Essays about: "Dataintegritet."

Showing result 1 - 5 of 20 essays containing the word Dataintegritet..

1. 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)

   Author : Alexander Florean; [2024]
   Keywords : Synthetic Data; Machine Learning; Population Fidelity Measures; Utility Metrics; Synthetic Data Quality Evaluation; Classification Algorithms; Utility Estimation; Data Privacy; Artificial Intelligence; Experiment Framework; Model Performance Assessment; Syntetisk Data; Maskininlärning; Population Fidelity Mätvärden; Användbarhetsmätvärden; Kvalitetsutvärdering av Syntetisk Data; Klassificeringsalgoritmer; Användbarhetsutvärdering; Dataintegritet; Artificiell Intelligens; AI; Experiment Ramverk; Utvärdering av Modellprestanda;

   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

3. 2. Deep Learning in the Web Browser for Wind Speed Forecasting using TensorFlow.js

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Sara Moazez Gharebagh; [2023]
   Keywords : TensorFlow.js; JavaScript; Artificial Neural Networks; Deep Learning; Recurrent Neural Networks; Long Short-Term Memory; GatedRecurrent Units; TensorFlow.js; JavaScript; Artificiella Neurala Nätverk; Djupinlärning; Recurrent Neural Networks; Long Short-Term Memory; GatedRecurrent Units;

   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

4. 3. Software Fault Detection in Telecom Networks using Bi-level Federated Graph Neural Networks

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Rémi Bourgerie; [2023]
   Keywords : 5G 4G; Federated Learning; Graoh Learning; Graph-based Federated Learning; Temporal Graph Neural Networks; Time Series; Anomaly Detection; Fault Detection; 5G 4G; Federerat lärande; Graf lärande; Grafbaserat federerat lärande; Temporal Graph Neural Networks; Tidsserier; Upptäckt av anomalier; Upptäckt av fel;

   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

5. 4. Federated Learning for Natural Language Processing using Transformers

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Gustav Kjellberg; [2022]
   Keywords : Machine Learning; Federated Learning; Distributed Machine Learning; Natural Language Processing; BERT; ALBERT; Transformers; Data Privacy.; Maskininlärning; Federerad inlärning; Distribuerad Maskininlärning; Språkteknologi; BERT; ALBERT; Transformers; Dataintegritet.;

   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

6. 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)

   Author : Pulasthi Raveen Bandara Harasgama; [2022]
   Keywords : Federated learning; Distributed machine learning; Clustering; Word Embeddings; Federerad inlärning; Distribuerad maskininlärning; Klustring; Ordinbäddningar;

   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