Search for dissertations about: "missing information"

Showing result 1 - 5 of 308 swedish dissertations containing the words missing information.

  2. 1. Predictive Techniques and Methods for Decision Support in Situations with Poor Data Quality

    Author : Rikard König; Högskolan i Skövde; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; Rule Extraction; Genetic Programming; Uncertainty estimation; Machine Learning; Artificial Neural Networks; Data Mining; Information Fusion; Information technology; Informationsteknik; Teknik; Technology;

    Abstract : Today, decision support systems based on predictive modeling are becoming more common, since organizations often collect more data than decision makers can handle manually. Predictive models are used to find potentially valuable patterns in the data, or to predict the outcome of some event. READ MORE

  4. 2. Expressing emotions through vibration for perception and control

    Author : Shafiq ur Réhman; Li Liu; Xiaoyi Jiang; Umeå universitet; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; NATURVETENSKAP; NATURAL SCIENCES; TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; Multimodal Signal Processing; Mobile Communication; Vibrotactile Rendering; Locally Linear Embedding; Object Detection; Human Facial Expression Analysis; Lip Tracking; Object Tracking; HCI; Expectation-Maximization Algorithm; Lipless Tracking; Image Analysis; Visually Impaired.; Signal processing; Signalbehandling; Image analysis; Bildanalys; Computer science; Datavetenskap; Telecommunication; Telekommunikation; Systems engineering; Systemteknik; datoriserad bildanalys; Computerized Image Analysis; business data processing; administrativ databehandling; Electronics; elektronik; Systems Analysis; systemanalys;

    Abstract : This thesis addresses a challenging problem: “how to let the visually impaired ‘see’ others emotions”. We, human beings, are heavily dependent on facial expressions to express ourselves. A smile shows that the person you are talking to is pleased, amused, relieved etc. READ MORE

  5. 3. Information Security in Distributed Healthcare : Exploring the Needs for Achieving Patient Safety and Patient Privacy

    Author : Rose-Mharie Åhlfeldt; Benkt Wangler; Eva Söderström; Simone Fischer-Hübner; Stockholms universitet; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; Computer and systems science; Data- och systemvetenskap; Computer and Systems Sciences; data- och systemvetenskap; Technology;

    Abstract : In healthcare, patient information is a critical factor. The right information at the right time is a necessity in order to provide the best possible care for a patient. Patient information must also be protected from unauthorized access in order to protect patient privacy. READ MORE

  6. 4. Precise Image-Based Measurements through Irregular Sampling

    Author : Teo Asplund; Robin Strand; Cris L. Luengo Hendriks; Matthew J. Thurley; Gunilla Borgefors; Hugues Talbot; Uppsala universitet; []
    Keywords : TEKNIK OCH TEKNOLOGIER; ENGINEERING AND TECHNOLOGY; NATURVETENSKAP; NATURAL SCIENCES; image analysis; image processing; mathematical morphology; irregular sampling; adaptive morphology; missing samples; continuous morphology; path opening.; Computerized Image Processing; Datoriserad bildbehandling;

    Abstract : Mathematical morphology is a theory that is applicable broadly in signal processing, but in this thesis we focus mainly on image data. Fundamental concepts of morphology include the structuring element and the four operators: dilation, erosion, closing, and opening. READ MORE

  7. 5. Interpretable machine learning models for predicting with missing values

    Author : Lena Stempfle; Chalmers tekniska högskola; []
    Keywords : NATURVETENSKAP; NATURAL SCIENCES; missing values; Machine learning; healthcare; interpretable machine learning;

    Abstract : Machine learning models are often used in situations where model inputs are missing either during training or at the time of prediction. If missing values are not handled appropriately, they can lead to increased bias or to models that are not applicable in practice without imputing the values of the unobserved variables. READ MORE