Copy-move is one of the most commonly used methods of tampering with digital images. Keypoint-based detection is recognized as effective in copy-move forgery detection (CMFD). This paper proposes an efficient CMFD method via clustering SIFT keypoints and searching the similar neighborhoods to locate tampered regions. In the proposed method, the keypoints are clustered based on scale and color, grouped into several smaller clusters and matched separately, which reduce the high time complexity caused in matching caused by the high dimensionality of SIFT. In order to locate the tampered regions accurately at pixel level finally, a novel localization algorithm is designed to compare the similar neighborhoods of matching pairs by two similarity measures, and mark the tampered regions in pixels iteratively. We experimented on three different image data sets including kinds of tampering means to compare and verify the effectiveness and robustness of proposed method. The experimental results show that the proposed method is superior to existing state-of-art methods in terms of matching time complexity, detection reliability and forgery location accuracy. INDEX TERMS Copy-move forgery detection, digital image forensics, keypoint clustering, similar neighborhood search algorithm.
Medical professionals review clinical narratives to assign medical codes as per the International Classification of Diseases (ICD) for billing and care management. This manual process is inefficient and error-prone as it involves a nuanced one-to-many mapping. Recent works on automated ICD coding learn mappings between low-dimensional representations of the reports and the codes. While they propose novel neural networks for encoding varied types of information about the codes, it is unclear as to what information in the medical codes is helpful for performance improvement and why. Here, we compare different ways to represent, or embed, the codes based on their textual, structural and statistical characteristics, using a single deep learning baseline model in quantitative evaluations on discharge reports from the MIMIC-III Intensive Care Unit database. We also qualitatively analyse the nature of the cases that benefit most from the code embeddings and demonstrate that code embeddings are important for predicting ambiguous and oblique codes.
With the popularization of electricity, the use of electric porcelain is becoming more and more extensive, but in the process of production, the waste of electric porcelain continues to accumulate and increase, which not only pollutes the environment but also affects the normal life of human beings. Therefore, it is urgent to deal with these electric porcelain wastes. In this paper, the electric ceramic waste is used as the main raw material, and starch and kaolin are added to optimize the formula to prepare porous ceramsite. The effects of the content of electrical ceramic waste, sintering temperature and holding time on the properties of porous ceramsite were studied. After research, the results show that with the increase of waste content, the porosity and water absorption first decrease and then increase, while the compressive strength first increases and then decreases. With the increase of the sintering temperature, the porosity and the water absorption rate decrease continuously at the beginning, and the compressive strength increases continuously at the beginning, but when the sintering temperature increases continuously, there will be a turning point or a flattening point. With the continuous extension of the holding time, the water absorption and porosity continue to increase, while the compressive strength continues to decrease, but the overall change is small. The optimization can be found that when the waste content is 75%, the sintering temperature is 1200 °C, and the holding time is 30 min, the prepared porous ceramsite has the best performance. Its water absorption rate: 13.87%, porosity: 27.52%, compressive strength: 20.06MPa.
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