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RETRACTION: Service Quality Evaluation of Satellite Data Distribution System Based on BP-IPA 基于BP-IPA的卫星数据分发系统服务质量评价
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-23 DOI: 10.1111/coin.70185

RETRACTION: J. Jin, L. Lu, and X. Yan, “ Service Quality Evaluation of Satellite Data Distribution System Based on BP-IPA,” Computational Intelligence 37 no. 3 (2021): 12361253, https://doi.org/10.1111/coin.12364.

The above article, published online on 09 July 2020 in Wiley Online Library (wileyonlinelibrary.com) has been retracted by agreement between the journal Editor-in-Chief, Diana Inkpen; and Wiley Periodicals LLC. The article was published as part of a guest-edited issue. Following an investigation by the publisher, all parties have concluded that this article was accepted solely on the basis of a compromised peer review process. The editors have therefore decided to retract the article. The investigation did not find evidence of ethical misconduct by the authors. The authors have been informed of the retraction. They do not agree with the decision to retract.

引用本文:金俊,陆磊,闫晓霞,“基于BP-IPA的卫星数据分发系统服务质量评估”,《计算机工程》第37期。3 (2021): 1236-1253, https://doi.org/10.1111/coin.12364.The上述文章于2020年7月9日在线发表在Wiley在线图书馆(wileyonlinelibrary.com)上,经主编Diana Inkpen同意撤回;和Wiley期刊有限责任公司。这篇文章是作为嘉宾编辑的一期的一部分发表的。经过出版商的调查,各方都得出结论,这篇文章被接受完全是基于一个妥协的同行评议过程。编辑们因此决定撤回这篇文章。调查没有发现作者存在道德不端行为的证据。作者已被告知撤稿。他们不同意撤回的决定。
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引用次数: 0
Monitoring a Community's Emotional Landscape: Harnessing Social Media Data to Enhance Telemedicine Services for Mental Health During Disaster 监测社区的情绪状况:利用社会媒体数据加强灾害期间心理健康的远程医疗服务
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-21 DOI: 10.1111/coin.70183
Divya Gupta, Shampa Chakraverty
<div> <p>Disasters severely impact community mental health, as exemplified by the COVID-19 pandemic. While the pandemic spurred social media and telemedicine adoption, a crucial gap persists in integrating them for assistance in <i>community-as-a-patient</i> care. To bridge this, we propose a novel methodology to correlate community emotional changes with external events during a disaster. For this, we curated two datasets for an urban community for the COVID-19 period: <i>UMBlog</i> (microblogs) and <i>EvLog</i> (real-world on-ground events). We fine-tuned <i>EmoRegressor</i> which predicts emotion intensities for <i>anger</i>, <i>fear</i>, <i>joy</i>, and <i>sadness</i> from text (Pearson Correlation coefficient <span></span><math> <semantics> <mrow> <mi>r</mi> <mo>></mo> <mn>0</mn> <mo>.</mo> <mn>8</mn> </mrow> <annotation>$$ r>0.8 $$</annotation> </semantics></math> and Mean Square Error <span></span><math> <semantics> <mrow> <mi>M</mi> <mi>S</mi> <mi>E</mi> <mo><</mo> <mn>0</mn> <mo>.</mo> <mn>02</mn> </mrow> <annotation>$$ MSE<0.02 $$</annotation> </semantics></math>). Applying <i>EmoRegressor</i> on <i>UMBlog</i> generated collective emotion time series. These emotion time series, alongside with event time series for <i>EvLog</i> are analyzed to reveal synchronized patterns and significant emotion transitions using change point detection and time coherence analysis. Subsequently, time-proximity analysis and event attribution yield Attribution Score (<span></span><math> <semantics> <mrow> <mi>A</mi> <mi>S</mi> </mrow> <annotation>$$ AS $$</annotation> </semantics></math>) values for events triggering emotional changes. To validate <span></span><math> <semantics> <mrow> <mi>A</mi> <mi>S</mi> </mrow> <annotation>$$ AS $$</annotation> </semantics></math> robustness, we conducted internal consistency check involving Large Language Models (LLMs) assessment and independent news validation. Further, we provide <span></span><math> <semantics> <mrow> <mi>A</mi> <mi>S</mi> </mrow> <annotation>$$ AS $$</annotation> </semantics></math>-informed recommendations for telemedicine to plan Psychological First Aid (PFA) targeting <i>community-as-a-patient</i> care. Our analysis underscores the synergy of social media and computational methods with telemedicine in foster
灾害严重影响社区心理健康,COVID-19大流行就是一个例子。虽然大流行刺激了社交媒体和远程医疗的采用,但在将它们纳入社区即患者护理方面仍然存在重大差距。为了解决这个问题,我们提出了一种新的方法,将社区情绪变化与灾难期间的外部事件联系起来。为此,我们为2019冠状病毒病期间的城市社区策划了两个数据集:UMBlog(微博)和EvLog(现实世界的现场事件)。我们对EmoRegressor进行了微调,它可以从文本中预测愤怒、恐惧、快乐和悲伤的情绪强度(Pearson相关系数r &gt; 0)。8 $$ r>0.8 $$和均方误差M S E &lt;02 $$ MSE<0.02 $$)。在UMBlog上应用EmoRegressor生成集体情绪时间序列。通过分析这些情绪时间序列以及EvLog的事件时间序列,利用变化点检测和时间一致性分析来揭示同步模式和重要的情绪转变。随后,对引发情绪变化的事件进行时间邻近分析和事件归因,得出归因得分(A S $$ AS $$)值。为了验证A S $$ AS $$稳健性,我们进行了内部一致性检查,包括大型语言模型(llm)评估和独立新闻验证。此外,我们为远程医疗计划心理急救(PFA)提供了A S $$ AS $$知情建议,目标是社区作为患者的护理。我们的分析强调了社交媒体和计算方法与远程医疗的协同作用,以促进社区规模的响应性医疗保健系统。
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引用次数: 0
DDoS Detection and Mitigation in Cloud via Hybrid Architecture With Extractive Feature Set 基于提取特征集混合架构的云DDoS检测与缓解
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-21 DOI: 10.1111/coin.70169
Sumalatha Ippa, Lakshmana Phaneendra Maguluri

Unlike traditional systems, cloud infrastructures are dynamic and distributed, necessitating specialized methods to handle distributed denial of service (DDoS) attacks that flood services with traffic, causing downtime or performance issues. This research introduces a novel approach for sensing and preventing Cloud DDoS attacks by addressing the unique challenges of dynamic and distributed cloud environments. The novelty of this work lies in three main contributions: (i) Preprocessing, (ii) Feature extraction, (iii) Attack detection, and (iv) Attack mitigation. In the preprocessing phase, the input dataset is balanced using random undersampling and processed with an enhanced decimal scaling (EDS) normalization technique. The feature extraction phase involves retrieving enhanced correntropy-based features, higher-order statistical (HOS) features, and raw features. Attack detection is carried out using a novel deep learning (DL)-based hybrid model named improved LinkNet-PolyNet (ILPNet), which combines improved LinkNet (ILNet) and PolyNet. This approach offers improved accuracy over the conventional LinkNet model by addressing bottlenecks between the encoder and decoder blocks. Lastly, an enhanced entropy-based mitigation approach is used to reduce detected attacks. The efficiency of this approach is evaluated through comparisons with existing methods using detailed performance, statistical, and ablation analyses. The ILPNet strategy demonstrated superior performance compared to conventional methods, achieving an accuracy of 0.960, a precision of 0.953, and an F-measure of 0.961.

与传统系统不同,云基础设施是动态和分布式的,因此需要专门的方法来处理分布式拒绝服务(DDoS)攻击,这种攻击会导致服务流量泛滥,导致停机或性能问题。本研究通过解决动态和分布式云环境的独特挑战,介绍了一种用于感知和预防云DDoS攻击的新方法。这项工作的新颖性在于三个主要贡献:(i)预处理,(ii)特征提取,(iii)攻击检测,(iv)攻击缓解。在预处理阶段,输入数据集使用随机欠采样进行平衡,并使用增强的十进制缩放(EDS)归一化技术进行处理。特征提取阶段包括检索增强的基于熵的特征、高阶统计(HOS)特征和原始特征。攻击检测使用一种新的基于深度学习(DL)的混合模型,称为改进的LinkNet-PolyNet (ILPNet),它结合了改进的LinkNet (ILNet)和PolyNet。这种方法通过解决编码器和解码器块之间的瓶颈,提高了传统LinkNet模型的准确性。最后,使用增强的基于熵的缓解方法来减少检测到的攻击。通过与现有方法的详细性能、统计和消融分析进行比较,评估了该方法的效率。与传统方法相比,ILPNet策略表现出优异的性能,准确度为0.960,精密度为0.953,F-measure为0.961。
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引用次数: 0
Classification of Malignant Lymphoma Using Interpretable Dilated MobileNetV2 With Convolutional Recurrent Neural Network 利用可解释的扩张MobileNetV2和卷积递归神经网络对恶性淋巴瘤进行分类
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-19 DOI: 10.1111/coin.70173
Priyaa Sri Ganesan, Ananthajothi Kaliyamoorthy

Lymphoma is commonly considered as cancer that affects the entire organs of the body. The accurate classification of malignant lymphomas is helpful for providing better treatment plans to patients. Usually, the lymphoma types are differentiated by cytologic features and growth patterns. Moreover, the abnormal cell variations are effectively identified through the immunologic, genetic, and clinical features that are useful in making the diagnosis. The gap between pattern analysis and cancer diagnostics is effectively handled with the development of computer vision methods. Computed tomography (CT)-based image analysis and Positron Emission Tomography (PET)-based image analysis for the classification of malignant lymphomas have some disadvantages, including a lack of inter and intra-observer variability. Thus, a robust deep learning-aided malignant lymphoma classification model is implemented to identify the specific type of lymphoma. Initially, the input images are acquired from benchmark databases. The required images are processed via the proposed Hybrid Adaptive and Attentive Networks (HAAN) for the malignant lymphoma classification. Therefore, it is the combination of Dilated MobilenetV2 with Convolutional-Recurrent Neural Network (RNN) for the specific cancer subtype classification process. The functionality of the suggested network is enhanced by tuning the parameters in the network via the Revised Iteration-based Peregrine Falcon Optimization (RIPFO). Thus, the proposed model processes large volumes of input images quickly and accurately for obtaining efficient malignant lymphoma classification results. Finally, the performance of the developed framework is estimated with conventional methods. The analysis results prove that the accuracy of the malignant lymphoma subtype classification framework is higher than the baseline classification mechanisms. In order to prove the model effectiveness, the accuracy of the developed technique shows 93.67%, 94.74%, and 95.36% in terms of diverse activation functions like linear, sigmoid, and ReLU, respectively.

淋巴瘤通常被认为是一种影响全身器官的癌症。恶性淋巴瘤的准确分类有助于为患者提供更好的治疗方案。通常,淋巴瘤的类型是由细胞学特征和生长模式来区分的。此外,通过免疫、遗传和临床特征有效地识别异常细胞变异,这些特征对诊断有用。随着计算机视觉方法的发展,模式分析与癌症诊断之间的差距得到了有效的解决。基于计算机断层扫描(CT)的图像分析和基于正电子发射断层扫描(PET)的图像分析用于恶性淋巴瘤的分类存在一些缺点,包括缺乏观察者之间和观察者内部的可变性。因此,实现了一个鲁棒的深度学习辅助恶性淋巴瘤分类模型来识别特定类型的淋巴瘤。最初,从基准数据库获取输入图像。通过提出的混合自适应和关注网络(HAAN)对所需的图像进行处理,用于恶性淋巴瘤分类。因此,将Dilated MobilenetV2与卷积-递归神经网络(Convolutional-Recurrent Neural Network, RNN)相结合进行特定的癌症亚型分类过程。该网络的功能是通过基于迭代的Peregrine Falcon Optimization (RIPFO)对网络中的参数进行调整而增强的。因此,该模型可以快速准确地处理大量输入图像,从而获得高效的恶性淋巴瘤分类结果。最后,用常规方法对所开发框架的性能进行了估计。分析结果证明,恶性淋巴瘤亚型分类框架的准确性高于基线分类机制。为了证明模型的有效性,所开发的技术在线性、sigmoid和ReLU等不同激活函数下的准确率分别为93.67%、94.74%和95.36%。
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引用次数: 0
An Enhanced Dynamic Autoencoder Based on Gaussian Mixture Model for Plant Disease Detection 基于高斯混合模型的植物病害检测增强动态自编码器
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-18 DOI: 10.1111/coin.70164
Dorra Zaibi, Alya Alkameli, Riadh Ksantini

Agriculture plays a fundamental role in the global economy, contributing about 6% to global gross domestic product, while in many low-income nations, it accounts for up to 25%–40% of gross domestic product. Plants provide around 75% of the food consumed by humans and are the primary nutritional source for animals, making plant health crucial to global food security. However, plant diseases represent a serious threat to agricultural productivity, causing 10%–30% loss in global crop yields annually. The early detection and diagnosis of such diseases are thus vital to sustaining agricultural productivity. While deep learning techniques—particularly deep clustering—have shown promise in image-based disease detection, they often face limitations when dealing with complex and multi-modal data. To overcome these limitations, we propose an enhanced Dynamic Autoencoder method based on a Gaussian Mixture Model for plant disease detection. Unlike conventional methods that rely heavily on labeled data, our approach leverages probabilistic clustering and adaptive feature learning to distinguish between healthy and diseased plants more effectively. We evaluate our method using the PlantVillage dataset and demonstrate that our approach outperforms traditional deep learning and clustering approaches. The experimental results highlight the model's superior accuracy and normalized mutual information, underscoring its potential for scalable and intelligent plant disease monitoring in smart agriculture.

农业在全球经济中发挥着重要作用,约占全球国内生产总值(gdp)的6%,而在许多低收入国家,农业占国内生产总值(gdp)的比例高达25%-40%。植物为人类提供了约75%的食物,也是动物的主要营养来源,因此植物健康对全球粮食安全至关重要。然而,植物病害对农业生产力构成严重威胁,每年造成全球作物产量损失10%-30%。因此,及早发现和诊断这些疾病对维持农业生产力至关重要。虽然深度学习技术——尤其是深度聚类——在基于图像的疾病检测中显示出了前景,但它们在处理复杂和多模态数据时往往面临限制。为了克服这些限制,我们提出了一种基于高斯混合模型的植物病害检测增强动态自编码器方法。与严重依赖标记数据的传统方法不同,我们的方法利用概率聚类和自适应特征学习来更有效地区分健康和患病植物。我们使用PlantVillage数据集评估了我们的方法,并证明我们的方法优于传统的深度学习和聚类方法。实验结果表明,该模型具有较高的准确性和规范化的互信息,在智能农业中具有可扩展性和智能化的植物病害监测潜力。
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引用次数: 0
DNN for Position and Orientation Control of Continuum Robots Based on Rotation Vectors 基于旋转矢量的连续体机器人位置和姿态深度神经网络控制
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-18 DOI: 10.1111/coin.70182
Weicheng Xu, Tianhao Ai, Yuhui Bao

Continuum robot is a type of robot with continuous flexible skeleton, which plays an important role in production and life. At present, the position and orientation control (POC) scheme is not widely implemented in continuum robots, and it does not pay attention to the dynamic tracking error of the end-effector orientation, which has obvious limitations for completing the task. In view of the shortcomings of the existing research, this paper proposes a hybrid neural network-based algorithm for controlling the position and orientation of a continuum robot. In this algorithm, the POC scheme is applied to the continuum robot. Based on the kinematics equation of the continuum robot, the corresponding dynamic neural network (DNN) and the dynamic tracking algorithm of the end-effector orientation are obtained by theoretical derivation. The simulation results show that the proposed scheme reduces the position error of the end-effector of the continuum robot and improves operability. Specifically, the tracking error of the Euler angle converges quickly, and the ideal Euler angle is basically consistent with the actual Euler angle. The comparison between the experimental results and the existing control methods verifies the feasibility and advancement of the scheme.

连续机器人是一种具有连续柔性骨架的机器人,在生产和生活中发挥着重要作用。目前,位置和姿态控制(POC)方案在连续体机器人中应用并不广泛,它没有考虑末端执行器姿态的动态跟踪误差,对完成任务有明显的局限性。针对现有研究的不足,本文提出了一种基于混合神经网络的连续体机器人位置和姿态控制算法。该算法将POC算法应用于连续体机器人。基于连续体机器人的运动学方程,通过理论推导得到了相应的动态神经网络(DNN)和末端执行器姿态的动态跟踪算法。仿真结果表明,该方案减小了连续体机器人末端执行器的位置误差,提高了可操作性。具体来说,欧拉角的跟踪误差收敛较快,理想欧拉角与实际欧拉角基本一致。实验结果与现有控制方法的对比验证了该方案的可行性和先进性。
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引用次数: 0
Low-Computational-Complexity Zeroing Neural Network for Resisting Multiple-Type Noise With Solution of Constrained Quadratic Programming 基于约束二次规划的低计算复杂度归零神经网络抗多类型噪声
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-14 DOI: 10.1111/coin.70181
Junpei Yang, Zhan Li

Harmonic noise interference is commonly encountered during the operation of robotic manipulators and is often accompanied by various types of non-harmonic noise (e.g., polynomial, constant, and random noise). To tackle the challenges presented by these mixed-harmonic noise environments, this paper proposes a multi-noise-resistance low-computational-complexity zeroing neural network (MNR-LCCZNN) model. The proposed framework integrates an adaptive compensation mechanism to effectively suppress harmonic noise and employs a low-computational-complexity zeroing neural network (LCCZNN) structure that eliminates the need for matrix inversion, thereby enabling efficient handling of multi-task constraints. Furthermore, the incorporation of an advanced activation function significantly improves the model's convergence speed and robustness under noise-mixture conditions. Theoretical analysis rigorously establishes the stability and noise resistance of the model. To validate its effectiveness, the MNR-LCCZNN is applied to numerical simulations involving multiple constraints, as well as trajectory control experiments on the UR3e robotic arm. These experiments are conducted under both non-harmonic and harmonic noise interference. The results demonstrate that the proposed model delivers superior accuracy, robustness, and practical applicability across a range of representative noise scenarios.

谐波噪声干扰是机器人在操作过程中经常遇到的问题,并且常常伴随着各种类型的非谐波噪声(如多项式噪声、常数噪声和随机噪声)。为了解决这些混合谐波噪声环境所带来的挑战,本文提出了一种多抗噪声低计算复杂度归零神经网络(MNR-LCCZNN)模型。该框架集成了自适应补偿机制以有效抑制谐波噪声,并采用了低计算复杂度的归零神经网络(LCCZNN)结构,消除了对矩阵反演的需要,从而能够有效地处理多任务约束。此外,引入了一种先进的激活函数,显著提高了模型在混合噪声条件下的收敛速度和鲁棒性。理论分析严密地证明了模型的稳定性和抗噪声性。为了验证其有效性,将MNR-LCCZNN应用于UR3e机械臂的多约束数值仿真和轨迹控制实验。这些实验是在非谐波和谐波噪声干扰下进行的。结果表明,所提出的模型在一系列代表性噪声场景中具有优异的准确性、鲁棒性和实用性。
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引用次数: 0
Enhancing Adversarial Attacks With Two-Way Gradient Adjustment and Neighborhood Resampling 利用双向梯度调整和邻域重采样增强对抗性攻击
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-14 DOI: 10.1111/coin.70178
Qikun Zhang, Wenhang Yi, Ruifang Wang, Junling Yuan, Mengjie Wang, Hongfei Zhu

Deep neural networks (DNNs) are highly susceptible to adversarial examples. By adding imperceptible perturbations to benign images, adversarial examples can mislead models into producing incorrect outputs, thereby posing significant security risks to DNN-based applications. However, most existing adversarial attack methods still achieve limited attack success rates. Focusing on black-box transfer attacks, we propose two novel approaches. First, TNI-FGSM aggregates gradients from the forward, backward, and current directions, enabling more stable updates and yielding more optimal perturbation directions. Second, NRSM initially performs random sampling on the input, followed by neighborhood resampling on both sides of the previous iteration's gradient. This process captures richer information, facilitates the discovery of optimal local extrema, and enhances transferability. Experiments conducted on ImageNet across conventional CNNs, four types of vision Transformers, and robust models demonstrate that NRSM consistently outperforms baseline methods. When Inception-v3 (Inc-v3) is used as the local model, NRSM achieves attack success rates (ASRs) that are 11.9% and 15.4% higher than LETM on Swin and HGD, respectively. Under the ensemble setting, NRSM attains an average ASR of 96.5%, surpassing Admix by 6.1%. Code is available at https://github.com/BreenoWH/NRSM-TNI.

深度神经网络(dnn)极易受到对抗性示例的影响。通过在良性图像中添加难以察觉的扰动,对抗性示例可能会误导模型产生不正确的输出,从而对基于dnn的应用程序构成重大的安全风险。然而,大多数现有的对抗性攻击方法的攻击成功率仍然有限。针对黑盒传输攻击,我们提出了两种新的攻击方法。首先,TNI-FGSM聚合了来自前向、后向和当前方向的梯度,从而实现更稳定的更新并产生更优的扰动方向。其次,NRSM首先对输入进行随机采样,然后在前一次迭代的梯度两侧进行邻域重采样。这一过程捕获了更丰富的信息,促进了最优局部极值的发现,并增强了可移植性。在ImageNet上对传统cnn、四种类型的视觉变形器和鲁棒模型进行的实验表明,NRSM始终优于基线方法。当采用Inception-v3 (incc -v3)作为局部模型时,NRSM在Swin和HGD上的攻击成功率分别比LETM高11.9%和15.4%。在集合设置下,NRSM的平均ASR达到96.5%,比Admix高出6.1%。代码可从https://github.com/BreenoWH/NRSM-TNI获得。
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引用次数: 0
Efficient Breast Cancer Detection and Classification Model by Analyzing Mammogram Images Using ViT-Aided MobileNet With LSTM Network Based on Adaptive Segmentation 基于自适应分割的LSTM网络viti辅助MobileNet分析乳腺x线图像的高效乳腺癌检测与分类模型
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-14 DOI: 10.1111/coin.70176
Amarjeet Poonia, Monalisa Meena, Amritpal Singh Yadav, Saurabh Maheshwari, Dalpat Songara

One of the prevalent types of malignancy that affects women is called breast cancer. Early identification and treatment of cancer lessen the mortality rate. It is more common in women than men, which significantly increases the death rate of women. Prognosis and early identification are essential for ultimately increasing survival rates. The developed model is used to create effective techniques for detecting cancer in the women's at prior stage utilizing mammogram scans. The breast X-Ray images is also called as mammogram image. To recognize breast cancer from Mammogram images, an intelligent cancer framework is implemented using deep learning. The images needed for the classification process is collected from standard datasets. Next, the gathered images are provided to the segmented phase. Here, the segmentation is performed using a Weighted Adaptive Mask Region-based Convolutional Neural Network (WAM-RCNN), where the weights are optimally tuned using the Enhanced Fennec Fox Optimization (EFFO) algorithm. The segmented outcomes are fed to Vision Transformer-based MobileNet with Long Short Term Memory (ViT-MobLSTM) for the “detection and classification of breast cancer.” Finally, the suggested model is correlated with numerous metrics to find the competence of the model. The validation result shows that the developed model outperforms with effective outcomes.

影响女性的一种常见的恶性肿瘤被称为乳腺癌。癌症的早期发现和治疗可以降低死亡率。妇女比男子更常见,这大大增加了妇女的死亡率。预后和早期诊断对于最终提高生存率至关重要。所开发的模型用于创建有效的技术,以检测早期妇女的癌症,利用乳房x光扫描。乳房x光图像也被称为乳房x光图像。为了从乳房x光照片中识别乳腺癌,使用深度学习实现了一个智能癌症框架。分类过程所需的图像是从标准数据集中收集的。接下来,将采集到的图像提供给分割阶段。在这里,使用加权自适应掩码区域卷积神经网络(WAM-RCNN)进行分割,其中使用增强型Fennec Fox优化(EFFO)算法对权重进行优化调整。分割后的结果被输入到基于视觉转换器的长短期记忆移动网络(viti - moblstm)中,用于“乳腺癌的检测和分类”。最后,将建议的模型与多个指标进行关联,以确定模型的胜任能力。验证结果表明,所建立的模型具有良好的效果。
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引用次数: 0
Data-Driven Fault Detection for Multi-Agent Systems With Data Quantization 基于数据量化的多智能体系统数据驱动故障检测
IF 1.7 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Pub Date : 2026-01-08 DOI: 10.1111/coin.70171
Yuan Wang

This paper focuses on data-driven fault detection for multi-agent systems subject to actuator faults. For fault detection, a quantized data-driven fault detection scheme is constructed by making full use of data quantization information, which can reduce the computational burden and overcome dependence on a precise system mathematical model. Moreover, the developed method has the fault detection capability, where each agent can detect faults in all other agents within the topology network. A simulation study is conducted in the final section to validate the effectiveness of the proposed scheme.

研究了多智能体系统在执行器故障情况下的数据驱动故障检测问题。在故障检测方面,充分利用数据量化信息,构建量化数据驱动的故障检测方案,减少了计算量,克服了对精确系统数学模型的依赖。此外,该方法还具有故障检测功能,每个代理都可以检测拓扑网络中所有其他代理的故障。最后进行了仿真研究,验证了所提方案的有效性。
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引用次数: 0
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Computational Intelligence
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