Sökning: "predictive augmentation"

Visar resultat 1 - 5 av 7 uppsatser innehållade orden predictive augmentation.

1. 1. Restaurant Daily Revenue Prediction : Utilizing Synthetic Time Series Data for Improved Model Performance

   Uppsats för yrkesexamina på avancerad nivå, Uppsala universitet/Avdelningen för beräkningsvetenskap

   Författare :Stella Jarlöv; Anton Svensson Dahl; [2023]
   Nyckelord :demand forecasting; data augmentation; time series data; machine learning; restaurant industry; generative adversarial networks; TimeGAN; XGBoost;

   Sammanfattning : This study aims to enhance the accuracy of a demand forecasting model, XGBoost, by incorporating synthetic multivariate restaurant time series data during the training process. The research addresses the limited availability of training data by generating synthetic data using TimeGAN, a generative adversarial deep neural network tailored for time series data. LÄS MER

3. 2. Deep Learning for Chromosome Segmentation with Uncertainty Estimation

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Arvid Norström; [2021]
   Nyckelord :;

   Sammanfattning : Karyotyping, the process of pairing and ordering chromosomes, is an important tool for cytogenetic analysis to detect chromosome abnormalities. A trained specialist analyses the resulting image of the karyotyping, known as a karyogram, paying attention to the size, shape, and number of the chromosomes. LÄS MER

4. 3. Time-series Generative Adversarial Networks for Telecommunications Data Augmentation

   Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Hamid Dimyati; [2021]
   Nyckelord :Telecommunication; Time- series Forecasting; Data Augmentation; Generative Adversarial Networks; Telekommunikation; Prognoser för Tidsserier; Dataförstoring; Generative Adversarial Networks;

   Sammanfattning : Time- series Generative Adversarial Networks (TimeGAN) is proposed to overcome the GAN model’s insufficiency in producing synthetic samples that inherit the predictive ability of the original timeseries data. TimeGAN combines the unsupervised adversarial loss in the GAN framework with a supervised loss adopted from an autoregressive model. LÄS MER

5. 4. Using Transfer Learning to classify different stages of Alzheimer’s disease

   Kandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Författare :Anton Danker; Jacob Wirgård Wiklund; [2021]
   Nyckelord :;

   Sammanfattning : The identification of Alzheimer’s disease through the application of various machine learning techniques on neuroimaging data of the likes of MRI is an area of study which has seen intense levels of research in recent years. While many machine learning techniques have existed for a long time, recent advances in Deep Learning and Computer Vision have allowed for better performing predictive models. LÄS MER

7. 5. Artificial Intelligence in the Pulp and Paper Industry : Current State and Future Trends

   Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

   Författare :Marcus Nystad; Lukas Lindblom; [2020]
   Nyckelord :Swedish Pulp and Paper Industry; Manufacturing Processes; AI; Artificial Intelligence; ML; Machine Learning; Current State; Future Trends; Management; Digital Innovation Management; Augmentation; Automation; Organizing R D; Svenska pappers- och massa industrin; skogsindustrin; tillverkningsprocesser; artificiell intelligens; AI; ML; maskininlärning; nuvarande tillstånd; framtida trender; management; digitalt innovationsarbete; automatisering; förstärkning; organisering av FoU.;

   Sammanfattning : The advancements in Artificial Intelligence (AI) have received large attention in recent years and increased awareness has led to massive societal benefits and new opportunities for industries able to capitalize on these emerging technologies. The pulp and paper industry is going through one of the most considerable transformations into Industry 4. LÄS MER