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Thermodynamic and Thermoelastic Properties of SiSn:Data Mining-Based Searches and High Compression Effect 认领 引用
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作者 Rabie Mezouar Fouad Okba +2 位作者 Dejan Zagorac Salah Daoud Abdelfateh Benmakhlouf 《Computers, Materials & Continua》 SCIE EI 2026年第7期423-439,共17页
The compression effects on the thermoelastic and thermodynamic properties of cubic zincblende silicon-tin alloy(SiSn)were explored using a multi-methodological approach,deploying data mining methods,theoretical equati... The compression effects on the thermoelastic and thermodynamic properties of cubic zincblende silicon-tin alloy(SiSn)were explored using a multi-methodological approach,deploying data mining methods,theoretical equation-of-state parameters,and the Quasi-Harmonic Debye Model.We analyze the relative volume,isothermal bulk modulus,thermal expansion coefficient,Debye temperature,sound velocity,and microhardness of the SiSn compound under pressures up to 8 GPa.The study commences with the data mining-based searches for a structural model and continues with an analysis of the pressure dependence of the relative volume using the Vinet equation of state,followed by an investigation of the bulk modulus and other related thermoelastic properties.Moreover,the variation of microhardness with temperature is predicted,demonstrating a patent progressive decline as the temperature rises from 0 to 800 K.The thermodynamic properties of the SiSn compound have been explored using the quasi-harmonic Debye model in temperatures ranging from 0 to 800 K and pressures ranging from 0 to 8 GPa,respectively.In addition to the information not found in the literature and offered by this study,our work also establishes a simplified model that can predict the evolution of microhardness as a function of temperature,firstly for the SiSn compound,and perhaps can extend to group-Ⅳ semiconductors. 展开更多
关键词 Silicon-tin SiSn equation of state high pressure thermo-elastic properties data mining microhardness PACS Classification:61.72.Tt 65.40.−b 62.50.−p 62.20.−x
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Medical data mining for diabetic retinopathy:Imaging modalities and algorithms 认领 引用
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作者 Bobbinpreet Kaur Sheenam +1 位作者 Aleem Ali Fakhrun Jamal 《Medical Data Mining》 CAS 2026年第3期62-73,共12页
Diabetic retinopathy(DR)is one of the primary issue of vision loss.It is caused by damages of blood vessels of the retina.Advances in imaging technology and the computational intelligence are turning out to be an effe... Diabetic retinopathy(DR)is one of the primary issue of vision loss.It is caused by damages of blood vessels of the retina.Advances in imaging technology and the computational intelligence are turning out to be an effective tool to accurately and early diagnosis of Diabetic retinopathy.This review article highlights a critical analysis of the existing literature on machine learning and deep learning model applied to fundus photography,optical coherence tomography(OCT),and RetCam imaging.Public datasets such as EyePACS,IDRiD,and Messidor have been widely used but remain challenged by variability,class imbalance,and annotation quality.Data mining techniques—such as clustering to discern disease progression trends,feature selection to minimize dimensionality are essential for deriving relevant clinical insights.The results demonstrate that deep learning-based CAD systems surpass typical machine learning methods,with classification accuracies greater than 90%in multi-stage DR severity assessment.Fundus photography integrated with CNN-based models exhibits significant promise for extensive screening,but OCT-based methods offer improved structural examination of retinal layers.Therefore,an overall computer-aided diagnosis(CAD)supported by medical data mining can enable cost-effective,scalable,and precise DR screening,thereby reducing the global burden of diabetes-related blindness. 展开更多
关键词 diabetic retinopathy medical data mining computer-aided diagnosis retinal imaging datasets deep learning algorithms health informatics
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Research on the Application Effect of Project-Based Learning(PBL)in the Practical Teaching of Big Data Mining 认领 引用
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作者 Yangping Tong 《Journal of Contemporary Educational Research》 2026年第5期337-342,共6页
With the rapid development of the big data industry,the demand for data mining talents in the market has significantly increased,and higher requirements have been imposed on the practical abilities of these talents.Th... With the rapid development of the big data industry,the demand for data mining talents in the market has significantly increased,and higher requirements have been imposed on the practical abilities of these talents.This has compelled schools to accelerate the innovation of teaching methods.Project-based learning has obvious application value in the practical teaching of big data mining.Based on this,this paper takes the textbook Python Data Mining Algorithms and Application Experiments and Course Training Guidance as the core carrier,explores the application path of project-based learning in the practical teaching of big data mining,evaluates the effect of teaching application,builds a systematic teaching system,and designs targeted teaching content and teaching plans.The research results can enhance students’practical operation ability and teamwork ability,and provide certain references and inspirations for promoting teaching reform. 展开更多
关键词 Big data Data mining Project-based learning
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Mechanisms of Chinese herbal medicine in modulating gut microbiota on primary open-angle glaucoma:a study based on data mining,network pharmacology,and Mendelian randomization 认领 引用
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作者 Yu TANG Bingyao ZHU +4 位作者 Qianhong LIU Kai WU Pai ZHOU Xiaolei YAO Qinghua PENG 《Digital Chinese Medicine》 CAS CSCD 2025年第4期491-503,共13页
Objective To elucidate the potential mechanisms by which Chinese herbal medicine(CHM)regulates gut microbiota(GM)to influence the development of primary open-angle glaucoma(POAG).Methods Data mining,network pharmacolo... Objective To elucidate the potential mechanisms by which Chinese herbal medicine(CHM)regulates gut microbiota(GM)to influence the development of primary open-angle glaucoma(POAG).Methods Data mining,network pharmacology,and Mendelian randomization(MR)analyses(two-sample design)were conducted in integration to systematically explore the CHMGM-POAG axis.Literature-based data mining method was applied to identify frequently used herbs and herb pairs for POAG,and the properties and meridian tropism of the herbs were analyzed as well.Target prediction and pathway enrichment analyses were performed to identify shared molecular pathways among CHM components,GM,and POAG.MR analysis was performed to assess the genetically predicted causal associations between specific microbial taxa and POAG risk.Results Our data mining work indicated that commonly used CHMs were mainly bitter and sweet in flavors and cold in property,with meridian tropism toward the liver,lung,and kidney.The predominant therapeutic effects of the CHMs included soothing the liver and regulating Qi,promoting blood circulation,and reducing fluid retention.Representative herb pairs were Shudihuang(Rehmanniae Radix Praeparata)-Gouqi(Lycii Fructus)with Zexie(Alismatis Rhizoma),Gouqi(Lycii Fructus)-Fuling(Poria)with Shudihuang(Rehmanniae Radix),and Juhua(Chrysanthemi Flos)-Gouqi(Lycii Fructus)with Zexie(Alismatis Rhizoma).Network pharmacology revealed overlapping targets involving antioxidative,anti-inflammatory,and metabolic regulation pathways.MR analysis demonstrated that higher abundances of Ruminiclostridium 6[odds ratio(OR)=0.73,95%confidence interval(CI):0.58–0.92,P=0.007],Ruminococcaceae UCG-002(OR=0.77,95%CI:0.63–0.96,P=0.018),Ruminococcus torques group(OR=0.71,95%CI:0.57–0.90,P=0.004),and Victivallis(OR=0.82,95%CI:0.70–0.96,P=0.016)were causally associated with reduced POAG risk,whereas Actinomyces(OR=1.34,95%CI:1.06–1.68,P=0.013)and Blautia(OR=1.39,95%CI:1.01–1.90,P=0.042)showed positive associations.Conclusion This study revealed potential causal links between GM and POAG and provided integrative evidence that CHM may modulate the microbiota to exert neuroprotective effects.These findings offer new integrative insights into the gut-eye axis and a theoretical basis for developing microbiota-targeted CHM strategies in glaucoma management. 展开更多
关键词 Chinese herbal medicine Primary open-angle glaucoma Gut microbiota Data mining Network pharmacology Mendelian randomization
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Unveiling core acupoints in acupuncture treatment for primary depressive disorder:integrating data mining and network acupuncture-based analysis 认领 引用
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作者 Siyu LIU Xinnan LUOa Jiayun XIE +2 位作者 Miqun ZHOU Xiaona HU Shuang SONG 《Digital Chinese Medicine》 CAS CSCD 2025年第4期504-516,共13页
Objective To identify core acupoint patterns and elucidate the molecular mechanisms of acupuncture for primary depressive disorder(PDD)through data mining and network analysis.Methods A comprehensive literature search... Objective To identify core acupoint patterns and elucidate the molecular mechanisms of acupuncture for primary depressive disorder(PDD)through data mining and network analysis.Methods A comprehensive literature search was conducted across PubMed,Embase,Ovid Technologies(OVID),Web of Science,Cochrane Library,China National Knowledge Infrastructure(CNKI),China National Knowledge Infrastructure Database(VIP),Wanfang Data,and SinoMed Database from database foundation to January 31,2025,for clinical studies on acupuncture treatment of PDD.Descriptive statistics,high-frequency acupoint analysis,degree and betweenness centrality evaluation,and core acupoint prescription mining identified predominant therapeutic combinations for PDD.Network acupuncture was used to predict therapeutic target for the core acupoint prescription.Subsequent protein-protein interaction(PPI)network and molecular complex detection(MCODE)analyses were conducted to identify the key targets and functional modules.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)analyses explored the underlying biological mechanisms of the core acupoint prescription in treating PDD.Results A total of 57 acupoint prescriptions underwent systematic analysis.The core therapeutic combinations comprised Baihui(GV20),Yintang(GV29),Neiguan(PC6),Hegu(LI4),and Shenmen(HT7).Network acupuncture analysis identified 88 potential therapeutic targets(79 overlapping with PDD),while PPI network analysis revealed central regulatory nodes,including interleukin(IL)-6,IL-1β,tumor necrosis factor(TNF)-α,toll-like receptor 4(TLR4),IL-10,brain-derived neurotrophic factor(BDNF),transforming growth factor(TGF)-β1,C-XC motif chemokine ligand 10(CXCL10),mitogen-activated protein kinase 3(MAPK3),and nitric oxide synthase 1(NOS1).MCODE-based modular analysis further elucidated three functionally coherent clusters:inflammation-homeostasis(score=6.571),plasticity-neurotransmission(score=3.143),and oxidative stress(score=3.000).GO and KEGG analyses demonstrated significant enrichment of the MAPK,phosphoinositide 3-kinase/protein kinase B(PI3K/Akt),and hypoxia-inducible factor(HIF)-1 signaling pathways.These mechanistic insights suggested that the antidepressant effects mediated through mechanisms of neuroinflammatory regulation,neuroplasticity restoration,and immune-oxidative stress homeostasis.Conclusion This study reveals that acupuncture alleviates depression through a multi-level mechanism,primarily involving the neuroinflammation suppression,neuroplasticity enhancement,and oxidative stress regulation.These findings systematically clarify the underlying mechanisms of acupuncture’s antidepressant effects and identify novel therapeutic targets for further mechanistic research. 展开更多
关键词 Acupuncture Primary depressive disorder(PDD) Data mining Network acupuncture Association analysis
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Data mining in neurosurgical emergencies: real-world impact of real-time intelligence 认领 引用
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作者 Yi-Rui Sun 《Medical Data Mining》 CAS 2025年第3期73-75,共3页
Introduction Neurosurgical emergencies such as spontaneous intracerebral hemorrhage(ICH),traumatic brain injury(TBI),and acute brain herniation are among the most time-sensitive and high-stakes conditions in modern me... Introduction Neurosurgical emergencies such as spontaneous intracerebral hemorrhage(ICH),traumatic brain injury(TBI),and acute brain herniation are among the most time-sensitive and high-stakes conditions in modern medicine.Clinical decisions often must be made within minutes,yet these decisions are traditionally guided by limited information,heuristic reasoning,and past experience.In this context,the rise of medical data mining and real-time analytics offers a transformative opportunity:to extract actionable intelligence from the flood of clinical,imaging,and physiological data already being collected,and to use this intelligence to guide care in real time[1–3](Figure 1). 展开更多
关键词 acute brain herniation extract actionable spontaneous intracerebral hemorrhage ich traumatic brain injury tbi data mining neurosurgical emergencies traumatic brain injury spontaneous intracerebral hemorrhage real time intelligence
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Optimization and principles of acupoint selection and coordination in the treatment of adult abdominal obesity using acupuncture and moxibustion over the past decade:A data mining 认领 引用 被引量：1
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作者 Jia-xin SHEN Tian-yun HUANG +4 位作者 Zhou HAO Guang-bin PENG Yue-ying MA Huan-gan WU Chun-hui BAO 《World Journal of Acupuncture-Moxibustion》 CAS CSCD 2025年第3期223-231,共9页
Objective To explore the optimization and principles of acupoint selection and coordination in the treatment of adult abdominal obesity using acupuncture and moxibustion over the past decade using data mining.Methods ... Objective To explore the optimization and principles of acupoint selection and coordination in the treatment of adult abdominal obesity using acupuncture and moxibustion over the past decade using data mining.Methods Clinical studies of abdominal obesity treated with acupuncture and moxibustion,collected in the past 10 years,were searched from China Biology Medicine disc(CBMdisc),China National knowledge infrastructure(CNKI),Wanfang,China Science and Technology Journal Database(VIP),Pubmed,Embase,Google Scholar,Web of Science,(The Cumulative Index to Nursing and Allied Health Literature)CINAHL,Psyclnfo and Scopus,dated from March 1,2013 to March 31,2023.Using IBM SPSS Modeler 18.0 and other software,the frequency analysis,association-rules analysis and cluster analysis were conducted on interventions,traditional Chinese medicine(TCM)patterns,use frequency of acupoint,meridian attribution of acupoint,acupoint location,etc.Results A total of 55 articles were included,with 102 prescriptions and 71 acupoints involved.The top 3 interventions were acupoint embedding method,simple electroacupuncture and simple filiform needling.Seventeen patterns/syndromes of TCM differentiation were collected,dominated by spleen deficiency and damp blockage,spleen and kidney yang deficiency and heat accumulation in stomach and intestines.The acupoints in clinical practice were mostly at the foot-yangming stomach meridian,the conception vessel and the foot-taiyin spleen meridian,and located at the abdominal region.The top 5 acupoints of high frequency were Tianshu(ST25),Zhongwan(CV12),Daheng(SP15),Zusanli(ST36),Huaroumen(ST24)and Daimai(GB26).The specific points of the high frequency were the crossing points and front-mu points,of which,ST25 and CV12 were the most prominent.After association-rules analysis on the high-frequency acupoints,20 groups of associated acupoints were obtained,in which,the core acupoints included ST25,CV12,SP15 and ST36.Conclusion In recent 10 years,abdominal obesity is treated by the acupoints of foot-yangming stomach meridian,the conception vessel and the foot-taiyin spleen meridian.Compared with the regimen for simple obesity,the acupoints at the abdominal region are specially selected in treatment of abdominal obesity,such as ST25,CV12,SP15 and ST36.Supplementary acupoints are selected based on syndrome differentiation to simultaneously address both the disease manifestations and root causes. 展开更多
关键词 Abdominal obesity Acupuncture and moxibustion Data mining Rules of acupoint selection
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Unveiling the prescription patterns and mechanisms of Chinese herbal compound patents in the management of acute appendicitis:A data mining investigation 认领 引用
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作者 Yuewen Li Qinsheng Zhang Suqin Hu 《Journal of Chinese Pharmaceutical Sciences》 CAS CSCD 2025年第6期566-580,共15页
In the present study,data mining and network pharmacology were utilized to explore the principles and mechanisms of traditional Chinese medicine(TCM)in treating acute appendicitis.The goal was to provide a scientific ... In the present study,data mining and network pharmacology were utilized to explore the principles and mechanisms of traditional Chinese medicine(TCM)in treating acute appendicitis.The goal was to provide a scientific basis for clinical treatment and further research on this disease.First,we searched the National Patent Database for Chinese herbal compound prescriptions used to treat acute appendicitis.We then applied frequency analysis,character and taste meridian analysis,association rule analysis,and hierarchical cluster analysis to identify the patterns of TCM treatment for acute appendicitis,selecting key combinations of Chinese medicines.Next,we screened the main active components of these key TCM based on quality markers.Using databases such as SwissTargetPrediction,SymMap,ETCM,and STRING,we analyzed the pharmacological mechanisms of these key TCM in treating acute appendicitis.Key active components and targets were further verified through molecular docking.We identified a total of 129 patents involving 316 Chinese medicines,with 24 being frequently used.The results indicated that most Chinese herbs used for acute appendicitis were heat-clearing drugs,blood-activating and stasis-removing drugs,and purging drugs.The primary active ingredients of the Rhubarb-cortex moutan-flos lonicerae combination for treating acute appendicitis included Emodin,Paeonol,Physcion,Chlorogenic acid,Chrysophanol,Rhein acid,and Aloe-emodin.These ingredients targeted key proteins such as ALB,TP53,BCL2,STAT3,IL-6,and TNF,and were involved in cellular responses to lipopolysaccharides,cell composition,and various cytokine-mediated biological processes.They also interacted with signaling pathways like AGE-RAGE,TNF,IL-17,and FoxO.Based on patent data,this study analyzed medication patterns in the treatment of acute appendicitis,discussed the possible mechanisms of key TCM combinations,and provided a scientific basis and new perspectives for the diagnosis and treatment of the disease. 展开更多
关键词 Acute appendicitis Data mining Rule of composition Hierarchical clustering Molecular docking
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Distributed anonymous data perturbation method for privacy-preserving data mining 认领 引用 被引量：5
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作者 Feng LI Jin MA Jian-hua LI 《Journal of Zhejiang University-SCIENCE A》 SCIE EI CAS 2009年第7期952-963,共12页
Privacy is a critical requirement in distributed data mining. Cryptography-based secure multiparty computation is a main approach for privacy preserving. However, it shows poor performance in large scale distributed s... Privacy is a critical requirement in distributed data mining. Cryptography-based secure multiparty computation is a main approach for privacy preserving. However, it shows poor performance in large scale distributed systems. Meanwhile, data perturbation techniques are comparatively efficient but are mainly used in centralized privacy-preserving data mining (PPDM). In this paper, we propose a light-weight anonymous data perturbation method for efficient privacy preserving in distributed data mining. We first define the privacy constraints for data perturbation based PPDM in a semi-honest distributed environment. Two protocols are proposed to address these constraints and protect data statistics and the randomization process against collusion attacks: the adaptive privacy-preserving summary protocol and the anonymous exchange protocol. Finally, a distributed data perturbation framework based on these protocols is proposed to realize distributed PPDM. Experiment results show that our approach achieves a high security level and is very efficient in a large scale distributed environment. 展开更多
关键词 Privacy-preserving data mining （PPDM） Distributed data mining Data perturbation
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Intelligent Educational Administration Management System Based on Data Mining Technology 认领 引用
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作者 Xiaofei Yang 《Journal of Contemporary Educational Research》 2025年第6期123-128,共6页
With the gradual acceleration of information construction in colleges and universities,digital campus and smart campus have gradually become important means for colleges and universities to scientifically manage the c... With the gradual acceleration of information construction in colleges and universities,digital campus and smart campus have gradually become important means for colleges and universities to scientifically manage the campus.They have been applied to teaching,scientific research,student management,and other fields,improving the quality and efficiency of management.This paper mainly studies the intelligent educational administration management system based on data mining technology.Firstly,this paper introduces the application process of data mining technology,and builds an intelligent educational administration management system based on data mining technology.Then,this paper optimizes the application of the Apriori algorithm in educational administration management through transaction compression and frequent sampling.Compared with the traditional Apriori algorithm,the optimized Apriori algorithm in this paper has a shorter execution time under the same minimum support. 展开更多
关键词 Data mining Educational administration management System construction Apriori algorithm
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Study on Screening of Main Acupoints and Pattern-Specific Acupoint Combination Rules for Acupuncture in Autism Spectrum Disorder Complicated with Sleep Disorder Based on Data Mining 认领 引用
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作者 Wei Du Hujie Song 《Journal of Clinical and Nursing Research》 2025年第8期241-249,共9页
Objective:To explore the core acupuncture acupoints and pattern-adapted acupoint combination rules for autism spectrum disorder(ASD)complicated with sleep disorder using clinical data mining technology.Methods:A retro... Objective:To explore the core acupuncture acupoints and pattern-adapted acupoint combination rules for autism spectrum disorder(ASD)complicated with sleep disorder using clinical data mining technology.Methods:A retrospective analysis was conducted on the diagnosis and treatment data of 104 children with ASD complicated with sleep disorder admitted to Xi’an Traditional Chinese Medicine(TCM)Encephalopathy Hospital from January 2022 to December 2024.Cross-pattern main acupoints were screened via frequency statistics,chi-square test,and factor analysis;pattern-specific auxiliary acupoints were extracted by combining multiple correspondence analysis,cluster analysis,and association rule mining.Results:Ten cross-pattern main acupoints(Baihui,Sishenzhen,Language Area 1,Language Area 2,Neiguan,Shenmen,Yongquan,Xuanzhong)were identified,and acupoint combination schemes for four major TCM patterns(Hyperactivity of Liver and Heart Fire,Deficiency of Kidney Essence,Deficiency of Both Heart and Spleen,Hyperactivity of Liver with Spleen Deficiency)were established.Conclusion:Acupuncture treatment should follow the principle of“regulating spirit and calming the brain as the root,and dredging collaterals based on pattern differentiation as the branch”.The synergy between main and auxiliary acupoints can accurately regulate the disease,providing a basis for precise clinical treatment. 展开更多
关键词 Autism Spectrum Disorder(ASD) Sleep disorder Acupoint selection rule Data mining
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Extensible Markup Language Data Mining System Model 认领 引用
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作者 李炜 宋瀚涛 《Journal of Beijing Institute of Technology》 EI CAS 2003年第1期28-32,共5页
The existing data mining methods are mostly focused on relational databases and structured data, but not on complex structured data (like in extensible markup language(XML)). By converting XML document type descriptio... The existing data mining methods are mostly focused on relational databases and structured data, but not on complex structured data (like in extensible markup language(XML)). By converting XML document type description to the relational semantic recording XML data relations, and using an XML data mining language, the XML data mining system presents a strategy to mine information on XML. 展开更多
关键词 extensible markup language(XML) document type description(DTD) data mining data mining language relational schema
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Data mining in clinical big data:the frequently used databases,steps,and methodological models 认领 引用 被引量：66
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作者 Wen-Tao Wu Yuan-Jie Li +4 位作者 Ao-Zi Feng Li Li Tao Huang An-Ding Xu Jun Lv 《Military Medical Research》 SCIE CAS CSCD 2021年第4期552-563,共12页
Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical I... Many high quality studies have emerged from public databases,such as Surveillance,Epidemiology,and End Results(SEER),National Health and Nutrition Examination Survey(NHANES),The Cancer Genome Atlas(TCGA),and Medical Information Mart for Intensive Care(MIMIC);however,these data are often characterized by a high degree of dimensional heterogeneity,timeliness,scarcity,irregularity,and other characteristics,resulting in the value of these data not being fully utilized.Data-mining technology has been a frontier field in medical research,as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models.Therefore,data mining has unique advantages in clinical big-data research,especially in large-scale medical public databases.This article introduced the main medical public database and described the steps,tasks,and models of data mining in simple language.Additionally,we described data-mining methods along with their practical applications.The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients. 展开更多
关键词 Clinical big data Data mining Machine learning Medical public database Surveillance Epidemiology and End Results National Health and Nutrition Examination Survey The Cancer Genome Atlas Medical Information Mart for Intensive Care
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Research on Tourism E-commerce based on Data Mining 认领 引用
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作者 Yan LIU 《International Journal of Technology Management》 2015年第1期123-125,共3页
This paper describes in detail the web data mining technology, analyzes the relationship between the data on the web site to the tourism electronic commerce （including the server log, tourism commodity database, user... This paper describes in detail the web data mining technology, analyzes the relationship between the data on the web site to the tourism electronic commerce （including the server log, tourism commodity database, user database, the shopping cart）, access to relevant user preference information for tourism commodity. Based on these models, the paper presents recommended strategies for the site registered users, and has had the corresponding formulas for calculating the current user of certain items recommended values and the corresponding recommendation algorithm, and the system can get a recommendation for user. 展开更多
关键词 Data mining Tourism e-commerce Web data mining recommended system
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Comparsion analysis of data mining models applied to clinical research in Traditional Chinese Medicine 认领 引用 被引量：23
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作者 Yufeng Zhao Qi Xie +7 位作者 Liyun He Baoyan Liu Kun Li Xiang Zhang Wenjing Bai Lin Luo Xianghong Jing Ruili Huo 《Journal of Traditional Chinese Medicine》 SCIE CSCD 2014年第5期627-634,共8页
OBJECTIVE: To help researchers selecting appropriate data mining models to provide better evidence for the clinical practice of Traditional Chinese Medicine(TCM) diagnosis and therapy.METHODS: Clinical issues based on... OBJECTIVE: To help researchers selecting appropriate data mining models to provide better evidence for the clinical practice of Traditional Chinese Medicine(TCM) diagnosis and therapy.METHODS: Clinical issues based on data mining models were comprehensively summarized from four significant elements of the clinical studies:symptoms, symptom patterns, herbs, and efficacy.Existing problems were further generalized to determine the relevant factors of the performance of data mining models, e.g. data type, samples, parameters, variable labels. Combining these relevant factors, the TCM clinical data features were compared with regards to statistical characters and informatics properties. Data models were compared simultaneously from the view of applied conditions and suitable scopes.RESULTS: The main application problems were the inconsistent data type and the small samples for the used data mining models, which caused the inappropriate results, even the mistake results. These features, i.e. advantages, disadvantages, satisfied data types, tasks of data mining, and the TCM issues, were summarized and compared.CONCLUSION: By aiming at the special features of different data mining models, the clinical doctors could select the suitable data mining models to resolve the TCM problem. 展开更多
关键词 Medicine, Chinese traditional, Biomedi-cal research Data mining Model Comparison anal-ysis
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Research on Component Law of Chinese Patent Medicine for Anti-influenza and Development of New Recipes for Anti-influenza by Unsupervised Data Mining Methods 认领 引用 被引量：17
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作者 唐仕欢 陈建新 +6 位作者 李耿 吴宏伟 陈畅 张娜 高娜 杨洪军 黄璐琦 《Journal of Traditional Chinese Medicine》 SCIE 2010年第4期288-293,共6页
Objective:To analyze the component law of Chinese patent medicines for anti-influenza and develop new prescriptions for anti-influenza by unsupervised data mining methods. Methods: Chinese patent medicine recipes for ... Objective:To analyze the component law of Chinese patent medicines for anti-influenza and develop new prescriptions for anti-influenza by unsupervised data mining methods. Methods: Chinese patent medicine recipes for anti-influenza were collected and recorded in the database, and then the correlation coefficient between herbs, core combinations of herbs and new prescriptions were analyzed by using modified mutual information, complex system entropy cluster and unsupervised hierarchical clustering, respectively. Results: Based on analysis of 126 Chinese patent medicine recipes, the frequency of each herb occurrence in these recipes, 54 frequently-used herb pairs, 34 core combinations were determined, and 4 new recipes for influenza were developed. Conclusion: Unsupervised data mining methods are able to mine the component law quickly and develop new prescriptions. 展开更多
关键词 influenza unsupervised data mining methods swine influenza new prescription discovery
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Data mining-based detection of acupuncture treatment on juvenile myopia 认领 引用 被引量：16
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作者 Xuming Yang Lingyu Xu +1 位作者 Fei Zhong Ying Zhu 《Journal of Traditional Chinese Medicine》 SCIE CSCD 2012年第3期372-376,共5页
OBJECTIVE:We applied data mining techniques to the study of acupuncture as a treatment for juvenile myopia,with the aim of identifying hidden patterns in the data.METHODS:Fifty patients with juvenile myopia were selec... OBJECTIVE:We applied data mining techniques to the study of acupuncture as a treatment for juvenile myopia,with the aim of identifying hidden patterns in the data.METHODS:Fifty patients with juvenile myopia were selected and treated with acupuncture,and data mining was used to analyze the effects of treatment and the influence of behavioral variables.Clustering analysis was used to divide myopia patients into two classifications before acupuncture treatment.Artificial neural network BP algorithm was adopted to analyze the roles of different factors in changes in diopters.An association algorithm was used to analyze factors associated with the subjective experience of acupuncture and average diopter.RESULTS:The two classification results were fully consistent with the understandings of the ophthalmic circles.The duration of using the Internet and watching TV every day was the main factor that affected vision.Acupuncture feelings and therapeutic effect have a strong correlativity.A good or above experience's score of acupuncture could slow the progression of juvenile myopia.CONCLUSION:Collecting data from patients with juvenile myopia by using data mining can extract hidden potential rules and knowledge from the research evidence.The decision support can be provided to improve the doctor's clinical acupuncture treatment effects. 展开更多
关键词 Acupuncture therapy Myopia Algorithms Data mining
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Optimization of Cooling Process of Iron Ore Pellets Based on Mathematical Model and Data Mining 认领 引用 被引量：8
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作者 Gui-ming YANG Xiao-hui FAN +2 位作者 Xu-ling CHEN Xiao-xian HUANG Xi LI 《Journal of Iron and Steel Research International》 SCIE CAS CSCD 2015年第11期1002-1008,共7页
Cooling process of iron ore pellets in a circular cooler has great impacts on the pellet quality and systematic energy exploitation.However,multi-variables and non-visualization of this gray system is unfavorable to e... Cooling process of iron ore pellets in a circular cooler has great impacts on the pellet quality and systematic energy exploitation.However,multi-variables and non-visualization of this gray system is unfavorable to efficient production.Thus,the cooling process of iron ore pellets was optimized using mathematical model and data mining techniques.A mathematical model was established and validated by steady-state production data,and the results show that the calculated values coincide very well with the measured values.Based on the proposed model,effects of important process parameters on gas-pellet temperature profiles within the circular cooler were analyzed to better understand the entire cooling process.Two data mining techniques—Association Rules Induction and Clustering were also applied on the steady-state production data to obtain expertise operating rules and optimized targets.Finally,an optimized control strategy for the circular cooler was proposed and an operation guidance system was developed.The system could realize the visualization of thermal process at steady state and provide operation guidance to optimize the circular cooler. 展开更多
关键词 iron ore pellet circular cooler model data mining optimization
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A generic and extensible model for the martensite start temperature incorporating thermodynamic data mining and deep learning framework 认领 引用 被引量：6
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作者 Chenchong Wang Kaiyu Zhu +2 位作者 Peter Hedström Yong Li Wei Xu 《Journal of Materials Science & Technology》 SCIE EI CAS CSCD 2022年第33期31-43,共13页
The martensite start temperature is a critical parameter for steels with metastable austenite.Although numerous models have been developed to predict the martensite start(Ms)temperature,the complexity of the martensit... The martensite start temperature is a critical parameter for steels with metastable austenite.Although numerous models have been developed to predict the martensite start(Ms)temperature,the complexity of the martensitic transformation greatly limits their performance and extensibility.In this work,we apply deep data mining of thermodynamic calculations and deep learning to develop a generic model for Msprediction.Deep data mining was used to establish a hierarchical database with three levels of information.Then,a convolutional neural network model,which can accurately treat the hierarchical data structure,was used to obtain the final model.By integrating thermodynamic calculations,traditional machine learning and deep learning modeling,the final predictor model shows excellent generalizability and extensibility,i.e.model performance both within and beyond the composition range of the original database.The effects of 15 alloying elements were considered successfully using the proposed methodology.The work suggests that,with the help of deep data mining considering the physical mechanisms,deep learning methods can partially mitigate the challenge with limited data in materials science and provide a means for solving complex problems with small databases. 展开更多
关键词 Martensite transformation Data mining Deep learning Extensibility Small-sample problem
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Feature Selection with Optimal Stacked Sparse Autoencoder for Data Mining 认领 引用 被引量：7
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作者 Manar Ahmed Hamza Siwar Ben Haj Hassine +5 位作者 Ibrahim Abunadi Fahd N.Al-Wesabi Hadeel Alsolai Anwer Mustafa Hilal Ishfaq Yaseen Abdelwahed Motwakel 《Computers, Materials & Continua》 SCIE EI 2022年第8期2581-2596,共16页
Data mining in the educational field can be used to optimize the teaching and learning performance among the students.The recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine ... Data mining in the educational field can be used to optimize the teaching and learning performance among the students.The recently developed machine learning(ML)and deep learning(DL)approaches can be utilized to mine the data effectively.This study proposes an Improved Sailfish Optimizer-based Feature SelectionwithOptimal Stacked Sparse Autoencoder(ISOFS-OSSAE)for data mining and pattern recognition in the educational sector.The proposed ISOFS-OSSAE model aims to mine the educational data and derive decisions based on the feature selection and classification process.Moreover,the ISOFS-OSSAEmodel involves the design of the ISOFS technique to choose an optimal subset of features.Moreover,the swallow swarm optimization(SSO)with the SSAE model is derived to perform the classification process.To showcase the enhanced outcomes of the ISOFSOSSAE model,a wide range of experiments were taken place on a benchmark dataset from the University of California Irvine(UCI)Machine Learning Repository.The simulation results pointed out the improved classification performance of the ISOFS-OSSAE model over the recent state of art approaches interms of different performance measures. 展开更多
关键词 Data mining pattern recognition feature selection data classification SSAE model
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