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A Robust Data Hiding Reversible Technique for Improving the Security in e-Health Care System

computer Modeling in engineering & sciences 2023年第1期134卷 201-219页

作者： Saima Kanwal Feng Tao Ahmad Almogren Ateeq Ur Rehman Rizwan Taj Ayman Radwan School of Computer and Communication Lanzhou University of TechnologyLanzhou730050China Department of Computer Science College of Computer and Information SciencesKing Saud UniversityRiyadh11633Saudi Arabia Department of Electrical Engineering Government College UniversityLahore54000Pakistan Instituto de Telecomunicaoes and Universidade de Aveiro Aveiro3810-193Portugal

The authenticity and integrity of healthcare is the primary *** reversible watermarking schemes have been developed to improve the primary objective but increasing the quantity of embedding data leads to covering image distortion and visual quality resulting in data security detection.A trade-off between robustness,imperceptibility,and embedded capacity is difficult to achieve with current algorithms due to limitations in their *** this purpose insight,an improved reversibility watermarking methodology is proposed to maximize data embedding capacity and imperceptibility while maintaining data security as a primary concern.A key is generated by a random path with minimum bit flipping is selected in the 4 × 4 block to gain access to the data embedding *** random path's complex structure ensures data *** of various sizes(8 KB,16 KB,32 KB)are used to analyze image imperceptibility and evaluate quality *** proposed reversible watermarking methodology performance is tested under standard structures PSNR,SSIM,and *** results revealed that the MRI watermarked images are imperceptible,like the cover image when LSB is 3 bits *** proposed reversible watermarking methodology outperforms other related techniques in terms of average PSNR(49.29).Experiment results show that the suggested reversible watermarking method improves data embedding capacity and imperceptibility compared to existing state-of-the-art approaches.

关键词： Medical images security data hiding LSB matching hiding capacity imperceptibility

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Multilayer Satellite Network Collaborative Mobile Edge Caching:A GCN-Based Multi-Agent Approach

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China Communications 2024年第11期21卷 56-74页

作者： Yang Jie He Jingchao Cheng Nan Yin Zhisheng Han Dairu Zhou Conghao Sun Ruijin School of Telecommunications Engineering Xidian UniversityXi’an 710071China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210000China Department of Electrical and Computer Engineering University of WaterlooWaterloo ON N2L 3G1Canada

With the explosive growth of highdefinition video streaming data,a substantial increase in network traffic has *** emergency of mobile edge caching(MEC)can not only alleviate the burden on core network,but also significantly improve user *** with the MEC and satellite networks,the network is empowered popular content ubiquitously and *** the research gap between multilayer satellite networks and MEC,we study the caching placement problem in this ***,we introduce a three-layer distributed network caching management architecture designed for efficient and flexible handling of large-scale *** the constraint on satellite capacity and content propagation delay,the cache placement problem is then formulated and transformed into a markov decision process(MDP),where the content coded caching mechanism is utilized to promote the efficiency of content ***,a new generic metric,content delivery cost,is proposed to elaborate the performance of caching decision in large-scale ***,we introduce a graph convolutional network(GCN)-based multi-agent advantage actor-critic(A2C)algorithm to optimize the caching ***,extensive simulations are conducted to evaluate the proposed algorithm in terms of content delivery cost and transferability.

关键词： cache placement coded caching graph convolutional network(GCN) mobile edge caching(MEC) multilayer satellite network

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Cloud-Enabled Neural Networks for Intelligent Vehicle Emissions Tracking and Analysis

Cloud-Enabled Neural Networks for Intelligent Vehicle Emissi...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Anusha, N. Jeslin, J Gnana Srividhya, V. Gupta, Nukala Sujata Meenakshi, R. Srinivasan, C. Vellore Institute of Technology School of Computer Science and Engineering Department of IoT Tamil Nadu Vellore India Rmk College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Meenakshi College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Chaitanya Bharathi Institute of Technology Department of Computer Engineering and Technology Hyderabad Gandipet India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350382723

A system for smart vehicle emissions monitoring and analysis using cloud computing and neural networks is presented in this paper. As global environmental concerns develop, monitoring and reducing vehicle emissions is crucial. Scalability and real-time data processing are issues for traditional approaches. Thus, cloud-enabled neural network architecture for scalable and efficient vehicle emissions monitoring using distributed computing is developed. A neural network model trained on various vehicle emission datasets, allowing for precise pattern analysis and prediction of emissions. The scalability of the system is guaranteed by using cloud resources, which can accommodate the rising amount of data created by an increasing number of vehicles. By incorporating cloud-based data processing seamlessly, real-time monitoring is accomplished, enabling immediate analysis and prompt emission alerts. The suggested emissions tracking platform is robust and scalable to intelligent transportation systems by revealing vehicle emissions' environmental effect. Future smart city and sustainable urban transportation developments are built on cloud computing and neural networks. The proposed system shows how cloud-enabled neural networks may solve complicated vehicle emissions problems. © 2024 IEEE.

关键词： Scalability

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Enhanced Efficiency in Fog Computing: A Fuzzy Data-Driven Machine Selection Strategy

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International Journal of Fuzzy Systems 2024年第1期26卷 368-389页

作者： Zavieh, Hadi Javadpour, Amir Ja’fari, Forough Sangaiah, Arun Kumar Slowik, Adam School of Economics and Statistics Guangzhou University Guangzhou510006 China Harbin Institute of Technology Shenzhen China Department of Computer Engineering Sharif University of Technology Tehran Iran International Graduate School of AI National Yunlin University of Science and Technology Douliu Taiwan Department of Electronics and Computer Science Koszalin University of Technology Koszalin Poland ADiT-Lab Electrotechnics and Telecommunications Department Instituto Politécnico de Viana do Castelo Porto4900–347 Portugal Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

With the rapid proliferation of IoT and Cloud networks and the corresponding number of devices, handling incoming requests has become a significant challenge. Task scheduling problems have emerged as a common concern, necessitating the exploration of new methods for request management. This paper proposes a novel approach called the Fuzzy Inverse Markov Data Envelopment Analysis Process (FIMDEAP). Our method combines the strengths of the Fuzzy Inverse Data Envelopment Analysis (FIDEA) and Fuzzy Markov Decision Process (FMDP) techniques to enable the efficient selection of physical and virtual machines while operating in a fuzzy mode. We represent data as triangular fuzzy numbers and employ the alpha-cut method to solve the proposed models. The paper provides a mathematical optimization model for the proposed method and presents a numerical example for illustration. Furthermore, we evaluate the performance of our method in a cloud environment through simulations. The results demonstrate that our approach outperforms existing methods, namely PSO + ACO and FBPSO + FBACO, in terms of key metrics, including energy consumption, execution cost, response time, gain of cost, and makespan. © The Author(s) 2023.

关键词： Markov processes

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Snippet Policy Network for Multi-Class Varied-Length ECG Early Classification

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IEEE Transactions on Knowledge and Data engineering 2023年第6期35卷 6349-6361页

作者： Huang, Yu Yen, Gary G. Tseng, Vincent S. National Yang Ming Chiao Tung University Department of Computer Science Hsinchu city300093 Taiwan Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74075 United States

Arrhythmia detection from ECG is an important research subject in the prevention and diagnosis of cardiovascular diseases. The prevailing studies formulate arrhythmia detection from ECG as a time series classification problem. Meanwhile, early detection of arrhythmia presents a real-world demand for early prevention and diagnosis. In this paper, we address a problem of cardiovascular diseases early classification, which is a varied-length and long-length time series early classification problem as well. For solving this problem, we propose a deep reinforcement learning-based framework, namely Snippet Policy Network (SPN), consisting of four modules, snippet generator, backbone network, controlling agent, and discriminator. Comparing to the existing approaches, the proposed framework features flexible input length, solves the dual-optimization solution of the earliness and accuracy goals. Experimental results demonstrate that SPN achieves an excellent performance of over 80% in terms of accuracy. Compared to the state-of-the-art methods, at least 7% improvement on different metrics, including the precision, recall, F1-score, and harmonic mean, is delivered by the proposed SPN. To the best of our knowledge, this is the first work focusing on solving the cardiovascular early classification problem based on varied-length ECG data. Based on these excellent features from SPN, it offers a good exemplification for addressing all kinds of varied-length time series early classification problems. © 1989-2012 IEEE.

关键词： Diseases

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A Dynamic Heuristic Set-based Particle Swarm Optimizer for the Multi-Objective Task Assigning Problem in Wireless Sensor Networks 11

A Dynamic Heuristic Set-based Particle Swarm Optimizer for t...

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11th International Conference on Machine Intelligence Theory and Applications, MiTA 2024

作者： Wei, Feng-Feng Geng, Ming-Can Hu, Xiao-Min Jeon, Sang-Woon Chen, Wei-Neng South China University of Technology School of Computer Science and Engineering Guangzhou China Hanyang University Department of Electrical and Electronic Engineering Ansan Korea Republic of

ISBN: (纸本)9798350385670

The task assigning problem in wireless sensor networks is a challenging NP-hard combinatorial optimization problem that has caught much attention. Although binary particle swarm optimizers have been proven to be effective for this problem, the prematurity remains severe. To fix the prematurity, this paper proposes a set-based particle swarm optimizer with dynamic heuristic substitution techniques. Differing from the original binary particle swarm optimizer, the representation scheme is based on the concept of set. The position of each particle is a set whose elements are defined by the problem. A particle position element denotes the allocation of a task to a sensor node. The proposed algorithm tries to select an optimal subset from the universal set based on the particle swarm optimization framework. Moreover, the comprehensive learning strategy that utilizes beneficial information from the best-ever positions of all particles is employed, which helps keep the swarm away from local optima. Considering the constraints in real-world situations, the position updating adopts a substitution operation, guided by heuristic information that dynamically alters. To improve particle diversity, mutation operations are carried out after renewing the particle positions. Simulation results demonstrate the superiority of the proposed approach over others. © 2024 IEEE.

关键词： Combinatorial optimization

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Risk-Aware Decentralized Safe Control via Dynamic Responsibility Allocation 37

Risk-Aware Decentralized Safe Control via Dynamic Responsibi...

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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Lyu, Yiwei Luo, Wenhao Dolan, John M. Department of Electrical and Computer Engineering Carnegie Mellon University United States Department of Computer Science University of North Carolina at Charlotte United States Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9781577358800

In this work, we present a novel risk-aware decentralized Control Barrier Function (CBF)-based controller for multi-agent systems. The proposed decentralized controller is composed based on pairwise agent responsibility shares (a percentage), calculated from the risk evaluation of each individual agent faces in a multi-agent interaction environment. With our proposed CBF-inspired risk evaluation framework, the responsibility portions between pairwise agents are dynamically updated based on the relative risk they face. Our method allows agents with lower risk to enjoy a higher level of freedom in terms of a wider action space, and the agents exposed to higher risk are constrained more tightly on action spaces, and are therefore forced to proceed with caution. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Multi agent systems

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UAMP-Based Delay-Doppler Channel Estimation for OTFS Systems

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China Communications 2023年第10期20卷 70-84页

作者： Zhongjie Li Weijie Yuan Qinghua Guo Nan Wu Ji Zhang Department of Electronic and Electrical Engineering Southern University of Science and TechonologyShenzhen 518055China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia School of Integrated Circuits and Electronics Beijing Institute of TechnologyBeijing 100081China School of Mathematics and Statistics Henan University of Science and TechnologyLuoyang 471000China

Orthogonal time frequency space(OTFS)technique,which modulates data symbols in the delay-Doppler(DD)domain,presents a potential solution for supporting reliable information transmission in highmobility vehicular *** this paper,we study the issues of DD channel estimation for OTFS in the presence of fractional *** first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing(UAMP),which exploits the structured sparsity of the effective DD domain channel using hidden Markov model(HMM).The empirical state evolution(SE)analysis is then leveraged to predict the performance of our proposed *** refine the hyperparameters in the proposed algorithm,we derive the update criterion for the hyperparameters through the expectation-maximization(EM)***,Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline schemes.

关键词： orthogonal time frequency space(OTFS) channel estimation hidden Markov model(HMM) unitary approximate message passing(UAMP)

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Noncrossing Longest Paths and Cycles 32

Noncrossing Longest Paths and Cycles

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32nd International Symposium on Graph Drawing and Network Visualization, GD 2024

作者： Aloupis, Greg Biniaz, Ahmad Bose, Prosenjit De Carufel, Jean-Lou Eppstein, David Maheshwari, Anil Odak, Saeed Smid, Michiel Tóth, Csaba D. Valtr, Pavel Khoury College of Computer Sciences Northeastern University BostonMA United States School of Computer Science University of Windsor Canada School of Computer Science Carleton University Ottawa Canada School of Electrical Engineering and Computer Science University of Ottawa Canada Computer Science Department University of California IrvineCA United States Department of Mathematics California State University Northridge Los AngelesCA United States Department of Computer Science Tufts University Medford United States Department of Applied Mathematics Charles University Prague Czech Republic

ISBN: (纸本)9783959773430

Edge crossings in geometric graphs are sometimes undesirable as they could lead to unwanted situations such as collisions in motion planning and inconsistency in VLSI layout. Short geometric structures such as shortest perfect matchings, shortest spanning trees, shortest spanning paths, and shortest spanning cycles on a given point set are inherently noncrossing. However, the longest such structures need not be noncrossing. In fact, it is intuitive to expect many edge crossings in various geometric graphs that are longest. Recently, Álvarez-Rebollar, Cravioto-Lagos, Marín, Solé-Pi, and Urrutia (Graphs and Combinatorics, 2024) constructed a set of points for which the longest perfect matching is noncrossing. They raised several challenging questions in this direction. In particular, they asked whether the longest spanning path, on any finite set of points in the plane, must have a pair of crossing edges. They also conjectured that the longest spanning cycle must have a pair of crossing edges. In this paper, we give a negative answer to the question and also refute the conjecture. We present a framework for constructing arbitrarily large point sets for which the longest perfect matchings, the longest spanning paths, and the longest spanning cycles are noncrossing. © Greg Aloupis, Ahmad Biniaz, Prosenjit Bose, Jean-Lou De Carufel, David Eppstein, Anil Maheshwari, Saeed Odak, Michiel Smid, Csaba D. Tóth, and Pavel Valtr.

关键词： Trees (mathematics)

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CapsNet-FR: Capsule Networks for Improved Recognition of Facial Features

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computers, Materials & Continua 2024年第5期79卷 2169-2186页

作者： Mahmood Ul Haq Muhammad Athar Javed Sethi Najib Ben Aoun Ala Saleh Alluhaidan Sadique Ahmad Zahid farid Department of Computer System Engineering University of Engineering&TechnologyPeshawar25000Pakistan College of Computer Science and Information Technology Al-Baha UniversityAlaqiq65779-7738Saudi Arabia REGIM-Lab:Research Groups in Intelligent Machines National School of Engineers of Sfax(ENIS)University of SfaxSfax3038Tunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh84428Saudi Arabia EIAS:Data Science and Blockchain Laboratory College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Computer Sciences Bahria University Karachi CampusKarachi541004Pakistan Department of Electrical Engineering Abasyn UniversityPeshawar25000Pakistan

Face recognition (FR) technology has numerous applications in artificial intelligence including biometrics, security,authentication, law enforcement, and surveillance. Deep learning (DL) models, notably convolutional neuralnetworks (CNNs), have shown promising results in the field of FR. However CNNs are easily fooled since theydo not encode position and orientation correlations between features. Hinton et al. envisioned Capsule Networksas a more robust design capable of retaining pose information and spatial correlations to recognize objects morelike the brain does. Lower-level capsules hold 8-dimensional vectors of attributes like position, hue, texture, andso on, which are routed to higher-level capsules via a new routing by agreement algorithm. This provides capsulenetworks with viewpoint invariance, which has previously evaded CNNs. This research presents a FR model basedon capsule networks that was tested using the LFW dataset, COMSATS face dataset, and own acquired photos usingcameras measuring 128 × 128 pixels, 40 × 40 pixels, and 30 × 30 pixels. The trained model outperforms state-ofthe-art algorithms, achieving 95.82% test accuracy and performing well on unseen faces that have been blurred orrotated. Additionally, the suggested model outperformed the recently released approaches on the COMSATS facedataset, achieving a high accuracy of 92.47%. Based on the results of this research as well as previous results, capsulenetworks perform better than deeper CNNs on unobserved altered data because of their special equivarianceproperties.

关键词： CapsNet face recognition artificial intelligence

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