Search for dissertations about: "Empirical data"

Showing result 1 - 5 of 2524 swedish dissertations containing the words Empirical data.

  2. 1. Data management and Data Pipelines: An empirical investigation in the embedded systems domain

    Author : Aiswarya Raj Munappy; Chalmers tekniska högskola; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; data management; empirical investigation; artificial intelligence; data pipelines; embedded systems; software engineering; machine learning;

    Abstract : Context: Companies are increasingly collecting data from all possible sources to extract insights that help in data-driven decision-making. Increased data volume, variety, and velocity and the impact of poor quality data on the development of data products are leading companies to look for an improved data management approach that can accelerate the development of high-quality data products. READ MORE

  4. 2. Health Data : Representation and (In)visibility

    Author : Pedro Sanches; Magnus Boman; Catherine Mulligan; KTH; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES;

    Abstract : Health data requires context to be understood. I show how, by examining two areas: self-surveillance, with a focus on representation of bodily data, and mass-surveillance, with a focus on representing populations. READ MORE

  5. 3. Order in the random forest

    Author : Isak Karlsson; Henrik Boström; Lars Asker; Pierre Geurts; Stockholms universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Machine learning; random forest; ensemble; time series; data series; sequential data; sparse data; high-dimensional data; Computer and Systems Sciences; data- och systemvetenskap;

    Abstract : In many domains, repeated measurements are systematically collected to obtain the characteristics of objects or situations that evolve over time or other logical orderings. Although the classification of such data series shares many similarities with traditional multidimensional classification, inducing accurate machine learning models using traditional algorithms are typically infeasible since the order of the values must be considered. READ MORE

  6. 4. Learning from Complex Medical Data Sources

    Author : Jonathan Rebane; Panagiotis Papapetrou; Isak Samsten; Myra Spiliopoulou; Stockholms universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Machine Learning; Data Science; Healthcare; Complex Data; Explainable AI; Deep Learning; data- och systemvetenskap; Computer and Systems Sciences;

    Abstract : Large, varied, and time-evolving data sources can be observed across many domains and present a unique challenge for classification problems, in which traditional machine learning approaches must be adapted to accommodate for the complex nature of such data. Across most domains, there is also a need for machine learning models that are both well-performing and interpretable, to help provide explanations of a model's decisions that stakeholders can trust and take appropriate actions with. READ MORE

  7. 5. Data-driven AI Techniques for Fashion and Apparel Retailing

    Author : Chandadevi Giri; Ulf Johansson; Jenny Balkow; Xianyi Zeng; Sebastien Thomessey; Maria Riveiro; Högskolan i Borås; []
    Keywords : SAMHÄLLSVETENSKAP; SOCIAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Digitalization; artificial intelligence; fashion and apparel industry; churn prediction; sales forecasting; campaign analysis; data driven AI decision-making; 数字化，人工智能，服装产业，客户流失预测，销售预测，竞争分析，数据驱动的人 工智能决策; Digitalisation; intelligence artificielle IA; industrie de la mode et de l habillement; prédiction de désabonnement; prévision des ventes; analyse des promotions; Prise de décision par IA axée sur les données; Digitalisering; Artificiell intelligens; Modeindustrin; Churnprediktion; Försäljningsprognoser; Kampanjanalys; Datadriven AI; Beslutsstöd; Business and IT; Handel och IT; Textil och mode generell ; Textiles and Fashion General ;

    Abstract : Digitalisation allows companies to develop many new ways of interacting with customers and other stakeholders. These digital interactions typically generate data that can be stored and later processed for different objectives. READ MORE