检索结果-内蒙古大学图书馆

Network intrusion monitoring based on margin distance pruning and RF algorithm

RESULTS IN ENGINEERING 2025年 26卷

作者： Gou, Xiangming Johar, Md Gapar Md Tham, Jacquline Management & Sci Univ Postgrduate Ctr Shah Alam 40100 Malaysia Gongqing Inst Sci & Technol Sch Informat Engn Jiujiang 332020 Peoples R China

With the rapid development of network technology, network intrusion events are becoming increasingly frequent and complex. Traditional intrusion detection systems often have low detection accuracy, posing a serious threat to information security. To address the shortcomings of traditional methods in handling large-scale imbalanced data and improve the accuracy and efficiency of network intrusion monitoring, an enhanced random forest and K-means algorithm-based network intrusion monitoring technique is suggested. Firstly, margin optimization distance pruning is adopted, and feature sorting and K-means++clustering are introduced to improve the random forest algorithm. Then, combining density based spatial clustering and local anomaly factor detection of abnormal data, and combining Apriori algorithm and K-means algorithm for clustering analysis. The results showed that the accuracy of the proposed intrusion detection model in the LUFlow and CIDDS datasets was 79.4 % and 91.2 %, respectively, with modeling times of 80 s and 118 s, respectively. The accuracy and F1 value of the anomaly detection algorithm are 0.88 and 0.90, respectively. In summary, the model constructed in this study has good accuracy in intrusion detection and abnormal data detection, which helps ensure network security. However, it may also lead to an increase in modeling time. In the future, modeling efficiency can be improved through introducing distributed and parallel computing.

关键词： Intrusion detection random forest algorithm Network security Data mining

来源：评论

学校读者我要写书评

暂无评论

Evaluation method of composite insulator aging status based on hyperspectral imaging technology

引用

MEASUREMENT 2024年 225卷

作者： Fan, Yihan Guo, Yujun Liu, Yang Xiao, Song Zhang, Xueqin Wu, Guangning Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Peoples R China

Silicone rubber insulators' long-term operation in complex environments can lead to accelerated aging, causing the risk of flashovers and grid outages. Timely detection of the insulators' aging status is crucial to ensure the stable operation of grid. Therefore, this paper proposes a pixel-level fast, non-contact detection method based on hyperspectral imaging (HSI) technology for insulators' aging state. Firstly, the hyperspectral spectral line of the samples' six aging levels was extracted, and 14 features were extracted using pre-processing methods like segmented principal component analysis (SPCA) to reduce irrelevant information interference. Secondly, the aging state classification model was established, and through comparison using overall accuracy, F1 score, Precision, and Recall, the random forest (RF) model demonstrated optimal performance with a classification accuracy of 96.81%. Finally, this study utilized HSI technology to achieve a pixel-level evaluation of composite insulators' aging status, and the technique's feasibility was verified through specific parameters such as roughness.

关键词： Silicone rubber insulator Hyperspectral imaging technology Insulator aging Segmented principal component analysis random forest algorithm

来源：评论

学校读者我要写书评

暂无评论

Discovery and validation of colorectal cancer tissue-specific methylation markers: a dual-center retrospective cohort study

引用

CLINICAL EPIGENETICS 2024年第1期16卷 1-14页

作者： Cao, Qinxing Dan, Zhenjia Hou, Nengyi Yan, Li Yuan, Xingmei Lu, Hejiang Yu, Song Zhang, Jiangping Xiao, Huasheng Liu, Qiang Zhang, Xiaoyong Zhang, Min Pang, Minghui Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Sch Med Dept Geriatr Gen Surg Chengdu 611731 Peoples R China Chongqing Bohao Diagnost Technol Co Ltd Chongqing 410010 Peoples R China Shanghai Biotechnol Corp Ltd Shanghai 200126 Peoples R China Univ Elect Sci & Technol China Sichuan Prov Peoples Hosp Sch Med Dept Outpatient Chengdu 611731 Peoples R China

Background and purposeEarly detection, diagnosis, and treatment of colorectal cancer and its precancerous lesions can significantly improve patients' survival rates. The purpose of this research is to identify methylation markers specific to colorectal cancer tissues and validate their diagnostic capability in colorectal cancer and precancerous changes by measuring the level of DNA methylation in stool *** analyzed samples from six cancer tissues and adjacent normal tissues and fecal samples from 758 participants, including 62 patients with interfering diseases. Bioinformatics databases were used to screen for candidate biomarkers for CRC, and quantitative methylation-specific PCR methods were applied for identification. The methylation levels of the candidate biomarkers in fecal and tissue samples were measured. Logistic regression and random forest models were built and validated using fecal sample data from one of the centers, and the independent or combined diagnostic value of the candidate biomarkers in fecal samples for CRC and precancerous lesions was analyzed. Finally, the diagnostic capability and stability of the model were validated at another medical *** study identified two colorectal cancer CpG sites with tissue specificity. These two biomarkers have certain diagnostic power when used individually, but their diagnostic value for colorectal cancer and colorectal adenoma is more significant when they are used in *** results indicate that a DNA methylation biomarker combined diagnostic model based on two CpG sites, cg13096260 and cg12587766, has the potential for screening and diagnosing precancerous lesions and colorectal cancer. Additionally, compared to traditional diagnostic models, machine learning algorithms perform better but may yield more false-positive results, necessitating further investigation.

关键词： DNA methylation markers Colorectal cancer Advanced adenoma Diagnostic model random forest algorithm

来源：评论

学校读者我要写书评

暂无评论

Construction of an Internet of Things Industry Chain Classification Model Based on IRFA and Text Analysis

引用

JOURNAL OF INFORMATION PROCESSING SYSTEMS 2024年第2期20卷 215-225页

作者： Wang, Zhimin Zhongyuan Univ Technol Sch Econ & Management Zhengzhou Peoples R China

With the rapid development of Internet of Things (IoT) and big data technology, a large amount of data will be generated during the operation of related industries. How to classify the generated data accurately has become the core of research on data mining and processing in IoT industry chain. This study constructs a classification model of IoT industry chain based on improved random forest algorithm and text analysis, aiming to achieve efficient and accurate classification of IoT industry chain big data by improving traditional algorithms. The accuracy, precision, recall, and AUC value size of the traditional random forest algorithm and the algorithm used in the paper are compared on different datasets. The experimental results show that the algorithm model used in this paper has better performance on different datasets, and the accuracy and recall performance on four datasets are better than the traditional algorithm, and the accuracy performance on two datasets, P -I Diabetes and Loan Default, is better than the random forest model, and its final data classification results are better. Through the construction of this model, we can accurately classify the massive data generated in the IoT industry chain, thus providing more research value for the data mining and processing technology of the IoT industry chain.

关键词： Industrial Chain IoT random forest algorithm Text Analysis Visualization

来源：评论

学校读者我要写书评

暂无评论

The machine learning and geostatistical approach for assessment of arsenic contamination levels using physicochemical properties of water

引用

WATER SCIENCE AND TECHNOLOGY 2023年第3期88卷 595-614页

作者： Chattopadhyay, Arghya Singh, Anand Prakash Kumar, Siddharth Pati, Jayadeep Rakshit, Amitava Banaras Hindu Univ Inst Agr Sci Dept Soil Sci & Agr Chem Varanasi 221005 Uttar Pradesh India Indian Inst Informat Technol Dept Comp Sci & Engn Ranchi 834010 Jharkhand India

Arsenic contamination in groundwater due to natural or anthropogenic sources is responsible for carcinogenic and non-carcinogenic risks to humans and the ecosystem. The physicochemical properties of groundwater in the study area were determined in the laboratory using the samples collected across the Varanasi region of Uttar Pradesh, India. This paper analyses the physicochemical properties of water using machine learning, descriptive statistics, geostatistical and spatial analysis. Pearson correlation was used for feature selection and highly correlated features were selected for model creation. Hydrochemical facies of the study area were analyzed and the hyperparameters of machine learning models, i.e., multilayer perceptron, random forest (RF), naive Bayes, and decision tree were optimized before training and testing the groundwater samples as high (1) or low (0) arsenic contamination levels based on the WHO 10 mu g/L guideline value. The overall performance of the models was compared based on accuracy, sensitivity, and specificity value. Among all models, the RF algorithm outclasses other classifiers, as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. The accuracy result was compared with other previous studies and the machine learning model can be used to continuously monitor the arsenic contamination level in the groundwater.

关键词： arsenic multilayer perceptron naive Bayes random forest algorithm spatial variability

来源：评论

学校读者我要写书评

暂无评论

A novel merging strategy model considering the remaining distance in the acceleration lane

引用

IET INTELLIGENT TRANSPORT SYSTEMS 2023年第9期17卷 1879-1890页

作者： Gu, Menglu Su, Yanqi Wang, Chang Fu, Rui Guo, Yingshi Yuan, Wei Changan Univ Sch Automobile Xian Peoples R China Changan Univ Middle Sect Naner Huan Rd Xian 710064 Shaanxi Peoples R China

The driver of a merging vehicle must account for both the lane-changing risk and the remaining distance in the acceleration lane (RD$RD$) during a merging decision-making process. To investigate the impact of RD$RD$ on merge decisions, a typical freeway merging section was selected and monitored with a millimetre radar and a high-resolution digital camera, which were mounted on the guardrail in the gore area. More than 2000 merging vehicles were captured during the data collection process. The effects of the surrounding vehicles on the merging behaviour were analyzed, and a merging strategy model considering RD$RD$ that was based on the random forest algorithm was constructed. The results show that the following vehicle in the target lane is the main factor that affects the merging behaviour of the merging vehicle. When the decision time window was set to 0.6 s, the proposed merging decision model could distinguish 'Merge' events and 'Wait' events with accuracies of 97.2% and 89.4%, respectively. The overall accuracy of the model was 94.9%, which was 3.9% higher than for a corresponding merging decision model that excluded RD$RD$ influence. The proposed merging decision model can aid merging processes and give cues for human-like merge decisions of automated vehicles.

关键词： driving aid merging strategy random forest algorithm remaining distance in the acceleration lane

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study of Supervised Classification Methods for Investigating Landslide Evolution in the Mianyuan River Basin,China

引用

Journal of Earth Science 2023年第2期34卷 316-329页

作者： Yujie Long Weile Li Runqiu Huang Qiang Xu Bin Yu Gang Liu State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of TechnologyChengdu 610059China Sichuan Metallurgical Geological Survey and Design Group Co. LtdChengdu 610081China College of Earth Sciences Chengdu University of TechnologyChengdu 610059China

The Ms8.0 Wenchuan earthquake of 2008 dramatically changed the terrain surface and caused long-term increases in the scale and frequency of landslides and debris *** changing trend of landslides in the earthquake-affected area over the decade since the earthquake remains largely *** this study,we were able to address this issue using supervised classification methods and multitemporal remote sensing images to study landslide evolution in the worst-affected area(Mianyuan River Basin)over a period of ten *** images were processed using the maximum likelihood method and random forest algorithm to automatically map landslide occurrence from 2007 to *** principal findings are as follows:(1)when compared with visual image analysis,the random forest algorithm had a good average accuracy rate of 87%for landslide identification;(2)postevent landslide occurrence has generally decreased with time,but heavy monsoonal seasons have caused temporary spikes in activity;and(3)the postearthquake landslide activity in the Mianyuan River Basin can be divided into a strong activity period(2008 to 2011),medium activity period(2012 to 2016),and weak activity period(post 2017).Landslide activity remains above the prequake level,with damaging events being rare but continuing to ***-term remote sensing and on-site monitoring are required to understand the evolution of landslide activity after strong earthquakes.

关键词： Wenchuan earthquake Mianyuan River Basin automatic detection evolutionary trend maximum likelihood method random forest algorithm engineering geology

来源：评论

学校读者我要写书评

暂无评论

Modeling proximate causes of deforestation in Antioquia, Colombia

引用

REGIONAL ENVIRONMENTAL CHANGE 2024年第4期24卷 149页

作者： Calderon-Caro, Jennifer Morales-Gomez, Luz Maria Gutierrez-Velez, Victor H. Gonzalez-Caro, Sebastian Benavides, Ana Maria Fdn Jardin Bot Joaquin Antonio Uribe Medellin Medellin Colombia Temple Univ Dept Geog Environm & Urban Studies Philadelphia PA USA Univ Nacl Colombia Dept Ciencias Forestales Sede Medellin Medellin Colombia Univ Exeter Coll Life & Environm Sci Sch Geog Exeter England

The tropical Andes, the highest global biodiversity hotspot, is highly threatened by habitat loss and intense land use. forest loss rates have remained persistent throughout the region during this century, resulting in a spread fragmented landscape composed of small forest fragments. However, the spatial distribution and magnitude of the proximate causes of associated land-cover change across the tropical Andes remain unclear at regional scales (e.g., provinces). Here, we combined climate, socioeconomic, and remote sensing data with machine-learning methods to model the proximate causes of forest loss across Antioquia, Colombia, where mountains dominate the landscape. We found that temperature is one of the main proximate causes of deforestation because it defines the distribution of crops and cattle (i.e., livestock) along an elevational gradient. We also detected that vegetation features and preceding deforestation predict current deforestation increasing our ability to identify proximate causes of deforestation. Additionally, there is a high influence of local-scale socioeconomic factors driving deforestation trends over the last decade, providing evidence that implementing control measures and economic alternatives tailored to each region can reduce deforestation rates. Our model would be used as a valuable tool to inform decisions aimed at reducing pressure on forests.

关键词： Andean forests Antioquia province forest loss Livestock random forest algorithm Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Evaluating completion rates of COVID-19 contact tracing surveys in New York City

引用

BMC PUBLIC HEALTH 2024年第1期24卷 414页

作者： He, Kaiyu Foerster, Steffen Vora, Neil M. Blaney, Kathleen Keeley, Chris Hendricks, Lisa Varma, Jay K. Long, Theodore Shaman, Jeffrey Pei, Sen Columbia Univ Mailman Sch Publ Hlth Dept Biostat New York NY 10032 USA New York City Dept Hlth & Mental Hyg DOHMH Long Isl City NY 11001 USA NYC Hlth Hosp New York NY USA Weill Cornell Med Coll Dept Populat Hlth Sci New York NY 10065 USA NYU Dept Populat Hlth New York NY 10016 USA Columbia Univ Mailman Sch Publ Hlth Dept Environm Hlth Sci New York NY 10032 USA Columbia Univ Columbia Climate Sch New York NY 10025 USA

Importance Contact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs. Objective To identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021. Methods We analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used. Results The percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation - compared with young adults (the reference group,24 years old < age < = 65 years old), the completion rate for seniors (age > 65 years old) were lower by 12.1% (95%CI: 11.1% - 13.3%), and the completion rate for youth group (age < = 24 years old) were lower by 1.6% (95%CI: 0.6% -2.6%). In addition, phone calls made from 6 to 9 pm had a 4.1% (95% CI: 1.8% - 6.3%) higher

关键词： COVID-19 Contact tracing surveys Survey completion rates random forest algorithm Log-binomial regression Model prediction

来源：评论

学校读者我要写书评

暂无评论

random forest Analysis Based on Fingertip Pulse for Changes in an Individual before and after Discontinuation of Medication

Random Forest Analysis Based on Fingertip Pulse for Changes ...

引用

2023 Cross Strait Radio Science and Wireless Technology Conference, CSRSWTC 2023

作者： Shan, Ying Yu, Junsheng Huang, Zhuya School of Electronic Engineering Beijing University of Posts and Telecommunications Beijing China School of Physics and Electronic Information Anhui Normal University Wuhu China School of Intelligence and Digital Engineering Luoyang Vocational College of Science and Technology Luoyang China

ISBN: (纸本)9798350358971

Depression is a common mental disorder, and medication is one of the common treatment methods. However, there is currently a lack of a simple and non-invasive method to assess the changes in depression patients before and after medication. In this study, the researchers combined depression with fingertip pulse and machine recognition. They designed a method that uses signal processing to extract peaks from fingertip pulse and calculate heart rate variability features. The random forest algorithm was used to identify the changes in depression patients before and after discontinuation of medication. The experimental results indicate that by utilizing the random forest algorithm in combination with features of heart rate variability, a classification accuracy of 72%, sensitivity of 68%, specificity of 78%, and an area under the ROC curve (AUC) of 80% were achieved for identifying differences before and after medication, demonstrating the best classification performance. © 2023 IEEE.

关键词： depression fingertip pulse heart rate variability random forest algorithm

来源：评论

学校读者我要写书评

暂无评论

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

请选择保存的检索档案：

请选择收藏分类：

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

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

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：