Radar-based Feature Design and Multiclass Classification for Road User Recognition

The classification of individual traffic participants is a complex task, especially for challenging scenarios with multiple road users or under bad weather conditions. Radar sensors provide an - with respect to well established camera systems - orthogonal way of measuring such scenes. In order to gain accurate classification results, 50 different features are extracted from the measurement data and tested on their performance. From these features a suitable subset is chosen and passed to random forest and long short-term memory (LSTM) classifiers to obtain class predictions for the radar input. Moreover, it is shown why data imbalance is an inherent problem in automotive radar classification when the dataset is not sufficiently large. To overcome this issue, classifier binarization is used among other techniques in order to better account for underrepresented classes. A new method to couple the resulting probabilities is proposed and compared to others with great success. Final results show substantial improvements when compared to ordinary multiclass classificationComment: 8 pages, 6 figure

QAmplifyNet: Pushing the Boundaries of Supply Chain Backorder Prediction Using Interpretable Hybrid Quantum - Classical Neural Network

Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. However, traditional machine-learning models struggle with large-scale datasets and complex relationships, hindering real-world data collection. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of handling large datasets. Our proposed model, QAmplifyNet, employs quantum-inspired techniques within a quantum-classical neural network to predict backorders effectively on short and imbalanced datasets. Experimental evaluations on a benchmark dataset demonstrate QAmplifyNet's superiority over classical models, quantum ensembles, quantum neural networks, and deep reinforcement learning. Its proficiency in handling short, imbalanced datasets makes it an ideal solution for supply chain management. To enhance model interpretability, we use Explainable Artificial Intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet seamlessly integrates into real-world supply chain management systems, enabling proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, providing superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management

J Biomed Inform

In the last decade, the widespread adoption of electronic health record documentation has created huge opportunities for information mining. Natural language processing (NLP) techniques using machine and deep learning are becoming increasingly widespread for information extraction tasks from unstructured clinical notes. Disparities in performance when deploying machine learning models in the real world have recently received considerable attention. In the clinical NLP domain, the robustness of convolutional neural networks (CNNs) for classifying cancer pathology reports under natural distribution shifts remains understudied. In this research, we aim to quantify and improve the performance of the CNN for text classification on out-of-distribution (OOD) datasets resulting from the natural evolution of clinical text in pathology reports. We identified class imbalance due to different prevalence of cancer types as one of the sources of performance drop and analyzed the impact of previous methods for addressing class imbalance when deploying models in real-world domains. Our results show that our novel class-specialized ensemble technique outperforms other methods for the classification of rare cancer types in terms of macro F1 scores. We also found that traditional ensemble methods perform better in top classes, leading to higher micro F1 scores. Based on our findings, we formulate a series of recommendations for other ML practitioners on how to build robust models with extremely imbalanced datasets in biomedical NLP applications.HHSN261201800032C/CA/NCI NIH HHSUnited States/HHSN261201800009C/CA/NCI NIH HHSUnited States/NU58DP006344/DP/NCCDPHP CDC HHSUnited States/HHSN261201800015I/CA/NCI NIH HHSUnited States/HHSN261201800013C/CA/NCI NIH HHSUnited States/HHSN261201800016I/CA/NCI NIH HHSUnited States/HHSN261201800014I/CA/NCI NIH HHSUnited States/HHSN261201800032I/CA/NCI NIH HHSUnited States/HHSN261201800013I/HL/NHLBI NIH HHSUnited States/U58 DP003907/DP/NCCDPHP CDC HHSUnited States/HHSN261201800015C/CA/NCI NIH HHSUnited States/HHSN261201800013I/CA/NCI NIH HHSUnited States/HHSN261201800014C/CA/NCI NIH HHSUnited States/HHSN261201800016C/CA/NCI NIH HHSUnited States/P30 CA177558/CA/NCI NIH HHSUnited States/HHSN261201300021C/CA/NCI NIH HHSUnited States/HHSN261201800009I/CA/NCI NIH HHSUnited States/HHSN261201800007C/CA/NCI NIH HHSUnited States

Using Transfer Learning to Train Individualized Models to Detect Eating Episodes from Daily Wrist Motion

This thesis considers the problem of detecting periods of eating in free-living conditions by analyzing wrist motion data collected using sensors embedded within a typical smartwatch. Previous work by our research group included the collection of a dataset containing 354 days of recorded wrist motion data from 351 different people (approximately one day of data per person) [42]. A machine learning model was then trained to classify this wrist motion data as either eating or non-eating [40]. We refer to this model as the group model. Subsequent work in our research group collected approximately ten days of data each for eight new individuals and trained a model for each person solely using their own data [51]. We refer to these models as individual models. It was observed that, in most cases, the individual models outperformed the group model when evaluating the data of their corresponding individual, but at the cost of requiring each individual to collect two weeks of additional data. The novelty of this work is using transfer learning to leverage features learned within the group model and apply them to new individual models to further increase performance and possibly reduce the amount of individual data needed. Two datasets were used in this work. The first was the Clemson All Day (CAD) dataset, which contains 354 days of recorded wrist motion data from 351 different participants (approximately one day of data per participant). The CAD dataset includes a total of 4,680 hours of data, including 1,063 meals. The second dataset used was the Multiday dataset, which is comprised of at least ten days of free-living wrist motion data each for eight individuals. Both datasets were pre-processed using smoothing and normalization techniques. Training samples were then generated using a sliding window approach with a window size of six minutes. All group, individual, and transfer learning models evaluated in this work utilized an identical convolutional neural network (CNN) architecture. For a given window, the classifier generated a value that represented the probability of eating (P(E)) in the window. Entire days of wrist motion data were passed to the network to produce a continuous P(E) sequence for an entire day. This sequence was processed using a dual thresholding technique to locate predicted segments of eating within the recording. In our results, the transfer learning model achieved an eating episode true positive rate (TPR) of 81% with a false positive per true positive ratio (FP/TP) of 1.40. Compared to the individual model, this was a 6% decrease in episode TPR but a 43% improvement in FP/TP. The transfer learning model showed a time weighted accuracy (AccW) of 80%, which was only a 1% decrease relative to the individual model. After removing an outlier from the Multiday dataset and rerunning our experiments, the transfer learning model showed an episode TPR of 86% with an FP/TP of 1.34. Compared to the individual model, this was only a 3% decrease in TPR and a 46% improvement in FP/TP. By excluding the outlier, the transfer learning model also showed an 83% AccW, which was a 1% increase relative to the individual model. Furthermore, the transfer learning model was able to reduce training times by 12% compared to the individual model. In conclusion, we were able to find evidence that transfer learning could be utilized in order to improve individualized eating detection models by increasing weighted accuracy and decreasing false detections

Addressing class imbalance in deep learning for acoustic target classification

Acoustic surveys provide important data for fisheries management. During the surveys, ship-mounted echo sounders send acoustic signals into the water and measure the strength of the reflection, so-called backscatter. Acoustic target classification (ATC) aims to identify backscatter signals by categorizing them into specific groups, e.g. sandeel, mackerel, and background (as bottom and plankton). Convolutional neural networks typically perform well for ATC but fail in cases where the background class is similar to the foreground class. In this study, we discuss how to address the challenge of class imbalance in the sampling of training and validation data for deep convolutional neural networks. The proposed strategy seeks to equally sample areas containing all different classes while prioritizing background data that have similar characteristics to the foreground class. We investigate the performance of the proposed sampling methodology for ATC using a previously published deep convolutional neural network architecture on sandeel data. Our results demonstrate that utilizing this approach enables accurate target classification even when dealing with imbalanced data. This is particularly relevant for pixel-wise semantic segmentation tasks conducted on extensive datasets. The proposed methodology utilizes state-of-the-art deep learning techniques and ensures a systematic approach to data balancing, avoiding ad hoc methods.Addressing class imbalance in deep learning for acoustic target classificationpublishedVersio