Essays about: "Dataintegritet"
Showing result 1 - 5 of 20 essays containing the word Dataintegritet.
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1. Measuring the Utility of Synthetic Data : An Empirical Evaluation of Population Fidelity Measures as Indicators of Synthetic Data Utility in Classification Tasks
University essay from Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)Abstract : In the era of data-driven decision-making and innovation, synthetic data serves as a promising tool that bridges the need for vast datasets in machine learning (ML) and the imperative necessity of data privacy. By simulating real-world data while preserving privacy, synthetic data generators have become more prevalent instruments in AI and ML development. READ MORE
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2. Deep Learning in the Web Browser for Wind Speed Forecasting using TensorFlow.js
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Deep Learning is a powerful and rapidly advancing technology that has shown promising results within the field of weather forecasting. Implementing and using deep learning models can however be challenging due to their complexity. READ MORE
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3. Software Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The increasing complexity of telecom networks, induced by the recent development of 5G, is a challenge for detecting faults in the telecom network. In addition to the structural complexity of telecommunication systems, data accessibility has become an issue both in terms of privacy and access cost. READ MORE
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4. Federated Learning for Natural Language Processing using Transformers
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : The use of Machine Learning (ML) in business has increased significantly over the past years. Creating high quality and robust models requires a lot of data, which is at times infeasible to obtain. As more people are becoming concerned about their data being misused, data privacy is increasingly strengthened. READ MORE
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5. Cluster selection for Clustered Federated Learning using Min-wise Independent Permutations and Word Embeddings
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Federated learning is a widely established modern machine learning methodology where training is done directly on the client device with local client data and the local training results are shared to compute a global model. Federated learning emerged as a result of data ownership and the privacy concerns of traditional machine learning methodologies where data is collected and trained at a central location. READ MORE