Sökning: "Experimental Learning"
Visar resultat 1 - 5 av 414 uppsatser innehållade orden Experimental Learning.
1. Predictive Modeling of Pipetting Dynamics. Multivariate Regression Analysis: PLS and ANN for Estimating Density and Volume from Pressure Recordings
Master-uppsats, Lunds universitet/Avdelningen för Biomedicinsk teknikSammanfattning : Thermo Fisher Scientific manufacture automatic pipetting instruments for diagnostic tests. These tests are sensitive to abnormalities and changes in e.g. volume or density could potentially lead to less precision or other issues in the pipetting work flow. LÄS MER
2. Collaborative Learning in an Immersive Virtual Environment: The Effects of Context and Retrieval Practice
Master-uppsats, Lunds universitet/Institutionen för psykologiSammanfattning : The accessibility of Virtual Reality (VR) enables the investigation of desirable difficulties originating from memory research with increased ecological validity. The two desirable difficulties include contextual variation and retrieval practice. LÄS MER
3. AI-based image generation: The impact of fine-tuning on fake image detection
Kandidat-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskapSammanfattning : Machine learning-based image generation models such as Stable Diffusion are now capable of generating synthetic images that are difficult to distinguish from real images, which gives rise to a number of legal and ethical concerns. As a potential measure of mitigation, it is possible to train neural networks to detect the digital artifacts present in the images synthesized by many generative models. LÄS MER
4. Quantum Optical Description of High-order Harmonic Generation
Master-uppsats, Lunds universitet/Atomfysik; Lunds universitet/Fysiska institutionenSammanfattning : High-order Harmonic Generation (HHG) is a highly non-linear process in which an atom interacts with a strong laser field. The laser field lowers the atomic potential barrier allowing bound electrons to escape into the continuum through tunnel ionization, propagate, and, with some probability, recombine with the parent ion. LÄS MER
5. Machine learning for molecular property prediction and drug safety
Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknikSammanfattning : Utilizing machine learning methods for the prediction of acid dissociation (pKa ) values of compounds holds great significance, as pKa is an important parameter, optimized frequently in drug discovery. Accurate prediction of pKa values could potentially provide valuable insights on other molecular properties and thereby support compound design. LÄS MER