Titlebook: Context-Aware Systems and Applications; 12th EAI Internation Phan Cong Vinh,Nguyen Thanh Tung Conference proceedings 2024 ICST Institute fo

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Venules

User-Based Collaborative Filtering Multi-criteria Recommender System Based on Interaction Between Crhe consulting system is special attented by researchers. Many decision-making solutions for multi-criteria recommender model have been proposed. However, with the intrinsic of the data, the latent values of the interaction relationship, the dominance between the criteria always changes the results o

dissolution

Application of Machine Learning Techniques to Classify Intention to Pay for Forest Ecosystem Serviceoncerned. This research selects several machine learning techniques, including single classifiers (Multilayer Perceptron, Naive Bayes, SMO) and ensemble classifiers (LogitBoost, Random Forest, Bagging) to evaluate and classify willingness-to-pay intention for mangrove ecosystem services of people in

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Anomaly Detection in Univariate Time Series: HOT SAX vesus LSTM-Based Methodsubsequences in a time series. The majority of these methods is classified into the window-based category, which applies a sliding window with a fixed length to extract subsequences before finding out anomaly subsequences. A well-known algorithm in this category is HOT SAX algorithm. Recently, deep

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Item-Based Energy Clustering Recommendationoduction about a cluster of the items based on the general characteristics of the item is more important than just focusing on the individual items. In this paper, we have proposed a new approach for the recommendation system, the proposed method uses the energy distance to group the items with simi

MERIT

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