Sökning: "Utility Metrics"

Visar resultat 1 - 5 av 28 uppsatser innehållade orden Utility Metrics.

  1. 1. Measuring the Utility of Synthetic Data : An Empirical Evaluation of Population Fidelity Measures as Indicators of Synthetic Data Utility in Classification Tasks

    Master-uppsats, Karlstads universitet/Institutionen för matematik och datavetenskap (from 2013)

    Författare :Alexander Florean; [2024]
    Nyckelord :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;

    Sammanfattning : 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. LÄS MER

  2. 2. Data streaming provenance in advanced metering infrastructures

    Master-uppsats, Göteborgs universitet/Institutionen för data- och informationsteknik

    Författare :Zozk Mohamed; [2023-11-24]
    Nyckelord :Advanced Metering Infrastructure; Ananke; Apache Flink; Göteborg Energi; Provenance; Stream processing; Stream Processing Engine;

    Sammanfattning : Increasing volumes of data in digital systems have made the traditional approach of gathering and storing all the data while analyzing it in bulks at periodic intervals challenging and costly. One such field is the electric grid market, which has started modernizing its aging grids into smart grids where Advanced Metering Infrastructures (AMIs) play a vital role. LÄS MER

  3. 3. Predicering av aktiekursutveckling för svenska aktier utifrån konjunkturdata

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

    Författare :Edward Ehrling; Felix Dahl; [2023]
    Nyckelord :;

    Sammanfattning : This study aims to investigate whether Swedish economic indicators can be used to predict stock market performance on the Stockholm Stock Exchange. The study is expected to contribute to new research in the field and also explore the potential utility of these predictions for individual investors. LÄS MER

  4. 4. Finding Causal Relationships Among Metrics In A Cloud-Native Environment

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

    Författare :Suresh Rishi Nandan; [2023]
    Nyckelord :Causality; Causal Discovery; Bayesian Network; Conditional Independence; Partial Correlation; Ensemble Causal Discovery; Anomaly Detection; Causal Graphs; Causality; Causal Discovery; Bayesian Network; Conditional Indeberoende; partiell korrelation; Ensemble Causal Discovery; Anomali Detektion; kausala grafer;

    Sammanfattning : Automatic Root Cause Analysis (RCA) systems aim to streamline the process of identifying the underlying cause of software failures in complex cloud-native environments. These systems employ graph-like structures to represent causal relationships between different components of a software application. LÄS MER

  5. 5. COMPARATIVE ANALYSIS OF MACHINE LEARNING LOAD FORECASTING TECHNIQUES

    Magister-uppsats, Stockholms universitet/Institutionen för data- och systemvetenskap

    Författare :Humphry Takang Bate; [2023]
    Nyckelord :;

    Sammanfattning : Load forecasting plays a critical role in energy management, and power systems, enabling efficient resource allocation, improved grid stability, and effective energy planning and distribution. Without accurate very short term load forecasting, utility management companies face uncertain load patterns, unrealistic prices, and poor infrastructure planning. LÄS MER