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Learning Vector Quantization Neural Network Method for Network Intrusion Detection 认领 引用
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作者 YANG Degang CHEN Guo +1 位作者 WANG Hui LIAO Xiaofeng 《Wuhan University Journal of Natural Sciences》 CAS 2007年第1期147-150,共4页
A new intrusion detection method based on learning vector quantization （LVQ） with low overhead and high efficiency is presented. The computer vision system employs LVQ neural networks as classifier to recognize intr... A new intrusion detection method based on learning vector quantization （LVQ） with low overhead and high efficiency is presented. The computer vision system employs LVQ neural networks as classifier to recognize intrusion. The recognition process includes three stages： （1） feature selection and data normalization processing;（2） learning the training data selected from the feature data set; （3） identifying the intrusion and generating the result report of machine condition classification. Experimental results show that the proposed method is promising in terms of detection accuracy, computational expense and implementation for intrusion detection. 展开更多
关键词 intrusion detection learning vector quantization neural network feature extraction
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Identification of dynamic systems using support vector regression neural networks 认领 引用 被引量：3
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作者 李军 刘君华 《Journal of Southeast University(English Edition)》 EI CAS 2006年第2期228-233,共6页
A novel adaptive support vector regression neural network （SVR-NN） is proposed, which combines respectively merits of support vector machines and a neural network. First, a support vector regression approach is appl... A novel adaptive support vector regression neural network （SVR-NN） is proposed, which combines respectively merits of support vector machines and a neural network. First, a support vector regression approach is applied to determine the initial structure and initial weights of the SVR-NN so that the network architecture is easily determined and the hidden nodes can adaptively be constructed based on support vectors. Furthermore, an annealing robust learning algorithm is presented to adjust these hidden node parameters as well as the weights of the SVR-NN. To test the validity of the proposed method, it is demonstrated that the adaptive SVR-NN can be used effectively for the identification of nonlinear dynamic systems. Simulation results show that the identification schemes based on the SVR-NN give considerably better performance and show faster learning in comparison to the previous neural network method. 展开更多
关键词 support vector regression neural network system identification robust learning algorithm adaptability
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An Adaptive Features Fusion Convolutional Neural Network for Multi-Class Agriculture Pest Detection 认领 引用
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作者 Muhammad Qasim Syed MAdnan Shah +4 位作者 Qamas Gul Khan Safi Danish Mahmood Adeel Iqbal Ali Nauman Sung Won Kim 《Computers, Materials & Continua》 SCIE EI 2025年第6期4429-4445,共17页
Grains are the most important food consumed globally,yet their yield can be severely impacted by pest infestations.Addressing this issue,scientists and researchers strive to enhance the yield-to-seed ratio through eff... Grains are the most important food consumed globally,yet their yield can be severely impacted by pest infestations.Addressing this issue,scientists and researchers strive to enhance the yield-to-seed ratio through effective pest detection methods.Traditional approaches often rely on preprocessed datasets,but there is a growing need for solutions that utilize real-time images of pests in their natural habitat.Our study introduces a novel twostep approach to tackle this challenge.Initially,raw images with complex backgrounds are captured.In the subsequent step,feature extraction is performed using both hand-crafted algorithms(Haralick,LBP,and Color Histogram)and modified deep-learning architectures.We propose two models for this purpose:PestNet-EF and PestNet-LF.PestNet-EF uses an early fusion technique to integrate handcrafted and deep learning features,followed by adaptive feature selection methods such as CFS and Recursive Feature Elimination(RFE).PestNet-LF utilizes a late fusion technique,incorporating three additional layers(fully connected,softmax,and classification)to enhance performance.These models were evaluated across 15 classes of pests,including five classes each for rice,corn,and wheat.The performance of our suggested algorithms was tested against the IP102 dataset.Simulation demonstrates that the Pestnet-EF model achieved an accuracy of 96%,and the PestNet-LF model with majority voting achieved the highest accuracy of 94%,while PestNet-LF with the average model attained an accuracy of 92%.Also,the proposed approach was compared with existing methods that rely on hand-crafted and transfer learning techniques,showcasing the effectiveness of our approach in real-time pest detection for improved agricultural yield. 展开更多
关键词 Artificial neural network(ANN) support vector machine(SVM) deep neural network(DNN) transfer learning(TL)
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Cryptocurrency Market Trends: A Machine Learning-Driven Time Series Forecasting with Twitter Sentiment Integration 认领 引用
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作者 Mubariz Khan Hafeez Ur Rehman Siddiqui +4 位作者 Adil Ali Saleem Muhammad Amjad Raza Lázaro Javier Hernández Rodríguez Pablo Herrero García Isabel de la Torre Díez 《Computers, Materials & Continua》 SCIE EI 2026年第9期1116-1136,共21页
Accurate forecasting of cryptocurrency prices remains an open challenge because classical statistical models cannot capture the non-linear,sentiment-driven dynamics of these markets.This study compares three hybrid de... Accurate forecasting of cryptocurrency prices remains an open challenge because classical statistical models cannot capture the non-linear,sentiment-driven dynamics of these markets.This study compares three hybrid deep learning architectures—VAR-LSTM,XGBoost-LSTM,and CNN-LSTM—to determine which best forecasts Bitcoin(BTC),Ethereum(ETH),and Dogecoin(DOGE)closing prices,and to quantify the marginal predictive value of Twitter sentiment integration.Six years of hourly OHLCV data(2017–2023)are augmented with VADER-scored Twitter sentiment polarity.Each model is formulated mathematically,implemented with documented hyperparameters(epochs,dropout,units;),and trained for one-step-ahead next-hour price prediction.Performance is measured by RMSE,MAE,MAPE,R²,and Directional Accuracy(DA)across five random seeds,with paired Wilcoxon significance tests.XGBoost-LSTM achieves the best performance(RMSE=81.547,R²=0.9254,DA=80.0%),outperforming all nine literature baselines.Removing Twitter sentiment degrades DA by 14.3 percentage points(p<0.01),confirming that social media signals carry independent predictive information.Hybrid architectures consistently outperform single-model baselines;XGBoost-LSTM offers the best accuracy-to-compute ratio.VADER-enriched Twitter sentiment is a significant predictor beyond price history.Limitations include reliance on a single sentiment platform and a training window that predates several structural market events. 展开更多
关键词 Cryptocurrency forecasting long short-term memory XGBoost convolutional neural network vector autoregression sentiment analysis Bitcoin time series hybrid models deep learning
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Machine Learning and Artificial Neural Network for Predicting Heart Failure Risk 认领 引用
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作者 Polin Rahman Ahmed Rifat +3 位作者 MD.IftehadAmjad Chy Mohammad Monirujjaman Khan Mehedi Masud Sultan Aljahdali 《Computer Systems Science & Engineering》 SCIE EI 2023年第1期757-775,共19页
Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learni... Heart failure is now widely spread throughout the world.Heart disease affects approximately 48%of the population.It is too expensive and also difficult to cure the disease.This research paper represents machine learning models to predict heart failure.The fundamental concept is to compare the correctness of various Machine Learning(ML)algorithms and boost algorithms to improve models’accuracy for prediction.Some supervised algorithms like K-Nearest Neighbor(KNN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF),Logistic Regression(LR)are considered to achieve the best results.Some boosting algorithms like Extreme Gradient Boosting(XGBoost)and Cat-Boost are also used to improve the prediction using Artificial Neural Networks(ANN).This research also focuses on data visualization to identify patterns,trends,and outliers in a massive data set.Python and Scikit-learns are used for ML.Tensor Flow and Keras,along with Python,are used for ANN model train-ing.The DT and RF algorithms achieved the highest accuracy of 95%among the classifiers.Meanwhile,KNN obtained a second height accuracy of 93.33%.XGBoost had a gratified accuracy of 91.67%,SVM,CATBoost,and ANN had an accuracy of 90%,and LR had 88.33%accuracy. 展开更多
关键词 Heart failure prediction data visualization machine learning k-nearest neighbors support vector machine decision tree random forest logistic regression xgboost and catboost artificial neural network
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A STUDY OF METHODS FOR IMPROVING LEARNING VECTOR QUANTIZATION 认领 引用
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作者 朱策 厉力华 +1 位作者 何振亚 王太君 《Journal of Electronics(China)》 1992年第4期312-320,共9页
Learning Vector Quantization(LVQ)originally proposed by Kohonen(1989)is aneurally-inspired classifier which pays attention to approximating the optimal Bayes decisionboundaries associated with a classification task.Wi... Learning Vector Quantization(LVQ)originally proposed by Kohonen(1989)is aneurally-inspired classifier which pays attention to approximating the optimal Bayes decisionboundaries associated with a classification task.With respect to several defects of LVQ2 algorithmstudied in this paper,some‘soft’competition schemes such as‘majority voting’scheme andcredibility calculation are proposed for improving the ability of classification as well as the learningspeed.Meanwhile,the probabilities of winning are introduced into the corrections for referencevectors in the‘soft’competition.In contrast with the conventional sequential learning technique,a novel parallel learning technique is developed to perform LVQ2 procedure.Experimental resultsof speech recognition show that these new approaches can lead to better performance as comparedwith the conventional 展开更多
关键词 Learning Vector Quantization(LVQ) Soft competition scheme Credibility Reference vector Parallel(sequential)learning technique
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A Comparative Study of Support Vector Machine and Artificial Neural Network for Option Price Prediction 认领 引用 被引量：1
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作者 Biplab Madhu Md. Azizur Rahman +3 位作者 Arnab Mukherjee Md. Zahidul Islam Raju Roy Lasker Ershad Ali 《Journal of Computer and Communications》 2021年第5期78-91,共14页
Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine lear... Option pricing has become one of the quite important parts of the financial market. As the market is always dynamic, it is really difficult to predict the option price accurately. For this reason, various machine learning techniques have been designed and developed to deal with the problem of predicting the future trend of option price. In this paper, we compare the effectiveness of Support Vector Machine (SVM) and Artificial Neural Network (ANN) models for the prediction of option price. Both models are tested with a benchmark publicly available dataset namely SPY option price-2015 in both testing and training phases. The converted data through Principal Component Analysis (PCA) is used in both models to achieve better prediction accuracy. On the other hand, the entire dataset is partitioned into two groups of training (70%) and test sets (30%) to avoid overfitting problem. The outcomes of the SVM model are compared with those of the ANN model based on the root mean square errors (RMSE). It is demonstrated by the experimental results that the ANN model performs better than the SVM model, and the predicted option prices are in good agreement with the corresponding actual option prices. 展开更多
关键词 Machine Learning Support Vector Machine Artificial Neural Network Prediction Option Price
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Reinforcement Learning Based Quantization Strategy Optimal Assignment Algorithm for Mixed Precision 认领 引用 被引量：1
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作者 Yuejiao Wang Zhong Ma +2 位作者 Chaojie Yang Yu Yang Lu Wei 《Computers, Materials & Continua》 SCIE EI 2024年第4期819-836,共18页
The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to d... The quantization algorithm compresses the original network by reducing the numerical bit width of the model,which improves the computation speed. Because different layers have different redundancy and sensitivity to databit width. Reducing the data bit width will result in a loss of accuracy. Therefore, it is difficult to determinethe optimal bit width for different parts of the network with guaranteed accuracy. Mixed precision quantizationcan effectively reduce the amount of computation while keeping the model accuracy basically unchanged. In thispaper, a hardware-aware mixed precision quantization strategy optimal assignment algorithm adapted to low bitwidth is proposed, and reinforcement learning is used to automatically predict the mixed precision that meets theconstraints of hardware resources. In the state-space design, the standard deviation of weights is used to measurethe distribution difference of data, the execution speed feedback of simulated neural network accelerator inferenceis used as the environment to limit the action space of the agent, and the accuracy of the quantization model afterretraining is used as the reward function to guide the agent to carry out deep reinforcement learning training. Theexperimental results show that the proposed method obtains a suitable model layer-by-layer quantization strategyunder the condition that the computational resources are satisfied, and themodel accuracy is effectively improved.The proposed method has strong intelligence and certain universality and has strong application potential in thefield of mixed precision quantization and embedded neural network model deployment. 展开更多
关键词 Mixed precision quantization quantization strategy optimal assignment reinforcement learning neural network model deployment
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Machine Learning Techniques in Predicting Hot Deformation Behavior of Metallic Materials 认领 引用 被引量：3
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作者 Petr Opela Josef Walek Jaromír Kopecek 《Computer Modeling in Engineering & Sciences》 SCIE EI 2025年第1期713-732,共20页
In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot al... In engineering practice,it is often necessary to determine functional relationships between dependent and independent variables.These relationships can be highly nonlinear,and classical regression approaches cannot always provide sufficiently reliable solutions.Nevertheless,Machine Learning(ML)techniques,which offer advanced regression tools to address complicated engineering issues,have been developed and widely explored.This study investigates the selected ML techniques to evaluate their suitability for application in the hot deformation behavior of metallic materials.The ML-based regression methods of Artificial Neural Networks(ANNs),Support Vector Machine(SVM),Decision Tree Regression(DTR),and Gaussian Process Regression(GPR)are applied to mathematically describe hot flow stress curve datasets acquired experimentally for a medium-carbon steel.Although the GPR method has not been used for such a regression task before,the results showed that its performance is the most favorable and practically unrivaled;neither the ANN method nor the other studied ML techniques provide such precise results of the solved regression analysis. 展开更多
关键词 Machine learning Gaussian process regression artificial neural networks support vector machine hot deformation behavior
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Machine learning for adjoint vector in aerodynamic shape optimization 认领 引用 被引量：4
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作者 Mengfei Xu Shufang Song +2 位作者 Xuxiang Sun Wengang Chen Weiwei Zhang 《Acta Mechanica Sinica》 SCIE EI CAS CSCD 2021年第9期1416-1432,I0003,共17页
Adjoint method is widely used in aerodynamic design because only once solution of flow field is required for it to obtain the gradients of all design variables. However, the computational cost of adjoint vector is app... Adjoint method is widely used in aerodynamic design because only once solution of flow field is required for it to obtain the gradients of all design variables. However, the computational cost of adjoint vector is approximately equal to that of flow computation. In order to accelerate the solution of adjoint vector and improve the efficiency of adjoint-based optimization, machine learning for adjoint vector modeling is presented. Deep neural network (DNN) is employed to construct the mapping between the adjoint vector and the local flow variables. DNN can efficiently predict adjoint vector and its generalization is examined by a transonic drag reduction of NACA0012 airfoil. The results indicate that with negligible computational cost of the adjoint vector, the proposed DNN-based adjoint method can achieve the same optimization results as the traditional adjoint method. 展开更多
关键词 Machine learning Deep neural network Adjoint vector modelling Aerodynamic shape optimization Adjoint method
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基于GASF-HOG-LVQ的油纸绝缘老化超声诊断 认领 引用
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作者 杨壮 尹智贤 +3 位作者 杨定坤 王畅鼎 陈伟根 王品一 《高电压技术》 EI CAS CSCD 北大核心 2025年第12期5779-5787,共9页
变压器是电力系统中的重要设备,为及时有效地评估变压器老化状态,该文提出了一种油纸绝缘老化超声特征提取及诊断方法。通过加速热老化试验,获得了涵盖全寿命周期的油纸绝缘样品。利用自主搭建的绝缘油超声检测平台获取了所有样本的超... 变压器是电力系统中的重要设备,为及时有效地评估变压器老化状态,该文提出了一种油纸绝缘老化超声特征提取及诊断方法。通过加速热老化试验,获得了涵盖全寿命周期的油纸绝缘样品。利用自主搭建的绝缘油超声检测平台获取了所有样本的超声信号,测量了其对应的绝缘纸聚合度并划分了老化状态。利用格拉姆求和角场(Gramian angular summation field,GASF)获取了超声信号的GASF图像,并基于方向梯度直方图(histogram of oriented gradient,HOG)提取了图像的HOG特征。基于学习向量化(learning vector quantization,LVQ)神经网络建立了基于油纸绝缘超声特征的老化诊断模型,通过试凑法确定最优竞争层神经元个数为9。研究结果表明,该诊断模型的诊断准确率超过90%,该方法具有监测油纸绝缘的潜力,具有一定的学术价值及工程应用意义。 展开更多
关键词 油纸绝缘老化 超声 格拉姆角场 方向梯度直方图 学习向量化神经网络
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Application of Feature Extraction through Convolution Neural Networks and SVM Classifier for Robust Grading of Apples 认领 引用 被引量：8
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作者 Yuan CAI Clarence W.DE SILVA +2 位作者 Bing LI Liqun WANG Ziwen WANG 《Instrumentation》 EI 2019年第4期59-71,共13页
This paper proposes a novel grading method of apples,in an automated grading device that uses convolutional neural networks to extract the size,color,texture,and roundness of an apple.The developed machine learning me... This paper proposes a novel grading method of apples,in an automated grading device that uses convolutional neural networks to extract the size,color,texture,and roundness of an apple.The developed machine learning method uses the ability of learning representative features by means of a convolutional neural network(CNN),to determine suitable features of apples for the grading process.This information is fed into a one-to-one classifier that uses a support vector machine(SVM),instead of the softmax output layer of the CNN.In this manner,Yantai apples with similar shapes and low discrimination are graded using four different approaches.The fusion model using both CNN and SVM classifiers is much more accurate than the simple k-nearest neighbor(KNN),SVM,and CNN model when used separately for grading,and the learning ability and the generalization ability of the model is correspondingly increased by the combined method.Grading tests are carried out using the automated grading device that is developed in the present work.It is verified that the actual effect of apple grading using the combined CNN-SVM model is fast and accurate,which greatly reduces the manpower and labor costs of manual grading,and has important commercial prospects. 展开更多
关键词 Apple Grading k-nearest Neighbour Method Convolutional Neural Network Support Vector Machine Machine Learning
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Using Neural Networks to Predict Secondary Structure for Protein Folding 认领 引用 被引量：2
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作者 Ali Abdulhafidh Ibrahim Ibrahim Sabah Yasseen 《Journal of Computer and Communications》 2017年第1期1-8,共8页
Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate predi... Protein Secondary Structure Prediction (PSSP) is considered as one of the major challenging tasks in bioinformatics, so many solutions have been proposed to solve that problem via trying to achieve more accurate prediction results. The goal of this paper is to develop and implement an intelligent based system to predict secondary structure of a protein from its primary amino acid sequence by using five models of Neural Network (NN). These models are Feed Forward Neural Network (FNN), Learning Vector Quantization (LVQ), Probabilistic Neural Network (PNN), Convolutional Neural Network (CNN), and CNN Fine Tuning for PSSP. To evaluate our approaches two datasets have been used. The first one contains 114 protein samples, and the second one contains 1845 protein samples. 展开更多
关键词 Protein Secondary Structure Prediction (PSSP) Neural Network (NN) α-Helix (H) β-Sheet (E) Coil (C) Feed Forward Neural Network (FNN) Learning Vector Quantization (LVQ) Probabilistic Neural Network (PNN) Convolutional Neural Network (CNN)
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Predicting Stock Movement Using Sentiment Analysis of Twitter Feed with Neural Networks 认领 引用
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作者 Sai Vikram Kolasani Rida Assaf 《Journal of Data Analysis and Information Processing》 2020年第4期309-319,共11页
External factors, such as social media and financial news, can have wide-spread effects on stock price movement. For this reason, social media is considered a useful resource for precise market predictions. In this pa... External factors, such as social media and financial news, can have wide-spread effects on stock price movement. For this reason, social media is considered a useful resource for precise market predictions. In this paper, we show the effectiveness of using Twitter posts to predict stock prices. We start by training various models on the Sentiment 140 Twitter data. We found that Support Vector Machines (SVM) performed best (0.83 accuracy) in the sentimental analysis, so we used it to predict the average sentiment of tweets for each day that the market was open. Next, we use the sentimental analysis of one year’s data of tweets that contain the “stock market”, “stocktwits”, “AAPL” keywords, with the goal of predicting the corresponding stock prices of Apple Inc. (AAPL) and the US’s Dow Jones Industrial Average (DJIA) index prices. Two models, Boosted Regression Trees and Multilayer Perceptron Neural Networks were used to predict the closing price difference of AAPL and DJIA prices. We show that neural networks perform substantially better than traditional models for stocks’ price prediction. 展开更多
关键词 Tweets Sentiment Analysis with Machine Learning Support Vector Machines (SVM) Neural Networks Stock Prediction
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基于6种机器学习模型的ICU患者多重耐药菌感染预测模型构建与评价 认领 引用 被引量：3
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作者 王珂璇 金晓灵 茅一萍 《中华医院感染学杂志》 CAS CSCD 北大核心 2026年第3期422-426,共5页
目的分析重症监护室患者感染多重耐药菌的危险因素,通过6种机器学习算法构建患者感染多重耐药菌的预测模型,通过评价模型相关指标选出最佳模型,为临床工作者早期识别高危患者,及时采取相应的预防措施提供参考。方法纳入2019年6月-2023年... 目的分析重症监护室患者感染多重耐药菌的危险因素,通过6种机器学习算法构建患者感染多重耐药菌的预测模型,通过评价模型相关指标选出最佳模型,为临床工作者早期识别高危患者,及时采取相应的预防措施提供参考。方法纳入2019年6月-2023年6月入住徐州医科大学附属医院重症监护室患者946例(多重耐药菌感染者473例,非感染者473例)。采用二元logistic回归分析,将筛选的危险因素作为构建预测模型的特征变量进行模型构建,分别构建并评价逻辑回归模型、人工神经网络模型、决策树模型、随机森林模型、支持向量机模型和极限梯度增强模型。结果从外院或急诊入院(OR=2.635)、入住重症监护室时长≥7 d(OR=1.291)、手术(OR=3.089)、慢性肺部疾病(OR=3.664)、外周静脉置管(OR=2.111)、留置腹腔引流管(OR=3.382)、抗菌药物使用种类≥3种(OR=1.001)、抗菌药物使用时长≥1周(OR=2.323)是重症监护室患者感染多重耐药菌的危险因素(P<0.05)。通过机器学习算法构建的重症监护室患者感染多重耐药菌预测模型中,逻辑回归模型受试者工作特征曲线下面积、灵敏度、特异度、阳性预测值、阴性预测值、F1值均优于其他模型,为最优模型。结论临床应重视患者易感染多重耐药菌的危险因素,尽早给予针对性干预,降低重症监护室患者感染多重耐药菌的风险。 展开更多
关键词 机器学习 重症监护室 多重耐药菌 预测模型 逻辑回归模型 人工神经网络模型 决策树模型 随机森林模型 支持向量机模型 极限梯度增强模型
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融合矢量结构特征的复杂建筑物形状识别方法 认领 引用
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作者 周旭升 谭永滨 +2 位作者 于忠海 王维 肖青云 《测绘学报》 EI CSCD 北大核心 2026年第5期838-849,共12页
建筑物形状识别与分类作为城市空间分析、智能制图与三维城市建模的关键环节,对高精度地图构建和智慧城市治理具有重要意义。然而,现有方法在应对复杂多样的建筑形态时仍面临显著挑战。基于人工定义几何特征的方法普遍存在泛化能力不足... 建筑物形状识别与分类作为城市空间分析、智能制图与三维城市建模的关键环节,对高精度地图构建和智慧城市治理具有重要意义。然而,现有方法在应对复杂多样的建筑形态时仍面临显著挑战。基于人工定义几何特征的方法普遍存在泛化能力不足的问题;而采用栅格化处理的方法则容易引入几何失真,难以准确保留建筑矢量轮廓的拓扑结构与精细特征。为此,本文提出一种面向矢量建筑物轮廓的端到端自动特征学习方法。该方法将建筑轮廓抽象为角点图结构,以实现建筑物轮廓的结构化表示;并设计结构特征模块嵌入图卷积网络(GCN)中,用于捕获凹凸转折、分支等形态特征,从而增强模型对复杂几何结构的判别能力。试验结果显示,该方法在公开及其扩展数据集上的准确率分别为99.20%和99.03%,Kappa系数均超过0.989,展现出优异的性能。试验误判分析表明,模型混淆主要集中于几何或拓扑高度相似的类别(如E/U形、X/O形),反映出其在细粒度结构建模与全局语义融合方面仍有提升空间。本文为矢量建筑轮廓的高精度识别提供了一种无须人工特征、具备良好泛化性的解决方法。 展开更多
关键词 建筑物形状分类 矢量轮廓 图神经网络 自动特征学习 城市空间分析
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基于多光谱与热红外影像的烤烟地土壤含水率反演 认领 引用
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作者 查宏波 赵芳 +5 位作者 王力 陈嘉航 徐凯 王海东 杨启良 吴立峰 《节水灌溉》 北大核心 2026年第3期26-33,41,共8页
为实现烤烟地土壤含水率快速精准监测,以云南省昭通市巧家县蒙姑镇拖坑村9块烤烟试验田为研究区,结合多光谱、热红外无人机影像与机器学习模型开展反演研究。获取DJI Mavic 3多光谱无人机(4波段,分辨率0.003 m)、DJI Matrice 4T热红外... 为实现烤烟地土壤含水率快速精准监测,以云南省昭通市巧家县蒙姑镇拖坑村9块烤烟试验田为研究区,结合多光谱、热红外无人机影像与机器学习模型开展反演研究。获取DJI Mavic 3多光谱无人机(4波段,分辨率0.003 m)、DJI Matrice 4T热红外无人机(VOx传感器,分辨率0.005 m)影像,以及土壤体积含水率数据。经ENVI与Pix4D预处理影像,提取多光谱敏感特征(9个)并结合热红外DN值构建特征集,采用随机森林、支持向量机、极端梯度提升、卷积神经网络算法构建单源/多源反演模型,以均方根误差(RMSE)与决定系数(R2)评估精度。结果表明:多源模型精度显著优于单源,RF在多光谱+DN输入下最优(RMSE=1.30%、R2=0.79),XGBoost性能与其接近(RMSE=1.31%、R2=0.78);SHAP分析显示NREI、NDVI_RVI及热红外DN值为关键特征。研究证实多光谱与热红外无人机影像协同机器学习可高效反演烤烟地土壤含水率,为产区精准灌溉与水资源管理提供技术支撑。 展开更多
关键词 无人机 机器学习 随机森林 支持向量机 XGBoost 卷积神经网 土壤含水率反演 多光谱 热红外 数据融合
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材料自然环境腐蚀预测模型应用进展 认领 引用
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作者 张彭辉 姜浩 +2 位作者 徐铖 白雪寒 李青音 《材料保护》 CAS CSCD 2026年第7期86-97,共12页
各种装备结构材料在自然环境中不可避免地会发生腐蚀。随着算法理论和计算机技术的发展,针对其腐蚀行为的预测模型得到了广泛关注与应用。为此,对灰色预测模型、支持向量机模型、人工神经网络模型等典型腐蚀预测模型进行了简介,介绍了... 各种装备结构材料在自然环境中不可避免地会发生腐蚀。随着算法理论和计算机技术的发展,针对其腐蚀行为的预测模型得到了广泛关注与应用。为此,对灰色预测模型、支持向量机模型、人工神经网络模型等典型腐蚀预测模型进行了简介,介绍了其在腐蚀领域的应用进展,并对深度学习、集成学习等机器学习方法进行了概述,并指出通过算法结构优化、性能提升及集成联用等方式可有效提高模型性能,是未来腐蚀预测模型发展与应用的重点方向。 展开更多
关键词 腐蚀预测 支持向量机 人工神经网络 机器学习
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基于经典机器学习模型的河流重点水质预测 认领 引用
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作者 吕婷 薛琼 +1 位作者 闵兴华 金哲 《净水技术》 CAS 2026年第1期150-156,共7页
近年来,水体污染问题日益突出,给河道环境造成巨大压力,故河道水环境破坏问题亟待解决。机器学习是一种基于大量监测数据的水质预测预警方法,是河道治理的新途径。【目的】本文旨在对比不同模型对不同水质指标的预测能力。【方法】本文... 近年来,水体污染问题日益突出,给河道环境造成巨大压力,故河道水环境破坏问题亟待解决。机器学习是一种基于大量监测数据的水质预测预警方法,是河道治理的新途径。【目的】本文旨在对比不同模型对不同水质指标的预测能力。【方法】本文以长江中下游平原某河流断面为例,首先通过显著性分析和主成分分析筛选出主要水质影响因子,随后根据自动监测站点数据选用支持向量机(SVM)和长短期记忆网络(LSTM)、门控循环单元(GRU)、时间卷积网络(TCN)神经网络模型对水质水平进行模拟。【结果】氨氮和溶解氧(DO)是筛选出的主要影响因子,4种模型对氨氮模拟的准确度高于DO。【结论】GRU模型对2种指标的模拟最具优势,SVM模型对氨氮和LSTM模型对DO水质模拟具有相对优势,而TCN模型对氨氮和DO的预测能力均相对较弱。 展开更多
关键词 水质预测 影响因子识别 机器学习 支持向量机(SVM) 人工神经网络(ANN)
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基于机器学习的肠球菌血流感染预后不良预测模型的构建与评估 认领 引用
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作者 韩亚飞 汪静 +3 位作者 张添添 陈莉 张浩 王强 《中华医院感染学杂志》 CAS CSCD 北大核心 2026年第5期790-795,共6页
目的构建基于机器学习肠球菌血流感染患者发生预后不良的多种预测模型,并评估其预测效能。方法回顾性分析2021年1月1日-2024年12月31日南京医科大学附属江宁医院收治的128例肠球菌血流感染患者的临床资料,采用Lasso回归和多因素logisti... 目的构建基于机器学习肠球菌血流感染患者发生预后不良的多种预测模型,并评估其预测效能。方法回顾性分析2021年1月1日-2024年12月31日南京医科大学附属江宁医院收治的128例肠球菌血流感染患者的临床资料,采用Lasso回归和多因素logistic回归筛选与其发生有关联的显著变量,并将其纳入机器学习模型。分别采用逻辑回归、决策树、随机森林、极限梯度提升、轻量级梯度提升机、支持向量机和人工神经网络7种机器学习方法构建预测模型,比较模型的精确率、准确率、灵敏度和F1分数等以评估不同模型的预测效能。结果逻辑回归、决策树、随机森林、极限梯度提升、轻量级梯度提升机、支持向量机和人工神经网络在测试集中的准确率分别为83.33、84.44、87.78、86.67、82.22、86.67和86.67;精确率分别为88.24、78.72、85.71、83.72、77.78、83.72和83.72;F1分数分别为0.800、0.841、0.867、0.857、0.814、0.857和0.857;AUC值分别为0.922、0.922、0.952、0.933、0.878、0.916和0.942。其中随机森林模型预测性提示,低蛋白血症是最具影响力的因素。结论成功构建出预测肠球菌血流感染患者发生预后不良的模型,其中随机森林模型预测效能最佳,可为该类患者临床护理工作提供一个早期预测和防治预后不良发生的有效工具。 展开更多
关键词 肠球菌 血流感染 机器学习 逻辑回归 决策树 随机森林 极限梯度提升 轻量级梯度提升机 支持向量机 人工神经网络 预后不良 预测模型
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