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Magesh, N.
- Improving the Image Retrieval Performance Using False Image Filtering Approach
Authors
1 Department of Computer Science and Engg., Institute of Road and Transport Technology, Erode, IN
2 Department of Computer Science and Engg, Bannariamman Institute of Technology, Sathyamangalam, Autonomous Institution Affiliated to Anna University, Chennai, IN
Source
Digital Image Processing, Vol 5, No 4 (2013), Pagination: 204-212Abstract
The novel approach combines color and texture features for content based image retrieval (CBIR). This paper is used to retrieve the images from the huge collection of image databases. Most of the research interest in recent years uses feature indexing techniques for the image retrieval. If the number of features are more, then the more time is spent on the comparing the features in low level image retrieval. The proposed system has focused on minimizing the number of comparision by considering the structure of the color theory which says that human color vision system is sensitive to light–dark variations. Here, the color theory is used to eliminate the irrelevant images from the huge collection of images. The feature extraction methods are used to retrive the relevant images. The irrelevant images are filtered by mesuring the deviation between light and dark colors. The opponent values of color and texture features of the image are taken. The image retrieval performance is improved by minimizing the number of comparisions. The proposed method outperforms the other previously developed methods by providing the classification accuracy of more than 89% for the various types of natural images taken from coral database. Hence, this paper concentrates on color and texture features for image retrieval in different directions. The proposed method significantly improves efficiency with less computational complexity.Keywords
Color, Texture, Tamura, Threshold, Retrieval, Image Database, Mean, Standard Deviation, Hash Queue, Color Theory, Median Features.
- Employee Appraisal Report Processing Using Weka
Authors
1 Dept. of Computer Science and Engineering, Institute of Road and Transport Technology, Erode-638316, IN
2 Dept. of Computer Science and Engineering, Bannariamman Institute of Technology, Sathyamangalam-638401, IN
Source
Data Mining and Knowledge Engineering, Vol 5, No 5 (2013), Pagination: 202-208Abstract
The main objective is to evaluate the appraisal report of an employee using a decision tree algorithm. The decision tree is one of inductive learning method used in artificial intelligence. It is used for data classification and prediction. The data mining applications use the decision tree for information retrieval and information extraction. This paper discuss about the method of applying decision tree for predicting the performance of an employee working in an organization. The decision tree is created by using WEKA tool which is used to evaluate the performance of an employee by processing the appraisal report of an employee. The processed data is mainly used for giving promotion, yearly increment and career advancement. In order to provide yearly increment for an employee, it should be evaluated by using past historical data of employees. The historical data are stored in the form of ARFF(Attribute-Relation File Format) and the performance are found by testing the attributes of an employee against the rules generated by the decision tree classifier in WEKA tool. This paper concentrates on collecting data about employees, generating a decision tree from the historical data, testing the decision tree with attributes of an employee and generating the output as whether to give the promotion or not using WEKA tool. The information about an employee are collected by using the user interface. This information is compared with the trained data stored in the decision tree. The final goal node is to determine whether the employee will get yearly increment, promotion or not.Keywords
Classification, Decision Tree, J48 Algorithm, Training.- Improving Text Summarization Using Latent Semantic Analysis
Authors
1 Assistant Professor (Senior) of Department of Computer Science & Engineering in Institute of Road & Transport Technology, Vasavi College Post, Erode - 638316, Tamilnadu, IN
2 M.E Computer Science and Engineering at Institute of Road & Transport Technology, Vasavi College Post, Erode - 638316, Tamil Nadu, IN
Source
Software Engineering, Vol 12, No 2 (2020), Pagination: 25-30Abstract
Text Summarization is a method of generating a shorter version of the given document using natural language processing that enables the users to quickly identify the major points of a document. Text summarization aims at getting the most symbolic content in a system in a compact form from given document while it retains the semantic information of text to a large extent. It is considered to be an effective way of attempting the information and solves the problem of presenting information in more condense form. There are different approaches to produce well defined form of summaries and one of the modern methods is Latent Semantic Analysis. Though the available information about any topic is large and incredible, so there is a need for rapid view of those articles to determine accordance of the article as per user’s wish.
In this paper, the successive way of summarizing the text document by involving the sequence of the techniques and its evaluation using rouge scores was engaged. The SVD plays an important role in separating important sentences from input document. Every sentence is enabled with rank based on its importance in original document. Sentence selection is done based on their ranks and the summary generated. The rouge will produce three distinct scores as, Recall, Precision and F-score. The F-score is considered for evaluating the correctness of summary. The observation of three distinct summaries by reducing input document by 1/2nd, 1/3rd, 1/4th rouge scores and f-score is found to provide the effective results in summarizing the text document.
Keywords
Information Retrieval (IR), Latent Semantic Analysis (LSA), Text Summarization Component.- Text Summarization Using Fuzzy Logic Approach
Authors
1 Computer Science and Engineering at Bharathiyar University, IN
2 Computer Science and Engineering at K.S.K College of Engineering &Technology, Anna University, Kumbakonam, IN
Source
Fuzzy Systems, Vol 12, No 2 (2020), Pagination: 17-21Abstract
It is tremendous to extract the information faster from internet nowadays. There are lot of materials available on the internet and in order to extract the most relevant information, a good mechanism is found to be used. This problem is settled by the Automatic Text Summarization mechanism. Text Summarization is the system of developing a shorter version of the text that involves the relevant information. Text summarization is classified as Extraction and Abstraction. Here this paper targets on the Fuzzy logic approach for processing text summarization.
In this paper, the efficient way of summarizing the text document is performed by involving the combination of the techniques such as fuzzy logic approach and then evaluation of the result with the rouge scores was calculated. The Singular value decomposition plays significant role on extracting the important sentences from the original document. Every sentence is enabled with a rank which is based on its importance in the original document. Sentence selection is involved according to the ranks and the summary are generated. The rouge will generate three scores as, Recall, Precision and F-score. F-score is found to be the evaluation metric for the correctness of a summary. The comparison of three different summaries by compressing the input document as 1/2nd, 1/3rd, 1/4th rouge scores and f-score provides the effective results towards summarizing the text document.