Essays about: "adrian"
Showing result 1 - 5 of 233 essays containing the word adrian.
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1. Towards Automated Log Message Embeddings for Anomaly Detection
University essay from Lunds universitet/Institutionen för reglerteknikAbstract : Log messages are implemented by developers to record important runtime information about a system. For that reason, system logs can provide insight into the state and health of a system and potentially be used to anticipate and discover errors. READ MORE
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2. Satellite signal attenuation due to atmospheric influences in northern Sweden
University essay from Umeå universitet/Institutionen för fysikAbstract : Earth-space traversing electromagnetic waves become attenuated as they propagate through the atmosphere. The sources of attenuation are weather phenomena in the Troposphere, and scintillation and absorption in the Ionosphere. READ MORE
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3. Exploring the Applications of Machine Learning in the Public Sector
University essay from Lunds universitet/Fysiska institutionen; Lunds universitet/Beräkningsbiologi och biologisk fysik - Genomgår omorganisationAbstract : Despite the many use cases for machine learning, it sees minimal usage in Sweden’s public sector today. It is important that the public sector in particular utilizes the most efficient tools available. READ MORE
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4. Downscaling the Doughnut Economics Model - Employing a Global Model at the Enterprise Level: A case study of Proton Group and Apotea AB
University essay from Jönköping University/Internationella HandelshögskolanAbstract : In a rapidly changing world, sustainability is becoming more and more of a priority for organizations. This paper evaluates the possibility of using the Doughnut Economics Model (DEM) as a tool to implement sustainability within an organization on the firm-level, highlighting the potential opportunities and limitations that it poses. READ MORE
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5. Over-the-Air Federated Learning with Compressed Sensing
University essay from Linköpings universitet/KommunikationssystemAbstract : The rapid progress with machine learning (ML) technology has solved previously unsolved problems, but training these ML models requires ever larger datasets and increasing amounts of computational resources. One potential solution is to enable parallelization of the computations and allow local processing of training data in distributed nodes, such as Federated Learning (FL). READ MORE