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Metaheuristic Optimization Through Deep Learning Classification of COVID-19 in Chest X-Ray Images

computers, Materials & Continua 2022年第11期73卷 4193-4210页

作者： Nagwan Abdel Samee El-Sayed M.El-Kenawy Ghada Atteia Mona M.Jamjoom Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Noha E.El-Attar Tarek Gaber Adam Slowik Mahmoud Y.Shams Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura 35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh 11671Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura University35516MansouraEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams University11566CairoEgypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityEgypt School of Science Engineeringand EnvironmentUniversity of SalfordUK Faculty of Computers and Informatics Suez Canal UniversityIsmailia41522Egypt Koszalin University of Technology Poland Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt

As corona virus disease(COVID-19)is still an ongoing global outbreak,countries around the world continue to take precautions and measures to control the spread of the *** of the excessive number of infected patients and the resulting deficiency of testing kits in hospitals,a rapid,reliable,and automatic detection of COVID-19 is in extreme need to curb the number of *** analyzing the COVID-19 chest X-ray images,a novel metaheuristic approach is proposed based on hybrid dipper throated and particle swarm *** lung region was segmented from the original chest X-ray images and augmented using various transformation ***,the augmented images were fed into the VGG19 deep network for feature *** the other hand,a feature selection method is proposed to select the most significant features that can boost the classification ***,the selected features were input into an optimized neural network for *** neural network is optimized using the proposed hybrid *** experimental results showed that the proposed method achieved 99.88%accuracy,outperforming the existing COVID-19 detection *** addition,a deep statistical analysis is performed to study the performance and stability of the proposed *** results confirm the effectiveness and superiority of the proposed approach.

关键词： Covid-19 feature selection dipper throated optimization particle swarm optimization deep learning

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CPL-SLAM: Centralized Collaborative Multi-Robot Visual-Inertial SLAM Using Point-and-Line Features

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IEEE Internet of Things Journal 2025年第12期12卷 21866-21875页

作者： Liu, Xin Wen, Shuhuan Liu, Huaping Richard Yu, F. Yanshan University Engineering Research Center The Ministry of Education for Intelligent Control System and Intelligent Equipment Key Lab of Intelligent Rehabilitation and Neuroregulation in Hebei Province Department of Key Lab of Industrial Computer Control Engineering of Hebei Province Qinhuangdao066004 China Tsinghua University Department of Computer Science and Technology Beijing100084 China Shenzhen University College of Computer Science and Software Engineering China Carleton University Canada

Traditional visual-inertial SLAM (Simultaneous Localization and Mapping) systems predominantly rely on feature point matching from a single robot to realize the robot pose estimation and environment map construction. However, in complex scenarios, these traditional systems struggle with issues such as tracking failures due to illumination changes, rapid movements, and low-texture environments, and they perform poorly in terms of mapping efficiency and global consistency. To address these challenges, we propose a centralized collaborative SLAM system that employs both point and line features for tracking in the robot and map fusion in the cloud. The proposed system leverages the fusion of point and line features across all instances in the process, which allows our method to achieve higher localization accuracy in structured, low-texture scenes. With the aid of classifying gravity-aligned vertical lines and spatial parallel lines, the proposed system can deliver faster and more accurate odometry in complex scenes. Furthermore, we developed intra-robot and inter-robot loop closure detection methods based on point and line features, generating a globally consistent sparse point cloud and structured scene map in the cloud. Our method is able to build richer maps while improving accuracy compared to existing methods. Experimental results on public datasets and in real-world environments show that, compared to existing advanced methods, our approach demonstrates better performance. © 2014 IEEE.

关键词： SLAM robotics

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Deep Reinforcement Learning for Resource Allocation in Multi-cell Cellular Networks

Deep Reinforcement Learning for Resource Allocation in Multi...

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作者： Sun, Ming Hu, Liangjin Wang, Yanchun Zhang, Hui Wang, Jiaqiu College of Computer and Control Engineering Qiqihar University Qiqihar161006 China Beijing China-Power Information Technology Co. Ltd. Beijing China

Resource allocation for full-frequency multiplexing cellular networks tends to involve two aspects of both the channel allocation and the power allocation. In order to maximize the energy efficiency of full-frequency multiplexing cellular networks, this paper proposes a deep reinforcement learning algorithm that constructs a deep Q network (DQN) with multiple hidden layers to output the channel allocation scheme and power control scheme. In the proposed DQN, the transition state is the power allocated for each channel. In contrast, the transition action consists of the channel allocation scheme, the power allocated for each channel, and the corresponding power adjustment amount. Besides, the reward of the transition of the DQN is the total energy efficiency. Simulation results show that the proposed DQN can ensure the low computation delay and that the energy efficiency obtained by the proposed method is larger than other methods at large numbers of channels. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Reinforcement learning

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Research on Multi Beam Sounding Based on Spatial Position Relationship and Backtracking Algorithm 2

Research on Multi Beam Sounding Based on Spatial Position Re...

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2nd IEEE International Conference on control, Electronics and computer Technology, ICCECT 2024

作者： Xian, Bensen Liu, Bo Li, Yufen Zhu, Baicheng Chen, Qiuyan Diao, Jinlong Sun, Wenkai Mo, Jiahuan Bai, Shaoyuan Zhang, Ya'nan College of Environmental Science and Engineering Guilin University of Technology Guilin China School of Computer Science and Engineering Guilin University of Technology Guilin China School of Mathematics and Statistics Guilin University of Technology Guilin China Collaborative Innovation Center for Water Pollution Control and Water Safety in Karst Area Guilin University of Technology Guilin China

ISBN: (纸本)9798350380958

The multi beam depth measurement system is an efficient underwater terrain measurement method, which has wide applications in marine terrain surveys, underwater archaeology, and other fields. Therefore, exploring the problem of multi beam survey lines is of great research significance for the exploration of marine disciplines. This article mainly uses SketChup for drawing. With the help of mathematical geometry and triangle theory, a seawater depth calculation model, a coverage width calculation model, and an optimal survey line design model are constructed. The indicator values of each measurement point are solved using Python. This paper establishes a mathematical model for the coverage width of multi beam depth measurement and the overlaps rate between adjacent bands. Firstly, the spatial structure is analyzed as a planar Fig. Then, this paper uses a plan for geometric analysis to obtain right angled triangles with similar ratios. Through physical analysis, a seawater depth measurement model was established. Then, geometric analysis is performed using the obtained planar Fig to obtain a right angled triangle with similar ratios. Then, a depth measurement model for seawater is obtained through physical angle analysis. The coverage width measurement model and the repetition rate measurement model are established using the sine theorem and equal substitution. Finally, using Python, we calculate the index values for the listed positions with an open angle of 120°, a slope of 1.5°, and a center point seawater depth of 70m. For example, the depth, coverage width, and overlap rate of the measuring line at a distance of 200m from the center point are 64.763m, 224.885m, and 13.837%, respectively. © 2024 IEEE.

关键词： Python

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An Efficient Hybrid Red Panda Optimization-Similarity-Navigated Graph Neural Network with Blockchain Technology for Shared Electric Vehicle Charging 4

An Efficient Hybrid Red Panda Optimization-Similarity-Naviga...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Sammy, F. Rajeshwari, S. Prashanth, G.K. Thirumalaimuthu, Babu Pokhariya, Hemant Singh Natrayan, L. Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522 302 India Bangalore Institute of Technology Department of Mathematics Karnataka Bengaluru560004 India Siddaganga Institute Technology Department of Mca Karnataka Tumkur-572103 India St. Joseph's College of Engineering Department of Instrumentation and Control Engineering Tamil Nandu Chennai600119 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Saveetha School of Engineering Simats Department of Mechanical Engineering Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331540364

Blockchain technology for shared electric vehicle (EV) charging makes use of a distributed ledger in handling different transactions which promotes authenticity in charging. However, this technology can be non-scalable at times which slows down the transaction rates as the scale increases in an organization and may also demand high computational as well as energy requirements which increases the inefficiency of the total system, the operating expenses of EV charging station, costs of energy utilized for charging, cost of maintenance and cost of station infrastructure may prove to be a weakness, impacting the feasibility of infrastructure deployment and upkeep expenses of the charging stations. To overcome these drawbacks, this paper proposes a hybrid method for blockchain technology with shared EV charging. The proposed approach combines the use of the Red Panda Optimization (RPO) with Similarity-Navigated Graph Neural Network (SNGNN). Hence, it is named as hybrid RPO-SNGNN approach. Reducing the operating cost of EV charging position and maximizes the security efficiency is the main aim of the proposed approach. To optimize scheduling and resource allocation by finding efficient paths by RPO and predict future demand and user behaviour patterns by SNGNN. By then, the proposed model has been put into practice on the MATLAB work platform and contrasted with other existing methods. The proposed technique outperforms all existing techniques, including Fuzzy-based Cross Hierarchical Reinforcement Q Learning (FCHRQL), Non-Dominated Sorting Genetic Algorithm-Long Short-Term Memory Network (NSGA-II-LSTMN), and Ant Colony Optimization-Proximal Policy Optimization (ACO-PPO). The proposed method shows the operating cost of $550 and the security rate is 97%. From the result, the proposed approach operating cost is low and the security rate is high when compared to the existing approaches. © 2024 IEEE.

关键词： Charging stations

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AeroGTO: An Efficient Graph-Transformer Operator for Learning Large-Scale Aerodynamics of 3D Vehicle Geometries 39

AeroGTO: An Efficient Graph-Transformer Operator for Learnin...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liu, Pengwei Wang, Pengkai Ren, Xingyu Yuan, Hangjie Hao, Zhongkai Xu, Chao Cai, Shengze Ni, Dong College of Control Science and Engineering Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (纸本)157735897X

Obtaining high-precision aerodynamics in the automotive industry relies on large-scale simulations with computational fluid dynamics, which are generally time-consuming and computationally expensive. Recent advances in operator learning for partial differential equations offer promising improvements in terms of efficiency. However, capturing intricate physical correlations from extensive and varying geometries while balancing large-scale discretization and computational costs remains a significant challenge. To address these issues, we propose AeroGTO, an efficient graph-transformer operator designed specifically for learning large-scale aerodynamics in engineering applications. AeroGTO combines local feature extraction through message passing and global correlation capturing via projection-inspired attention, employing a frequency-enhanced graph neural network augmented with k-nearest neighbors to handle three-dimensional (3D) irregular geometries. Moreover, the transformer architecture adeptly manages multi-level dependencies with only linear complexity concerning the number of mesh points, enabling fast inference of the model. Given a car's 3D mesh, AeroGTO accurately predicts surface pressure and estimates drag. In comparisons with five advanced models, AeroGTO is extensively tested on two industry-standard benchmarks, Ahmed-Body and DrivAerNet, achieving a 7.36% improvement in surface pressure prediction and a 10.71% boost in drag coefficient estimation, with fewer FLOPs and only 1% of the parameters used by the prior leading method. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Graph neural networks

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Robust control for Bidirectional Thrust Quadrotors under Instantaneously Drastic Disturbances

Robust Control for Bidirectional Thrust Quadrotors under Ins...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zujian Chen Shaolin Mo Botao Zhang Jiyu Li Hui Cheng School of Computer Science and Engineering Sun Yat-sen University Guangzhou China College of Mechatronics and Control Engineering Shenzhen University Shenzhen China College of Engineering South China Agricultural University Guangzhou China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Quadrotors may crash and cause severe accidents under instantaneously drastic disturbances. To mitigate the effect of such disturbances, these critical issues should be considered: efficient disturbance observation and compensation, full attitude controllability, and instant output power generation of the quadrotor. In this paper, to keep the quadrotor stable even under suddenly drastic disturbances, a novel control framework is presented to by integrating the advantages of active disturbance rejection control (ADRC) as well as geometric control for a quadrotor with bidirectional thrust capabilities. Moreover, to strengthen the adaptability under significant disturbances, a novel switching strategy is introduced into the control framework by virtue of the quadrotor’s bidirectional thrust capabilities. The ADRC scheme is performed when the disturbances are within a range; alternatively, if the disturbances surpass the preset range and the desired control is beyond the ultimate output of the quadrotor, the quadrotor compliantly responds by executing a 180° flip reverse flight to handle such drastic disturbances. Numerical and real-world experiments demonstrate that the proposed robust control strategy has superior performance adapts to instantaneously drastic disturbances.

关键词： Robust control Attitude control Switches Numerical simulation controllability computer crashes Robotics and automation

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FlexPDD: Enabling Proportional Delay Differentiation Service on Programmable Switches

FlexPDD: Enabling Proportional Delay Differentiation Service...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Xinyue Jiang Dezhang Kong Zhengyan Zhou Di Wang Shuangxi Chen Chunming Wu College of Computer Science and Technology Zhejiang University Hangzhou China College of Control Science and Engineering Zhejiang University Hangzhou China Jiaxing Vocational and Technical College Jiangxing China

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Quality-of-Service (QoS) guarantees are crucial for meeting the diverse performance requirements of applications in packet networks. The Proportional Delay Differentiation (PDD) model offers relative service differentiation based on the delay requirements of different traffic classes. However, implementing PDD on current hardware switches faces challenges due to the lack of inherent queuing behavior description in switch ASICs. This paper introduces FlexPDD, a dynamic and adaptive packet prioritization mechanism designed to implement the PDD model on programmable switches. FlexPDD leverages the flexibility of programmable switch to adjust the mapping between packet classes and output queues dynamically, ensuring precise control over delay differentiation. Our implementation of FlexPDD on a Barefoot Tofino switch and an NS3 simulator demonstrates its feasibility and effectiveness. The results indicate that FlexPDD successfully maintains approximate delay differentiation among service classes proportional to their delay weights, highlighting its potential as a practical solution for achieving advanced service differentiation in modern network infrastructures.

关键词： Adaptation models Quality of service Switches control systems Hardware Scheduling Delays Global communication Faces

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Interior Scene Intelligent Generated for Smart Homes Based on GAN-ID Algorithms

Journal of Network Intelligence

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Journal of Network Intelligence 2024年第2期9卷 1046-1055页

作者： Zhang, Fu-Quan Ma, Lin-Juan Zhou, Jun-Xian Wang, Run-Hong Zhang, Guang-Zhen College of Computer and Control Engineering Minjiang University Fuzhou China Digital Media Art Key Laboratory of Sichuan Province Sichuan Conservatory of Music Chengdu China Fuzhou Technology Innovation Center of Intelligent Manufacturing Information System Minjiang University Fuzhou China Fujian Province University Fuzhou China School of Computer Science and Technology Beijing Institute of Technology Beijing China Minjiang University Fuzhou China Information Media School of Tianjin Technician Institute of Mechanical & Electrical Technology Tianjin China

The purpose of this paper is to present a new GAN-ID algorithm to gener-ate interior scene intelligently based on Artificial Intelligence of Things (AIoT). Although great progress has been made when GAN algorithm applied in smart homes and smart cities, the issue of the trade-off between the accuracy and the efficiency in interior scene has been overlooked. A number of experiments were performed to check the performance and it is indicated that the results of the proposed GAN-ID has great advantages in model sample generation than other common methods when generating interior scene intelligently, which is similar to state-of-the-art. It is meaningful when it can be applied in smart cities and smart homes filed based on AIoT in the future. © 2024, J. Network Intell. All rights reserved.

关键词： Automation

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Discrete memristive neuron model and its interspike interval-encoded application in image encryption

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Science China(Technological Sciences) 2021年第10期64卷 2281-2291页

作者： BAO Han HUA ZhongYun LIU WenBo BAO BoCheng College of Automation Engineering Nanjing University of Aeronautics and AstronauticsNanjing 210016China School of Computer Science and Technology Harbin Institute of TechnologyShenzhenShenzhen 518055China School of Microelectronics and Control Engineering Changzhou UniversityChangzhou 213164China

Bursting is a diverse and common phenomenon in neuronal activation patterns and it indicates that fast action voltage spiking periods are followed by resting *** interspike interval(ISI)is the time between successive action voltage spikes of neuron and it is a key indicator used to characterize the ***,a three-dimensional memristive Hindmarsh-Rose(mHR)neuron model was constructed to generate hidden chaotic ***,the properties of the discrete mHR neuron model have not been investigated,*** this article,we first construct a discrete mHR neuron model and then acquire different hidden chaotic bursting sequences under four typical sets of *** make these sequences more suitable for the application,we further encode these hidden chaotic sequences using their ISIs and the performance comparative results show that the ISI-encoded chaotic sequences have much more complex chaos properties than the original *** addition,we apply these ISI-encoded chaotic sequences to the application of image *** image encryption scheme has a symmetric key structure and contains plain-text permutation and bidirectional diffusion *** results and security analyses prove that it has excellent robustness against various possible attacks.

关键词： chaos complexity chaotic bursting sequence memristive neuron model interspike interval(ISI) image encryption

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