iosr-JCE   Volume-10 ~ Issue-6

Abstract: Data mining is the knowledge discovery process by analyzing the large volumes of data from various perspectives and summarizing it into useful information; data mining has become an essential component in various fields of human life. It is used to identify hidden patterns in a large data set. Classification techniques are supervised learning techniques that classify data item into predefined class label. It is one of the most useful techniques in data mining to build classification models from an input data set; these techniques commonly build models that are used to predict future data trends. In this paper we have worked with different data mining applications and various classification algorithms, these algorithms have been applied on different dataset to find out the efficiency of the algorithm and improve the performance by applying data preprocessing techniques and feature selection and also prediction of new class labels.

Keywords: Classification, Mining Techniques, Algorithms.

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Abstract: This work presents and investigates the discriminatory capability of contourlet coefficient co-occurrence matrix features in the analysis of mammogram images and its classification. It has been revealed that contourlet transform has a remarkable potential for analysis of images representing smooth contours and fine geometrical structures, thus suitable for textural details. Initially the ROI (Region of Interest) is cropped from the original image and its contrast is enhanced using histogram equalization. The ROI is decomposed using contourlet transform and the co-occurrence matrices are generated for four different directions (θ=0°, 45°, 90° and 135°) and distance (d= 1 pixel). For each co-occurrence matrix a variety of second order statistical texture features are extracted and the dimensionality of the features is reduced using Sequential Floating Forward Selection (SFFS) algorithm. A PNN is used for the purpose of classification. For experimental evaluation, 200 images are taken from mini MIAS (Mammographic Image Analysis Society) database. Experimental results show that the proposed methodology is more efficient and maximum classification accuracy of 92.5% is achieved. The results prove that contourlet coefficient co-occurrence matrix texture features can be successfully applied for the classification of mammogram images.

Keywords-Contourlet Transform, Mammogram, SFFS, PNN, ROI, MIAS

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Abstract: Using AI Hans peter Wickelgren applying the usage of text-based passwords is common authentication system in any Application. This conventional authentication scheme faces some kind of limitations and drawbacks with usability and crypto-graphical security issues that bring troubles to users. For example, user tends to pick passwords that can be easily guessed. On the contrary, if a password is hard to guess, then it is often hard to remember. An alternative system is required to overcome these problems. To deal with these drawbacks, authentication scheme that use photo ,image, or set of pattern as password is proposed using knowledge Recall-Based System(KRBS).Graphical passwords consist of clicking or dragging activities on the pictures rather than typing textual characters, might be the option to overcome the problems that arise from the text-based passwords authentication system. In this paper, a comprehensive Artificial Intelligence(AI) study of the existing graphical password schemes is performed. The graphical password authentication systems are categorized into two AI approach types: An approach on recognition-based System (RBS) and second approach on Recall-based system (RCBS). We discuss adequately the strengths and limitations of each method in terms of usability and security aspects .

Keywords- Graphical Passwords using Hans peter Wickelgren, Recognition-Based Graphical User Authentication, Recall-Based Graphical User Authentication, Pure Recall-Based Authentication, Knowledge Recall-Based Authentication System, Usability, Security , Artificial Intelligence(AI) ,Knowledge-Based Development Systems(KBDS).

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