[Figs 8](#fig8){ref-type=”fig”} and [9](#fig9){ref-type=”fig”} summarize the proposed model and show the built up layers and parameterization. The hidden layers in the first phase is used to construct a common representation between the input and output while the second phase consists of a histogram representation to increase the detail of the output. The model is then trained with a back-propagation algorithm to extract the feature representation for the predicted output. A custom MATLAB script is used to classify the applications. The results are compared to the output generated by hand and no difference could be found. The top score applications are shown in [Fig. 9](#fig9){ref-type=”fig”} with most of the apps in the top ten scoring applications being academic apps (i.e. mathematics, calculus, and computer science).Fig. 8Proposed model to classify text documents with the proposed histogram based approach. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)Fig. 8Fig. 9Most scored apps on the Apple App Store. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)Fig. 9
The research attempts to address an important problem (i.e. that of the timely repair and un-installation of malware) that has not been addressed before. The proposed algorithm is simple and generic, and the evaluation shows that the proposed model is able to produce accurate results. However, the proposed model is restricted to specific app categories and the users that are trained to extract the feature representation of the apps. A weakness in the proposed system is that it uses a small dataset. The proposed model is also dependent on the data available in the app store and the time available to complete the extraction of feature representation for the classification system. In future work, we plan to expand the dataset to increase the number of apps used to evaluate the model to include other categories of apps available in the store. The next stage of the work will involve extending the proposed model to more applications available in the Apple App store. The more apps used to evaluate the proposed model, the more advanced the model will be. However, extracting
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Simply go to the start menu and navigate to „All programs”. Then right click in the title bar of windows explorer and select „Send To”.
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The code below outputs as the filename/filepath from where the files are running from.
import os
os.system(‘pwd’)
A:
You can use os.path.join() and the os.sep parameter:
# Test if there is a path in dir1 and a filename in dir2
dir1, file = os.path.split(dir2)
# Test if there is a path in dir2 and a filename in dir1
dir2, file = os.path.split(dir1)
# If there is no path in dir1 and a filename in dir2, add.. to dir1
dir1, file = os.path.split(os.path.join(dir1, file))
Circulating levels of alpha-fetoprotein and its mRNA in patients with hepatocellular carcinoma.
Morphological diagnosis of hepatocellular carcinoma (HCC) is difficult in the early stages, especially in the presence of cirrhosis and hepatitis. In this situation, differential diagnosis of HCC from chronic hepatitis or cirrhosis is very important. Hepatocellular carcinoma cells produce high levels of alpha-fetoprotein (AFP), and measurement of AFP in the serum is the most commonly used tool for diagnosing and monitoring HCC. AFP mRNA is also a diagnostic marker for HCC. To determine whether the serum levels of AFP and its mRNA are suitable tools for diagnosis and monitoring of HCC, we assayed AFP and AFP mRNA in the serum from 26 patients with HCC (17 male, 9 female; 16 with cirrhosis and 10 without cirrhosis), 8 patients with chronic hepatitis (5 with cirrhosis and 3 without cirrhosis), and 13 healthy subjects (9 male, 4 female). The mean+/-SD of the serum AFP level in patients with HCC was 2743+/-3918 microg/L, significantly higher than that in patients with chronic hepatitis (1596+/-1364 microg/L; p
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