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Fast detection of the early decay in oranges using visible-LED structuredillumination imaging combined with spiral phase transform and feature-based classification model

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International Journal of Agricultural and Biological Engineering 2024年第3期17卷 185-192,F0003页

作者： Zhonglei Cai Chanjun Sun Yizhi Zhang Ruiyao Shi Junyi Zhang Hailiang Zhang Jiangbo Li College of Mechanical and Electrical Engineering Shihezi UniversityShihezi 832003XinjiangChina Jiangsu Province and Education Ministry Co-sponsored Synergistic Innovation Center of Modern Agricultural Equipment Jiangsu UniversityZhenjiang 212013JiangsuChina Intelligent Equipment Research Center Beijing Academy of Agriculture and Forestry SciencesBeijing100097China College of Electrical and Automation Engineering East China Jiaotong UniversityNanchang 330013China National Engineering Research Center for Information Technology in Agriculture Beijing 100089China

The early decay of citrus can cause economic and serious food safety *** early decayed area has no obvious visual characteristics,making effective detection of this damage very difficult for the citrus *** study constructed a new detection system based on visible-light emitting diode(LED)structured-illumination imaging and proposed an effective methodology combined with a spiral phase transform(SPT)algorithm for the early detection of decayed *** sample obtained three phase-shifting pattern images with phase shifts of−2π/3,0,and 2π/3 at a spatial frequency of 0.25 cycles/*** strategies(i.e.,the conventional three-phase-shifting method,2-phase SPT,and 1-phase SPT)were used to demodulate the original patterned images to recover the direct component(DC)and amplitude component(AC)*** partial least squares discriminant analysis(PLS-DA)and least squares support vector machine(LS-SVM)classification models were established based on the texture features of DC,AC,and RT(*** ratio of AC to DC)***,the random frog(RF)algorithm was used to simplify the optimal full-featured ***,the LS-SVM model constructed using 7 texture features from the RT image obtained an average classification accuracy of 95.1%for all tested *** study indicates that the proposed structured-illumination imaging technique combined with 2-phase SPT and feature-based classification model can achieve the fast identification of early decayed oranges.

关键词： oranges early decay detection structured-illumination imaging spiral phase transform classification model

STOCHASTIC SIMULATION OF TROPICAL CYCLONE TRACKS IN THE NORTHWEST PACIFIC REGION WITH classification model

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Journal of Tropical Meteorology 2020年第1期26卷 47-56页

作者： HUANG Wen-feng LIU Da-wei SHAO Meng-kai School of Civil and Hydraulic Engineering Hefei University of Technology Anhui 230009 China

Accurate simulation of tropical cyclone tracks is a prerequisite for tropical cyclone risk *** the spatial characteristics of tropical cyclone tracks in the Northwest Pacific region,stochastic simulation method based on classification model is used to simulate tropical cyclone tracks in this *** simulation includes the classification method,the genesis model,the traveling model,and the lysis *** cyclone tracks in the Northwest Pacific region are classified into five categories on the basis of its movement characteristics and steering *** the genesis model,Gaussian kernel probability density functions with the biased cross validation method are used to simulate the annual occurrence number and genesis *** traveling model is established on the basis of the mean and mean square error of the historical 6 h latitude and longitude *** termination probability is used as the discrimination standard in the lysis ***,this stochastic simulation method of tropical cyclone tracks is applied and qualitatively evaluated with different *** show that the tropical cyclone tracks in Northwest Pacific can be satisfactorily simulated with this classification model.

关键词： classification model genesis model lysis model traveling model tropical cyclone track

FLIGHT classification model BASED ON TRANSITIVE CLOSURE ALGORITHM AND APPLICATION TO FLIGHT SEQUENCING PROBLEM

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Transactions of Nanjing University of Aeronautics and Astronautics 2007年第1期24卷 31-35页

作者：李雄徐肖豪李冬宾南京航空航天大学民航学院中国民航大学空中交通管理学院

A new arrival and departure flight classification method based on the transitive closure algorithm （TCA） is proposed. Firstly, the fuzzy set theory and the transitive closure algorithm are introduced. Then four different factors are selected to establish the flight classification model and a method is given to calculate the delay cost for each class. Finally, the proposed method is implemented in the sequencing problems of flights in a terminal area, and results are compared with that of the traditional classification method（TCM）. Results show that the new classification model is effective in reducing the expenses of flight delays, thus optimizing the sequences of arrival and departure flights, and improving the efficiency of air traffic control.

关键词： air traffic control transitive closure algorithm cost of flight delay classification model

Employee Attrition classification model Based on Stacking Algorithm

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Psychology Research 2023年第6期13卷 279-285页

作者： CHEN Yanming LIN Xinyu ZHAN Kunye Shantou University South China Normal University Shenzhen University

This paper aims to build an employee attrition classification model based on the Stacking *** algorithm is applied to address the issue of data imbalance and the Randomforest feature importance ranking method is used to resolve the overfitting problem after data cleaning and ***,different algorithms are used to establish classification models as control experiments,and R-squared indicators are used to ***,the Stacking algorithm is used to establish the final classification *** model has practical and significant implications for both human resource management and employee attrition analysis.

关键词： employee attrition classification model machine learning ensemble learning oversampling algorithm Randomforest stacking algorithm

Data-driven casting defect prediction model for sand casting based on random forest classification algorithm

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China Foundry 2024年第2期21卷 137-146页

作者： Bang Guan Dong-hong Wang Da Shu Shou-qin Zhu Xiao-yuan Ji Bao-de Sun Shanghai Key Lab of Advanced High-Temperature Materials and Precision Forming School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China State Key Lab of Metal Matrix Composites Shanghai Jiao Tong UniversityShanghai 200240China Hefei Casting and Forging Factory of Anhui Heli Co. LtdHefei 230000China State Key Laboratory of Materials Processing and Die&Mould Technology Huazhong University of Science and TechnologyWuhan 430074China

The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo *** collected data includes four types of defects and corresponding process parameters were used to construct the RF *** results show a recall rate above 90% for all *** Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF ***,the classification model was applied to different production conditions for quality *** the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature *** prediction model,when applied to the factory,greatly improved the efficiency of defect *** show that the scrap rate decreased from 10.16% to 6.68%.

关键词： sand casting process data-driven method classification model quality prediction feature importance

Artificial Intelligence-Based Fusion model for Paddy Leaf Disease Detection and classification

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Computers, Materials & Continua 2022年第7期72卷 1391-1407页

作者： Ahmed SAlmasoud Abdelzahir Abdelmaboud Taiseer Abdalla Elfadil Eisa Mesfer Al Duhayyim Asma Abbas Hassan Elnour Manar Ahmed Hamza Abdelwahed Motwakel Abu Sarwar Zamani Department of Information Systems College of Computer and Information SciencesPrince Sultan UniversityRiyadh11586Saudi Arabia Department of Information Systems College of Science and ArtsKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Information Systems-Girls Section King Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAflaj16828Saudi Arabia Computer Science Department Community College-Girls SectionKing Khalid UniversityMuhayel Aseer62529Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

In agriculture,rice plant disease diagnosis has become a challenging issue,and early identification of this disease can avoid huge loss incurred from less crop *** of the recently-developed computer vision and Deep Learning(DL)approaches can be commonly employed in designing effective models for rice plant disease detection and classification *** this motivation,the current research work devises an Efficient Deep Learning based Fusionmodel for Rice Plant Disease(EDLFM-RPD)detection and *** aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient *** addition,EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected *** study also used a fusion of handcrafted Gray Level Co-occurrence Matrix(GLCM)and Inception-based deep features to derive the ***,Salp Swarm Optimization with Fuzzy Support Vector Machine(FSVM)model is utilized for *** order to validate the enhanced outcomes of EDLFM-RPD technique,a series of simulations was *** results were assessed under different *** obtained values infer the improved performance of EDLFM-RPD technique over recent approaches and achieved a maximum accuracy of 96.170%.

关键词： Rice plant disease classification model artificial intelligence deep learning fusion model parameter optimization

Cardiac Arrhythmia Disease Classifier model Based on a Fuzzy Fusion Approach

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Computers, Materials & Continua 2023年第5期75卷 4485-4499页

作者： Fatma Taher Hamoud Alshammari Lobna Osman Mohamed Elhoseny Abdulaziz Shehab Eman Elayat College of Technological Innovation Zayed UniversityDubaiUAE Department of Information Systems College of Computer and Information SciencesJouf UniversitySakakaSaudi Arabia Department of Electronics and Communications Engineering Delta Higher Institute for Engineering&TechnologyMansouraEgypt College of Computing and Informatics University of SharjahSharjahUnited Arab Emirates Department of Information Systems Mansoura UniversityMansoura35516Egypt Department of Teacher Preparation Faculty of Specific EducationMansoura UniversityMansouraEgypt

Cardiac diseases are one of the greatest global health *** to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent *** article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia *** fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection *** ensemble of classifiers is then applied to the fusion’s *** proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of ***,at the preprocessing steps,for the miss-valued attributes,we used the average value in the linear attributes group by the same class and the most frequent value for nominal ***,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized *** preprocessing step led to 161 out of 279 attributes(features).Thereafter,a fuzzy-based feature-selection fusion method is applied to fuse high-ranked features obtained from different heuristic feature-selection *** short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive *** proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art *** achieves 98.5%accuracy for binary class mode and 98.9%accuracy for categorized class mode.

关键词： Cardiac arrhythmia preprocessing missing values classification model fusion

Building classification models with Combined Biomarker Tests： Application to Early Detection of Liver Cancer

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Journal of Statistical Science and Application 2017年第3期5卷 91-103页

作者： Dion Chen Surbhi Jain Ying-Hsu Su Wei Song Biostatistics Janssen R&D LLC Spring House PA 19477 USA Biomarkers JBS Science Inc.Doylestown PA 18902 USA Biomarkers The Baruch S. Blumberg Institute Doylestown PA 18902 USA

Early detection of hepatocellular carcinoma （HCC） is critical for the effective treatment. Alpha fetoprotein （AFP） serum level is currently used for HCC screening, but the cutoff of the AFP test has limited sensitivity （-50%）, indicating a high false negative rate. We have successfully demonstrated that cancer derived DNA biomarkers can be detected in urine of patients with cancer and can be used for the early detection of cancer （Jain et al., 2015; Lin et al., 2011; Song et al., 2012; Su, Lin, Song, ＆ Jain, 2014; Su, Wang, Norton, Brenner, ＆ Block, 2008）. By combining urine biomarkers （uBMK） values and serum AFP （sAFP） level, a new classification model has been proposed for more efficient HCC screening. Several criterions have been discussed to optimal the cutoff for uBMK score and sAFP score. A joint distribution of sAFP and uBMK with point mass has been fitted using maximum likelihood method. Numerical results show that the sAFP data and uBMK data are very well described by proposed model. A tree-structured sequential test can be optimized by selecting the cutoffs. Bootstrap simulations also show the robust classification results with the optimal cuto~..

关键词： classification model Biomarker Data Analysis Joint Distribution Sensitivity Specificity LiverCancer.

Supervised learning with probability interpretation in airfoil transition judgment

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Chinese Journal of Aeronautics 2023年第1期36卷 91-104页

作者： Binbin WEI Yongwei GAO Dong LI Lei DENG School of Aeronautics Northwestern Polytechnical UniversityXi’an 710072China

Transition prediction has always been a frontier issue in the field of aerodynamics.A supervised learning model with probability interpretation for transition judgment based on experimental data was developed in this *** solved the shortcomings of the point detection method in the experiment,that which was often only one transition point could be obtained,and comparison of multi-point data was ***,the Variable-Interval Time Average(VITA)method was used to transform the fluctuating pressure signal measured on the airfoil surface into a sequence of states which was described by Markov chain ***,a feature vector consisting of one-step transition matrix and its stationary distribution was ***,the Hidden Markov model(HMM)was used to pre-classify the feature vectors marked using the traditional Root Mean Square(RMS)***,a classification model with probability interpretation was established,and the cross-validation method was used for model *** research results show that the developed model is effective and reliable,and it has strong Reynolds number generalization *** developed model was theoretically analyzed in depth,and the effect of parameters on the model was studied in *** with the traditional RMS criterion,a reasonable transition zone can be obtained using the developed classification *** addition,the developed model does not require comparison of multi-point *** developed supervised learning model provides new ideas for the transition detection in flight experiments and other experiments.

关键词： classification model Hidden Markov model Markov chain model Supervised learning Transition judgment