correlations can improve the performances of classifiers. Current multi-label classification methods mainly consider the global label correlations. However, the label correlations may be different over different data groups. In this paper, we propose a simple and efficient framework for multi-label classification,...
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