Sökning: "project classification"

Visar resultat 1 - 5 av 608 uppsatser innehållade orden project classification.

  1. 1. Adapted data collection in field utilizing RMR and the Q-system

    Uppsats för yrkesexamina på avancerad nivå, Luleå tekniska universitet/Institutionen för samhällsbyggnad och naturresurser

    Författare :Amanda Olsson; [2024]
    Nyckelord :Rock mass classification; Q-slope; SMR; Slope stability;

    Sammanfattning : The focus on slope stability has increased rapidly in Norway over the last years due to several unwanted landslides. In Norway, the most used method today to classify the rock mass and to determine the required reinforcement, is the Q-system. In addition to that the RMR method is also a commonly used method. LÄS MER

  3. 2. Land cover classification using machine-learning techniques applied to fused multi-modal satellite imagery and time series data

    Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

    Författare :Anastasia Sarelli; [2024]
    Nyckelord :Geography; GIS; Land Cover Classification; Landsat; Machine Learning; Earth and Environmental Sciences;

    Sammanfattning : Land cover classification is one of the most studied topics in the field of remote sensing, involving the use of data from satellite sensors to analyze and categorize different land surface types. There are numerous satellite products available, each offering different spatial, spectral, and temporal resolutions. LÄS MER

  4. 3. Visualization and analysis of object states using diffusion models and PyTorch

    Kandidat-uppsats, Mälardalens universitet/Akademin för innovation, design och teknik

    Författare :Christopher Nyberg; [2024]
    Nyckelord :;

    Sammanfattning : Artificial Intelligence (AI) is an extremely rapidly growing field in modern technology. As the applications of AI expand, the ability to accurately analyze and predict the condition of various objects through various models has profound implications across numerous industries. LÄS MER

  5. 4. Self-Supervised Learning for Tabular Data: Analysing VIME and introducing Mix Encoder

    Kandidat-uppsats, Lunds universitet/Fysiska institutionen

    Författare :Max Svensson; [2024]
    Nyckelord :Machine Learning; Self-supervised learning; AI; Physics; Medicine; Physics and Astronomy;

    Sammanfattning : We introduce Mix Encoder, a novel self-supervised learning framework for deep tabular data models based on Mixup [1]. Mix Encoder uses linear interpolations of samples with associated pretext tasks to form useful pre-trained representations. LÄS MER

  7. 5. Improving echocardiogram view classification using diffusion models

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

    Författare :Luis Arevalo; Anouka Ranby; [2023-10-23]
    Nyckelord :Computer; science; computer science; engineering; project; artificial intelligence; machine learning; deep neural networks; diffusion models; synthetic data; echocardiogram classification;

    Sammanfattning : In the field of medical science datasets are often highly imbalanced, where rare datapoints are of high importance. This study aims to explore the usage of synthetic datasets to improve the classification of echocardiogram views. LÄS MER