Searched for: subject%3A%22random%255C+forest%22
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Gheorghe, Andrada (author)
Predicting aircraft Take-Off Weight (TOW) has been a long-sought task by aviation stakeholders, especially for operational and regulatory bodies involved in flight planning. Unfortunately, TOW being a sensitive parameter to operational trends and cost indices, aircraft operators tend to keep it confidential. In recent years, Machine Learning (ML...
master thesis 2024
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van Winden, Brian (author)
Introduction<br/>Approximately 9 in 1000 children are born with congenital heart disease (CHD), of whom a quarter are classified as critical CHD (CCHD) and require an intervention within their first year. Monitoring these patients in the Paediatric Intensive Care Unit (PICU) is crucial, yet with increasing amounts of data, detecting subtle...
master thesis 2024
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Kan, Leo (author)
The Earth's surface is a complex landscape that is essential for a wide range of applications, from urban planning to environmental monitoring. Digital models of the Earth's surface are generated through mathematical calculations using elevation data collected from various sources and the Digital Terrain Model (DTM) which captures the bare earth...
master thesis 2024
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Weißhaar, Thaddäus (author)
European cities are implementing diverse strategies to curtail car usage. Understanding the impact of these policies necessitates insights into mode choice behaviour. However, for conventional discrete choice models, utility specifications must be defined upfront, potentially leading to misleading policy recommendations. This problem is solved...
student report 2023
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van Marrewijk, Josine (author)
In this research the possibilities of the application of machine learning models at ‘Hoogheemraadschap van Delfland’ are studied. A random forest (RF) and an LSTM model are used for the prediction of the sum of the discharge in the next 2, 8 and 12 hours from the polders to the boezem canals. This research has showed the potential of machine...
master thesis 2023
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Baez Lozada, Luis Carlos (author)
This research evaluates the applicability of Multivariate Imputation by Chained Equations (MICE) for estimating missing well-log data across different sedimentary basis. Utilizing various machine learning techniques including XGBoost (XGB), Random Forest (RF), K-Nearest Neighbors (KNR), and Bayesian Ridge (BR), the performance of MICE was tested...
master thesis 2023
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Lu, Jenny (author)
Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease with a high mortality rate, poor prognosis, and a mere 7.7% 5-year survival rate [1] compared to 65% for all cancer types [2]. Approximately 80% of patients are diagnosed at the advanced stage [3], for which only palliative chemo(radio)therapy remains as treatment option. However,...
master thesis 2023
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Lückerath, Femke (author)
During the initial phase of diagnosis, patients with anti-NDMA-receptor encephalitis (anti-NMDARE) often experience severe symptoms that significantly impact their quality of life. Anti-NDMARE is an autoimmune disorder affecting the brain, with electroencephalography (EEG) playing a vital role in diagnosis and treatment. Identifying EEG patterns...
master thesis 2023
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Manoli, Calin (author)
Machine Learning (ML) is a rapidly growing field, therefore ensuring that students deeply understand such concepts is of key importance in order to certify that they are prepared for the challenges and opportunities of the future workforce. Despite this, literature on teaching ML and assessing students' understanding with regard to this field is...
bachelor thesis 2023
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de Boer, Adam (author)
The following report will enlighten to which extent LiDAR data could enhance land cover classification. It focuses on the area Lemps (26510) in Southwest France where a land cover classification was made using Sentinel-2 spectral images during the fieldwork. Using the additional LiDAR data, the focus shifts to distinguish coniferous and...
bachelor thesis 2023
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Thanh, Hung Vo (author), Ebrahimnia Taremsari, Sajad (author), Ranjbar, Benyamin (author), Mashhadimoslem, Hossein (author), Rahimi, E. (author), Rahimi, Mohammad (author), Elkamel, Ali (author)
Porous carbons as solid adsorbent materials possess effective porosity characteristics that are the most important factors for gas storage. The chemical activating routes facilitate hydrogen storage by adsorbing on the high surface area and microporous features of porous carbon-based adsorbents. The present research proposed to predict H<sub...
journal article 2023
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Sun, Yubo (author), Cheng, H. (author), Zhang, Shizhe (author), Mohan, Manu K. (author), Ye, G. (author), De Schutter, Geert (author)
Alkali-activated concrete (AAC) is regarded as a promising alternative construction material to reduce the CO<sub>2</sub> emission induced by Portland cement (PC) concrete. Due to the diversity in raw materials and complexity of reaction mechanisms, a commonly applied design code is still absent to date. This study attempts to directly...
journal article 2023
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Tepeli, Y.I. (author), Seale, C.F. (author), P. Gonçalves, Joana (author)
Motivation<br/><br/>Anti-cancer therapies based on synthetic lethality (SL) exploit tumour vulnerabilities for treatment with reduced side effects, by targeting a gene that is jointly essential with another whose function is lost. Computational prediction is key to expedite SL screening, yet existing methods are vulnerable to prevalent selection...
journal article 2023
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Hernández, J.I. (author), van Cranenburgh, S. (author), Chorus, C.G. (author), Mouter, N. (author)
We propose three procedures based on association rules (AR) learning and random forests (RF) to support the specification of a portfolio choice model applied in data from complex choice experiment data, specifically a Participatory Value Evaluation (PVE) choice experiment. In a PVE choice experiment, respondents choose a combination of...
journal article 2023
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Mensi, Antonella (author), Tax, D.M.J. (author), Bicego, Manuele (author)
Because outliers are very different from the rest of the data, it is natural to represent outliers by their distances to other objects. Furthermore, there are many scenarios in which only pairwise distances are known, and feature-based outlier detection methods cannot directly be applied. Considering these observations, and given the success...
journal article 2023
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Koohmishi, Mehdi (author), Guo, Y. (author)
Parent rock strength and crumb rubber modification are two critical mechanical parameters that significantly decide the ballast layer degradation subjected to train dynamic loading. Using machine learning to predict ballast degradation considering these two parameters is helpful for deciding ballasted track maintenance cycle. In the current...
journal article 2023
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Labaar, Anna Lisa (author)
For European Union member states, it is mandatory to assign Natura 2000 areas and regularly monitor them. Currently, vegetation mapping is done mainly manually, which is a time-consuming and expensive practise. Unmanned Aerial Vehicles (UAVs or drones), manoeuvrable vehicles with which high-resolution measurements can be done, could increase...
master thesis 2022
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Groot Beumer, Morris (author)
Recent advancements in causal inference and machine learning research have brought forward methods to estimate effects of interventions from observational data. The augmented inverse probability weighted (AIPW) estimator is such a method, which can be used to obtain estimates of potential outcomes. Potential outcomes are defined as a...
master thesis 2022
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Vierling, Koen (author)
About 75 percent of CO2 emissions caused by the combustion of fossil fuels stem from cities. Climate policy minimising the amount of emitted carbon can not be evaluated without accurate quantification of CO2 emissions.<br/>Not only the amount of emitted carbon is of interest, but also being able to place emissions in both the spatial as well as...
master thesis 2022
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Velthoen, J.J. (author)
In this thesis we develop several statistical methods to estimate high conditional quantiles to use for statistical post-processing of weather forecasts. We propose methodologies that combine theory from extreme value statistics and machine learning algorithms in order to estimate high conditional quantiles in large covariate spaces. In...
doctoral thesis 2022
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