000 Allgemeines, Informatik, Informationswissenschaft
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The e-commerce turnover has a constant growth rate of about 10%. An additional increase
in complexity and traffic spikes clarify the need for a scalable software architecture to prevent
a potential technical debt, higher financial cost, longer maintenance, or a reduced reliability.
Due to the fact, that existing approaches like the Palladio Approach require a high modelling
overhead and the importance of dropping this overhead was identified this master thesis is
focused on the modelling and simulation of e-commerce web application architectures using
a high-level approach to provide a faster, but possibly more inaccurate prediction of the
scalability.
This is done by the usage of the Design Science Research Process as a frame, a scientific
literature review for use of the existing knowledge base and the Conical Methodology for the
artefact creation. The artefact is a graphical model which is evaluated using a simulation
developed with Python and its framework SimPy. For model creation and evaluation a total
of twelve papers investigating the scalability of e-commerce web application architectures is
split into a test and train group. The training group and parts of the scientific research are
used to identify the components load balancer, application server, web tier, ERP system,
legacy system and database as well as some general characteristics that need to be considered.
The components with the most modelling variables are the application server and web
tier with a total of thirteen, while the ERP and legacy system only required five.
The model is evaluated using a total of three papers from the test group, where an average
throughput error of 5.78% and a response time error of 46.55% or 26.46% was identified. An
additional evaluation based on two non-e-commerce architectures shows the usability of the
model for other types of architectures. Even though the average error gives the impression,
that the model is not providing a good estimation, the graphical results show, that the model
and its simulation can be used to provide a faster scalability prediction. The model is least
accurate for the prediction of the situation, where the response time increases exponentially,
as this is the point, where variables, only accountable for some percentage and thus ignored
for the model, have the highest influence.
Future research can be found in the extension of the model by either adding or investigating
additional components, adding features ignored within this work or applying it to other
types of web application architectures. Additionally, both the low-level and the high-level
approaches can be brought together to combine the advantages from both approaches.
In this bachelor thesis, different models for predicting the influenza virus are
examined in more detail.
The focus is on epidemiological compartmental models, as well as on different
Machine Learning approaches.
In particular, the basics chapter presents the SIR model and its various extensions.
Furthermore, Deep Learning and Social Network approaches are
investigated and the applied methods of a selected article are analysed in more
detail.
The practical part of this work consists in the implementation of a Multiple
Linear Regression model and an Artificial Neural Network. For the development
of both models the programming language Python was chosen using the
Deep Learning Framework Keras.
Tests were performed with real data from the Réseau Sentinelles, a French
organisation for monitoring national health.
The results of the tests show that the Neural Network is able to make better
predictions than the Multiple Linear Regression model.
The discussion shows ideas for improving influenza prediction including the
establishment of a worldwide collaboration between the surveillance centres as
well as the consolidation of historical data with real-time social media data.
Therefore, this work consists of a state-of-the art of models regarding the
spread of influenza virus, the development and comparison of several models
programmed in Python, evaluated on real data.
Alzheimer’s Disease affects millions of people worldwide, but till today, the gold standard
for definitive diagnosis of this disease is a biopsy. Nevertheless, with the progress
of the disease, a volume loss in the Hippocampus can be observed. Therefore, good
segmentation methods are crucial to facilitate quantification of this loss.
The focus of this work is on the development of a Machine Learning algorithm, more
precisely a Generative Adversarial Network, for the automated segmentation of the
human Hippocampus and its substructures in Magnetic Resonance Images. In particular,
the task is to determine if the integration of a pre-trained network that generates
segmentations into a Generative Adversarial Network scheme can improve generated
segmentations. In this context, a segmentation network in form of a U-net corresponds
to the generator. The discriminator is developed separately and merged in a second
step with the generator for combined training.
With a literature review regarding the automated segmentation of the Hippocampus,
current methods in this field and their medical and technological basics were identified.
The datasets were preprocessed to make them suitable for the use in a neural
network. In the training process, the generator was trained first until convergence.
Then, the Generative Adversarial Network including the pre-trained generator was
trained. The outcomes were evaluated via cross-validation in two different datasets
(Kulaga-Yoskovitz and Winterburn). The Generative Adversarial Network scheme
was tested regarding different architectural and training aspects, including the usage
of skip-connections and a combined loss function.
The best results were achieved in the Kulaga-Yoskovitz dataset with a Dice coefficient
of 90.84 % after the combined training of generator and discriminator with a joined
loss function. This improves the current state of the art method in the same task and
dataset with a Dice index of 88.79 % by Romero [Rom17]. Except of two cases in the
Winterburn dataset, the proposed combined method could always improve the Dice
results after the training of only the generator, even though only by a small amount.
Medical imaging produces many images every day in clinical routine. Keeping up with the
daily image analysis task and this vast amount of data is quite a challenge for radiologists.
However, these analysis tasks can be automated with well-proven automatic segmentation
methods. Segmentation reviewing of an expert is necessary because learningbased
automatic segmentation methods may not perform well on exceptional image
data. Creating valid segmentations by reviewing them also improve the learning-based
methods.
Combining established standards with modern technologies creates a flexible environment
to efficiently evaluate multiple segmentation algorithm outputs based on different metrics
and visualizations and report these analysis results back to a clinical system environment.
The presented software system can inspect such quantitative results in a fast and intuitive
way, potentially improving the daily repetitive segmentation review and rework of a
research radiologist. The presented system is designed to be integrated into a virtual
distributed computing environment with other systems and analysis methods. Critical
factors for this particular environment are the handling of many patient data and routine
automated analysis with state of the art technology.
First experiments show that the time to review automatic segmentation results can be
roughly divided in half while the confidence of the radiologist is enhanced. The system
is also able to highlight individual slices which are essential for the expert’s review
decision. For this highlighting, different metric scores are compared and evaluated.