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A reliable neural network framework for the Zika system based reservoirs and human movement

Knowledge-Based Systems 2024年 292卷

作者： Sabir, Zulqurnain Khan, Sundas Naqeeb Raja, Muhammad Asif Zahoor Babatin, M.M. Hashem, Atef F. Abdelkawy, M.A. Department of Computer Science and Mathematics Lebanese American University Beirut Lebanon Department of Graphics Computer Vision and Digital Systems Silesian University of Technology Gliwice Poland Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Riyadh11566 Saudi Arabia Department of Mathematics and Information Science Faculty of Science Beni-Suef University Egypt

The motive of current investigations provides the numerical solutions of the neuro computing solver based on the Levenberg-Marquardt backpropagation neural network approach (LMB) to solve the Zika virus system of reservoirs and human movement. The mathematical form of the human movement model is based on ten different classes, which makes the model nonlinear. The solution of this nonlinear mathematical model is presented by using the stochastic LMB neural network process. A dataset is designed based on the Adam solver in order to reduce the mean square error by dividing the data as training, endorsement, and testing with the use of 75%, 10% and 15%. Ten numbers of neurons have been taken by using the log-sigmoid transfer in the hidden layer for solving the human movement model. The precision of the proposed solver is authenticated by using the valuation of reference and proficient outcomes, while the reducible absolute error presents the correctness of the scheme. Moreover, the statistical performances based different proportional schemes have been used to authenticate the reliability of the scheme. © 2024 Elsevier B.V.

关键词： Neural networks

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On heterogeneity or sub-classes aspect in construction of stylometric input datasets

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Procedia computer science 2022年 207卷 2526-2535页

作者： Urszula Stańczyk Grzegorz Baron Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

Stylometric analysis of texts relies on learning characteristic traits of writing styles for authors. Once these patterns are discovered, they can be compared to the ones present in other text samples, to recognise their authorship. This recognition can be compromised if input datasets are prepared without taking into consideration possible stratification of the input space, leading to specific grouping of datapoints, or sub-classes within distinguished classes. The paper shows research dedicated to construction of various structures of input datasets, and combinations of such structures between train and test sets. In the research the influence of different stratification forms on the performance of selected popular classification systems was observed. To minimise the number of influencing factors, a task of authorship attribution was performed as binary classification with balanced classes. Stylometric descriptors exploited belonged to lexical and syntactic group, giving frequencies of occurrence for chosen style-markers. It resulted in real-valued attributes and these values were explored without applying discretisation, in order to avoid the possible bias of this procedure on observations.

关键词： Stylometry Dataset construction Heterogeneity Sub-classes Stratification Authorship attribution

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IEEE T-BIOM Editorial Board Changes

IEEE Transactions on Biometrics, Behavior, and Identity Scie...

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IEEE Transactions on Biometrics, Behavior, and Identity science 2024年第1期7卷 1-2页

作者： Naser Damer Weihong Deng Jianjiang Feng Vishal M. Patel Ajita Rattani Mark Nixon Smart Living & Biometric Technologies Fraunhofer Institute for Computer Graphics Research IGD Darmstadt Germany Pattern Recognition and Intelligent System Laboratory Beijing University of Posts and Telecommunications Beijing China Department of Automation Tsinghua University Beijing China Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA Department of Computer Science and Engineering College of Engineering University of North Texas Denton TX USA

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To the Point: From Dynamic Heatmap Video to Gaze Points 25

To the Point: From Dynamic Heatmap Video to Gaze Points

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Proceedings of the 2025 Symposium on Eye Tracking Research and Applications

作者： Beryl Gnanaraj Swetha Manivasagam Jaya Sreevalsan-Nair Graphics-Visualization-Computing Lab International Institute of Information Technology Bangalore Bangalore Karnataka India Department of Computer Science and Engineering PSG College of Technology Coimbatore Tamil Nadu India

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Continual Learning of a Time Series Model Using a Mixture of HMMs with Application to the IoT Fuel Sensor Verification 18

Continual Learning of a Time Series Model Using a Mixture of...

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18th Conference on computer science and Intelligence Systems, FedCSIS 2023

作者： Glomb, Przemyslaw Cholewa, Michal Foszner, Pawel Bularz, Jakub Institute of Theoretical and Applied Informatics Polish Academy of Sciences Batycka 5 Gliwice44-100 Poland Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A Gliwice44-100 Poland Aiut Sp. Z O.o. ul. Wyczókowskiego 113 Gliwice44-109 Poland

ISBN: (纸本)9788396744784

This paper presents an application of a mixture of Hidden Markov Models (HMMs) as a tool for verification of IoT fuel sensors. The IoT fuel sensors report the level of fuel in tanks of a petrol station, and are a key component for monitoring system reliability (billing), safety (fuel/oil leak detection) and security (theft prevention). We propose an algorithm for learning a mixture of HMMs based on a continual learning principle, i.e. it adapts the model while monitoring a sensor over time, signalling unexpected or anomalous sensor reports. We have tested the proposed approach on a real-life data of 15 fuel tanks being monitored with the FuelPrime system, where it has shown a very good performance (average area under ROC curve of 0.94) of detecting anomalies in the sensor data. Additionally we show that the proposed method can be used for trend monitoring and present qualitative analysis of the short and long term learning performance. The proposed method has promising performance score, the resulting model has a high degree of explainability, limited memory and computation requirements and can be easily generalized to other domains of sensor verification. © 2023 Polish Information Processing Society.

关键词： Hidden Markov models

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How Novices Use LLM-based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment 23

How Novices Use LLM-based Code Generators to Solve CS1 Codin...

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Proceedings of the 23rd Koli Calling International Conference on Computing Education Research

作者： Majeed Kazemitabaar Xinying Hou Austin Henley Barbara Jane Ericson David Weintrop Tovi Grossman Department of Computer Science Dynamic Graphics Project (DGP) University of Toronto Canada School of Information University of Michigan USA Microsoft Research USA College of Education University of Maryland USA

ISBN: (纸本)9798400716539

As Large Language Models (LLMs) gain in popularity, it is important to understand how novice programmers use them and the effect they have on learning to code. We present the results of a thematic analysis on a data set from 33 learners, aged 10-17, as they independently learned Python by working on 45 code-authoring tasks with access to an AI Code Generator based on OpenAI Codex. We explore several important questions related to how learners used LLM-based AI code generators, and provide an analysis of the properties of the written prompts and the resulting AI generated code. Specifically, we explore (A) the context in which learners use Codex, (B) what learners are asking from Codex in terms of syntax and logic, (C) properties of prompts written by learners in terms of relation to task description, language, clarity, and prompt crafting patterns, (D) properties of the AI-generated code in terms of correctness, complexity, and accuracy, and (E) how learners utilize AI-generated code in terms of placement, verification, and manual modifications. Furthermore, our analysis reveals four distinct coding approaches when writing code with an AI code generator: AI Single Prompt, where learners prompted Codex once to generate the entire solution to a task; AI Step-by-Step, where learners divided the problem into parts and used Codex to generate each part; Hybrid, where learners wrote some of the code themselves and used Codex to generate others; and Manual coding, where learners wrote the code themselves. Our findings reveal consistently positive trends between learners’ utilization of the Hybrid coding approach and their post-test evaluation scores, while showing consistent negative trends between the AI Single Prompt and the post-test evaluation scores. Furthermore, we offer insights into novice learners’ use of AI code generators in a self-paced learning environment, highlighting signs of over-reliance, self-regulation, and opportunities for enhancing AI-assisted learnin

关键词： ChatGPT Copilot Introductory Programming Large Language Models OpenAI Codex Self-paced Learning Self-regulation

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Facial Feature Removal for Anonymization of Neurological Image Data

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Current Directions in Biomedical Engineering 2021年第1期7卷 130-134页

作者： Gießler, Fina Reithmann, Max Preim, Bernhard Behme, Daniel Saalfeld, Sylvia Department for Simulation and Graphics Otto-von-Guericke University Magdeburg Germany Department of Neuroradiology University Hospital of Magdeburg Germany Faculty of Computer Science and Research Campus STIMULATE Otto-von-Guericke University Magdeburg Germany

Interdisciplinary exchange of medical datasets between clinicians and engineers is essential for clinical research. Due to the Data Protection Act, which preserves the rights of patients, full anonymization is necessary before any exchange can take place. Due to the continuous improvement of image quality of tomographic datasets, anonymization of patient-specific information is not sufficient. In this work, we present a prototype that allows to reliably obscure the facial features of patient data, thus enabling anonymization of neurological datasets in image space. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Clinical research

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An over view on intuitionistic fuzzy topological spaces 3

An over view on intuitionistic fuzzy topological spaces

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3rd International Conference on Statistics, Mathematics, Teaching, and Research 2019, ICSMTR 2019

作者： Abdy, M. Zenin, S. Irwan Department of Mathematics FMIPA Universitas Negeri Makassar Indonesia Department of Mathematics with Computer Graphics Faculty of Science and Natural Resources Universiti Malaysia Sabah Malaysia

We present a brief overview some fundamental results on the intuitionistic fuzzy topological spaces, and give some introductory results about fuzzy open set, fuzzy closed set, fuzzy neighborhood, fuzzy interior set, fuzzy continuity, fuzzy compactness and fuzzy connectedness in these spaces. © Published under licence by IOP Publishing Ltd.

关键词： Fuzzy sets

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Topologically Robust B-spline Reconstruction of Fibers from 3D Images 8th

Topologically Robust B-spline Reconstruction of Fibers from ...

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8th Workshop on Topological Methods in Data Analysis and Visualization, TopoInVis 2019

作者： Mosbach, Dennis Schladitz, Katja Hamann, Bernd Hagen, Hans Computer Graphics and HCI Group University of Kaiserslautern Kaiserslautern67663 Germany Image Processing Department Fraunhofer ITWM Kaiserslautern67663 Germany Department of Computer Science University of California DavisCA95616 United States

ISBN: (纸本)9783030834999

The micro-structure of wood-based insulation materials is analyzed to gain insight into how features on microscopic scales influence macroscopic thermal conductivity. Three-dimensional (3D) image data obtained by micro-computed tomography reveals a complex structure formed by cellulose fibers. To study the effect of geometry changes, simple B-spline representations of these fibers are highly desirable. A straightforward solution is to extract a triangulated isosurface from the 3D image and partition it into quadrilateral macro-cells with disk-like topology. For each cell, a B-spline surface is constructed by minimizing a least squares error term. However, the physical processing of the material affects the structure of the fibers. The resulting changes in surface topology cause difficulties for the quadrilateral partitioning. Image processing tools can solve these topological issues, but they also impact geometry. We present a novel approach that splits geometry and topology processing of the data. It allows for topological simplification while still preserving the geometry of a scanned object. Established B-spline approximation methods are used to create a model. The involved mathematical equations are described in detail with a focus on simple implementation. Our presented results demonstrate that smooth and accurate models can be created for challenging data. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fibers

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COMFIS - Comparative Visualization of Simulated Medical Flow Data

COMFIS - Comparative Visualization of Simulated Medical Flow...

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2022 Eurographics Workshop on Visual Computing for Biology and Medicine, EG VCBM 2022

作者： Meuschke, M. Voß, S. Eulzer, P. Janiga, G. Arens, C. Wickenhöfer, R. Preim, B. Lawonn, K. Department of Simulation and Graphics University of Magdeburg Germany Institute of Fluid Mechanics and Thermodynamics University of Magdeburg Germany Institute of Computer Science University of Jena Germany Department of Otorhinolaryngology University Hospital Gießen Germany Herz-Jesu Krankenhaus Dernbach Germany

ISBN: (纸本)9783038681779

Simulations of human blood and airflow are playing an increasing role in personalized medicine. Comparing flow data of different treatment scenarios or before and after an intervention is important to assess treatment options and success. However, existing visualization tools are either designed for the evaluation of a single data set or limit the comparison to a few partial aspects such as scalar fields defined on the vessel wall or internal flow patterns. Therefore, we present COMFIS, a system for the comparative visual analysis of two simulated medical flow data sets, e.g. before and after an intervention. We combine various visualization and interaction methods for comparing different aspects of the underlying, often time-dependent data. These include comparative views of different scalar fields defined on the vessel/mucous wall, comparative depictions of the underlying volume data, and comparisons of flow patterns. We evaluated COMFIS with CFD engineers and medical experts, who were able to efficiently find interesting data insights that help to assess treatment options. © 2022 The Author(s) © 2022 The Eurographics Association.

关键词： Flow patterns

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