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The Semantic web deliberates machines to develop conceptual information themselves by understanding its implications. Semantic web creates overt resources through ontology. Ontology widely offers the representation of conceptual knowledge. It has the ability to signify the domain knowledge in a distinct and unambiguous manner. We propose Ontology-based structure for personalized library usage environment which discover user's behaviour by their learning feedback. The personalized library Ontology and user's feedback is developed using Protégé editor 4.3. To identify user behaviour we determine 225 students' library learning feedback which enhance the reference information for the future users. For data classification, we propose semantic similarity-based Improved J48 induction learning algorithm which facilitates the system to identify patterns and regularities for the extracted ontology data. Impurity from the classified data is evaluated using entropy and information gain. The new decisionmaking for ontology subpopulation is made by estimating the highest GainRatio. Finally, the system performance is evaluated with k-fold cross-validation, accuracy, F1-score, and execution time. The proposed algorithm reduces dissimilar data and handles missing data therefore, the system can improve its efficiency and performance with enhanced accuracy up to 10%-12% compare to existing algorithms as Random Forest, Naive Bayes, Multilayer Perceptron and J48 algorithm. It can also reduce its execution time more than 20 (ms) for the different size of datasets compare to existing C4.5 algorithm. Theoretical analysis and comparison of existing algorithms with its experimental results and system evaluation show the effectiveness and performance of the proposed system. [ABSTRACT FROM AUTHOR]

Automated diagnosis of cancer from skin lesion data has been the focus of numerous research. Despite that it can be challenging to interpret these images because of features like colour illumination changes, variation in the sizes and forms of the lesions. To tackle these problems, the proposed model develops an ensemble of deep learning techniques for skin cancer diagnosis. Initially, skin imaging data are collected and preprocessed using resizing and anisotropic diffusion to enhance the quality of the image. Preprocessed images are fed into the Fuzzy-C-Means clustering technique to segment the region of diseases. Stacking-based ensemble deep learning approach is used for classification and the LSTM acts as a meta-classifier. Deep Neural Network (DNN) and Convolutional Neural Network (CNN) are used as input for LSTM. This segmented images are utilized to be input into the CNN, and the local binary pattern (LBP) technique is employed to extract DNN features from the segments of the image. The output from these two classifiers will be fed into the LSTM Meta classifier. This LSTM classifies the input data and predicts the skin cancer disease. The proposed approach had a greater accuracy of 97%. Hence, the developed model accurately predicts skin cancer disease.