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In the current era of web applications such as e-retail business, the web services focused to provide personalized search systems to the targeted user intents based on the navigation patterns. Intelligent collaborative filtering recommender system tries to recommend the web pages considering the similar patterns of the other users along with the usage knowledge of the current user session. This recommender systems strategy lacks of the domain knowledge in comparing the usage patterns of the other users in serving with recommendations. This paper mainly focused on incorporating the domain knowledge and usage knowledge in personalization as well as in comparing the similar user patterns for recommender systems. This novel strategy builds a model to recommend the web pages that can help the new search scenarios and can improve the likelihood of a user towards the host website. Experimental results shown that the proposed novel strategy yields to gain in performance of the recommender system in terms of the quality of the web page recommendations.

In this paper, an autonomous brain tumor segmentation and detection model is developed utilizing a convolutional neural network technique that included a local binary pattern and a multilayered support vector machine. The detection and classification of brain tumors are a key feature in order to aid physicians; an intelligent system must be designed with less manual work and more automated operations in mind. The collected images are then processed using image filtering techniques, followed by image intensity normalization, before proceeding to the patch extraction stage, which results in patch extracted images. During feature extraction, the RGB image is converted to a binary image by grayscale conversion via the colormap process, and this process is then completed by the local binary pattern (LBP). To extract feature information, a convolutional network can be utilized, while to detect objects, a multilayered support vector machine (ML-SVM) can be employed. CNN is a popular deep learning algorithm that is utilized in a wide variety of engineering applications. Finally, the classification approach used in this work aids in determining the presence or absence of a brain tumor. To conduct the comparison, the entire work is tested against existing procedures and the proposed approach using critical metrics such as dice similarity coefficient (DSC), Jaccard similarity index (JSI), sensitivity (SE), accuracy (ACC), specificity (SP), and precision (PR).

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