Now showing items 1-3 of 3

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    A Collaborative Filtering Recommendation Based on User Profile and User Behavior in Online Social Networks 

    Lu, Yang; Gopalakrishnan, Anilkumar Kothalil (2014)

    This paper aims to present and discuss the similarity among users in a social network based on CF (Collaborative Filtering) algorithm and SimRank (Similarity Based on Random Walk) algorithm. The CF algorithm used to predict the relationship between users based on the user rating on items (movies and books) and the user’s profile. The SimRank algorithm calculates the similarity among users through finding the nearest neighbors for each user in the social network. At last, the combination of these two algorithms will be used to get “people ...
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    License Plate Extraction and Recognition of a Thai Vehicle Based on MSER and BPNN 

    Tao, Hong; Gopalakrishnan, Anilkumar Kothalil (2015)

    The extraction and recognition of a Thai vehicle license plate based on Maximally Stable Extremal Regions (MSER) and Back-Propagation Neural Network (BPNN) is presented. The license plate area is in a maximally stable extremal region of a car image. It can be effectively extracted from MSERs by multiple classifications. The feature extraction of characters from the license plate is based on Zernike moment. The feature is used as a training dataset for the BPNN to recognize the characters. The experimental results indicate that the proposed ...
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    Myanmar Paper Currency Recognition Using GLCM and k-NN 

    Hlaing, Khin Nyein Nyein; Gopalakrishnan, Anilkumar Kothalil (2016-01)

    Paper currency recognition depends on the currency note characteristics of a particular country. And the features extraction directly affects the recognition ability. Paper currency recognition is one of the important applications of pattern recognition. This paper aims to present a model for automatic classification of currency notes using k-Nearest Neighbor (k-NN) classifier that is the most important and simplest method in pattern recognition. The proposed model is based on textural feature such as Gray Level Co-occurrence Matrix ...