AI applications have become ubiquitous,bringing significant convenience to various industries.In e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions ...AI applications have become ubiquitous,bringing significant convenience to various industries.In e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy development.However,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI applications.Therefore,it is essential to verify the integrity of e-commerce data.Although existing Provable Data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in PDP.To address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded data.Additionally,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its practicality.Finally,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.展开更多
With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis o...With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis of these data and insight into user behavior patterns and preferences.This paper elaborates on the application of big data technology in the analysis of user behavior on e-commerce platforms,including the technical methods of data collection,storage,processing and analysis,as well as the specific applications in the construction of user profiles,precision marketing,personalized recommendation,user retention and churn analysis,etc.,and discusses the challenges and countermeasures faced in the application.Through the study of actual cases,it demonstrates the remarkable effectiveness of big data technology in enhancing the competitiveness of e-commerce platforms and user experience.展开更多
In the research, the crawler technology was used to evaluate the Taobao silkie, and product evaluation data is the case object. After word segmentation, naive Bayesian, K-means, and TF-IDF related algorithms were used...In the research, the crawler technology was used to evaluate the Taobao silkie, and product evaluation data is the case object. After word segmentation, naive Bayesian, K-means, and TF-IDF related algorithms were used to complete word frequency statistics, sentiment analysis, and cluster analysis. It was found that the quality of rural specialty products was more recognized by e-commerce customers, but its overall emotional score was slightly lower than that of non-origin products. After summing up the drawbacks of the former, targeted countermeasures and suggestions were proposed.展开更多
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
The technique of data mining was provided to predict gas disaster in view of the characteristics of coal mine gas disaster and feature knowledge based on gas disaster. The rough set theory was used to establish data m...The technique of data mining was provided to predict gas disaster in view of the characteristics of coal mine gas disaster and feature knowledge based on gas disaster. The rough set theory was used to establish data mining model of gas disaster prediction, and rough set attributes relations was discussed in prediction model of gas disaster to supplement the shortages of rough intensive reduction method by using information en- tropy criteria.The effectiveness and practicality of data mining technology in the prediction of gas disaster is confirmed through practical application.展开更多
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
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
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
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
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
A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safe...A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.展开更多
In e-commerce the multidimensional data analysis based on the Web data needs integrating various data sources such as XML data and relational data on the conceptual level. A conceptual data description approach to mul...In e-commerce the multidimensional data analysis based on the Web data needs integrating various data sources such as XML data and relational data on the conceptual level. A conceptual data description approach to multidimensional data model the UML galaxy diagram is presented in order to conduct multidimensional data analysis for multiple subjects. The approach is illuminated using a case of 2_roots UML galaxy diagram that takes marketing analysis of TV products involved one retailer and several suppliers into consideration.展开更多
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
Previous weighted frequent pattern （WFP） mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted freque...Previous weighted frequent pattern （WFP） mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted frequent pattern over data streams. SWFP-Miner is based on sliding window and can discover important frequent pattern from the recent data. A new refined weight definition is proposed to keep the downward closure property, and two pruning strategies are presented to prune the weighted infrequent pattern. Experimental studies are performed to evaluate the effectiveness and efficiency of SWFP-Miner.展开更多
A large amount of data is present on the web which can be used for useful purposes like a product recommendation,price comparison and demand forecasting for a particular product.Websites are designed for human underst...A large amount of data is present on the web which can be used for useful purposes like a product recommendation,price comparison and demand forecasting for a particular product.Websites are designed for human understanding and not for machines.Therefore,to make data machine-readable,it requires techniques to grab data from web pages.Researchers have addressed the problem using two approaches,i.e.,knowledge engineering and machine learning.State of the art knowledge engineering approaches use the structure of documents,visual cues,clustering of attributes of data records and text processing techniques to identify data records on a web page.Machine learning approaches use annotated pages to learn rules.These rules are used to extract data from unseen web pages.The structure of web documents is continuously evolving.Therefore,new techniques are needed to handle the emerging requirements of web data extraction.In this paper,we have presented a novel,simple and efficient technique to extract data from web pages using visual styles and structure of documents.The proposed technique detects Rich Data Region(RDR)using query and correlative words of the query.RDR is then divided into data records using style similarity.Noisy elements are removed using a Common Tag Sequence(CTS)and formatting entropy.The system is implemented using JAVA and runs on the dataset of real-world working websites.The effectiveness of results is evaluated using precision,recall,and F-measure and compared with five existing systems.A comparison of the proposed technique to existing systems has shown encouraging results.展开更多
The diversity of e-commerce Business to Consumer systems and the significant increase in their use during the COVID-19 pandemic as a one of the primary channels of retail commerce, has made all the most important the ...The diversity of e-commerce Business to Consumer systems and the significant increase in their use during the COVID-19 pandemic as a one of the primary channels of retail commerce, has made all the most important the need to measuring their quality using practical methods. This paper presents a quality evaluation framework for web metrics that are B2C specific. The framework uses three dimensions based on end-user interaction categories, metrics internal specs and quality sub-characteristics as defined by ISO25010. Beginning from the existing large corpus of general-purpose web metrics, e-commerce specific metrics are chosen and categorized. Analysis results are subjected to a data mining analysis to provide association rules between the various dimensions of the framework. Finally, an ontology that corresponds to the framework is developed to answer to complicated questions related to metrics use and to facilitate the production of new, user defined meta-metrics.展开更多
Statistics is a powerful tool for data measurement. Statistical techniques properly planned and executed give meaning to meaningless data. The difficulty some practitioners encounter hinges on the fact that though the...Statistics is a powerful tool for data measurement. Statistical techniques properly planned and executed give meaning to meaningless data. The difficulty some practitioners encounter hinges on the fact that though there are numerous statistical methods available for use in analysis, the extent of their understanding and ease of using these tools for analysis is limited. This study has twofold purpose: firstly, literature on categorical data commonly used in research w</span><span style="font-family:Verdana;">as</span><span style="font-family:Verdana;"> reviewed</span><span style="font-family:Verdana;">;</span><span style="font-family:""><span style="font-family:Verdana;"> next, we reported the results of a survey we designed and executed. Categorical data was collected via questionnaire and analyzed to serve as a backbone of the robustness of categorical data. Several conjec</span><span style="font-family:Verdana;">tures about the independence of the socio-economic variables and e-commence</span><span style="font-family:Verdana;"> were tested. Some of the factors influencing patronage of e-commerce were </span><span style="font-family:Verdana;">identified. It is clear from the literature that as one’s academic qualification</span><span style="font-family:Verdana;"> improves</span></span><span style="font-family:Verdana;">, </span><span style="font-family:""><span style="font-family:Verdana;">there is an associated improvement in their preference for e-commerce, but the results revealed otherwise. Size of family was found to influence e-commerce. Both income and social status positively affected pa</span><span style="font-family:Verdana;">tronage in e-commerce. Gender also appeared to affect patronage in e-commerce</span><span style="font-family:Verdana;">. 62.3% of staff had patronized e-commerce</span></span><span style="font-family:Verdana;">.</span><span style="font-family:Verdana;"> This shows that e-commerce patronage was gradually increasing. It is therefore our considered view that policy documents regulating and monitoring the use of e-commerce be developed to increase e-commerce participation across the globe</span><span style="font-family:Verdana;">. </span><span style="font-family:Verdana;">It is also recommended that the bottlenecks which obstruct patronage in e-commence be addressed so that a lot more staff will develop a positive attitude towards e-commerce.展开更多
In this paper, we conduct research on the modern precision e-commerce marketing model under the big data and pattern recognition background. Large amount of consumption data provides the electricity enterprises grasp ...In this paper, we conduct research on the modern precision e-commerce marketing model under the big data and pattern recognition background. Large amount of consumption data provides the electricity enterprises grasp the user consumption pattern and the basis of the electric business enterprise through the use of big data can be personalized, accurate and intelligent advertising push service, service mode for the creation of more interesting and effective. Under this basis, electricity companies can also pass the assurance of pair of big data, looking for better increase user stickiness, development of new products and services, the ways and methods to reduce operational costs and accordingly, we propose the novel perspectives on the corresponding issues for the systematic level enhancement that provides the novel methodology of precision e-commerce marketing.展开更多
In today's connected world,the generation of massive streaming data across diverse domains has become commonplace.In the presence of concept drift,class imbalance,label scarcity,and new class emergence,these chall...In today's connected world,the generation of massive streaming data across diverse domains has become commonplace.In the presence of concept drift,class imbalance,label scarcity,and new class emergence,these challenges jointly degrade representation stability,bias learning toward outdated distributions,and reduce the resilience and reliability of detection in dynamic environments.This paper proposes a streaming classincremental learning(SCIL)framework to address these issues.The SCIL framework integrates an autoencoder(AE)with a multi-layer perceptron for multi-class prediction,employs a dual-loss strategy(classification and reconstruction)for prediction and new class detection,uses corrected pseudo-labels for online training,manages classes with queues,and applies oversampling to handle imbalance.The rationale behind the method's structure is elucidated through ablation studies,and a comprehensive experimental evaluation is performed using both real-world and synthetic datasets that feature class imbalance,incremental classes,and concept drifts.Our results demonstrate that SCIL outperforms strong baselines and state-of-the-art methods.In line with our commitment to Open Science,we make our code and datasets available to the community.展开更多
基金funded by the Taiwan Comprehensive University System and the National Science and Technology Council of Taiwan under grant number NSTC 111-2410-H-019-006-MY3Additionally,this work was financially/partially supported by the Advanced Institute of Manufacturing with High-tech Innovations(AIM-HI)from the Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education(MOE)in Taiwan+1 种基金the National Natural Science Foundation of China,No.62402444the Zhejiang Provincial Natural Science Foundation of China,No.LQ24F020012.
文摘AI applications have become ubiquitous,bringing significant convenience to various industries.In e-commerce,AI can enhance product recommendations for individuals and provide businesses with more accurate predictions for market strategy development.However,if the data used for AI applications is damaged or lost,it will inevitably affect the effectiveness of these AI applications.Therefore,it is essential to verify the integrity of e-commerce data.Although existing Provable Data Possession(PDP)protocols can verify the integrity of cloud data,they are not suitable for e-commerce scenarios due to the limited computational capabilities of edge servers,which cannot handle the high computational overhead of generating homomorphic verification tags in PDP.To address this issue,we propose PDP with Outsourced Tag Generation for AI-driven e-commerce,which outsources the computation of homomorphic verification tags to cloud servers while introducing a lightweight verification method to ensure that the tags match the uploaded data.Additionally,the proposed scheme supports dynamic operations such as adding,deleting,and modifying data,enhancing its practicality.Finally,experiments show that the additional computational overhead introduced by outsourcing homomorphic verification tags is acceptable compared to the original PDP.
文摘With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis of these data and insight into user behavior patterns and preferences.This paper elaborates on the application of big data technology in the analysis of user behavior on e-commerce platforms,including the technical methods of data collection,storage,processing and analysis,as well as the specific applications in the construction of user profiles,precision marketing,personalized recommendation,user retention and churn analysis,etc.,and discusses the challenges and countermeasures faced in the application.Through the study of actual cases,it demonstrates the remarkable effectiveness of big data technology in enhancing the competitiveness of e-commerce platforms and user experience.
文摘In the research, the crawler technology was used to evaluate the Taobao silkie, and product evaluation data is the case object. After word segmentation, naive Bayesian, K-means, and TF-IDF related algorithms were used to complete word frequency statistics, sentiment analysis, and cluster analysis. It was found that the quality of rural specialty products was more recognized by e-commerce customers, but its overall emotional score was slightly lower than that of non-origin products. After summing up the drawbacks of the former, targeted countermeasures and suggestions were proposed.
文摘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.
基金the National Natural Science Foundation of China(70572070)the Liaoning Province Talents Fund Projects(2005219005)the Technology Key Project of Liaoning Province(2006220019)
文摘The technique of data mining was provided to predict gas disaster in view of the characteristics of coal mine gas disaster and feature knowledge based on gas disaster. The rough set theory was used to establish data mining model of gas disaster prediction, and rough set attributes relations was discussed in prediction model of gas disaster to supplement the shortages of rough intensive reduction method by using information en- tropy criteria.The effectiveness and practicality of data mining technology in the prediction of gas disaster is confirmed through practical application.
文摘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.
基金Henan Province Special Research Project of Tra ditional Chinese Medicine(Grant No.2022ZY1090).
文摘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.
文摘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.
基金Supported by Shanghai College Students Innovation and Entrepreneurship Training Program Project:202310268066The 16th Batch of Science And Technology Innovation Projects of Shanghai University of Traditional Chinese Medicine:SHUTCM2023010+1 种基金2024 Shanghai Oriental Talent Program Youth Project2021 High-level Local University Innovation Team Project of Shanghai University of Traditional Chinese Medicine:No.3 Shanghai Education Commission Personnel [2022]。
文摘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.
基金Song Hujie’s Inheritance Studio of National Renowned Traditional Chinese Medicine Experts.
文摘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.
基金the National Key Research and Development Program of China(No.2023 YFC2811600)the National Natural Science Foundation of China(Nos.52301349,52088102)+1 种基金the Major Science and Technology Innovation Program of Qingdao(No.223-3-hygg-10-hy)the Qingdao Science Foundation for Post-doctoral Scientists(Nos.QDBSH20220202070,QDBSH20220201015)。
文摘A deep-sea riser is a crucial component of the mining system used to lift seafloor mineral resources to the vessel.Even minor damage to the riser can lead to substantial financial losses,environmental impacts,and safety hazards.However,identifying modal parameters for structural health monitoring remains a major challenge due to its large deformations and flexibility.Vibration signal-based methods are essential for detecting damage and enabling timely maintenance to minimize losses.However,accurately extracting features from one-dimensional(1D)signals is often hindered by various environmental factors and measurement noises.To address this challenge,a novel approach based on a residual convolutional auto-encoder(RCAE)is proposed for detecting damage in deep-sea mining risers,incorporating a data fusion strategy.First,principal component analysis(PCA)is applied to reduce environmental fluctuations and fuse multisensor strain readings.Subsequently,a 1D-RCAE is used to extract damage-sensitive features(DSFs)from the fused dataset.A Mahalanobis distance indicator is established to compare the DSFs of the testing and healthy risers.The specific threshold for these distances is determined using the 3σcriterion,which is employed to assess whether damage has occurred in the testing riser.The effectiveness and robustness of the proposed approach are verified through numerical simulations of a 500-m riser and experimental tests on a 6-m riser.Moreover,the impact of contaminated noise and environmental fluctuations is examined.Results show that the proposed PCA-1D-RCAE approach can effectively detect damage and is resilient to measurement noise and environmental fluctuations.The accuracy exceeds 98%under noise-free conditions and remains above 90%even with 10 dB noise.This novel approach has the potential to establish a new standard for evaluating the health and integrity of risers during mining operations,thereby reducing the high costs and risks associated with failures.Maintenance activities can be scheduled more efficiently by enabling early and accurate detection of riser damage,minimizing downtime and avoiding catastrophic failures.
基金This project was supported by China Postdoctoral Science Foundation (2005037506) and the National Natural ScienceFoundation of China (70472029)
文摘In e-commerce the multidimensional data analysis based on the Web data needs integrating various data sources such as XML data and relational data on the conceptual level. A conceptual data description approach to multidimensional data model the UML galaxy diagram is presented in order to conduct multidimensional data analysis for multiple subjects. The approach is illuminated using a case of 2_roots UML galaxy diagram that takes marketing analysis of TV products involved one retailer and several suppliers into consideration.
文摘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).
文摘Previous weighted frequent pattern （WFP） mining algorithms are not suitable for data streams for they need multiple database scans. In this paper, we present an efficient algorithm SWFP-Miner to mine weighted frequent pattern over data streams. SWFP-Miner is based on sliding window and can discover important frequent pattern from the recent data. A new refined weight definition is proposed to keep the downward closure property, and two pruning strategies are presented to prune the weighted infrequent pattern. Experimental studies are performed to evaluate the effectiveness and efficiency of SWFP-Miner.
文摘A large amount of data is present on the web which can be used for useful purposes like a product recommendation,price comparison and demand forecasting for a particular product.Websites are designed for human understanding and not for machines.Therefore,to make data machine-readable,it requires techniques to grab data from web pages.Researchers have addressed the problem using two approaches,i.e.,knowledge engineering and machine learning.State of the art knowledge engineering approaches use the structure of documents,visual cues,clustering of attributes of data records and text processing techniques to identify data records on a web page.Machine learning approaches use annotated pages to learn rules.These rules are used to extract data from unseen web pages.The structure of web documents is continuously evolving.Therefore,new techniques are needed to handle the emerging requirements of web data extraction.In this paper,we have presented a novel,simple and efficient technique to extract data from web pages using visual styles and structure of documents.The proposed technique detects Rich Data Region(RDR)using query and correlative words of the query.RDR is then divided into data records using style similarity.Noisy elements are removed using a Common Tag Sequence(CTS)and formatting entropy.The system is implemented using JAVA and runs on the dataset of real-world working websites.The effectiveness of results is evaluated using precision,recall,and F-measure and compared with five existing systems.A comparison of the proposed technique to existing systems has shown encouraging results.
文摘The diversity of e-commerce Business to Consumer systems and the significant increase in their use during the COVID-19 pandemic as a one of the primary channels of retail commerce, has made all the most important the need to measuring their quality using practical methods. This paper presents a quality evaluation framework for web metrics that are B2C specific. The framework uses three dimensions based on end-user interaction categories, metrics internal specs and quality sub-characteristics as defined by ISO25010. Beginning from the existing large corpus of general-purpose web metrics, e-commerce specific metrics are chosen and categorized. Analysis results are subjected to a data mining analysis to provide association rules between the various dimensions of the framework. Finally, an ontology that corresponds to the framework is developed to answer to complicated questions related to metrics use and to facilitate the production of new, user defined meta-metrics.
文摘Statistics is a powerful tool for data measurement. Statistical techniques properly planned and executed give meaning to meaningless data. The difficulty some practitioners encounter hinges on the fact that though there are numerous statistical methods available for use in analysis, the extent of their understanding and ease of using these tools for analysis is limited. This study has twofold purpose: firstly, literature on categorical data commonly used in research w</span><span style="font-family:Verdana;">as</span><span style="font-family:Verdana;"> reviewed</span><span style="font-family:Verdana;">;</span><span style="font-family:""><span style="font-family:Verdana;"> next, we reported the results of a survey we designed and executed. Categorical data was collected via questionnaire and analyzed to serve as a backbone of the robustness of categorical data. Several conjec</span><span style="font-family:Verdana;">tures about the independence of the socio-economic variables and e-commence</span><span style="font-family:Verdana;"> were tested. Some of the factors influencing patronage of e-commerce were </span><span style="font-family:Verdana;">identified. It is clear from the literature that as one’s academic qualification</span><span style="font-family:Verdana;"> improves</span></span><span style="font-family:Verdana;">, </span><span style="font-family:""><span style="font-family:Verdana;">there is an associated improvement in their preference for e-commerce, but the results revealed otherwise. Size of family was found to influence e-commerce. Both income and social status positively affected pa</span><span style="font-family:Verdana;">tronage in e-commerce. Gender also appeared to affect patronage in e-commerce</span><span style="font-family:Verdana;">. 62.3% of staff had patronized e-commerce</span></span><span style="font-family:Verdana;">.</span><span style="font-family:Verdana;"> This shows that e-commerce patronage was gradually increasing. It is therefore our considered view that policy documents regulating and monitoring the use of e-commerce be developed to increase e-commerce participation across the globe</span><span style="font-family:Verdana;">. </span><span style="font-family:Verdana;">It is also recommended that the bottlenecks which obstruct patronage in e-commence be addressed so that a lot more staff will develop a positive attitude towards e-commerce.
文摘In this paper, we conduct research on the modern precision e-commerce marketing model under the big data and pattern recognition background. Large amount of consumption data provides the electricity enterprises grasp the user consumption pattern and the basis of the electric business enterprise through the use of big data can be personalized, accurate and intelligent advertising push service, service mode for the creation of more interesting and effective. Under this basis, electricity companies can also pass the assurance of pair of big data, looking for better increase user stickiness, development of new products and services, the ways and methods to reduce operational costs and accordingly, we propose the novel perspectives on the corresponding issues for the systematic level enhancement that provides the novel methodology of precision e-commerce marketing.
基金supported by the European Research Council(ERC)under Grant Agreement No.951424(Water-Futures)by the Republic of Cyprus through the Deputy Ministry of Research,Innovation and Digital Policy.
文摘In today's connected world,the generation of massive streaming data across diverse domains has become commonplace.In the presence of concept drift,class imbalance,label scarcity,and new class emergence,these challenges jointly degrade representation stability,bias learning toward outdated distributions,and reduce the resilience and reliability of detection in dynamic environments.This paper proposes a streaming classincremental learning(SCIL)framework to address these issues.The SCIL framework integrates an autoencoder(AE)with a multi-layer perceptron for multi-class prediction,employs a dual-loss strategy(classification and reconstruction)for prediction and new class detection,uses corrected pseudo-labels for online training,manages classes with queues,and applies oversampling to handle imbalance.The rationale behind the method's structure is elucidated through ablation studies,and a comprehensive experimental evaluation is performed using both real-world and synthetic datasets that feature class imbalance,incremental classes,and concept drifts.Our results demonstrate that SCIL outperforms strong baselines and state-of-the-art methods.In line with our commitment to Open Science,we make our code and datasets available to the community.
