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I develop a program called DAVID (you can find full documentation here(https://www.researchgate.net/profile/Amit_Sharma183)). Now in the next level, a mechanical structure is being engineered. The same program will not only assist the user but also make the mechanical structure function accordingly. This presentation describes the WHAT, WHEN, WHY, HOW of this project. Enjoy the presentation...
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Hamid Motahari
Â
Abstract: Information retrieval is the major task in present scenario as quantum of data is increasing with a tremendous speed. So, to manage & mine knowledge for different users as per their interest, is the goal of every organization whether it is related to grid computing, business intelligence, distributed databases or any other. To achieve this goal of extracting quality information from large databases, software agents have proved to be a strong pillar. Over the decades, researchers have implemented the concept of multi agents to get the process of data mining done by focusing on its various steps. Among which data pre-processing is found to be the most sensitive and crucial step as the quality of knowledge to be retrieved is totally dependent on the quality of raw data. Many methods or tools are available to pre-process the data in an automated fashion using intelligent (self learning) mobile agents effectively in distributed as well as centralized databases but various quality factors are still to get attention to improve the retrieved knowledge quality. This article will provide a review of the integration of these two emerging fields of software agents and knowledge retrieval process with the focus on data pre-processing step. Keywords: Data Mining, Multi Agents, Mobile Agents, Preprocessing, Software Agents
A Survey of Agent Based Pre-Processing and Knowledge Retrieval
A Survey of Agent Based Pre-Processing and Knowledge Retrieval
IOSR Journals
Â
With the proliferation of electronic commerce and knowledge economy environment both organizations and individuals generate and consume a large amount of online information. With the huge availability of product information on website, many times it becomes difficult for a consumer to locate item he wants to buy. Recommendation Systems [RS] provide a solution to this. Many websites such as YouTube, e-Bay, Amazon have come up with their own versions of Recommendation Systems. However Issues like lack of data, changing data, changing user preferences and unpredictable items are faced by these recommendation systems. In this paper we propose a model of Recommendation systems in e-commerce domain which will address issues of cold start problem and change in user preference problem. Our work proposes a novel recommendation system which incorporates user profile parameters obtained from Social Networking website. Our proposed model SNetRS is a collaborative filtering based algorithm, which focuses on user preferences obtained from FaceBook. We have taken domain of books to illustrate our model.
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ijcseit
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Seminar
1.
Intelligent Software Assistants
Meghaditya Roy Chaudhury Exam Roll ~ CSE-1241040 Class Roll ~ 000810501052 BCSE Final Year Seminar-I Jadavpur University
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Hard Part: Interacting
with the World
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Â
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Harder Part: Interacting
with the userâs friends and other contacts
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Emerging community-based QA
system such as âQuoraâ
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Hardest Part: Interacting
with the User
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Thank You