Essays about: "learning journal"
Showing result 1 - 5 of 17 essays containing the words learning journal.
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1. Privacy Preserving Biometric Multi-factor Authentication
University essay from Lunds universitet/Institutionen för elektro- och informationsteknikAbstract : This thesis investigates the viability of using Fully Homomorphic Encryption and Machine Learning to construct a privacy-preserving biometric multi-factor authentication system. The system is based on the architecture described as ”Model K - Store distributed, compare distributed” in ISO/IEC 24745:2022 and uses the Torus Fully Homomorphic Encryption scheme proposed by Chillotti et al. READ MORE
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2. The Cognitive Revolution – Fact or Fiction? : Using topic modelling to look for signs of a paradigm shift in a Swedish journal
University essay from Linköpings universitet/Institutionen för datavetenskapAbstract : Traditionally, when social scientists wanted to analyze large amounts of documents, they have resorted to using manual coding techniques. This process can be made easier by using machine learning approaches. READ MORE
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3. What do we talk about when we talk about algorithmic literacy? : A scoping review
University essay from Högskolan i Borås/Akademin för bibliotek, information, pedagogik och ITAbstract : Problem formulation, goal and objectives: Algorithms are ubiquitous in digital society, yet complex to understand and often hidden. Algorithmic literacy can be a useful concept when educating and empowering users. However, it is not uniformly defined or used, and the state of knowledge is unclear. READ MORE
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4. Discovering Implant Terms in Medical Records
University essay from Linköpings universitet/Institutionen för datavetenskapAbstract : Implant terms are terms like "pacemaker" which indicate the presence of artifacts in the body of a human. These implant terms are key to determining if a patient can safely undergo Magnetic Resonance Imaging (MRI). READ MORE
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5. Semantic Topic Modeling and Trend Analysis
University essay from Linköpings universitet/Statistik och maskininlärningAbstract : This thesis focuses on finding an end-to-end unsupervised solution to solve a two-step problem of extracting semantically meaningful topics and trend analysis of these topics from a large temporal text corpus. To achieve this, the focus is on using the latest develop- ments in Natural Language Processing (NLP) related to pre-trained language models like Google’s Bidirectional Encoder Representations for Transformers (BERT) and other BERT based models. READ MORE