检索结果-南通市图书馆

Comparison of debris flow susceptibility assessment methods:support vector machine,particle swarm optimization,and feature selection techniques

引用

Journal of Mountain Science 2024年第2期21卷 397-412页

作者： ZHAO Haijun WEI Aihua MA Fengshan DAI Fenggang JIANG Yongbing LI Hui Key Laboratory of Shale Gas and Geoengineering Institute of Geology and GeophysicsChinese Academy of SciencesBeijing 100029China Innovation Academy for Earth Science Chinese Academy of SciencesBeijing 100029China Hebei Province Key Laboratory of Sustained Utilization&Development of Water Recourse Hebei GEO UniversityShijiazhuang 050031China School of Water Resources and Environment Hebei GEO UniversityShijiazhuang 050031China The Sixth Geological Brigade of Geological Mineral Exploration and Development Bureau of Hebei Province Shijiazhuang 050085China

The selection of important factors in machine learning-based susceptibility assessments is crucial to obtain reliable susceptibility *** this study,metaheuristic optimization and feature selection techniques were applied to identify the most important input parameters for mapping debris flow susceptibility in the southern mountain area of Chengde City in Hebei Province,China,by using machine learning *** total,133 historical debris flow records and 16 related factors were *** support vector machine(SVM)was first used as the base classifier,and then a hybrid model was introduced by a two-step ***,the particle swarm optimization(PSO)algorithm was employed to select the SVM model ***,two feature selection algorithms,namely principal component analysis(PCA)and PSO,were integrated into the PSO-based SVM model,which generated the PCA-PSO-SVM and FS-PSO-SVM models,*** statistical metrics(accuracy,recall,and specificity)and the area under the receiver operating characteristic curve(AUC)were employed to evaluate and validate the performance of the *** results indicated that the feature selection-based models exhibited the best performance,followed by the PSO-based SVM and SVM ***,the performance of the FS-PSO-SVM model was better than that of the PCA-PSO-SVM model,showing the highest AUC,accuracy,recall,and specificity values in both the training and testing *** was found that the selection of optimal features is crucial to improving the reliability of debris flow susceptibility assessment ***,the PSO algorithm was found to be not only an effective tool for hyperparameter optimization,but also a useful feature selection algorithm to improve prediction accuracies of debris flow susceptibility by using machine learning *** high and very high debris flow susceptibility zone appropriately covers 38.01%of the study area,where debris flow may occur under intens

关键词： Chengde Feature selection support vector machine Particle swarm optimization Principal component analysis Debris flow susceptibility

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Enhanced Steganalysis for Color Images Using Curvelet Features and support vector machine

引用

Computers, Materials & Continua 2024年第1期78卷 1311-1328页

作者： Arslan Akram Imran Khan Javed Rashid Mubbashar Saddique Muhammad Idrees Yazeed Yasin Ghadi Abdulmohsen Algarni Faculty of Computer Science and Information Technology Department of Computer ScienceSuperior UniversityLahore54000Pakistan machine Learning Code(MLC)Lab Meharban HouseHouse Number 209Zafar ColonyOkara56300Pakistan Information Technology Services University of OkaraOkara56310Pakistan Department of Computer Science&Engineering University of Engineering&Technology LahoreNarowal CampusNarowal51601Pakistan Department of Computer Science and Software Engineering Al Ain UniversityAl Ain11671Abu Dhabi Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

Algorithms for steganography are methods of hiding data transfers in media *** machine learning architectures have been presented recently to improve stego image identification performance by using spatial information,and these methods have made it feasible to handle a wide range of problems associated with image *** with little information or low payload are used by information embedding methods,but the goal of all contemporary research is to employ high-payload images for *** address the need for both low-and high-payload images,this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of *** vector machine(SVM),a commonplace classification technique,has been employed to determine whether the image is a stego or *** Wavelet Obtained Weights(WOW),Spatial Universal Wavelet Relative Distortion(S-UNIWARD),Highly Undetectable Steganography(HUGO),and Minimizing the Power of Optimal Detector(MiPOD)steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the *** WOW at several payloads,the proposed approach proves its classification accuracy of 98.60%.It exhibits its superiority over SOTA methods.

关键词： Curvelets fast fourier transformation support vector machine high pass filters steganography

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

HHO optimized support vector machine classifier for traditional Chinese medicine syndrome differentiation of diabetic retinopathy

引用

International Journal of Ophthalmology(English edition) 2024年第6期17卷 991-1000页

作者： Li Xiao Cheng-Wu Wang Ying Deng Yi-Jing Yang Jing Lu Jun-Feng Yan Qing-Hua Peng School of Chinese Medicine Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina School of Informatics Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina Hunan Provincial Key Laboratory for Prevention and Treatment of Ophthalmology and Otolaryngology Diseases with Chinese Medicine Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina Hunan Provincial Engineering and Technological Research Center for Prevention and Treatment of Ophthalmology and Otolaryngology Diseases with Chinese Medicine and Protecting Visual Function Hunan University of Chinese MedicineChangsha 410208Hunan ProvinceChina

AIM:To develop a classifier for traditional Chinese medicine(TCM)syndrome differentiation of diabetic retinopathy(DR),using optimized machine learning algorithms,which can provide the basis for TCM objective and intelligent syndrome ***:Collated data on real-world DR cases were collected.A variety of machine learning methods were used to construct TCM syndrome classification model,and the best performance was selected as the basic *** Algorithm(GA)was used for feature selection to obtain the optimal feature *** Hawk Optimization(HHO)was used for parameter optimization,and a classification model based on feature selection and parameter optimization was *** performance of the model was compared with other optimization *** models were evaluated with accuracy,precision,recall,and F1 score as ***:Data on 970 cases that met screening requirements were *** vector machine(SVM)was the best basic classification *** accuracy rate of the model was 82.05%,the precision rate was 82.34%,the recall rate was 81.81%,and the F1 value was 81.76%.After GA screening,the optimal feature combination contained 37 feature values,which was consistent with TCM clinical *** model based on optimal combination and SVM(GA_SVM)had an accuracy improvement of 1.92%compared to the basic *** model based on HHO and GA optimization(HHO_GA_SVM)had the best performance and convergence speed compared with other optimization *** with the basic classification model,the accuracy was improved by 3.51%.CONCLUSION:HHO and GA optimization can improve the model performance of SVM in TCM syndrome differentiation of *** provides a new method and research idea for TCM intelligent assisted syndrome differentiation.

关键词： traditional Chinese medicine diabetic retinopathy Harris Hawk Optimization support vector machine syndrome differentiation

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Performance Analysis of support vector machine (SVM) on Challenging Datasets for Forest Fire Detection

引用

International Journal of Communications, Network and System Sciences 2024年第2期17卷 11-29页

作者： Ankan Kar Nirjhar Nath Utpalraj Kemprai Aman Department of Computer Science Chennai Mathematical Institute Chennai India Department of Mathematics & Computer Science Chennai Mathematical Institute Chennai India Department of Data Science Chennai Mathematical Institute Chennai India

This article delves into the analysis of performance and utilization of support vector machines (SVMs) for the critical task of forest fire detection using image datasets. With the increasing threat of forest fires to ecosystems and human settlements, the need for rapid and accurate detection systems is of utmost importance. SVMs, renowned for their strong classification capabilities, exhibit proficiency in recognizing patterns associated with fire within images. By training on labeled data, SVMs acquire the ability to identify distinctive attributes associated with fire, such as flames, smoke, or alterations in the visual characteristics of the forest area. The document thoroughly examines the use of SVMs, covering crucial elements like data preprocessing, feature extraction, and model training. It rigorously evaluates parameters such as accuracy, efficiency, and practical applicability. The knowledge gained from this study aids in the development of efficient forest fire detection systems, enabling prompt responses and improving disaster management. Moreover, the correlation between SVM accuracy and the difficulties presented by high-dimensional datasets is carefully investigated, demonstrated through a revealing case study. The relationship between accuracy scores and the different resolutions used for resizing the training datasets has also been discussed in this article. These comprehensive studies result in a definitive overview of the difficulties faced and the potential sectors requiring further improvement and focus.

关键词： support vector machine Challenging Datasets Forest Fire Detection Classification

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Stability prediction of hard rock pillar using support vector machine optimized by three metaheuristic algorithms

引用

矿业科学技术学报:英文版 2023年第8期33卷 1019-1036页

作者： Chuanqi Li Jian Zhou Kun Du Daniel Dias School of Resources and Safety Engineering Central South UniversityChangsha 410083China Laboratory 3SR CNRS UMR 5521Grenoble Alpes UniversityGrenoble 38000France

Hard rock pillar is one of the important structures in engineering design and excavation in underground *** and convenient prediction of pillar stability is of great significance for underground space *** paper aims to develop hybrid support vector machine(SVM)models improved by three metaheuristic algorithms known as grey wolf optimizer(GWO),whale optimization algorithm(WOA)and sparrow search algorithm(SSA)for predicting the hard rock pillar *** integrated dataset containing 306 hard rock pillars was established to generate hybrid SVM *** parameters including pillar height,pillar width,ratio of pillar width to height,uniaxial compressive strength and pillar stress were set as input *** global indices,three local indices and the receiver operating characteristic(ROC)curve with the area under the ROC curve(AUC)were utilized to evaluate all hybrid models’*** results confirmed that the SSA-SVM model is the best prediction model with the highest values of all global indices and local ***,the performance of the SSASVM model for predicting the unstable pillar(AUC:0.899)is not as good as those for stable(AUC:0.975)and failed pillars(AUC:0.990).To verify the effectiveness of the proposed models,5 field cases were investigated in a metal mine and other 5 cases were collected from several published *** validation results indicated that the SSA-SVM model obtained a considerable accuracy,which means that the combination of SVM and metaheuristic algorithms is a feasible approach to predict the pillar stability.

关键词： Underground pillar stability Hard rock support vector machine Metaheuristic algorithms

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Lung Cancer Detection Using Modified AlexNet Architecture and support vector machine

引用

Computers, Materials & Continua 2023年第1期74卷 2039-2054页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Arfan Jaffar Muhammad Rashid Muhammad Amjad Iqbal Faculty of Computer Science and Information Technology The Superior UniversityLahore54000Pakistan Department of Computer Science National University of TechnologyIslamabad45000Pakistan Faculty of IT University of Central PunjabLahore54100Pakistan

Lung cancer is the most dangerous and death-causing disease indicated by the presence of pulmonary nodules in the *** is mostly caused by the instinctive growth of cells in the *** nodule detection has a significant role in detecting and screening lung cancer in Computed tomography(CT)scan *** detection plays an important role in the survival rate and treatment of lung cancer ***,pulmonary nodule classification techniques based on the convolutional neural network can be used for the accurate and efficient detection of lung *** work proposed an automatic nodule detection method in CT images based on modified AlexNet architecture and support vector machine(SVM)algorithm namely *** proposed model consists of seven convolutional layers,three pooling layers,and two fully connected layers used to extract *** vector machine classifier is applied for the binary classification of nodules into benign *** experimental analysis is performed by using the publicly available benchmark dataset Lung nodule analysis 2016(LUNA16).The proposed model has achieved 97.64%of accuracy,96.37%of sensitivity,and 99.08%of specificity.A comparative analysis has been carried out between the proposed LungNet-SVM model and existing stateof-the-art approaches for the classification of lung *** experimental results indicate that the proposed LungNet-SVM model achieved remarkable performance on a LUNA16 dataset in terms of accuracy.

关键词： Lung cancer alexnet luna16 computed tomography support vector machine

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

A new hybrid approach to assessing soil quality using neutrosophic fuzzy-AHP and support vector machine algorithm in sub-humid ecosystem

引用

Journal of Mountain Science 2023年第11期20卷 3186-3202页

作者： ÖZKAN Barış DENGIZ Orhan ALABOZ Pelin KAYA NursaçSerda Department of Industrial Engineering Faculty of EngineeringOndokuz Mayis UniversitySamsun 55200Türkiye Department of Soil Sciences and Plant Nutrition Faculty of AgricultureOndokuz Mayis UniversitySamsun 55200Türkiye Department of Soil Science and Plant Nutrition Faculty of AgricultureIsparta University of Applied SciencesIsparta32000Türkiye

Soil quality determination and estimation is an important issue not only for terrestrial ecosystems but also for sustainable management of *** this study,soil quality was determined by linear and nonlinear standard scoring function methods integrated with a neutrosophic fuzzy analytic hierarchy process in the micro *** addition,soil quality values were estimated using a support vector machine(SVM)in machine learning *** order to generate spatial distribution maps of soil quality indice values,different interpolation methods were evaluated to detect the most suitable semivariogram *** the soil quality index values obtained by the linear method were determined between 0.458-0.717,the soil quality index with the nonlinear method showed variability at the levels of *** was no statistical difference between the two methods,and they were determined to be *** the estimation of soil quality with SVM,the normalized root means square error(NRMSE)values obtained in the linear and nonlinear method estimation were determined as 0.057 and 0.047,*** spherical model of simple kriging was determined as the interpolation method with the lowest RMSE value in the actual and predicted values of the linear method while,in the nonlinear method,the lowest error in the distribution maps was determined with exponential of the simple kriging.

关键词： Soil quality support vector machine Neutrosophic fuzzy Humid ecosystem

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Analysis of Water Pollution Causes and Control Countermeasures in Liaohe Estuary via support vector machine Particle Swarm Optimization under Deep Learning

引用

Computer Modeling in Engineering & Sciences 2022年第1期130卷 315-329页

作者： Guize Liu Jinqing Ye Yuan Chen Xiaolong Yang Yanbin Gu College of Environmental Science and Engineering Ocean University of ChinaQingdao266003China NationalMarine EnvironmentalMonitoring Center Ministry of Ecology and EnvironmentDalian116023China

This study explores the loss or degradation of the ecosystem and its service function in the Liaohe estuary coastal zone due to the deterioration ofwater *** systembased on support vectormachine(SVM)-particle swarm optimization(PSO)(SVM-PSO)algorithm is proposed under the background of deep ***-PSO algorithm is employed to analyze the pollution status of the Liaohe estuary,so is the difference in water pollution of different sea consuming *** on the analysis results for causes of pollution,the control countermeasures of water pollution in Liaohe estuary are put *** results suggest that the water pollution index prediction model based on SVM-PSO algorithm shows the maximum error of 2.41%,the average error of 1.24%in predicting the samples,the root mean square error(RMSE)of 5.36×10^(−4),and the square of correlation coefficient of ***,the prediction system in this study is *** present,the water pollution status of Liaohe estuary is of moderate and severe levels of eutrophication,and the water pollution status basically remains at the level of mild *** general trend is fromphosphorus moderate restricted eutrophication to phosphorus restricted potential *** sumup,the SVM-PSO algorithm shows good sewage prediction ability,which can be applied and promoted in water pollution control and has reliable reference significance.

关键词： Water pollution countermeasure analysis support vector machine particle swarm optimization

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

On the prediction of methane adsorption in shale using grey wolf optimizer support vector machine approach

引用

Petroleum 2022年第2期8卷 264-269页

作者： Rahmad Syah Mohammad Hossein Towfighi Naeem Reza Daneshfar Hossein Dehdar Bahram Soltani Soulgani DS&CI Research Group Universitas Medan AreaMedanIndonesia Department of Petroleum Engineering Faculty of Chemical EngineeringTarbiat Modares UniversityP.O.Box14155-4838TehranIran Department of Petroleum Engineering Ahwaz Faculty of Petroleum EngineeringPetroleum University of Technology(PUT)AhwazIran

With the advancement of technology,gas shales have become one of the most prominent energy sources all over the ***,estimating the amount of adsorbed gas in shale resources is necessary for the technical and economic foresight of the production *** paper presents a novel machine learning method called grey wolf optimizer support vector machine(GWO-SVM)to predict adsorbed *** this purpose,a data set containing temperature,pressure,total organic carbon(TOC),and humidity has been collected from several sources,and the GWO-SVM model was created based on *** results show that this model has R-squared and root mean square error equal to 0.982 and 0.08,***,the results ensure that the proposed model gives an excellent prediction of the amount of adsorbed gas compared to previously proposed ***,according to the sensitivity analysis,among the input parameters,humidity has the highest effect on gas adsorption.

关键词： Gas adsorption Shale machine learning Model support vector machine Grey wolf optimizer

来源：

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Enhanced Nature Inspired-support vector machine for Glaucoma Detection

引用

Computers, Materials & Continua 2023年第7期76卷 1151-1172页

作者： Jahanzaib Latif Shanshan Tu Chuangbai Xiao Anas Bilal Sadaqat Ur Rehman Zohaib Ahmad Engineering Research Center of Intelligent Perception and Autonomous Control Faculty of Information TechnologyBeijing University of Technology100124BeijingChina College of Information Science Technology Hainan Normal UniversityHainan ProvinceHaikou571158China Department of Computer Science University of SalfordManchesterUK Faculty of Information Technology Beijing University of Technology Chaoyang District BeijingChina

Glaucoma is a progressive eye disease that can lead to blindness if left *** detection is crucial to prevent vision loss,but current manual scanning methods are expensive,time-consuming,and require specialized *** study presents a novel approach to Glaucoma detection using the Enhanced Grey Wolf Optimized support vector machine(EGWO-SVM)*** proposed method involves preprocessing steps such as removing image noise using the adaptive median filter(AMF)and feature extraction using the previously processed speeded-up robust feature(SURF),histogram of oriented gradients(HOG),and Global *** enhanced Grey Wolf Optimization(GWO)technique is then employed with SVM for *** evaluate the proposed method,we used the online retinal images for glaucoma analysis(ORIGA)database,and it achieved high accuracy,sensitivity,and specificity rates of 94%,92%,and 92%,*** results demonstrate that the proposed method outperforms other current algorithms in detecting the presence or absence of *** study provides a novel and effective approach to Glaucoma detection that can potentially improve the detection process and outcomes.

关键词： Glaucoma detection grey golf optimization support vector machine feature extraction image classification

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：