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  <doc>
    <id>2153</id>
    <completedYear>2025</completedYear>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>88</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Radio Astronomy Essentials: Calibration Techniques with the 2.3m Radio Telescope at Rhine-Waal University</title>
    <abstract language="eng">This thesis discusses calibration and, in this context, multiple ways of determining the system temperature of the 2.3-metre radio telescope at Rhine-Waal University of Applied Sciences in Kamp-Lintfort. Two methods for determining the system temperature are described here: the hot/cold-method and the derivation from the radiometer equation using measurement data from a source with a known brightness temperature, in this case, S7. &#13;
Both methods deliver different results, depending on the measurements and assumptions on which they are based. The results show that the data basis and uncertainties should always be considered when analysing the observational data to be able to evaluate the results correctly. Overall, the telescope is quite suitable for educational observations, especially of the 21-cm line of neutral hydrogen.&#13;
Furthermore, instructions have been created for the various tasks so that the results are reproducible and comprehensible. The entire process is made transparent, from planning the observations and setting up the telescope to measuring and analysing the data. In addition, basic concepts of radio astronomy are described in theory to provide valuable background knowledge. On this basis, the use of the telescope enables students and amateur astronomers to understand and apply the basic principles of radio astronomy and to plan and carry out their own observations.</abstract>
    <identifier type="urn">urn:nbn:de:hbz:1383-opus4-21533</identifier>
    <enrichment key="opus.import.date">2025-02-18T10:02:32+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">swordtest</enrichment>
    <licence>CC BY-NC 4.0 International - Namensnennung-Nicht kommerziell</licence>
    <author>Sarah-Maria Rostalski</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Radio Astronomy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Radio Telescope</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Interdisciplinary Education</value>
    </subject>
    <collection role="institutes" number="">Fakultät Kommunikation und Umwelt</collection>
    <collection role="Import" number="import">Import</collection>
    <thesisPublisher>Hochschule Rhein-Waal</thesisPublisher>
    <thesisGrantor>Hochschule Rhein-Waal</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-rhein-waal/files/2153/Rostalski_SarahMaria_Masterthesis.pdf.pdf</file>
  </doc>
  <doc>
    <id>189</id>
    <completedYear/>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2017-12-05</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>2017-10-27</thesisDateAccepted>
    <title language="eng">Workload Prediction using Apache Spark and R in a Mainframe Operating System</title>
    <abstract language="eng">This master thesis presents a machine learning approach using the distributed data- processing framework Apache Spark and the programming language R. The aim is to predict the time series of the Appl Percentage (ApplPerc) from the workload manager of the z/OS mainframe system using SMF 72.3 and SMF 70.1 records as input. System Management Facility (SMF) data are binary log files that are used to collect system performance data and information about system behaviour. The SMFs show how Apache Spark can be used for the pre-processing. This includes data collection, data extraction, and data selection. The machine learning algorithms are implemented in R. The input SMF datasets will be split into training and test data, and thereby applied to different machine learning and deep learning models such as random forest regression, recurrent neural network, and k-nearest neighbor regression to predict the ApplPerc. The validation of the prediction models will be proved by using cross-validation techniques to evaluate the best applied parameters for each model and therefore to locate the model with the best performance. The process of data analysis is followed by a data-mining methodology called Cross-Industry Standard Process for Data Mining (CRISP-DM), which outlines the steps involved in performing the analysis.</abstract>
    <author>Daniel Röchert</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Prediction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Workload management</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Apache Spark</value>
    </subject>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="institutes" number="">Fakultät Kommunikation und Umwelt</collection>
    <thesisPublisher>Hochschule Rhein-Waal</thesisPublisher>
    <thesisGrantor>Hochschule Rhein-Waal</thesisGrantor>
  </doc>
  <doc>
    <id>802</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>88</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-05-07</completedDate>
    <publishedDate>2020-12-10</publishedDate>
    <thesisDateAccepted>2020-12-17</thesisDateAccepted>
    <title language="eng">Development of a Self Learning Agent to control a Gas Network Simulator</title>
    <abstract language="eng">Natural gas is an indispensable source of energy for industrial and domestic purposes, hence distribution and trade demands competition. In gas distribution, optimizing is a crucial task involving control decision on the network elements with respect to trader’s demand at the boundary nodes. The application of artificial intelligence is becoming popular in diverse domains, making its role in gas transport networks more and more interesting. The opposite interest of the players in the network is the reason behind the thought of establishing a game analogy here. The agents as players act on the basis of the opponent’s action. Since both agents perform actions from different space, a neural network model is designed for the dispatcher agent to learn to make decisions for active elements based on its experience from the sample gas transport network. Behavior of the network and its elements is depicted using an optimized gas network simulator. The representation of the gas transport network and its state is digitally transformed so that it is able to extract the features by an artificial neural network representing the environment and its rules. With the help of the trained neural network, the dispatcher agent decides the control decisions with less accumulated penalties. The results and findings of the proposed method are subjected to a comparison with the interval halving method. The results of the study will open the door for further research possibilities.</abstract>
    <identifier type="urn">urn:nbn:de:hbz:1383-opus4-8025</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <licence>CC BY 4.0 International - Namensnennung</licence>
    <author>Bitty Samuel Varghese</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Gas transport network simulator</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Trader agent</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Dispatcher agent</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep reinforcement learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Interval halving</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Monte-Carlo tree search</value>
    </subject>
    <collection role="institutes" number="">Fakultät Kommunikation und Umwelt</collection>
    <thesisPublisher>Hochschule Rhein-Waal</thesisPublisher>
    <thesisGrantor>Hochschule Rhein-Waal</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-rhein-waal/files/802/MasterThesis_BittySamuelVarghese_24873.pdf</file>
  </doc>
  <doc>
    <id>1813</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>96</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2023-10-02</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>2023-07-27</thesisDateAccepted>
    <title language="eng">The Impact of Leader Behavior on Team Processes</title>
    <abstract language="eng">The modern work environment is subject to constant change with a notable shift from individual to team-based work structures. This transition necessitates an improved un-derstanding of how managerial actions influence the dynamics within these teams, to optimally align them for success. Despite the substantial research about leader effec-tiveness and team effectiveness based on organizational performance indicators, it is rather infrequent that the direct influence of leader behavior on teams is explored. This thesis aims to address this gap by answering how leader behaviors impact team pro-cesses in the context of German medium to large-scale IT-enterprises. Using a quanti-tative cross-sectional methodology, individuals working in team-based structures under direct supervision were surveyed. Analyzing data from 94 respondents via hierarchical linear regression analysis, several significant relationships were identified. In general, leader behavior has a strong positive impact on team processes. Furthermore, task-oriented leaders have the greatest positive influence on action processes, while relation-oriented leaders have the greatest positive influence on interpersonal processes. Change-oriented leaders also significantly influence action processes and interpersonal processes, albeit less strongly than their counterparts. These results provide implications for team management practices, emphasizing the importance of leader behavior for aligning team processes in the direction of success.</abstract>
    <identifier type="urn">urn:nbn:de:hbz:1383-opus4-18136</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>CC BY-NC-ND 4.0 International - Namensnennung-Nicht kommerziell-Keine Bearbeitungen</licence>
    <author>Sven Langer</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Leader Behavior</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Team Processes</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>IT-Management</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Leader Effectiveness</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Team Effectiveness</value>
    </subject>
    <collection role="ddc" number="331">Arbeitsökonomie</collection>
    <collection role="institutes" number="">Fakultät Kommunikation und Umwelt</collection>
    <thesisPublisher>Hochschule Rhein-Waal</thesisPublisher>
    <thesisGrantor>Hochschule Rhein-Waal</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-rhein-waal/files/1813/Langer_Leader_Behavior_and_Team_Processes.pdf</file>
  </doc>
  <doc>
    <id>1878</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>132</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-02-02</completedDate>
    <publishedDate>2024-02-02</publishedDate>
    <thesisDateAccepted>2024-12-05</thesisDateAccepted>
    <title language="eng">VISUALIZATION OF FLOOD SCENARIOS IN GERMANY IN 2021 AND FORECASTING FLOOD USING MACHINE-LEARNING MODELS</title>
    <abstract language="eng">Floods are the most common, devastating, and frequently occurring natural disaster nowadays.&#13;
Because of climate change, Europe is expected to see an even higher number of floods in the coming decade. As floods cannot be prevented, understanding the pattern and causes and being able to forecast could significantly reduce the losses during the flood. In July 2021, Germany faced a catastrophic flood, taking more than 180 people’s lives and causing around 40 billion euros of economic loss. To study the flooded region and the region at high risk, the Flood event 2021 has been visualized in this research. The Flood map has been created using a sentinel-1 image. In the visualization, floods in the Rhine River and Arh River can be seen, and the change in the pattern of flood from 12th July to 16th July has also been observed.&#13;
Different models exist that try to predict floods and warn as early as possible. Since Machine learning is popular nowadays in every sector, ML models have been implemented in this study to forecast the flood. The SARIMA, Random Forest, and LSTM have been implemented using historical data to predict the flood. The Random Forest model performed better than the remaining two models. The Isolation Forest model has also been implemented to classify the data into Flood and No Flood. This model performs well with the Flood classes but struggles in capturing the No Flood class.</abstract>
    <identifier type="urn">urn:nbn:de:hbz:1383-opus4-18780</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>CC0 1.0 Universell - Gemeinfreiheit (Public Domain)</licence>
    <author>Bhuwan Acharya</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Flood</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Machine learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>sentinel</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>visualization</value>
    </subject>
    <collection role="institutes" number="">Fakultät Kommunikation und Umwelt</collection>
    <thesisPublisher>Hochschule Rhein-Waal</thesisPublisher>
    <thesisGrantor>Hochschule Rhein-Waal</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-rhein-waal/files/1878/Final_Master_Thesis.pdf</file>
  </doc>
  <doc>
    <id>1924</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>74</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-03-25</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>2024-03-18</thesisDateAccepted>
    <title language="eng">Analyzing Customer Behvior Patterns &amp; Predicting Online Product Return Intentions: A Data Mining Approach</title>
    <abstract language="eng">During the past years, it is noticeable that the e-commerce industry has emerged drastically, offering accessibility to a variety of products to the customer where they can buy products from the comfort of their home. However, it is associated with a lot of new challenges for e-commerce businesses, particularly in understanding and managing the customer behavior patterns with the rising online product returns. Since the accessibility of online shopping has risen, assessing the critical factors related to product return and prediction has become a really challenging task for e-commerce vendors. This study also aims to segment the customers based on customer behavior prediction into two categories, i.e., high return risk and low return risk customers, and further develop strategies to reduce the online returns.&#13;
This study is rather structured into four integral parts, where each part provides the comprehensive analysis. The first aspect involves analyzing and identifying the customer behavior patterns leading to product returns. The second aspect predicts the online product return based on selected features and the third is to segment the customers based on online product return prediction and categorize them into high return risk, and low return risk customers. The final step involves development of strategies to reduce the product return based on the intense analysis conducted.&#13;
To achieve this meaningful research outcome, data analysis is conducted to understand the customer behavior patterns, and a Random Forest feature selector is used to identify the customer behavior patterns that lead to product return. Based on the identified features, classification models were applied to classify and predict whether the customer is going to return product or not. Furthermore, in these seven classification models such as Logistic Regression, Ada Boost, Decision Tree, Naive Bayes, XG Boost, K-Nearest Neighbors, and Random Forest were implemented and used to compare the performance of the classification models to find out the best performing model. Lastly, the segmentation of customers is carried out based on the online product prediction using Logistic regression with classification threshold method into high return risk and low return risk customer categories.&#13;
The results obtained help in understanding the customer behavior and reduce the online product return by developing the strategies. This study will eventually help the online businesses in reducing returns which will also enhance the customer satisfaction.</abstract>
    <identifier type="urn">urn:nbn:de:hbz:1383-opus4-19240</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>CC BY 4.0 International - Namensnennung</licence>
    <author>Neetu Sharma</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Customer behavior</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Product return</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data mining</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Customer segmentation</value>
    </subject>
    <collection role="ddc" number="004">Datenverarbeitung; Informatik</collection>
    <collection role="institutes" number="">Fakultät Kommunikation und Umwelt</collection>
    <thesisPublisher>Hochschule Rhein-Waal</thesisPublisher>
    <thesisGrantor>Hochschule Rhein-Waal</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-rhein-waal/files/1924/Neetu_Sharma_Master_thesis.pdf</file>
  </doc>
  <doc>
    <id>2144</id>
    <completedYear>2025</completedYear>
    <publishedYear>2025</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Development of an ML-based multivariate anomaly detection model for beehive monitoring using sensor and environmental data.</title>
    <abstract language="eng">Anomaly detection is an important aspect of beehive monitoring, enabling beekeepers to take timely action and preventing economic losses caused by the decline of the bee population. Most existing beehive monitoring systems lack machine learning (ML) capabilities for anomaly detection and rely on threshold-based methods or expensive, complex designs. This thesis utilized the DigiBee prototype, a cost- effective monitoring system, to develop an ML-based anomaly detection model using Isolation Forest. Data on temperature, humidity, sound, vibration, and weight from four active beehives was collected between May 1, 2024, and July 25, 2024, alongside environmental data—temperature, relative humidity, and precipitation—from nearby meteorological stations. Exploratory data analysis revealed several limitations: uniform outputs from vibration sensors, calibration errors in weight sensors, and multiple gaps during the data collection period. Correlation analysis indicated a limited influence of external weather variables on internal beehive conditions, while internal sensor parameters displayed stronger correlations with each other. Five models were developed, using either beehive data alone or a combination of beehive and weather data. Results showed that beehive-specific models achieved higher accuracy in detecting localized anomalies, whereas models combining data from multiple beehives generalized better but underrepresented hive-specific issues. Models incorporating weather parameters, especially precipitation, introduced noise and unnecessary dimensions, which, when excluded, improved the model’s prediction by focusing on hive-specific patterns and ensuring anomaly detection remained contextually relevant.</abstract>
    <enrichment key="opus.import.date">2025-02-12T12:37:19+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">swordtest</enrichment>
    <licence>Bestimmungen des deutschen Urheberrechts</licence>
    <author>Zaid Saleem Syed</author>
    <collection role="Import" number="import">Import</collection>
    <thesisPublisher>Hochschule Rhein-Waal</thesisPublisher>
    <thesisGrantor>Hochschule Rhein-Waal</thesisGrantor>
  </doc>
  <doc>
    <id>1864</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-01-08</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>2023-11-17</thesisDateAccepted>
    <title language="eng">API Driven Form Rendering</title>
    <abstract language="eng">This master’s thesis investigates the challenges and opportunities associated with dynamic form rendering in user interfaces, specifically in the context of Galeria Karstadt Kaufhof (GKK), a key player in the German retail sector. Collaborating with CODUCT Solutions GmbH, this research aims to enhance GKK’s operational efficiency and user experience in their digital transformation journey, focusing on Project Baldeney, a cloud-native platform that automates content management workflows.&#13;
The thesis begins by outlining the limitations of traditional, static methods of form rendering, highlighting the implications for development time and error frequency. It identifies the fragmentation in the existing API-driven architectures as a critical issue affecting system reliability and user experience. Against this backdrop, the work proposes a unified, API-driven framework to dynamically render forms for many user actions, such as data input and transactions.&#13;
The research takes a deep dive into existing systems—particularly Informatica’s Product Information Management (PIM) software and the new software in development Article Workbench to identify user experience issues, inefficiencies, and the complexities involved.&#13;
Through this examination, it offers a targeted solution to integrate various components of form management, like building, parsing, validation, and rendering, into a cohesive system.&#13;
The thesis aims to contribute to software development and user experience design substantially, by targeting improved development efficiency, reduced errors, and enhanced system reliability. By achieving these objectives, the research aspires to furnish GKK with&#13;
a robust solution for dynamic form rendering, thereby enhancing both user satisfaction and system performance in the fast-evolving digital landscape.</abstract>
    <enrichment key="opus.source">publish</enrichment>
    <licence>CC BY-NC 4.0 International - Namensnennung-Nicht kommerziell</licence>
    <author>Ahmed Hanafy</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Dynamic Form Rendering</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>API-Driven Architecture</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Retail Software Integration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>User Interface Efficiency</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Product Information Management</value>
    </subject>
    <collection role="institutes" number="">Fakultät Kommunikation und Umwelt</collection>
    <thesisPublisher>Hochschule Rhein-Waal</thesisPublisher>
    <thesisGrantor>Hochschule Rhein-Waal</thesisGrantor>
  </doc>
  <doc>
    <id>1967</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-07-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>2024-07-17</thesisDateAccepted>
    <title language="eng">Sentiment Analysis in Nepali Tweets: Leveraging TransformerBased Pre-trained Models</title>
    <abstract language="eng">Despite the remarkable achievements of large transformer-based pre-trained models like BERT, GPT in several Natural Language Processing (NLP) tasks including Sentiment Analysis (SA), challenges are still present for subdued source languages like Nepali. Nepali language is written in Devanagari script, has complex grammatical structure and diverse linguistic features. Due to the absence of balanced datasets, and computational resources for Nepali, achieving optimal result with the latest architecture remains challenging. For this reason, publicly available NLP modelsfor Nepali are very less, making research in this area difficult. This paper attempts to addressthis gap through the use of pre-trained transformer models specially tailored for Nepali from Hugging Face including BERT, DistilBERT, ALBERT, and DeBERTa for sentiment analysis in Nepali tweets on relatively balanced datasets. The models are trained on large Nepali datasets and optimized for NLP tasks involving Devanagari scripts. To evaluate the model’s performance, various tokenization strategies are investigated in order to capitalize on transformer-based embedding with the SoftMax function and confusion matrix. The outcomes of models are compared using the same datasets. The study’s results shows that DistilBERT achieved the highest accuracy rate of 88% in Nepali sentiment analysis tasks, followed by BERT and DeBERTa at 83% and 80%, respectively. However, ALBERT showed a low accuracy of 70%. The result of this approach shares valuable viewpoints for the field of sentiment analysis in diverse linguistic contexts.</abstract>
    <identifier type="urn">urn:nbn:de:hbz:1383-opus4-19677</identifier>
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    <licence>CC0 1.0 Universell - Gemeinfreiheit (Public Domain)</licence>
    <author>Purnima Gurung</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Sentiment Analysis</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>NLP</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nepali Tweets</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Monolingual</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>DistilBERT</value>
    </subject>
    <collection role="institutes" number="">Fakultät Kommunikation und Umwelt</collection>
    <thesisPublisher>Hochschule Rhein-Waal</thesisPublisher>
    <thesisGrantor>Hochschule Rhein-Waal</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-rhein-waal/files/1967/Purnima_Gurung_27993.pdf</file>
  </doc>
  <doc>
    <id>1987</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-07-19</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>2024-07-09</thesisDateAccepted>
    <title language="eng">Named Entity Recognition for Nepali Text Using Pre-Trained BERT-Based Model</title>
    <abstract language="eng">The popularity of using transformer-based models like Bidirectional Encoder Representations from Transformers (BERT) for various Natural Language Processing (NLP) tasks is increasing rapidly. Unfortunately, the research is very limited for low-resource languages like Nepali. This study examines the utilisation of pre-trained BERT-based NERNepal for Named Entity Recognition (NER) tasks in Nepali text. The main goal is to investigate the efficiency of the NERNepal model, which has been pre-trained and fine-tuned on Nepali corpus data for NER. This research provides new insights by evaluating the NERNepal model on two distinct datasets. It addresses unique linguistic challenges specific to Nepali. The study also offers a detailed analysis of the model's strengths and weaknesses. By focusing on diverse datasets, this study shows how adaptable the model is and how its performance varies. These aspects have not been explored extensively before. The EverestNER dataset is one of the largest human-annotated datasets in Nepal so far, and the Nepali_NER dataset is also BIO-annotated for the NER task, which helped to compare the model’s prediction. The performance was better on the Nepali_NER dataset in comparison with another selected dataset. The EverestNER dataset contained many complex words for ORG connected with many tokens for a single entity with different contextual meanings and different annotations for the same word in different tokens as per context. Because of this, it created more confusion for the prediction, especially for the ORG entity. It had a similar issue with another dataset as well, but the label annotation was better in comparison. Furthermore, the research tries to clarify the difficulties and constraints related to utilising pre-trained BERT models for NER in low-resource languages such as Nepali. The study focuses on research areas on the efficacy of the model and its performance on two different datasets. Post-training with the EverestNER train dataset was attempted, but due to computational resource limitations, only a maximum of 3 epochs was possible, which did not improve the evaluation. It also has implications for enhancing language processing tools for Nepali. The results show the ability of a pre-trained BERT-based model to improve NER skills for Nepali text. However, further study is required to overcome the current obstacles to identifying complex words. The availability of high computational resources and the possibility of combining other NER approaches with a transformer-based model could increase the performance and robustness of the model.&#13;
&#13;
Keywords: Named Entity Recognition, Natural Language Processing, BERT, low resources language</abstract>
    <identifier type="urn">urn:nbn:de:hbz:1383-opus4-19870</identifier>
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    <licence>CC0 1.0 Universell - Gemeinfreiheit (Public Domain)</licence>
    <author>Sabita Shrestha</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Named Entity Recognition</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Natural Language Processing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>BERT</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>low resources language</value>
    </subject>
    <collection role="ddc" number="000">Informatik, Informationswissenschaft, allgemeine Werke</collection>
    <collection role="institutes" number="">Fakultät Kommunikation und Umwelt</collection>
    <thesisPublisher>Hochschule Rhein-Waal</thesisPublisher>
    <thesisGrantor>Hochschule Rhein-Waal</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-rhein-waal/files/1987/SabitaShrestha_15533.pdf</file>
  </doc>
  <doc>
    <id>2025</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-08-16</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>2024-06-05</thesisDateAccepted>
    <title language="eng">Data Analysis of the "Loyalty Program-Valeo Specialist Club" to Improve the  Independent Aftermarket: Designing an Efficient Marketing Strategy</title>
    <abstract language="eng">Nowadays, a loyalty program is one of the most significant marketing tools for winning genuine customer loyalty. Valeo, a global automotive supplier, also offers the Valeo Specialist Club loyalty program and rewards garage or workshop members who only purchase Valeo spare parts. The core intent of this study is to understand the importance of the Valeo loyalty application, members’ insights, and their experiences for improving the future planning, aftermarket performance, and management of the Value Specialist Club. In today’s competitive market, products, services, and customer loyalty are valuable assets, so the program was created to enhance member satisfaction and loyalty with the Value Specialist Club. A mixed-method approach was used to answer the study question and findings, improve member satisfaction, and refine marketing strategies, using a survey as primary data and Qlik Sense as secondary data. Besides, using a survey methodology, the study collected 79 responses from different countries (the United Kingdom, Germany, Belgium, and the Netherlands) and analyzed the data with only 58 Specialist Club member's responses. A reliability examination was additionally accomplished to measure the accuracy of the collected survey data. Qlik Sense, Microsoft 365, and Excel 2016 were used for data analysis and visualization, which involved descriptive analysis, correlation coefficients, and text sentiment analysis. The study results show positive trends in member satisfaction across four markets, but a decreasing engagement rate is noticeable. In general, members from all countries were satisfied with Valeo products and services, contrasting with moderate dissatisfaction with the Valeo Specialist Club app and customer service. The study also found a strong correlation between the Valeo Specialist Club app, loyalty service, and customer service, suggesting improvements to meet member expectations and preferences. Nevertheless, the study also emphasizes the need to enhance the Valeo Specialist Club registration process, loyalty program features, and benefits to align with current trends, foster increased member satisfaction, and sustain loyalty within the independent aftermarket. Furthermore, the conclusions of this study will aid in developing upcoming marketing plans.</abstract>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>CC BY-NC-ND 4.0 International - Namensnennung-Nicht kommerziell-Keine Bearbeitungen</licence>
    <author>Most Sarmin Akter</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Loyalty Program</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Insights</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Member Loyalty</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Marketing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Valeo Specialist Club</value>
    </subject>
    <collection role="institutes" number="">Fakultät Kommunikation und Umwelt</collection>
    <thesisPublisher>Hochschule Rhein-Waal</thesisPublisher>
    <thesisGrantor>Hochschule Rhein-Waal</thesisGrantor>
  </doc>
  <doc>
    <id>2159</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>77</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <type>masterthesis</type>
    <publisherName/>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2024-07-23</completedDate>
    <publishedDate>2024-07-23</publishedDate>
    <thesisDateAccepted>2024-07-23</thesisDateAccepted>
    <title language="eng">Interactive Visualisation Tool for Teaching Environmental Data: A Guideline of how to set up the tool taking user feedback into account</title>
    <abstract language="eng">This thesis will study the creation of an interactive data visualization tool for environmental dataset that concentrates specifically on Nitrate concentrations in groundwater. This project is intended to create a tool for exploratory/experiential learning - one that allows learners to investigate regional data and pursue genuine learning opportunities. The plan is to use regional data in a specific user journey to create this interactive learning experience. The project uses open environmental data: Hygris dataset of LANUV(Landesamt für Natur, Umwelt und Verbraucherschutz) related to the regions Kleve and Wesel in Germany, and realizes this with help of python programming language and python streamlit framework, web development tools and data visualization libraries (pandas , plotly.express, streamlite.components.v1).&#13;
This study explores tailored visualization strategies that address key challenges such as the inherent complexity of environmental datasets and the need for more engaging and comprehensible educational tools. These strategies include deploying interactive visualizations that allow hands-on data exploration, and temporal visualizations. The role of user feedback in refining educational tools for teaching environmental data is examined, particularly in designing effective visualization techniques like line char, bar chart, bubble chart, pie chart, scatter plot and intensity graph for representing groundwater nitrate concentrations. The impact of different visualization techniques on the understanding and interpretation of open environmental data among educators and students is analyzed, with an emphasis on the spatial distribution of groundwater nitrate concentrations over time. This approach aims to increase user engagement and deepen understanding of environmental data, facilitating a more profound embedding of information within learners' cognitive processes.</abstract>
    <identifier type="urn">urn:nbn:de:hbz:1383-opus4-21594</identifier>
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    <enrichment key="opus.doi.autoCreate">false</enrichment>
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    <licence>CC BY-NC-ND 4.0 International - Namensnennung-Nicht kommerziell-Keine Bearbeitungen</licence>
    <author>Khem Raj Regmi</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Interactive data visualization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data representation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Environmental data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Nitrate concentration</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Groundwater</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Open data</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>User feedback</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Education</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data accessibility</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data inclusivity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Educational tools</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Digital teaching</value>
    </subject>
    <collection role="institutes" number="">Fakultät Kommunikation und Umwelt</collection>
    <thesisPublisher>Hochschule Rhein-Waal</thesisPublisher>
    <thesisGrantor>Hochschule Rhein-Waal</thesisGrantor>
    <file>https://opus4.kobv.de/opus4-rhein-waal/files/2159/MasterThesiskhemrajregmi.pdf</file>
  </doc>
</export-example>
