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Mobile web browsing signifies accessing the content on web pages using a mobile device. It is common for Internet search engines to use keyword searching in which rank is assigned to each page based on several features. But it is an arduous task for a user to inscribe a keyword in such a delicate small mobile screen
based clustering is the most appropriate clustering algorithm. Here we used tweets from Twitter, while the DBSCAN method is used for generating clusters. Here for finding similarity between tweets cosine similarity is used, but because of its low value we increase its value by adding weight to it by matching the keywords
sending a simple keyword query to the hotspot (BlueInfo Pull). The BlueInfo hotspot requests the service from the origin server in the Internet and relays the response to the mobile device, possibly after adaptation for mobile viewing. The usability of BlueInfo Pull in comparison to a mobile phone browser is demonstrated
application. We believe that context-sensitive service discovery is the key success factor for mobile applications and could have the same impact and level of success as search engines on desktop computers. Keyword-based searches using typical search engines or portals for browsing services with a mobile device often do not
multimedia user-generated content. An authorized person or body filters it before being published. Once user-generated multimedia tourist contents are accepted, they are published using a tool into a web page. This GUI allows browsing for contents provided by individual users or those including a tag or keyword. Finally, the
information retrieve modes as Fuzzy Constraint Keywords based Searching Mode, Location-based E-Map Browsing Mode, 3-D Tag-Cloud based Collaborative Sharing Mode, which can enable the user to choose suitable retrieve mode and find the useful entity information much quickly. Furthermore, this paper introduces the latest Web2.0
The existing search engine system almost based on keywords from users inputting. In a network environment, people can make use of the pc or mobile device for information retrieval. The way of inputting keywords has lasted for more than 20 years until Siri appeared in 2011. Siri can do information retrieval and process
In order to solve the limitations of most of the current search engines only through the user's handwriting input keywords to read information, described new search engine system based on computer vision. Containing the information search and problem solve two basic parts. And for the needs of the people in daily
mobile device and query terms submitted by a user are formatted into the specific format. Then these treated elements are sent to an expert system to match with the rules. Finally, according to the matched rules, the additional keywords are appended to the query terms so as to catch on the user's search intention
information through the use of Short Message Service (SMS) and improve price transparency by creating a small database containing keywords that will easily provide consumers with detailed information about cheaper and branded drugs. The methodology being used consists of three parts. The first part includes interviewing key
in front of him/her by the shutter clicks. One of the great advantages of our approach is to use open image database such as Flickr, Picasa, or so on, to identify a landmark. Images on such open databases are automatically updated and some proper keywords are given by photographers in the world. Therefore, we need not
. Simultaneously, it generates a personal context-aware dictionary dynamically from the keywords gotten via some APIs in the Internet. Currently, the information of user's context is also provided by NGN. In this paper, we explain the overview of our proposal and prototype implementation in Japanese.
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.