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arXiv 2025年

作者： Vo, Tat Thang Le, Tran Trong Khoi Afach, Sivem Vansteelandt, Stijn Research Group EPIDERME Faculty of Medicine University Paris Est Creteil France Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

Obtaining causally interpretable meta-analysis results is challenging when there are differences in the distribution of effect modifiers between eligible trials. To overcome this, recent work on transportability methods has considered standardizing results of individual studies over the case-mix of a target population, prior to pooling them as in a classical random-effect meta-analysis. One practical challenge, however, is that case-mix standardization often requires individual participant data (IPD) on outcome, treatments and case-mix characteristics to be fully accessible in every eligible study, along with IPD case-mix characteristics for a random sample from the target population. In this paper, we aim to develop novel strategies to integrate aggregated-level data from eligible trials with non-accessible IPD into a causal meta-analysis, by extending moment-based methods frequently used for population-adjusted indirect comparison in health technology assessment. Since valid inference for these moment-based methods by M-estimation theory requires additional aggregated data that are often unavailable in practice, computational methods to address this concern are also developed. We assess the finite-sample performance of the proposed approaches by simulated data, and then apply these on real-world clinical data to investigate the effectiveness of risankizumab versus ustekinumab among patients with moderate to severe psoriasis. © 2025, CC BY.

关键词： Inverse problems

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Wearable Fall Detection Based on Motion Signals Using Hybrid Deep Residual Neural Network 1

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15th Multi-disciplinary International Conference on Artificial Intelligence, MIWAI 2022

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Hnoohom, Narit Jitpattanakul, Anuchit Department of Computer Engineering School of Information and Communication Technology University of Phayao Phayao Mueang Phayao Thailand Image Information and Intelligence Laboratory Department of Computer Engineering Faculty of Engineering Mahidol University Nakhon Pathom Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9783031209925

ISBN: (纸本)9783031209918

There have been several approaches for wearable fall detection devices during the last twenty years. The majority of technologies relied on machine learning. Although the given findings appear that the issue is practically addressed, critical problems remain about feature extraction and selection. In this research, the constraint of machine learning on feature extraction is addressed by including a hybrid convolutional operation in our proposed deep residual network, called the DeepFall model. The proposed network automatically generates high-level motion signal characteristics that can be utilized to track falls and everyday activities. FallAllD dataset, a publicly available standard dataset for fall detection that gathered motion signals of falls and other events, was utilized to analyze the proposed network. We performed investigations using a 5-fold cross-validation technique to determine overall accuracy and F-measure. The experimental outcomes show that the proposed DeepFall performs better accuracy (95.19%) and F-measure (92.79%) than the state-of-the-art baseline deep learning networks. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Fall detection

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On First-Order Meta-Reinforcement Learning with Moreau Envelopes

On First-Order Meta-Reinforcement Learning with Moreau Envel...

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IEEE Conference on Decision and Control

作者： Mohammad Taha Toghani Sebastian Perez-Salazar César A. Uribe Department of Electrical and Computer Engineering Rice University Houston TX USA Computational Applied Mathematics and Operations Research Rice University Houston TX USA

Meta-Reinforcement Learning (MRL) is a promising framework for training agents that can quickly adapt to new environments and tasks. In this work, we study the MRL problem under the policy gradient formulation, where we propose a novel algorithm that uses Moreau envelope surrogate regularizers to jointly learn a meta-policy that is adjustable to the environment of each individual task. Our algorithm, called Moreau Envelope Meta-Reinforcement Learning (MEMRL), learns a meta-policy that can adapt to a distribution of tasks by efficiently updating the policy parameters using a combination of gradient-based optimization and Moreau Envelope regularization. Moreau Envelopes provide a smooth approximation of the policy optimization problem, which enables us to apply standard optimization techniques and converge to an appropriate stationary point. We provide a detailed analysis of the MEMRL algorithm, where we show a sublinear convergence rate to a first-order stationary point for non-convex policy gradient optimization. We finally show the effectiveness of MEMRL on a multi-task 2D-navigation problem.

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Free vibration analysis of functionally graded porous curved nanobeams on elastic foundation in hygro-thermo-magnetic environment

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Frontiers of Structural and Civil engineering 2023年第4期17卷 584-605页

作者： Quoc-Hoa PHAM Parviz MALEKZADEH Van Ke TRAN Trung NGUYEN-THOI Faculty of Engineering and Technology Nguyen Tat Thanh UniversityHo Chi Minh City 700000Vietnam Department of Mechanical Engineering Persian Gulf UniversityBushehr ***Iran Faculty of Mechanical Engineering Le Quy Don Technical UniversityHanoi 100000Vietnam Laboratory for Applied and Industrial Mathematics Institute for Computational Science and Artificial IntelligenceVan Lang UniversityHo Chi Minh City 700000Vietnam Faculty of Mechanical-Electrical and Computer Engineering School of TechnologyVan Lang UniversityHo Chi Minh City 700000Vietnam Bualuang ASEAN Chair Professor Thammasat School of EngineeringThammasat UniversityPathumtani 12120Thailand

Herein,a two-node beam element enriched based on the Lagrange and Hermite interpolation function is proposed to solve the governing equation of a functionally graded porous(FGP)curved nanobeam on an elastic foundation in a hygro–thermo–magnetic *** material properties of curved nanobeams change continuously along the thickness via a power-law distribution,and the porosity distributions are described by an uneven porosity *** effects of magnetic fields,temperature,and moisture on the curved nanobeam are assumed to result in axial loads and not affect the mechanical properties of the *** equilibrium equations of the curved nanobeam are derived using Hamilton’s principle based on various beam theories,including the classical theory,first-order shear deformation theory,and higher-order shear deformation theory,and the nonlocal elasticity *** accuracy of the proposed method is verified by comparing the results obtained with those of previous reliable ***,the effects of different parameters on the free vibration behavior of the FGP curved nanobeams are investigated comprehensively.

关键词： functionally graded porous material curved nanobeam hygro-thermo-magnetic enriched finite element method

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Extended Information Geometry: Large Deviation Theory, Statistical Thermodynamics, and Empirical Counting Frequencies

arXiv

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arXiv 2025年

作者： Muppirala, Viswa Virinchi Qian, Hong Department of Electrical and Computer Engineering University of Washington SeattleWA98195 United States Department of Applied Mathematics University of Washington SeattleWA98195 United States

Combinatorics, probabilities, and measurements are fundamental to understanding information. This work explores how the application of large deviation theory (LDT) in counting phenomena leads to the emergence of various entropy functions, including Shannon’s entropy, mutual information, and relative and conditional entropies. In terms of these functions, we reveal an inherent geometrical structure through operations, including contractions, lift, change of basis, and projections. Legendre-Fenchel (LF) transform that is central to both LDT and Gibbs’ method of thermodynamics offers a novel energetic description of data. The manifold of empirical mean values of statistical data ad infinitum has a parametrization using LF conjugates w.r.t. an entropy function;this gives rise to the additivity known in statistical thermodynamic energetics. This work extends current information geometry to information projection as defined through conditional expectations in Kolmogorov’s probability theory. Copyright © 2025, The Authors. All rights reserved.

关键词： Entropy

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A Pseudo-Spectral Scheme for Systems of Two-Point Boundary Value Problems with Left and Right Sided Fractional Derivatives and Related Integral Equations

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computer Modeling in engineering & Sciences 2021年第7期128卷 21-41页

作者： I.G.Ameen N.A.Elkot M.A.Zaky A.S.Hendy E.H.Doha Department of Mathematics Faculty of ScienceAl-Azhar UniversityCairoEgypt Department of Mathematics Faculty of ScienceCairo UniversityGiza12613Egypt Department of Applied Mathematics Physics DivisionNational Research CentreDokkiCairo12622Egypt Department of Computational Mathematics and Computer Science Institute of Natural Sciences and MathematicsUral Federal UniversityYekaterinburg620002Russia Department of Mathematics Faculty of ScienceBenha UniversityBenha13511Egypt

We target here to solve numerically a class of nonlinear fractional two-point boundary value problems involving left-and right-sided fractional *** main ingredient of the proposed method is to recast the problem into an equivalent system of weakly singular integral ***,a Legendre-based spectral collocation method is developed for solving the transformed ***,we can make good use of the advantages of the Gauss quadrature *** present the construction and analysis of the collocation *** results can be indirectly applied to solve fractional optimal control problems by considering the corresponding Euler–Lagrange *** numerical examples are given to confirm the convergence analysis and robustness of the scheme.

关键词： Spectral collocation method weakly singular integral equations two-point boundary value problems convergence analysis

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Perioperative Anesthesia Data: Visualization, Effects, Analysis with Artificial Intelligence 4

Perioperative Anesthesia Data: Visualization, Effects, Analy...

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4th IEEE International Conference on Electronic Communications, Internet of Things and Big Data, ICEIB 2024

作者： Liu, Xiaoxiao McGrath, Sean Flanagan, Colin Lei, Yiming Zeng, Liaoyuan Electronic and Computer Engineering University of Limerick Limerick Ireland School of Mathematics and Information HeBei University HeBei China Mobile Digital Hospital System School of Electronics Peking University Ministry of Education Engineering Research Center Beijing China University of Electronic Science and Technology of China Department of Information and Communication Engineering Chengdu China

ISBN: (纸本)9798350360721

A comprehensive understanding of physiological data is essential for anesthesiologists to monitor and maintain the health of surgical patients. Understanding anesthetic signals is essential to improve over the past ten decades to comprehend a variety of diseases, ensure the safety of patients, and accelerate their recovery. We identified and summarized four themes of artificial intelligence (AI) research in anesthesia: applications of AI in anesthesiology, signals monitored in different anesthesia stages, commonly monitored signs, and data standardization. The effect of various AI technologies on data analysis was studied, and the types, functions, sensors, characteristics, and intelligent analysis of physiological signals monitored during anesthesia are presented in this article. AI influences anesthesia practices in many ways, including preoperative data evaluation, anesthetic depth monitoring, and postoperative event prediction. © 2024 IEEE.

关键词： Machine learning

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EIROB : An Emotionally Intelligent ROBot for Empathetic Support

EIROB : An Emotionally Intelligent ROBot for Empathetic Supp...

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IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES)

作者： Safad Ismail Harsha Vasudev Department of Computer Science and Engineering Saintgits College of Engineering Kerala India Department of Mathematics Human Inspired Technology Research Centre University of Padua Italy

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

Social assistive robots enhance social interaction and well-being by providing companionship and support. Emotional Intelligence in these robots refers to their ability to identify, understand, and respond to human emotions. This study investigates how well a social robot named ‘EIROB’ can engage elderly persons by using Artificial Emotional Intelligence. EIROB employs a Multimodal Emotion Recognition Algorithm and a Multimodal Emotion Expression System to detect the user’s emotional state and generate appropriate responses through an effective dialogue manager. Interaction with EIROB is facilitated by an Android app, which uses the mobile phone’s inbuilt camera and microphone for facial expression and speech sentiment detection. The captured data is processed in the cloud that enables the model to be deployed remotely. This study aims to create an empathetic interaction model for senior citizens particularly during pandemics like COVID-19 to prevent feelings of isolation. The performance of the emotion recognition models is evaluated via accuracy, precision, recall, and F1-score to ensure the effectiveness of the system in recognizing and responding to user’s emotional states.

关键词： Emotion recognition Adaptation models Social robots Medical treatment Signal processing algorithms Signal processing Assistive robots SPICE Older adults Smart phones

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Autoencoder-Driven Anomaly Detection for Efficient Malware Identification

Autoencoder-Driven Anomaly Detection for Efficient Malware I...

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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Neda Firoz Mohammad Sadman Tahsin Md. Zobayer Hossain Institute of Applied Mathematics and Computer Science Tomsk State University Tomsk Russia Department of Computer Science and Engineering BRAC Univeristy Dhaka Bangladesh Department of Electrical and Electronic Engineering University of Asia Pacific Dhaka Bangladesh

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Recently, the exponential rise of malicious software, particularly repackaged Android malware, has become a significant concern for mobile devices. Dealing with Android malware detection through dynamic analysis is helpful, but it is also very important to make a connection between an app's features and the parts that are needed to make it work. To tackle this challenge, we present an innovative approach for malware detection utilizing autoencoders. Our method uses a mixed deep-learning model that combines autoencoders (AE) for feature extraction and convolutional neural networks (CNN) for classifying malware. We used autoencoders to pull out attributes from noisy input. This creates groups of bad predictions through reconstruction error and effectively lowers the number of dimensions in the large feature set. The recently reconstructed input features are subsequently utilized in machine learning and deep learning models for predictions. To assess the effectiveness of our suggested model, we carried out experiments on three datasets, each containing two labels in the ‘class’ attribute: ‘0’ for Benign and ‘1’ for Malicious. We achieved 99.99% accuracy with an improved MAE value of 0.001 and RMSE value of 0.063 across three datasets. The results illustrate that our model attained exceptional accuracy and holds promise for delivering robust malware detection outcomes.

关键词： Visualization Accuracy Target recognition Operating systems Receivers Feature extraction Malware Convolutional neural networks Viruses (medical) Unsupervised learning

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Robust Attacks Detection Model for Internet of Flying Things based on Generative Adversarial Network (GAN) and Adversarial Training

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IEEE Internet of Things Journal 2025年第13期12卷 23961-23974页

作者： Gaber, Tarek Ali, Tarek Nicho, Mathew Torky, Mohamed Engineering and Environment University of Salford School of Science Manchester United Kingdom Suez Canal University Faculty of Computers and Informatics Ismailia Egypt Manchester Metropolitan University Department of Computing and Mathematics Manchester United Kingdom Rabdan Academy Research and Innovation Centre Abu Dhabi United Arab Emirates University of Queensland School of Electrical Engineering and Computer Science Australia Egyptian Russian University-Badr City Faculty of Artificial Intelligence Cairo Egypt

The Internet of Flying Things (IoFT) holds significant promise in fields like disaster management and surveillance. However, it is increasingly vulnerable to cyberattacks that can compromise the confidentiality, integrity, and availability (CIA) of sensitive data. Despite the growing interest in proposing Intrusion Detection Systems (IDSs) for IoFT networks, current literature faces key limitations, particularly the shortage of publicly available IoFT datasets with diverse attacks, and the fact that existing IDSs lack robustness against sophisticated adversarial machine learning attacks. This paper is the first study to address these limitations by proposing a more resilient and accurate IDS tailored for IoFT networks (RIDS-IoFT). We introduce a novel IDS that leverages Generative Adversarial Networks (GANs) to generate a hybrid dataset that combines real IoFT traffic data with GAN-generated adversarial attacks, addressing the dataset diversity issue. Additionally, we introduce an innovative adversarial training method to enhance the system’s defense against evolving threats, such as Fast Gradient Sign Method (FGSM), Basic Iterative Method (BIM), and Carlini & Wagner (C&W) attacks. The proposed RIDS-IoFT was evaluated using four machine learning models, Random Forest (RF), Decision Tree (DT), Support Vector Machine (SVM), and Logistic Regression (LR), on two datasets: ECU-IoFT and CICIDS2018. The IDS’s performance was assessed based on its ability to detect both traditional and adversarial attacks. The results show that the Random Forest model achieved the highest detection accuracy, up to 96.5%, demonstrating superior performance across both real and hybrid datasets. The proposed RIDS-IoFT not only enhances detection accuracy but also strengthens resilience against adversarial threats, making it suitable for resource-constrained IoFT environments. In conclusion, this study presents a comprehensive approach to securing IoFT networks by combining real and synthetic d

关键词： Generative adversarial networks

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