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computational intelligence in Health and Medicine (CIHM), ieee symposium on

作者： Johannes Kummert Alexander Schulz Moriz Habigt Maike Stemmler Christina Kohler Dirk Abel Rolf Rossaint Barbara Hammer Machine Learning Group Bielefeld University Bielefeld Germany Anaesthesiology Clinic RWTH Aachen University Aachen Germany Institute of Automatic Control RWTH Aachen University Aachen Germany

ISBN: (数字)9798331508333

ISBN: (纸本)9798331508340

Medical data is a valuable resource that is under privacy constraints and often requires a substantial effort and cost to obtain. This is the case for physiological cardiovascular data that is among others utilized to develop assistive heart devices. In the present work, we aim to improve a GAN based generative approach from the literature by integrating physiological knowledge in form of pressure-value curves together with a dynamic time warping cost function into the model. We demonstrate that our adapted model generates more realistic cardiovascular signals as measured by targeted physiological markers and showcase that such generated signals can be used to improve a downstream task on real data.

关键词： Heart Adaptation models Visualization data privacy Time series analysis Focusing Inspection Linear programming Physiology data models

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Synthetic Feature Augmentation Improves Generalization Performance of Language Models

Synthetic Feature Augmentation Improves Generalization Perfo...

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computational intelligence in Natural Language Processing and Social Media (CI-NLPSoMe Companion), ieee symposium on

作者： Ashok Choudhary Cornelius Thiels Hojjat Salehinejad Department of Surgery Mayo Clinic Rochester MN USA Kern Center for the Science of Health Care Delivery Mayo Clinic Rochester MN USA Department of Artificial Intelligence and Informatics Mayo Clinic Rochester MN USA

ISBN: (数字)9798331519742

ISBN: (纸本)9798331519759

Training and fine-tuning deep learning models, especially large language models (LLMs), on limited and imbalanced datasets poses substantial challenges. These issues often result in poor generalization, where models overfit to dominant classes and underperform on minority classes, leading to biased predictions and reduced robustness in real-world applications. To overcome these challenges, we propose augmenting features in the embedding space by generating synthetic samples using a range of techniques. By upsampling underrepresented classes, this method improves model performance and alleviates data imbalance. We validate the effectiveness of this approach across multiple open-source text classification benchmarks, demonstrating its potential to enhance model robustness and generalization in imbalanced data scenarios.

关键词： Training Deep learning Social networking (online) Large language models Text categorization Predictive models data models Robustness Natural language processing Synthetic data

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RIAND: Robustness-Improved and Accelerated Neural-Deduplication

RIAND: Robustness-Improved and Accelerated Neural-Deduplicat...

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computational intelligence in Natural Language Processing and Social Media (CI-NLPSoMe), ieee symposium on

作者： Bowen Chen Department of Computer Science The University of Tokyo Tokyo Japan

ISBN: (数字)9798331508418

ISBN: (纸本)9798331508425

The predominant approach to text deduplication is still lexical-based, failing short in detecting near-duplicates that have low lexical similarity but high semantic similarity. Neural deduplication methods address those issues using embed dings to capture surface text and semantic meaning. However, they face challenges such as sensitivity to noise, limited scalability, and lacked effectiveness in real-world corpora. This study aims to overcome these weaknesses. We enhance noise robustness through noised data augmentation and contrastive learning, ensuring similar embed dings for sentences and their noisy counterparts. We also improve scalability with lower theoretical complexity, an optimized running pipeline, and distributed processing, resulting in a significant speed improvement. Applying our method to real-world corpora demonstrates its effectiveness, and we observe performance gains in language models trained on deduplicated data. Qualitative analysis confirms the superiority of the RIAND method in identifying near-duplicates. 1 1 https://***/PosoSAgapo/TextDupRIAND

关键词： Social networking (online) computational modeling Scalability Semantics Pipelines Noise Contrastive learning data augmentation Natural language processing data models

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Pool-based Active Learning with Decision Trees: Incorporate the Tree Structure to Explore and Exploit

Pool-based Active Learning with Decision Trees: Incorporate ...

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Trustworthy, Explainable and Responsible computational intelligence (CITREx), ieee symposium on

作者： Marvin Schöne Bjarne Jaster Julian Bültemeier Justus Kösters Christoph-Alexander Holst Martin Kohlhase Center for Applied Data Science Gütersloh Bielefeld University of Applied Sciences and Arts Germany Institut für Industrielle Informationstechnik Technische Hochschule Ostwestfalen-Lippe Germany

ISBN: (数字)9798331520151

ISBN: (纸本)9798331520168

Active Learning (AL) is a powerful method to efficiently gather data for the training of machine learning models. Particularly effective are AL methods that consider both the representativeness and informativeness of the data, allowing the methods to balance the exploration and exploitation. However, when AL is used in real applications, such as modelling an industrial process or a product, the resulting models often cannot be validated sufficiently due to a lack of independent test data. In this case, Decision Trees (DTs) offer a major advantage over other more established models in the field of AL. The intrinsic interpretability of DTs allows for validation without the use of independent test data. In this work, we present a novel AL method based on DTs. In addition to an intrinsically interpretable classification model, our new method utilizes different exploration and exploitation properties of the learned tree structure to gather training data. The behavior of our method is illustrated and the performance is compared to other established AL methods in an experimental study using third-party data sets. Our comparison demonstrates that the DTs trained with our method converge to a lower test error at a faster rate than DTs trained with the competing methods.

关键词： Training Active learning Training data Machine learning data models Decision trees computational intelligence

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ETL Model for Pandemic data Processing

ETL Model for Pandemic Data Processing

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International symposium on Applied Machine intelligence and Informatics (SAMI)

作者： Lukáš Tomaščik Norbert Ádám Nikola Geciová Branislav Madoš Peter Poprík Heidar Khorshidiyeh Department of Computers and Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia Department of Mathematics and Theoretical Informatics Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This paper presents the development and implementation of an Extract-Transform-Load (ETL) model aimed at processing pandemic data. The model gathers data from various official sources, transforms it for easy analysis, and provides insights through graphical representations. Implemented using Python, the system automates data extraction and visualization, offering an accessible tool for understanding pandemic trends.

关键词： Analytical models Pandemics data visualization Transforms Market research data processing data models data mining Informatics Machine intelligence

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Fairness and Trust in data-Driven Decisions: Analyzing Discrepancies in Ordinal Decisions

Fairness and Trust in Data-Driven Decisions: Analyzing Discr...

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Trustworthy, Explainable and Responsible computational intelligence (CITREx), ieee symposium on

作者： Edward Abel Sajid Siraj University of Southern Denmark Denmark Leeds University Business School Leeds United Kingdom

ISBN: (数字)9798331520151

ISBN: (纸本)9798331520168

When evaluating decisions founded on data driven procedures, it is crucial to verify that the decisions align with the procedures, to ensure fairness for those affected by the outcomes and to maintain trust. For decisions for which there is a set of ordinal outcomes, fairness considerations extend beyond considering parity in appropriate outcomes (true positives) across different groups. It is also important to consider how inappropriate outcomes (non-true positives) can result in differing consequences depending on if, and by how much, the outcome is an overestimate (falls on a higher position in the ordinal set of outcomes) or an underestimate (falls on a lower position in the ordinal set of outcomes).In this work, we analyze the fairness of historical decisions through investigating inconsistencies between inappropriate outcomes for different groups in terms of underestimates and overestimates. For a pair of groups, we train a classification model using data from one group, and then test the model with respect to data for a second group. We compare the results to the inverse, a model trained on the second group and tested on the first group. From this, any variations between the models’ tendencies to underestimate or overestimate, compared to the actual historical outcomes, can be uncovered. We use a variety of algorithmic models to minimize the potential for any uncovered tendencies to arise simply due to reliance on a single *** explore an application case study of the United Kingdom government’s Covid-19 tiered restriction decisions, which took a localized strategy to allocate different tiers of restrictions of movement to different geographic areas based on each’s data. Here, the different tiers represent a set of ordinal decision outcomes. The decisions sparked public debate regarding their transparency and fairness, and the application of our approach reveals inconsistencies in how the North and the South of England were treated.

关键词： COVID-19 Analytical models computational modeling Government data models computational intelligence

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Evolutionary Neural Architecture Search Framework With Masked Encoding Mechanism for Point Cloud Registration

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ieee TRANSACTIONS ON EMERGING TOPICS IN computational intelligence 2025年

作者： Wu, Yue Gong, Peiran Yuan, Yongzhe Gong, Maoguo Ma, Wenping Miao, Qiguang Xidian Univ Sch Comp Sci & Technol Minist Educ Key Lab Collaborat Intelligence Syst Xian 710071 Peoples R China Xidian Univ Sch Elect Engn Minist Educ Key Lab Collaborat Intelligence Syst Xian 710071 Peoples R China Inner Mongolia Normal Univ Acad Artificial Intelligence Coll Math Sci Hohhot 010022 Peoples R China Xidian Univ Sch Artificial Intelligence Minist Educ Key Lab Intelligent Percept & Image Understanding Xian 710071 Peoples R China

Deep learning-based point cloud registration (PCR) has received increasing attention in the field of computer vision. However, the lack of expertise in deep neural network architecture design remains a large obstacle for end-users with registration needs. While neural architecture search (NAS) indeed addresses the architecture automation design problem and has undergone substantial development, the majority of existing NAS approaches are tailored for image processing. Because of significant differences in data characteristics and network structures, directly transferring the existing NAS approaches for PCR is infeasible. Hence, this work proposes an evolutionary neural architecture search framework for PCR, which can fit the different types of PCR networks. The framework, equipped with the designed masked encoding mechanism, can seamlessly integrate with the majority of evolutionary computation-based optimization algorithms, thereby reducing the additional cost of operator design. Based on the above framework, the users can freely select the advanced network components and optimization algorithms depending on their needs. Experimental results on synthetic and real-world datasets, ModelNet40 and KITTI, demonstrate the superiority of the proposed framework in terms of registration precision and architecture complexity.

关键词： Feature extraction Point cloud compression Encoding Computer architecture data mining Accuracy Three-dimensional displays Decoding Transformers Neural architecture search Point cloud registration evolutionary neural architecture search novel encoding mechanism

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Mental Health Diagnostics Using Machine Learning: A Clustering and Predictive Modeling Approach 19

Mental Health Diagnostics Using Machine Learning: A Clusteri...

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19th International Conference on Ubiquitous Information Management and Communication, IMCOM 2025

作者： Chauhan, Ritu Mishra, Aarushi Jena, Mehak Yafi, Eiad Zuhairi, Megat F. Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP India Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia Malaysian Institute of Information Technology Universiti Kuala Lumpur Kuala Lumpur50250 Malaysia

ISBN: (纸本)9798331507817

The application of machine learning has rapidly advanced as a transformative approach in healthcare, offering significant potential in the analysis of huge amounts of acquired data on Mental health across the world enhancing the treatment of mental health issues. Machine learning algorithms are being utilized to analyse patterns and trends in mental health datasets. This study investigates various machine learning methodologies and descriptive analytics techniques to detect patterns and trends in mental health-related factors, focusing on predicting the likelihood of individuals seeking mental health treatment. We utilized clustering, association rule mining, feature selection, and machine learning classifiers such as Random Forest, Decision Tree, XGBoost, and Logistic Regression. By applying these techniques to a dataset of mental health factors, we identified key predictors influencing mental health outcomes and developed models to enhance predictive accuracy. Our analysis highlighted the effectiveness of machine learning algorithms in extracting meaningful patterns from the dataset, with Random Forest and XGBoost emerging as particularly strong performers with accuracies of 81% and 84% . Other applied algorithms that are Decision Tree, Gradient Boosting and logistic regression have the accuracy of 81%, 77%, and 73% respectively. © 2025 ieee.

关键词： Electronic health record

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Simulating Illiquid Markets: Insights from Fractional Ownership Trading and Agent-Based Models

Simulating Illiquid Markets: Insights from Fractional Owners...

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computational intelligence for Financial Engineering and Economics Companion (CiFer Companion), ieee symposium on

作者： Lars Fluri A. Ege Yilmaz Denis Bieri Thomas Ankenbrand Aurelio Perucca Universität Basel Basel Switzerland Hochschule Luzern Rotkreuz Switzerland MARK Investment Holding AG Zug Switzerland

ISBN: (数字)9798331519704

ISBN: (纸本)9798331519711

This research investigates liquidity dynamics in fractional ownership markets, focusing on illiquid alternative investments traded on a FinTech platform. By leveraging empirical data and employing agent-based modeling (ABM), the study simulates trading behaviors in sell offer-driven systems, offering insights into the mechanisms that influence liquidity in these markets. The ABM-based simulation model provides a data augmentation environment which allows for the exploration of diverse trading architectures and rules, offering an alternative to direct experimentation. This approach bridges academic theory and practical application, supported by collaboration with industry and Swiss federal funding. The paper lays the foundation for extensions, including the identification of a liquidity-maximizing trading environment and the design of a market maker, by simulating modifications to the current design of the investment platform using an ABM specified with empirical data 1 1 An extended version of this research paper including additional results and supplementary material can be found on arXiv (DOI: arXiv: 2411.13381v2).

关键词： Economics Industries Fintech Focusing data augmentation Agent-based modeling data models Stability analysis Investment Synthetic data

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A Deep Ensemble Learning Approach for Imbalanced data in Bankruptcy Prediction

A Deep Ensemble Learning Approach for Imbalanced Data in Ban...

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computational intelligence for Financial Engineering and Economics (CiFer), ieee symposium on

作者： Peter Gnip Peter Drotár Róbert Kanász Martin Zoričak Faculty of Electrical Engineering and Informatics Technical University of Košice Košice Slovakia Faculty of Economics Technical University of Košice Košice Slovakia

ISBN: (数字)9798331508319

ISBN: (纸本)9798331508326

Skewed data distribution poses many challenges in various domains, including the financial sector. Information about a company's potential bankruptcy is crucial for financial institutions and decision-making managers. As bankruptcy prediction has been a key concern for practitioners and research workers for decades, advancements in machine learning offer numerous methods to address imbalanced data scenarios with promising results. Among these, ensembles and neural networks showed remarkable results regarding prediction effectiveness. In this study, we propose a novel deep ensemble boosting approach to overcome imbalanced scenarios called Boosting TabNet. Achieved results showed promising results, specifically in the case of realworld datasets characterized by significant class imbalance ratios. Boosting TabNet outperformed or achieved results comparable to other approaches in most utilized datasets. The highest achieved score in terms of geometric mean (GM) score was over $\mathbf{9 6 \%}$ .

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