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Unascertained measurement classifying model of goal collapse prediction 认领 引用 被引量：37
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作者 董陇军 彭刚剑 +2 位作者 付玉华 白云飞 刘有芳 《Journal of Coal Science & Engineering(China)》 2008年第2期221-224,共4页
Based on optimized forecast method of unascertained classifying,a unascer- tained measurement classifying model (UMC) to predict mining induced goaf collapse was established,The discriminated factors of the model are ... Based on optimized forecast method of unascertained classifying,a unascer- tained measurement classifying model (UMC) to predict mining induced goaf collapse was established,The discriminated factors of the model are influential factors including over- burden layer type,overburden layer thickness,the complex degree of geologic structure, the inclination angle of coal bed,volume rate of the cavity region,the vertical goaf depth from the surface and space superposition layer of the goaf region.Unascertained mea- surement (UM) function of each factor was calculated.The unascertained measurement to indicate the classification center and the grade of waiting forecast sample was determined by the UM distance between the synthesis index of waiting forecast samples and index of every classification.The training samples were tested by the established model,and the correct rate is 100%.Furthermore,the seven waiting forecast samples were predicted by the UMC model.The results show that the forecast results are fully consistent with the ac- tual situation. 展开更多
关键词 unascertained measurement classifying model goaf collapse prediction mining engineering
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Subaxial cervical spine injury classification system: is it most appropriate for classifying cervical injury? 认领 引用 被引量：4
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作者 Rafael Martínez-Pérez Francisco Fuentes Víctor S.Alemany 《Neural Regeneration Research》 SCIE CAS CSCD 2015年第9期1416-1417,共2页
The cervical spine injury represents a potential devastating disease with 6% associated in-hospital mortality （lain et al., 2015）. Neurological deterioration ranging from complete spinal cord injury （SCI） to incom... The cervical spine injury represents a potential devastating disease with 6% associated in-hospital mortality （lain et al., 2015）. Neurological deterioration ranging from complete spinal cord injury （SCI） to incomplete SCI or single radiculopathy are potential consequences of the blunt trauma over this region. The subaxial cervical spine accounts the vast majority of cervical injuries, making up two thirds of all cervical fractures （Alday, 1996）. Few classifications （Holdsworth, 1970; White et al., 1975; Mien et al., 1982; Denis, 1984; Vaccaro et al., 2007） have been proposed to describe injuries of the cervical spine for several reasons. First, to delineate the best treatment in each case; second, to determinate an accurate neurological prognosis, and third, to establish a standard way to communicate and describe specific characteristics of cervical injuries patterns. Classical systems are primarily descriptive and no single system has gained widespread use, largely because of restrictions in clinical relevance and its complexity. 展开更多
关键词 is it most appropriate for classifying cervical injury SLIC Subaxial cervical spine injury classification system
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Gaussian mixture models for clustering and classifying traffic flow in real-time for traffic operation and management 认领 引用 被引量：2
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作者 孙璐 张惠民 +3 位作者 高荣 顾文钧 徐冰 陈鲤梁 《Journal of Southeast University(English Edition)》 EI CAS 2011年第2期174-179,共6页
Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM ... Based on Gaussian mixture models（GMM）, speed, flow and occupancy are used together in the cluster analysis of traffic flow data. Compared with other clustering and sorting techniques, as a structural model, the GMM is suitable for various kinds of traffic flow parameters. Gap statistics and domain knowledge of traffic flow are used to determine a proper number of clusters. The expectation-maximization （E-M） algorithm is used to estimate parameters of the GMM model. The clustered traffic flow pattems are then analyzed statistically and utilized for designing maximum likelihood classifiers for grouping real-time traffic flow data when new observations become available. Clustering analysis and pattern recognition can also be used to cluster and classify dynamic traffic flow patterns for freeway on-ramp and off-ramp weaving sections as well as for other facilities or things involving the concept of level of service, such as airports, parking lots, intersections, interrupted-flow pedestrian facilities, etc. 展开更多
关键词 traffic flow patterns Gaussian mixture model level of service data mining cluster analysis classifier
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Classifying and ranking DMUs in interval DEA 认领 引用 被引量：3
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作者 郭均鹏 吴育华 李汶华 《Journal of Harbin Institute of Technology(New Series)》 EI CAS 2005年第4期405-407,共3页
During efficiency evaluating by DEA, the inputs and outputs of DMUs may be intervals because of insufficient information or measure error. For this reason, interval DEA is proposed. To make the efficiency scores more ... During efficiency evaluating by DEA, the inputs and outputs of DMUs may be intervals because of insufficient information or measure error. For this reason, interval DEA is proposed. To make the efficiency scores more discriminative, this paper builds an Interval Modified DEA (IMDEA) model based on MDEA. Furthermore, models of obtaining upper and lower bounds of the efficiency scores for each DMU are set up. Based on this, the DMUs are classified into three types. Next, a new order relation between intervals which can express the DM’s preference to the three types is proposed. As a result, a full and more convictive ranking is made on all the DMUs. Finally an example is given. 展开更多
关键词 interval modified DEA（IMDEA） decision making units（DMUs） order relation classify rank
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A Geographic Information Systems approach for classifying and mapping forest management category in Baihe Forestry Bureau, Northeast China 认领 引用 被引量：2
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作者 WANG Shun-zhong SHAO Guo-fan +2 位作者 GU Hui-yan WANG Qing-li DAI Li-min 《Journal of Forestry Research》 CAS 2006年第3期211-215,共5页
This paper demonstrates a Geographic Information Systems （GIS） procedure of classifying and mapping forest management category in Baihe Forestry Burea, Jilin Province, China. Within the study area, Baihe Forestry Bu... This paper demonstrates a Geographic Information Systems （GIS） procedure of classifying and mapping forest management category in Baihe Forestry Burea, Jilin Province, China. Within the study area, Baihe Forestry Bureau land was classified into a two-hierarchy system. The top-level class included the non-forest and forest. Over 96% of land area is forest in the study area, which was further divided into key ecological service forest （KES）, general ecological service forest （GES）, and commodity forest （COM）. COM covered 45.0% of the total land area and was the major forest management type in Baihe Forest Bureau. KES and GES accounted for 21.2% and 29.9% of the total land area, respectively. The forest management zones designed with GIS in this study were then compared with the forest management zones established using the hand draw by the local agency. There were obvious differences between the two products. It suggested that the differences had some to do with the data sources, basic unit and mapping procedures. It also suggested that the GIS method was a useful tool in integrating forest inventory data and other data for classifying and mapping forest zones to meet the needs of the classified forest management system. 展开更多
关键词 Classified forest management Key ecological service forest： GIS： Baihe Forestry Bureau
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The Motion Trace of Particles in Classifying Flow Field 认领 引用 被引量：2
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作者 黎国华 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS 2005年第2期71-73,共3页
According to the theory of the stochastic trajectory model of particle in the gas-solid two-phase flows, the two-phase turbulence model between the blades in the inner cavity of the FW-Φ150 horizontal turbo classifie... According to the theory of the stochastic trajectory model of particle in the gas-solid two-phase flows, the two-phase turbulence model between the blades in the inner cavity of the FW-Φ150 horizontal turbo classifier was established, and the commonly-used PHOENICS code was adopted to carried out the numerical simulation. It was achieved the flow characteristics under a certain condition as well as the motion trace of particles with different diameters entering from certain initial location and passing through the flow field between the blades under the correspondent condition. This research method quite directly demonstrates the motion of particles. An experiment was executed to prove the accuracy of the results of numerical simulation. 展开更多
关键词 stochastic trajectory model turbo classifier numerical simulation motion trace
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METHOD OF CLASSIFYING GRASPS BY ROBOT HANDS 认领 引用 被引量：2
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作者 Zhang Yuru 《Chinese Journal of Mechanical Engineering》 CAS 1996年第4期271-277,共7页
This research characterizes grasping by multifingered robot hands through investiga- tion of the space of contact forces into four subspaces , a method is developed to determine the di- mensions of the subspaces with ... This research characterizes grasping by multifingered robot hands through investiga- tion of the space of contact forces into four subspaces , a method is developed to determine the di- mensions of the subspaces with respect to the connectivity of the object. The relationship reveals the differences between three types of grasps classified and indicates how the contact force can be decomposed corresponding to each type of grasp. The subspaces and the determination of their di- mensions are illlustrated by examples. 展开更多
关键词 Robot hand Classifying grasp Contact force
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Classifying Abdominal Fat Distribution Patterns byUsing Body Measurement Data 认领 引用
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作者 Jingjing Sun Bugao Xu +1 位作者 Jane Lee Jeanne H.Freeland-Graves 《Computer Modeling in Engineering & Sciences》 SCIE EI 2021年第3期1189-1202,共14页
This study aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues(VAT and SAT)measured by magnetic resonance imaging(MRI),to analyze the relationsh... This study aims to explore new categorization that characterizes the distribution clusters of visceral and subcutaneous adipose tissues(VAT and SAT)measured by magnetic resonance imaging(MRI),to analyze the relationship between the VAT-SAT distribution patterns and the novel body shape descriptors(BSDs),and to develop a classifier to predict the fat distribution clusters using the BSDs.In the study,66 male and 54 female participants were scanned by MRI and a stereovision body imaging(SBI)to measure participants’abdominal VAT and SAT volumes and the BSDs.A fuzzy c-means algorithm was used to form the inherent grouping clusters of abdominal fat distributions.A support-vector-machine(SVM)classifier,with an embedded feature selection scheme,was employed to determine an optimal subset of the BSDs for predicting internal fat distributions.A fivefold cross-validation procedure was used to prevent over-fitting in the classification.The classification results of the BSDs were compared with those of the traditional anthropometric measurements and the Dual Energy X-Ray Absorptiometry(DXA)measurements.Four clusters were identified for abdominal fat distributions:(1)low VAT and SAT,(2)elevated VAT and SAT,(3)higher SAT,and(4)higher VAT.The cross-validation accuracies of the traditional anthropometric,DXA and BSD measurements were 85.0%,87.5% and 90%,respectively.Compared to the traditional anthropometric and DXA measurements,the BSDs appeared to be effective and efficient in predicting abdominal fat distributions. 展开更多
关键词 Abdominal fat distribution body shape descriptor SVM classifier
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Narrowing the particle size distribution of powder coatings via a reformed classifying cyclone:Improved flowability and film qualities 认领 引用 被引量：1
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作者 Junqing Xie Jing Fu +3 位作者 Yuanyuan Shao Haiping Zhang Hui Zhang Jesse Zhu 《Particuology》 SCIE EI CAS CSCD 2025年第8期67-77,共11页
In this study,a new design of classifying cyclone separator with secondary air inlet and air guider(SAG type)was proposed to narrow the particle size distribution(PSD).CFD simulation reveals that only adding a seconda... In this study,a new design of classifying cyclone separator with secondary air inlet and air guider(SAG type)was proposed to narrow the particle size distribution(PSD).CFD simulation reveals that only adding a secondary air inlet(SA type)at the bottom of the cyclone will create a strong updraft.After adding an air guider,the rotating air enhances the strength of the outer vortex,which is conducive to the particle collection.The experiment results also verify these findings.SA cyclone separator effectively narrows the PSD,but the particle collection efficiency is greatly reduced.In contrast,SAG cyclone separator can decrease the PSD while maintaining a high particle collection efficiency.In addition,it is also found that the collection efficiency of particles decreases with the increase of the opening size of the secondary air inlet,but the SAG cyclone separator is less sensitive to this,making it more controllable in practical applications.To further assess the impact of cyclone modification on coating powder and film properties,the powder coatings with wide PSD and narrow PSD were prepared by original cyclone and SAG cyclone,respectively.It was found that reducing the span of the powder by modifying the cyclone can effectively improve the flowability(angle of repose(AOR),avalanche angle(AVA)and rotating bed expansion rate(RBER))and film properties(distinctness of image(DOI)and surface flatness)of the particles.This study provides guidance for narrowing the PSD and improving the flowability and film properties of powder coatings. 展开更多
关键词 Classifying cyclone PSD Powder coating Flowability Film properties
Handling the Challenges of Small-Scale Labeled Data and Class Imbalances in Classifying the N and K Statuses of Rubber Leaves Using Hyperspectroscopy Techniques 认领 引用 被引量：2
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作者 Wenfeng Hu Weihao Tang +5 位作者 Chuang Li Jinjing Wu Hong Liu Chao Wang Xiaochuan Luo Rongnian Tang 《Plant Phenomics》 SCIE EI CSCD 2024年第2期281-294,共14页
The nutritional status of rubber trees(Hevea brasiliensis)is inseparable from the production of natural rubber.Nitrogen(N)and potassium(K)levels in rubber leaves are 2 crucial criteria that reflect the nutritional sta... The nutritional status of rubber trees(Hevea brasiliensis)is inseparable from the production of natural rubber.Nitrogen(N)and potassium(K)levels in rubber leaves are 2 crucial criteria that reflect the nutritional status of the rubber tree.Advanced hyperspectral technology can evaluate N and K statuses in leaves rapidly.However,high bias and uncertain results will be generated when using a small size and imbalance dataset to train a spectral estimaion model.A typical solution of laborious long-term nutrient stress and high-intensive data collection deviates from rapid and flexible advantages of hyperspectral tech.Therefore,a less intensive and streamlined method,remining information from hyperspectral image data,was assessed. 展开更多
关键词 handling data ersp challenges classifying imbalances labeled leaves rubber small-scale
Machine Learning Prediction of Crystal Structure Stability toward the Design of High-Entropy Oxides 认领 引用
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作者 Qiancheng Zhou Chunchun Wang +4 位作者 Liyuan Wang Zhouzhou Wang Ming Qiu Mingdong Dong Ying Yu 《Chinese Physics Letters》 SCIE EI CAS CSCD 2026年第5期314-347,共34页
Energy above the convex hull(Ehull)is a key thermodynamic criterion for assessing phase stability.However,the enormous computational cost required for phase diagram construction hinders the prediction of Ehull,undersc... Energy above the convex hull(Ehull)is a key thermodynamic criterion for assessing phase stability.However,the enormous computational cost required for phase diagram construction hinders the prediction of Ehull,underscoring the need for data-driven approaches.Here,a hybrid framework integrating an autoencoder with a random forest classifier was proposed to effectively categorize crystal structures into stable,metastable,and unstable regimes according to Ehull thresholds,achieving an overall accuracy above 84%.More importantly,physically interpretable latent features associated with density,symmetry,and lattice were identified for stability prediction.Application to high-entropy oxides(HEOs)further demonstrates the effectiveness of the framework,revealing that structures with high configurational entropies and low cation radius mismatch are overwhelmingly classified as stable or metastable.Beyond confirming the dominant role of density and lattice features in stability prediction,SHAP analysis further suggests that larger disparities in atomic thermal conductivities and the regulation of the magnetic moment by limited magnetic atoms play a critical role in governing the stability of HEO structures.The interpretable and effective AE-RF algorithm developed in this work holds great potential for accelerating the discovery of novel HEOs and multicomponent materials. 展开更多
关键词 high entropy oxides random forest classifier crystal structure stability convex hull hybrid framework machine learning crystal structures phase diagram construction
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A method for classifying whistles of bottlenose dolphin(Tursiops truncates) based on syntactic pattern reorganization 认领 引用
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作者 YANG Wuyi SUN Xinzhe +3 位作者 ZHANG Yu WEI Chong YANG Yanming NIU Fuqiang 《Chinese Journal of Acoustics》 CSCD 2017年第3期362-372,共11页
A method based on syntactic pattern recognition was presented to automatically classify whistles of bottlenose dolphin. Dolphin whistles have typically been characterized in terms of their instantaneous frequency as a... A method based on syntactic pattern recognition was presented to automatically classify whistles of bottlenose dolphin. Dolphin whistles have typically been characterized in terms of their instantaneous frequency as a function of time, which is also known as ＂whistle contour＂. The frequency variation features of a whistle were extracted according to its contour. Then, the frequency variation features were used for learning grammatical patterns. A whistle was classified according to grammatical pattern of its frequency variation features. The exper- imental results showed that the classification accuracy of the proposed method was 95%. The method can provide technical support for acoustic study of dolphins＇ biological behavior. 展开更多
关键词 In Tursiops truncates A method for classifying whistles of bottlenose dolphin
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Research on intelligent identification of Traditional Chinese Medicine constitutions based on multi-model fusion 认领 引用 被引量：1
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作者 YUE Yao CAI Xiaohong 《Journal of Traditional Chinese Medicine》 SCIE CAS CSCD 2026年第1期226-235,共10页
OBJECTIVE:To develop an automated system for identifying and classifying constitution types in Traditional Chinese Medicine(TCM)by leveraging multi-model fusion algorithms.METHODS:A condensed version of a physical inf... OBJECTIVE:To develop an automated system for identifying and classifying constitution types in Traditional Chinese Medicine(TCM)by leveraging multi-model fusion algorithms.METHODS:A condensed version of a physical information collection form was designed to facilitate efficient data acquisition.The collected data were analyzed using a multi-model fusion approach,which integrated several machine learning techniques.These included support vector machines,Naive Bayes,decision trees,random forests,logistic regression,multilayer perceptrons,K-nearest neighbors,gradient boosting,adaptive ensemble learning,and recurrent neural networks.A soft voting strategy was used to combine the predictive outputs of each model,enabling the selection of the most effective model combination.RESULTS:The classification models demonstrated consistent and robust performance across most TCM constitution types when enhanced by the multi-model fusion strategy.In particular,high levels of accuracy,precision,recall,and F1-score were achieved for constitution types such as Yang deficiency,Qi deficiency,and Qi stagnation.However,the classification performance for the Yin deficiency constitution was relatively lower,indicating the need for further refinement and optimization in future research.CONCLUSION:This study introduces a novel,automated method for classifying TCM constitution types through the application of multi-model fusion algorithms.The approach simplifies the complex task of constitution identification while offering a practical and theoretical framework for the intelligent diagnosis of TCM body types.The findings have the potential to enhance personalized health management and support clinical decision-making in TCM diagnosis and treatment. 展开更多
关键词 multi-model fusion algorithms Traditional Chinese Medicine constitution voting classifier
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EDTM:Efficient Domain Transition for Multi-Source Domain Adaptation 认领 引用
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作者 Mangyu Lee Jaekyun Jeong +2 位作者 Yun Wook Choo Keejun Han Jungeun Kim 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第2期955-970,共16页
Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional ... Domain adaptation aims to reduce the distribution gap between the training data(source domain)and the target data.This enables effective predictions even for domains not seen during training.However,most conventional domain adaptation methods assume a single source domain,making them less suitable for modern deep learning settings that rely on diverse and large-scale datasets.To address this limitation,recent research has focused on Multi-Source Domain Adaptation(MSDA),which aims to learn effectively from multiple source domains.In this paper,we propose Efficient Domain Transition for Multi-source(EDTM),a novel and efficient framework designed to tackle two major challenges in existing MSDA approaches:(1)integrating knowledge across different source domains and(2)aligning label distributions between source and target domains.EDTM leverages an ensemble-based classifier expert mechanism to enhance the contribution of source domains that are more similar to the target domain.To further stabilize the learning process and improve performance,we incorporate imitation learning into the training of the target model.In addition,Maximum Classifier Discrepancy(MCD)is employed to align class-wise label distributions between the source and target domains.Experiments were conducted using Digits-Five,one of the most representative benchmark datasets for MSDA.The results show that EDTM consistently outperforms existing methods in terms of average classification accuracy.Notably,EDTM achieved significantly higher performance on target domains such as Modified National Institute of Standards and Technolog with blended background images(MNIST-M)and Street View House Numbers(SVHN)datasets,demonstrating enhanced generalization compared to baseline approaches.Furthermore,an ablation study analyzing the contribution of each loss component validated the effectiveness of the framework,highlighting the importance of each module in achieving optimal performance. 展开更多
关键词 Multi-source domain adaptation imitation learning maximum classifier discrepancy ensemble based classifier EDTM
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Machine learning hybrid dynamic best model selection algorithm for real-time fire prediction using IoT-enabled multi-sensor data in buildings 认领 引用
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作者 Mujeeb Ali Khan Weiguo Song +3 位作者 Abbas Khan Mazhar Ali Rehmat Karim Jun Zhang 《Journal of Safety Science and Resilience》 EI CSCD 2026年第2期329-346,共18页
Fire disasters in urban areas,including homes,offices,and industrial facilities,have increased significantly over the past decade,causing extensive damage and loss of life.Integrating intelligent fire detection system... Fire disasters in urban areas,including homes,offices,and industrial facilities,have increased significantly over the past decade,causing extensive damage and loss of life.Integrating intelligent fire detection systems with machine learning(ML)is crucial for providing early warnings and facilitating effective response coordination.In this research,a novel hybrid dynamic best model selection(HDBMS)ML-based algorithm is proposed for internet of things(IoT)-enabled fire detection in buildings,which outperforms traditional static models by providing higher accuracy and adaptability across diverse fire scenarios.The proposed algorithm employs feature selection pre-processing techniques,followed by the synergistic integration of five classifiers:support vector classifier(SVC),logistic regression,random forest,Gaussian Naïve Bayes(Gaussian NB),and decision tree,aiming to enhance prediction accuracy and robustness across various fire scenarios.This system dynamically selects the optimal classifier based on real-time performance metrics such as precision,accuracy,F1-score,and recall.This approach was rigorously validated using our developed dataset and real-time sensor data to monitor smoke,temperature,and humidity under various fire scenarios.Following algorithm validation,a laboratory-constructed multi-sensor fire detection node prototype wirelessly feeds sensor data to the ThingSpeak cloud platform for real-time data analysis and communication with ML algorithms in the back-end.The system's improved precision,accuracy,and root mean squared error(RMSE)confirm its effectiveness.The results demonstrate that the proposed approach achieves superior classification accuracy compared with existing methods in the literature.Furthermore,this study's novelty lies in the dynamic selection of the most effective model in real-time,a characteristic that is currently lacking in fire detection systems,thereby enhancing the system's flexibility and efficiency in various fire scenarios. 展开更多
关键词 Machine learning optimization Adaptive classifier selection Sensor fusion Fire hazard mitigation Real-time analytics
Mapping fruit orchard species for smallholders in drought-vulnerable agroforestry systems of China 认领 引用
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作者 ZHANG Jingge HUANG Huaguo +1 位作者 YUAN Xiaotian TAN Shen 《Journal of Arid Land》 SCIE CAS CSCD 2026年第5期886-902,共17页
Fruit trees are typically organized at the orchard level,where the tree-based ecosystem is characterized by high homogeneity,leading to clustered distributions with distinct boundaries.While remote sensing-based class... Fruit trees are typically organized at the orchard level,where the tree-based ecosystem is characterized by high homogeneity,leading to clustered distributions with distinct boundaries.While remote sensing-based classification techniques are well established,most studies have not treated fruit orchards as a distinct category.Whether remote sensing can effectively address orchard classification and distribution remains uncertain.This study focused on the Guanzhong Plain on the southern part of the Loess Plateau as a representative drought-vulnerable region in China,characterized by mixed orchard–cropland landscapes.Sentinel-2 imagery was used as the primary classification feature,supplemented by topographic characteristics.A Random Forest classifier was trained and validated using 1980 ground samples across major planting regions in May 2024.The final classification results were satisfactory,with an overall accuracy of 0.86.Meanwhile,a comparison against statistical data demonstrated the reasonableness of fruit orchard area:the correlation coefficients for three major fruit types(apple,grape,and kiwi)are greater than 0.75.Compared with existing land cover products,which often misclassify fruit trees as cropland or forestland,our results demonstrated that combining band reflectance time series,vegetation index time series,and topographic features can effectively differentiate fruit orchards from spectrally similar cropland and forestland.This study facilitates precise fruit orchard mapping,supporting targeted production management and ecological carbon sequestration estimation in similar regions with drought-vulnerable agroforestry systems. 展开更多
关键词 fruit orchard classification Sentinel-2 imagery Random Forest classifier phenology Guanzhong Plain
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Traffic Vision:UAV-Based Vehicle Detection and Traffic Pattern Analysis via Deep Learning Classifier 认领 引用
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作者 Mohammed Alnusayri Ghulam Mujtaba +4 位作者 Nouf Abdullah Almujally Shuoa S.Aitarbi Asaad Algarni Ahmad Jalal Jeongmin Park 《Computers, Materials & Continua》 SCIE EI 2026年第3期266-284,共19页
This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized... This paper presents a unified Unmanned Aerial Vehicle-based(UAV-based)traffic monitoring framework that integrates vehicle detection,tracking,counting,motion prediction,and classification in a modular and co-optimized pipeline.Unlike prior works that address these tasks in isolation,our approach combines You Only Look Once(YOLO)v10 detection,ByteTrack tracking,optical-flow density estimation,Long Short-Term Memory-based(LSTM-based)trajectory forecasting,and hybrid Speeded-Up Robust Feature(SURF)+Gray-Level Co-occurrence Matrix(GLCM)feature engineering with VGG16 classification.Upon the validation across datasets(UAVDT and UAVID)our framework achieved a detection accuracy of 94.2%,and 92.3%detection accuracy when conducting a real-time UAV field validation.Our comprehensive evaluations,including multi-metric analyses,ablation studies,and cross-dataset validations,confirm the framework’s accuracy,efficiency,and generalizability.These results highlight the novelty of integrating complementary methods into a single framework,offering a practical solution for accurate and efficient UAV-based traffic monitoring. 展开更多
关键词 Smart traffic system drone devices machine learner dynamic complex scenes VGG-16 classifier
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RankXLAN:An explainable ensemble-based machine learning framework for biomarker detection,therapeutic target identification,and classification using transcriptomic and epigenomic stomach cancer data 认领 引用
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作者 Kasmika Borah Himanish Shekhar Das +1 位作者 Mudassir Khan Saurav Mallik 《Medical Data Mining》 CAS 2026年第1期13-31,共19页
Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-through... Background:Stomach cancer(SC)is one of the most lethal malignancies worldwide due to late-stage diagnosis and limited treatment.The transcriptomic,epigenomic,and proteomic,etc.,omics datasets generated by high-throughput sequencing technology have become prominent in biomedical research,and they reveal molecular aspects of cancer diagnosis and therapy.Despite the development of advanced sequencing technology,the presence of high-dimensionality in multi-omics data makes it challenging to interpret the data.Methods:In this study,we introduce RankXLAN,an explainable ensemble-based multi-omics framework that integrates feature selection(FS),ensemble learning,bioinformatics,and in-silico validation for robust biomarker detection,potential therapeutic drug-repurposing candidates’identification,and classification of SC.To enhance the interpretability of the model,we incorporated explainable artificial intelligence(SHapley Additive exPlanations analysis),as well as accuracy,precision,F1-score,recall,cross-validation,specificity,likelihood ratio(LR)+,LR−,and Youden index results.Results:The experimental results showed that the top four FS algorithms achieved improved results when applied to the ensemble learning classification model.The proposed ensemble model produced an area under the curve(AUC)score of 0.994 for gene expression,0.97 for methylation,and 0.96 for miRNA expression data.Through the integration of bioinformatics and ML approach of the transcriptomic and epigenomic multi-omics dataset,we identified potential marker genes,namely,UBE2D2,HPCAL4,IGHA1,DPT,and FN3K.In-silico molecular docking revealed a strong binding affinity between ANKRD13C and the FDA-approved drug Everolimus(binding affinity−10.1 kcal/mol),identifying ANKRD13C as a potential therapeutic drug-repurposing target for SC.Conclusion:The proposed framework RankXLAN outperforms other existing frameworks for serum biomarker identification,therapeutic target identification,and SC classification with multi-omics datasets. 展开更多
关键词 stomach cancer bioinformatics ensemble learning classifier biomarker targets
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Fault Diagnosis Based on BA-SVM Trapezoidal Region Pole Classification 认领 引用
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作者 DU Zi-wei YAO Bo WANG Fu-zhong 《Chinese Quarterly Journal of Mathematics》 2026年第2期197-206,共10页
For a class of linear constant systems, the problem of continuous gain type fault diagnosis and reliable control of a single component of an actuator is investigated based on the trapezoidal region. Firstly, in order ... For a class of linear constant systems, the problem of continuous gain type fault diagnosis and reliable control of a single component of an actuator is investigated based on the trapezoidal region. Firstly, in order to solve the problem that the pole information of closed-loop system is difficult to observe, a design scheme of full-dimensional state observer is given to realize the real-time observation of pole information and form a pole classification database for system failure. Secondly, according to the characteristics that the poles are located in different areas when different channels have faults, support vector machine is applied to design a pole classifier to diagnose faults in the system and achieve accurate and reliable control of the system based on the fault diagnosis results.Then, in order to solve the problem of difficulty in selecting parameters for support vector machines, the bat algorithm(BA) is proposed to achieve automatic parameter optimization, which has the advantages of strong robustness and easy to combine with other methods. 展开更多
关键词 Pole classifier Support vector machine Bat algorithm Regional pole assignment Fault diagnosis
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Indoor metabolites and chemicals outperform microbiome in classifying childhood asthma and allergic rhinitis 认领 引用 被引量：2
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作者 Yu Sun Hao Tang +15 位作者 Shuang Du Yang Chen Zheyuan Ou Mei Zhang Zhuoru Chen Zhiwei Tang Dongjun Zhang Tianyi Chen Yanyi Xu Jiufeng Li Dan Norback Jamal Hisham Hashim Zailina Hashim Jie Shao Xi Fu Zhuohui Zhao 《Eco-Environment & Health》 2023年第4期208-218,共11页
Indoor microorganisms impact asthma and allergic rhinitis(AR),but the associated microbial taxa often vary extensively due to climate and geographical variations.To provide more consistent environmental assessments,ne... Indoor microorganisms impact asthma and allergic rhinitis(AR),but the associated microbial taxa often vary extensively due to climate and geographical variations.To provide more consistent environmental assessments,new perspectives on microbial exposure for asthma and AR are needed.Home dust from 97 cases(32 asthma alone,37 AR alone,28 comorbidity)and 52 age-and gender-matched controls in Shanghai,China,were analyzed using high-throughput shotgun metagenomic sequencing and liquid chromatography-mass spectrometry.Homes of healthy children were enriched with environmental microbes,including Paracoccus,Pseudomonas,and Psychrobacter,and metabolites like keto acids,indoles,pyridines,and flavonoids(astragalin,hesperidin)(False Discovery Rate<0.05).A neural network co-occurrence probability analysis revealed that environmental microorganisms were involved in producing these keto acids,indoles,and pyridines.Conversely,homes of diseased children were enriched with mycotoxins and synthetic chemicals,including herbicides,insecticides,and food/cosmetic additives.Using a random forest model,characteristic metabolites and microorganisms in Shanghai homes were used to classify high and low prevalence of asthma/AR in an independent dataset in Malaysian schools(N=1290).Indoor metabolites achieved an average accuracy of 74.9%and 77.1%in differentiating schools with high and low prevalence of asthma and AR,respectively,whereas indoor microorganisms only achieved 51.0%and 59.5%,respectively.These results suggest that indoor metabolites and chemicals rather than indoor microbiome are potentially superior environmental indicators for childhood asthma and AR.This study extends the traditional risk assessment focusing on allergens or air pollutants in childhood asthma and AR,thereby revealing potential novel intervention strategies for these diseases. 展开更多
关键词 Dust Environmental classifier Home Indoor Allergy
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