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Structured Random Cycle-Guided Algorithm (SRCA): An Adaptive Metaheuristic Combining Directionally-Guided and Stochastic Search Strategies 认领 引用
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作者 Giuseppe Marannano Antonino Cirello Tommaso Ingrassia 《Computers, Materials & Continua》 SCIE EI 2026年第6期1873-1898,共26页
In response to the growing need for adaptive optimization algorithms capable of handling complex,multimodal,and high-dimensional search spaces,this paper introduces the Structured Random Cycle-guided Algorithm(SRCA).S... In response to the growing need for adaptive optimization algorithms capable of handling complex,multimodal,and high-dimensional search spaces,this paper introduces the Structured Random Cycle-guided Algorithm(SRCA).SRCA is not presented as a fundamentally new optimization paradigm,but rather as an architectural synthesis and a unified adaptive framework for dynamic operator selection.Based on a cycle-structured architecture,directional and stochastic search behaviors are dynamically selected at the individual level.The algorithm orchestrates well-established structured movements with a diverse pool of stochastic exploration strategies,enabling a coherent and adaptive balance between exploration and exploitation throughout the optimization process.Unlike traditional metaheuristics that rely on fixed behavioral roles or static movement schemes,SRCA allows each individual to adapt its search strategy based on real-time population feedback,monitored through convergence and dispersion indicators.The performance of SRCA is quantitatively assessed under strictly identical experimental conditions on a comprehensive set of 23 benchmark functions,including multimodal and high-dimensional problems,as well as on six classical constrained engineering design problems.Numerical results demonstrate competitive convergence reliability and robustness across diverse optimization tasks,confirming the effectiveness of the proposed adaptive cycle-based framework. 展开更多
关键词 Metaheuristic algorithms optimization constrained optimization benchmark functions structured random cycle-guided algorithm
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Random forest algorithm reveals novel sites in HA protein that shift receptor binding preference of the H9N2 avian influenza virus 认领 引用 被引量：2
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作者 Yuncong Yin Wen Li +7 位作者 Rujian Chen Xiao Wang Yiting Chen Xinyuan Cui Xingbang Lu David M.Irwin Xuejuan Shen Yongyi Shen 《Virologica Sinica》 SCIE CAS CSCD 2025年第1期109-117,共9页
A switch from avian-typeα-2,3 to human-typeα-2,6 receptors is an essential element for the initiation of a pandemic from an avian influenza virus.Some H9N2 viruses exhibit a preference for binding to human-typeα-2,... A switch from avian-typeα-2,3 to human-typeα-2,6 receptors is an essential element for the initiation of a pandemic from an avian influenza virus.Some H9N2 viruses exhibit a preference for binding to human-typeα-2,6 receptors.This identifies their potential threat to public health.However,our understanding of the molecular basis for the switch of receptor preference is still limited.In this study,we employed the random forest algorithm to identify the potentially key amino acid sites within hemagglutinin(HA),which are associated with the receptor binding ability of H9N2 avian influenza virus(AIV).Subsequently,these sites were further verified by receptor binding assays.A total of 12 substitutions in the HA protein(N158D,N158S,A160 N,A160D,A160T,T163I,T163V,V190T,V190A,D193 N,D193G,and N231D)were predicted to prefer binding toα-2,6 receptors.Except for the V190T substitution,the other substitutions were demonstrated to display an affinity for preferential binding toα-2,6 receptors by receptor binding assays.Especially,the A160T substitution caused a significant upregulation of immune-response genes and an increased mortality rate in mice.Our findings provide novel insights into understanding the genetic basis of receptor preference of the H9N2 AIV. 展开更多
关键词 H9N2 Hemagglutinin(HA) Receptor binding preference Random forest algorithm Host shift Interspecies transmission
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Enhanced diagnosis of diabetic retinopathy:integrating advanced algorithms for automated detection and classification 认领 引用
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作者 E Murali T Prasad +2 位作者 JHari Krishna Abdul Hussain Sharief Peta Nandini 《International Journal of Ophthalmology(English edition)》 SCIE CAS 2026年第4期637-645,共9页
AIM:To develop an automated diagnostic system for early detection of diabetic retinopathy(DR)using fundus images by identifying exudates,hemorrhages,and microaneurysms with advanced image processing and machine learni... AIM:To develop an automated diagnostic system for early detection of diabetic retinopathy(DR)using fundus images by identifying exudates,hemorrhages,and microaneurysms with advanced image processing and machine learning techniques.METHODS:Fundus images from the IDRiD dataset and additional Kaggle datasets were used.A wavelet-based band-pass filter was applied for edge enhancement of retinal features.Gaussian mixture model(GMM)clustering was used to segment and extract texture features.These extracted features were classified using machine learning algorithms,including a random forest classifier and a multilayer perceptron neural network.Performance metrics such as sensitivity,specificity,and accuracy were computed to evaluate the proposed model’s diagnostic effectiveness.RESULTS:The random forest-based classification system achieved a sensitivity of 95.08%,specificity of 86.67%,and overall accuracy of 95.20%in detecting DR lesions.The combination of wavelet-based edge enhancement,GMM clustering,and neural network-based feature classification demonstrated high reliability in lesion identification.CONCLUSION:The proposed method effectively detects early signs of DR from fundus images,offering a highaccuracy,automated,and scalable solution for assisting ophthalmologists.Its application can support large-scale screening programs,particularly in regions with limited access to specialized eye care. 展开更多
关键词 diabetic retinopathy random forest algorithm Gaussian mixture modelclustering multilayer perceptron fundus image analysis
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Random State Approach to Quantum Computation of Electronic-Structure Properties 认领 引用
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作者 Yiran Bai Feng Xiong Xueheng Kuang 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第1期89-104,共16页
Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and v... Classical computation of electronic properties in large-scale materials remains challenging.Quantum computation has the potential to offer advantages in memory footprint and computational scaling.However,general and viable quantum algorithms for simulating large-scale materials are still limited.We propose and implement random-state quantum algorithms to calculate electronic-structure properties of real materials.Using a random state circuit on a small number of qubits,we employ real-time evolution with first-order Trotter decomposition and Hadamard test to obtain electronic density of states,and we develop a modified quantum phase estimation algorithm to calculate real-space local density of states via direct quantum measurements.Furthermore,we validate these algorithms by numerically computing the density of states and spatial distributions of electronic states in graphene,twisted bilayer graphene quasicrystals,and fractal lattices,covering system sizes from hundreds to thousands of atoms.Our results manifest that the random-state quantum algorithms provide a general and qubit-efficient route to scalable simulations of electronic properties in large-scale periodic and aperiodic materials. 展开更多
关键词 periodic materials random state circuit random state quantum algorithms electronic structure properties density states aperiodic materials quantum algorithms quantum computation
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Prostate cancer prediction forest algorithm that takes using the random into account transrectal ultrasound findings, age, and serum levels of prostate-specific antigen 认领 引用 被引量：7
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作者 Li-Hong Xiao Pei-Ran Chen +4 位作者 Zhong-Ping Gou Yong-Zhong Li Mei Li Liang-Cheng Xiang Ping Feng 《Asian Journal of Andrology》 SCIE CAS CSCD 2017年第5期586-590,共5页
The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. ... The aim of this study is to evaluate the ability of the random forest algorithm that combines data on transrectal ultrasound findings, age, and serum levels of prostate-specific antigen to predict prostate carcinoma. Clinico-demographic data were analyzed for 941 patients with prostate diseases treated at our hospital, including age, serum prostate-specific antigen levels, transrectal ultrasound findings, and pathology diagnosis based on ultrasound-guided needle biopsy of the prostate. These data were compared between patients with and without prostate cancer using the Chi-square test, and then entered into the random forest model to predict diagnosis. Patients with and without prostate cancer differed significantly in age and serum prostate-specific antigen levels （P 〈 0.001）, as well as in all transrectal ultrasound characteristics （P 〈 0.05） except uneven echo （P = 0.609）. The random forest model based on age, prostate-specific antigen and ultrasound predicted prostate cancer with an accuracy of 83.10%, sensitivity of 65.64%, and specificity of 93.83%. Positive predictive value was 86.72%, and negative predictive value was 81.64%. By integrating age, prostate-specific antigen levels and transrectal ultrasound findings, the random forest algorithm shows better diagnostic performance for prostate cancer than either diagnostic indicator on its own. This algorithm may help improve diagnosis of the disease by identifying patients at high risk for biopsy. 展开更多
关键词 diagnosis prostate cancer prostate-specific antigen random forest algorithm transrectal ultrasound characteristics
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A real-time intelligent lithology identification method based on a dynamic felling strategy weighted random forest algorithm 认领 引用 被引量：11
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作者 Tie Yan Rui Xu +2 位作者 Shi-Hui Sun Zhao-Kai Hou Jin-Yu Feng 《Petroleum Science》 SCIE EI CAS CSCD 2024年第2期1135-1148,共14页
Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face ... Real-time intelligent lithology identification while drilling is vital to realizing downhole closed-loop drilling. The complex and changeable geological environment in the drilling makes lithology identification face many challenges. This paper studies the problems of difficult feature information extraction,low precision of thin-layer identification and limited applicability of the model in intelligent lithologic identification. The author tries to improve the comprehensive performance of the lithology identification model from three aspects: data feature extraction, class balance, and model design. A new real-time intelligent lithology identification model of dynamic felling strategy weighted random forest algorithm(DFW-RF) is proposed. According to the feature selection results, gamma ray and 2 MHz phase resistivity are the logging while drilling(LWD) parameters that significantly influence lithology identification. The comprehensive performance of the DFW-RF lithology identification model has been verified in the application of 3 wells in different areas. By comparing the prediction results of five typical lithology identification algorithms, the DFW-RF model has a higher lithology identification accuracy rate and F1 score. This model improves the identification accuracy of thin-layer lithology and is effective and feasible in different geological environments. The DFW-RF model plays a truly efficient role in the realtime intelligent identification of lithologic information in closed-loop drilling and has greater applicability, which is worthy of being widely used in logging interpretation. 展开更多
关键词 Intelligent drilling Closed-loop drilling Lithology identification Random forest algorithm Feature extraction
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Prediction of hot-rolled strip crown based on Boruta and extremely randomized trees algorithms 认领 引用 被引量：4
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作者 Li Wang Song-lin He +1 位作者 Zhi-ting Zhao Xian-du Zhang 《Journal of Iron and Steel Research International》 SCIE EI CAS CSCD 2023年第5期1022-1031,共10页
The quality of hot-rolled steel strip is directly affected by the strip crown.Traditional machine learning models have shown limitations in accurately predicting the strip crown,particularly when dealing with imbalanc... The quality of hot-rolled steel strip is directly affected by the strip crown.Traditional machine learning models have shown limitations in accurately predicting the strip crown,particularly when dealing with imbalanced data.This limitation results in poor production quality and efficiency,leading to increased production costs.Thus,a novel strip crown prediction model that uses the Boruta and extremely randomized trees(Boruta-ERT)algorithms to address this issue was proposed.To improve the accuracy of our model,we utilized the synthetic minority over-sampling technique to balance the imbalance data sets.The Boruta-ERT prediction model was then used to select features and predict the strip crown.With the 2160 mm hot rolling production lines of a steel plant serving as the research object,the experimental results showed that 97.01% of prediction data have an absolute error of less than 8 lm.This level of accuracy met the control requirements for strip crown and demonstrated significant benefits for the improvement in production quality of steel strip. 展开更多
关键词 Hot-rolled strip Data improvement Strip crown Feature selection Boruta algorithm Extremely randomized trees algorithm
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Investigation of Nuclear Binding Energy and Charge Radius Based on Random Forest Algorithm 认领 引用 被引量：1
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作者 CAI Boshuai YU Tianjun +3 位作者 LIN Xuan ZHANG Jilong WANG Zhixuan YUAN Cenxi 《原子能科学技术》 EI CAS CSCD 北大核心 2023年第4期704-712,共9页
The random forest algorithm was applied to study the nuclear binding energy and charge radius.The regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random forest.RMSE ... The random forest algorithm was applied to study the nuclear binding energy and charge radius.The regularized root-mean-square of error(RMSE)was proposed to avoid overfitting during the training of random forest.RMSE for nuclides with Z,N>7 is reduced to 0.816 MeV and 0.0200 fm compared with the six-term liquid drop model and a three-term nuclear charge radius formula,respectively.Specific interest is in the possible(sub)shells among the superheavy region,which is important for searching for new elements and the island of stability.The significance of shell features estimated by the so-called shapely additive explanation method suggests(Z,N)=(92,142)and(98,156)as possible subshells indicated by the binding energy.Because the present observed data is far from the N=184 shell,which is suggested by mean-field investigations,its shell effect is not predicted based on present training.The significance analysis of the nuclear charge radius suggests Z=92 and N=136 as possible subshells.The effect is verified by the shell-corrected nuclear charge radius model. 展开更多
关键词 nuclear binding energy nuclear charge radius random forest algorithm
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Randomized scheduling algorithm for input-queued switches 认领 引用 被引量：2
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作者 吴俊 罗军舟 《Journal of Southeast University(English Edition)》 EI CAS 2005年第1期6-10,共5页
The sampling problem for input-queued (IQ) randomized scheduling algorithms is analyzed.We observe that if the current scheduling decision is a maximum weighted matching (MWM),the MWM for the next slot mostly falls in... The sampling problem for input-queued (IQ) randomized scheduling algorithms is analyzed.We observe that if the current scheduling decision is a maximum weighted matching (MWM),the MWM for the next slot mostly falls in those matchings whose weight is closed to the current MWM.Using this heuristic,a novel randomized algorithm for IQ scheduling,named genetic algorithm-like scheduling algorithm (GALSA),is proposed.Evolutionary strategy is used for choosing sampling points in GALSA.GALSA works with only O(N) samples which means that GALSA has lower complexity than the famous randomized scheduling algorithm,APSARA.Simulation results show that the delay performance of GALSA is quite competitive with respect to that of APSARA. 展开更多
关键词 switches input-queued randomized algorithm
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Randomized Algorithms for Probabilistic Optimal Robust Performance Controller Design 认领 引用 被引量：1
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作者 宋春雷 谢玲 《Journal of Beijing Institute of Technology》 EI CAS 2004年第1期15-19,共5页
Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach wa... Polynomial-time randomized algorithms were constructed to approximately solve optimal robust performance controller design problems in probabilistic sense and the rigorous mathematical justification of the approach was given. The randomized algorithms here were based on a property from statistical learning theory known as (uniform) convergence of empirical means (UCEM). It is argued that in order to assess the performance of a controller as the plant varies over a pre-specified family, it is better to use the average performance of the controller as the objective function to be optimized, rather than its worst-case performance. The approach is illustrated to be efficient through an example. 展开更多
关键词 randomized algorithms statistical learning theory uniform convergence of empirical means (UCEM) probabilistic optimal robust performance controller design
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Random forest algorithm and regional applications of spectral inversion model for estimating canopy nitrogen concentration in rice 认领 引用 被引量：26
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作者 LI Xuqing LIU Xiangnan LIU Meiling WU Ling 《遥感学报》 CSCD 北大核心 2014年第4期923-945,共23页
Canopy Nitrogen Concentration （CNC） is a key indicator of crop yields. It is feasible to establish a real- time regional model to estimate CNC by upscaling the field-scale spectral model. This study focuses on monit... Canopy Nitrogen Concentration （CNC） is a key indicator of crop yields. It is feasible to establish a real- time regional model to estimate CNC by upscaling the field-scale spectral model. This study focuses on monitoring the CNC in rice on a large scale in real-time. The Random Forest （RF） algorithm is used to establish the CNC spectral inversion model, and some vegetation indexes that are sensitive to nitrogen were selected as input parameters for the RF. CNC was selected as an output parameter. The hyperspectral and biochemical data were collected in a paddy in Changchun City, Jilin Province, China, and the data in Suzhou was used to test the model＇s universality and effectiveness. Two regional-scale models were developed by applying scale transformation based on the input and output variables respectively. The results show that the RFCNC model （CNC spectral inversion model based on the RF algorithm） performed accurately and significantly improved upon existing methods. R2, used to validate method accuracy in Changchun and Suzhou, was 0.82 and 0.73 respectively. The regional application accuracy increased （R2= 0. 81） through the two upscaling methods using hyperspectral remote sensing satellite images. This study suggests that this method is promising for estimating regional CNC in rice by upscaling a field-scale spectral model if the strategy is appropriately selected. 展开更多
关键词 random forest algorithm, nitrogen inversion model, upscaling, regional application
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Winter Wheat Yield Estimation Based on Sparrow Search Algorithm Combined with Random Forest:A Case Study in Henan Province,China 认领 引用 被引量：1
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作者 SHI Xiaoliang CHEN Jiajun +2 位作者 DING Hao YANG Yuanqi ZHANG Yan 《Chinese Geographical Science》 SCIE CSCD 2024年第2期342-356,共15页
Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous r... Precise and timely prediction of crop yields is crucial for food security and the development of agricultural policies.However,crop yield is influenced by multiple factors within complex growth environments.Previous research has paid relatively little attention to the interference of environmental factors and drought on the growth of winter wheat.Therefore,there is an urgent need for more effective methods to explore the inherent relationship between these factors and crop yield,making precise yield prediction increasingly important.This study was based on four type of indicators including meteorological,crop growth status,environmental,and drought index,from October 2003 to June 2019 in Henan Province as the basic data for predicting winter wheat yield.Using the sparrow search al-gorithm combined with random forest(SSA-RF)under different input indicators,accuracy of winter wheat yield estimation was calcu-lated.The estimation accuracy of SSA-RF was compared with partial least squares regression(PLSR),extreme gradient boosting(XG-Boost),and random forest(RF)models.Finally,the determined optimal yield estimation method was used to predict winter wheat yield in three typical years.Following are the findings:1)the SSA-RF demonstrates superior performance in estimating winter wheat yield compared to other algorithms.The best yield estimation method is achieved by four types indicators’composition with SSA-RF)(R2=0.805,RRMSE=9.9%.2)Crops growth status and environmental indicators play significant roles in wheat yield estimation,accounting for 46%and 22%of the yield importance among all indicators,respectively.3)Selecting indicators from October to April of the follow-ing year yielded the highest accuracy in winter wheat yield estimation,with an R2of 0.826 and an RMSE of 9.0%.Yield estimates can be completed two months before the winter wheat harvest in June.4)The predicted performance will be slightly affected by severe drought.Compared with severe drought year(2011)(R2=0.680)and normal year(2017)(R2=0.790),the SSA-RF model has higher prediction accuracy for wet year(2018)(R2=0.820).This study could provide an innovative approach for remote sensing estimation of winter wheat yield.yield. 展开更多
关键词 winter wheat yield estimation sparrow search algorithm combined with random forest(SSA-RF) machine learning multi-source indicator optimal lead time Henan Province,China
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Randomized Kaczmarz algorithm for CT reconstruction 认领 引用 被引量：1
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作者 赵可 潘晋孝 孔慧华 《Journal of Measurement Science and Instrumentation》 CAS 2013年第1期34-37,共4页
The order of the projection in the algebraic reconstruction technique(ART)method has great influence on the rate of the convergence.Although many scholars have studied the order of the projection,few theoretical proof... The order of the projection in the algebraic reconstruction technique(ART)method has great influence on the rate of the convergence.Although many scholars have studied the order of the projection,few theoretical proofs are given.Thomas Strohmer and Roman Vershynin introduced a randomized version of the Kaczmarz method for consistent,and over-determined linear systems and proved whose rate does not depend on the number of equations in the systems in 2009.In this paper,we apply this method to computed tomography(CT)image reconstruction and compared images generated by the sequential Kaczmarz method and the randomized Kaczmarz method.Experiments demonstrates the feasibility of the randomized Kaczmarz algorithm in CT image reconstruction and its exponential curve convergence. 展开更多
关键词 Kaczmarz method iterative algorithm randomized Kaczmarz method computed tomography(CT) CT image reconstruction exponent curve fitting
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Object-based classification of hyperspectral data using Random Forest algorithm 认领 引用 被引量：8
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作者 Saeid Amini Saeid Homayouni +1 位作者 Abdolreza Safari Ali A.Darvishsefat 《Geo-Spatial Information Science》 SCIE EI CSCD 2018年第2期127-138,共12页
This paper presents a new framework for object-based classification of high-resolution hyperspectral data.This multi-step framework is based on multi-resolution segmentation(MRS)and Random Forest classifier(RFC)algori... This paper presents a new framework for object-based classification of high-resolution hyperspectral data.This multi-step framework is based on multi-resolution segmentation(MRS)and Random Forest classifier(RFC)algorithms.The first step is to determine of weights of the input features while using the object-based approach with MRS to processing such images.Given the high number of input features,an automatic method is needed for estimation of this parameter.Moreover,we used the Variable Importance(VI),one of the outputs of the RFC,to determine the importance of each image band.Then,based on this parameter and other required parameters,the image is segmented into some homogenous regions.Finally,the RFC is carried out based on the characteristics of segments for converting them into meaningful objects.The proposed method,as well as,the conventional pixel-based RFC and Support Vector Machine(SVM)method was applied to three different hyperspectral data-sets with various spectral and spatial characteristics.These data were acquired by the HyMap,the Airborne Prism Experiment(APEX),and the Compact Airborne Spectrographic Imager(CASI)hyperspectral sensors.The experimental results show that the proposed method is more consistent for land cover mapping in various areas.The overall classification accuracy(OA),obtained by the proposed method was 95.48,86.57,and 84.29%for the HyMap,the APEX,and the CASI datasets,respectively.Moreover,this method showed better efficiency in comparison to the spectralbased classifications because the OAs of the proposed method was 5.67 and 3.75%higher than the conventional RFC and SVM classifiers,respectively. 展开更多
关键词 Object-based classification Random Forest algorithm multi-resolution segmentation(MRS) hyperspectral imagery
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MATRIX ALGEBRA ALGORITHM OF STRUCTURE RANDOM RESPONSE NUMERICAL CHARACTERISTICS 认领 引用
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作者 Mei YulinWang XiaomingWang DelunDepartment of Mechanical Engineering,Dalian University of Technology,Dalian 116024,China 《Chinese Journal of Mechanical Engineering》 EI CAS 2003年第2期149-152,共4页
A new algorithm of structure random response numerical characteristics, namedas matrix algebra algorithm of structure analysis is presented. Using the algorithm, structurerandom response numerical characteristics can ... A new algorithm of structure random response numerical characteristics, namedas matrix algebra algorithm of structure analysis is presented. Using the algorithm, structurerandom response numerical characteristics can easily be got by directly solving linear matrixequations rather than structure motion differential equations. Moreover, in order to solve thecorresponding linear matrix equations, the numerical integration fast algorithm is presented. Thenaccording to the results, dynamic design and life-span estimation can be done. Besides, the newalgorithm can solve non-proportion damp structure response. 展开更多
关键词 matrix algebra algorithm structure random response numericalcharacteristics numerical integration fast algorithm non-proportion damp
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Impacts of random negative training datasets on machine learning-based geologic hazard susceptibility assessment 认领 引用 被引量：2
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作者 Hao Cheng Wei Hong +3 位作者 Zhen-kai Zhang Zeng-lin Hong Zi-yao Wang Yu-xuan Dong 《China Geology》 CAS CSCD 2025年第4期676-690,共15页
This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,... This study investigated the impacts of random negative training datasets(NTDs)on the uncertainty of machine learning models for geologic hazard susceptibility assessment of the Loess Plateau,northern Shaanxi Province,China.Based on randomly generated 40 NTDs,the study developed models for the geologic hazard susceptibility assessment using the random forest algorithm and evaluated their performances using the area under the receiver operating characteristic curve(AUC).Specifically,the means and standard deviations of the AUC values from all models were then utilized to assess the overall spatial correlation between the conditioning factors and the susceptibility assessment,as well as the uncertainty introduced by the NTDs.A risk and return methodology was thus employed to quantify and mitigate the uncertainty,with log odds ratios used to characterize the susceptibility assessment levels.The risk and return values were calculated based on the standard deviations and means of the log odds ratios of various locations.After the mean log odds ratios were converted into probability values,the final susceptibility map was plotted,which accounts for the uncertainty induced by random NTDs.The results indicate that the AUC values of the models ranged from 0.810 to 0.963,with an average of 0.852 and a standard deviation of 0.035,indicating encouraging prediction effects and certain uncertainty.The risk and return analysis reveals that low-risk and high-return areas suggest lower standard deviations and higher means across multiple model-derived assessments.Overall,this study introduces a new framework for quantifying the uncertainty of multiple training and evaluation models,aimed at improving their robustness and reliability.Additionally,by identifying low-risk and high-return areas,resource allocation for geologic hazard prevention and control can be optimized,thus ensuring that limited resources are directed toward the most effective prevention and control measures. 展开更多
关键词 Landslides Debris flows Collapses Ground fissures Geologic hazard prevention and control engineering Geologic hazard susceptibility assessment Negative training dataset Average spatial correlation Random forest algorithm Risk and return analysis Geological survey engineering Loess Plateau area
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Pseudo-Random Coding Side-Lobe Suppression Method Based on CLEAN Algorithm 认领 引用 被引量：1
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作者 Liyu Tian Lianjuan Zhang Jiaying Li 《Journal of Beijing Institute of Technology》 EI CAS 2017年第3期375-380,共6页
A pseudo-random coding side-lobe suppression method based on CLEAN algorithm is introduced.The CLEAN algorithm mainly processes pulse compression results of a pseudo-random coding,and estimates a target＇s distance by... A pseudo-random coding side-lobe suppression method based on CLEAN algorithm is introduced.The CLEAN algorithm mainly processes pulse compression results of a pseudo-random coding,and estimates a target＇s distance by a method named interpolation method,so that we can get an ideal pulse compression result of the target,and then use the adjusted ideal pulse compression side-lobe to cut the actual pulse compression result,so as to achieve the remarkable performance of side-lobe suppression for large targets,and let the adjacent small targets appear.The computer simulations by MATLAB with this method analyze the effect of side-lobe suppression in an ideal or noisy environment.It is proved that this method can effectively solve the problem due to the side-lobe of pseudo-random coding being too high,and can enhance the radar＇s multi-target detection ability. 展开更多
关键词 CLEAN algorithm pseudo-random coding pulse compression interpolation method
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Artificial intelligence in the service of entrepreneurial finance:knowledge structure and the foundational algorithmic paradigm 认领 引用
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作者 Robert Kudelić Tamara Šmaguc Sherry Robinson 《Financial Innovation》 2025年第1期2021-2063,共43页
The study conducts a bibliometric review of artificial intelligence applications in two areas:the entrepreneurial finance literature,and the corporate finance literature with implications for entrepreneurship.A rigoro... The study conducts a bibliometric review of artificial intelligence applications in two areas:the entrepreneurial finance literature,and the corporate finance literature with implications for entrepreneurship.A rigorous search and screening of the web of science core collection identified 1,890 journal articles for analysis.The bibliometrics provide a detailed view of the knowledge field,indicating underdeveloped research directions.An important contribution comes from insights through artificial intelligence methods in entrepreneurship.The results demonstrate a high representation of artificial neural networks,deep neural networks,and support vector machines across almost all identified topic niches.In contrast,applications of topic modeling,fuzzy neural networks,and growing hierarchical self-organizing maps are rare.Additionally,we take a broader view by addressing the problem of applying artificial intelligence in economic science.Specifically,we present the foundational paradigm and a bespoke demonstration of the Monte Carlo randomized algorithm. 展开更多
关键词 Bibliometrics Artificial intelligence Entrepreneurship Finance Randomized algorithm
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A Regularized Randomized Kaczmarz Algorithm for Large Discrete Ill-Posed Problems 认领 引用
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作者 LIU Fengming WANG Zhengsheng +1 位作者 YANG Siyu XU Guili 《Transactions of Nanjing University of Aeronautics and Astronautics》 EI CSCD 2020年第5期787-795,共9页
Tikhonov regularization is a powerful tool for solving linear discrete ill-posed problems.However,effective methods for dealing with large-scale ill-posed problems are still lacking.The Kaczmarz method is an effective... Tikhonov regularization is a powerful tool for solving linear discrete ill-posed problems.However,effective methods for dealing with large-scale ill-posed problems are still lacking.The Kaczmarz method is an effective iterative projection algorithm for solving large linear equations due to its simplicity.We propose a regularized randomized extended Kaczmarz(RREK)algorithm for solving large discrete ill-posed problems via combining the Tikhonov regularization and the randomized Kaczmarz method.The convergence of the algorithm is proved.Numerical experiments illustrate that the proposed algorithm has higher accuracy and better image restoration quality compared with the existing randomized extended Kaczmarz(REK)method. 展开更多
关键词 ill-posed problem Tikhonov regularization randomized extended Kaczmarz(REK)algorithm image restoration
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Enhancing rock slope stability prediction using random forest machine learning:A case study 认领 引用
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作者 Afiqah Ismail Ahmad Safuan A Rashid +10 位作者 Ali Dehghanbanadaki Rafiuddin Hakim Roslan Mohd Firdaus Md Dan@Azlan Abd Wahid Rasib Radzuan Saari Mushairry Mustaffar Azman Kassim Rini Asnida Abdullah Khairul Hazman Padil Norbazlan Mohd Yusof Norisam Abd Rahaman 《China Geology》 CAS CSCD 2025年第4期691-706,共16页
The prediction of slope stability is a complex nonlinear problem.This paper proposes a new method based on the random forest(RF)algorithm to study the rocky slopes stability.Taking the Bukit Merah,Perak and Twin Peak(... The prediction of slope stability is a complex nonlinear problem.This paper proposes a new method based on the random forest(RF)algorithm to study the rocky slopes stability.Taking the Bukit Merah,Perak and Twin Peak(Kuala Lumpur)as the study area,the slope characteristics of geometrical parameters are obtained from a multidisciplinary approach(consisting of geological,geotechnical,and remote sensing analyses).18 factors,including rock strength,rock quality designation(RQD),joint spacing,continuity,openness,roughness,filling,weathering,water seepage,temperature,vegetation index,water index,and orientation,are selected to construct model input variables while the factor of safety(FOS)functions as an output.The area under the curve(AUC)value of the receiver operating characteristic(ROC)curve is obtained with precision and accuracy and used to analyse the predictive model ability.With a large training set and predicted parameters,an area under the ROC curve(the AUC)of 0.95 is achieved.A precision score of 0.88 is obtained,indicating that the model has a low false positive rate and correctly identifies a substantial number of true positives.The findings emphasise the importance of using a variety of terrain characteristics and different approaches to characterise the rock slope. 展开更多
关键词 Slope stability prediction Random Forest Algorithm Remote sensing in Geology Factor of Safety(FOS) Geometrical parameters Rock quality designation(RQD) Multilayer perceptron(MLP)
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