Blar i NTNU Open på tittel
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Machine Learning Aided Static Malware Analysis: A Survey and Tutorial
(Chapter, 2018)Malware analysis and detection techniques have been evolving during the last decade as a reflection to development of different malware techniques to evade network-based and host-based security protections. The fast growth ... -
Machine learning algorithm for prediction of stuck pipe incidents using statistical data: case study in middle east oil fields
(Peer reviewed; Journal article, 2022)One of the most troublesome issues in the drilling industry is stuck drill pipes. Drilling activities will be costly and time-consuming due to stuck pipe issues. As a result, predicting a stuck pipe can be more useful. ... -
Machine learning algorithms performed no better than regression models for prognostication in traumatic brain injury
(Peer reviewed; Journal article, 2020)Objective We aimed to explore the added value of common machine learning (ML) algorithms for prediction of outcome for moderate and severe traumatic brain injury. Study Design and Setting We performed logistic regression ... -
Machine Learning and Atomic Simulations to predict Nanoscale Ice Adhesion
(Master thesis, 2020)Det er kjent at overflateruhet påvirker hvor godt is fester seg til materialer, men de underliggende mekanismene er ikke fullstendig forstått. I denne masteroppgaven blir to tilnærminger, én fysikkbasert, og én svart ... -
A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism
(Peer reviewed; Journal article, 2022)In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud ... -
Machine learning and CFD for mapping and optimization of CO2 ejectors
(Peer reviewed; Journal article, 2021)In this study, a novel simulation-based algorithm for CO2 ejector design and performance evaluation is presented. The algorithm is based on an automated Computational Fluid Dynamics (CFD) workflow that can account for ... -
Machine Learning and Cross-Sectional Returns: An empirical analysis of machine learning for return prediction in the Norwegian equities market
(Master thesis, 2023)Denne masteroppgaven bruker maskinløring for å predikere fremtidig aksjeavkastning for aksjer som er en del av Oslo Stock Exchange All Share Index (OSEAX). Maskinglæringsalgoritmer som Random Forest og Gradient Boosted ... -
Machine Learning and First Principles Modeling Applied to Multiphase Flow Estimation
(Doctoral theses at NTNU;2020:407, Doctoral thesis, 2020)Accurate knowledge of multiphase flowrates produced by each well in an oil and gas production system is important for performing production optimization, flow assurance and reservoir management. Among the alternatives, ... -
Machine Learning and Image Processing for the Study of Fluid Particle Breakage in Turbulent Flow
(Master thesis, 2020)Hovedformålet med denne oppgaven er å lage maskinlæringsmodeller for bruk i en allerede eksisterende bildeanalysekode for å kunne spore oljedråper i en turbulent strøm av vann. I tillegg, har prosjektet som mål å beskrive ... -
Machine learning applied to GPU test generation
(Master thesis, 2023)Denne oppgaven har undersøkt hvordan finne effektiv tester og stimuli for simulerings basert verifikasjons av digital kretser. Nåværende metoder er baseres på avgrenset tilfeldig simulering og menneskelig involvering. Dette ... -
A Machine Learning Approach for Determining Reference Wells in the Norwegian Continental Shelf
(Master thesis, 2018)An unsupervised learning solution for selecting relevant reference wells for a new well that is to be drilled in the Norwegian Continental Shelf (NCS), shall be made with readily available software tools such as Python and ... -
Machine Learning Approach for Hydrogen Safety in Confined Spaces: Forecasting Pressure Peaking Phenomenon
(Master thesis, 2023)Hydrogen is the most abundant element in the universe and has gained significant attention as a clean and renewable energy source. However, hydrogen also poses significant safety risks, especially when it is stored, ... -
A Machine Learning Approach to Analyze Natural Hazards Accidents Scenarios
(Peer reviewed; Journal article, 2022)Climate change has contributed to an increasing frequency and severity of natural hazards accidents over recent years, and the increasing trend is expected to continue and escalate. Globally, demographics are changing and ... -
A Machine Learning Approach to Investigate Fronto-Parietal Neural Ensemble Dynamics During Complex
(Chapter, 2020)Brain circuits exhibit very complex dynamics, where individual neurons fire action potentials determining coordinated activity patterns. During behavior, a multitude of brain areas are engaged in planning and execution. A ... -
A machine learning approach to predict chattering alarms
(Peer reviewed; Journal article, 2020)The alarm system plays a vital role to ensure safety and reliability in the process industry. Ideally, an alarm should inform the operator about critical conditions only and provide guidance to a set of corrective actions ... -
A Machine Learning Approach to Predict Post-stroke Fatigue. The Nor-COAST study
(Journal article; Peer reviewed, 2014)Objective: This study aimed to predict fatigue 18 months post-stroke by utilizing comprehensive data from the acute and sub-acute phases after stroke in a machine-learning set-up. Design: A prospective multicenter ... -
A Machine Learning Approach to Predict the Materials' Susceptibility to Hydrogen Embrittlement
(Journal article; Peer reviewed, 2023) -
Machine Learning Approaches to Automatically Detect Glacier Snow Lines on Multi-Spectral Satellite Images
(Peer reviewed; Journal article, 2022)Documenting the inter-annual variability and the long-term trend of the glacier snow line altitude is highly relevant to document the evolution of glacier mass changes. Automatically identifying the snow line on glaciers ... -
Machine learning assisted low-frequency model building for AVO inversion
(Master thesis, 2018)AVO inversion is a valuable tool to estimate absolute reservoir properties from prestack seismic data. The bandwidth limitation of the seismic requires that the missing low frequencies must be added to the inversion in ... -
Machine learning augmented reduced-order models for FFR-prediction
(Peer reviewed; Journal article, 2021)Computational predictions in cardiovascular medicine have largely relied on explicit models derived from physical and physiological principles. Recently, the application of artificial intelligence in cardiovascular medicine ...