Browsing by Author "Gopalakrishnan, Anilkumar Kothalil"
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    A Collaborative Filtering Recommendation Based on User Profile and User Behavior in Online Social Networks
    ( 2014) Lu, Yang ; Gopalakrishnan, Anilkumar Kothalil
    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 may interest each other” from users’ database. In the experimental analysis, a data set “DouBan” (a data set is collected from a Chinese website) will be used and demonstrates the performance of the improved technique with a website. And the website will be developed to show the recommended processing of the proposed algorithm. Finally, the recommendation accuracy of the proposed method is evaluated by comparing with the existing recommendation algorithms.
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    Diagnosing prostate cancer using backpropagation neural network and greedy decision procedure
    ( 2010-05) Gopalakrishnan, Anilkumar Kothalil ; Thitipong Tanprasert ; Faculty of Science and Technology
    A novel procedure for diagnosing prostate cancer (PC) based on Back propagation Neural Network (BPNN) is proposed. Elderly men with symptoms such as urinary retention, urinary hesitancy, urinary dribbling, burning urination, hematuria, etc. are considered as primary attributes. Prostate-specific antigen (PSA) level and Gleason score are the secondary attributes. Initial dataset is generated based on the clinical database. The BPNN assigns symptom levels of a set of patients based on their primary attributes. A greedy decision procedure predicts tumor stages of patients based on their strong symptom levels and secondary attributes. The simulation shows that the proposed procedure is an effective way for diagnosing prostate tumor stages.
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    License Plate Extraction and Recognition of a Thai Vehicle Based on MSER and BPNN
    ( 2015) Tao, Hong ; Gopalakrishnan, Anilkumar Kothalil
    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 approach is an effective method for the extraction and recognition of a Thai license plate.
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    Myanmar Paper Currency Recognition Using GLCM and k-NN
    ( 2016-01) Hlaing, Khin Nyein Nyein ; Gopalakrishnan, Anilkumar Kothalil
    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 (GLCM). The recognition system is composed of four parts. The skew correction of rotated image is first. The captured image is second preprocessing and the third part is extracting its features by using GLCM. The last one is recognition, in which the core is k-Nearest Neighbor classifier. Experimental results are presented on a dataset of 500 images consisting of 5 classes of currency notes which are 100 Kyat, 200 Kyat, 500 Kyat, 1000 Kyat, and 5000 Kyat notes. It is shown that a good performance can be achieved using k-NN classifier algorithm. The recognition system presented in this paper indicates that the proposed approach is one of the most effective strategies of identifying currency pattern to read its face value.
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