Essays about: "artificiellt neuronnät"

Showing result 16 - 18 of 18 essays containing the words artificiellt neuronnät.

  1. 16. Smartphone sensors are sufficient to measure smoothness of car driving

    University essay from KTH/Skolan för datavetenskap och kommunikation (CSC)

    Author : Jesper Bränn; [2017]
    Keywords : machine learning; sensor fusion; sensor readings; car; smartphone; smartphone sensors; GPS; accelerometer; gyroscope; convolutional neural network; artificial neural network; maskininlärning; sensorfusion; sensorläsning; bil; smartphone; smartphonesensorer; artificiella neuronnät;

    Abstract : This study aims to look at whether or not it is sufficient to only use smartphone sensors to judge if someone who is driving a car is driving aggressively or smoothly. To determine this, data were first collected from the accelerometer, gyroscope, magnetometer and GPS sensors in the smartphone as well as values based on these sensors from the iOS operating system. READ MORE

  3. 17. Investigation of the prognostic value of CT and PET-based radiomic image features in oropharyngeal squamous cell carcinoma

    University essay from Lunds universitet/Medicinsk strålningsfysik, Lund; Lunds universitet/Sjukhusfysikerutbildningen

    Author : Mohammed Mosad Said; [2016]
    Keywords : Medicine and Health Sciences;

    Abstract : Background Medical data in the form of radiographic routine scans is steadily accumulating. The analysis of such data through automated quantitative methods is believed to produce new information which would allow for more personalization of therapy. The present thesis investigated the use of such methods in head and neck cancer. READ MORE

  4. 18. Peptide Retention Time Prediction using Artificial Neural Networks

    University essay from KTH/Matematisk statistik

    Author : Sara Väljamets; [2016]
    Keywords : ;

    Abstract : This thesis describes the development and evaluation of an artificial neural network, trained to predict the chromatographic retention times of peptides, based on their amino acid sequence. The purpose of accurately predicting retention times is to increase the number of protein identifications in shotgun proteomics and to improve targeted mass spectrometry experiment. READ MORE