Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning

Radiologists are in short supply globally, and deep learning models offer a promising solution to address this shortage as part of clinical decision-support systems. However, training such models often requires expensive and time-consuming manual labeling of large datasets. Automatic label extraction from radiology reports can reduce the time required to obtain labeled datasets, but this task is challenging due to semantically similar words and missing annotated data. In this work, we explore the potential of weak supervision of a deep learning-based label prediction model, using a rule-based labeler. We propose a deep learning-based CheXpert label prediction model, pre-trained on reports labeled by a rule-based German CheXpert model and fine-tuned on a small dataset of manually labeled reports. Our results demonstrate the effectiveness of our approach, which significantly outperformed the rule-based model on all three tasks. Our findings highlight the benefits of employing deep learning-based models even in scenarios with sparse data and the use of the rule-based labeler as a tool for weak supervision

Cloud-Based Dynamic Programming for an Electric City Bus Energy Management Considering Real-Time Passenger Load Prediction

Electric city bus gains popularity in recent years for its low greenhouse gas emission, low noise level, etc. Different from a passenger car, the weight of a city bus varies significantly with different amounts of onboard passengers, which is not well studied in existing literature. This study proposes a passenger load prediction model using day-of-week, time-of-day, weather, temperatures, wind levels, and holiday information as inputs. The average model, Regression Tree, Gradient Boost Decision Tree, and Neural Networks models are compared in the passenger load prediction. The Gradient Boost Decision Tree model is selected due to its best accuracy and high stability. Given the predicted passenger load, dynamic programming algorithm determines the optimal power demand for supercapacitor and battery by optimizing the battery aging and energy usage in the cloud. Then rule extraction is conducted on dynamic programming results, and the rule is real-time loaded to onboard controllers of vehicles. The proposed cloud-based dynamic programming and rule extraction framework with the passenger load prediction shows 4% and 11% fewer bus operating costs in off-peak and peak hours, respectively. The operating cost by the proposed framework is less than 1% shy of the dynamic programming with the true passenger load information

Building a finite state automaton for physical processes using queries and counterexamples on long short-term memory models

Most neural networks (NN) are commonly used as black-box functions. A network takes an input and produces an output, without the user knowing what rules and system dynamics have produced the specific output. In some situations, such as safety-critical applications, having the capability of understanding and validating models before applying them can be crucial. In this regard, some approaches for representing NN in more understandable ways, attempt to accurately extract symbolic knowledge from the networks using interpretable and simple systems consisting of a finite set of states and transitions known as deterministic finite-state automata (DFA). In this thesis, we have considered a rule extraction approach developed by Weiss et al. that employs the exact learning method L* to extract DFA from recurrent neural networks (RNNs) trained on classifying symbolic data sequences. Our aim has been to study the practicality of applying their rule extraction approach on more complex data based on physical processes consisting of continuous values. Specifically, we experimented with datasets of varying complexities, considering both the inherent complexity of the dataset itself and complexities introduced from different discretization intervals used to represent the continuous data values. Datasets incorporated in this thesis encompass sine wave prediction datasets, sequence value prediction datasets, and a safety-critical well-drilling pressure scenario generated through the use of the well-drilling simulator OpenLab and the sparse identification of nonlinear dynamical systems (SINDy) algorithm. We observe that the rule extraction algorithm is able to extract simple and small DFA representations of LSTM models. On the considered datasets, extracted DFA generally demonstrates worse performance than the LSTM models used for extraction. Overall, for both increasing problem complexity and more discretization intervals, the performance of the extracted DFA decreases. However, DFA extracted from datasets discretized using few intervals yields more impressive results, and the algorithm can in some cases extract DFA that outperforms their respective LSTM models.Masteroppgave i informatikkINF399MAMN-INFMAMN-PRO

Self learning neuro-fuzzy modeling using hybrid genetic probabilistic approach for engine air/fuel ratio prediction

Machine Learning is concerned in constructing models which can learn and make predictions based on data. Rule extraction from real world data that are usually tainted with noise, ambiguity, and uncertainty, automatically requires feature selection. Neuro-Fuzzy system (NFS) which is known with its prediction performance has the difficulty in determining the proper number of rules and the number of membership functions for each rule. An enhanced hybrid Genetic Algorithm based Fuzzy Bayesian classifier (GA-FBC) was proposed to help the NFS in the rule extraction. Feature selection was performed in the rule level overcoming the problems of the FBC which depends on the frequency of the features leading to ignore the patterns of small classes. As dealing with a real world problem such as the Air/Fuel Ratio (AFR) prediction, a multi-objective problem is adopted. The GA-FBC uses mutual information entropy, which considers the relevance between feature attributes and class attributes. A fitness function is proposed to deal with multi-objective problem without weight using a new composition method. The model was compared to other learning algorithms for NFS such as Fuzzy c-means (FCM) and grid partition algorithm. Predictive accuracy and the complexity of the Fuzzy Rule Base System (FRBS) including number of rules and number of terms in each rule were taken as terms of evaluation. It was also compared to the original GA-FBC depending on the frequency not on Mutual Information (MI). Experimental results using Air/Fuel Ratio (AFR) data sets show that the new model participates in decreasing the average number of attributes in the rule and sometimes in increasing the average performance compared to other models. This work facilitates in achieving a self-generating FRBS from real data. The GA-FBC can be used as a new direction in machine learning research. This research contributes in controlling automobile emissions in helping the reduction of one of the most causes of pollution to produce greener environment

AsyLex: A Dataset for Legal Language Processing of Refugee Claims

Advancements in natural language processing (NLP) and language models have demonstrated immense potential in the legal domain, enabling automated analysis and comprehension of legal texts. However, developing robust models in Legal NLP is significantly challenged by the scarcity of resources. This paper presents AsyLex, the first dataset specifically designed for Refugee Law applications to address this gap. The dataset introduces 59,112 documents on refugee status determination in Canada from 1996 to 2022, providing researchers and practitioners with essential material for training and evaluating NLP models for legal research and case review. Case review is defined as entity extraction and outcome prediction tasks. The dataset includes 19,115 gold-standard human-labeled annotations for 20 legally relevant entity types curated with the help of legal experts and 1,682 gold-standard labeled documents for the case outcome. Furthermore, we supply the corresponding trained entity extraction models and the resulting labeled entities generated through the inference process on AsyLex. Four supplementary features are obtained through rule-based extraction. We demonstrate the usefulness of our dataset on the legal judgment prediction task to predict the binary outcome and test a set of baselines using the text of the documents and our annotations. We observe that models pretrained on similar legal documents reach better scores, suggesting that acquiring more datasets for specialized domains such as law is crucial. The dataset is available at https://huggingface. co/datasets/clairebarale/AsyLex

Supporting Telecommunication Alarm Management System with Trouble Ticket Prediction

Fault alarm data emanated from heterogeneous telecommunication network services and infrastructures are exploding with network expansions. Managing and tracking the alarms with Trouble Tickets using manual or expert rule- based methods has become challenging due to increase in the complexity of Alarm Management Systems and demand for deployment of highly trained experts. As the size and complexity of networks hike immensely, identifying semantically identical alarms, generated from heterogeneous network elements from diverse vendors, with data-driven methodologies has become imperative to enhance efficiency. In this paper, a data-driven Trouble Ticket prediction models are proposed to leverage Alarm Management Systems. To improve performance, feature extraction, using a sliding time-window and feature engineering, from related history alarm streams is also introduced. The models were trained and validated with a data-set provided by the largest telecommunication provider in Italy. The experimental results showed the promising efficacy of the proposed approach in suppressing false positive alarms with Trouble Ticket prediction

Rule Extraction from Support Vector Machines: A Geometric Approach

Despite the success of connectionist systems in prediction and classi¯cation problems, critics argue that the lack of symbol processing and explanation capability makes them less competitive than symbolic systems. Rule extraction from neural networks makes the interpretation of the behaviour of connectionist networks possible by relating sub-symbolic and symbolic process- ing. However, most rule extraction methods focus only on speci¯c neural network architectures and present limited generalization performance. Support Vector Machine is an unsupervised learning method that has been recently applied successfully in many areas, and o®ers excellent generalization ability in comparison with other neural network, statistical, or symbolic machine learning models. In this thesis, an algorithm called Geometric and Oracle-Based Support Vector Machines Rule Extraction (GOSE) has been proposed to overcome the limitations of other rule-extraction methods by extracting comprehensible models from Support Vector Machines (SVM). This algorithm views the extraction as a geometric task. Given a trained SVM network, GOSE queries the synthetic instances and draws conjunction rules by approximating the optimization problem. The extracted rule set also represents the approximation of the SVM classi¯cation boundary. Unlike previous works in SVM rule-extraction, GOSE is broadly applicable to different networks and problems because it need not rely on training examples and network architectures. Theoretical proof guarantees that GOSE is capable of approximating the behavior of SVM networks. Empirical experiments are conducted on di®erent SVM networks from binary classification networks to multi-class networks in various classi¯cation domains. The result of experiments demonstrates that GOSE can extract comprehensible rules with high levels of accuracy and ¯delity for their corresponding networks. GOSE also exhibits superior consistency. After analyzing and applying several optimizing measures, the complexity of GOSE was improved. In brief, GOSE provides a novel way to explain how an SVM network functions