Data Mining - Concepts and Techniques.

Today, Internet and Web technologies not only provide students opportunities for flexible interactivity with study materials, peers and instructors, but also generate large amounts of usage data that can be processed and reveal behavioral patterns of study and learning. This study analyzed data extracted from a Moodle-based blended learning course, to build a student model that predicts course performance. CART decision tree algorithm was used to classify students and predict those at risk, based on the impact of four online activities: message exchanging, group wiki content creation, course files opening and online quiz taking. The overall percentage of correct classifications was about 99.1%, proving the model sensitive to identify very specific

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Classification and Regression Trees (CART) for Predictive Modeling in Blended Learning

Several applications including access control, system security need a robust and real-time face identification systems. The last is almost performed by two processes: the detection of the face and then the recognition. In literature, it can be found that some techniques are used exclusively for the detection process and some others are dedicated for the recognition. In this paper, we propose to test and evaluate a common technique for both processes. More particularly, we consider in this paper only the detection process. In order to do that, we have investigated three descriptors: Local Binary Pattern (LBP), Local Binary Pattern Histogram (LBPH), and Histogram Of Gradient (HOG) to get features of faces. These features are used for training process with the same Support Vector Machine classifier taken in different conditions in order to have training dataset. The use of LBPH desciptor followed by SVM classifier for the detection process consitite the second contribution of the paper. Two sets composed of negative images (non-faces) and positive images (faces) are used to generate the training dataset. The test is performed on another dataset. We evaluate the three techniques for face detection on the standard FERET datasets in order to compare the efficiency of these three methods under different face variations and different illuminations. The Local Binary Pattern Histogram with Support Vector Machine classifier outperform the others techniques in terms of efficiency and robustness of face detection. It has been found that the correct detection rate is greater than 98.04% in average.

A novel face detection approach using local binary pattern histogram and support vector machine

A review on the studies employing artificial bee colony algorithm to solve combinatorial optimization problems

Automated tumor delineation is complex and an interesting area of research in medical imaging. The tumor delineation in oncology needs to be precise for treatment planning and delivery. Early detection of the lung cancer is possible using Computed Tomography (CT) Images. In this approach, the CT scan images process through lung volume segmentation, lung nodule segmentation, feature extraction and classification. The lung volumes are segmented by thresholding method while lung nodules were segmented using k-means clustering. The features were extracted using Gray Level Co-occurrence Matrix (GLCM). The texture features like contrast, dissimilarity, Homogeneity, Angular Second Moment (ASM), Energy, Mean and Standard deviation were extracted to represent a non-tumorous and tumorous image. Finally, the features are trained and tested by using K-Nearest Neighbor (KNN) and Multi-Layer Perceptron (MLP) algorithm. The proposed approach achieved the classification accuracy of 98.30% using KNN and 98.31 % using MLP. The experiments were performed using the NSCLC, LIDC datasets.

Multi-Layer Perceptron Based Lung Tumor Classification

In recent years, the biometrics has achieved a great attention on a world level. A Biometric System operates by getting biometric information from a personal that extracts a feature set from the data which is acquired, and helps in comparing this feature set against the template stored in the database. There are biometric technologies which could either be physiological or behavioral. Face Recognition is having the importance to provide biometric authentication with easy image acquisition that can be used for online and offline applications. There are number of existing approaches for biometric facial recognition and classification. This paper gives a review on some of the common and reliable approaches which include PCA, LDA, SVM, SIFT, SURF, etc. General Terms Your general terms must be any term which can be used for general classification of the submitted material such as Pattern Recognition, Security, Algorithms et. al.

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A Study based on Various Face Recognition Algorithms

Association rule mining is virtually importance and its use is one of a essential method for data mining. The association rule mining approach significant have been with many minute changes in the apriori although their fundamental opinion proceed same i.e use of support and confidence threshold(s). This paper to find that there is no tasks that have been done in the region of E-Apriori. In this paper have to introduce new algorithm Enhance Apriori i.e(E-Apriori).The E-Apriori algorithm is advance to Enhance the Apriori algorithm by using the median support (supmedian) alternatively of minimum support, to deliver probabilistic item-set alternatively of large item-set. In this paper for optimization the rule with the help of ABC technique i.e (Artificial Bee Colony) and E-Apriori and Apriori algorithm situated on ABC technique (Artificial Bee Colony).

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Improved Association Rule Mining based on ABC

The Data Mining is extracting or mining knowledge from large volume of data. Classification technique is used in different-2 application. In this paper proposes a new classifier utilizing MLP approach by grouping based on nearest neighbor i.e. improved MLP-NN. The MLP-NN approach can handle noisy data and reduce complexity. This technique has been applied for medical diagnosis. This paper analyzes the lung images (i.e. CTscan images) for identifying and classifying them among the various lung diseases (i.e. bronchitis, emphysema, pleural effusion or normal) using 100 images data set and 80 images data set. Keyword Data Mining; Classification; Multilayer Perceptron.

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Improved MLP-NN based approach for Lung Diseases Classification

This paper consists of review methods used for face recognitionSIFT, SURF, PCA, PCASIFT, etc. for recognition and matching. SIFT and SURF is used to extract features to perform reliable matching from the images. PCA eigenfaces are used, they are entered into SIFT. The basic process of face recognition system is described here and improvement is shown in matching the invariant faces in this paper. SIFT and SURF are used to extract the features and then applying PCA to the image for the better performance in terms of rotation, expression and contrast. Performance can be seen on the basis of Recognition rate. Image Processing Toolbox under MATLAB Software is used for the implementation of this proposed work.

Face Recognition using SIFT, SURF and PCA for Invariant Faces

rule mining is a vital technique of data mining which is of great use and importance. For Association Rule mining various new techniques have been developed but concept for the newly developed algorithms remains the same. These recent developments are basically modifications to previous ones which introduce some minute changes in existing algorithms for better results. This paper addresses these basic algorithms and techniques in an elaborative way. So the reader can understand each of these techniques along with their pros and cons, without any additional effort. The techniques discussed here involve AIS, SETM, Apriori and FP-tree in detail.

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Prince Verma

Papers

A Study based on Various Face Recognition Algorithms

Improved Association Rule Mining based on ABC

Improved MLP-NN based approach for Lung Diseases Classification

Face Recognition using SIFT, SURF and PCA for Invariant Faces

Association Rule Mining Algorithm's Variant Analysis