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4th International Workshop on Information Technologies: Theoretical and Applied Problems, ITTAP 2024

作者： Pawuś, Dawid Paszkiel, Szczepan Porażko, Tomasz University of Technology Faculty of Electrical Engineering Automatic Control and Informatics Prószkowska 76 Street Opole45-758 Poland Departament of Internal Medicine and Nephrology Institute of Medical Sciences University of Opole Oleska 48 Street Opole45-052 Poland Departament of Nephrology with Dialysis Unit Opole University Hospital Witosa 26 Street Opole46-020 Poland

This study presents a comparative analysis of various artificial intelligence (AI) and machine learning (ML) algorithms for risk classification in idiopathic membranous nephropathy (IMN), a complex kidney disease. The research evaluates seven different models, including K-Nearest Neighbors, Decision Trees, Random Forests, Support Vector Machines, Adaptive Boosting, LightGBM, and Multilayer Perceptron. The results reveal that ensemble methods, particularly Random Forests, achieve the highest precision in classifying IMN risk levels, highlighting their potential in improving diagnostic accuracy and patient management in nephrology. The study underscores the importance of model selection and fine-tuning to optimize AI applications in clinical settings, providing a basis for future advancements in AI-driven nephrology. © 2023 Copyright for this paper by its authors.

关键词： Expert systems

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Novel Systems Based on Artificial Intelligence and Numerical Algorithms for Predicting Laboratory Results: A Comparative Study of Original automatic Prediction Model with Advances in the Field 18th

Novel Systems Based on Artificial Intelligence and Numeric...

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18th International Conference on Interdisciplinarity in Engineering, INTER-ENG 2024

作者： Pawuś, Dawid Porazko, Tomasz Paszkiel, Szczepan Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska Street 76 Opole45–758 Poland Department of Internal Medicine and Nephrology Institute of Medical Sciences University of Opole Oleska 48 Street Opole45–052 Poland Department of Nephrology with Dialysis Unit Opole University Hospital Witosa 26 Street 46–020Opole Poland

ISBN: (纸本)9783031816840

This study develops and evaluates a predictive model for estimating glomerular filtration rate using advanced machine learning algorithms. We trained and tested seven models, including K-nearest neighbors, support vector machine, gradient boosting, AdaBoost, XGBoost, LightGBM, and linear support vector regression, with a focus on clinical parameters like creatinine, urea, sodium, potassium, age, and gender. Gradient boosting and XGBoost demonstrated superior performance, achieving over 96% accuracy and mean absolute percentage errors around 2.5%. Additionally, we reviewed recent advancements in the field and compared our findings with existing research. The comparative analysis highlights the effectiveness of our proposed models in accurately predicting laboratory test results. This underscores the potential of machine learning to enhance diagnostic accuracy and improve patient outcomes in nephrology. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Transient Vibrations Suppression in Parametrically Excited Resonators 1

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作者： Ramírez-Barrios, Miguel Dohnal, Fadi Collado, Joaquín Professional Interdisciplinary Unit of Biotechnology National Polytechnic Institute Mexico City Mexico Division for Mechatronics Lienz UMIT - Private University for Health Sciences Medical Informatics and Technology Lienz Austria Automatic Control Department Cinvestav Mexico City Mexico

ISBN: (数字)9783030454029

ISBN: (纸本)9783030454012;9783030454043

Micromechanical resonators with a high-quality factor have been studied in recent years due to their excellent performance and potential applications. Some of these micromechanical resonators describe a long settling time, which can be disabled for fast switching applications. A novel design reduces the settling time by coupling two micromechanical resonators with parametric excitation;the parametric excitation tuned at the anti-resonant frequency increases the damping into the system;thus, the responses vibrations are attenuated, and the settling time is minimized. The scheme can be classified as open-loop control and belongs to the vibrational control field. Through a mathematical approach, this article describes this phenomenon in detail;by the averaging technique, the parametric excitation effects are analyzed. Moreover, we give new analytic conditions and formulas to minimize the settling time;these formulas might be helpful for future experimentation. Finally, using the current analysis, this contribution proposes a methodology to suppress the transient vibrations in this class of systems. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Vibration analysis

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Multi-method analysis of emotional and affective states

Multi-method analysis of emotional and affective states

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Progress in Applied Electrical Engineering (PAEE)

作者： Adrian Luckiewicz Bogdan Ruszczak Mariusz Pelc Dariusz Mikołajewski Adam Sudoł Barbara Grochowicz Edward J. Gorzelanczyk Patryk Mendon Aleksandra Kawala-Sterniu Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science University of Opole Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Neuropsychological Research Unit 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation Medical University in Lublin Lublin Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

This study explores the neurological basis of emotions using a multi-method approach, analysing functional near-infrared spectroscopy (fNIRS) data obtained from a 26-channel device. Author’s primary objective was to examine the brain’s response to tasks that provoke emotional, imagery and affective reactions through the observation of hemodynamic changes in data. By applying data processing methods and techniques, cerebral activations corresponding to different emotional states were shown. This research enriches comprehension of how emotion and imagery tasks are processed by the brain and offers insights into aspects of brain activity during emotionally charged engagements.

关键词： Electrical engineering Brain Data processing Hemodynamics Functional near-infrared spectroscopy

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ML-Powered Biomedical Signals Processing using Hybrid Filters

ML-Powered Biomedical Signals Processing using Hybrid Filter...

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Progress in Applied Electrical Engineering (PAEE)

作者： Mariusz Pelc Dariusz Mikołajewski Patryk Mendoń Adrian Luckiewicz Adam Sudoł Edward Jacek Gorzelańczyk Aleksandra Kawala-Sterniuk Institute of Computer Science University of Opole Opole Poland School of Computing and Mathematical Sciences University of Greenwich London UK Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland Neuropsychological Research Unit 2nd Clinic of the Psychiatry and Psychiatric Rehabilitation Medical University in Lublin Lublin Poland Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

ISBN: (数字)9798331527563

ISBN: (纸本)9798331527570

This paper deals with the problem of signals filtering using hybrid filters. Such solutions may prove to be especially beneficial in the context of biomedical signals filtering where the signals’ amplitudes are typically not very high and they can be easily affected by different kinds of disturbances. Having a number of different types of filters tuned for some specific purposes we will show how the best filter choice is affected by selecting one ML model over another.

关键词： Electrical engineering Filters Biological system modeling Biomedical communication

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Mathieu Equations Utilizing Symplectic Properties 1

Mathieu Equations Utilizing Symplectic Properties

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1st International Nonlinear Dynamics Conference, NODYCON 2019

作者： Barrios, Miguel Ramírez Collado, Joaquín Dohnal, Fadi Professional Interdisciplinary Unit of Biotechnology National Polytechnic Institute México City Mexico Automatic Control Department CINVESTAV México City Mexico Division for Mechatronics Lienz UMIT - Private University for Health Sciences Medical Informatics and Technology Lienz Austria

ISBN: (纸本)9783030347123

Several theoretical studies deal with the stability transition curves of the Mathieu equation. A few others present numerical and asymptotic methods to describe the stability of coupled Mathieu equations. However, sometimes the averaging and perturbation techniques deal with cumbersome computations, and the numerical methods spend considerable resources and computation time. This contribution extends the definition of linear Hamiltonian systems to periodic Hamiltonian systems with a particular dissipation. This leads naturally to a generalization of symplectic matrices, to μ-symplectic matrices. This definition enables an efficient way for calculating the stability transition curves of coupled Mathieu equations. © 2020 Nonlinear Dynamics of Structures, Systems and Devices - Proceedings of the 1st International Nonlinear Dynamics Conference, NODYCON 2019. All rights reserved.

关键词： Perturbation techniques

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Stochastic model predictive control for constrained networked control systems with random time delay

Stochastic model predictive control for constrained networke...

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作者： Patrinos, Panagiotis Sopasakis, Pantelis Sarimveis, Haralambos Department of Mechanical and Structural Engineering University of Trento Via Mesiano 77 38100 Italy Unit of Automatic Control and Informatics School of Chemical Engineering National Technical University of Athens Heroon Politechniou 9 GR-15780 Greece

ISBN: (纸本)9783902661937

In this paper the continuous time stochastic constrained optimal control problem is formulated for the class of networked control systems assuming that time delays follow a discrete-time, nite Markov chain . Polytopic overapproximations of the system's trajectories are employed to produce a polyhedral inner approximation of the non-convex constraint set resulting from imposing the constraints in continuous time. The problem is cast in a Markov jump linear systems (MJLS) framework and a stochastic MPC controller is calculated explicitly, offline, coupling dynamic programming with parametric piecewise quadratic (PWQ) optimization. The calculated control law leads to stochastic stability of the closed loop system, in the mean square sense and respects the state and input constraints in continuous time. © 2011 IFAC.

关键词： Networked control systems

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Stochastic Model Predictive control for Constrained Networked control Systems with Random Time Delay

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IFAC Proceedings Volumes 2011年第1期44卷 12626-12631页

作者： Panagiotis Patrinos Pantelis Sopasakis Haralambos Sarimveis Department of Mechanical and Structural Engineering University of Trento Via Mesiano 77 38100 Italy National Technical University of Athens Heroon Politechniou 9 NTUA Zografou Campus GR-15780 School of Chemical Engineering Unit of Automatic Control & Informatics

Abstract In this paper the continuous time stochastic constrained optimal control problem is formulated for the class of networked control systems assuming that time delays follow a discrete-time, finite Markov chain. Polytopic overapproximations of the system's trajectories are employed to produce a polyhedral inner approximation of the non-convex constraint set resulting from imposing the constraints in continuous time. The problem is cast in a Markov jump linear systems (MJLS) framework and a stochastic MPC controller is calculated explicitly, offline, coupling dynamic programming with parametric piecewise quadratic (PWQ) optimization. The calculated control law leads to stochastic stability of the closed loop system, in the mean square sense and respects the state and input constraints in continuous time.

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