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New numerical Approach for Solution of Nonlinear Differential Equations

Hunan Daxue Xuebao/Journal of Hunan University Natural sciences 2022年第7期49卷 163-170页

作者： Zarnan, Jumah Aswad Hameed, Wafaa Mustafa Kanbar, Asan Baker Numerical Analysis Iraq Department of Computer Science Cihan University of Sulaimaniya Kurdistan Region Sulaimaniya46001 Iraq

Nonlinear differential equations extensively used mathematical models for many interesting and important phenomena observed in numerous areas of science and technology. They are inspired by problems in diverse fields such as economics, biology, fluid dynamics, physics, differential geometry, engineering, control theory, materials science, and quantum mechanics. This special issue aims to highlight recent developments in methods and applications of nonlinear differential equations. In addition, there are papers analyzing equations that arise in engineering, classical and fluid mechanics, and finance. In this paper, we present a new numerical approach, which is concerned with the solutions of Nonlinear Differential Equations determined by a new approximation system based on inverse Laplace transforms using Chebyshev polynomials functional matrix of integration. The obtained solutions are novel, and previous literature lacks such derivations. The reliability and accuracy of our approach were shown by comparing our derived solutions with solutions obtained by other existing methods. The efficiency of the proposed numerical technique is exhibited through graphical illustrations and results drafted in tabular form for specific values of the parameters to validate the numerical investigation. The system capability is clarified through several standard nonlinear differential equations: Duffing, Van der Pol, Blasius, and Haul. The numerical results illustrate that the estimated result is in good agreement with exact or numerical styles available in literature whenever the exact results are unknown. Errors estimation to the corresponding numerical scheme also is carried out. © 2022 Hunan University. All rights reserved.

关键词： Chebyshev polynomials

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Existence of solutions to the generalized periodic fractional boundary value problem

Existence of solutions to the generalized periodic fractiona...

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作者： Fečkan, Michal Marynets, Kateryna Wang, JinRong Department of Mathematical Analysis and Numerical Mathematics Comenius University in Bratislava Bratislava Slovakia Mathematical Institute of Slovak Academy of Sciences Bratislava Slovakia Delft Institute of Applied Mathematics Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Department of Mathematics Guizhou University Guiyang China

We study a boundary value problem for a Caputo-type fractional differential equation subjected to periodic boundary conditions. For an auxiliary problem with the simplified right-hand side, we explicitly construct its unique solution. In addition, based on the theory of the topological index, we prove existence of at least one solution to the original problem. © 2023 The Authors. Mathematical Methods in the Applied sciences published by John Wiley & Sons Ltd.

关键词： Boundary value problems

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DREAMS: A python framework to train deep learning models with model card reporting for medical and health applications

arXiv

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arXiv 2024年

作者： Khadka, Rabindra Lind, Pedro G. Yazidi, Anis Belhadi, Asma Department of Computer Science OsloMet – Oslo Metropolitan University OsloN-0130 Norway Simula Research Laboratory Numerical Analysis and Scientific Computing OsloN-0164 Norway

Electroencephalography (EEG) data provides a non-invasive method for researchers and clinicians to observe brain activity in real time. The integration of deep learning techniques with EEG data has significantly improved the ability to identify meaningful patterns, leading to valuable insights for both clinical and research purposes. However, most of the frameworks so far, designed for EEG data analysis, are either too focused on pre-processing or in deep learning methods per, making their use for both clinician and developer communities problematic. Moreover, critical issues such as ethical considerations, biases, uncertainties, and the limitations inherent in AI models for EEG data analysis are frequently overlooked, posing challenges to the responsible implementation of these technologies. In this paper, we introduce a comprehensive deep learning framework tailored for EEG data processing, model training and report generation. While constructed in way to be adapted and developed further by AI developers, it enables to report, through model cards, the outcome and specific information of use for both developers and clinicians. In this way, we discuss how this framework can, in the future, provide clinical researchers and developers with the tools needed to create transparent and accountable AI models for EEG data analysis and diagnosis. © 2024, CC BY.

关键词： Electroencephalography

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Cross-Modality Gait Recognition: Bridging LiDAR and Camera Modalities for Human Identification

Cross-Modality Gait Recognition: Bridging LiDAR and Camera M...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Rui Wang Chuanfu Shen Manuel J. Marin-Jimenez George Q. Huang Shiqi Yu Department of Computer Science and Engineering Southern University of Science and Technology Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Department of Computing and Numerical Analysis University of Cordoba Department of Industrial and Systems Engineering The Hong Kong Polytechnic University

ISBN: (数字)9798350364132

ISBN: (纸本)9798350364149

Current gait recognition research mainly focuses on identifying pedestrians captured by the same type of sensor, neglecting the fact that individuals may be captured by different sensors in order to adapt to various environments. A more practical approach should involve cross-modality matching across different sensors. Hence, this paper focuses on investigating the problem of cross-modality gait recognition, with the objective of accurately identifying pedestrians across diverse vision sensors. We present CrossGait inspired by the feature alignment strategy, capable of cross retrieving diverse data modalities. Specifically, we investigate the cross-modality recognition task by initially extracting features within each modality and subsequently aligning these features across modalities. To further enhance the cross-modality performance, we propose a Prototypical Modality-shared Attention Module that learns modality-shared features from two modality-specific features. Additionally, we design a Cross-modality Feature Adapter that transforms the learned modality-specific features into a unified feature space. Extensive experiments conducted on the SUSTech1K dataset demonstrate the effectiveness of CrossGait: (1) it exhibits promising cross-modality ability in retrieving pedestrians across various modalities from different sensors in diverse scenes, and (2) CrossGait not only learns modality-shared features for cross-modality gait recognition but also maintains modality-specific features for single-modality recognition.

关键词： Performance evaluation Visualization Pedestrians Laser radar Transforms Vision sensors Feature extraction Cameras Gait recognition Wireless fidelity

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Ying Bi, Bing Xue, Mengjie Zhang: Genetic programming for image classification—an automated approach to feature learning

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Genetic Programming and Evolvable Machines 2022年第4期23卷 589-590页

作者： Zafra, Amelia Department of Computer Science and Numerical Analysis Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Cordoba Cordoba Spain

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A two-stage algorithm in evolutionary product unit neural networks for classification

arXiv

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arXiv 2024年

作者： Tallón-Ballesteros, Antonio J. Hervás-Martínez, César Department of Languages and Computer Systems University of Seville Reina Mercedes Avenue Seville41012 Spain Department of Computer Science and Numerical Analysis University of Córdoba Campus of Rabanales Albert Einstein Building Córdoba14071 Spain

This paper presents a procedure to add broader diversity at the beginning of the evolutionary process. It consists of creating two initial populations with different parameter settings, evolving them for a small number of generations, selecting the best individuals from each population in the same proportion and combining them to constitute a new initial population. At this point the main loop of an evolutionary algorithm is applied to the new population. The results show that our proposal considerably improves both the efficiency of previous methodologies and also, significantly, their efficacy in most of the data sets. We have carried out our experimentation on twelve data sets from the UCI repository and two complex real-world problems which differ in their number of instances, features and classes. Copyright © 2024, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Evaluating Automatic Annotation Techniques for Fine-Tuning Large Language Models in Financial Sentiment analysis

Evaluating Automatic Annotation Techniques for Fine-Tuning L...

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Sentiment analysis and Deep Learning (ICSADL), International Conference on

作者： Ibtissam Youb Mohamed Hamlich Sebastian Ventura Feras Al-Obeidat Department of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain Department of Electronic CCPS Laboratory ENSAM University of Hassan II Casablanca Morocco College of Technological Innovation Zayed University Abu Dhabi UAE

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Accurate sentiment analysis in financial contexts is crucial for market analysis and investment decisions. However, manually annotating large datasets for training models is time-consuming and costly. This study evaluates two automatic an-notation tools, VADER and TextBlob, for their effectiveness in generating labels for training large language models (LLMs) like BERT and GPT-2 in financial sentiment analysis. We used a large dataset of stock market-related tweets, including a manually annotated subset for benchmarking. The remaining tweets were automatically labeled using VADER and TextBlob. We trained BERT and GPT-2 models with these sentiment labels and compared their performance against the benchmark dataset. Our findings show that models trained with VADER annotations had a higher correlation with human-labeled data (62% accuracy) compared to those trained with TextBlob annotations (48% accuracy). These results suggest that VADER is more suitable for automatic annotation in financial sentiment analysis, providing more accurate and reliable sentiment labels, which can significantly improve the efficiency of training large language models for financial applications.

关键词： Training Sentiment analysis Accuracy Annotations Social networking (online) Large language models Benchmark testing Data models Reliability Investment

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Marker-based Visual SLAM leveraging Hierarchical Representations

arXiv

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arXiv 2023年

作者： Tourani, Ali Bavle, Hriday Sanchez-Lopez, Jose Luis Salinas, Rafael Muñoz Voos, Holger University of Luxembourg Luxembourg The Faculty of Science Technology and Medicine University of Luxembourg Luxembourg The Department of Computer Science and Numerical Analysis University of Córdoba Rabanales Campus Spain

Fiducial markers can encode rich information about the environment and can aid Visual SLAM (VSLAM) approaches in reconstructing maps with practical semantic information. Current marker-based VSLAM approaches mainly utilize markers for improving feature detections in low-feature environments and/or for incorporating loop closure constraints, generating only low-level geometric maps of the environment prone to inaccuracies in complex environments. To bridge this gap, this paper presents a VSLAM approach utilizing a monocular camera along with fiducial markers to generate hierarchical representations of the environment while improving the camera pose estimate. The proposed approach detects semantic entities from the surroundings, including walls, corridors, and rooms encoded within markers, and appropriately adds topological constraints among them. Experimental results on a real-world dataset collected with a robot demonstrate that the proposed approach outperforms a traditional marker-based VSLAM baseline in terms of accuracy, given the addition of new constraints while creating enhanced map representations. Furthermore, it shows satisfactory results when comparing the reconstructed map quality to the one reconstructed using a LiDAR SLAM approach. © 2023, CC BY.

关键词： Cameras

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A Non-Parametric Identification Scheme of SISO-LTI Systems Using Blaschke-Products*

A Non-Parametric Identification Scheme of SISO-LTI Systems U...

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Mediterranean Conference on Control and Automation (MED)

作者： Gergő Ungvári Alexandros Soumelidis Tamás Dózsa Department of Numerical Analysis Faculty of Informatics Eötvös Loránd University Budapest Hungary Systems and Control Laboratory HUN-REN Institute for Computer Science and Control Budapest Hungary

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this manuscript, a novel algorithm is presented for the identification of single input single output linear time invariant (SISO-LTI) systems. The proposed method is able to find poles of the transfer function describing the system without any assumptions on the system order. The results presented in this paper generalize earlier identification schemes based on expansions of the transfer function using generalized rational orthogonal basis functions (GOBFs). These previous methods depended on a convergent sequence constructed from the coefficients of the orthogonal GOBF expansion. The first contribution of this study is showing that a wider family of functions may be used to construct similar pole finding algorithms. The aforementioned previous approaches can be interpreted as special cases of the results presented here. In addition, using this generalization a new pole identification scheme is proposed using finite Blaschke-products. Through experiments, the practical utility of this scheme is verified when compared to earlier variations of the pole finding scheme.

关键词： Automation Transfer functions

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dlordinal: a Python package for deep ordinal classification

arXiv

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arXiv 2024年

作者： Bérchez-Moreno, Francisco Vargas, Víctor M. Ayllón-Gavilán, Rafael Guijo-Rubio, David Hervás-Martínez, César Fernández, Juan C. Gutiérrez, Pedro A. Department of Computer Science and Numerical Analysis University of Córdoba Córdoba Spain Department of Clinical-Epidemiological Research in Primary Care Instituto Maimónides de Investigación Biomédica de Córdoba Spain

dlordinal is a new Python library that unifies many recent deep ordinal classification methodologies available in the literature. Developed using PyTorch as underlying framework, it implements the top performing state-of-the-art deep learning techniques for ordinal classification problems. Ordinal approaches are designed to leverage the ordering information present in the target variable. Specifically, it includes loss functions, various output layers, dropout techniques, soft labelling methodologies, and other classification strategies, all of which are appropriately designed to incorporate the ordinal information. Furthermore, as the performance metrics to assess novel proposals in ordinal classification depend on the distance between target and predicted classes in the ordinal scale, suitable ordinal evaluation metrics are also included. dlordinal is distributed under the BSD-3-Clause license and is available at https://***/ayrna/dlordinal. © 2024, CC BY-NC-SA.

关键词： Python

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