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arXiv 2025年

作者： Haddadnia, Mohammad Grashoff, Leonie Strieth-Kalthoff, Felix Harvard University Department of Biological Chemistry & Molecular Pharmacology BostonMA United States Harvard University Dana-Farber Cancer Institute Department of Cancer Biology BostonMA United States University of Wuppertal School of Mathematics and Natural Sciences Wuppertal Germany University of Wuppertal Interdisciplinary Center for Machine Learning and Data Analytics Wuppertal Germany

Scientific optimization problems are usually concerned with balancing multiple competing objectives, which come as preferences over both the outcomes of an experiment (e.g. maximize the reaction yield) and the corresponding input parameters (e.g. minimize the use of an expensive reagent). Typically, practical and economic considerations define a hierarchy over these objectives, which must be reflected in algorithms for sample-efficient experiment planning. Herein, we introduce BoTier, a composite objective that can flexibly represent a hierarchy of preferences over both experiment outcomes and input parameters. We provide systematic benchmarks on synthetic and real-life surfaces, demonstrating the robust applicability of BoTier across a number of use cases. Importantly, BoTier is implemented in an auto-differentiable fashion, enabling seamless integration with the BoTorch library, thereby facilitating adoption by the scientific community. © 2025, CC BY.

关键词： Bot (Internet)

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NC-ALG: Graph-Based Active learning Under Noisy Crowd

NC-ALG: Graph-Based Active Learning Under Noisy Crowd

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International Conference on data Engineering

作者： Wentao Zhang Yexin Wang Zhenbang You Yang Li Gang Cao Zhi Yang Bin Cui Center for Machine Learning Research Peking University Institute of Advanced Algorithms Research Shanghai National Engineering Labratory for Big Data Analytics and Applications Key Lab of High Confidence Software Technologies Peking University Department of Data Platform TEG Tencent Inc. Beijing Academy of Artificial Intelligence Institute of Computational Social Science Peking University Qingdao

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Graph Neural Networks (GNNs) have achieved great success in various data mining tasks but they heavily rely on a large number of annotated nodes, requiring considerable human efforts. Despite the effectiveness of existing GNN-based Active learning (AL) methods, they assume that the annotated labels are always correct, which is contradictory to the error-prone labeling process in a practical crowdsourcing environment. Besides, due to this impractical assumption, existing works only focus on optimizing the node selection in AL but neglect optimizing the labeling process. Therefore, we present NC-ALG, the first GNN-based AL framework that optimizes both the node selection and node labeling process under a noisy crowd. For node selection, NC-ALG introduces a new measurement to model influence reliability and an effective influence maximization objective to select nodes. For node labeling, NC-ALG significantly reduces the labeling cost by considering the model-predicted labels and the labels of mirror nodes. To the best of our knowledge, this is the first attempt to consider GNN-based AL under the practical noisy crowd. Empirical studies on public datasets demonstrate that NC-ALG significantly outperforms existing methods in terms labeling efficiency. Notably, it only takes NC-ALG one-third of the labeling budget that the competitive baseline GRAIN needs to achieve an accuracy of 70.7 % on PubMed.

关键词： Crowdsourcing Costs Noise Predictive models Graph neural networks Labeling Noise measurement

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machine learning approach for feature interpretation and classification of genetic mutations leading to tumor and cancer 1st

Machine learning approach for feature interpretation and cla...

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1st International Conference on Advances in Electrical and Computer Technologies, ICAECT 2019

作者： Sah, Ankit Kumar Mishra, Abinash Reddy, U. Srinivasulu Machine Learning and Data Analytics Lab Department of Computer Applications National Institute of Technology TiruchirappalliTamil Nadu620015 India

ISBN: (纸本)9789811555572

As the interpretation of genetic mutation is done manually, it is difficult to diagnose a large number of patients and get reports of the same in a quick time. Hence, it needs to be automated using machine learning approach. Towards the same, natural language processing (NLP) technique, viz. term frequency-inverse document frequency (TF-IDF), is used to represent documents as fixed-size depiction for interpreting the given nine classes of genetic mutations. The main aim of this study is to identify the well-suited machine learning model which will give better results in terms of multi-class log-loss. Another important aspect of this study is to interpret the features since feature interpretability is very important in healthcare domain using various machine learning algorithms. Logistic regression (LR) with class balancing was implemented by taking top 1000 words of 3-gram TF-IDF generated features that outperformed the other classifiers to give a test log-loss of 0.98. © Springer Nature Singapore Pte Ltd 2020.

关键词： machine learning

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Automated Long-Term Monitoring of Stereotypical Movement in Polar Bears Under Human Care Using machine learning

SSRN

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SSRN 2024年

作者： Zuerl, Matthias Stoll, Philip Brehm, Ingrid Sueskind, Jonas Raab, René Petermann, Jan Zanca, Dario Simon, Ralph von Fersen, Lorenzo Eskofier, Bjoern Machine Learning and Data Analytics Lab Department Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91052 Germany Animal Physiology Department of Biology Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen91058 Germany Behavioral Ecology and Conservation Lab Nuremberg Zoo Nuremberg90480 Germany

The welfare of captive animals is often assessed by observing behaviours indicative of stress or discomfort, such as stereotypical behaviour (SB), which often shows as repetitive, invariant *** behaviour monitoring methods, however, are labour-intensive and subject to observer *** study presents an innovative automated approach utilising computer vision and machine learning to non-invasively detect and analyse SB in captive animals, exemplified by a longitudinal study of two polar *** designed an animal tracking framework to localise and identify individual animals in the *** determining their position on the enclosure map via homographic transformation, we cleaned the resulting trajectories using a particle ***, we classified the trajectory patterns as SB or normal behaviour using a lightweight random forest approach with an accuracy of 94.9 %.The system not only allows for continuous, objective monitoring of animal behaviours but also provides insights into seasonal variations in SB, illustrating its potential for improving animal welfare in zoological *** work advances the field of animal welfare research by introducing a scalable, efficient method for the long-term, automated monitoring of stereotypical behaviour, paving the way for its application across different species and settings. © 2024, The Authors. All rights reserved.

关键词： Enclosures

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DISCO: Internal Evaluation of Density-Based Clustering

arXiv

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arXiv 2025年

作者： Beer, Anna Krieger, Lena Weber, Pascal Ritzert, Martin Assent, Ira Plant, Claudia Faculty of Computer Science University of Vienna Vienna Austria IAS-8: Data Analytics and Machine Learning Forschungszentrum Jülich Jülich Germany UniVie Doctoral School Computer Science University of Vienna Vienna Austria Data Science @ Uni Vienna University of Vienna Vienna Austria Institute of Computer Science and Campus Institute Data Science University of Göttingen Göttingen Germany Department of Computer Science Aarhus University Aarhus Denmark

In density-based clustering, clusters are areas of high object density separated by lower object density areas. This notion supports arbitrarily shaped clusters and automatic detection of noise points that do not belong to any cluster. However, it is challenging to adequately evaluate the quality of density-based clustering results. Even though some existing cluster validity indices (CVIs) target arbitrarily shaped clusters, none of them captures the quality of the labeled noise. In this paper, we propose DISCO, a Density-based Internal Score for Clustering Outcomes, which is the first CVI that also evaluates the quality of noise labels. DISCO reliably evaluates density-based clusters of arbitrary shape by assessing compactness and separation. It also introduces a direct assessment of noise labels for any given clustering. Our experiments show that DISCO evaluates density-based clusterings more consistently than its competitors. It is additionally the first method to evaluate the complete labeling of density-based clustering methods, including noise labels. © 2025, CC BY.

关键词： Cluster analysis

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Thai Conversational Chatbot Classification Using BiLSTM and data Augmentation 1

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1st International Conference on data Science and Artificial Intelligence, DSAI 2023

作者： Lhasiw, Nunthawat Tanantong, Tanatorn Sanglerdsinlapachai, Nuttapong Thammasat Research Unit in Data Innovation and Artificial Intelligence Department of Computer Science Faculty of Science and Technology Thammasat University Pathum Thani Thailand Strategic Analytics Networks with Machine Learning and AI Research Team National Electronics and Computer Technology Center Pathum Thani Thailand

ISBN: (数字)9789819979691

ISBN: (纸本)9789819979684

Chatbot platforms, e.g., Facebook and Line, have revolutionized human interaction in the digital age. In order to develop an automatic chatbot classification, there are several challenges especially for Thai chat messages. Conversational messages are usually short and ambiguous. Therefore, it is difficult to find a dataset for constructing an effective classification model. To address the limited size of the dataset, data augmentation techniques can be possibly applied. data augmentation involves generating synthetic messages by applying various transformations to existing data samples while preserving their original meaning. In this study, the size and diversity of the dataset is increased by two methods, i.e., text augmentation using word2vec from Thai2Fit and English-Thai machine translation models proposed by VISTEC. Based on the augmented messages, a Deep learning technique, BiLSTM, is used to construct a chatbot classification model. The experimental obtained results demonstrate that data augmentation can help to increase the classification performance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Classification (of information)

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learning Clinical Concepts for Predicting Risk of Progression to Severe COVID-19

arXiv

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arXiv 2022年

作者： Zhou, Helen Cheng, Cheng Shields, Kelly J. Kochhar, Gursimran Cheema, Tariq Lipton, Zachary C. Weiss, Jeremy C. Machine Learning Department Heinz College Carnegie Mellon University United States Highmark Health Enterprise Data & Analytics Datasicence R&D United States Allegheny Health Network United States

With COVID-19 now pervasive, identification of high-risk individuals is crucial. Using data from a major healthcare provider in Southwestern Pennsylvania, we develop survival models predicting severe COVID-19 progression. In this endeavor, we face a tradeoff between more accurate models relying on many features and less accurate models relying on a few features aligned with clinician intuition. Complicating matters, many EHR features tend to be under-coded, degrading the accuracy of smaller models. In this study we develop two sets of high-performance risk scores: (i) an unconstrained model built from all available features;and (ii) a pipeline that learns a small set of clinical concepts before training a risk predictor. Learned concepts boost performance over the corresponding features (C-index 0.858 vs. 0.844) and demonstrate improvements over (i) when evaluated out-of-sample (subsequent time periods). Our models outperform previous works (C-index 0.844–0.872 vs. 0.598–0.810). Copyright © 2022, The Authors. All rights reserved.

关键词： COVID-19

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Prediction of Stress Coping Capabilities from Nightly Heart Rate Patterns using machine learning

Prediction of Stress Coping Capabilities from Nightly Heart ...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Linda Vorberg Siri Pflueger Robert Richer Katharina M. Jaeger Arne Küderle Nicolas Rohleder Bjoern M. Eskofier Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Chair of Health Psychology Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Stress is related to short- and long-term alterations in stress systems, including the hypothalamic-pituitary-adrenal (HPA) axis and the sympathetic nervous system (SNS). While it is well established that stress experienced during the day can affect sleep quality, less is known about how it affects stress systems during the night. We assume that stress coping strategies can have an impact on how stress carries over into the night and that individuals with bad coping mechanisms show elevated activation of stress systems during sleep. For that reason, we recorded the heart rate (HR) and heart rate variability (HRV) of 21 healthy participants on two consecutive nights during sleep and the first hour after awakening and extracted cortisol and alpha-amylase from saliva samples collected in the first hour after awakening. Stress coping capabilities were assessed using self-reports. We performed backward stepwise multiple regression models to analyze the relationship between HR(V) and stress coping and trained different machine learning-based regression algorithms to predict positive (SVF Pos ) and negative (SVF Neg ) stress coping capabilities, respectively. Our results show that individuals with higher SVF Neg scores showed higher SNS activity during the night, whereas higher SVF Pos scores indicated lower SNS activity. SVF Pos was predicted with a mean absolute error (MAE) of 1.51 ± 0.73 and SVF Neg with an MAE of 2.79 ± 1.53. Our findings indicate an association between nightly HR(V) and the individual’s capability of coping with stress. This provides further information about how stress influences sleep and might be used for tailored intervention and feedback on successful stress coping.

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VoStress – Voice-based Detection of Acute Psychosocial Stress

VoStress – Voice-based Detection of Acute Psychosocial Stre...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Marie Oesten Robert Richer Luca Abel Nicolas Rohleder Bjoern M. Eskofier Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Chair of Health Psychology Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Current stress assessment methods include self-reports and biomarkers which are evaluated in often complex, laboratory procedures. Due to that investigating new indicators for acute stress is crucial for the development of automatic stress detection systems. A promising extension might be provided by investigating speech, which has been shown to be affected by negative emotions and threatening situations. For that reason, we extracted verbal acoustics from audio data collected during a study where N=21 participants underwent the Trier Social Stress Test (TSST), the gold standard for laboratory stress induction, and a stress-free control condition (friendly-TSST) while concurrently collecting cortisol via saliva samples to assess the biological response to stress. Our results show that acute stress leads to significant (p < 0.05) alterations of acoustic features. A stepwise backward multiple linear regression model explained 58.8 % of the variance of the maximum cortisol increase. In addition to that, we performed classification experiments that distinguished stress from non-stress situations with an accuracy of 80.0 ± 12.7 %. While further research is needed to validate our approach, we are convinced that the information extracted from speech can be a valuable indicator for automatic stress detection systems and can even predict the biological response to stress situations.

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WebPPG: Feasibility and Usability of Self-Performed, Browser-Based Smartphone Photoplethysmography

WebPPG: Feasibility and Usability of Self-Performed, Browser...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Michael Nissen Madeleine Flaucher Katharina M. Jaeger Hanna Huebner Nina Danzberger Adriana Titzmann Constanza A. Pontones Peter A. Fasching Bjoern M. Eskofier Heike Leutheuser Department Artificial Intelligence in Biomedical Engineering (AIBE) Machine Learning and Data Analytics Lab (MaD Lab) Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany Department of Gynecology and Obstetrics Erlangen University Hospital Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Erlangen Germany

Smartphones enable and facilitate biomedical studies as they allow the recording of various biomedical signals, including photoplethysmograms (PPG). However, user engagement rates in mobile health studies are reduced when an application (app) needs to be installed. This could be alleviated by using installation-free web apps. We evaluate the feasibility of browser-based PPG recording, conducting the first usability study on smartphone-based PPG. We present an at-home study using a web app and library for PPG recording using the rear camera and flash. The underlying library is freely made available to researchers. 25 Android users participated, using their own smartphones. The study consisted of a demographic and anamnestic questionnaire, the signal recording itself (60 s), and a consecutive usability questionnaire. After filtering, heart rate was extracted (14/17 successful), signal-to-noise ratios assessed (0.64 ± 0.50 dB, mean ± standard deviation), and quality was visually inspected (12/17 usable for diagnosis). Recording was not supported in 9 cases. This was due to the browser’s insufficient support for the flash light API. The app received a System Usability Scale score of 82 ± 9, which is above the 90th percentile. Overall, browser flash light support is the main limiting factor for broad device support. Thus, browser-based PPG is not yet widely applicable, although most participants feel comfortable with the recording itself. The utilization of the user-facing camera might represent a more promising approach. This study contributes to the development of low-barrier, user-friendly, installation-free smartphone signal acquisition. This enables profound, comprehensive data collection for research and clinical *** relevance— WebPPG offers low-barrier remote diagnostic capabilities without the need for app installation

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