ITRACE MEDICAL CA MOVIE
To the best of our knowledge, it is the first time that RTE concept has been implemented to Information Search in the form of Movie Search Engine. The experiments have shown encouraging results, reduced the time of search and provided more accurate results. The complete concept presented in the paper has been implemented in the form of a movie search engine wherein the knowledge based RTE concept has been employed on " summaries or plots of the movies " internally to get best possible classification of the movies. The data mined is used by an Inference system to give the output to the problem. Different Lexical resources like WordNet, VerbNet, ConceptNet have been integrated into a rich knowledge base, to provide semantics and structural information on English words. In this paper, Knowledge based approach has been proposed, utilizing data mining concepts on large text which is appropriately classified. Natural Language Inference has dealt with approaches like the bag of words approach, formal methods (First order Logic) and pattern relation extraction which usually do not show satisfactory results. Recognizing Textual Entailment (RTE) is a relatively new problem necessary for Natural Language Understanding (NLU) and Automated Knowledge Discovery. In This paper we proposed a semantic query interface which creates a semantic query according the user input query and study of current semantic search engine techniques for semantic search. Semantic search engines – only solution to this which returns concepts not documents according to user query matching. But they seemed to be less efficient to understand the relationship between the keywords which had an adverse effect on the results it produced. The reason behind this is the search engines performs search based on the syntax not on semantics. But the relevancy of results in most cases may not be satisfactory and the users may not be patient enough to browse through complete list of pages to get a relevant result. The users may want to define their search by using attributes of the search target. This approach clearly puts the users in a critical situation of guessing the exact keyword. These search engines use traditional search service based on "static keywords", which require the users to type in the exact keywords. When user not found relevant information he has to shortlist the results. Which information we retrieved via search engine may not be relevant to the search target in the search engine user's mind.
Medical cannabis has been legal in Canada since 2001, and its recreational use was completely legalized in October 2018.Ībout 5.4 per cent of Canadians have purchased cannabis since October, including some 600,000 who said they had recently tried it for the first time, according to Statistics Canada.These days we are using some popular web search engines for information retrieval in all areas, such engine are as Google, Yahoo!, and Live Search, etc.
The pilot project is slated to be launched this summer for deployment in pharmacies by November 2019, according to Shoppers, which has 1,300 stores. Popularized through its use in the creation of cryptocurrencies like Bitcoin, blockchain is a decentralized digital ledger that can be used to verifiably record transactions. "Although that's not always a guarantee within the medical cannabis industry at the moment, we're hoping this new program can help change that," he said. You can sign up here to receive it each weekday night.
ITRACE MEDICAL CA SOFTWARE
Shoppers Drug Mart said it was teaming up with Canadian software firm TruTrace to develop and deploy a pilot project to ensure the traceability of medical cannabis. The Pot Post: Sign up to get the newsletter delivered to your inbox.
Canada's biggest drug store chain said Monday it plans to use blockchain technology to trace the source of the cannabis it distributes, as part of an effort to set standards for the flourishing industry.
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