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Texas A&M University
1.
Ku, Nian-Wei.
Integration of Lidar Remote Sensing from Multiple Platforms to Assess Vegetation Biophysical Parameters.
Degree: PhD, Ecosystem Science and Management, 2018, Texas A&M University
URL: http://hdl.handle.net/1969.1/173611
► This research concentrates on using multiple platforms of lidar remote sensing for assessing vegetation biophysical parameters. Airborne and spaceborne light detection and ranging (lidar) (i.e.,…
(more)
▼ This research concentrates on using multiple platforms of lidar remote sensing for assessing vegetation biophysical parameters. Airborne and spaceborne light detection and ranging (lidar) (i.e., ICESat) remote sensing can characterize the three-dimensional structure of vegetation and therefore can provide useful information for assessing
forest and rangeland woody plant biomass. The objectives of this research are 1) developing robust methods using airborne lidar and multispectral data to generate a local woody plant biomass map in northern Texas, 2) investigating the accuracy of existing global
forest canopy height maps using airborne lidar data in multiple ecoregions in the southern United States, and 3) upscaling local
forest aboveground biomass estimates to regional scale in an ecoregion. This research integrates statistical methods and remote sensing techniques to develop the procedure for building the regional
forest aboveground biomass map. First, this research results in an approach for employing both airborne lidar and multispectral data with statistical methods to create a local scale woody plant aboveground biomass map in northern Texas. Then, the validation and calibration of the global
forest canopy height map (GCHM) are used throughout rangelands and forests in the southern United States. A calibrated global
forest canopy height map (cGCHM) serves as a primary data source for upscaling the
forest aboveground biomass map from the local- to regional-scale in the South Central Plains ecoregion. In summary, the research utilized lidar data which was collected from multiple platforms to estimate aboveground biomass at multiple scales.
Advisors/Committee Members: Popescu, Sorin C (advisor), Eriksson, Marian (committee member), Wu, X. Ben (committee member), Filippi, Anthony (committee member).
Subjects/Keywords: Lidar; Forest; Biomass; Random forests
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APA (6th Edition):
Ku, N. (2018). Integration of Lidar Remote Sensing from Multiple Platforms to Assess Vegetation Biophysical Parameters. (Doctoral Dissertation). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/173611
Chicago Manual of Style (16th Edition):
Ku, Nian-Wei. “Integration of Lidar Remote Sensing from Multiple Platforms to Assess Vegetation Biophysical Parameters.” 2018. Doctoral Dissertation, Texas A&M University. Accessed April 22, 2021.
http://hdl.handle.net/1969.1/173611.
MLA Handbook (7th Edition):
Ku, Nian-Wei. “Integration of Lidar Remote Sensing from Multiple Platforms to Assess Vegetation Biophysical Parameters.” 2018. Web. 22 Apr 2021.
Vancouver:
Ku N. Integration of Lidar Remote Sensing from Multiple Platforms to Assess Vegetation Biophysical Parameters. [Internet] [Doctoral dissertation]. Texas A&M University; 2018. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/1969.1/173611.
Council of Science Editors:
Ku N. Integration of Lidar Remote Sensing from Multiple Platforms to Assess Vegetation Biophysical Parameters. [Doctoral Dissertation]. Texas A&M University; 2018. Available from: http://hdl.handle.net/1969.1/173611
2.
Thureborn, Oscar.
AI på spårbilbana - Maskinen mot människan
.
Degree: Chalmers tekniska högskola / Institutionen för data och informationsteknik, 2020, Chalmers University of Technology
URL: http://hdl.handle.net/20.500.12380/301928
► Artificiell Intelligens blir allt vanligare för att övervaka prestanda hos system. Ofta skickas data från flera system till en molntjänst som sedan behandlar den. Desto…
(more)
▼ Artificiell Intelligens blir allt vanligare för att övervaka prestanda hos system. Ofta skickas data från flera system
till en molntjänst som sedan behandlar den. Desto fler system som kopplas upp, desto mer data skickas. För
att undvika att överbelasta uppkopplingar hos system kan istället datan behandlas direkt på plats.
Ekkono har utvecklat ett koncept som de kallar ”AI on the edge”. Detta innebär att maskininlärning sker direkt
hos det system som ska tränas istället för att behöva skicka data till en molntjänst eller liknande. Cybercom, som är ett IT-konsultbolag, vill lära sig mer om hur Ekkonos SDK (Software Development Kit) fungerar. Studenterna kom tillsammans med Cybercom och Ekkono fram till idén om skapa ett sk. ”showcase” som kan användas för att öka intresset för deras koncept. Detta realiserades genom att modifiera en bil från en spårbilbana till att bli maskinstyrd och med hjälp av Ekkonos SDK ta bättre och/eller snabbare beslut än människan. Det huvudsakliga syftet med projektet var således att undersöka om den maskinstyrda bilen kunde köra runt en bana snabbare än en bil styrd av en människa.
Bilen utrustades med ett accelerometer- och motorstyrningskort samt en Raspberry Pi Zero W, en liten 1GHz
enkortsdator som kör ett linuxbaserat operativsystem. På Raspberry Pi Zero W installerades sedan Ekkonos
SDK som användes med accelerometern för att prediktera de krafter som skulle komma att påverka bilen och
därefter bestämma hur mycket gaspådrag bilen skulle få i nästa steg.
Bilen lyckades köra runt banan med hjälp av Ekkonos AI men inte snabbare än vad en människa kunde göra.
Detta berodde i stor del på tidsbegränsningar. Konstruktionen av bilen tog mer tid än väntat vilket resulterade
i att den tid som kunde läggas ner på att implementera en väl fungerande AI-modell inte räckte till. Dessutom
så finns det mycket att önska hos beslutsfattningsprocessen, den processen som tar det predikterade värdet och fattar ett beslut baserat på det. En större utmaning än väntat var också att inte designa programmet på ett
sådant sätt att det blir väldigt skräddarsytt för en viss ban-design men inte fungerar på andra typer av banor.
AI-modellen som användes var Random forest, vilket är en bra utgångspunkt då den är snabb att ställa upp
utan att behöva vikta in-parametrarna. Hade det funnits mer tid till att implementera AI-modellen hade en
Multilayer Perceptron modell valts att implementeras istället.
Subjects/Keywords: Artificiell Intelligens;
Spårbilbana;
Random Forest
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APA (6th Edition):
Thureborn, O. (2020). AI på spårbilbana - Maskinen mot människan
. (Thesis). Chalmers University of Technology. Retrieved from http://hdl.handle.net/20.500.12380/301928
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Thureborn, Oscar. “AI på spårbilbana - Maskinen mot människan
.” 2020. Thesis, Chalmers University of Technology. Accessed April 22, 2021.
http://hdl.handle.net/20.500.12380/301928.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Thureborn, Oscar. “AI på spårbilbana - Maskinen mot människan
.” 2020. Web. 22 Apr 2021.
Vancouver:
Thureborn O. AI på spårbilbana - Maskinen mot människan
. [Internet] [Thesis]. Chalmers University of Technology; 2020. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/20.500.12380/301928.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Thureborn O. AI på spårbilbana - Maskinen mot människan
. [Thesis]. Chalmers University of Technology; 2020. Available from: http://hdl.handle.net/20.500.12380/301928
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Waterloo
3.
Jin, Jiao.
A Random Forest Based Method for Urban Land Cover Classification using LiDAR Data and Aerial Imagery.
Degree: 2012, University of Waterloo
URL: http://hdl.handle.net/10012/6770
► Urban land cover classification has always been crucial due to its ability to link many elements of human and physical environments. Timely, accurate, and detailed…
(more)
▼ Urban land cover classification has always been crucial due to its ability to link many elements of human and physical environments. Timely, accurate, and detailed knowledge of the urban land cover information derived from remote sensing data is increasingly required among a wide variety of communities. This surge of interest has been predominately driven by the recent innovations in data, technologies, and theories in urban remote sensing. The development of light detection and ranging (LiDAR) systems, especially incorporated with high-resolution camera component, has shown great potential for urban classification. However, the performance of traditional and widely used classification methods is limited in this context, due to image interpretation complexity. On the other hand, random forests (RF), a newly developed machine learning algorithm, is receiving considerable attention in the field of image classification and pattern recognition. Several studies have shown the advantages of RF in land cover classification. However, few have focused on urban areas by fusion of LiDAR data and aerial images.
The performance of the RF based feature selection and classification methods for urban areas was explored and compared to other popular feature selection approach and classifiers. Evaluation was based on several criteria: classification accuracy, impact of different training sample size, and computational speed. LiDAR data and aerial imagery with 0.5-m resolution were used to classify four land categories in the study area located in the City of Niagara Falls (ON, Canada). The results clearly demonstrate that the use of RF improved the classification performance in terms of accuracy and speed. Support vector machines (SVM) based and RF based classifiers showed similar accuracies. However, RF based classifiers were much quicker than SVM based methods. Based on the results from this work, it can be concluded that the RF based method holds great potential for recent and future urban land cover classification problem with LiDAR data and aerial images.
Subjects/Keywords: Urban Land Cover; Random Forest
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Jin, J. (2012). A Random Forest Based Method for Urban Land Cover Classification using LiDAR Data and Aerial Imagery. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/6770
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Jin, Jiao. “A Random Forest Based Method for Urban Land Cover Classification using LiDAR Data and Aerial Imagery.” 2012. Thesis, University of Waterloo. Accessed April 22, 2021.
http://hdl.handle.net/10012/6770.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Jin, Jiao. “A Random Forest Based Method for Urban Land Cover Classification using LiDAR Data and Aerial Imagery.” 2012. Web. 22 Apr 2021.
Vancouver:
Jin J. A Random Forest Based Method for Urban Land Cover Classification using LiDAR Data and Aerial Imagery. [Internet] [Thesis]. University of Waterloo; 2012. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/10012/6770.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Jin J. A Random Forest Based Method for Urban Land Cover Classification using LiDAR Data and Aerial Imagery. [Thesis]. University of Waterloo; 2012. Available from: http://hdl.handle.net/10012/6770
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Georgia
4.
Ganesan, Sivanesan.
Regression Leaf Forest.
Degree: 2014, University of Georgia
URL: http://hdl.handle.net/10724/27128
► There are a number of learning methods that provide solutions to classification and regression problems, including Linear Regression, Decision Trees, KNN, and SVMs. These methods…
(more)
▼ There are a number of learning methods that provide solutions to classification and regression problems, including Linear Regression, Decision Trees, KNN, and SVMs. These methods work well in many applications, but they are challenged for
real world problems that are noisy, non-linear or high dimensional. Furthermore, missing data (e.g., missing historical features of companies in stock data), is not managed well by current approaches. We present an implementation of a hybrid learning
system that combines an ensemble of decision trees (Random Forest) with of Linear Regression. Linear Regression (LR) is fast but not accurate because it assumes linearity, while Random Forests are not as fast as LR but have been shown to be accurate for
high dimensional and large data sets. By combining these approaches we address the weaknesses of each approach and exploit their strengths both in terms of real time performance and accuracy. In this thesis, we evaluate a hybrid Random Forest and Linear
Regression implementation called "Regression Leaf Forest", which is a forest of trees with regression leaves for supervised learning problems. The approach extends Random Forests by introducing Linear Regression learners at the leaf nodes of the trees
for predicting functions. Our empirical analysis on both real and artificial data shows that the proposed algorithm requires less computation time for both large and high-dimensional datasets while providing comparable or better accuracy when compared
to: Single Tree, a Single Linear Regression Tree, and Random Forest algorithms.
Subjects/Keywords: Random Forest; Linear Regression
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Ganesan, S. (2014). Regression Leaf Forest. (Thesis). University of Georgia. Retrieved from http://hdl.handle.net/10724/27128
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Ganesan, Sivanesan. “Regression Leaf Forest.” 2014. Thesis, University of Georgia. Accessed April 22, 2021.
http://hdl.handle.net/10724/27128.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Ganesan, Sivanesan. “Regression Leaf Forest.” 2014. Web. 22 Apr 2021.
Vancouver:
Ganesan S. Regression Leaf Forest. [Internet] [Thesis]. University of Georgia; 2014. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/10724/27128.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Ganesan S. Regression Leaf Forest. [Thesis]. University of Georgia; 2014. Available from: http://hdl.handle.net/10724/27128
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Melbourne
5.
FEDRIGO, MELISSA.
Where is the forest carbon?: Characterising and modelling the structure and extent of south-eastern Australian temperate forests for improved estimation of carbon stocks.
Degree: 2015, University of Melbourne
URL: http://hdl.handle.net/11343/57411
► Forests play a vital role in the global carbon cycle as both a source and sink of carbon. Increasingly, remote sensing data is being used…
(more)
▼ Forests play a vital role in the global carbon cycle as both a source and sink of carbon. Increasingly, remote sensing data is being used to facilitate the identification, quantification, and management of forest carbon and associated resources at a variety of spatial scales. Efforts to extrapolate carbon estimates from site-specific observations and laboratory experiments to landscape scales require baseline estimates of forest distribution and structure. Model-data synthesis approaches are continually being improved to allow for field observations and remote sensing data to scale carbon estimates from the plot to the landscape scale. In the Central Highlands region of south-eastern Australia, recently suggested as the most carbon dense forests in the world (Keith et al., 2009), carbon stock estimates span wide ranges based on spatially limited field campaigns that have been scaled to the landscape using coarse resolution distribution maps of forest types and homogenising assumptions of forest structure.
This study aimed to improve the resolution and accuracy of past studies by characterising and modelling the structure and extent of south-eastern Australian temperate forests for improved estimation of carbon stocks. Plot level uncertainties in carbon estimates were reduced by assessing carbon stocks in multiple above- and belowground forest components, including the first estimates of two dominant tree fern species that occur in the region. Utilising three-dimensional structural information from light detection and ranging (lidar) remote sensing data, hyperspectral remote sensing techniques were used to analyse the lidar data in a novel approach to map forest structure and distribution for these forests. A predictive ecosystem map (PEM) was generated to incorporate species distributions with lidar-measured forest structure to spatially identify the likely distribution of key rainforest and eucalypt stand types in the Central Highlands. Using detailed plot carbon estimates by stand types and the distribution map generated by the PEM, a model-data synthesis approach was used to predict forest carbon at 20 m spatial resolution for the study area. Carbon estimates for all stand types generated by the model were between regional and continental scale carbon predictions for the study area.
The important outcomes of this research is a series of new approaches to characterising and modelling the structure and extent of forest ecosystems through the use of both detailed field assessments and remote sensing data. High spatial resolution map outputs of forest stand type distribution, structure, and estimates of carbon can be used by local management authorities as a baseline for informed decision making on managing and reporting the contribution of south-eastern Australian temperate forests to global carbon cycles.
Subjects/Keywords: lidar; remote sensing; forest carbon; forest structure; random forest
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
FEDRIGO, M. (2015). Where is the forest carbon?: Characterising and modelling the structure and extent of south-eastern Australian temperate forests for improved estimation of carbon stocks. (Doctoral Dissertation). University of Melbourne. Retrieved from http://hdl.handle.net/11343/57411
Chicago Manual of Style (16th Edition):
FEDRIGO, MELISSA. “Where is the forest carbon?: Characterising and modelling the structure and extent of south-eastern Australian temperate forests for improved estimation of carbon stocks.” 2015. Doctoral Dissertation, University of Melbourne. Accessed April 22, 2021.
http://hdl.handle.net/11343/57411.
MLA Handbook (7th Edition):
FEDRIGO, MELISSA. “Where is the forest carbon?: Characterising and modelling the structure and extent of south-eastern Australian temperate forests for improved estimation of carbon stocks.” 2015. Web. 22 Apr 2021.
Vancouver:
FEDRIGO M. Where is the forest carbon?: Characterising and modelling the structure and extent of south-eastern Australian temperate forests for improved estimation of carbon stocks. [Internet] [Doctoral dissertation]. University of Melbourne; 2015. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/11343/57411.
Council of Science Editors:
FEDRIGO M. Where is the forest carbon?: Characterising and modelling the structure and extent of south-eastern Australian temperate forests for improved estimation of carbon stocks. [Doctoral Dissertation]. University of Melbourne; 2015. Available from: http://hdl.handle.net/11343/57411

University of Alberta
6.
Zhou,Yi.
Persistent Homology on Time series.
Degree: MS, Department of Mathematical and Statistical
Sciences, 2016, University of Alberta
URL: https://era.library.ualberta.ca/files/cvx021f40p
► Topology is a useful tool of mathematics studying how objects are related to one another by investigating their qualitative structural properties, such as connectivity and…
(more)
▼ Topology is a useful tool of mathematics studying how
objects are related to one another by investigating their
qualitative structural properties, such as connectivity and shape.
In this thesis, we applied the method of topological data analysis
(TDA) on sequence data and adopt the theory of persistent homology
for time series, based on topological features computed over the
persistence diagram. Aiming to analyze sequence data from diverse
views, we investigate topological features (in a persistent
homology perspective) of both traditional statistical tools (i.e.
time series) and machine learning methods (i.e. random forest).
Combining the advantages of three different ideas, we finally have
a way to solve clustering (unsupervised learning) and predicting
problems (supervised learning) for our two datasets respectively.
There are two main contributions in this thesis. In Chapter 2, we
applied persistent homology on the cross correlation matrices and
partial correlation matrices of time series, and obtain topological
features from the persistence diagrams and barcodes. With this
information, we generated consistent clusters and loops from our
data and this solution for unsupervised learning problems of
unlabeled datasets constitutes my first contribution in this
thesis. The second contribution lies in considering landscape as an
important covariate for supervised learning problems. In Chapter 3,
we applied persistent homology on polysomnography (PSG) time series
and took the integrals of landscapes as covariates generated from
time series. A random forest model is built with these covariates
to predict Obstructive Apnea-Hypopnea (3% desaturation) Index of
new incoming patient.
Subjects/Keywords: Persistent Homology; Time Series; Random Forest
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhou,Yi. (2016). Persistent Homology on Time series. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/cvx021f40p
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Chicago Manual of Style (16th Edition):
Zhou,Yi. “Persistent Homology on Time series.” 2016. Masters Thesis, University of Alberta. Accessed April 22, 2021.
https://era.library.ualberta.ca/files/cvx021f40p.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
MLA Handbook (7th Edition):
Zhou,Yi. “Persistent Homology on Time series.” 2016. Web. 22 Apr 2021.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Vancouver:
Zhou,Yi. Persistent Homology on Time series. [Internet] [Masters thesis]. University of Alberta; 2016. [cited 2021 Apr 22].
Available from: https://era.library.ualberta.ca/files/cvx021f40p.
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete
Council of Science Editors:
Zhou,Yi. Persistent Homology on Time series. [Masters Thesis]. University of Alberta; 2016. Available from: https://era.library.ualberta.ca/files/cvx021f40p
Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vanderbilt University
7.
Knorr, David Christopher.
Using Machine Learning to Identify and Predict Gentrification in Nashville, Tennessee.
Degree: MS, Earth and Environmental Sciences, 2019, Vanderbilt University
URL: http://hdl.handle.net/1803/13285
► Gentrification is a polarizing and elusive type of neighborhood change that disproportionately threatens our community’s most vulnerable populations. The lived consequences of gentrification have merited…
(more)
▼ Gentrification is a polarizing and elusive type of neighborhood change that disproportionately threatens our community’s most vulnerable populations. The lived consequences of gentrification have merited a substantial amount of academic focus over the course of more than five decades. This paper takes a critical examination of the prescriptive frameworks that previous researchers have used to distinguish gentrification, identifying errors of inclusion, exclusion, as well as major methodological inconsistencies. We advance a k-means clustering approach of six change variables to identify four dominant trajectories of neighborhood change in Nashville, TN (Davidson County) between 2000 and 2016. One specific typology indicated the tell-tale patterns of gentrification and evidence for residential displacement in 13% of inner-city census tracts. A significant join-count statistic revealed that these change typologies were clustered spatially; the observed spatial phenomena gives promise to the potential to predict gentrification as a rational process. We used these outcomes to train a
random forest binary classification model to predict susceptibility to gentrification based on starting housing, demographic, transportation, amenity, and locational characteristics. The mapped predictions of gentrification reveal continuing gentrification in south and east Nashville as well as possible expansion beyond previously identified areas, predominantly along highway corridors to the north and southeast where the majority of the affordable housing stock remains. This research contributes to the quantitative efforts to identify gentrification by advancing a more holistic and less biased alternative. This work also contributes to the forward-looking literature on gentrification. It is designed to serve communities as well as policymakers to optimize intervention strategies in an effort to increase equitable development and socially just cities.
Advisors/Committee Members: Jonathan Gilligan (committee member), Janey Camp (committee member).
Subjects/Keywords: machine learning; random forest; spatial analysis; Gentrification
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Knorr, D. C. (2019). Using Machine Learning to Identify and Predict Gentrification in Nashville, Tennessee. (Thesis). Vanderbilt University. Retrieved from http://hdl.handle.net/1803/13285
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Knorr, David Christopher. “Using Machine Learning to Identify and Predict Gentrification in Nashville, Tennessee.” 2019. Thesis, Vanderbilt University. Accessed April 22, 2021.
http://hdl.handle.net/1803/13285.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Knorr, David Christopher. “Using Machine Learning to Identify and Predict Gentrification in Nashville, Tennessee.” 2019. Web. 22 Apr 2021.
Vancouver:
Knorr DC. Using Machine Learning to Identify and Predict Gentrification in Nashville, Tennessee. [Internet] [Thesis]. Vanderbilt University; 2019. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/1803/13285.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Knorr DC. Using Machine Learning to Identify and Predict Gentrification in Nashville, Tennessee. [Thesis]. Vanderbilt University; 2019. Available from: http://hdl.handle.net/1803/13285
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
8.
Sieben, Wiebke.
Datengestützte Regelgenerierung
für die Alarmgebung im Online-Monitoring von
Intensivpatienten.
Degree: 2009, Technische Universität Dortmund
URL: http://hdl.handle.net/2003/26009
► In dieser Dissertation wird die Eignung datengestützt generierter Alarmregeln für die Überwachung von Patienten auf der Intensivstation untersucht. Hierfür wird ein bekantes Klassifikationsverfahren modifiziert, so…
(more)
▼ In dieser Dissertation wird die
Eignung datengestützt generierter Alarmregeln für die Überwachung
von Patienten auf der Intensivstation untersucht. Hierfür wird ein
bekantes Klassifikationsverfahren modifiziert, so dass es
Alarmregeln mit einer wählbar hohen Sensitivität erzeugt. Dieses
neue Verfahren wird online an Patientenmonitoring-Daten getestet.
Die Reduktion von Fehlalarmen im Patientenmonitoring auf der
Intensivstation ist aufgrund von Fehlalarmraten von bis zu 90%
notwendig. Eine einfache Schwellwertüberwachung der Vitalparameter
ist jedoch trotz wissenschaftlicher Fortschritte gegenwärtiger
Stand der Technik. In einer Anforderungsanalyse werden Bedingungen
für die überführbarkeit neuer Verfahren in die Praxis ermittelt,
unter deren Berücksichtigung ein Klassifikationsverfahren zur
Alarmregelgenerierung gewählt wird. Mit dessen Hilfe können die
Alarme des Standard-Patientenmonitors validiert, d.h. unterdrückt
oder ausgegeben werden. Hier bilden umfangreiche Aufzeichnungen von
Daten eines Standard-Patientenmonitors sowie klinische Annotationen
die benötigte Datengrundlage. In der Datenvorverarbeitung werden,
entsprechend der Vorgehensweise von ärzten, Charakteristika
konstruiert, die den gesundheitlichen Verlauf eines Patienten kurz
vor Auslösung eines Alarms wiedergeben. Sie werden zum einen aus
lokalen linearen Regressionen und zum anderen aus
Wavelet-Zerlegungen abgeleitet. Übliche Klassifikationsverfahren
eignen sich nicht, um Alarmregeln mit einer wählbaren und
kontrollierbaren Zielsensitivität zu generieren. Aus diesem Grund
wird das häufig genutzte Klassifikationsverfahren "
Random Forest"
gemäß dem Neyman-Pearson-Prinzip modifiziert, um die Konstruktion
von Alarmregeln mit einer vorgegebenen Sensitivität zu ermöglichen.
Die Eignung dieses Verfahrens wird für Zielsensitivitäten von 95%
und 98% gezeigt. Die vorgegebenen Sensitivitäten werden in den
betrachteten Fällen im arithmetischen Mittel und Median bei
geringer Variabilität in den Ergebnissen erreicht. Gleichzeitig
können die Fehlalarme um bis zu 55% im Median reduziert werden.
Charakteristika des gesundheitlichen Verlaufs können diese
Ergebnisse nicht weiter steigern. Die Allgemeingültigkeit der
erzeugten Alarmregeln wird anhand einer nach Patienten
stratifizierten Stichprobe überprüft. In diesem Fall bleibt die
Reduktion der Fehlalarme bei hohen Sensitivitäten deutlich hinter
den bisherigen Ergebnissen zurück. Dies deutet darauf hin, dass die
vorliegenden Daten nicht ausreichen, um allgemein gültige Regeln zu
generieren. Das im Rahmen dieser Arbeit entwickelte Verfahren kann
bei Erhebung weiterer Daten zur Alarmregelgenerierung für die
Patientenüberwachung genutzt werden. Das vorgeschlagene Konzept,
die Klassifikations-Entscheidung eines gebaggten Ensembles von
Klassifizierern analog zu einem Neyman-Pearson-Test zu bilden, ist
auf neue Fragestellungen leicht übertragbar und daher in vielen
Anwendungsgebieten von Interesse.
Advisors/Committee Members: Gather, Ursula.
Subjects/Keywords: Klassifikation;
Patienten-Monitoring; Random Forest; 310
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Sieben, W. (2009). Datengestützte Regelgenerierung
für die Alarmgebung im Online-Monitoring von
Intensivpatienten. (Thesis). Technische Universität Dortmund. Retrieved from http://hdl.handle.net/2003/26009
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Sieben, Wiebke. “Datengestützte Regelgenerierung
für die Alarmgebung im Online-Monitoring von
Intensivpatienten.” 2009. Thesis, Technische Universität Dortmund. Accessed April 22, 2021.
http://hdl.handle.net/2003/26009.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Sieben, Wiebke. “Datengestützte Regelgenerierung
für die Alarmgebung im Online-Monitoring von
Intensivpatienten.” 2009. Web. 22 Apr 2021.
Vancouver:
Sieben W. Datengestützte Regelgenerierung
für die Alarmgebung im Online-Monitoring von
Intensivpatienten. [Internet] [Thesis]. Technische Universität Dortmund; 2009. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/2003/26009.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Sieben W. Datengestützte Regelgenerierung
für die Alarmgebung im Online-Monitoring von
Intensivpatienten. [Thesis]. Technische Universität Dortmund; 2009. Available from: http://hdl.handle.net/2003/26009
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
9.
Voigt, Tobias.
Threshold optimization and variable construction for classification in the MAGIC and FACT experiments.
Degree: 2014, Technische Universität Dortmund
URL: http://dx.doi.org/10.17877/DE290R-14114
► In the MAGIC and FACT experiments, random forests are usually used for a classification of a gamma ray signal and hadronic background. Random forests use…
(more)
▼ In the MAGIC and FACT experiments,
random forests are usually used for a classification of a gamma ray signal and hadronic background.
Random forests use a set of tree classifiers and aggregate the single decisions of the trees into one overall decision. In this work a method to choose an optimal threshold value for the
random forest classification is introduced. The method is based on the minimization of the MSE of an estimator for the number of gamma particles in the data set. In a second step, new variables for the classification are introduced in this work. The idea of these variables is to fit bivariate distributions to images recorded by the two telescopes and using distance measures for densities to calculate the distance between the observed and fitted distributions. With a reasonable choice of distributions to fit, it can be expected that such distances are smaller for gamma observations than for the hadronic background. In a third step,
the new threshold optimization and the new variable construction are combined and compared to the methods currently in use. It can be seen that the new methods lead to substantial improvements of the classification with regard to the aim of the analysis.
Advisors/Committee Members: Fried, Roland (advisor), Weihs, Claus (referee).
Subjects/Keywords: Classification; Astronomy; Random forest; 310; 570
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Voigt, T. (2014). Threshold optimization and variable construction for classification in the MAGIC and FACT experiments. (Doctoral Dissertation). Technische Universität Dortmund. Retrieved from http://dx.doi.org/10.17877/DE290R-14114
Chicago Manual of Style (16th Edition):
Voigt, Tobias. “Threshold optimization and variable construction for classification in the MAGIC and FACT experiments.” 2014. Doctoral Dissertation, Technische Universität Dortmund. Accessed April 22, 2021.
http://dx.doi.org/10.17877/DE290R-14114.
MLA Handbook (7th Edition):
Voigt, Tobias. “Threshold optimization and variable construction for classification in the MAGIC and FACT experiments.” 2014. Web. 22 Apr 2021.
Vancouver:
Voigt T. Threshold optimization and variable construction for classification in the MAGIC and FACT experiments. [Internet] [Doctoral dissertation]. Technische Universität Dortmund; 2014. [cited 2021 Apr 22].
Available from: http://dx.doi.org/10.17877/DE290R-14114.
Council of Science Editors:
Voigt T. Threshold optimization and variable construction for classification in the MAGIC and FACT experiments. [Doctoral Dissertation]. Technische Universität Dortmund; 2014. Available from: http://dx.doi.org/10.17877/DE290R-14114
10.
Barroso, João António Araújo.
Modelo preditivo para o risco de readmissão hospitalar
.
Degree: 2017, Universidade de Aveiro
URL: http://hdl.handle.net/10773/25092
► O desenvolvimento deste projeto passou por 4 fases: duas pesquisas preliminares em momentos distintos; construção e avaliação de modelos preditivos; desenvolvimento de uma aplicação web…
(more)
▼ O desenvolvimento deste projeto passou por 4 fases: duas pesquisas preliminares
em momentos distintos; construção e avaliação de modelos preditivos;
desenvolvimento de uma aplicação web com a solução de um desses modelos.
A primeira fase do projeto consistiu numa pesquisa em que o objetivo foi a
recolha de um grande número de artigos relacionados com problemáticas no
meio hospitalar. Para esta fase dedicou-se cerca de um mês de trabalho.
Desta primeira etapa, determinou-se o objetivo do projeto: o desenvolvimento
de um modelo preditivo para o risco de readmissão hospitalar.
A segunda fase do projeto foi sem dúvida a que mais tempo se despendeu,
onde se dedicaram mais de dois meses de trabalho. Para esta fase foi disponibilizada
uma base de dados hospitalar real (de vários hospitais) com cerca
de um milhão de observações e centenas de variáveis. Esta fase pode ser
dividida em várias etapas: numa etapa inicial procurou-se compreender e
resolver incoerências através de modificações e transformações da base de
dados original; na segunda fase efetuaram-se modificações ao formato de algumas
variáveis e criaram-se outras novas variáveis com recurso às variáveis
já existentes; na terceira etapa, após finalizadas as transformações à base de
dados, selecionaram-se alguns conjuntos de variáveis por ordem de significância;
na última fase construiram-se e testaram-se vários modelos
random
forest com os conjuntos de treino selecionados na etapa anterior.
Na a terceira fase, o objetivo englobava selecionar uma tecnologia Machine
Learning (ML) para posterior desenvolvimento de uma aplicação web. Assim,
procedeu-se a uma nova pesquisa sobre tecnologias ML, nomeadamente o
Apache Spark, o H2O, o H2O Sparkling Water, o Microsoft Azure ML e o
OpenCPU. Para esta fase, foi dedicado cerca de um mês de trabalho.
Após a pesquisa e decisão da tecnologia a utilizar, desenvolveu-se uma aplicação
web. Mais uma vez, foi dedicado cerca de um mês de trabalho para se
finalizar esta fase.
As fases de trabalho do estágio seguiram a ordem temporal acima descrita,
contudo foram desenvolvidos outros trabalhos complementares, como por
exemplo, a criação de dashboards usando o software Microsoft Power BI.
Para além do que foi mencionado, sempre que existiu oportunidade, discutiuse
informalmente com trabalhadores desta área (maioritariamente médicos)
acerca do tema, de forma a compreender melhor o problema e descobrir a
melhor forma de o abordar.
Advisors/Committee Members: Silva, Luís Miguel Almeida da (advisor), Marques, Bernardo (advisor).
Subjects/Keywords: Random forest;
Readmissão hospitalar;
OpenCPU;
Modelo preditivo
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Barroso, J. A. A. (2017). Modelo preditivo para o risco de readmissão hospitalar
. (Thesis). Universidade de Aveiro. Retrieved from http://hdl.handle.net/10773/25092
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Barroso, João António Araújo. “Modelo preditivo para o risco de readmissão hospitalar
.” 2017. Thesis, Universidade de Aveiro. Accessed April 22, 2021.
http://hdl.handle.net/10773/25092.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Barroso, João António Araújo. “Modelo preditivo para o risco de readmissão hospitalar
.” 2017. Web. 22 Apr 2021.
Vancouver:
Barroso JAA. Modelo preditivo para o risco de readmissão hospitalar
. [Internet] [Thesis]. Universidade de Aveiro; 2017. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/10773/25092.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Barroso JAA. Modelo preditivo para o risco de readmissão hospitalar
. [Thesis]. Universidade de Aveiro; 2017. Available from: http://hdl.handle.net/10773/25092
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

KTH
11.
Röhss, Josefine.
A Statistical Framework for Classification of Tumor Type from microRNA Data.
Degree: Mathematical Statistics, 2016, KTH
URL: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191990
► Hepatocellular carcinoma (HCC) is a type of liver cancer with low survival rate, not least due to the difficulty of diagnosing it in an…
(more)
▼ Hepatocellular carcinoma (HCC) is a type of liver cancer with low survival rate, not least due to the difficulty of diagnosing it in an early stage. The objective of this thesis is to build a random forest classification method based on microRNA (and messenger RNA) expression profiles from patients with HCC. The main purpose is to be able to distinguish between tumor samples and normal samples by measuring the miRNA expression. If successful, this method can be used to detect HCC at an earlier stage and to design new therapeutics. The microRNAs and messenger RNAs which have a significant difference in expression between tumor samples and normal samples are selected for building random forest classification models. These models are then tested on paired samples of tumor and surrounding normal tissue from patients with HCC. The results show that the classification models built for classifying tumor and normal samples have high prediction accuracy and hence show high potential for using microRNA and messenger RNA expression levels for diagnosis of HCC.
Hepatocellulär cancer (HCC) är en typ av levercancer med mycket låg överlevnadsgrad, inte minst på grund av svårigheten att diagnosticera i ett tidigt skede. Syftet med det här projektet är att bygga en klassificeringsmodell med random forest, baserad på uttrycksprofiler av mikroRNA (och budbärar-RNA) från patienter med HCC. Målet är att kunna skilja mellan tumörprover och normala prover genom att mäta uttrycket av mikroRNA. Om detta mål uppnås kan metoden användas för att upptäcka HCC i ett tidigare skede och för att utveckla nya läkemedel. De mikroRNA och budbärar-RNA som har en signifikant skillnad i uttryck mellan prover från tumörvävnad och intilliggande normal vävnad väljs ut för att bygga klassificaringsmodeller med random forest. Dessa modeller testas sedan på parade prover av tumörvävnad och intilliggande vävnad från patienter med HCC. Resultaten visar att modeller som byggs med denna metod kan klassificera tumörprover och normala prover med hög noggrannhet. Det finns således stor potential för att använda uttrycksprofiler från mikroRNA och budbärar-RNA för att diagnosticera HCC.
Subjects/Keywords: miRNA; mRNA; random forest; classification; HCC; diagnosis
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Röhss, J. (2016). A Statistical Framework for Classification of Tumor Type from microRNA Data. (Thesis). KTH. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191990
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Röhss, Josefine. “A Statistical Framework for Classification of Tumor Type from microRNA Data.” 2016. Thesis, KTH. Accessed April 22, 2021.
http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191990.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Röhss, Josefine. “A Statistical Framework for Classification of Tumor Type from microRNA Data.” 2016. Web. 22 Apr 2021.
Vancouver:
Röhss J. A Statistical Framework for Classification of Tumor Type from microRNA Data. [Internet] [Thesis]. KTH; 2016. [cited 2021 Apr 22].
Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191990.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Röhss J. A Statistical Framework for Classification of Tumor Type from microRNA Data. [Thesis]. KTH; 2016. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191990
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Southern California
12.
Zhu, Xun.
Identifying important microRNAs in progression of breast
cancer.
Degree: MS, Applied Mathematics, 2014, University of Southern California
URL: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/435497/rec/3334
► Stefan Wuchty et. al. from PLOS summarized a workflow to identify important miRs of pathways for a type of tumor, which uses GSEA to measure…
(more)
▼ Stefan Wuchty et. al. from PLOS summarized a workflow
to identify important miRs of pathways for a type of tumor, which
uses GSEA to measure the importance of each pathway. We try to
apply this kind of workflow on a new set of data on breast cancer
(acquired from TCGA). Because the number of samples is very
limited, which is different from the situation in their original
paper, instead of selecting miRs with high FDR, we instead selected
the important miRs based on their permuted p‐values. ❧ We created
three kinds of measurements trying to characterize the important
miRs, which corresponds to three matrices—a binary matrix
indicating whether an pathway is the target of miR, an integer
matrix indicating the total number of binding sites between an
miR‐pathway pair, and a real number matrix indicating the ""bind
score"" of an miR‐pathway pair. We selected the important miRs
through permutation tests with p < 0.01 and compared the
selected important miRs from the three metrics.
Advisors/Committee Members: Lototsky, Sergey V. (Committee Chair), Garmire, Lana (Committee Member), Wang, Chunming (Committee Member).
Subjects/Keywords: microRNA; GSEA; random‐forest; p‐value
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhu, X. (2014). Identifying important microRNAs in progression of breast
cancer. (Masters Thesis). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/435497/rec/3334
Chicago Manual of Style (16th Edition):
Zhu, Xun. “Identifying important microRNAs in progression of breast
cancer.” 2014. Masters Thesis, University of Southern California. Accessed April 22, 2021.
http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/435497/rec/3334.
MLA Handbook (7th Edition):
Zhu, Xun. “Identifying important microRNAs in progression of breast
cancer.” 2014. Web. 22 Apr 2021.
Vancouver:
Zhu X. Identifying important microRNAs in progression of breast
cancer. [Internet] [Masters thesis]. University of Southern California; 2014. [cited 2021 Apr 22].
Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/435497/rec/3334.
Council of Science Editors:
Zhu X. Identifying important microRNAs in progression of breast
cancer. [Masters Thesis]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/435497/rec/3334

University of Cambridge
13.
Bland, Matthew Paul.
Targeting domestic abuse by mining police records.
Degree: PhD, 2020, University of Cambridge
URL: https://doi.org/10.17863/CAM.46479
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793089
► This dissertation presents findings from analyses of three large datasets of domestic abuse records sourced from multiple police forces in England and Wales. It seeks…
(more)
▼ This dissertation presents findings from analyses of three large datasets of domestic abuse records sourced from multiple police forces in England and Wales. It seeks to address research questions in relation to the extent of repeat and serial abuse, concentration and escalation of harm, and the forecasting of future serious crimes. Using a variety of statistics, it shows that most victims and offenders report domestic abuse to the police forces just once in a multi-year period. Among these cases however, are many of the individuals who comprise very small 'power few' groups that account for most of total crime harm. Using the Cambridge Crime Harm Index as the instrument of measurement, analysis shows that 80% of cumulative harm is attributable to fewer than 3% of victims and offenders, and almost half of these most harmed victims or harmful offenders have only one record of domestic abuse in police databases. Police forces are therefore presented with a substantial challenge when it comes to preventing serious harm from domestic abuse, because in more than 40% of the most harmful cases they have not dealt with the victims or offenders of domestic abuse before. Furthermore, among the victims and offenders who are linked to multiple records of domestic abuse, analysis detects no pattern of escalating severity. In fact, the first crime reported is, on average, the most harmful domestic crime reported to the police. This runs contrary to popular theories of escalation and further illustrates the forecasting challenge facing police agencies. Contemporary harm reduction strategies have placed some emphasis on the management of serial perpetrators of abuse, but analysis shows that these do not offer a complete solution for harm reduction either. These analyses show that serial offenders account for only between 10% and 15% of all domestic offenders and contribute no more to the 'power few' than repeat offenders who have just one victim. However, analysis of the non-domestic crime records of domestic offenders does show that serial perpetrators are less specialised in domestic abuse than repeat or single-time offenders - they commit more non-domestic types of crime and account for more total crime harm overall. Serial and repeat offenders with the greatest generalist tendencies were shown to be attributed to the most domestic abuse harm of any domestic offender types, indicating potential relevance to non-domestic offending records in the pursuit of predicting serious domestic crimes. How then, might police agencies seek to identify the most harmful cases before they occur? This research explores a large bank of records relating to arrests for any type of crime, using a statistical model created by a supervised machine learning algorithm. This model processes each arrestee every time they enter custody and using predictors from 35 pieces of information primarily concerning the prior offending history of the arrestee, generates a forecast of future arrest for a domestic crime within two years. The forecast has three…
Subjects/Keywords: Domestic Abuse; Police Data; Random Forest
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Bland, M. P. (2020). Targeting domestic abuse by mining police records. (Doctoral Dissertation). University of Cambridge. Retrieved from https://doi.org/10.17863/CAM.46479 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793089
Chicago Manual of Style (16th Edition):
Bland, Matthew Paul. “Targeting domestic abuse by mining police records.” 2020. Doctoral Dissertation, University of Cambridge. Accessed April 22, 2021.
https://doi.org/10.17863/CAM.46479 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793089.
MLA Handbook (7th Edition):
Bland, Matthew Paul. “Targeting domestic abuse by mining police records.” 2020. Web. 22 Apr 2021.
Vancouver:
Bland MP. Targeting domestic abuse by mining police records. [Internet] [Doctoral dissertation]. University of Cambridge; 2020. [cited 2021 Apr 22].
Available from: https://doi.org/10.17863/CAM.46479 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793089.
Council of Science Editors:
Bland MP. Targeting domestic abuse by mining police records. [Doctoral Dissertation]. University of Cambridge; 2020. Available from: https://doi.org/10.17863/CAM.46479 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.793089

University of Montana
14.
Elmore, Nathan J.
CLASSIFYING EMOTION USING STREAMING OF PHYSIOLOGICAL CORRELATES OF EMOTION.
Degree: MS, 2012, University of Montana
URL: https://scholarworks.umt.edu/etd/199
► The ability for a computer to recognize emotions would have many uses. In the field of human-computer interaction, it would be useful if computers could…
(more)
▼ The ability for a computer to recognize emotions would have many uses. In the field of human-computer interaction, it would be useful if computers could sense if a user is frustrated and offer help (Lisetti & Nasoz, 2002), or it could be used in cars to predict stress or road rage (Nasoz, Lisetti, & Vasilakos, 2010). Also, it has uses in the medical field with emotional therapy or monitoring patients (Rebenitsch, Owen, Brohil, Biocca, & Ferydiansyah, 2010). Emotion recognition is a complex subject that combines psychology and computer science, but it is not a new problem. When the question was first posed, researchers examined at physiological signals that could help differentiate an emotion (Schachter & Singer, 1962). As the research progressed, researchers examined ways in which computers could recognize emotions, many of which were successful. Previous research has not yet looked at the emotional data as streaming data, or attempted to classify emotion in real time. This thesis extracts features from a window of simulated streaming data to attempt to classify emotions in real time. As a corollary, this method can also be used to attempt to identify the earliest point an emotion can be predicted. The results show that emotions can be classified in real time, and applying a window and feature extraction leads to better classification success. It shows that this method may be used to determine if an emotion could be predicted before it is cognitively experienced, but it could not predict the emotion transitional state. More research is required before that goal can be achieved.
Subjects/Keywords: emotion; machine learning; random forest; streaming
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APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
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APA (6th Edition):
Elmore, N. J. (2012). CLASSIFYING EMOTION USING STREAMING OF PHYSIOLOGICAL CORRELATES OF EMOTION. (Masters Thesis). University of Montana. Retrieved from https://scholarworks.umt.edu/etd/199
Chicago Manual of Style (16th Edition):
Elmore, Nathan J. “CLASSIFYING EMOTION USING STREAMING OF PHYSIOLOGICAL CORRELATES OF EMOTION.” 2012. Masters Thesis, University of Montana. Accessed April 22, 2021.
https://scholarworks.umt.edu/etd/199.
MLA Handbook (7th Edition):
Elmore, Nathan J. “CLASSIFYING EMOTION USING STREAMING OF PHYSIOLOGICAL CORRELATES OF EMOTION.” 2012. Web. 22 Apr 2021.
Vancouver:
Elmore NJ. CLASSIFYING EMOTION USING STREAMING OF PHYSIOLOGICAL CORRELATES OF EMOTION. [Internet] [Masters thesis]. University of Montana; 2012. [cited 2021 Apr 22].
Available from: https://scholarworks.umt.edu/etd/199.
Council of Science Editors:
Elmore NJ. CLASSIFYING EMOTION USING STREAMING OF PHYSIOLOGICAL CORRELATES OF EMOTION. [Masters Thesis]. University of Montana; 2012. Available from: https://scholarworks.umt.edu/etd/199

Delft University of Technology
15.
Kaandorp, Mikael (author).
Machine Learning for Data-Driven RANS Turbulence Modelling.
Degree: 2018, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:f833e151-7c0f-414c-8217-5af783c88474
► The application of machine learning algorithms as data-driven turbulence modelling tools for Reynolds Averaged Navier-Stokes (RANS) simulations is presented. A novel machine learning algorithm, called…
(more)
▼ The application of machine learning algorithms as data-driven turbulence modelling tools for Reynolds Averaged Navier-Stokes (RANS) simulations is presented. A novel machine learning algorithm, called the Tensor Basis Random Forest (TBRF) is introduced, which is able to predict the Reynolds stress anisotropy tensor. The algorithm is trained on several flow cases using DNS/LES data, and used to predict the Reynolds stress anisotropy tensor on multiple test flow cases. Predictions are then propagated with a custom OpenFOAM solver to yield an improved flow field. Results show that the TBRF algorithm is able to accurately predict the anisotropy tensor for various flow cases, with most of the predictions being realizable and close to the DNS/LES reference data. Resulting mean flows for a square duct and a backward facing step show great resemblance to corresponding DNS and experimental data-sets.
Aerospace Engineering
Advisors/Committee Members: Dwight, R.P. (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: machine learning; RANS; Random Forest; Turbulence
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❌
APA ·
Chicago ·
MLA ·
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CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Kaandorp, M. (. (2018). Machine Learning for Data-Driven RANS Turbulence Modelling. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:f833e151-7c0f-414c-8217-5af783c88474
Chicago Manual of Style (16th Edition):
Kaandorp, Mikael (author). “Machine Learning for Data-Driven RANS Turbulence Modelling.” 2018. Masters Thesis, Delft University of Technology. Accessed April 22, 2021.
http://resolver.tudelft.nl/uuid:f833e151-7c0f-414c-8217-5af783c88474.
MLA Handbook (7th Edition):
Kaandorp, Mikael (author). “Machine Learning for Data-Driven RANS Turbulence Modelling.” 2018. Web. 22 Apr 2021.
Vancouver:
Kaandorp M(. Machine Learning for Data-Driven RANS Turbulence Modelling. [Internet] [Masters thesis]. Delft University of Technology; 2018. [cited 2021 Apr 22].
Available from: http://resolver.tudelft.nl/uuid:f833e151-7c0f-414c-8217-5af783c88474.
Council of Science Editors:
Kaandorp M(. Machine Learning for Data-Driven RANS Turbulence Modelling. [Masters Thesis]. Delft University of Technology; 2018. Available from: http://resolver.tudelft.nl/uuid:f833e151-7c0f-414c-8217-5af783c88474

Brunel University
16.
Zhang, Fan.
Music emotion recognition based on feature combination, deep learning and chord detection.
Degree: PhD, 2019, Brunel University
URL: http://bura.brunel.ac.uk/handle/2438/18140
;
https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774573
► As one of the most classic human inventions, music appeared in many artworks, such as songs, movies and theatres. It can be seen as another…
(more)
▼ As one of the most classic human inventions, music appeared in many artworks, such as songs, movies and theatres. It can be seen as another language, used to express the authors thoughts and emotion. In many cases, music can express the meaning and emotion emerged which is the authors hope and the audience feeling. However, the emotions which appear during human enjoying the music is complex and difficult to precisely explain. Therefore, Music Emotion Recognition (MER) is an interesting research topic in artificial intelligence field for recognising the emotions from the music. The recognition methods and tools for the music signals are growing fast recently. With recent development of the signal processing, machine learning and algorithm optimization, the recognition accuracy is approaching perfection. In this thesis, the research is focused on three differentsignificantpartsofMER,thatarefeatures, learningmethodsandmusicemotion theory, to explain and illustrate how to effectively build MER systems. Firstly, an automatic MER system for classing 4 emotions was proposed where OpenSMILE is used for feature extraction and IS09 feature was selected. After the combination with STAT statistic features, Random Forest classifier produced the best performance than previous systems. It shows that this approach of feature selection and machine learning can indeed improve the accuracy of MER by at least 3.5% from other combinations under suitable parameter setting and the performance of system was improved by new features combination by IS09 and STAT reaching 83.8% accuracy. Secondly, another MER system for 4 emotions was proposed basedon the dynamic property of music signals where the features are extracted from segments of music signals instead of the whole recording in APM database. Then Long Shot-Term Memory (LSTM) deep learning model was used for classification. The model can use the dynamic continuous information between the different time frame segments for more effective emotion recognition. However, the final performance just achieved 65.7% which was not as good as expected. The reason might be that the database is not suitable to the LSTM as the initial thoughts. The information between the segments might be not good enough to improve the performance of recognition in comparison with the traditional methods. The complex deep learning method do not suitable for every database was proved by the conclusion,which shown that the LSTM dynamic deep learning method did not work well in this continuous database. Finally, it was targeted to recognise the emotion by the identification of chord inside as these chords have particular emotion information inside stated in previous theoretical work. The research starts by building a new chord database that uses the Adobe audition to extract the chord clip from the piano chord teaching audio. Then the FFT features based on the 1000 points sampling pre-process data and STAT features were extracted for the selected samples from the database. After the calculation and comparison using Euclidean…
Subjects/Keywords: Long-short term memory; Random Forest
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Zhang, F. (2019). Music emotion recognition based on feature combination, deep learning and chord detection. (Doctoral Dissertation). Brunel University. Retrieved from http://bura.brunel.ac.uk/handle/2438/18140 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774573
Chicago Manual of Style (16th Edition):
Zhang, Fan. “Music emotion recognition based on feature combination, deep learning and chord detection.” 2019. Doctoral Dissertation, Brunel University. Accessed April 22, 2021.
http://bura.brunel.ac.uk/handle/2438/18140 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774573.
MLA Handbook (7th Edition):
Zhang, Fan. “Music emotion recognition based on feature combination, deep learning and chord detection.” 2019. Web. 22 Apr 2021.
Vancouver:
Zhang F. Music emotion recognition based on feature combination, deep learning and chord detection. [Internet] [Doctoral dissertation]. Brunel University; 2019. [cited 2021 Apr 22].
Available from: http://bura.brunel.ac.uk/handle/2438/18140 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774573.
Council of Science Editors:
Zhang F. Music emotion recognition based on feature combination, deep learning and chord detection. [Doctoral Dissertation]. Brunel University; 2019. Available from: http://bura.brunel.ac.uk/handle/2438/18140 ; https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.774573

Penn State University
17.
Xia, Zhiyue.
HARNESSING THE POWER OF GEOSPATIAL DATA WITH RANDOM FOREST TO FORECAST GYPSY MOTH OUTBREAK.
Degree: 2018, Penn State University
URL: https://submit-etda.libraries.psu.edu/catalog/15704zzx13
► The gypsy moth (Lymantria dispar) is a non-native forest pest that was introduced to the USA in 1869. Since then it has spread continuously across…
(more)
▼ The gypsy moth (Lymantria dispar) is a non-native
forest pest that was introduced to the USA in 1869. Since then it has spread continuously across most of the northeastern US. Larvae of this insect prefer feeding on oak species, although other species may also serve as host trees. During an outbreak, larvae defoliate forests across large regions and repeated defoliation can predispose the trees to attacks by secondary insect pests or fungal infections causing tree mortality.
Gypsy moth outbreaks are episodic and are difficult to predict. Development of forecasting models remains a challenge despite their potential usefulness in effectively mobilizing resources to deal with the outbreaks. Previous studies indicate that vegetation attributes measured through remote sensing, terrain, and climate characteristics influence the likelihood of gypsy moth outbreaks. In addition, temporal and spatial variables describing the cyclic and spatial patterns of the outbreaks could be very valuable in forecasting outbreaks.
In this thesis, a model is developed to forecast gypsy moth outbreaks using Pennsylvania as a case study. Systematic sampling was used to locate 5,042 sample pixels across
forest areas of Pennsylvania and focus on defoliation episodes during the time period 2000-2016 to develop the model. For each pixel, a large suite of temporal and spatial predictor variables is derived from inventory data, climate, topography, and remote sensing measures of vegetation status, while the occurrence of defoliation is obtained from annual defoliation sketch maps. Machine learning modeling algorithm
Random Forests was used in this study, which has a well-documented predictive ability and can deal with a large number of variables. The model performance is assessed by hindcasting defoliations in 1985, 1990 and 1995, and by cross validation leaving out one year of the fit dataset at a time. An accurate forecasting model is of critical importance for projecting the spatial extent of future defoliations and for
forest management planning.
Advisors/Committee Members: Douglas A Miller, Thesis Advisor/Co-Advisor, Laura P Leites, Thesis Advisor/Co-Advisor, Shelby Fleischer, Committee Member.
Subjects/Keywords: Gypsy moth; Random Forest; Random Forests; Forest disturbance; Spatial modeling; Ecological modeling
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Xia, Z. (2018). HARNESSING THE POWER OF GEOSPATIAL DATA WITH RANDOM FOREST TO FORECAST GYPSY MOTH OUTBREAK. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15704zzx13
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Xia, Zhiyue. “HARNESSING THE POWER OF GEOSPATIAL DATA WITH RANDOM FOREST TO FORECAST GYPSY MOTH OUTBREAK.” 2018. Thesis, Penn State University. Accessed April 22, 2021.
https://submit-etda.libraries.psu.edu/catalog/15704zzx13.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Xia, Zhiyue. “HARNESSING THE POWER OF GEOSPATIAL DATA WITH RANDOM FOREST TO FORECAST GYPSY MOTH OUTBREAK.” 2018. Web. 22 Apr 2021.
Vancouver:
Xia Z. HARNESSING THE POWER OF GEOSPATIAL DATA WITH RANDOM FOREST TO FORECAST GYPSY MOTH OUTBREAK. [Internet] [Thesis]. Penn State University; 2018. [cited 2021 Apr 22].
Available from: https://submit-etda.libraries.psu.edu/catalog/15704zzx13.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Xia Z. HARNESSING THE POWER OF GEOSPATIAL DATA WITH RANDOM FOREST TO FORECAST GYPSY MOTH OUTBREAK. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/15704zzx13
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Brno University of Technology
18.
Jurák, Martin.
Detekce objektů na GPU: Object Detection on GPU.
Degree: 2019, Brno University of Technology
URL: http://hdl.handle.net/11012/52221
► This thesis is focused on the acceleration of Random Forest object detection in an image. Random Forest detector is an ensemble of independently evaluated random…
(more)
▼ This thesis is focused on the acceleration of
Random Forest object detection in an image.
Random Forest detector is an ensemble of independently evaluated
random decision trees. This feature can be used to acceleration on graphics unit. Development and increasing performance of graphics processing units allow the use of GPU for general-purpose computing (GPGPU). The goal of this thesis is describe how to implement
Random Forest method on GPU with OpenCL standard.
Advisors/Committee Members: Juránek, Roman (advisor), Hradiš, Michal (referee).
Subjects/Keywords: Detekce objektů; GPGPU; GPU; OpenCL; Random Forest; Object detection; GPGPU; GPU; OpenCL; Random Forest
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Jurák, M. (2019). Detekce objektů na GPU: Object Detection on GPU. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/52221
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Jurák, Martin. “Detekce objektů na GPU: Object Detection on GPU.” 2019. Thesis, Brno University of Technology. Accessed April 22, 2021.
http://hdl.handle.net/11012/52221.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Jurák, Martin. “Detekce objektů na GPU: Object Detection on GPU.” 2019. Web. 22 Apr 2021.
Vancouver:
Jurák M. Detekce objektů na GPU: Object Detection on GPU. [Internet] [Thesis]. Brno University of Technology; 2019. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/11012/52221.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Jurák M. Detekce objektů na GPU: Object Detection on GPU. [Thesis]. Brno University of Technology; 2019. Available from: http://hdl.handle.net/11012/52221
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
19.
Verica, Weverton Rodrigo.
Mapeamento semiautomático por meio de padrão espectro-temporal de áreas agrícolas e alvos permanentes com evi/modis no Paraná.
Degree: 2018, Universidade Estadual do Oeste do Paraná; Cascavel; Programa de Pós-Graduação em Engenharia Agrícola; UNIOESTE; Brasil; Centro de Ciências Exatas e Tecnológicas
URL: http://tede.unioeste.br/handle/tede/3916
► Submitted by Neusa Fagundes ([email protected]) on 2018-09-06T19:38:50Z No. of bitstreams: 2 Weverton_Verica2018.pdf: 4544186 bytes, checksum: 766200b4dea97433d3d88b08cbe3e548 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)
Made available…
(more)
▼ Submitted by Neusa Fagundes ([email protected]) on 2018-09-06T19:38:50Z No. of bitstreams: 2 Weverton_Verica2018.pdf: 4544186 bytes, checksum: 766200b4dea97433d3d88b08cbe3e548 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5)
Made available in DSpace on 2018-09-06T19:38:50Z (GMT). No. of bitstreams: 2 Weverton_Verica2018.pdf: 4544186 bytes, checksum: 766200b4dea97433d3d88b08cbe3e548 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-02-16
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES
Knowledge of location and quantity of areas for agriculture or either native or planted forests is relevant for public managers to make their decisions based on reliable data. In addition, part of ICMS revenues from the Municipal Participation Fund (FPM) depends on agricultural production data, number of rural properties and the environmental factor.
The objective of this research was to design an objective and semiautomatic methodology to map agricultural areas and targets permanent, and later to identify areas of soybean, corn 1st and 2nd crops, winter crops, semi-perennial agriculture, forests and other permanent targets in the state of Paraná for the harvest years (2013/14 to 2016/17), using temporal series of EVI/Modis vegetation indexes. The proposed methodology follows the steps of the Knowledge Discovery Process in Database – KDD, in which the classification task was performed by the Random Forest algorithm. For the validation of the mappings, samples extracted from Landsat-8 images were used, obtaining the global accuracy indices greater than 84.37% and a kappa index ranging from 0.63 to 0.98, hence considered mappings with good or excellent spatial accuracy. The municipal data of the area of soybean, corn 1st crop, corn 2nd crop and winter crops mapped were confronted with the official statistics obtaining coefficients
of linear correlation between 0.61 to 0.9, indicating moderate or strong correlation with the data officials. In this way, the proposed semi-automatic methodology was successful in the mapping, as well as the automation of the process of elaboration of the metrics, thus generating a script in the software R in order to facilitate future mappings with low processing time.
O conhecimento da localização e da quantidade de áreas destinadas a agricultura ou a florestas nativas ou plantadas é relevante para que os gestores públicos tomem suas decisões pautadas em dados fidedignos com a realidade. Além disto, parte das receitas de ICMS advindas do Fundo de Participação aos Municípios (FPM) depende de dados de produção agropecuária, número de propriedades rurais e fator ambiental. Diante disso, esta dissertação teve como objetivo elaborar uma metodologia objetiva e semiautomática para mapear áreas agrícolas e alvos permanente e posteriormente identificar áreas de soja, milho 1ª e 2ª
safras, culturas de inverno, agricultura semi-perene, florestas e demais alvos permanentes no estado do Paraná…
Advisors/Committee Members: Johann, Jerry Adriani, Johann, Jerry Adriani, Silva Junior, Carlos Antonio da, Mercante , Erivelto, Gurgacz, Flávio.
Subjects/Keywords: KDD; Random Forest; Classificação; KDD; Random forest; Classification; CIENCIAS AGRARIAS::ENGENHARIA AGRICOLA
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Verica, W. R. (2018). Mapeamento semiautomático por meio de padrão espectro-temporal de áreas agrícolas e alvos permanentes com evi/modis no Paraná. (Masters Thesis). Universidade Estadual do Oeste do Paraná; Cascavel; Programa de Pós-Graduação em Engenharia Agrícola; UNIOESTE; Brasil; Centro de Ciências Exatas e Tecnológicas. Retrieved from http://tede.unioeste.br/handle/tede/3916
Chicago Manual of Style (16th Edition):
Verica, Weverton Rodrigo. “Mapeamento semiautomático por meio de padrão espectro-temporal de áreas agrícolas e alvos permanentes com evi/modis no Paraná.” 2018. Masters Thesis, Universidade Estadual do Oeste do Paraná; Cascavel; Programa de Pós-Graduação em Engenharia Agrícola; UNIOESTE; Brasil; Centro de Ciências Exatas e Tecnológicas. Accessed April 22, 2021.
http://tede.unioeste.br/handle/tede/3916.
MLA Handbook (7th Edition):
Verica, Weverton Rodrigo. “Mapeamento semiautomático por meio de padrão espectro-temporal de áreas agrícolas e alvos permanentes com evi/modis no Paraná.” 2018. Web. 22 Apr 2021.
Vancouver:
Verica WR. Mapeamento semiautomático por meio de padrão espectro-temporal de áreas agrícolas e alvos permanentes com evi/modis no Paraná. [Internet] [Masters thesis]. Universidade Estadual do Oeste do Paraná; Cascavel; Programa de Pós-Graduação em Engenharia Agrícola; UNIOESTE; Brasil; Centro de Ciências Exatas e Tecnológicas; 2018. [cited 2021 Apr 22].
Available from: http://tede.unioeste.br/handle/tede/3916.
Council of Science Editors:
Verica WR. Mapeamento semiautomático por meio de padrão espectro-temporal de áreas agrícolas e alvos permanentes com evi/modis no Paraná. [Masters Thesis]. Universidade Estadual do Oeste do Paraná; Cascavel; Programa de Pós-Graduação em Engenharia Agrícola; UNIOESTE; Brasil; Centro de Ciências Exatas e Tecnológicas; 2018. Available from: http://tede.unioeste.br/handle/tede/3916

Delft University of Technology
20.
van Schetsen, Anouk (author).
Impact of graph-based features on Bitcoin prices.
Degree: 2019, Delft University of Technology
URL: http://resolver.tudelft.nl/uuid:363d443c-64f6-4c35-9671-4092aa334923
► Predicting the trends in Bitcoin market prices is a very challenging task due to the many uncertainties and variables influencing the market value. The market…
(more)
▼ Predicting the trends in Bitcoin market prices is a very challenging task due to the many uncertainties and variables influencing the market value. The market is susceptible to quick changes, causing seemingly random fluctuations in the Bitcoin price. Due to the chaotic and highly volatile nature of Bitcoin behavior, investments come with high risk. To minimize the risk involved, knowledge of the Bitcoin price movement in the future is desirable. Different studies have shown that Machine Learning algorithms can predict, to varying degrees, the price fluctuations of Bitcoin. However, most researches do not explore the relationship between the price and other features outside the transaction network, such as market capitalization, Bitcoin mining speed, or entity behavior. Also, most of the features are extracted from the network level, which means obtaining the number of transactions, users, Bitcoins mined, etc. In this research, we focus on additional features, such as features outside the transaction network and node-based features inside the transaction network, which could improve the price prediction of Bitcoin. The investigated features are the “fairness and goodness” measure and the “1-ARW-betweenness cen- trality” measure. Fairness and goodness are entity behavior measures. The goodness of a Bitcoin address captures how much this address is liked/trusted by other addresses, while the fairness of a Bitcoin address captures how fair the address is in rating other addresses’ likeability or trust level. The 1-ARW-betweenness centrality is a feature based on absorbing random walks. The feature captures the extent to which a Bitcoin address has control over the money flow between different addresses. A benchmark, based on the machine learning algorithm Random Forest with commonly used features, is used to test the impact of the additional features. The Random Forest tries to predict the sign (up-down movement) of the price per day, using data from the two previous days. Comparing this benchmark with a similar model, but then including the additional features, will gain more information about how these additional features influence the Bitcoin price.
Applied Mathematics | Financial Engineering
Advisors/Committee Members: Oosterlee, Kees (mentor), Delft University of Technology (degree granting institution).
Subjects/Keywords: Machine Learning; Random Forest; Bitcoin; random walk; graph analysis
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APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
van Schetsen, A. (. (2019). Impact of graph-based features on Bitcoin prices. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:363d443c-64f6-4c35-9671-4092aa334923
Chicago Manual of Style (16th Edition):
van Schetsen, Anouk (author). “Impact of graph-based features on Bitcoin prices.” 2019. Masters Thesis, Delft University of Technology. Accessed April 22, 2021.
http://resolver.tudelft.nl/uuid:363d443c-64f6-4c35-9671-4092aa334923.
MLA Handbook (7th Edition):
van Schetsen, Anouk (author). “Impact of graph-based features on Bitcoin prices.” 2019. Web. 22 Apr 2021.
Vancouver:
van Schetsen A(. Impact of graph-based features on Bitcoin prices. [Internet] [Masters thesis]. Delft University of Technology; 2019. [cited 2021 Apr 22].
Available from: http://resolver.tudelft.nl/uuid:363d443c-64f6-4c35-9671-4092aa334923.
Council of Science Editors:
van Schetsen A(. Impact of graph-based features on Bitcoin prices. [Masters Thesis]. Delft University of Technology; 2019. Available from: http://resolver.tudelft.nl/uuid:363d443c-64f6-4c35-9671-4092aa334923

University of Edinburgh
21.
Fedrigo, Melissa.
Estimating Biomass in the Mountain Regions of Bwindi Impenetrable National Park, Uganda using Radar and Optical Remote Sensing.
Degree: 2009, University of Edinburgh
URL: http://hdl.handle.net/1842/3094
► Field measured estimates of aboveground biomass (AGB) for 15 transects in Bwindi Impenetrable National Park (BINP), Uganda were used to generate a number of prediction…
(more)
▼ Field measured estimates of aboveground biomass (AGB) for 15 transects in Bwindi Impenetrable National Park (BINP), Uganda were used to generate a number of prediction models for estimating aboveground biomass (AGB) over the full extent of BINP. AGB estimates were extrapolated from the field data using dual-polarization radar satellite data alone, optical satellite data, and a combination of both. The effectiveness of the dual-polarization radar remote sensing data alone was limited due to the difficulties of geocoding and terrain correction in this mountainous region, producing problems with layover and shadowing. The optical-only method demonstrated that perhaps thermal bands may be more sensitive to biomass in tropical forests than visible bands. The radar and optical combined method, generated using the non-parametric algorithm
Random Forest (RF) in R, provided the lowest RMSE error (~120 Mg ha-1). The analysis also demonstrated that a number of radar backscatter variables had greater utility for generating a predictive model of biomass than many optical bands in this mountainous region. The combined optical and radar remote sensing model was used to produce a final AGB map over the full 331 km2 extent of BINP; AGB in BINP was estimated at 89.1 million Mg ± 3.9 million Mg, with a mean carbon density of 44.5 million Mg C ± 60 Mg C ha-1.
Advisors/Committee Members: Meir, Patrick.
Subjects/Keywords: biomass; remote sensing; random forest; tropical forest; carbon
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Export
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APA (6th Edition):
Fedrigo, M. (2009). Estimating Biomass in the Mountain Regions of Bwindi Impenetrable National Park, Uganda using Radar and Optical Remote Sensing. (Thesis). University of Edinburgh. Retrieved from http://hdl.handle.net/1842/3094
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Fedrigo, Melissa. “Estimating Biomass in the Mountain Regions of Bwindi Impenetrable National Park, Uganda using Radar and Optical Remote Sensing.” 2009. Thesis, University of Edinburgh. Accessed April 22, 2021.
http://hdl.handle.net/1842/3094.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Fedrigo, Melissa. “Estimating Biomass in the Mountain Regions of Bwindi Impenetrable National Park, Uganda using Radar and Optical Remote Sensing.” 2009. Web. 22 Apr 2021.
Vancouver:
Fedrigo M. Estimating Biomass in the Mountain Regions of Bwindi Impenetrable National Park, Uganda using Radar and Optical Remote Sensing. [Internet] [Thesis]. University of Edinburgh; 2009. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/1842/3094.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Fedrigo M. Estimating Biomass in the Mountain Regions of Bwindi Impenetrable National Park, Uganda using Radar and Optical Remote Sensing. [Thesis]. University of Edinburgh; 2009. Available from: http://hdl.handle.net/1842/3094
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

NSYSU
22.
Kuo, Bo-Wen.
Interpretable representation learning based on Deep Rule Forests.
Degree: Master, Information Management, 2018, NSYSU
URL: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727118-134901
► The spirit of tree-based methods is to learn rules. A large number of machine learning techniques are tree-based. More complicated tree learners may result in…
(more)
▼ The spirit of tree-based methods is to learn rules. A large number of machine learning techniques are tree-based. More complicated tree learners may result in higher predictive models, but may sacrifice for model interpretability. On the other hand, the spirit of representation learning is to extract abstractive concepts from manifestations of the data.
For instance, Deep Neural networks (DNNs) is the most popular method in representation learning. However, unaccountable feature representation is the shortcoming of DNNs. In this paper, we proposed an approach, Deep Rule
Forest (DRF), to learn region representations based on
random forest in the deep layer-wise structures. The learned interpretable rules region representations combine other machine learning algorithms. We trained CART which learned from DRF region representations, and found that the prediction accuracies sometime are better than ensemble learning methods.
Advisors/Committee Members: Keng-Pei Lin (chair), Yihuang, Kang (committee member), Pei-Ju, Lee (chair).
Subjects/Keywords: Rule Learning; Random Forest; Representation Learning; Interpretability; Deep Rule Forest
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APA ·
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CSE |
Export
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Manager
APA (6th Edition):
Kuo, B. (2018). Interpretable representation learning based on Deep Rule Forests. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727118-134901
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Kuo, Bo-Wen. “Interpretable representation learning based on Deep Rule Forests.” 2018. Thesis, NSYSU. Accessed April 22, 2021.
http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727118-134901.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Kuo, Bo-Wen. “Interpretable representation learning based on Deep Rule Forests.” 2018. Web. 22 Apr 2021.
Vancouver:
Kuo B. Interpretable representation learning based on Deep Rule Forests. [Internet] [Thesis]. NSYSU; 2018. [cited 2021 Apr 22].
Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727118-134901.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Kuo B. Interpretable representation learning based on Deep Rule Forests. [Thesis]. NSYSU; 2018. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0727118-134901
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Missouri – Columbia
23.
Serna, Erica.
Quantifying error in vegetation mapping.
Degree: 2011, University of Missouri – Columbia
URL: http://hdl.handle.net/10355/11508
► Understanding the current distribution and structure of forest vegetation is important for designing forest management plans and prioritizing restoration at landscape scales. This project provides…
(more)
▼ Understanding the current distribution and structure of
forest vegetation is important for designing
forest management plans and prioritizing restoration at landscape scales. This project provides information on
Random Forest, a relatively new statistical package in the field of forestry, and patterns in mapping errors, a less explored field of study particularly in the forests of the Midwest United States. Vegetation maps can be made from classification and regression trees, such as
Random Forest, by integrating environmental variables with vegetation information. An understanding of the accuracy of the maps is important because management plans and restoration efforts are more effective with accurate data. This study was done in forested regions in Minnesota with the purpose of 1) analyzing physiographic factors controlling tree species distribution; 2) mapping potential species distributions; 3) quantifying error in vegetation mapping; and 4) understanding map accuracy by evaluating minimum amounts of sample data necessary for reliable mapping. The results from
Random Forest were found to be realistic ecologically and biologically. Also, tree species required records of 1-2 trees per 10,000 ha to produce accurate maps. Knowing the minimum amount of data points necessary for acceptable accuracy assists scientists mapping vegetation. This study demonstrates the effectiveness of
Random Forest in vegetation mapping, which can be useful for future vegetation mapping.
Advisors/Committee Members: He, Hong S. (advisor), Dey, Daniel C. (advisor), Fresen, John (advisor).
Subjects/Keywords: random forest; Vegetation mapping; Forest management; Forest restoration; Regression analysis; Trees (Graph theory)
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Serna, E. (2011). Quantifying error in vegetation mapping. (Thesis). University of Missouri – Columbia. Retrieved from http://hdl.handle.net/10355/11508
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Serna, Erica. “Quantifying error in vegetation mapping.” 2011. Thesis, University of Missouri – Columbia. Accessed April 22, 2021.
http://hdl.handle.net/10355/11508.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Serna, Erica. “Quantifying error in vegetation mapping.” 2011. Web. 22 Apr 2021.
Vancouver:
Serna E. Quantifying error in vegetation mapping. [Internet] [Thesis]. University of Missouri – Columbia; 2011. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/10355/11508.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Serna E. Quantifying error in vegetation mapping. [Thesis]. University of Missouri – Columbia; 2011. Available from: http://hdl.handle.net/10355/11508
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Michigan Technological University
24.
Cleaver, Megan.
USING RANDOM FOREST MODELING TO PREDICT EARTHWORM DISTRIBUTION IN THE OTTAWA NATIONAL FOREST.
Degree: MS, College of Forest Resources and Environmental Science, 2018, Michigan Technological University
URL: https://digitalcommons.mtu.edu/etdr/716
► Our study applies the machine learning method, Random Forest (RF), to understand distribution patterns and predictive powers of environmental variables determining earthworm occurrence in…
(more)
▼ Our study applies the machine learning method,
Random Forest (RF), to understand distribution patterns and predictive powers of environmental variables determining earthworm occurrence in northern hardwood forests of the Great Lakes region. In our study we found earthworm species: Dendrobaena octaedra (Savigny), Lumbricus rubellus (Hoffmeister), Lumbricus terrestris (L.), Aporrectodea rosea (Saigny), Aporrectodea calignosa (Saigny), and Aporrectodea tuberculata (Eisen). Presence/absence data of L. terrestris were used in predictive distribution modeling for the Ottawa National
Forest in the Upper Peninsula of Michigan based on the following Geographic Information Systems (GIS) variables:
forest cover type, soil texture, soil pH, and distance from roads.
Random Forest results were successful in producing models with high predictive accuracies and stable environmental variables when predicting L. terrestris occurrence. Deciduous cover type contributed the most to the outcome of the RF models, followed by soil texture, distance from roads and soil pH. The effectiveness of this approach in modeling earthworm distribution could be the first step in leading a large-scale predictive modeling effort to determine earthworm distribution for all of the Great Lakes region and other northern hardwood
forest ecosystems. Having this insight would advance
forest management efforts and regional studies addressing earthworm ecological effects.
Advisors/Committee Members: Blair Orr, Erik Lilleskov.
Subjects/Keywords: Earthworms; Distribution; Predictive Modeling; Random Forest; GIS; Ottawa National Forest; Ecology and Evolutionary Biology; Forest Management; Forest Sciences
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Cleaver, M. (2018). USING RANDOM FOREST MODELING TO PREDICT EARTHWORM DISTRIBUTION IN THE OTTAWA NATIONAL FOREST. (Masters Thesis). Michigan Technological University. Retrieved from https://digitalcommons.mtu.edu/etdr/716
Chicago Manual of Style (16th Edition):
Cleaver, Megan. “USING RANDOM FOREST MODELING TO PREDICT EARTHWORM DISTRIBUTION IN THE OTTAWA NATIONAL FOREST.” 2018. Masters Thesis, Michigan Technological University. Accessed April 22, 2021.
https://digitalcommons.mtu.edu/etdr/716.
MLA Handbook (7th Edition):
Cleaver, Megan. “USING RANDOM FOREST MODELING TO PREDICT EARTHWORM DISTRIBUTION IN THE OTTAWA NATIONAL FOREST.” 2018. Web. 22 Apr 2021.
Vancouver:
Cleaver M. USING RANDOM FOREST MODELING TO PREDICT EARTHWORM DISTRIBUTION IN THE OTTAWA NATIONAL FOREST. [Internet] [Masters thesis]. Michigan Technological University; 2018. [cited 2021 Apr 22].
Available from: https://digitalcommons.mtu.edu/etdr/716.
Council of Science Editors:
Cleaver M. USING RANDOM FOREST MODELING TO PREDICT EARTHWORM DISTRIBUTION IN THE OTTAWA NATIONAL FOREST. [Masters Thesis]. Michigan Technological University; 2018. Available from: https://digitalcommons.mtu.edu/etdr/716
25.
Tran khac, Viet.
Le rôle des facteurs environnementaux sur la concentration des métaux-tracesdans les lacs urbains -Lac de Pampulha, Lac de Créteil et 49 lacs péri-urbains d’Ile de France : The role of environmental factors on trace-metalconcentrations in urban lakes - Lake Pampulha, Lake Créteil and 49 lakes in the Ile-de-France region.
Degree: Docteur es, Sciences et Techniques de l'Environnement, 2016, Université Paris-Est
URL: http://www.theses.fr/2016PESC1160
► Les lacs jouent un rôle particulier dans le cycle de l’eau dans les bassins versants urbains. La stratification thermique et le temps de séjour de…
(more)
▼ Les lacs jouent un rôle particulier dans le cycle de l’eau dans les bassins versants urbains. La stratification thermique et le temps de séjour de l’eau élevé favorisent le développement phytoplanctonique. La plupart des métaux sont naturellement présents dans l’environnement à l’état de traces. Ils sont essentiels pour les organismes vivants. Néanmoins, certains métaux sont connus pour leurs effets toxiques sur les animaux et les humains. La concentration totale des métaux ne reflète pas leur toxicité. Elle dépend de leurs propriétés et de leur spéciation (fractions particulaires, dissoutes: labiles ou biodisponibles et inertes). Dans les systèmes aquatiques, les métaux peuvent être absorbés par des ligands organiques ou minéraux. Leur capacité à se complexer avec la matière organique dissoute (MOD), particulièrement les substances humiques, a été largement étudiée. Dans les lacs, le développement phytoplanctonique peut produire de la MOD non-humique, connue pour sa capacité complexante des métaux. Pourtant, peu de recherche sur la spéciation des métaux dans la colonne d’eau des lacs urbains a été réalisée jusqu’à présent.Les objectifs principaux de cette thèse sont (1) d’obtenir une base de données fiables des concentrations en métaux traces dans la colonne d’eau de lacs urbains représentatifs; (2) d’évaluer leur biodisponibilité via une technique de spéciation adéquate ; (3) d’analyser leur évolution saisonnière et spatiale et leur spéciation; (4) d’étudier l’impact des variables environnementales, en particulier de la MOD autochtone sur leur biodisponibilité; (5) de lier la concentration des métaux au mode d’occupation du sol du bassin versant.Notre méthodologie est basée sur un suivi in-situ des lacs en complément d’analyses spécifiques en laboratoire. L’étude a été conduite sur trois sites: le lac de Créteil (France), le lac de Pampulha (Brésil) et 49 lacs péri-urbains (Ile de France). Sur le lac de Créteil, plusieurs dispositifs de mesure en continu nous ont fourni une partie de la base de données limnologiques. Dans le bassin versant du lac de Pampulha, la pression anthropique est très importante. Le climat et le régime hydrologique des 2 lacs sont très différents. Les 49 lacs de la région d’Ile de France ont été échantillonnés une fois pendant trois étés successifs (2011-2013). Ces lacs nous ont fourni une base de données synoptique, représentative de la contamination métallique à l’échelle d’une région fortement anthropisée.Afin d’expliquer le rôle des variables environnementales sur la concentration métallique, le modèle
Random Forest a été appliqué sur les bases de données du lac de Pampulha et des 49 lacs urbains avec 2 objectifs spécifiques: (1) dans le lac de Pampulha, comprendre le rôle des variables environnementales sur la fraction labile des métaux traces, potentiellement biodisponible et (2) dans les 49 lacs, comprendre la relation des variables environnementales, particulièrement au niveau du bassin versant, sur la concentration dissoute des métaux. L’analyse des relations entre métaux et…
Advisors/Committee Members: Vinçon-Leite, Brigitte (thesis director), De Oliveira Nascimento, Nilo (thesis director), Varrault, Gilles (thesis director).
Subjects/Keywords: Lacs urbains; Métaux traces; Spéciation; Biodisponibilité; Random Forest model; Urban lakes; Trace metal; Speciation; Bioavailability; Landuse; Random Forest model
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Tran khac, V. (2016). Le rôle des facteurs environnementaux sur la concentration des métaux-tracesdans les lacs urbains -Lac de Pampulha, Lac de Créteil et 49 lacs péri-urbains d’Ile de France : The role of environmental factors on trace-metalconcentrations in urban lakes - Lake Pampulha, Lake Créteil and 49 lakes in the Ile-de-France region. (Doctoral Dissertation). Université Paris-Est. Retrieved from http://www.theses.fr/2016PESC1160
Chicago Manual of Style (16th Edition):
Tran khac, Viet. “Le rôle des facteurs environnementaux sur la concentration des métaux-tracesdans les lacs urbains -Lac de Pampulha, Lac de Créteil et 49 lacs péri-urbains d’Ile de France : The role of environmental factors on trace-metalconcentrations in urban lakes - Lake Pampulha, Lake Créteil and 49 lakes in the Ile-de-France region.” 2016. Doctoral Dissertation, Université Paris-Est. Accessed April 22, 2021.
http://www.theses.fr/2016PESC1160.
MLA Handbook (7th Edition):
Tran khac, Viet. “Le rôle des facteurs environnementaux sur la concentration des métaux-tracesdans les lacs urbains -Lac de Pampulha, Lac de Créteil et 49 lacs péri-urbains d’Ile de France : The role of environmental factors on trace-metalconcentrations in urban lakes - Lake Pampulha, Lake Créteil and 49 lakes in the Ile-de-France region.” 2016. Web. 22 Apr 2021.
Vancouver:
Tran khac V. Le rôle des facteurs environnementaux sur la concentration des métaux-tracesdans les lacs urbains -Lac de Pampulha, Lac de Créteil et 49 lacs péri-urbains d’Ile de France : The role of environmental factors on trace-metalconcentrations in urban lakes - Lake Pampulha, Lake Créteil and 49 lakes in the Ile-de-France region. [Internet] [Doctoral dissertation]. Université Paris-Est; 2016. [cited 2021 Apr 22].
Available from: http://www.theses.fr/2016PESC1160.
Council of Science Editors:
Tran khac V. Le rôle des facteurs environnementaux sur la concentration des métaux-tracesdans les lacs urbains -Lac de Pampulha, Lac de Créteil et 49 lacs péri-urbains d’Ile de France : The role of environmental factors on trace-metalconcentrations in urban lakes - Lake Pampulha, Lake Créteil and 49 lakes in the Ile-de-France region. [Doctoral Dissertation]. Université Paris-Est; 2016. Available from: http://www.theses.fr/2016PESC1160

Brno University of Technology
26.
Grolig, Lukáš.
Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#.
Degree: 2020, Brno University of Technology
URL: http://hdl.handle.net/11012/189709
► This bachelor thesis is focused on selection of data mining algorithms based on decision trees for an analytical system developed under the project System for…
(more)
▼ This bachelor thesis is focused on selection of data mining algorithms based on decision trees for an analytical system developed under the project System for the Internet security increase based on malware spreading analysis. Selected algorithms are described in greater detais, as well as their implementation in the C# language. These algorithms are then tested with regards to their training speed and classification accuracy. Finally, this thesis presents further conclusions and recommendations based on performed experiments.
Advisors/Committee Members: Stríž, Rostislav (advisor), Pešek, Martin (referee).
Subjects/Keywords: Dolování z dat; dolovací algoritmy; klasifikace; rozhodovací stromy; random forest; sprint; Data mining; mining algorithms; classification; decision trees; random forest; sprint
Record Details
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Record Details
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Grolig, L. (2020). Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/189709
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Grolig, Lukáš. “Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#.” 2020. Thesis, Brno University of Technology. Accessed April 22, 2021.
http://hdl.handle.net/11012/189709.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Grolig, Lukáš. “Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#.” 2020. Web. 22 Apr 2021.
Vancouver:
Grolig L. Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/11012/189709.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Grolig L. Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/189709
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

University of Helsinki
27.
Roivainen, Hege.
Document classification based on library catalogue metadata.
Degree: Department of Modern Languages; Helsingfors universitet, Humanistiska fakulteten, Institutionen för moderna språk, 2017, University of Helsinki
URL: http://hdl.handle.net/10138/229702
► Kansalliskirjastojen metadataluettelot ovat hyviä informaatiolähteitä, sillä ne sisältävät tiedon lähes kaikesta tiettynä aikana ja tietyllä alueella julkaistusta aineistosta. Yleensä ne ovat kattavasti kuvailtuja, joten niitä…
(more)
▼ Kansalliskirjastojen metadataluettelot ovat hyviä informaatiolähteitä, sillä ne sisältävät tiedon lähes kaikesta tiettynä aikana ja tietyllä alueella julkaistusta aineistosta. Yleensä ne ovat kattavasti kuvailtuja, joten niitä voi käyttää kvantitatiivisen tutkimuksen lähteinä. Usein tutkimusta tehtäessä tutkimusaineisto kannattaa jakaa pienempiin osiin esimerkiksi genren perusteella. Monissa tapauksissa aineiston aukkoisuus kuitenkin vähentää aineiston käytettävyyttä. Tämä pro gradu -työ arvioi mahdollisuutta hyödyntää koneoppimista etsittäessä tutkimukselle relevantteja osajoukkoja kirjastoluetteloista. Esimerkkitapaukseksi valitsin English Short Title Cataloguen (ESTC) ja etsittäväksi osajoukoksi runokirjat. Runokirjojen genretiedon kuuluisi olla annotoitu, mutta todellisista kirjastoluetteloista tämä tieto usein puuttuu.
Käytin random forest -algoritmiä perinteisillä tekijän tunnistuksessa ja genreluokittelussa käytetyillä erityyppisillä piirrevektoreilla sekä metadatakenttien arvoilla parhaan tuloksen saamiseksi. Koska kirjastoluettelot eivät sisällä kirjojen koko tekstiä, piirteiden valinta keskittyi otsikoissa käytettyihin sanoihin ja lingvistisiin ominaisuuksiin. Otsikot ovat yleensä lyhyitä ja sisältävät hyvin vähän informaatiota, minkä vuoksi yhdistin piirrevektoreiden parhaiten toimivat piirteet yhteen ja tein lopullisen haun niillä. Tutkimuksen päätulos oli varmistus siitä, että otsikoiden käyttö piirteiden muodostamisessa on käyttökelpoinen strategia. Tutkimus avaa mahdollisuuksia määrittää osajoukkoja tulevaisuudessa koneoppimisen keinoin ja lisätä kirjastoluetteloiden hyödyntämistä kvantitatiivisessa tutkimuksessa.
Subjects/Keywords: random forest; machine learning; genre classification; library catalogues; kieliteknologia; Language Technology; Språkteknologi; random forest; machine learning; genre classification; library catalogues
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Roivainen, H. (2017). Document classification based on library catalogue metadata. (Masters Thesis). University of Helsinki. Retrieved from http://hdl.handle.net/10138/229702
Chicago Manual of Style (16th Edition):
Roivainen, Hege. “Document classification based on library catalogue metadata.” 2017. Masters Thesis, University of Helsinki. Accessed April 22, 2021.
http://hdl.handle.net/10138/229702.
MLA Handbook (7th Edition):
Roivainen, Hege. “Document classification based on library catalogue metadata.” 2017. Web. 22 Apr 2021.
Vancouver:
Roivainen H. Document classification based on library catalogue metadata. [Internet] [Masters thesis]. University of Helsinki; 2017. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/10138/229702.
Council of Science Editors:
Roivainen H. Document classification based on library catalogue metadata. [Masters Thesis]. University of Helsinki; 2017. Available from: http://hdl.handle.net/10138/229702

Brno University of Technology
28.
Grolig, Lukáš.
Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#.
Degree: 2020, Brno University of Technology
URL: http://hdl.handle.net/11012/188334
► This bachelor thesis is focused on selection of data mining algorithms based on decision trees for an analytical system developed under the project System for…
(more)
▼ This bachelor thesis is focused on selection of data mining algorithms based on decision trees for an analytical system developed under the project System for the Internet security increase based on malware spreading analysis. Selected algorithms are described in greater detais, as well as their implementation in the C# language. These algorithms are then tested with regards to their training speed and classification accuracy. Finally, this thesis presents further conclusions and recommendations based on performed experiments.
Advisors/Committee Members: Stríž, Rostislav (advisor), Pešek, Martin (referee).
Subjects/Keywords: Dolování z dat; dolovací algoritmy; klasifikace; rozhodovací stromy; random forest; sprint; Data mining; mining algorithms; classification; decision trees; random forest; sprint
Record Details
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Share »
Record Details
Similar Records
Cite
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Grolig, L. (2020). Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/188334
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Grolig, Lukáš. “Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#.” 2020. Thesis, Brno University of Technology. Accessed April 22, 2021.
http://hdl.handle.net/11012/188334.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Grolig, Lukáš. “Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#.” 2020. Web. 22 Apr 2021.
Vancouver:
Grolig L. Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/11012/188334.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Grolig L. Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/188334
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
29.
Ebrahimi, Shahin.
Contribution to automatic adjustments of vertebrae landmarks on x-ray images for 3D reconstruction and quantification of clinical indices : Contribution aux ajustements automatiques de points anatomiques des vertèbres pour la reconstruction 3D et la quantification d’indices cliniques.
Degree: Docteur es, Informatique-traitement du signal, 2017, Paris, ENSAM
URL: http://www.theses.fr/2017ENAM0050
► L’exploitation de données radiographiques, en particulier pour la reconstruction 3D du rachis de patients scoliotiques, est un prérequis à la modélisation personnalisée. Les méthodes actuelles,…
(more)
▼ L’exploitation de données radiographiques, en particulier pour la reconstruction 3D du rachis de patients scoliotiques, est un prérequis à la modélisation personnalisée. Les méthodes actuelles, bien qu’assez robustes pour la routine clinique, reposent sur des ajustements manuels fastidieux. Dans ce contexte, ce travail de thèse vise à la détection automatisée de points anatomiques spécifiques des vertèbres, permettant ainsi des ajustements automatisés. Nous avons développé premièrement une méthode originale de localisation de coins de vertèbres cervicales et lombaires sur les radiographies sagittales. L’évaluation rigoureuse de cette méthode suggère sa robustesse et sa précision. Nous avons ensuite développé un algorithme pour le problème pertinent cliniquement de localisation des pédicules sur les radiographies coronales. Cet algorithme se compare favorablement aux méthodes similaires dans la littérature, qui nécessitent une saisie manuelle. Enfin, nous avons soulevé les problèmes, relativement peu étudiés, de détection, identification et segmentation des apophyses épineuses du rachis cervical dans les radiographies sagittales. Toutes les tâches mentionnées ont été réalisées grâce à une combinaison originale de descripteurs visuels et une classification multi-classe par Random Forest, menant à une nouvelle et puissante approche de localisation et de segmentation. Les méthodes proposées dans cette thèse suggèrent un grand potentiel pour être intégré à la reconstruction 3D du rachis, utilisée quotidiennement en routine clinique.
Exploitation of spine radiographs, in particular for 3D spine shape reconstruction of scoliotic patients, is a prerequisite for personalized modelling. Current methods, even though robust enough to be used in clinical routine, still rely on tedious manual adjustments. In this context, this PhD thesis aims toward automated detection of specific vertebrae landmarks in spine radiographs, enabling automated adjustments. In the first part, we developed an original Random Forest based framework for vertebrae corner localization that was applied on sagittal radiographs of both cervical and lumbar spine regions. A rigorous evaluation of the method confirms robustness and high accuracy of the proposed method. In the second part, we developed an algorithm for the clinically-important task of pedicle localization in the thoracolumbar region on frontal radiographs. The proposed algorithm compares favourably to similar methods from the literature while relying on less manual supervision. The last part of this PhD tackled the scarcely-studied task of joint detection, identification and segmentation of spinous processes of cervical vertebrae in sagittal radiographs, with again high precision performance. All three algorithmic solutions were designed around a generic framework exploiting dedicated visual feature descriptors and multi-class Random Forest classifiers, proposing a novel solution with computational and manual supervision burdens aiming for translation into clinical use. Overall, the presented…
Advisors/Committee Members: Skalli, Wafa (thesis director), Angelini, Elsa (thesis director), Gajny, Laurent (thesis director).
Subjects/Keywords: Rachis; Radiographies; Vertèbres; Random forest; Descripteurs visuels; Descripteurs contextuels; Spine; X-Ray; Vertebrae landmarks; Random Forest; Visual features; Contextuel Features
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APA (6th Edition):
Ebrahimi, S. (2017). Contribution to automatic adjustments of vertebrae landmarks on x-ray images for 3D reconstruction and quantification of clinical indices : Contribution aux ajustements automatiques de points anatomiques des vertèbres pour la reconstruction 3D et la quantification d’indices cliniques. (Doctoral Dissertation). Paris, ENSAM. Retrieved from http://www.theses.fr/2017ENAM0050
Chicago Manual of Style (16th Edition):
Ebrahimi, Shahin. “Contribution to automatic adjustments of vertebrae landmarks on x-ray images for 3D reconstruction and quantification of clinical indices : Contribution aux ajustements automatiques de points anatomiques des vertèbres pour la reconstruction 3D et la quantification d’indices cliniques.” 2017. Doctoral Dissertation, Paris, ENSAM. Accessed April 22, 2021.
http://www.theses.fr/2017ENAM0050.
MLA Handbook (7th Edition):
Ebrahimi, Shahin. “Contribution to automatic adjustments of vertebrae landmarks on x-ray images for 3D reconstruction and quantification of clinical indices : Contribution aux ajustements automatiques de points anatomiques des vertèbres pour la reconstruction 3D et la quantification d’indices cliniques.” 2017. Web. 22 Apr 2021.
Vancouver:
Ebrahimi S. Contribution to automatic adjustments of vertebrae landmarks on x-ray images for 3D reconstruction and quantification of clinical indices : Contribution aux ajustements automatiques de points anatomiques des vertèbres pour la reconstruction 3D et la quantification d’indices cliniques. [Internet] [Doctoral dissertation]. Paris, ENSAM; 2017. [cited 2021 Apr 22].
Available from: http://www.theses.fr/2017ENAM0050.
Council of Science Editors:
Ebrahimi S. Contribution to automatic adjustments of vertebrae landmarks on x-ray images for 3D reconstruction and quantification of clinical indices : Contribution aux ajustements automatiques de points anatomiques des vertèbres pour la reconstruction 3D et la quantification d’indices cliniques. [Doctoral Dissertation]. Paris, ENSAM; 2017. Available from: http://www.theses.fr/2017ENAM0050

Brno University of Technology
30.
Grolig, Lukáš.
Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#.
Degree: 2020, Brno University of Technology
URL: http://hdl.handle.net/11012/54939
► This bachelor thesis is focused on selection of data mining algorithms based on decision trees for an analytical system developed under the project System for…
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▼ This bachelor thesis is focused on selection of data mining algorithms based on decision trees for an analytical system developed under the project System for the Internet security increase based on malware spreading analysis. Selected algorithms are described in greater detais, as well as their implementation in the C# language. These algorithms are then tested with regards to their training speed and classification accuracy. Finally, this thesis presents further conclusions and recommendations based on performed experiments.
Advisors/Committee Members: Stríž, Rostislav (advisor), Pešek, Martin (referee).
Subjects/Keywords: Dolování z dat; dolovací algoritmy; klasifikace; rozhodovací stromy; random forest; sprint; Data mining; mining algorithms; classification; decision trees; random forest; sprint
Record Details
Similar Records
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Share »
Record Details
Similar Records
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❌
APA ·
Chicago ·
MLA ·
Vancouver ·
CSE |
Export
to Zotero / EndNote / Reference
Manager
APA (6th Edition):
Grolig, L. (2020). Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#. (Thesis). Brno University of Technology. Retrieved from http://hdl.handle.net/11012/54939
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Chicago Manual of Style (16th Edition):
Grolig, Lukáš. “Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#.” 2020. Thesis, Brno University of Technology. Accessed April 22, 2021.
http://hdl.handle.net/11012/54939.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
MLA Handbook (7th Edition):
Grolig, Lukáš. “Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#.” 2020. Web. 22 Apr 2021.
Vancouver:
Grolig L. Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#. [Internet] [Thesis]. Brno University of Technology; 2020. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/11012/54939.
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
Council of Science Editors:
Grolig L. Implementace algoritmů založených na rozhodovacích stromech v jazyce C#: Implementation of Algorithms Based on Decision Trees in C#. [Thesis]. Brno University of Technology; 2020. Available from: http://hdl.handle.net/11012/54939
Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation
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