TY - THES A1 - Ghosh, Anup Kumar T1 - Analyzing individual chickens' life data to predict egg and growth performance N2 - The 2022 ban on killing male chicks, implemented in Germany and France, tackled ethical problems within egg-laying hen farming. Because of this policy, alternative poultry production methods were created to better meet animal welfare guidelines. To meet this challenge, the University of Bonn launched research into dual-purpose chickens, specifically the British Ixworth breed. Integrating male and female chicken farming offers a new, innovative solution to ethical and sustainability issues in traditional farming by combining egg and meat production. The importance of this research for revolutionizing poultry farming cannot be overstated (Becker et al., 2023). A University of Bonn research team is studying this alternative farming method to determine its productivity and viability. To meet industry needs while prioritizing animal welfare, they evaluate dual-purpose chicken potential by analysing egg (number and weight) and meat (weight) yields. This thesis has four sections, exploring different facets of dual-purpose chickens across four generations via diverse statistical and machine learning methods. First, it examines existing research on various facets of analysing and forecasting individual chicken egg and growth data. It further explores Becker et al.’s 2023 study, “The British Ixworth: individual growth and egg production of a purebred dual-purpose chicken.” Moreover, the linear regression, SVR, KNN, and decision tree models were applied to this data for advanced analysis and prediction. The thesis’s second part details the methodology, including requirements analysis and dataset preparation. This thesis's third section details how data from four generations of chicken life were analysed to predict future egg production and individual chicken growth. Across generations, individual chickens' average body weight and egg weight all showed an increase in the results. Predictions from linear regression and decision tree models show an increase in individual chickens' growth across generations to come. Egg production forecasts were better with the KNN model. This research will use advanced statistical methods and machine learning to model and assess the generational performance of dual-purpose chickens. These tools will forecast future egg and meat production, thus giving key insights to enhance breeding programs. This research aims to optimize dual-purpose chicken production for growth and egg-laying performance. The researchers eagerly anticipate the results, which will help to shape their future poultry farming projects. KW - Dual-purpose chickens KW - British Ixworth KW - Poultry farming KW - Machine learning KW - Statistical analysis KW - Predictive modeling KW - Linear regression KW - K-nearest neighbors (KNN) KW - Support Vector KW - Regression (SVR) KW - Decision tree KW - Sustainable agriculture KW - Generational analysis KW - Chicken growth prediction KW - Egg-laying performance KW - Alternative poultry production Y1 - 2025 ER - TY - THES A1 - Acharya, Bhuwan T1 - VISUALIZATION OF FLOOD SCENARIOS IN GERMANY IN 2021 AND FORECASTING FLOOD USING MACHINE-LEARNING MODELS N2 - Floods are the most common, devastating, and frequently occurring natural disaster nowadays. 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. 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. KW - Flood KW - Machine learning KW - sentinel KW - visualization Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:1383-opus4-18780 ER - TY - THES A1 - Karsten, Can T1 - Using machine learning to create dynamic customer journeys in digital environments. N2 - This research aims at analysing how machine learning can be used to create individualised customer journeys to improve customer experience. This work develops the new model of machine learning keters inaided dynamic customer journeys , which should help marimproving customer journey design to address customers' needs. Additionally, the model could be a comprehensible tool for marketing strategy formulation. In an empirical study consisting of a survey with 168 participants and a trained machine learning mod el with a dataset size of 1000 cases, this model is validated. Results show that a machine learning model can be used commercially to cluster users into personas and that a recommendation system can be built to dynamically adapt the customer journey to give each persona an individualised experience. Future research should aim at further practical validation of the correlation between user experience improvement and persona based customer journey design. KW - Customer journey KW - Customer centricity KW - Machine learning KW - Individualisation KW - Customer experience design KW - Digital marketing KW - Personas Y1 - 2023 ER - TY - THES A1 - Röchert, Daniel T1 - Workload Prediction using Apache Spark and R in a Mainframe Operating System N2 - 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. KW - Machine learning KW - Deep learning KW - Prediction KW - Workload management KW - Apache Spark Y1 - 2017 ER -