Chemometrics and Intelligent Laboratory Systems最新文献_第6页

Machine learning and evolutionary computation on e-nose datasets: A preliminary approach to ergot alkaloid detection in wheat 基于电子鼻数据集的机器学习和进化计算：小麦麦角生物碱检测的初步方法

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-11-13 DOI: 10.1016/j.chemolab.2025.105574

Chiara Giliberti , Giulia Magnani , Monica Mattarozzi , Marco Giannetto , Federica Bianchi , Maria Careri , Stefano Cagnoni

To the best of the authors' knowledge, this is the first time that an approach based on the use of machine learning (ML) algorithms combined with genetic programming (GP) was used to process small-sample-size e-nose data. The approach was proposed to classify the volatile compound information of wheat samples based on the contamination of ergot alkaloids, a class of emerging mycotoxins which pose a severe threat to food safety and consumer health. Unlike previous studies that applied convolutional neural networks to full e-nose response profiles, our approach focused on a small set of features extracted from the steady-state region of each response curve. Despite the low dimensionality, using GP to generate optimal features significantly improved the classification performance of several ML models. Different classifiers, including Decision Tree, Linear Discriminant Analysis, the Mahalanobis Distance Classifier, an artificial neural network-based method and ensemble methods were assessed and applied to a dataset of 21 wheat samples. These samples were classified according to their compliance with the EU maximum limit of 150 μg/kg for ergot alkaloids in wheat. The combined application of GP-based feature transformations, specifically using M3GP, and ML classifiers resulted in significant improvements in accuracy, F1 score, precision and recall compared to models trained on untransformed features. These findings highlight the unexplored potential of GP as a powerful tool for feature construction in sensor-based classification tasks for food safety signal processing.

据作者所知，这是第一次使用基于机器学习（ML）算法结合遗传编程（GP）的方法来处理小样本电子鼻数据。麦角生物碱是一类严重威胁食品安全和消费者健康的新型真菌毒素，提出了基于麦角生物碱污染对小麦样品挥发性化合物信息进行分类的方法。与之前将卷积神经网络应用于完整电子鼻响应剖面的研究不同，我们的方法侧重于从每个响应曲线的稳态区域提取的一小部分特征。尽管维数较低，但使用GP生成最优特征显著提高了几种ML模型的分类性能。采用决策树、线性判别分析、马氏距离分类器、基于人工神经网络的方法和集成方法对21个小麦样本数据集进行了评估和应用。这些样品符合欧盟对小麦中麦角生物碱的最高限量150 μg/kg进行分类。与未转换特征训练的模型相比，基于gp的特征转换（特别是使用M3GP）和ML分类器的组合应用在准确性、F1分数、精度和召回率方面都有显著提高。这些发现突出了GP作为基于传感器的食品安全信号处理分类任务中特征构建的强大工具的潜力。

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引用次数: 0

Nondestructive detection of total flavonoids content in daylily using Vis-NIR and NIR hyperspectral imaging: data fusion combined with SHAP for model interpretability 利用近红外和近红外高光谱成像无损检测黄花菜中总黄酮含量：数据融合结合SHAP提高模型可解释性

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-11-13 DOI: 10.1016/j.chemolab.2025.105575

Xuexia Ma, Na Li, Ruifeng Wang, Jiaxue Ma, Ninghua Zhu, Tingting Li, Zhongxiong Zhang, Haifeng Li, Songlei Wang, Haihong Zhang

Flavonoids, vital bioactive compounds in daylily (a nutritionally and medicinally valuable food), have antioxidant, anti-inflammatory, antibacterial, and antidepressant properties, which boost its nutritional value, health benefits, and quality. Hyperspectral Imaging (HSI) for detecting trace flavonoids usually uses single systems, failing to leverage multispectral complementarity and restricting detection performance. This study integrates a data fusion strategy with two HSI techniques (visible–near-infrared (Vis-NIR) and near-infrared (NIR)) for the non-destructive detection of total flavonoids content (TFC) in daylily. The investigation employed partial least squares regression (PLSR) and least squares support vector machine (LS-SVM) for spectral data. Additionally, data-level and feature-level fusion strategies are implemented for data fusion modeling, while the SHapley Additive exPlanations (SHAP) methodology is used to comprehensively evaluate spectral feature contribution rates. The findings demonstrate that modeling based on the fusion strategy of LS-SVM yields substantially superior results compared to single-system approaches. Notably, the Mid-level fusion model incorporating competitive adaptive reweighted sampling (CARS) and LS-SVM demonstrates optimal performance. The determination coefficient (R²_P), root mean square prediction error (RMSEP) and residual prediction deviation (RPD) of the prediction set were 0.9332, 0.0186 and 3.3560, respectively. This study confirms the feasibility of HSI technology in non-destructively detecting flavonoids in daylily. Furthermore, the collaborative optimization of multi-spectral HSI systems through a data fusion strategy effectively enhances the accuracy of non-destructive flavonoids detection. This study presents innovative technical approaches for non-destructive trace substance detection and agricultural product quality and safety monitoring, thereby providing essential technical support for developing intelligent agricultural product quality and safety monitoring systems.

黄花菜（一种有营养和药用价值的食物）中的黄酮类化合物是重要的生物活性化合物，具有抗氧化、抗炎、抗菌和抗抑郁的特性，这提高了黄花菜的营养价值、健康益处和质量。用于检测痕量黄酮类化合物的高光谱成像（HSI）通常使用单一系统，不能充分利用多光谱的互补性，限制了检测性能。本研究将两种HSI技术（可见-近红外（Vis-NIR）和近红外（NIR））的数据融合策略用于黄花菜中总黄酮含量（TFC）的无损检测。采用偏最小二乘回归（PLSR）和最小二乘支持向量机（LS-SVM）对光谱数据进行分析。此外，采用数据级和特征级融合策略进行数据融合建模，采用SHapley加性解释（SHAP）方法综合评估光谱特征贡献率。研究结果表明，与单系统方法相比，基于LS-SVM融合策略的建模结果明显优于单系统方法。值得注意的是，结合竞争自适应重加权采样（CARS）和LS-SVM的中级融合模型表现出最优的性能。预测集的决定系数（R2P）、均方根预测误差（RMSEP）和残差预测偏差（RPD）分别为0.9332、0.0186和3.3560。本研究证实了HSI技术无损检测黄花菜中黄酮类化合物的可行性。此外，通过数据融合策略对多光谱HSI系统进行协同优化，有效提高了黄酮类化合物无损检测的准确性。本研究提出了微量物质无损检测和农产品质量安全监测的创新技术途径，为发展农产品质量安全智能监测系统提供必要的技术支持。

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引用次数: 0

A combination of gas detection system and adaptive deep learning network (GFC-Net) to identify different production batches of beer 结合气体检测系统和自适应深度学习网络（GFC-Net）来识别不同生产批次的啤酒

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-11-04 DOI: 10.1016/j.chemolab.2025.105557

Junliang Han , Feifei Tong , Chuansheng Tang , Titi Liu

Even for products of the same brand, the quality of beer may vary across different production batches. Strict quality testing is essential to ensure product consistency, safety, and consumer satisfaction. In this work, an e-nose system, combined with the proposed deep learning algorithm, achieves the qualitative identification of beers from different production batches. First, the e-nose system is applied to acquire the gas information of beers from different production batches. Then, to comprehensively extract features characterizing the gas information, a fusion computational module that integrates local and global features from convolution and self-attention mechanism is proposed, called the Gas Features Calculation Module (GFCM). Finally, a Gas Features Classification Network (GFC-Net) is designed to enable the adaptive identification of beers from different production batches. Through structural optimization, ablation experiments, and comparison with state-of-the-art gas classification methods, GFC-Net achieves an accuracy of 98.50 %, a precision of 98.70 %, and a recall of 98.58 %. The integration of gas information that characterizes the overall chemical quality, along with GFC-Net, enables the qualitative identification of beers from different batches, providing an effective approach for quality monitoring.

即使是同一品牌的产品，不同批次的啤酒质量也会有所不同。严格的质量检测对于确保产品的一致性、安全性和消费者满意度至关重要。在这项工作中，电子鼻系统结合所提出的深度学习算法，实现了不同生产批次啤酒的定性识别。首先，利用电子鼻系统采集不同生产批次啤酒的气体信息。然后，为了全面提取表征气体信息的特征，提出了一种融合卷积和自关注机制的局部特征和全局特征的融合计算模块，称为气体特征计算模块（GFCM）。最后，设计了气体特征分类网络（GFC-Net），实现了不同生产批次啤酒的自适应识别。通过结构优化、烧蚀实验以及与现有气体分类方法的比较，GFC-Net的准确率为98.50%，精密度为98.70%，召回率为98.58%。整合表征整体化学质量的气体信息，以及GFC-Net，可以对不同批次的啤酒进行定性鉴定，为质量监测提供了有效的方法。

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引用次数: 0

Sampling-based computation of the sets of feasible solutions and feasible bands for noisy data 基于采样的噪声数据可行解集和可行带的计算

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-11-04 DOI: 10.1016/j.chemolab.2025.105565

Mathias Sawall , Tomass Andersons , Chunhong Wei , Christoph Kubis , Klaus Neymeyr

Multivariate curve resolution often suffers from solution ambiguity, with many nonnegative factorizations fitting the data equally well. Building on the algorithm of Laursen and Hobolth (2022), we present an efficient sampling algorithm that can handle noisy data even containing negative entries. The algorithm iteratively updates factor columns via affine combinations within a nested loop structure, effectively approximating the sets of feasible solutions, the feasible bands, as well as the dual profiles. We apply the algorithm to two in situ FTIR spectroscopic data sets tracking the decomposition and activation of rhodium carbonyl complexes for the hydroformylation process. A comparison against established algorithms for these data sets indicates the robustness and computational efficiency of the algorithm.

多元曲线分辨率经常受到解模糊的影响，许多非负因子分解同样可以很好地拟合数据。基于Laursen和Hobolth（2022）的算法，我们提出了一种有效的采样算法，即使包含负项也可以处理噪声数据。该算法通过嵌套循环结构内的仿射组合迭代更新因子列，有效地逼近可行解集、可行带集以及双剖面集。我们将该算法应用于两个原位FTIR光谱数据集，跟踪氢甲酰化过程中铑羰基配合物的分解和活化。对这些数据集与已有算法的比较表明了该算法的鲁棒性和计算效率。

引用次数: 0

Investigation on different strategies of significance testing in ANOVA-simultaneous component analysis (ASCA) anova -同步成分分析（ASCA）中不同显著性检验策略的探讨

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-11-04 DOI: 10.1016/j.chemolab.2025.105573

Faezeh Maddahi , Mahsa Akbari Lakeh , Jamile Mohammad Jafari , Farnoosh Koleini , Siewert Hugelier , Paul J. Gemperline , Hamid Abdollahi

ANOVA Simultaneous Component Analysis (ASCA) integrates analysis of variance with multivariate modelling to quantify how experimental factors and their interactions affect complex multivariate measurements. Statistical significance in ASCA is typically assessed by permutation testing; however, different permutation strategies imply distinct null hypothesis and exchangeability assumptions. In this study, we systematically compare three widely used approaches embedded in popular chemometric software packages where the permutation strategy is often predefined and not always transparent to the user. The restricted permutation method shuffles observations only within experimental strata, preserving the structure of the null hypothesis. The reduced‐model permutation contrasts the full ASCA model with a simplified version in which selected effects are removed. Permutation of marginal design matrices isolates interaction effects by permuting marginal matrices derived from the design matrix. We evaluate these methods on simulated datasets with varying patterns of main effects and interactions, as well as on an experimental study of feral cabbage (Brassica oleracea) under treatment and time factors. Our results show that the restricted permutation method reliably detects main effects, reduced‐model permutation excels at identifying interactions, and permutation of marginal design matrices consistently captures both. By examining the assumptions and performance of each method, we provide practical guidance for selecting the optimal permutation strategy in ASCA-based chemometric analysis, particularly for balanced experimental designs. As a baseline, we additionally assessed unrestricted permutation of the raw data using two test statistics: the sum of squares and the F-ratio. The results demonstrated that when employing the F-ratio, this approach was also capable of accurately detecting statistical significance.

ANOVA同时成分分析（ASCA）将方差分析与多变量建模相结合，量化实验因素及其相互作用如何影响复杂的多变量测量。ASCA的统计显著性通常通过排列测试来评估；然而，不同的排列策略意味着不同的零假设和互换性假设。在这项研究中，我们系统地比较了三种广泛使用的方法，这些方法嵌入在流行的化学计量软件包中，其中排列策略通常是预定义的，并不总是对用户透明。限制排列法只在实验层内打乱观察结果，保留原假设的结构。简化模型排列对比了完整的ASCA模型与简化版本，其中选择的影响被删除。边际设计矩阵的置换通过置换由设计矩阵导出的边际矩阵来隔离交互效应。我们在具有不同主效应和相互作用模式的模拟数据集上评估了这些方法，并在处理和时间因素下对野生卷心菜（芸苔甘蓝）进行了实验研究。我们的研究结果表明，限制排列方法可靠地检测主效应，简化模型排列在识别相互作用方面表现出色，而边缘设计矩阵的排列一致地捕获了两者。通过检查每种方法的假设和性能，我们为基于asca的化学计量分析中选择最佳排列策略提供了实用指导，特别是对于平衡实验设计。作为基线，我们使用两个检验统计量（平方和和f比）额外评估了原始数据的无限制排列。结果表明，当采用f比时，该方法也能够准确地检测统计显著性。

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引用次数: 0

Impact of converting graphs into spanning trees on node and graph classification in Graph Neural Network 图神经网络中生成树对节点和图分类的影响

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-11-03 DOI: 10.1016/j.chemolab.2025.105562

Mohammadmahdi Taheri , Mahdi Eftekhari , Gholamreza Aghamollaei

This paper investigates the impact of graph reduction to their spanning trees on Graph Neural Network (GNN) performance in node and graph classification across six GNN architectures. The proposed approach leverages spanning trees for graph sparsification while preserving critical structural information, achieving performance comparable to or better than full-graph models. A theoretical connection is established between edge sampling via determinantal point processes (DPPs) and the transfer-current matrix, showing minimum spanning trees effectively approximate DPP-selected subgraphs. Four edge-weighting schemes are analyzed such that their sparsification trade-offs are revealed. The method consistently reduces memory usage and computation while maintaining accuracy. Findings indicate that spanning tree pruning offers a scalable, theoretically grounded strategy for efficient GNN training without compromising classification accuracy. Experiments on node classification benchmarks (Cora, Citeseer, PubMed, PPI) and graph classification biological and chemical datasets (AIDS, MUTAG, PROTEINS, NCI1, IMDB-BINARY) demonstrate excellent graph classification results, notably 98.27% accuracy on AIDS, with reduced computational overhead.

本文研究了生成树的图约简对图神经网络（GNN）节点和图分类性能的影响。所提出的方法利用生成树进行图稀疏化，同时保留关键的结构信息，实现与全图模型相当或更好的性能。通过确定性点过程（DPPs）和传输电流矩阵建立了边缘采样的理论联系，表明最小生成树有效地近似dpp选择的子图。分析了四种边加权方案，从而揭示了它们的稀疏性权衡。该方法在保持准确性的同时持续减少内存使用和计算。研究结果表明，生成树修剪为有效的GNN训练提供了一种可扩展的，理论基础的策略，而不会影响分类精度。在节点分类基准（Cora, Citeseer, PubMed， PPI）和图分类生物和化学数据集（AIDS， MUTAG， PROTEINS, NCI1, IMDB-BINARY）上的实验显示了出色的图分类结果，在AIDS上准确率达到98.27%，计算开销降低。

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引用次数: 0

A reliable deep neural network using the radial basis for the spreading virus in computers with kill signals 一种基于径向基的可靠深度神经网络，用于具有杀伤信号的计算机中病毒的传播

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-11-01 DOI: 10.1016/j.chemolab.2025.105560

Zulqurnain Sabir , Bahaa Basbous , Basma Souayeh , Muhammad Umar , Soheil Salahshour

Purpose

The purpose of this work is to provide a reliable neural network process for the spreading virus in computers with kill signals. The mathematical model shows susceptible, exposed, infected individuals to form the virus inactive, and kill signals classes.

Method

A structure of deep neural network (DNN) is designed by using two different hidden layers having radial basis activation functions in both layers, optimization through the Bayesian regularization, twenty and thirty numbers of neurons in primary and secondary hidden layers for the spreading virus in computers with kill signals. The stochastic DNN framework is presented to solve the spreading virus in computers with kill signals by selecting the data for training as 70 %, and 15 %, 15 % for both validation and testing.

Results

The accuracy of the scheme is observed through the overlapping of the solutions along with negligible absolute error for solving the model. The consistency of the solver is observed through the process of error histogram, regression, and state transition.

Novelty

The proposed DNN structure having radial basis activation function has never been applied for the spreading virus in computers with kill signals.

目的为具有杀伤信号的计算机中病毒的传播提供一种可靠的神经网络过程。该数学模型显示了易感、暴露、感染个体形成的病毒灭活和杀伤信号等级。方法采用两层具有径向基激活函数的不同隐层设计深度神经网络（DNN）结构，通过贝叶斯正则化优化，在主隐层和次隐层分别设置20和30个神经元，用于在具有杀伤信号的计算机中传播病毒。提出了随机深度神经网络框架，通过选择训练数据为70%，验证数据为15%，测试数据为15%，来解决具有杀死信号的计算机中病毒的传播问题。结果通过解的重叠观察到该方案的精度，求解模型的绝对误差可以忽略不计。通过误差直方图、回归和状态转移的过程来观察求解器的一致性。新颖提出的具有径向基激活函数的深度神经网络结构尚未应用于具有杀伤信号的计算机中病毒的传播。

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引用次数: 0

Instrumental prediction of in vivo sensory properties of emollients to allow the development of new biobased ingredients 用仪器预测润肤剂的体内感官特性，以开发新的生物基成分

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-10-30 DOI: 10.1016/j.chemolab.2025.105559

Floriane Rischard , Amandine Flourat , Ecaterina Gore , Géraldine Savary

An important step in the development of novel cosmetic ingredients is the setting up of sensory analyses to assess their tactile properties. A recent work allowed the obtention of 12 novel biobased emollients with interesting physico-chemical properties. Four of the most promising emollients were selected in the present study and their safety was tested to ensure they are suitable for use on human skin. Their tactile properties, along with ten commercial emollients, were assessed by 16 expert assessors: circular spreading behavior, thickness of residual film and slippery after feel. In addition to characterizing a wide range of emollients, the results made possible the establishment of three predictive models using Partial Least Squares regressions. These original models correspond to various sensory attributes of the emollients, both during and after their application on the skin. All predictive models were then validated by leave-one-out cross validations. Only three instrumental parameters (viscosity, friction, stickiness) were necessary to build the models and predict the tactile properties. This approach was then applied to the eight other biobased emollients that were not initially used to establish the predictions in order to validate the models. Results demonstrate the significant value of such models for developing new ingredients. Ultimately, these predictive models could override the time-consuming and costly process of safety testing and sensory analyses in the research in development of future newly produced emollients for dermocosmetic applications.

开发新型化妆品成分的一个重要步骤是建立感官分析来评估其触觉特性。最近的一项工作使人们注意到12种具有有趣的物理化学性质的新型生物基润肤剂。在本研究中选择了四种最有前途的润肤剂，并对其安全性进行了测试，以确保它们适合用于人体皮肤。他们的触觉特性，连同十种商业润肤剂，由16位专家评估：圆形扩散行为，残余膜的厚度和光滑后的感觉。除了表征范围广泛的润肤剂外，结果还可以使用偏最小二乘回归建立三种预测模型。这些原始模型对应于润肤剂的各种感官属性，无论是在他们的应用在皮肤上。然后通过留一交叉验证验证所有预测模型。只需要三个仪器参数（粘度，摩擦力，粘性）就可以建立模型并预测触觉特性。然后将这种方法应用于其他八种最初未用于建立预测的生物基润肤剂，以验证模型。结果表明，这些模型对开发新成分具有重要的价值。最终，这些预测模型可以在未来新生产的皮肤美容应用润肤剂的研究开发中超越耗时和昂贵的安全测试和感官分析过程。

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引用次数: 0

Enhancing IoT anomaly detection with the Dwarf Mongoose-Chaos optimized deep belief framework 利用矮猫鼬-混沌优化的深度信念框架增强物联网异常检测

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-10-30 DOI: 10.1016/j.chemolab.2025.105558

Veena Potdar , Mohan Govindasa Kabadi

Anomaly detection is essential for identifying deviations from normal patterns in data, enabling the detection of security breaches or system faults, particularly in Internet of Things (IoT) networks. However, traditional machine learning (ML) and deep learning (DL) methods often struggle with the dynamic and complex nature of IoT environments, where attack patterns are non-linear, continuously evolving, and context-dependent. These models typically require large labeled datasets and retraining to adapt to new threats, which limits their responsiveness and scalability. Additionally, their high computational demands make real-time deployment on resource-constrained IoT devices challenging. Furthermore, many ML/DL models exhibit poor generalization, performing well in controlled scenarios but failing to maintain accuracy across diverse, real-world IoT settings with varying devices, protocols, and data distributions. To address these issues, this work proposes the Dwarf Mongoose-Chaos Optimized Deep Belief (DCODB) Framework, which combines advanced preprocessing, feature selection (FS), and classification techniques. Initial preprocessing involves Min-Max Normalization and One-Hot Encoding to scale numerical features and transform categorical data for effective model input. FS is optimized by the novel Dwarf Mongoose-Chaos Fusion Optimization (DMCFO), which is a swarm intelligence algorithm that leverages chaotic maps to improve the effectiveness of the Dwarf Mongoose Optimization Algorithm (DMO), reducing dimensionality and improving classification accuracy. The refined features are then classified using a Deep Belief Network (DBN), which processes hierarchical feature representations to differentiate between normal and anomalous behaviors in the NSL-KDD dataset. The proposed framework has been thoroughly assessed using diverse metrics, demonstrating its effectiveness in anomaly detection by achieving above 99 % Balanced Accuracy, along with exceptional Precision, Recall, F1 Score, Specificity, and the AUC-ROC curve. These high-performance metrics affirm the model's capability to deliver reliable and scalable anomaly detection in IoT environments, strengthening overall security.

异常检测对于识别数据中正常模式的偏差至关重要，能够检测安全漏洞或系统故障，特别是在物联网（IoT）网络中。然而，传统的机器学习（ML）和深度学习（DL）方法经常与物联网环境的动态性和复杂性作斗争，其中攻击模式是非线性的，不断发展的，并且依赖于上下文。这些模型通常需要大型标记数据集和重新训练以适应新的威胁，这限制了它们的响应能力和可扩展性。此外，它们的高计算需求使得在资源受限的物联网设备上进行实时部署具有挑战性。此外，许多ML/DL模型表现出较差的泛化，在受控场景中表现良好，但无法在具有不同设备，协议和数据分布的各种现实世界物联网设置中保持准确性。为了解决这些问题，本工作提出了矮猫鼬-混沌优化深度信念（DCODB）框架，该框架结合了先进的预处理、特征选择（FS）和分类技术。初始预处理包括Min-Max归一化和One-Hot编码，以缩放数值特征并将分类数据转换为有效的模型输入。该算法是一种基于混沌映射的群智能算法，利用混沌映射来提高小猫鼬优化算法（DMO）的有效性，降低了分类维数，提高了分类精度。然后使用深度信念网络（DBN）对精炼的特征进行分类，DBN处理分层特征表示以区分NSL-KDD数据集中的正常和异常行为。所提出的框架已经使用不同的指标进行了彻底的评估，证明了其在异常检测方面的有效性，达到了99%以上的平衡准确率，以及出色的精度、召回率、F1评分、特异性和AUC-ROC曲线。这些高性能指标肯定了该模型在物联网环境中提供可靠和可扩展异常检测的能力，从而增强了整体安全性。

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引用次数: 0

Solving the puzzle: Simulation of multivariate data with control over the structure of columns and rows using the two-sided orthogonal procrustes problem 解决难题：用双侧正交procrustes问题控制多变量数据的列和行结构的模拟

IF 3.8 2区化学 Q2 AUTOMATION & CONTROL SYSTEMS

Chemometrics and Intelligent Laboratory Systems

Pub Date : 2025-10-30 DOI: 10.1016/j.chemolab.2025.105556

Francisco Arteaga , José Camacho , Alberto Ferrer

Researchers interested in developing new multivariate statistical methods often need to be able to generate multivariate datasets with specific characteristics to test the effectiveness of their data analysis algorithms under specific conditions.

In this paper, we present a family of methods for generating multivariate centred datasets by simultaneously controlling features of the cross-product matrices

X^{⊤} X

and

X X^{⊤}

. This provides an interesting trade-off to control for the variance structure in the data, important for the family of algorithms that operate on the data matrix, like, e.g., Principal Component Analysis, and control for the distances among objects, important for algorithms that operate on the distance matrix, like Multidimensional Scaling. The proposed methods form a general framework that can be understood as a jigsaw puzzle, joining pieces obtained from the spectral decomposition of a target covariance matrix and the singular value decomposition of a target data matrix. These methods have in common that they are derived from a two-sided orthogonal Procrustes problem.

对开发新的多元统计方法感兴趣的研究人员通常需要能够生成具有特定特征的多元数据集，以测试其数据分析算法在特定条件下的有效性。在本文中，我们提出了一组通过同时控制交叉积矩阵X， X和XX， X的特征来生成多元中心数据集的方法。这为控制数据中的方差结构提供了一个有趣的权衡，这对于在数据矩阵上操作的算法家族很重要，例如，主成分分析，以及控制对象之间的距离，对于在距离矩阵上操作的算法很重要，例如多维缩放。所提出的方法形成了一个总体框架，可以理解为拼图游戏，将从目标协方差矩阵的谱分解和目标数据矩阵的奇异值分解中获得的碎片连接起来。这些方法的共同之处在于它们都是由一个双边正交的Procrustes问题导出的。

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引用次数: 0