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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Liu, Bo Dang, Fangfang Yang, Ying Li, Dingding Song, Yifan HeNan ZhengZhou450052 China

ISBN: (纸本)9798350383348

With the rapid development of information technology, the problem of network security has become increasingly prominent. Camouflage intrusion, as a common means of network attack, has strong concealment and destructiveness, which brings great security threats to enterprises and organizations. In order to effectively deal with camouflage intrusion, more and more researchers apply machine learning and data mining technology to the field of intrusion detection. Among them, Random Forest (rf) algorithm, as an ensemble learning algorithm, has the advantages of high accuracy and low complexity, and has been widely concerned. However, the traditional rf algorithm still has some problems when dealing with camouflage intrusion detection, such as single feature selection, strong correlation between base classifiers and so on. © 2024 IEEE.

关键词： Channel cybersecurity Data completeness intrusion detection rf algorithm tampering rate the amount of data transferred transmission speed

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Land-Cover Classification of Coastal Wetlands Using the rf algorithm for Worldview-2 and Landsat 8 Images

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REMOTE SENSING 2019年第16期11卷 1927-1927页

作者： Wang, Xiaoxue Gao, Xiangwei Zhang, Yuanzhi Fei, Xianyun Chen, Zhou Wang, Jian Zhang, Yayi Lu, Xia Zhao, Huimin Jiangsu Ocean Univ Sch Geomat & Marine Informat Lianyungang 222002 Peoples R China Univ Chinese Acad Sci Sch Astron & Space Sci Beijing 100049 Peoples R China Chinese Acad Sci Natl Astron Observ Key Lab Lunar Sci & Deep Space Explorat Beijing 100101 Peoples R China

Wetlands are one of the world's most important ecosystems, playing an important role in regulating climate and protecting the environment. However, human activities have changed the land cover of wetlands, leading to direct destruction of the environment. If wetlands are to be protected, their land cover must be classified and changes to it monitored using remote sensing technology. The random forest (rf) machine learning algorithm, which offers clear advantages (e.g., processing feature data without feature selection and preferable classification result) for high spatial image classification, has been used in many study areas. In this research, to verify the effectiveness of this algorithm for remote sensing image classification of coastal wetlands, two types of spatial resolution images of the Linhong Estuary wetland in Lianyungang-Worldview-2 and Landsat-8 images-were used for land cover classification using the rf method. To demonstrate the preferable classification accuracy of the rf algorithm, the support vector machine (SVM) and k-nearest neighbor (k-NN) methods were also used to classify the same area of land cover for comparison with the results of rf classification. The study results showed that (1) the overall accuracy of the rf method reached 91.86%, higher than the SVM and k-NN methods by 4.68% and 4.72%, respectively, for Worldview-2 images;(2) at the same time, the classification accuracies of rf, SVM, and k-NN were 86.61%, 79.96%, and 77.23%, respectively, for Landsat-8 images;(3) for some land cover types having only a small number of samples, the rf algorithm also achieved better classification results using Worldview-2 and Landsat-8 images, and (4) the addition texture features could improve the classification accuracy of the rf method when using Worldview-2 images. Research indicated that high-resolution remote sensing images are more suitable for small-scale land cover classification image and that the rf algorithm can provide better classificat

关键词： coastal wetland classification rf algorithm

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Nonparametric monitoring of high-dimensional processes via EWMA control charts based on random forest learning

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COMPUTERS & INDUSTRIAL ENGINEERING 2025年 204卷

作者： Wu, Cang Wang, Dong Luo, Min Huang, Wenpo Si, Zexin Lanzhou Univ Technol Sch Mech Engn Lanzhou Gansu Peoples R China Hangzhou Dianzi Univ Sch Management Hangzhou Zhejiang Peoples R China PipeChina Gansu Pipeline Co Lanzhou Gansu Peoples R China

Multi-sensor systems have been widely used in manufacturing processes to perceive environmental or operating conditions and quality information, collecting large amounts of high-dimensional quality data. Because these systems are so common in practice, the important question has emerged as to how to leverage the full potential of rich data to monitor the various processes. Most traditional multivariate control charts in the field of highdimensional data monitoring are no longer relevant because of their high dimensionality and typically unknown prior distribution of variables. In response to this need, this paper proposes a novel nonparametric Exponentially Weighted Moving Average (EWMA) control scheme by employing the random forest (rf) algorithm and log-likelihood method to transform high-dimensional data into one-dimensional data serving as the input of monitoring statistics. The simulation results indicate that the proposed control scheme outperforms its competitors in terms of various distributions and data types, especially for only one out-of-control (OC) cluster in the data stream. We also extend our scheme to the additional cases of a disk monitoring study and a breast cancer monitoring study prove the robustness and effectiveness of the proposed scheme.

关键词： Nonparametric EWMA rf algorithm Log-likelihood High-dimensional

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Classification of Wetlands in the Liaohe Estuary Based on MRMR-rf-CV Feature Preference of Multisource Remote Sensing Images

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2025年 18卷 6116-6133页

作者： Ke, Lina Zhang, Shilin Lu, Yao Lei, Nan Yin, Changkun Tan, Qin Wang, Lei Liu, Daqi Wang, Quanming Liaoning Normal Univ Sch Geog Dalian 116029 Peoples R China Natl Marine Environm Monitoring Ctr Dalian 116023 Peoples R China

Coastal wetland ecosystems, shaped by the interaction between ocean and land, play a pivotal role in blue carbon sequestration. Understanding the distribution and evolutionary patterns of complex wetland types is essential for advancing wetland conservation, exploring blue carbon potentials, and supporting national carbon neutrality goals. This study utilized the Google Earth Engine cloud platform alongside Sentinel-1 synthetic aperture radar data, Sentinel-2 multispectral data, digital elevation model data, and unmanned aerial vehicle data to develop a feature preference algorithm integrating the minimum redundancy maximum relevance (MRMR) and random forest (rf) methods. Using eCognition, ArcMap, and ENVI platforms, we achieved an object-oriented classification of the Liaohekou Shuangtaizi wetlands. Results revealed that: 1) The MRMR-rf optimized 40 features, ranked by importance as Sentinel-2 spectral > Sentinel-1 index > Sentinel-1 radar > topographic > Sentinel-1 texture;2) Six sets of comparison schemes were established, and the classification scheme based on the MRMR-rf model achieved the best classification performance, with an overall accuracy of 90.89% and a Kappa coefficient of 0.9;3) The Liaohekou Estuary wetland was predominantly composed of Phragmites australis (***), shallow sea, and tidal flat, followed by cropland, rivers, breeding pools, Suaeda salsa (***), reservoirs, puddles, bare soil, and building sites as secondary components;and 4) Between 2000 and 2023, the wetland area of different types in the study area changed significantly, with the changes mainly concentrated in the coastal aquaculture areas, tidal flat areas, and *** growth areas.

关键词： Wetlands Remote sensing Indexes Classification algorithms Feature extraction Vegetation mapping Optical sensors Optical imaging Laser radar Estuaries Coastal wetlands minimum redundancy maximum correlation algorithm multisource remote sensing imagery object-oriented classification rf algorithm

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Research on fault diagnosis of electric gate valve in nuclear power plant based on the VMD-MDI-ISSA-rf model

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ANNALS OF NUCLEAR ENERGY 2024年 207卷

作者： Gao, Jia-rong Liu, Yong-kuo Duan, Cheng-jie Ding, Peng Song, Ju-qing Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Harbin 150001 Peoples R China China Nucl Power Technol Res Inst Co Ltd Lead Bismuth Fast Reactor Project Dept Shenzhen 518028 Peoples R China Dongguan Univ Technol Sch Mech Engn Songshan Lake Dongguan 523808 Peoples R China

Electric gate valve (EGV) is an essential equipment within nuclear power plant (NPP). This paper presents an advanced fault diagnosis (FD) approach, leveraging Variational Modal Decomposition (VMD), Mutual Dimensionless Indicator (MDI) and the Random Forest (rf) optimized through Improved Sparrow Search algorithm (ISSA), aimed at improving the accuracy of fault diagnosis and optimizing the FD model during EGV failure events. To commence, we employ the VMD algorithm for modal decomposition of raw electric gate valve signals. This process yields several Intrinsic Mode Function (IMF) components with diverse frequencies, enabling the capture of the underlying dynamics of the signals and facilitating a more comprehensive analysis of the fault conditions. We subsequently apply the K -L divergence to identify key IMF components that closely resemble the original signals. These selected key IMF components serve as the foundation for extracting dimensional indicators (DI) and mutual dimensionless indicators (MDI) as signal features. Furthermore, the Improved Sparrow Search algorithm (ISSA) is enlisted to optimize the maximum feature count and the number of decision trees in the Random Forest (rf) algorithm. Ultimately, the optimized rf algorithm is deployed for fault diagnosis. Our paper offers a comparative analysis, pitting the VMD method against Empirical Mode Decomposition (EMD) and Local Mean Decomposition (LMD). Additionally, we compare our proposed fault diagnosis model with traditional rf algorithm and the SSA-rf algorithm. Through rigorous experimentation, our results achieved an average fault diagnosis accuracy of up to 96.375%.

关键词： Nuclear Power Plant Electric Gate Valves Fault Diagnosis VMD MDI ISSA algorithm rf algorithm

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Machine learning for the prediction of postoperative nosocomial pulmonary infection in patients with spinal cord injury

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EUROPEAN SPINE JOURNAL 2023年第11期32卷 3825-3835页

作者： Li, Meng-Pan Liu, Wen-Cai Wu, Jia-Bao Luo, Kun Liu, Yu Zhang, Yu Xiao, Shi-Ning Liu, Zhi-Li Huang, Shan-Hu Liu, Jia-Ming Nanchang Univ Affiliated Hosp 1 Dept Orthoped Surg 17 Yongwaizheng St Nanchang 330006 Jiangxi Peoples R China Nanchang Univ Clin Med Coll 1 Nanchang 330006 Peoples R China Shanghai Jiao Tong Univ Affiliated Peoples Hosp 6 Dept Orthopaed Shanghai Peoples R China Nanchang Univ Inst Spine & Spinal Cord 17 Yongwaizheng St Nanchang 330006 Jiangxi Peoples R China

PurposeThe purpose of this study was to establish the best prediction model for postoperative nosocomial pulmonary infection through machine learning (ML) and assist physicians to make accurate diagnosis and treatment *** with spinal cord injury (SCI) who admitted to a general hospital between July 2014 and April 2022 were included in this study. The data were segmented according to the ratio of seven to three, 70% were randomly selected to train the model, and the other 30% were used for testing. We used LASSO regression to screen the variables, and the selected variables were used in the construction of six different ML models. Shapley additive explanations and permutation importance were used to explain the output of the ML models. Finally, sensitivity, specificity, accuracy and area under receiver operating characteristic curve (AUC) were used as the evaluation index of the *** total of 870 patients were enrolled in this study, of whom 98 (11.26%) developed pulmonary infection. Seven variables were used for ML model construction and multivariate logistic regression analysis. Among these variables, age, ASIA scale and tracheotomy were found to be the independent risk factors for postoperative nosocomial pulmonary infection in SCI patients. Meanwhile, the prediction model based on rf algorithm performed best in the training and test sets. (AUC = 0.721, accuracy = 0.664, sensitivity = 0.694, specificity = 0.656).ConclusionAge, ASIA scale and tracheotomy were the independent risk factors of postoperative nosocomial pulmonary infection in SCI. The prediction model based on rf algorithm had the best performance.

关键词： Pulmonary infection SCI Machine learning rf algorithm Complication

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Determining the most appropriate drought index using the random forest algorithm with an emphasis on agricultural drought

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NATURAL HAZARDS 2023年第1期115卷 923-946页

作者： Zarei, Abdol Rassoul Mahmoudi, Mohammad Reza Moghimi, Mohammad Mehdi Fasa Univ Dept Range & watershed management Nat Engn Fac Agr Fasa Iran Fasa Univ Dept Stat Fac Sci Fasa Iran Fasa Univ Dept water Sci Engn Coll Agr Fasa Iran

Since drought can mainly affect agriculture, especially rain-fed agriculture with high dependence on rainwater, and consequently jeopardize food security and social protection, it is absolutely essential to determine an appropriate drought index to assess the agricultural drought more accurately. In this paper, the random forest algorithm (rf) was employed to compare the capabilities of six more commonly used drought indices in agricultural drought assessment, namely standardized precipitation evapotranspiration index (SPEI), modified SPEI (M-SPEI), reconnaissance drought index (RDI), modified RDI (M-RDI), standardized precipitation index (SPI) and modified SPI (M-SPI) based on the correlation between the drought indices (i.e., independent variables) and yield of rain-fed barley (i.e., response variable). In this study, the data series of 10 stations with appropriate climate variety from 1968 to 2017 were used on 1-, 3-, 6-, and 12-month time scales (27 time periods in total). The results indicated that the linear regression between the simulated and predicted yield of rain-fed barley using the AquaCrop model and the rf algorithm (respectively) had no difference with a perfect reliable line (Y = X) in 0.05 or 0.01 significant levels. The R-2 between simulated and predicted barley yield (YB) was also significant at 0.01 levels in all stations, indicating the appropriate capability of the rf algorithm in YB prediction. The results also showed that some indices had better outcomes than others in stations;for example, the SPEI index in Arak, Mashhad, Shahre Kord, and Shiraz stations, the M-SPEI index in Babolsar, Gazvin, and Ramsar, the SPEI and M-SPEI indices in Esfahan and Gorgan stations, and the RDI and SPI indices in Rasht stations had the best outcome. Finally, it was recommended to use the SPEI and M-SPEI indices for agricultural drought assessment.

关键词： rf algorithm Drought indices Yield Rain-fed barley Agricultural drought

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A Comparative Analysis of Machine Learning Techniques for Breast Cancer Prediction 2

A Comparative Analysis of Machine Learning Techniques for Br...

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2nd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2024

作者： Dutta, Umesh Kaushik, Simran Iyer, Srinidhi Singh, Ina Manav Rachna International Institute of Research and Studies Department of Electronics and Communication Engineering Haryana Faridabad India Manav Rachna International Institute of Research and Studies Manav Rachna Innovation & Incubation Centre Haryana Faridabad India

ISBN: (纸本)9798350327533

One of the major diseases found in women worldwide is Breast Cancer. More than 2.2M cases have been reported with about a 30% mortality rate in accordance with WHO. The rapid advancement of technology has led to breakthroughs in different fields such as medicine, automotive, etc. Despite advancements in screening technologies, achieving high sensitivity and specificity for early detection remains a challenge. Machine Learning is one of the front runners and is currently playing a vital role in solving real-world problems, particularly in the healthcare domain. Using different Machine Learning algorithms prediction of Breast Cancer has been tallied in this work. Various algorithms such as SVM (Support Vector Machine), KNN (K-Nearest Neighbours), DT (Decision Tree), and rf (Random Forest) have vast application areas. In this study, these algorithms have been tested and evaluated on the basis of precision, recall, and accuracy which are the key performance metrics for the evaluation of the model. The dataset for breast Cancer has been pulled from Kaggle, which is the most prominently used dataset by researchers. The simulation results revealed that Random Forest outperformed the others with the precision of 94.5%, recall of 94%, and accuracy of 95%, clearly showing its potential to be used for early prediction of Breast Cancer. © 2024 IEEE.

关键词： Breast Cancer Prediction DT algorithm KNN algorithm Modelling using ML rf algorithm SVM algorithm

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Revolutionizing Agriculture through Leaf Disease Prediction in Plants

Revolutionizing Agriculture through Leaf Disease Prediction ...

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2023 IEEE Global Conference on Information Technologies and Communications, GCITC 2023

作者： Anand, Sahana Shri Sankaran, Sariga Ravi, Sandhiya Hariharan, Shanmugasundaram Kukreja, Vinay Vasantha, S.V. Sri Sairam Engineering College Department of Information Technology Chennai India Vardhaman College of Engineering Department of Cse Hyderabad India Chitkara University Institute of Engineering and Technology Department of Computer Science and Engineering Punjab India

ISBN: (纸本)9798350308167

India's agricultural strength is hindered by plant diseases due to limited understanding of disease markers and remedies. Deep learning, particularly Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), offers hope for enhanced disease detection in agriculture. Our proposal centers on CNNs for accurate plant disease identification, streamlining the process. Urgency arises from economic repercussions and global food security. Deep learning is employed in various sectors such as healthcare, finance, and agriculture. Its applications range from identifying diseases, classifying weeds and leaves, to predicting soil moisture levels. The survey tackles current trends and objections in deep learning for plant disease prediction, guiding aspiring researchers. Machine Learning (ML) and Deep Learning (DL) tools replace costly visual inspection. Our study deploys ML algorithms including K Nearest Neighbors (KNN), Logistic Regression, Random Forest(rf) algorithm, Naïve Bayes, Decision Trees, and Support Vector Machines(SVM) on a diverse crop image dataset. A CNN-based system, coupled with ML soil analysis, suggests fitting crops, enabling informed planting choices. Classifier comparison validates Support Vector Classifier's (SVC) superiority. Swift disease detection is critical for food demand and economic growth. This study proposes a modern evaluation system using ML and pre-trained models (VGG16, InceptionV3, ResNet152) on datasets to classify plant diseases. IoT further augments disease and yield prediction, vital for modern agriculture. © 2023 IEEE.

关键词： Artificial Intelligence(AI) CNN Deep learning Internet Of Things (IOT) KNN Logistic Regression Machine learning pre-trained models rf algorithm RNN SVM swift disease diagnosis

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Determination of the most important meteorological parameters affecting the yield and biomass of barley and winter wheat using the random forest algorithm

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PADDY AND WATER ENVIRONMENT 2021年第1期19卷 199-216页

作者： Zarei, Abdol Rassoul Mahmoudi, Mohammad Reza Shabani, Ali Achite, Mohammed Fasa Univ Fac Agr Dept Range & Watershed Management Nat Engn Fasa Iran Fasa Univ Dept Stat Fac Sci Fasa Iran Fasa Univ Dept Water Engn Coll Agr Sci Fasa Iran Hassiba Benbouali Univ Chlef Fac Nat & Life Sci Water & Environm Lab Chlef Algeria

Considering the very important role of climatic parameters on the yield and biomass of plants (especially in rain-fed agriculture), in this research, using data series of 9 stations during 1968-2017 the influence of 6 climatic parameters including the average of annual maximum and minimum temperature (Max-T and Min-T), the average of annual sunshine (Su-Sh), the average of annual relative humidity (H), the average of annual wind speed (Wi) and the average annual precipitation (P) on the yield and biomass of winter wheat (YWW and BWW) and barley (YB and BB) as two important and strategic species to provide the human food and the livestock feed in Iran, was investigated and prioritized. To assess the effectiveness rate of the climatic parameters on the response variables (YWW, BWW, YB and BB), the random forest algorithm (rf) was used. The results indicated that the rf algorithm had a good ability to predict the response variables because (1) the linear regression between simulated and predicted YWW, BWW, YB and BB using the AquaCrop model and the rf algorithm (respectively) had no difference with perfect reliable line (Y = X) in 0.05 or 0.01 significant levels and (2) R-2 between simulated and predicted YWW, BWW, YB and BB was significant at 0.01 levels. According to the results, the P, Wi and Min-T parameters were the most influential climatic parameters on the YWW (respectively), the Min-T, Wi and P parameters were the most influential parameters on the YB and the Wi and Min-T parameters were the most influential parameters on the BWW and BB. On the other hand, in all stations, the Su-Sh and Hu were the least influential parameters on the YWW, BWW, YB and BB.

关键词： rf algorithm AquaCrop model Climatic parameters Annual yield Annual biomass

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