scholarly journals Portraying Privacy Leakage of Public WiFi Systems for Users on Travel Spam Detection in Social Bookmarking System

In this paper, we depicts spam revelation, in perspective of the examination of posts, in social bookmarking districts. For consistent acknowledgment of spam posts, we propose a name estimation plot and a specific evaluation procedure for picking marks. The label estimation scores each tag. In the particular evaluation, the label scores in perspective of the utilization repeat and the degree of spammers are estimated and the thoughts of white tag and dim tag are introduced. Using these thoughts, names are proficiently arranged into the names demolishing the execution of spam revelation, the names pleasing in getting spammers, and the marks which should achieve a discipline. Finally, we propose semantic components to moreover upgrade the spam distinguishing proof

1978 ◽  
Vol 17 (01) ◽  
pp. 16-23 ◽  
Author(s):  
Ch. L. Zollikofer ◽  
J. Wewerka ◽  
Th. Frank

35 patients with scintigraphically silent thyroid regions without palpable cold nodules were further evaluated by ultrasonography. In 33 cases the sonographic diagnosis was confirmed by other examinations or the clinical course. 2 cases were misinterpreted right at the beginning of our series.The use of ultrasonography in evaluating silent thyroid regions in the totally decompensated autonomous adenoma, in unilateral thyroid aplasia, thyroiditis and hyperthyroidism is shown to be a reliable and valuable supplement to the clinical and radioisotopic evaluation procedures. When differentiating the totally decompensated autonomous adenoma from unilateral thyroid aplasia a stimulation test need not be performed in most cases. Suspected thyroiditis can be confirmed in a simple way. Being a non-invasive evaluation procedure, ultrasonography should be used before performing a needle biopsy.


2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.


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