A nonparticipant netnographic qualitative study of a list of prodrug websites (blogs, fora, and drug marketplaces) located into the surface web was here carried out. The paper aims at providing an overview to mental health's and addiction's professionals on current knowledge about prodrug activities on the deep web.
The hidden " deep web" is facilitating this phenomenon. The easily renewable and anarchic online drug-market is gradually transforming indeed the drug market itself, from a "street" to a "virtual" one, with customers being able to shop with a relative anonymity in a 24-hr marketplace. Nowadays, the web is rapidly spreading, playing a significant role in the marketing or sale or distribution of "quasi" legal drugs, hence facilitating continuous changes in drug scenarios. Orsolini, Laura Papanti, Duccio Corkery, John Schifano, Fabrizio PMID:27313603Īn insight into the deep web why it matters for addiction psychiatry?
Finally, a careful performance evaluation of our algorithm confirms that our approach can effectively detect outliers in deep web. Neighborhood sampling and uncertainty sampling are developed in this paper with the goal of improving recall and precision based on stratification. In our approach, the query space of a deep web data source is stratified based on a pilot sample. The primary contribution of this paper is to develop a new data mining method for outlier detection over deep web. Therefore, traditional data mining methods cannot be directly applied. In the context of deep web, users must submit queries through a query interface to retrieve corresponding data. In this paper, we argue that, for many scenarios, it is more meaningful to detect outliers over deep web. Existing work in outlier detection never considers the context of deep web. Fang, Ligang Gu, Caidong Yang, Yuanfeng Cui, Zhimingįor many applications, finding rare instances or outliers can be more interesting than finding common patterns. Xian, Xuefeng Zhao, Pengpeng Sheng, Victor S. Stratification-Based Outlier Detection over the Deep Web Xian, Xuefeng Zhao, Pengpeng Sheng, Victor S Fang, Ligang Gu, Caidong Yang, Yuanfeng Cui, Zhimingįor many applications, finding rare instances or outliers can be more interesting than finding common patterns.
Stratification-Based Outlier Detection over the Deep Web. This video provides an introduction to the deep web search engine. The deep web includes content in searchable databases available to web users but not accessible by popular search engines, such as Google. To make the web work better for science, OSTI has developed state-of-the-art technologies and services including a deep web search capability.
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