S. Sadesh^1,*, Osamah Ibrahim Khalaf², Mohammad Shorfuzzaman³, Abdulmajeed Alsufyani³, K. Sangeetha⁴, Mueen Uddin⁵

Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 3365-3384, 2023, DOI:10.32604/iasc.2023.030751

Abstract Web usage mining, content mining, and structure mining comprise the web mining process. Web-Page Recommendation (WPR) development by incorporating Data Mining Techniques (DMT) did not include end-users with improved performance in the obtained filtering results. The cluster user profile-based clustering process is delayed when it has a low precision rate. Markov Chain Monte Carlo-Dynamic Clustering (MC²-DC) is based on the User Behavior Profile (UBP) model group’s similar user behavior on a dynamic update of UBP. The Reversible-Jump Concept (RJC) reviews the history with updated UBP and moves to appropriate clusters. Hamilton’s Filtering Framework (HFF) is designed to filter user data… More >

Varghese S. Chooralil¹, Vinodh P. Vijayan², Biju Paul¹, M. M. Anishin Raj³, B. Karthikeyan^4,*, G. Manikandan⁴

CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2891-2903, 2021, DOI:10.32604/cmc.2021.012796

Abstract Key information extraction can reduce the dimensional effects while evaluating the correct preferences of users during semantic data analysis. Currently, the classifiers are used to maximize the performance of web-page recommendation in terms of precision and satisfaction. The recent method disambiguates contextual sentiment using conceptual prediction with robustness, however the conceptual prediction method is not able to yield the optimal solution. Context-dependent terms are primarily evaluated by constructing linear space of context features, presuming that if the terms come together in certain consumer-related reviews, they are semantically reliant. Moreover, the more frequently they coexist, the greater the semantic dependency is.… More >