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

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 simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In 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 distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results 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

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classification model with High Deviation for Intrusion Detection on System Call Traces

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Journal of Beijing Institute of Technology 2005年第3期14卷 260-263页

作者：彭新光刘玉树吴裕树杨勇 School of Information Science and Technology Beijing Institute of Technology Beijing100081 China Department of Computer Science and Technology Taiyuan University of Technology Taiyuan030024 China

A new classification model for host intrusion detection based on the unidentified short sequences and RIPPER algorithm is proposed. The concepts of different short sequences on the system call traces are strictly defined on the basis of in-depth analysis of completeness and correctness of pattern databases. Labels of short sequences are predicted by learned RIPPER rule set and the nature of the unidentified short sequences is confirmed by statistical method. Experiment results indicate that the classification model increases clearly the deviation between the attack and the normal traces and improves detection capability against known and unknown attacks.

关键词： network security intrusion detection system calls unidentified sequences classification model

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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 productivity.Some 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 processes.With this motivation,the current research work devises an Efficient Deep Learning based Fusionmodel for Rice Plant Disease(EDLFM-RPD)detection and classification.The aim of the proposed EDLFM-RPD technique is to detect and classify different kinds of rice plant diseases in a proficient manner.In addition,EDLFM-RPD technique involves median filtering-based preprocessing and K-means segmentation to determine the infected portions.The study also used a fusion of handcrafted Gray Level Co-occurrence Matrix(GLCM)and Inception-based deep features to derive the features.Finally,Salp Swarm Optimization with Fuzzy Support Vector Machine(FSVM)model is utilized for classification.In order to validate the enhanced outcomes of EDLFM-RPD technique,a series of simulations was conducted.The results were assessed under different measures.The 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

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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.Oversampling 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 preprocessing.Then,different algorithms are used to establish classification models as control experiments,and R-squared indicators are used to compare.Finally,the Stacking algorithm is used to establish the final classification model.This 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

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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：黄文锋刘大伟邵梦凯 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 assessment.Against 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 region.Such simulation includes the classification method,the genesis model,the traveling model,and the lysis model.Tropical cyclone tracks in the Northwest Pacific region are classified into five categories on the basis of its movement characteristics and steering positions.In the genesis model,Gaussian kernel probability density functions with the biased cross validation method are used to simulate the annual occurrence number and genesis positions.The traveling model is established on the basis of the mean and mean square error of the historical 6 h latitude and longitude displacements.The termination probability is used as the discrimination standard in the lysis model.Then,this stochastic simulation method of tropical cyclone tracks is applied and qualitatively evaluated with different diagnostics.Results 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

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一种阶段重置的知识蒸馏方法研究与仿真

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建模与仿真 2024年第2期13卷 1455-1465页

作者：陈骏立孙占全上海理工大学光电信息与计算机工程学院，上海

知识蒸馏是一种将知识从教师网络传递到学生网络的模型压缩方法。目前的知识蒸馏方法存在教师网络和学生网络之间的语义信息不一致的问题，具体而言，师生模型之间的前向推理距离不一致导致语义信息不一致，最终损耗蒸馏性能。为了解决... 详细信息

知识蒸馏是一种将知识从教师网络传递到学生网络的模型压缩方法。目前的知识蒸馏方法存在教师网络和学生网络之间的语义信息不一致的问题，具体而言，师生模型之间的前向推理距离不一致导致语义信息不一致，最终损耗蒸馏性能。为了解决这个问题，本文探索一种新的阶段重置知识蒸馏方法。该方法设计了以阶段为单位的知识蒸馏，师生网络相同阶段共享输出，降低了由学生与教师推理路径长度差异过大造成的特征语义不匹配的影响，从而提升学生网络的性能。最后，本文用提出的方法在公共数据集上进行仿真实验，并与最新的方法进行比较，实验结果表明本文提出的方法更具优势。

关键词：神经网络分类模型模型压缩知识蒸馏阶段重置 Neural Network classification model model Compression Knowledge Distillation Stage Reset

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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 challenges.Due to the high annual mortality rates,cardiac diseases have attracted the attention of numerous researchers in recent years.This article proposes a hybrid fuzzy fusion classification model for cardiac arrhythmia diseases.The fusion model is utilized to optimally select the highest-ranked features generated by a variety of well-known feature-selection algorithms.An ensemble of classifiers is then applied to the fusion’s results.The proposed model classifies the arrhythmia dataset from the University of California,Irvine into normal/abnormal classes as well as 16 classes of arrhythmia.Initially,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 attributes.However,in order to ensure the model optimality,we eliminated all attributes which have zero or constant values that might bias the results of utilized classifiers.The 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 algorithms.In short,our study comprises three main blocks:(1)sensing data and preprocessing;(2)feature queuing,selection,and extraction;and(3)the predictive model.Our proposed method improves classification performance in terms of accuracy,F1measure,recall,and precision when compared to state-of-the-art techniques.It 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

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Study on classification model Based on Relevance Vector Machine with Genetic Algorithm

Study on Classification Model Based on Relevance Vector Mach...

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2010 2nd IEEE International Conference on Information and Financial Engineering(ICIFE2010)

作者： Yanhong Li Mathematical College Beihua University Jilin 132013 China Taihui Liu Computer College Beihua University Jilin 132013 China

A novel classification method based on relevance vector machine with genetic algorithm is presented in the paper.In the model,genetic algorithm is applied to gain the suitable training parameters of relevance vector machine.State classification of roll bearing is applied to testify the classification ability of the proposed method,and state classification data of roll bearing are given.The experimental results show that relevance vector machine with genetic algorithm has higher classification accuracy than backpropagation neural network and support vector machine.

关键词： relevance vector machine classification model genetic algorithm Bayesian

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From beasts to bytes:Revolutionizing zoological research with artificial intelligence

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Zoological Research 2023年第6期44卷 1115-1131页

作者： Yu-Juan Zhang Zeyu Luo Yawen Sun Junhao Liu Zongqing Chen Chongqing Key Laboratory of Vector Insects Chongqing Key Laboratory of Animal BiologyCollege of Life ScienceChongqing Normal UniversityChongqing 401331China School of Mathematical Sciences Chongqing Normal UniversityChongqing 401331China Chongqing National Center for Applied Mathematics Chongqing Normal UniversityChongqing 401331China

Since the late 2010s,Artificial Intelligence(AI)including machine learning,boosted through deep learning,has boomed as a vital tool to leverage computer vision,natural language processing and speech recognition in revolutionizing zoological research.This review provides an overview of the primary tasks,core models,datasets,and applications of AI in zoological research,including animal classification,resource conservation,behavior,development,genetics and evolution,breeding and health,disease models,and paleontology.Additionally,we explore the challenges and future directions of integrating AI into this field.Based on numerous case studies,this review outlines various avenues for incorporating AI into zoological research and underscores its potential to enhance our understanding of the intricate relationships that exist within the animal kingdom.As we build a bridge between beast and byte realms,this review serves as a resource for envisioning novel AI applications in zoological research that have not yet been explored.

关键词： Animal science Data extraction classification model Behavior analysis Biomolecular sequences analysis

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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 paper.It 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 necessary.First,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 model.Second,a feature vector consisting of one-step transition matrix and its stationary distribution was extracted.Then,the Hidden Markov model(HMM)was used to pre-classify the feature vectors marked using the traditional Root Mean Square(RMS)criteria.Finally,a classification model with probability interpretation was established,and the cross-validation method was used for model validation.The research results show that the developed model is effective and reliable,and it has strong Reynolds number generalization ability.The developed model was theoretically analyzed in depth,and the effect of parameters on the model was studied in detail.Compared with the traditional RMS criterion,a reasonable transition zone can be obtained using the developed classification model.In addition,the developed model does not require comparison of multi-point data.The 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

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