PPT Topics

Aug 5, 2016

Multichannel Recordings for Data-Mining Algorithms Full Paper ieee

Processing of Multichannel Recordings for Data-Mining Algorithms pdf full abstract presentation paper journal ppt
Abstract: Data Mining, or knowledge discovery, is the computer-assisted process of digging through and analyzing large quantity of data in order to extract meaningful knowledge. Data mining methods are used in many studies to identify phenomena quicker and better than human experts. One class of these methods was designed for dealing with time series data. However, when several channels of data are collected simultaneously, data mining algorithms encounter numerous difficulties since channels may be measured in different units, may be recorded at different sampling-rates, or may have completely different characteristics. Furthermore, as the size of these data increases, the amount of irrelevant data usually increases as well and the process becomes impractical. Hence, in such cases, the analyst must be capable of focusing on the informational parts while ignoring the noise data. These kinds of difficulties complicate the analysis of multichannel data as compared to the analysis of single-channel data. This paper presents a useful technique for preprocessing multi channel data. Our technique supplies tools for coping with all the above-mentioned difficulties, and prepares the data for further analysis (using common algorithms, especially from the data mining field).

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Jul 4, 2016

Automatic Discovery of Personal Name Aliases from the Web full paper ppt ieee

Automatic Discovery of Personal Name Aliases from the Web
Abstract: In this paper we proposed a lexical-pattern-based approach to extract aliases of a given name. We use a set of names and their aliases as training data to extract lexical patterns that describe numerous ways in which information related to aliases of a name is presented on the web. An individual is typically referred by numerous name aliases on the web. Accurate identification of aliases of a given person name is useful in various web related tasks such as information retrieval, sentiment analysis, personal name disambiguation, and relation extraction. We propose a method to extract aliases of a given personal name from the web. Given a personal name, the proposed method first extracts a set of candidate aliases. Second, we rank the extracted candidates according to the likelihood of a candidate being a correct alias of the given name. We evaluate the proposed method on three data sets: an English personal names data set, an English place names data set, and a Japanese personal names data set. The proposed method outperforms numerous baselines and previously proposed name alias extraction methods, achieving a statistically significant mean reciprocal rank (MRR) of 0.67. Keywords: Automated Discovery, Aliases, lexical patterns.

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Accurate Knowledge Evaluation by Deep Datamining in Telecommunication Engineering Studies

Accurate Knowledge Evaluation by Deep Datamining in Telecommunication Engineering Studies Abstract-This paper describes a novel approach for the evaluation of the alumni knowledge acquisition based on deep data-mining techniques. Cooperative learning and data-mining can be combined to foster the knowledge acquisition process of the students. This approach has been introduced in the subject ldquoAnaacutelisis de Sistemas Contiacutenuosrdquo in the second term of the first year of the Telecommunication Engineering Degree studies at the Escuela Politeacutecnica Superior de Gandia from the Universidad Politeacutecnica de Valencia (UPV), Spain. The evaluation method is based on laboratory lessons and seminars. The laboratory lessons include an on-line exam with a specific set of variables targeted for data-mining processing. Interactive groupal seminars are also introduced in the subject. In this case, the accurate knowledge of the skills acquired by individual students is evaluated by which decorrelation techniques on a large set of variables which are also processed in the data-mining.

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A Genetic Based Wrapper Feature Selection Approach Using Nearest Neighbour Distance Matrix

A Genetic Based Wrapper Feature Selection Approach Using Nearest Neighbour Distance Matrix Abstract-Feature selection for data mining optimization receives quite a high demand especially on high-dimensional feature vectors of a data. Feature selection is a method used to select the best feature (or combination of features) for the data in order to achieve similar or better classification rate. Currently, there are three types of feature selection methods: filter, wrapper and embedded. This paper describes a genetic based wrapper approach that optimizes feature selection process embedded in a classification technique called a supervised Nearest Neighbour Distance Matrix (NNDM). This method is implemented and tested on several datasets obtained from the UCI Machine Learning Repository and other datasets. The results demonstrate a significant impact on the predictive accuracy for feature selection combined with the supervised NNDM in classifying new instances. Therefore it can be used in other applications that require feature dimension reduction such as image and bioinformatics classifications.

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Mar 24, 2013

Text Image Separation in Devanagari Documents ieee paper presentation download

This Paper is Submitted by R.Suman Varma (Chaitanya Bharathi Institute of Technology) Paper Presentations ppts topics with full doc abstract on Text - Image Separation in Devanagari Documents (Seminar Paper Presentations) 
 Abstract:In this paper we present a top-down, projection-profile based algorithm to separate text blocks from image blocks in a Devanagari document. We use a distinctive feature of Devanagari text, called Shirorekha (Header Line) to analyze the pattern produced by Devanagari text in the horizontal profile. The horizontal profile corresponding to a text block possesses certain regularity in frequency, orientation and shows spatial cohesion. The algorithm uses these features to identify text blocks in a document image containing both text and graphics.

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Jun 30, 2012

Data Mining of the E-Government on the Basis of Fuzzy Logic Paper Presentation

Seminar Papers Presentations IEEE ppt topics abstract on Data Mining of the E-Government on the Basis of Fuzzy Logic (Seminar Paper Presentations)
Abstract: The technology of data mining is widely used in various fields. E-Government is a grand new domain in recent years. When we use the E-Government system to process data, we need to choose what data is useful and what kind of new information we can get from the log file or from the database.
Because of the special characters of knowledge, this paper presents an algorithm of the fuzzy data mining, and put great importance on the steps of the Fuzzy data mining. At the end of this paper it improves the values of Fuzzy data mining in the E-government by a real example.
Index Terms –mining; E-government; Fuzzy logic

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Oct 16, 2011

Data mining techniques paper presentation | Datamining multi agent system seminar paper

Paper Pharmacy Presentations technical seminar ppts Abstract papers on Data mining techniques with multi-agent systems
The integration of data mining techniques with multi-agent systems to assist in dealing with information overload has received some attention during the last years. Agent Academy, a platform for training agents, introduces a whole new perspective on the improvement of agent intelligence.  Data mining techniques are used in order to extract useful patterns on real high-risk and time-efficient applications, and to provide the  platform with rules, decisions and classes on test case data. These metadata are embedded into agents in order to improve their existing intelligence. This paper describes the Agent Academy platform and focuses on the issues and challenges its development has revealed through the prism of data mining.

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