Sökning: "Convolutional NeuralNetworks"

Visar resultat 1 - 5 av 11 uppsatser innehållade orden Convolutional NeuralNetworks.

1. 1. Accuracy and Robustness of State of the Art Deepfake Detection Models

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

   Författare :Tobias Carlsson; Oskar Strömberg; [2023]
   Nyckelord :;

   Sammanfattning : With the evolution of artificial intelligence a lot of people have started getting worried about the potential dangers of deepfake images and videos, such as spreading fake videos of influential people. Several solutions to this problem have been proposed with some of the most efficient being convolutional neural networks for face detection in order to differentiate real images from deepfake images generated with a generative adversarial network. LÄS MER

3. 2. Real-time Human Detection using Convolutional Neural Networks with FMCW RADAR RGB data

   Master-uppsats, KTH/Skolan för industriell teknik och management (ITM)

   Författare :Anna Phan; Rogelio Medina; [2022]
   Nyckelord :Human Detection; Machine Learning; Convolutional Neural Networks; YOLO; FMCW Radar; Human Detection Evaluation; Människodetektering; Maskininlärning; Neurala faltningsnät; Djupa faltningsnät; YOLO; FMCW Radar; Utvärdering;

   Sammanfattning : Machine learning has been employed in the automotive industry together with cameras to detect objects in surround sensing technology. You Only Look Once is a state-of-the-art object detection algorithm especially suitable for real-time applications due to its speed and relatively high accuracy compared to competing methods. LÄS MER

4. 3. Developing an AI based approach to histological tissue type classification

   Master-uppsats, Uppsala universitet/Institutionen för informationsteknologi

   Författare :Sanjith Bonela; [2022]
   Nyckelord :;

   Sammanfattning : Histological tissue type classification is a profound research topic. However, most of the research in this area is confined to either to differentiate cancerous tissue from non-cancerous tissue or to classify connective, epithelial, muscleand nervous tissue rather than classifying an organ specific tissue from another. LÄS MER

5. 4. Artificial intelligence for segmentation of nuclei from transmitted images

   Master-uppsats, KTH/Skolan för kemi, bioteknologi och hälsa (CBH)

   Författare :Norah Klintberg Sakal; [2020]
   Nyckelord :image segmentation; U2OS convolutional neural network; artificial intelligence; human bone osteosarcoma;

   Sammanfattning : State-of-the-art fluorescent imaging research is strictly limited to eight fluorophore labels duringthe study of intercellular interactions among organelles. The number of excited fluorophore colorsis restricted due to overlap in the narrow spectra of visual wavelength. LÄS MER

7. 5. Deep Learning for Sea-Ice Classification on Synthetic Aperture Radar (SAR) Images in Earth Observation : Classification Using Semi-Supervised Generative Adversarial Networks on Partially Labeled Data

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

   Författare :Francesco Staccone; [2020]
   Nyckelord :Earth Observation; Classification; Deep Learning; Convolutional NeuralNetworks; Semi-Supervised Learning; GenerativeAdversarialNetworks; Jordobservation; Klassificering; Djupinlärning; IhopveckladeNeurala Nätverk; Halvövervakad Inlärning; Generativa Fientliga Nätverk;

   Sammanfattning : Earth Observation is the gathering of information about planet Earth’s system via Remote Sensing technologies for monitoring land cover types and their changes. Through the years, image classification techniques have been widely studied and employed to extract useful information from Earth Observation data such as satellite imagery. LÄS MER