Master's Thesis
Im Rahmen der Masterthesis „Analyse des Managements invasiver gebietsfremder Arten am Beispiel des Roten Amerikanischen Sumpfkrebses (Procambarus clarkii) während und im Anschluss an notwendige Sanierungsarbeiten am Hochwasserrückhaltebecken Breitenauer See östlich von Heilbronn“ wurde das Vorkommen des invasiven Roten Amerikanischen Sumpfkrebses am Breitenauer See umfangreich kartiert. Auch die nahegelegene Sulm mit bekanntem Vorkommen des Signalkrebses sowie das Nonnenbachsystem mit bekanntem Vorkommen des Steinkrebses wurden erfasst. Der Fokus lag auf der Beantwortung dreier Kernfragen. Zunächst wurde untersucht, ob und wie ein dauerhaftes IAS-Management (invasive alien species) des Roten Amerikanischen Sumpfkrebses am Breitenauer See nachhaltig durchgeführt werden kann, um inakzeptable ökologische Effekte zu vermeiden. Die zweite Fragestellung bezog sich auf die Wirksamkeit ergriffener Risikomanagementmaßnahmen während der Ablassaktion des Breitenauer Sees. Abschließend war fraglich, wie sich der Rote Amerikanische Sumpfkrebs verhält, wenn sein besiedeltes Gewässer trockenfällt.
This thesis explores and examines the effectiveness and efficacy of traditional machine learning (ML), advanced neural networks (NN) and state-of-the-art deep learning (DL) models for identifying mental distress indicators from the social media discourses based on Reddit and Twitter as they are immensely used by teenagers. Different NLP vectorization techniques like TF-IDF, Word2Vec, GloVe, and BERT embeddings are employed with ML models such as Decision Tree (DT), Random Forest (RF), Logistic Regression (LR) and Support Vector Machine (SVM) followed by NN models such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) to methodically analyse their impact as feature representation of models. DL models such as BERT, DistilBERT, MentalRoBERTa and MentalBERT are end-to-end fine tuned for classification task. This thesis also compares different text preprocessing techniques such as tokenization, stopword removal and lemmatization to assess their impact on model performance. Systematic experiments with different configuration of vectorization and preprocessing techniques in accordance with different model types and categories have been implemented to find the most effective configurations and to gauge the strengths, limitations, and capability to detect and interpret the mental distress indicators from the text. The results analysis reveals that MentalBERT DL model significantly outperformed all other model types and categories due to its specific pretraining on mental data as well as rigorous end-to-end fine tuning gave it an edge for detecting nuanced linguistic mental distress indicators from the complex contextual textual corpus. This insights from the results acknowledges the ML and NLP technologies high potential for developing complex AI systems for its intervention in the domain of mental health analysis. This thesis lays the foundation and directs the future work demonstrating the need for collaborative approach of different domain experts as well as to explore next generational large language models to develop robust and clinically approved mental health AI systems.
In contemporary decision-making systems, the integration of machine learning (ML) models such as CatBoost, Random Forest, and Decision Tree has become ubiquitous, exerting substantial influence on societal dynamics. This pervasive adoption accentuates the critical necessity for efficacious fairness interventions to mitigate inherent biases and discrimination. However, prevailing approaches predominantly address binary classifications and frequently draw upon limited, region-specific datasets, thereby constraining their relevance and applicability. To address these shortcomings, we propose an extension to the fairness projection model that uses ensemble learning tree-based classifiers as the base classifying model. The proposed model is named Fairness Projection with Ensemble Trees (FPET), an innovative post-processing intervention specifically designed for multiclass classification tasks. Fairness Projection with Ensemble Trees is uniquely designed to accommodate multiple and overlapping protected groups, rendering it versatile and inclusive. A distinguishing feature of FPET lies in its model-agnostic nature and scalability to large datasets, facilitated by an information-theoretic framework centered around information projection. This approach furnishes robust theoretical assurances regarding convergence and sample complexity, thereby ensuring its practical viability. Furthermore, FPET’s design is augmented by its support for parallel processing, further enhancing its suitability for large-scale applications. Comprehensive evaluation against diverse datasets, including Brazil’s ENEM exam dataset, HSLS, and COMPAS, demonstrates the superior performance of our proposed model, Fairness Projection with Ensemble Trees (FPET), which uses the Cat-Boost classifier for both binary and multi-class classification tasks. In all datasets, CatBoost performed exceptionally well. Our fairness method also outperformed other benchmark models, such as Equality of Odds (EqOdds), Level Equal Opportunity (LevEqOpp), reduction method, and rejection methods. The results were compared using two metrics: Mean Equal Opportunity and Statistical Parity. These findings highlight the effectiveness of FPET across various contexts and introduce a novel approach to fairness in machine learning, ensuring equitable and inclusive decision-makings.