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INITIAL VALUE PROBLEMS FOR NONLINEAR NEUTRAL DELAY Ψ-CAPUTO FRACTIONAL HYBRID DIFFERENTIAL EQUATIONS WITH BOUNDED DELAYS

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Journal of Mathematical sciences (United States) 2025年 1-11页

作者： Malghi, Hayat Taqbibt, Abdellah El Omari, M’hamed Qaffou, Abdelaziz Hilal, Khalid Department of Mathematics Applied Mathematics and Scientific Computing Laboratory Faculty of Sciences and Technology Beni Mellal Sultan Moulay Slimane University P.O. Box 523 Beni Mellal 23000 Morocco

In this work, we explore the existence of solutions to an initial value problem for nonlinear neutral delay Ψ-Caputo fractional hybrid differential equations with bounded delays. The existence results are established using fixed point theorems. To demonstrate the practical applicability of our findings, we provide an illustrative example. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Fixed point Hybrid differential equation Neutral delay differential equation Ψ-Caputo derivative

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GenAI as a Tool for Content Generation in Hypermedia Edutainment Applications: Potential and Limitations 17

GenAI as a Tool for Content Generation in Hypermedia Edutain...

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17th International Conference on Computer Supported Education, CSEDU 2025

作者： Heiden, Wolfgang Saitova, Veronika Kless, Tea Wegner, Valeska Rötter, David Neteler, Thomas Institute of Visual Computing Bonn-Rhein-Sieg University of Applied Sciences Sankt Augustin Germany Department of Mathematics Justus-Liebig-Universität Gießen Gießen Germany

ISBN: (纸本)9789897587467

Generative AI can considerably speed up the process of producing narrative content including different media. This may be particularly helpful for the generation of modular variations on narrative themes in hypermedia, crossmedia, or transmedia contexts, thereby enabling personalized access to the content by heterogenous target groups. We present an example where GenAI has been applied for image creation and translation of a text to multiple languages for a crossmedia edutainment project transferring IT security knowledge to vulnerable groups. GenAI still seems inadequate to produce interesting narrative text integrating dedicated educational content. AI-generated illustrations often require manual rework. However, LLM support in multilingual translations displays more intelligent solutions than expected, including the implementation of a password generation process from a narrated description. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Hypermedia systems

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Parameter identification in PDEs by the solution of monotone inclusion problems

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Numerische Mathematik 2025年 1-31页

作者： Gautam, Pankaj Grasmair, Markus Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand India Department of Mathematical Sciences Norwegian University of Science and Technology Trondheim Norway

In this paper we consider the solution of monotone inverse problems using the particular example of a parameter identification problem for a semilinear parabolic PDE. For the regularized solution of this problem, we introduce a total variation based regularization method requiring the solution of a monotone inclusion problem. We show well-posedness in the sense of inverse problems of the resulting regularization scheme. In addition, we introduce and analyze a numerical algorithm for the solution of this inclusion problem using a nested inertial primal dual method. We demonstrate by means of numerical examples the convergence of both the numerical algorithm and the regularization method.

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A Novel Approach for Performance Evaluation and Effectiveness of Data-Driven Heart Disease Diagnosis

A Novel Approach for Performance Evaluation and Effectivenes...

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International Conference on Advances and Applications of Artificial Intelligence and Machine Learning, ICAAAIML 2023

作者： Islam, Md Aminul Nag, Anindya Das, Ayontika Faruque, Jobaer Shuvo, Shabbir Ahmed Nur, Abdullah Hafez Rahman, Md Habibur School of Engineering Computing and Mathematics Oxford Brookes University Oxford United Kingdom Department of Computer Science and Engineering Adamas University Kolkata India IRD Dhaka Bangladesh Offenburg University of Applied Sciences Offenburg Germany International Islamic University Chittagong Sonaichhari Bangladesh Department of Mathematics University of Chittagong Chittagong Bangladesh

ISBN: (纸本)9789819795062

Cardiovascular disease (CVD) has emerged as a prominent cause of mortality globally during the past few decades. To receive medical care for heart illness in a clinic or medical center, it is necessary to undergo costly diagnostic tests. Implementing machine learning (ML) algorithms presents a viable approach to monitoring the health of individuals with cardiac conditions, offering a potential solution to mitigate the financial burden of medical expenses. This study employed ten ML algorithms to predict heart disease, which distinguishes itself from others by incorporating three distinct cross-validation approaches and three sample techniques, enhancing our findings’ accuracy. Three different cross-validation strategies have been implemented in this study: holdout cross-validation, tenfold cross-validation, and stratified tenfold cross-validation. Each technique has been applied to every sample technique used in the study. DTC, RFC, KNN, and XGB algorithms exhibited the most favorable outcomes. Our recommended model is the RF Classifier implemented with near-miss under-sampling out of the four models considered. The model under consideration demonstrates the maximum level of accuracy, specifically 97.54%, 97.11%, and 99%, while employing the Extra Tree, Random Forest Classifier, and XGB. The ML model's performance was assessed using metrics like accuracy, error rate, precision, recall, F1-score, ROC AUC curve etc. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cardiology

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) Architecture for Lung Cancer Subtype Classification

Convolutional Neural Network-Long Short-Term Memory (CNN-LST...

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Artificial Intelligence, Applications, Innovation and Ethics (AI2E), International Conference for

作者： S. Ahamed Nishath Suresh Rasappan Francis Saviour Devaraj Information Technology Department College of Computing and Information Sciences University of Technology and Applied Sciences-Ibri Ibri Sultanate of Oman Mathematics Department University of Technology and Applied Sciences-Ibri Ibri Sultanate of Oman

ISBN: (数字)9798331513733

ISBN: (纸本)9798331513740

Lung cancer is a leading cause of cancer-related deaths worldwide. Accurate classification of lung cancer subtypes is crucial for effective treatment. This study proposes a Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) architecture for classifying lung cancer subtypes: Adenocarcinoma, Squamous Cell Carcinoma, and Lymph Node involvement. Our model achieves 95.38% on a dataset LC105K of 105,000 images, outperforming traditional methods. The CNN-LSTM architecture extracts features from images and models temporal dependencies, improving classification accuracy.

关键词： Technological innovation Accuracy Squamous cell carcinoma Memory architecture Lung cancer Feature extraction Convolutional neural networks Reliability Lymph nodes Long short term memory

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Reported Challenges in Deep Learning-Based Human Pose Estimation: A Systematic Review

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IEEE Access 2025年 13卷 80520-80539页

作者： Fisal, Nagwa R. Fathalla, Ahmed Elmanakhly, Dina Salah, Ahmad Suez Canal University Faculty of Science Department of Mathematics Ismailia Egypt University of Technology and Applied Sciences College of Computing and Information Sciences Ibri Oman Faculty of Computers and Informatics Zagazig University Department of Computer Science Egypt

Human Pose Estimation (HPE) is the technique of precisely estimating the locations of human joints in images and videos. Deep learning has been frequently applied recently to improve HPE performance. However, a number of challenges, including occlusions, crowded environments, and hidden joints, make it difficult to create an effective HPE model. This systematic review provides a comprehensive analysis of HPE reported challenges, focusing on hidden joint issues due to their significant impact on performance. This study systematically examines a number of HPE models, choosing 74 papers on deep learning-based HPE that have been published since 2018 based on specific criteria. The reported challenges are listed along with their frequency and related references, the review also covers datasets, loss functions, evaluation metrics, and pre-trained models used in HPE. Our analysis demonstrates that occlusion and crowded scenes are still the main issues that affect the model performance, highlighting the importance of addressing hidden joint problem. The review presents a number of findings, such as the most widely utilized architecture in HPE literature is the residual network, the mean square error is the most frequently used loss function. Furthermore, MPII and Human3.6M are the most used datasets for 2D and 3D pose estimation, respectively. Finally, this study highlights the gaps in the field, which could lead to new research opportunities. © 2013 IEEE.

关键词： Challenges deep learning hidden joints human pose estimation

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Artificial Intelligence in Financial Derivatives: A Comparative Analysis of PSO and ACO for American-Style Put Options

Artificial Intelligence in Financial Derivatives: A Comparat...

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Advances in Electrical, computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Afroza Akter Salma Parvin A B M Shahadat Hossain MD Morshedul Islam Department of Mathematics Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Applied Mathematics University of Dhaka Dhaka Bangladesh Mathematical & Computing Sciences Concordia University of Edmonton Edmonton AB Canada

ISBN: (数字)9798350356236

ISBN: (纸本)9798350356243

Artificial Intelligence (AI) enhances option pricing accuracy and risk management, enabling precise valuation of complex derivatives across various market scenarios. This paper compares Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) in valuing American-style put options. We apply these metaheuristic algorithms to tackle the complexity of option pricing due to the early exercise feature, evaluating their performance across various boundary functions. We assess each method's accuracy and computational efficiency through extensive simulations. These simulations are benchmarked against findings from existing literature. Python's Computer Algebra System (CAS) is used for the simulations. The findings highlight AI's potential to enhance option valuation and contribute valuable insights for practitioners in finance.

关键词： Accuracy Computational modeling Metaheuristics Finance Pricing Numerical models Risk management Artificial intelligence Particle swarm optimization Cost accounting

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A SHORT PROOF OF ISSERLIS’ THEOREM

arXiv

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arXiv 2025年

作者： Munthe-Kaas, Hans Z. Verdier, Olivier Vilmart, Gilles Department of Mathematics and Statistics UiT – The Arctic University of Norway Tromsø Norway Department of Mathematics University of Bergen Bergen Norway Department of Computing Electrical Engineering and Mathematical Sciences Western Norway Norway University of Applied Sciences Bergen Norway Section of Mathematics University of Geneva Switzerland

We show that Isserlis’ theorem follows as a corollary to the invariant tensor theorem for isotropic *** Codes Primary: 60C05, 15A72, Secondary: 41A58, 60H35 Copyright © 2025, The Authors. All rights reserved.

关键词： Tensors

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The Distributionally Robust Optimization Model of Sparse Principal Component Analysis

arXiv

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arXiv 2025年

作者： Wang, Lei Liu, Xin Chen, Xiaojun Department of Applied Mathematics The Hong Kong Polytechnic University Hong Kong State Key Laboratory of Scientific and Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

We consider sparse principal component analysis (PCA) under a stochastic setting where the underlying probability distribution of the random parameter is uncertain. This problem is formulated as a distributionally robust optimization (DRO) model based on a constructive approach to capturing uncertainty in the covariance matrix, which constitutes a nonsmooth constrained min-max optimization problem. We further prove that the inner maximization problem admits a closed-form solution, reformulating the original DRO model into an equivalent minimization problem on the Stiefel manifold. This transformation leads to a Riemannian optimization problem with intricate nonsmooth terms, a challenging formulation beyond the reach of existing algorithms. To address this issue, we devise an efficient smoothing manifold proximal gradient algorithm. We prove the Riemannian gradient consistency and global convergence of our algorithm to a stationary point of the nonsmooth minimization problem. Moreover, we establish the iteration complexity of our algorithm. Finally, numerical experiments are conducted to validate the effectiveness and scalability of our algorithm, as well as to highlight the necessity and rationality of adopting the DRO model for sparse PCA. Copyright © 2025, The Authors. All rights reserved.

关键词： Optimization algorithms

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