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Showing 1 to 7 of 7 for “"User classification"”.
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A framework for understanding user interaction with content-based image retrieval: model, interface and users
User interaction is essential to the communication between users and content-based image retrieval (CBIR) systems. User interaction covers three key elements: an interaction model, an interactive interface and users. The three key elements combine to enable effective interaction to happen. Many …
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An Exploration of End-User Critical Incident Classification
… the expert. Techniques are needed that maintain user involvement while reducing both the cost of user involvement and the time required to analyze data. The User Action Framework (UAF) is a classification scheme for usability problems that facilitates data analysis and reusability of information …
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Sentiment analysis using unsupervised learning for local government elections in South Africa
… patterns and K-Means methods to classify the users as either bots and human using to be able to identify the user behind the keyboard. The study also make use of OpenAI GPT model to label the dataset for fine-tuning and addresses the issue of class imbalance. VADER and TextBlob results show a …
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Classification of London's public transport users using smart card data
Understanding transit users in terms of their travel patterns can support the planning and design of better services. User classification can improve market research through more targeted access to groups of interest. It facilitates planning through better survey design, as well as more detailed …
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Intent-based user segmentation with query enhancement
… of the internet, accurate prediction of user's online intent underlying their search queries has received increasing attention from the online advertising community. As a rich source of information on web user's behavior, query logs have been leveraged by advertising companies to deliver …
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Event-related Collections Understanding and Services
… develop TwiRole, an integrated model for 3-way user classification on Twitter, which detects brand-related, female-related, and male-related tweeters through multiple features with both machine learning (i.e., random forest classifier) and deep learning (i.e., an 18-layer ResNet) techniques. As …
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Discriminative Classification Models for Internet Security
… exposed to fraudulent use. Maintaining good user experience for legitimate users often requires the classification of entities as malicious or legitimate in order to initiate countermeasures. As an example, inbound email spam filters decide for spam or non-spam. They can base their decision …