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Enhanced Neutrosophic Set and Machine Learning Approach for Kidney Disease Prediction

Neutrosophic Sets and systems 2025年 80卷 465-477页

作者： Al-Doori, Humam M. Alkellezli, Tareef S. Abdelhafeez, Ahmed Eassaa, Mohamed Sawah, Mohamed S. El-Douh, Ahmed A. Cybersecurity Sciences Department College of Science Ashur University Baghdad Iraq Cybersecurity Engineering Department College of Engineering Ashur University Baghdad Iraq Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science Faculty of Information Technology Ajloun National University P.O.43 Ajloun26810 Jordan Cybersecurity Technology Engineering Department College of Engineering Technology Ashur University Baghdad Iraq

Kidney disease (KD) is a gradually increasing global health concern. It is a chronic illness linked to higher rates of morbidity and mortality, a higher risk of cardiovascular disease and numerous other illnesses, and expensive medical expenses. The machine learning (ML) models are applied for KD prediction with higher accuracy and precision. The KD dataset has uncertainty and vague information, so, we used the neutrosophic set (NS) to deal with vague and uncertainty information in the KD dataset. The KD dataset is converted into the N-KD dataset with three membership functions: truth, indeterminacy, and falsity. Three ML models are used in this study such as logistic regression (LR), support vector machine (SVM), and k-nearest neighbor (KNN). These ML models are applied to the N-KD dataset. The results show the LR has higher accuracy and precision on the N-KD dataset than the original KD dataset. © 2025, University of New Mexico. All rights reserved.

关键词： Logistic regression

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

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38th Conference on Neural information Processing systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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Predefined Performance Control for Saturation Nonlinear systems in Finite-Time 7

Predefined Performance Control for Saturation Nonlinear Syst...

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7th International Symposium on Autonomous systems, ISAS 2024

作者： Ju, Hanzheng Tao, Fazhan Fu, Zhumu Henan University of Science and Technology College of Information Engineering Luoyang China Intelligent System Science and Technology Innovation Center Longmen Laboratory Luoyang China

ISBN: (纸本)9798350363173

This paper investigates predefined performance control (PPC) in finite-time for nonlinear systems under saturation nonlinearity and immeasurable states. A modified finite-time performance function is devised to obtain expected performance indexes within finite-time. Meanwhile, the impact of input saturation is overcome by using mean-value theorem. By integrating backstepping method with adaptive technique, an observer-based PPC algorithm is presented, which guarantees that tracking error converges into predefined range within finite-time. Finally, the validity of presented control algorithm is demonstrated via simulation. © 2024 IEEE.

关键词： Backstepping

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Distributed Constrained Optimization with Uncoordinated Step Sizes: A Case Study on Ethylene Plant

Distributed Constrained Optimization with Uncoordinated Step...

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2024 China Automation Congress, CAC 2024

作者： Liu, Bing Li, Zhongmei Du, Wenli School of Information Science and Engineering East China University of Science and Technology Shanghai China Engineering Research Center of Process System Engineering Ministry of Education East China University of Science and Technology Shanghai China

ISBN: (纸本)9798350368604

This paper investigates an uncoordinated step sizes distributed optimization algorithm based on an undirected topology network structure, which can handle distributed optimization problems with inequality constraints and heterogeneity (losing the global clock) by only communicating with neighboring nodes. Firstly, an "Adapt-Then-Combine Distributed Inexact Gradient tracking subject to Inequality Constraints (ATC-DIGing-IC)" algorithm is proposed for μ-strongly convex and l-smooth objective functions. Using the "Adapt-Then-Combine (ATC)" framework and the gradient information tracking structure, the flexibility and fastness of the algorithm can be guaranteed under the uncoordinated step size. Then, a parameter projection technique is introduced to handle the inequality constraints without introducing additional auxiliary variables. Subsequently, the convergence results of the algorithm and the bounds on the uncoordinated step sizes are theoretically analyzed. Finally, the algorithm is applied to a plant-wide separation production process of an ethylene plant, and the experimental results show that the proposed algorithm requires a shorter optimization time than the existing methods. © 2024 IEEE.

关键词： Constrained optimization

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Fast Parallel Recovery for Transactional Stream Processing on Multicores 40

Fast Parallel Recovery for Transactional Stream Processing o...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhao, Jianjun Liu, Haikun Zhang, Shuhao Duan, Zhuohui Liao, Xiaofei Jin, Hai Zhang, Yu School of Computer Science and Technology Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Wuhan China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (纸本)9798350317152

Transactional stream processing engines (TSPEs) have gained increasing attention due to their capability of processing real-time stream applications with transactional semantics. However, TSPEs remain susceptible to system failures and power outages. Existing TSPEs mainly focus on performance improvement, but still face a significant challenge to guarantee fault tolerance while offering high-performance services. We revisit commonly-used fault tolerance approaches in stream processing and database systems, and find that these approaches do not work well on TSPEs due to complex data dependencies. In this paper, we propose a novel TSPE called MorphStreamR to achieve fast failure recovery while guaranteeing low performance overhead at runtime. The key idea of MorphStreamR is to record intermediate results of resolved dependencies at runtime, and thus eliminate data dependencies to improve task parallelism during failure recovery. MorphStreamR further mitigates the runtime overhead by selectively tracking data dependencies and incorporating workload-aware log commitment. Experimental results show that MorphStreamR can significantly reduce the recovery time by up to 3.1 x while experiencing much less performance slowdown at runtime, compared with other applicable fault tolerance approaches. © 2024 IEEE.

关键词： Fault tolerance

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Image Enhancement via Associated Perturbation Removal and Texture Reconstruction Learning

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IEEE/CAA Journal of Automatica Sinica 2024年第11期11卷 2253-2269页

作者： Kui Jiang Ruoxi Wang Yi Xiao Junjun Jiang Xin Xu Tao Lu IEEE the School of Computer Science and Technology Harbin Institute of Technology Zhengzhou Research Institute Harbin Institute of Technology the School of Artificial Intelligence Jianghan University the School of Geodesy and Geomatics Wuhan University the Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology the School of Computer Science and Engineering and also with the Hubei Province Key Laboratory of Intelligent Robot Wuhan Institute of Technology

Degradation under challenging conditions such as rain, haze, and low light not only diminishes content visibility, but also results in additional degradation side effects, including detail occlusion and color distortion. However, current technologies have barely explored the correlation between perturbation removal and background restoration, consequently struggling to generate high-naturalness content in challenging scenarios. In this paper, we rethink the image enhancement task from the perspective of joint optimization: Perturbation removal and texture reconstruction. To this end, we advise an efficient yet effective image enhancement model, termed the perturbation-guided texture reconstruction network(PerTeRNet). It contains two subnetworks designed for the perturbation elimination and texture reconstruction tasks, respectively. To facilitate texture recovery,we develop a novel perturbation-guided texture enhancement module(PerTEM) to connect these two tasks, where informative background features are extracted from the input with the guidance of predicted perturbation priors. To alleviate the learning burden and computational cost, we suggest performing perturbation removal in a sub-space and exploiting super-resolution to infer high-frequency background details. Our PerTeRNet has demonstrated significant superiority over typical methods in both quantitative and qualitative measures, as evidenced by extensive experimental results on popular image enhancement and joint detection tasks. The source code is available at https://***/kuijiang94/PerTeRNet.

关键词： Association learning attention mechanism image enhancement perturbation modeling

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Coverage Probability of Distributed CoMP UAV-Assisted Cellular Networks 22

Coverage Probability of Distributed CoMP UAV-Assisted Cellul...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Li, Yan Long, Qingmin Tang, Qiang He, Shiming Xiong, Bing Changsha University of Science and Technology School of Computer and Communication Engineering Changsha410004 China National University of Defense Technology Science and Technology on Information System Engineering Laboratory Changsha410073 China

ISBN: (纸本)9798331509712

It is well-established that terrestrial communication systems may fail during emergencies such as earthquakes, tsunamis, and floods. Fortunately, with the rapid advancement of unmanned aerial vehicle (UAV) network technology, deploying UAV nodes as aerial base stations (BSs) is assuming an increasingly crucial role in facilitating downlink transmissions and restoring ground communication capabilities. However, a single UAV node is not sufficient to meet the requirements. Inspired by distributed communication, we introduce a performance analysis framework based on stochastic geometry to analyze the distributed coordinated multi-point (CoMP) UAV-assisted communication network. Specifically, we assume that all UAV nodes follow a homogeneous Poisson point process (PPP) and maintain a constant altitude. The entire space is tessellated by multiple hexagons, with multiple UAV nodes within each hexagonal region working together to serve terrestrial user equipments (UEs). For this region-centric cooperative model, we derive an exact expression for the coverage probability to quantify the performance improvement enabled by UAVs, analyze the upper bound of the coverage probability, and provide a simplified approximation. We then compare this model to a user-centric model. Our numerical findings demonstrate that the cooperation of UAV nodes can significantly enhance the coverage probability and save spectrum resources. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Real-Time On-Site Ground-Motion Prediction Using ResNet

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Seismological Research Letters 2025年第3期96卷 1834-1845页

作者： Yang, Tzu-Yi Chen, Da-Yi Chin, Tai-Lin Mobile Computing and Networking Lab Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Seismological Center of Central Weather Administration Taipei Taiwan

On-site warnings can decrease the range of late alert zone during earthquakes. This study develops a deep learning model to predict whether the maximum peak ground acceleration at a station exceeds 25 Gal based on the waveform of the initial P-wave. A ResNet architecture model is developed to address the degradation problem in multilayered models, thereby enhancing performance. The model exhibits high performance for a test dataset, achieving F1-scores higher than 90% over various time windows, and outperforms the traditional progressive displacement value (Pdv) method and newer convolutional neural network (CNN) methods. Ground motion of two actual earthquakes are applied to test the model and validate its prediction capabilities, and the results indicate that the proposed model has higher accuracy and speed than Pdv and CNN methods. In addition, the model is integrated with the Earthworm earthquake early warning system for real-time waveform analysis and prediction, extending the model’s applicability beyond experimental stages to online systems. The proposed method is also tested using data from earthquakes that occurred in March 2023. © Seismological Society of America.

关键词： Convolutional neural networks

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Optimization of Anti-Air Defense Missile system Based on Kill Chain 3

Optimization of Anti-Air Defense Missile System Based on Kil...

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3rd International Symposium on Computer technology and information science, ISCTIS 2023

作者： Hu, Tao Luo, Xueshan Zhang, Xiaoxue Chen, Tao Cao, Tianruo Yuan, Hao National University of Defense Technology Science and Technology on Information System Engineering Laboratory Hunan Changsha China

ISBN: (纸本)9798350325386

The issue of resource allocation is the key to design the Anti-Air Defense Missile system As this is an NP-hard problem, allocating only end-point firepower cannot thoroughly address the issue. To better cater to real combat environments, this article uses a kill chain-based system resource allocation model that includes task completion rate, firepower cost, and completion time by utilizing discrete resources. To address the resource allocation problem, we optimized the non-dominated sorting genetic algorithm III (NSGA-III) by incorporating k-means clustering and the average clustering distance for congestion resolution. The results demonstrate that this method is effective in solving complex resource allocation problems, providing strong support for the command and decision-making of actual defense forces. © 2023 IEEE.

关键词： Resource allocation

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Self-assembled Fe_(3)O_(4)nanoparticles on V2C MXene for enhanced supercapacitor and microwave absorption applications

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Journal of Advanced Ceramics 2025年第3期14卷 183-196页

作者： Qixun Xia Ranlu Miao Wen Guo Min Xia Libo Wang Qianku Hu Aiguo Zhou School of Materials Science and Engineering Henan Polytechnic UniversityJiaozuo 454003China Henan Province Expressway Efficient Energy Storage Technology and Application Engineering Research Center Zhengzhou 450121China Department of Macromolecular Science Fudan UniversityShanghai 200438China Henan Radio Requlation Information System Backup Center Zhengzhou 450008China

Rapid advancements in modern electronic devices necessitate the development of materials that can simultaneously provide efficient energy storage and effective microwave ***,a novel composite material was prepared via the self-assembly of Fe_(3)O_(4)nanoparticles on the surface of V2C *** composite exhibited characteristics suitable for supercapacitor and electromagnetic absorption applications,highlighting the synergistic relationship between energy storage and electromagnetic wave absorption *** electrochemical tests revealed that the specific capacity of the V_(2)C MXene/Fe_(3)O_(4)composite(42.67 mAh·g^(−1))considerably improved compared with those of the raw *** prepared V2C MXene/Fe_(3)O_(4)//V_(2)C MXene/Fe_(3)O_(4)symmetric supercapacitor(SSC)demonstrated an energy density of 44.8 Wh·L^(−1),a power density of 959.4 W·L−1,and a capacity retention of 80.14%after 8000 ***,the V2C MXene/Fe_(3)O_(4)composite exhibited an optimal reflection loss(RL)of−42.4 dB in the Ku band,with an effective absorption bandwidth of 1.9 GHz(14.6–16.5 GHz).This composite material has broad application potential in modern electronic devices owing to its high energy storage capacity and effective electromagnetic wave *** dual functionality improves device performance and offers a compact solution for energy storage and effective microwave absorption.

关键词： MXenes Fe_(3)O_(4) two-dimensional(2D)materials supercapacitors microwave absorption

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