Sökning: "fredrik erik"
Visar resultat 1 - 5 av 105 uppsatser innehållade orden fredrik erik.
1. Att vara smart i det smarta hemmet
Kandidat-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskapSammanfattning : Smart homes are becoming more popular and there are more and more different devices for smart homes being made. Discussions have increased about the low security level of these devices, how much data is shared with companies that provide these services and how that data is used. LÄS MER
2. Internet of Kegs : En IoT-lösning för visualisering av sensordata för öl
Kandidat-uppsats, Linköpings universitet/Institutionen för datavetenskapSammanfattning : Rapporten behandlar projektet Internet of Kegs som genomfördes av nio studenter som en del av kursen TDDD96 – Kandidatprojekt i programvaruutveckling vid Linköpings universitet under vårterminen 2023. Projektets beställare var Neue AB. LÄS MER
3. Exploratory Data Analysis of Live 5G Radio Access Network Configuration Data Using Interpretable Machine Learning
Master-uppsats, Linköpings universitet/Databas och informationsteknikSammanfattning : In the increasingly connected world of today, people are more reliant on online services than ever before. To enhance network performance, efforts needs to be made to better understand the telecommunication system. LÄS MER
4. Energy efficient solutions within a manufacturing facility : Solutions and suggestions to develop the energy efficiency at Wavin
Kandidat-uppsats, Uppsala universitet/Institutionen för samhällsbyggnad och industriell teknikSammanfattning : This bachelor's thesis examines four scenarios related to energy efficiency at the Wavin manufacturing facility in Eskilstuna, Sweden. The four scenarios are: (i) keeping the current production processes, (ii) replacing the electric boilers with biofuel boilers, (iii) installing solar cells and (iv) installing biofuel boilers and solar cells. LÄS MER
5. Data Complexity and its effect on Classification Accuracy in Multi Class Classification Problems : A study using synthetic datasets
Kandidat-uppsats, KTH/DatavetenskapSammanfattning : This study investigates how the performance of a selection of machine learning classifiers is affected by the data complexity, measured by F1, N1, N2, and N3 in a multi class classification setting. This study uses synthetic datasets that span across the range of possible complexity levels for each complexity measure, allowing us to target the desired level of complexity for each dataset. LÄS MER