Ejercicio

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Ejercicio

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With the boom in IT technology, the data sets used in application are more and more larger and are described by a huge number of attributes, therefore, the feature selection become an important discipline in Knowle dge discovery and data mining, allowing the experts to select the most relevant features to impr ove the quality of their studies and to reduce the time processing of their algorithm. In addition to that, the data used by the applications become richer. They are now represented by a set of complex and structured objects, instead of simple numerical ma trixes. The purpose of our algorithm is to do feature selection on rich data, called Boolean Symbolic Objects (BSOs) . These objects are desc ribed by multivalued features. The BSOs are considered as higher level units which can model complex data, such as c luster of individuals, aggregated data or taxonomies. In this paper we will introduce a new feature selection criterion for BSOs , and we will explain how we improved its complexity.

Feature selection on boolean symbolic objects

Feature selection on boolean symbolic objects

Feature selection on boolean symbolic objects

The large a v ailable am ou n t of non - structured texts that b e - long to differe n t domains su c h as healthcare (e.g. medical records), justice (e.g. l a ws, declarations), insurance (e.g. declarations), etc. increases the effort required for the analysis of information in a decision making pro - cess. Differe n t pr o jects and t o ols h av e pro p osed strategies to reduce this complexi t y b y classifying, summarizing or annotating the texts. P artic - ularl y , text summary strategies h av e pr ov en to b e v ery useful to pr o vide a compact view of an original text. H ow e v er, the a v ailable strategies to generate these summaries do not fit v ery w ell within the domains that require ta k e i n to consideration the tem p oral dimension of the text (e.g. a rece n t piece of text in a medical record is more im p orta n t than a pre - vious one) and the profile of the p erson who requires the summary (e.g the medical s p ecialization). T o co p e with these limitations this pa p er prese n ts ”GRe A T” a m o del for automatic summary generation that re - lies on natural language pr o cessing and text mining te c hniques to extract the most rele v a n t information from narrati v e texts and disc o v er new in - formation from the detection of related information. GRe A T M o del w as impleme n ted on sof tw are to b e v alidated in a health institution where it has sh o wn to b e v ery useful to displ a y a preview of the information a b ou t medical health records and disc o v er new facts and h y p otheses within the information. Se v eral tests w ere executed su c h as F unctional - i t y , Usabili t y and P erformance regarding to the impleme n ted sof t w are. In addition, precision and recall measures w ere applied on the results ob - tained through the impleme n ted t o ol, as w ell as on the loss of information obtained b y pr o viding a text more shorter than the original

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You have a great mobile recruiting strategy. But are you using one of the most powerful social networks available today? Google Plus can provide a powerful boost to your mobile recruiting efforts as long as you know how to make the best use of the platform. In this presentation, I provide an overview of Google Plus, the personal Google Plus profile, Google Plus Communities and Hangouts on Air. The presentation concludes with three key tactics you can begin using today to improve your use of Google Plus in your mobile recruiting strategy. This presentation was delivered at the 2013 Mobile Recruiting Conference in Atlanta, GA on September 24, 2013 (www.mrec.net).

Google Plus and Your Mobile Recruiting Strategy

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This project is to retrieve the similar geographic images from the dataset based on the features extracted. Retrieval is the process of collecting the relevant images from the dataset which contains more number of images. Initially the preprocessing step is performed in order to remove noise occurred in input image with the help of Gaussian filter. As the second step, Gray Level Co-occurrence Matrix (GLCM), Scale Invariant Feature Transform (SIFT), and Moment Invariant Feature algorithms are implemented to extract the features from the images. After this process, the relevant geographic images are retrieved from the dataset by using Euclidean distance. In this, the dataset consists of totally 40 images. From that the images which are all related to the input image are retrieved by using Euclidean distance. The approach of SIFT is to perform reliable recognition, it is important that the feature extracted from the training image be detectable even under changes in image scale, noise and illumination. The GLCM calculates how often a pixel with gray level value occurs. While the focus is on image retrieval, our project is effectively used in the applications such as detection and classification.

Feature extraction based retrieval of

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34번 서누리 예방논문1 발표

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Mining academic social network is becoming increasingly necessary with the increasing amount of data. It is a favorite topic of research for many researchers. The data mining techniques are used for the mining of academic social networks. In this paper, we are presenting an efficient frequent item set mining technique for social academic network. The proposed framework first processes the research documents and then the enhanced frequent item set mining is applied to find the strength of relationship between the researchers. The proposed method will be fast in comparison to older algorithms. Also it will takes less main memory space for computation purpose.

Modern association rule mining methods

Modern association rule mining methods

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Face recognition is one of the most challenging problems in the domain of image processing and machine vision. Face recognition system is critical when individuals have very similar biometric signature such as identical twins. In this paper, new efficient facial-based identical twins recognition is proposed according to the geometric moment. The utilized geometric moment is Pseudo-Zernike Moment (PZM) as a feature extractor inside the facial area of identical twins images. Also, the facial area inside an image is detected using Ada Boost approach. The proposed method is evaluated on two datasets, Twins Days Festival and Iranian Twin Society which contain scaled, which contain the shifted and rotated facial images of identical twins in different illuminations. The results prove the ability of proposed method to recognize a pair of identical twins. Also, results show that the proposed method is robust to rotation, scaling and changing illumination.

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A mobile peer-to-peer computer network is the one in which each computer in the network can act as a client or server for the other computers in the network. The communication process among the nodes in the mobile peer to peer network requires more no of messages. Due to this large number of messages passing, propose an interconnection structure called distributed Spanning Tree (DST) and it improves the efficiency of the mobile peer to peer network. The proposed method improves the data availability and consistency across the entire network and also reduces the data latency and the required number of message passes for any specific application in the network. Further to enhance the effectiveness of the proposed system, the DST network is optimized with the Ant Colony Optimization method. It gives the optimal solution of the DST method and increased availability, enhanced consistency and scalability of the network. The simulation results shows that reduces the number of message sent for any specific application and average delay and increases the packet delivery ratio in the network.

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By the advances in the Evolution Algorithms (EAs) and the intelligent optimization metho ds we witness the big revolutions in solving the optimization problems. The application of the evolution algorithms are not only not limited to the combined optimization problems, but also are vast in domain to the continuous optimization problems. In this paper we analyze and study the Genetic Algorithm (GA) and the Artificial Immune System (AIS) algorithm which are capable in escaping the local optimization and also fastening reaching the global optimization and to show the efficiency of the GA and AIS th e application of them in Solving Continuous Optimization Functions (SCOFs) are studied. Because of the multi variables and the multi - dimensional spaces in SCOFs the use of the classic optimization methods, is generally non - efficient and high cost. In other words the use of the classic optimization methods for SCOFs generally leads to a local optimized solution. A possible solution for SCOFs is to use the EAs which are high in probability of succeeding reaching the local optimized solution. The results in pa per show that GA is more efficient than AIS in reaching the optimized solution in SCOFs.

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Modern association rule mining methods

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An efficient feature extraction method with pseudo zernike moment for facial ...

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ANALYSE THE PERFORMANCE OF MOBILE PEER TO PEER NETWORK USING ANT COLONY OPTIM...

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Convergence tendency of genetic algorithms and artificial immune system in so...

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Ejercicio

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