Browsing Institutt for datateknologi og informatikk by Title
Now showing items 1585-1604 of 6828
-
Deep Convolutional Encoder-Decoder Networks for Digital Rock Porosity Segmentation
(Master thesis, 2019)Digital rock physics (DRP) er en moderne metode for å karakterisere de fysiske egenskapene til ulike typer stein. Ved å modellere korn-, flerfaset- og porevolum i forksjellige bergarter, kan en assistere institusjoner med ... -
Deep Detection of Hate Speech in Text Through a Two-Pronged Approach
(Master thesis, 2018)With the widespread use of online services like Facebook and Twitter, disseminating hateful messages has become a simple matter. These messages not only spoil the experience for other users of a service. There is also an ... -
A Deep Diagnostic Framework Using Explainable Artificial Intelligence and Clustering
(Journal article; Peer reviewed, 2023)An important part of diagnostics is to gain insight into properties that characterize a disease. Machine learning has been used for this purpose, for instance, to identify biomarkers in genomics. However, when patient data ... -
Deep Evolvable-Substrate HyperNEAT - Extending ES-HyperNEAT with Multiple Substrates in an Evolving Topology
(Master thesis, 2020)Abstract Neuroevolution is a technique that evolves artificial neural networks through evolutionary algorithms, inspired by the natural evolution of biological brains. HyperNEAT is one such method, evolving patterns to ... -
Deep Evolvable-Substrate HyperNEAT - Extending ES-HyperNEAT with Multiple Substrates in an Evolving Topology
(Master thesis, 2020)Neuroevolusjon er en metode som utvikler kunstige neurale nettverk via evolusjonære algoritmer og er inspirert av den naturlige evolusjon av biologiske hjerner. HyperNEAT er en slik metode. Den utvikler mønstre til å ... -
Deep Generative Models for Realistic Image Anonymization
(Doctoral theses at NTNU;2023:421, Doctoral thesis, 2023)The following pages explore the use of generative models for realistic image anonymization. In summary, this thesis aims to address two primary objectives. First, develop generative models for synthesizing human figures ... -
Deep HDR Fusion: Deep Learning-based HDR Fusion of RAW images
(Master thesis, 2023)Capturing scenes with a wide dynamic range presents inherent challenges due to sensor limitations, resulting in a loss of dynamic range in captured images. By fusing multiple images captured at varying exposures, a single ... -
Deep Hyperspectral Prior: Single-Image Denoising, Inpainting, Super-Resolution
(Chapter, 2019)Deep learning algorithms have demonstrated state-of-the-art performance in various tasks of image restoration. This was made possible through the ability of CNNs to learn from large exemplar sets. However, the latter becomes ... -
Deep Learning Applied to Automatic Anomaly Detection in Capsule Video Endoscopy
(Master thesis, 2018)Introduction: Regularly screening of the gastrointestinal tract for polyps is the an important measure for preventing colorectal cancer. Screening large population's gastrointestinal tract is with todays common methods too ... -
A deep learning approach for brain tumor classification using MRI images
(Journal article; Peer reviewed, 2022) -
Deep Learning Approaches for Whiteboard Image Quality Enhancement
(Peer reviewed; Journal article, 2019)Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image ... -
Deep Learning based frameworks for 3D registration of differential and multimodal data
(Doctoral theses at NTNU;2023:153, Doctoral thesis, 2023)In recent decades, Visual Computing methodologies such as image processing and computer vision have addressed problems in the field of Cultural Heritage (CH)resulting in significant benefits. Specifically, accurate ... -
Deep Learning Controlled Temporal Upsampling - An Efficient Recurrent Convolutional Neural Network Controlled Architecture for Temporal Upsampling
(Master thesis, 2021)Rendering i sanntid blir stadig dyrere på grunn av skjermer med høyere oppløsning, høyere bildefrekvens og fotorealistisk grafikk. Kostnaden av rendering kan reduseres ved å rendere ved en lavere oppløsning enn skjermen, ... -
Deep Learning Enhanced Human Activity Recognition for Internet of Healthcare Things
(Peer reviewed; Journal article, 2020)Along with the advancement of several emerging computing paradigms and technologies, such as cloud computing, mobile computing, artificial intelligence, and big data, Internet of Things (IoT) technologies have been applied ... -
Deep Learning for Blind Calibration of Wireless Sensor Networks
(Master thesis, 2020)Temporal drift of low-cost sensors is a crucial problem when considering the applicability of wireless sensor networks (WSN). Since they provide highly local measurements, which is key to combat the ever increasing problem ... -
Deep Learning for Fault Prediction in Offshore Wind Turbines
(Master thesis, 2019)Denne oppgaven beskriver et feilprediksjonssystem for offshore vindturbiner basert på SCADA data. Hensikten med systemet er å gi tidlig advarsel for kommende feil. Dette vil kunne bidra til reduserte vedlikeholdskostnader ... -
Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions
(Peer reviewed; Journal article, 2022)Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using ... -
Deep Learning for Improved Diagnosis of Pathologies in Wireless Capsule Endoscopy with Focus on Data Efficiency and Transparency
(Doctoral theses at NTNU;2023:272, Doctoral thesis, 2023)Over the past decade the use of deep learning in different application areas has increased steadily. These areas include automated decisions in industries to autonomous vehicles, defence, and even medical. Each field gives ... -
Deep Learning for Mobile Crowdsourcing Techniques, Methods, and Challenges: A Survey
(Peer reviewed; Journal article, 2021)With the ever-increasing popularity of mobile computing technology and the wide adoption of outsourcing strategy in labour-intensive industrial domains, mobile crowdsourcing has recently emerged as a promising resolution ... -
Deep learning for privacy preservation in autonomous moving platforms enhanced 5G heterogeneous networks
(Peer reviewed; Journal article, 2021)5G heterogeneous networks have become a promising platform to connect a growing number of Internet-of-Things (IoT) devices and accommodate a wide variety of vertical services. IoT has not been limited to traditional sensing ...