Sökning: "learning on demand"

Visar resultat 16 - 20 av 458 uppsatser innehållade orden learning on demand.

  1. 16. Comparison of Recommendation Systems for Auto-scaling in the Cloud Environment

    Master-uppsats, Blekinge Tekniska Högskola/Institutionen för datavetenskap

    Författare :Sai Nikhil Boyapati; [2023]
    Nyckelord :Auto-Scaling; Auto-Scaling Recommendations; Cloud Environment; K-Nearest Neighbors; Machine Learning; Recommendation Systems; Random Forests; Support Vector Machines;

    Sammanfattning : Background: Cloud computing’s rapid growth has highlighted the need for efficientresource allocation. While cloud platforms offer scalability and cost-effectiveness for a variety of applications, managing resources to match dynamic workloads remains a challenge. LÄS MER

  3. 17. Assessing Electricity Prices and Their Driving Mechanisms in Brazil with Neural Networks

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

    Författare :Henrique Costabile; [2023]
    Nyckelord :;

    Sammanfattning : In general, electricity prices are very volatile and derive from many external variables. In Brazil, this price is determined by computer models developed and operated by government organizations. The supply and demand relationships are not enough to determine prices in Brazilian submarkets. LÄS MER

  4. 18. Federated Machine Learning for Resource Allocation in Multi-domain Fog Ecosystems

    Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

    Författare :Weilin Zhang; [2023]
    Nyckelord :Workload Allocation; Federated Learning; Deep Q-network; Fog networks; Federated Average Aggregation;

    Sammanfattning : The proliferation of the Internet of Things (IoT) has increasingly demanded intimacy between cloud services and end-users. This has incentivised extending cloud resources to the edge in what is deemed fog computing. The latter is manifesting as an ecosystem of connected clouds, geo-dispersed and of diverse capacities. LÄS MER

  5. 19. Demand Forecasting of Outbound Logistics Using Neural Networks

    Master-uppsats, Malmö universitet/Fakulteten för teknik och samhälle (TS)

    Författare :Enobong Paul Otuodung; Gulten Gorhan; [2023]
    Nyckelord :Time Series Prediction; Demand Forecasting; Outbound Logistics; Machine Learning; Deep Learning; Univariate Forecasting; Multivariate Forecasting; Multi-Step Forecasting; LSTM; CNN-LSTM; ConvLSTM; Encoder-Decoder; Design science; Design science;

    Sammanfattning : Long short-term volume forecasting is essential for companies regarding their logistics service operations. It is crucial for logistic companies to predict the volumes of goods that will be delivered to various centers at any given day, as this will assist in managing the efficiency of their business operations. LÄS MER

  7. 20. Inclusion of differential pricing in congestion charging scheme: The case of Stockholm and Curitiba

    Master-uppsats, KTH/Transportplanering

    Författare :Joaquin Franco; [2023]
    Nyckelord :;

    Sammanfattning : Among transport demand management (TDM) strategies, congestion pricing has been one of the most widely applied, but at the same time one of the most criticised. The reason is that this measure is considered regressive, exclusionary, and inequitable, since having a flat rate ignores people's ability to pay, i.e. LÄS MER