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Myocarditis Diagnosis: A Method using Mutual learning-Based ABC and Reinforcement learning

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Myocarditis Diagnosis: A Method using Mutual Learning-Based ...

International Symposium on Computational Intelligence and Informatics

作者： Saba Danaei Arsam Bostani Seyed Vahid Moravvej Fardin Mohammadi Roohallah Alizadehsani Afshin Shoeibi Hamid Alinejad-Rokny Saeid Nahavandi Adiban Institute of Higher Education Semnan Iran Department of mechanical engineering of biosystems Urmia university Department of exercise physiology & health science University of tehran Internship in UNSW BioMedical Machine Learning Lab Sydney NSW Australia Institute for Intelligent Systems Research and Innovation (IISRI) Deakin University Waurn Ponds Victoria Australia UNSW Data Science Hub The University of New South Wales (UNSW Sydney) Sydney New South Wales Australia BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney Sydney NSW Australia

ISBN: (纸本)9798350398830

Myocarditis occurs when the heart muscle becomes inflamed and inflammation occurs when your body’s immune system responds to infections. It can be diagnosed using cardiac magnetic resonance image (MRI), a non-invasive imaging technique with the possibility of operator bias. This paper proposes a hybrid method of deep reinforcement learning-based algorithms and meta-heuristics algorithms. A mutual learning-based artificial bee colony (ML-ABC) is employed for initial weight, which adjusts the candidate food source generated with the higher fitness between two individuals determined by a mutual learning factor. Moreover, a sequential decision-making process investigates the imbalanced classification issue, in which a convolutional neural network (CNN) is used as the foundation for policy architecture. At first, initial weights are produced using the ML-ABC algorithm. After that, the agent receives a sample at each phase and classifies it, obtaining environmental rewards. The minority class receives more rewards than the majority class. Eventually, the agent discovers an ideal strategy with the aid of a specific reward function and a beneficial learning environment. We evaluate our proposed approach on the Z-Alizadeh Sani myocarditis dataset based on standard criteria and demonstrate that the proposed method gives superior myocarditis diagnosis performance.

关键词： Mechatronics Computational modeling Metaheuristics Reinforcement learning Myocardium Markov processes Classification algorithms

Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials

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arXiv 2023年

作者： Lee, Siwoo Heinen, Stefan Khan, Danish Von Lilienfeld, O. Anatole Department of Chemistry University of Toronto St. George campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Acceleration Consortium University of Toronto 80 St George St TorontoONM5S 3H6 Canada Department of Materials Science and Engineering University of Toronto St. George campus TorontoON Canada Department of Physics University of Toronto St. George campus TorontoON Canada Machine Learning Group Technische Universität Berlin Berlin Institute for the Foundations of Learning and Data Berlin Germany

We present an automated data-collection pipeline involving a convolutional neural network and a large language model to extract user-specified tabular data from peer-reviewed literature. The pipeline is applied to 74 reports published between 1957 and 2014 with experimentally-measured oxidation potentials for 592 organic molecules (-0.75-3.58 V). After data curation (solvents, reference electrodes, and missed data points), we trained multiple supervised machine learning models reaching prediction errors similar to experimental uncertainty (∼0.2 V). For experimental measurements of identical molecules reported in multiple studies, we identified the most likely value based on out-of-sample machine learning predictions. Using the trained machine learning models, we then estimated oxidation potentials of ∼132k small organic molecules from the QM9 data set, with predicted values spanning 0.21-3.46 V. Analysis of the QM9 predictions in terms of plausible descriptor-property trends suggests that aliphaticity increases the oxidation potential of an organic molecule on average from ∼1.5 V to ∼2 V, while an increase in number of heavy atoms lowers it systematically. The pipeline introduced offers significant reductions in human labor otherwise required for conventional manual data collection of experimental results, and exemplifies how to accelerate scientific research through automation. © 2023, CC0.

关键词： Pipelines

Two-Sample Testing on Ranked Preference data and the Role of Modeling Assumptions

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arXiv 2020年

作者： Rastogi, Charvi Balakrishnan, Sivaraman Shah, Nihar B. Singh, Aarti Machine Learning Department Department of Statistics Computer Science Department Carnegie Mellon University PittsburghPA15213 United States

A number of applications require two-sample testing on ranked preference data. For instance, in crowdsourcing, there is a long-standing question of whether pairwise comparison data provided by people is distributed similar to ratings-converted-to-comparisons. Other examples include sports data analysis and peer grading. In this paper, we design two-sample tests for pairwise comparison data and ranking data. For our two-sample test for pairwise comparison data, we establish an upper bound on the sample complexity required to correctly distinguish between the distributions of the two sets of samples. Our test requires essentially no assumptions on the distributions. We then prove complementary lower bounds showing that our results are tight (in the minimax sense) up to constant factors. We investigate the role of modeling assumptions by proving lower bounds for a range of pairwise comparison models (WST, MST, SST, parameter-based such as BTL and Thurstone). We also provide testing algorithms and associated sample complexity bounds for the problem of two-sample testing with partial (or total) ranking data. Furthermore, we empirically evaluate our results via extensive simulations as well as two real-world datasets consisting of pairwise comparisons. By applying our two-sample test on real-world pairwise comparison data, we conclude that ratings and rankings provided by people are indeed distributed differently. On the other hand, our test recognizes no significant difference in the relative performance of European football teams across two seasons. Finally, we apply our two-sample test on a real-world partial and total ranking dataset and find a statistically significant difference in Sushi preferences across demographic divisions based on gender, age and region of residence. Copyright © 2020, The Authors. All rights reserved.

关键词： Grading

Software for dataset-wide XAI: From Local Explanations to Global Insights with Zennit, CoRelAy, and ViRelAy

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arXiv 2021年

作者： Anders, Christopher J. Neumann, David Samek, Wojciech Müller, Klaus-Robert Lapuschkin, Sebastian Machine Learning Group Department of Electrical Engineering and Computer Science Technische Universität Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin Germany Machine Learning and Communications Group Department of Electrical Engineering and Computer Science Technische Universität Berlin Germany Department of Artificial Intelligence Korea University Seoul Korea Republic of Max Planck Institut für Informatik Saarbrücken Germany

Deep Neural Networks (DNNs) are known to be strong predictors, but their prediction strategies can rarely be understood. With recent advances in Explainable Artificial Intelligence (XAI), approaches are available to explore the reasoning behind those complex models’ predictions. Among post-hoc attribution methods, Layer-wise Relevance Propagation (LRP) shows high performance. For deeper quantitative analysis, manual approaches exist, but without the right tools they are unnecessarily labor intensive. In this software paper, we introduce three software packages targeted at scientists to explore model reasoning using attribution approaches and beyond: (1) Zennit – a highly customizable and intuitive attribution framework implementing LRP and related approaches in PyTorch, (2) CoRelAy – a framework to easily and quickly construct quantitative analysis pipelines for dataset-wide analyses of explanations, and (3) ViRelAy – a web-application to interactively explore data, attributions, and analysis results. With this, we provide a standardized implementation solution for XAI, to contribute towards more reproducibility in our field. © 2021, CC BY-SA.

关键词： Deep neural networks

Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features

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arXiv 2021年

作者： Pargent, Florian Pfisterer, Florian Thomas, Janek Bischl, Bernd Department of Psychology Psychological Methods and Assessment LMU Munich Leopold-straße 13 Munich80802 Germany Department of Statistics Statistical Learning and Data Science LMU Munich Ludwigstraße 33 Munich80539 Germany

Since most machine learning (ML) algorithms are designed for numerical inputs, efficiently encoding categorical variables is a crucial aspect in data analysis. A common problem are high cardinality features, i.e. unordered categorical predictor variables with a high number of levels. We study techniques that yield numeric representations of categorical variables which can then be used in subsequent ML applications. We focus on the impact of these techniques on a subsequent algorithm's predictive performance, and - if possible - derive best practices on when to use which technique. We conducted a large-scale benchmark experiment, where we compared different encoding strategies together with five ML algorithms (lasso, random forest, gradient boosting, k-nearest neighbors, support vector machine) using datasets from regression, binary- and multiclass- classification settings. In our study, regularized versions of target encoding (i.e. using target predictions based on the feature levels in the training set as a new numerical feature) consistently provided the best results. Traditionally widely used encodings that make unreasonable assumptions to map levels to integers (e.g. integer encoding) or to reduce the number of levels (possibly based on target information, e.g. leaf encoding) before creating binary indicator variables (one-hot or dummy encoding) were not as effective in comparison. © 2021, CC BY.

关键词： Signal encoding

On the Utility Function of Experiments in Fundamental science

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arXiv 2025年

作者： Dorigo, Tommaso Doro, Michele Aehle, Max Gauger, Nicolas R. Awais, Muhammad Izbicki, Rafael Kieseler, Jan Masserano, Luca Nardi, Federico Vergara, Luis Recabarren Luleå University of Technology Laboratorievägen 14 Luleå97187 Sweden INFN - Sezione di Padova via F. Marzolo 8 Padova35131 Italy Universal Scientific Education and Research Network Italy Università di Padova Dipartimento di Fisica e Astronomia "G.Galilei" via F. Marzolo 8 Padova35131 Italy Gottlieb-Daimler-Strase Kaiserslautern67663 Germany Karlsruhe Institute for Technology Kaiserstrase 12 Karlsruhe76131 Germany Laboratoire de Physique de Clermont Auvergne 4 Avenue Blaise Pascal Aubière63170 France Centro di Ateneo di Studi e Attività Spaziali "Giuseppe Colombo" Via Venezia 15 PadovaI-35131 Italy Department of Statistics & Data Science Department of Machine Learning Carnegie Mellon University Pittsburgh United States Department of Statistics Federal University of São Carlos São Carlos Brazil

The majority of experiments in fundamental science today are designed to be multi-purpose: their aim is not simply to measure a single physical quantity or process, but rather to enable increased precision in the measurement of a number of different observable quantities of a natural system, to extend the search for new phenomena, or to exclude a larger phase space of candidate theories. Most of the time, a combination of the above goals is pursued;this breadth of scope adds a layer of complexity to the already demanding task of designing the measurement apparatus in an optimal way, by defining suitable geometries and choosing the most advantageous materials and appropriate detection technologies. The precise definition of a global optimality criterion may then require experimentalists to find a consensus on the relative scientific worth of those goals. In this work, we discuss the problem of formulating a utility function for multipurpose experiments, as an enabling step to employ artificial intelligence tools to explore the design space and assist humans in finding solutions at the Pareto front. Copyright © 2025, The Authors. All rights reserved.

关键词： Phase space methods

Media and responsible AI governance: a game-theoretic and LLM analysis

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arXiv 2025年

作者： Balabanova, Nataliya Bashir, Adeela Bova, Paolo Buscemi, Alessio Cimpeanu, Theodor da Fonseca, Henrique Correia Di Stefano, Alessandro Duong, Manh Hong Domingos, Elias Fernández Fernandes, Antonio Han, The Anh Krellner, Marcus Ogbo, Ndidi Bianca Powers, Simon T. Proverbio, Daniele Santos, Fernando P. Shamszaman, Zia Ush Song, Zhao School of Mathematics University of Birmingham United Kingdom School Computing Engineering and Digital Technologies Teesside University United Kingdom Luxembourg Institute of Science and Technology Luxembourg School of Mathematics and Statistics University of St Andrews United Kingdom INESC-ID and Instituto Superior Técnico Universidade de Lisboa Portugal Machine Learning Group Université libre de Bruxelles Belgium AI Lab Vrije Universiteit Brussel Belgium Division of Computing Science and Mathematics University of Stirling United Kingdom Department of Industrial Engineering University of Trento Italy University of Amsterdam Netherlands

This paper investigates the complex interplay between AI developers, regulators, users, and the media in fostering trustworthy AI systems. Using evolutionary game theory and large language models (LLMs), we model the strategic interactions among these actors under different regulatory regimes. The research explores two key mechanisms for achieving responsible governance, safe AI development and adoption of safe AI: incentivising effective regulation through media reporting, and conditioning user trust on commentariats’ recommendation. The findings highlight the crucial role of the media in providing information to users, potentially acting as a form of"soft" regulation by investigating developers or regulators, as a substitute to institutional AI regulation (which is still absent in many regions). Both game-theoretic analysis and LLM-based simulations reveal conditions under which effective regulation and trustworthy AI development emerge, emphasising the importance of considering the influence of different regulatory regimes from an evolutionary game-theoretic perspective. The study concludes that effective governance requires managing incentives and costs for high quality commentaries. © 2025, CC BY.

关键词： Digital elevation model

Automated question-answer medical model based on deep learning technology 20

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Automated question-answer medical model based on deep learni...

6th International Conference on Engineering and MIS, ICEMIS 2020

作者： Abdallah, Abdelrahman Kasem, Mahmoud Hamada, Mohamed A. Sdeek, Shaymaa MSc Machine Learning and Data Science Satbayev University Almaty Kazakhstan Information Technology Assiut University Assiut Egypt IS Department International IT University Almaty Kazakhstan Dept. Information System Assiut University Qena Egypt

ISBN: (纸本)9781450377362

Artificial intelligence can now provide more solutions for different problems, especially in the medical field. One of those problems is the lack of answers to any given medical/health-related question. The Internet is full of forums that allow people to ask some specific questions and get great answers for them. Nevertheless, browsing these questions to locate a similar case to your own question, also finding a satisfying accurate answer is difficult and timeconsuming task. This research will introduce a solution to these problems by automating the process of generating qualified answers to these questions and creating a kind of digital doctor. Furthermore, this research will train an end-to-end model using the framework of RNN and the encoder decoder to generate sensible and useful answers to a small set of medical/health issues. The proposed model was trained and evaluated using data from various online services, such as WebMD, HealthTap, eHealthForums, and iCliniq. © 2020 ACM.

关键词： Deep learning

Reconstructing Galaxy Cluster Mass Maps using Score-based Generative Modeling

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arXiv 2024年

作者： Hsu, Alan Ho, Matthew Lin, Joyce Markey, Carleen Ntampaka, Michelle Trac, Hy Póczos, Barnabás Department of Astronomy Harvard University CambridgeMA02138 United States Department of Physics Carnegie Mellon University PittsburghPA15213 United States UMR 7095 98 bis bd Arago ParisF-75014 France Columbia Astrophysics Laboratory Columbia University 550 West 120th Street New YorkNY10027 United States Department of Physics University of Wisconsin-Madison MadisonWI53726 United States McWilliams Center for Cosmology and Astrophysics Carnegie Mellon University PittsburghPA15213 United States Data Science Mission Office Space Telescope Science Institute BaltimoreMD21218 United States Department of Physics & Astronomy Johns Hopkins University BaltimoreMD21218 United States Machine Learning Department Carnegie Mellon University PittsburghPA15213 United States

We present a novel approach to reconstruct gas and dark matter projected density maps of galaxy clusters using score-based generative modeling. Our diffusion model takes in mock SZ and X-ray images as conditional inputs, and generates realizations of corresponding gas and dark matter maps by sampling from a learned data posterior. We train and validate the performance of our model by using mock data from a hydrodynamical cosmological simulation. The model accurately reconstructs both the mean and spread of the radial density profiles in the spatial domain to within 5%, indicating that the model is able to distinguish between clusters of different sizes. In the spectral domain, the model achieves close-to-unity values for the bias and cross-correlation coefficients, indicating that the model can accurately probe cluster structures on both large and small scales. Our experiments demonstrate the ability of score models to learn a strong, nonlinear, and unbiased mapping between input observables and fundamental density distributions of galaxy clusters. These diffusion models can be further fine-tuned and generalized to not only take in additional observables as inputs, but also real observations and predict unknown density distributions of galaxy clusters. © 2024, CC BY.

关键词： Galaxies