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CiiT International Journal of Artificial Intelligent Systems and Machine Learning
Print: ISSN 0974 – 9667 & Online: ISSN 0974 – 9543

20082009 2010 2011 2012 2013
   April May June July September October November

  Issue : April 2009
  DOI: AIML042009001
  Title: An Improved Clustering Technique Based On Statistical Model Preprocessing Using Gene Expression Data
  Authors: R. Mallika and G. Selvanayaki
  Keywords: Clustering, Feature selection, Gene expression
  Abstract:
         Micro arrays have become the effective, broadly used tools in biological and medical research to address a wide range of problems, including classification of disease subtypes and tumors. Many statistical methods are available for analyzing and systematizing these complex data into meaningful information, and one of the main goals in analyzing gene expression data is the detection of samples or genes with similar expression patterns. In this work, a comparison of performance of several feature selection methods based on data preprocessing including strategies of normalization or data reduction is studied and a new classical statistic technique is proposed for preprocessing. Then clustering technique is applied and promising results were achieved. The work also proves choice of a good preprocessing technique prior to clustering improves the performance. The results were proven to be the best in comparison with previous work.

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  Issue : April 2009
  DOI: AIML042009002
  Title: An Image Spam Classification Model Based on File Features Using Neural Networks
  Authors: Ms. M. Soranamageswari, Dr. C. Meena
  Keywords: Back propagation, Image Spam, Machine Learning and Spam Filtering
  Abstract:
         Spam is an unauthorized intrusion into a virtual space, which caused serious economy loss and social issues. Recently, Spammers have spreading new kind of email spamming method called image spamming, which uses simple image processing technologies like varied borders or backgrounds, randomly varied spacing or margins, and adding artifacts to the images. Priceless effort, time, and money of the users and organizations are wasted in handling them. Because of the recent upsurge in image spam, the proposed system is developed to classify image spam based on file features of an image, rather than text contents by using Back propagation neural networks, which classify the incoming image as a spam or ham. The experimental result show the system correctly classifies 95% of spam images with minimum false positives.

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  Issue : April 2009
  DOI: AIML042009003
  Title: Automatic Tamil Document Categorization Based on the Naive Bayes Algorithm
  Authors: S. Kohilavani, T. Mala and T. V. Geetha
  Keywords: Document Categorization, Naïve Bayes, Stopwords, Preprocessing, Classifier
  Abstract:
         This paper deals with automatic classification of tamil documents. Documents are repositories of knowledge. There are numerous documents available and effective search in documents is time consuming. To make document search a simpler task and for various other applications like event detection and tracking, document clustering and grouping we need to perform document categorization. Document categorization is a challenging task. Document categorization has recently become an active research topic in the area of information retrieval. The objective of document categorization is to assign entries from a set of prespecified categories to a document. Traditionally this categorization task is performed manually by domain experts. Each incoming document is read and comprehended by the expert and then it is assigned to a number of categories chosen from the set of prespecified categories. It is inevitable that a large amount of manual effort is required. A promising way to deal with this problem is to learn a categorization scheme automatically from training examples. In the training phase we are given a set of documents with class labels attached, and a classification system is built using a learning method. Once the categorization scheme is learned, it can be used for classifying future documents. Document category can be found out using various techniques. In this paper, Naive Bayes (NB) which is a statistical machine learning algorithm, is used to classify tamil documents to one of pre-defined categories. Experiments are used to evaluate the Naive Bayes categorizer. The data set used during these experiments consists of 50 documents per category. The experimental results shows that the Naive Bayes classifier performs well and its effectiveness is achieved with 89.8% accuracy.

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  Issue : April 2009
  DOI: AIML042009004
  Title: Semi-Automatic Domain Ontology Construction for Tamil Documents
  Authors: M. S. Girija, T. Mala and T. V. Geetha
  Keywords: Ontology, Semi-automatic Ontology, Semantic Relationship Extraction, Content Bearing Words, TF-IDF, Morphological analysis and Clustering
  Abstract:
         Ontology is an explicit specification of a conceptualization. That is, ontology is a description of the concepts and relationships that can exist for an agent or a community of agents. Ontology construction is a challenging task and in this paper a new technique is employed for the semi-automatic construction of ontology. It involves two modules. They are ontological word selection and semantic relationship extraction. Ontological nodes and semantically related words are selected from tamil text corpus. The input to the system is the tamil text documents. Each and every tamil text document is word segmented and then morphologically analyzed to find out the parts of speech. This is because, ontological words are supposed to be nouns. The confinement of the noun list is performed using TF-IDF technique. Semantically related words are identified based on the notion of serial clustering of words in text and by exploring the value of such clustering as an indicator of a word’s bearing content. This approach is flexible in the sense that is it is sensitive to context. A term is assessed as content bearing within one collection, but not another. In this way, a domain ontology is constructed semi-automatically for tamil text documents.

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  Issue : April 2009
  DOI: AIML042009005
  Title: Automatic Clustering and Normalised Cut Based Image Retrieval Techniques
  Authors: S. Vinodkumar, P.R. Lakshmi
  Keywords: Back Propagation, CBIR, KCLUE
  Abstract:
         The KCLUster-based rEtrieval(KCLUE), groups the image based on the similarity measure, so that there is maximum similarity with in the cluster and minimum similarity between the two cluster and then retrieve the images related to the query. The cluster based retrieval of images tackles the semantic gap problem. The Content-Based Image Retrieval (CBIR) extract the feature of the images and the images with maximum similarity with that of the query is retrieved. This paper makes use of both the concept to retrieve the images. The CBIR system-using KCLUE is called as Content-Based Image Clusters Retrieval (CBICR).The keyword-based retrieval along with the CBIR system retrieves the relevant images more effectively and it consumes less amount of time. The keyword based retrieval is done and the Nearest Neighbor Method is used to locate neighbor of the target image. The N-cut algorithm is used to organize the cluster.

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