Search for dissertations about: "e learning models"
Showing result 1 - 5 of 229 swedish dissertations containing the words e learning models.
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1. Visual Representations and Models: From Latent SVM to Deep Learning
Abstract : Two important components of a visual recognition system are representation and model. Both involves the selection and learning of the features that are indicative for recognition and discarding those features that are uninformative. READ MORE
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2. Touching the Essence of Life : Haptic Virtual Proteins for Learning
Abstract : This dissertation presents research in the development and use of a multi-modal visual and haptic virtual model in higher education. The model, named Chemical Force Feedback (CFF), represents molecular recognition through the example of protein-ligand docking, and enables students to simultaneously see and feel representations of the protein and ligand molecules and their force interactions. READ MORE
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3. Visual Analytics for Explainable and Trustworthy Machine Learning
Abstract : The deployment of artificial intelligence solutions and machine learning research has exploded in popularity in recent years, with numerous types of models proposed to interpret and predict patterns and trends in data from diverse disciplines. However, as the complexity of these models grows, it becomes increasingly difficult for users to evaluate and rely on the model results, since their inner workings are mostly hidden in black boxes, which are difficult to trust in critical decision-making scenarios. READ MORE
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4. Chemogenomics: Models of Protein-Ligand Interaction Space
Abstract : The large majority of the currently used drugs are small molecules that interact with proteins. Understanding protein-ligand recognition is thus central to drug discovery and design. Improved experimental techniques have resulted in an immense growth of drug target information. READ MORE
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5. Environmental Modelling : Learning from Uncertainty
Abstract : Environmental models are important tools; however uncertainty is pervasive in the modeling process. Current research has shown that understanding and representing these uncertainties is critical when decisions are expected to be made from the modeling results. READ MORE