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Decomposition method-based global Mittag-Leffler synchronization for fractional-order Clifford-valued neural networks with transmission delays and impulses

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Neural Networks 2025年 189卷 107564页

作者： Sriraman, R. Manoj, N. Pratap, Anbalagan Department of Mathematics College of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

This study examines the global Mittag-Leffler synchronization (GMLS) problem for fractional-order Clifford-valued neural networks (FOCLVNNs) including transmission delays and impulses. Firstly, a novel kind of FOCLVNNs is developed that incorporates impulses and transmission delays, which can be useful for better understanding of Clifford-valued neural networks (CLVNNs) dynamics more precisely. Then, because of the non-commutative multiplication feature of the Clifford algebra, the systems of FOCLVNNs can be decomposed into multi-dimensions real-valued neural networks (RVNNs), that avoid the difficulties associated with Clifford number multiplication. By using Lyapunov functions and inequality techniques, several novel solid conditions for the GMLS of FOCLVNNs under the state feedback controller that has been formulated. In addition, this paper presents novel results that are easy to solve and new insights into GMLS of FOCLVNNs. Then, a numerical verification is given along with graphical analysis to validate the applicability of the obtained theories. Based on the considered master–slave FOCLVNNs, an image encryption algorithm for color images is provided. In addition, simulation and performance analysis are stated to prove the validity of the given algorithms. © 2025 Elsevier Ltd

关键词： Lyapunov functions

Automated model design and benchmarking of 3D deep learning models for COVID-19 detection with chest CT scans

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arXiv 2021年

作者： He, Xin Wang, Shihao Chu, Xiaowen Shi, Shaohuai Tang, Jiangping Liu, Xin Yan, Chenggang Zhang, Jiyong Ding, Guiguang Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong Hong Kong School of Automation Hangzhou Dianzi University Hang Zhou China School of Software Tsinghua University Beijing China

The COVID-19 pandemic has spread globally for several months. Because its transmissibility and high pathogenicity seriously threaten people’s lives, it is crucial to accurately and quickly detect COVID-19 infection. Many recent studies have shown that deep learning (DL) based solutions can help detect COVID-19 based on chest CT scans. However, most existing work focuses on 2D datasets, which may result in low quality models as the real CT scans are 3D images. Besides, the reported results span a broad spectrum on different datasets with a relatively unfair comparison. In this paper, we first use three state-of-the-art 3D models (ResNet3D101, DenseNet3D121, and MC3 18) to establish the baseline performance on the three publicly available chest CT scan datasets. Then we propose a differentiable neural architecture search (DNAS) framework to automatically search for the 3D DL models for 3D chest CT scans classification with the Gumbel Softmax technique to improve the searching efficiency. We further exploit the Class Activation Mapping (CAM) technique on our models to provide the interpretability of the results. The experimental results show that our automatically searched models (CovidNet3D) outperform the baseline human-designed models on the three datasets with tens of times smaller model size and higher accuracy. Furthermore, the results also verify that CAM can be well applied in CovidNet3D for COVID-19 datasets to provide interpretability for medical diagnosis. Code: https://***/HKBU-HPML/CovidNet3D. © 2021, CC BY.

关键词： Diagnosis

Privacy-Aware Key Task Scheduling in Vehicular Networks Based on Incentive Mechanism 22

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Privacy-Aware Key Task Scheduling in Vehicular Networks Base...

22nd IEEE International Conference on High Performance Computing and Communications, 18th IEEE International Conference on Smart City and 6th IEEE International Conference on Data Science and Systems, HPCC-SmartCity-DSS 2020

作者： Xia, Youhua Wu, Libing Jin, Jiong Zhang, Tiehua Zheng, Xi Wuhan University School of Computer Science Wuhan China School of Cyber Science and Engineering Wuhan University Wuhan China Swinburne University of Technology School of Software and Electrical Engineering Melbourne Australia Macquarie University Department of Computing Sydney Australia

ISBN: (纸本)9781728176499

The introduction of a lightweight encryption scheme in the vehicular network has improved the reliability and security of data transmission substantially among vehicles. However, it is still not guaranteed that all vehicles comply with the encryption scheme throughout the whole operation period, considering the inherent selfishness of each participant. Apart from that, the data scheduling issue is another major concern in the vehicular network due to the high-speed mobility of vehicles. To tackle these issues, a multi-objective and multi-dimensional incentive mechanism is developed in this paper to achieve privacy-Aware data scheduling for vehicles. This mechanism is designed to encourage vehicles to carry out different tasks by providing relevant incentives while maximizing the overall utility of the network. Additionally, security and privacy of data transmission between vehicles and cloud servers are realized through a data perturbation approach. Experimental evaluation shows that the proposed incentive mechanism is better than the traditional methods when it comes to maximizing the number of participants and completed key tasks. © 2020 IEEE.

关键词： Cryptography

A Hybrid Cross-Scale Transformer Architecture for Robust Medical Image Segmentation

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A Hybrid Cross-Scale Transformer Architecture for Robust Med...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yang Ning Shouyi Zhang Wei Zhong Peide Liu Caiming Zhang School of Computer Science and Technology Shandong Jianzhu University Jinan China School of Informatics The University of Edinburgh Edinburgh UK Department of Network Information Security Guangdong Police College Guangzhou China School of Management Science and Engineering Shandong University of Finance and Economics Jinan China School of Software Shandong University Jinan China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Transformer architecture has emerged to be successful in many natural language processing tasks. However, its applications to clinical practice remain largely unexplored. In this study, we propose a Robust Cross-Scale Hybrid Transformer (RCSHT) architecture for medical image segmentation, which can effectively enhance the multi-scale feature representations while integrating local features with global dependencies. Specifically, we propose two new modules based on the self-attention mechanism: PSCM and PCCM, which perform matrix low-rank transformation on cross-scale images in spatial and channel space, respectively, to effectively enhance the discriminant ability of long-range dependencies of multi-scale images and effectively reduce the computational complexity. Meanwhile, we use spatial positional encoding to perform spatial regularization on the two attention modules, so that the model has more opportunities to obtain valuable spatial details and further enhance the spatial retention ability. Besides, by fusing outputs of the two modules, the robustness and discriminant ability of the feature representations are further improved. Note that our hybrid architecture allows transformers to be initialized as convolutional networks without pre-training. Extensive experiments conducted on our newly proposed dataset have demonstrated our RCSHT notably outperforms the state-of-the-art methods by a large margin, holding the promise to generalize well on other medical image segmentation.

关键词： Measurement Image segmentation Image coding Computed tomography computer architecture Transformers Robustness

An Auto-Contouring Method for Kidney based on SVM

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An Auto-Contouring Method for Kidney based on SVM

5th International Conference on Data Mining, Communications and Information technology, DMCIT 2021

作者： Li, Bo Xue, Jing Wang, Ping Gu, Saikun Chen, Wei Qian, Pengjiang Jiang, Yizhang School of Artificial Intelligence and Computer Science Jiangnan University 1800 Lihu Avenue Jiangsu Wuxi214122 China Department of Nephrology Affiliated Wuxi People's Hospital of Nanjing Medical University 299 Qingyang Rd Jiangsu Wuxi214023 China Jiangsu Key Laboratory of Media Design and Software Technology 1800 Lihu Avenue Jiangsu Wuxi214122 China

In adaptive radiotherapy planning, the contour map of the treatment target area is still the most difficult problem. Usually this work is done by a professional radiologist oncologist, which is very time-consuming and labor-intensive. In order to solve this problem, this paper proposes an automatic contour method based on support vector machine. Experiments were conducted on the lower abdomen MR data set of eight patients. In the experiment, the simulated data was only used to train the classifier, and the treatment day images were used to evaluate the performance of the classifier. DSI was used to compare the manual contour and automatic contour of the kidney. The experiment showed that, The automatic contour method based on support vector machine has better classification performance than most classification algorithms. Among the eight sets of results, the DSI value of the six sets of results is 1, and the smallest DSI value is also greater than 9.423. © Published under licence by IOP Publishing Ltd.

关键词： Support vector machines

Spatio-temporal graph neural network based child action recognition using data-efficient methods: A systematic analysis

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computer Vision and Image Understanding 2025年

作者： Sanka Mohottala Asiri Gawesha Dhrashana Karshutrirathna Pradeepa Samarasinghe Charith Abhayaratne Department of Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Electrical and Electronic Engineering Faculty of Engineering Sri Lanka Institute of Information Technology Sri Lanka Department of Computer Science Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Sri Lanka School of Electrical and Electronic Engineering The University of Sheffield United Kingdom

This paper presents implementations on child activity recognition (CAR) using spatial–temporal graph neural network (ST-GNN)-based deep learning models with the skeleton modality. Prior implementations in this domain have predominantly utilized CNN, LSTM, and other methods, despite the superior performance potential of graph neural networks. To the best of our knowledge, this study is the first to use an ST-GNN model for child activity recognition employing both in-the-lab, in-the-wild, and in-the-deployment skeleton data. To overcome the challenges posed by small publicly available child action datasets, transfer learning methods such as feature extraction and fine-tuning were applied to enhance model performance. As a principal contribution, we developed an ST-GNN-based skeleton modality model that, despite using a relatively small child action dataset, achieved superior performance (94.81%) compared to implementations trained on a significantly larger (x10) adult action dataset (90.6%) for a similar subset of actions. With ST-GCN-based feature extraction and fine-tuning methods, accuracy improved by 10%–40% compared to vanilla implementations, achieving a maximum accuracy of 94.81%. Additionally, implementations with other ST-GNN models demonstrated further accuracy improvements of 15%–45% over the ST-GCN baseline. The results on activity datasets empirically demonstrate that class diversity, dataset size, and careful selection of pre-training datasets significantly enhance accuracy. In-the-wild and in-the-deployment implementations confirm the real-world applicability of above approaches, with the ST-GNN model achieving 11 FPS on streaming data. Finally, preliminary evidence on the impact of graph expressivity and graph rewiring on accuracy of small dataset-based models is provided, outlining potential directions for future research.

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Prediction of ultraslow magnetic solitons via plasmon-induced transparency by artificial neural networks

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arXiv 2021年

作者： Cheng, Jiaxi Xu, Siliu Jiang, Shengwang Bo, Zhiqiang Department of Computer Science University of Waikato Hamilton New Zealand School of Electronic and Information Engineering Hubei University of Science and Technology Xianning China University of Electronic Science and Technology of China Chengdu China Software Engineering Institute Xidian University Xi'an China

Plasmon-induced transparency (PIT) in advanced materials has attracted extensive attention for both theoretical and applied physics. Here, we considered a scheme that can produce PIT and studied the characteristics of ultraslow low-power magnetic solitons. The PIT metamaterial is constructed as an array of unit cells that consist of two coupled varactor-loaded split-ring resonators. Simulations verified that ultraslow magnetic solitons can be generated in this type of metamaterial. To solve nonlinear equations, various types of numerical methods can be applied by virtue of exact solutions, which are always difficult to acquire. However, the initial conditions and propagation distance impact the ultimate results. In this article, an artificial neural network (ANN) was used as a supervised learning model to predict the evolution and final mathematical expressions through training based on samples with disparate initial conditions. Specifically, the influences of the number of hidden layers were discussed. Additionally, the learning results obtained by employing several training algorithms were analyzed and compared. Our research opens a route for employing machine learning algorithms to save time in both physical and engineering applications of Schrödinger-type systems. Copyright © 2021, The Authors. All rights reserved.

关键词： Numerical methods

A Brief Review of Explainable Artificial Intelligence in Healthcare

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arXiv 2023年

作者： Sadeghi, Zahra Alizadehsani, Roohallah Cifci, Mehmet Akif Kausar, Samina Rehman, Rizwan Mahanta, Priyakshi Bora, Pranjal Kumar Almasri, Ammar Alkhawaldeh, Rami S. Hussain, Sadiq Alatas, Bilal Shoeibi, Afshin Moosaei, Hossein Hladík, Milan Nahavandi, Saeid Pardalos, Panos M. Institute for Big Data Analytics Faculty of Computer Science Dalhousie University Canada Deakin University Geelong Australia The Institute of Computer Technology Tu Wien University Vienna1040 Austria University of Kotli Azad Jammu and Kashmir Azad Kashmir Kotli Pakistan Centre for Computer Science and Applications Dibrugarh University Assam India Department of Management Information Sys Al-Balqa Applied University Salt19117 Jordan Department of Computer Information Systems The University of Jordan Aqaba77110 Jordan Examination Branch Dibrugarh University Assam Dibrugarh India Department of Software Eng. Firat University Elazig23100 Turkey Data Science and Computational Intelligence Institute University of Granada Spain Department of Informatics Faculty of Science Jan Evangelista Purkyně University in Ústí nad Labem Czech Republic Department of Applied Mathematics School of Computer Science Faculty of Mathematics and Physics Charles University Prague Czech Republic Harvard Paulson School of Engineering and Applied Sciences Harvard University AllstonMA02134 United States Center for Applied Optimization Department of Industrial and Systems Engineering University of Florida Gainesville32611 United States

XAI refers to the techniques and methods for building AI applications which assist end users to interpret output and predictions of AI models. Black box AI applications in high-stakes decision-making situations, such as medical domain have increased the demand for transparency and explainability since wrong predictions may have severe consequences. Model explainability and interpretability are vital successful deployment of AI models in healthcare practices. AI applications' underlying reasoning needs to be transparent to clinicians in order to gain their trust. This paper presents a systematic review of XAI aspects and challenges in the healthcare domain. The primary goals of this study are to review various XAI methods, their challenges, and related machine learning models in healthcare. The methods are discussed under six categories: Features-oriented methods, global methods, concept models, surrogate models, local pixel-based methods, and human-centric methods. Most importantly, the paper explores XAI role in healthcare problems to clarify its necessity in safety-critical applications. The paper intends to establish a comprehensive understanding of XAI-related applications in the healthcare field by reviewing the related experimental results. To facilitate future research for filling research gaps, the importance of XAI models from different viewpoints and their limitations are investigated. © 2023, CC BY-NC-ND.

关键词： Decision making

Work fluctuations due to partial thermalizations in two-level systems

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arXiv 2021年

作者： Quadeer, Maria Korzekwa, Kamil Tomamichel, Marco Centre for Quantum Software and Information School of Computer Science University of Technology Sydney NSW2007 Australia Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Kraków30-348 Poland Department of Electrical and Computer Engineering Centre for Quantum Technologies National University of Singapore Singapore119077 Singapore

We study work extraction processes mediated by finite-time interactions with an ambient bath—partial thermalizations—as continuous time Markov processes for two-level systems. Such a stochastic process results in fluctuations in the amount of work that can be extracted and is characterized by the rate at which the system parameters are driven in addition to the rate of thermalization with the bath. We analyze the distribution of work for the case where the energy gap of a two-level system is driven at a constant rate. We derive analytic expressions for average work and lower bound for the variance of work showing that such processes cannot be fluctuation-free in general. We also observe that an upper bound for the Monte Carlo estimate of the variance of work can be obtained using Jarzynski’s fluctuation-dissipation relation for systems initially in equilibrium. Finally, we analyse work extraction cycles by modifying the Carnot cycle, incorporating processes involving partial thermalizations and obtain efficiency at maximum power for such finite-time work extraction cycles under different sets of constraints. Copyright © 2021, The Authors. All rights reserved.

关键词： Carnot cycle