By using CiteSpace software to create a knowledge map of authors,institutions and keywords,the literature on the spatio-temporal behavior of Chinese residents based on big data in the architectural planning discipline...By using CiteSpace software to create a knowledge map of authors,institutions and keywords,the literature on the spatio-temporal behavior of Chinese residents based on big data in the architectural planning discipline published in the China Academic Network Publishing Database(CNKI)was analyzed and discussed.It is found that there was a lack of communication and cooperation among research institutions and scholars;the research hotspots involved four main areas,including“application in tourism research”,“application in traffic travel research”,“application in work-housing relationship research”,and“application in personal family life research”.展开更多
By analyzing the correlation between courses in students’grades,we can provide a decision-making basis for the revision of courses and syllabi,rationally optimize courses,and further improve teaching effects.With the...By analyzing the correlation between courses in students’grades,we can provide a decision-making basis for the revision of courses and syllabi,rationally optimize courses,and further improve teaching effects.With the help of IBM SPSS Modeler data mining software,this paper uses Apriori algorithm for association rule mining to conduct an in-depth analysis of the grades of nursing students in Shandong College of Traditional Chinese Medicine,and to explore the correlation between professional basic courses and professional core courses.Lastly,according to the detailed analysis of the mining results,valuable curriculum information will be found from the actual teaching data.展开更多
In recent years improper allocation of safety input has prevailed in coal mines in China, which resulted in the frequent accidents in coal mining operation. A comprehensive assessment of the input efficiency of coal m...In recent years improper allocation of safety input has prevailed in coal mines in China, which resulted in the frequent accidents in coal mining operation. A comprehensive assessment of the input efficiency of coal mine safety should lead to improved efficiency in the use of funds and management resources. This helps government and enterprise managers better understand how safety inputs are used and to optimize allocation of resources. Study on coal mine＇s efficiency assessment of safety input was con- ducted in this paper. A C^2R model with non-Archimedean infinitesimal vector based on output is established after consideration of the input characteristics and the model properties. An assessment of an operating mine was done using a specific set of input and output criteria. It is found that the safety input was efficient in 2002 and 2005 and was weakly efficient in 2003. However, the efficiency was relatively low in both 2001 and 2004. The safety input resources can be optimized and adjusted by means of projection theory. Such analysis shows that, on average in 2001 and 2004, 45% of the expended funds could have been saved. Likewise, 10% of the safety management and technical staff could have been eliminated and working hours devoted to safety could have been reduced by 12%. These conditions could have Riven the same results.展开更多
Springback of sheet metal induced by elastic recovery is one of major defects in sheet metal forming processed. Springback is influenced by many factors including properties of the sheet material and processing condit...Springback of sheet metal induced by elastic recovery is one of major defects in sheet metal forming processed. Springback is influenced by many factors including properties of the sheet material and processing conditions. In this paper, a springback simulation was conducted and comparisons between the results based on different processing variables were illustrated. The discovery of knowledge of the effects of geometry and process parameters on springback from FEM results becomes increasingly important, as the number of numerical simulation has grown exponentially. Data mining is an effective tool to realize knowledge discovery in simulation results. A data-mining algorithm, rough sets theory (RST), was applied to analyze the effects of process parameters on springback in U-bending.展开更多
The buildings and structures of mines were monitored automatically using modern surveying technology. Through the analysis of the monitoring data, the deformation characteristics were found out from three aspects cont...The buildings and structures of mines were monitored automatically using modern surveying technology. Through the analysis of the monitoring data, the deformation characteristics were found out from three aspects containing points, lines and regions, which play an important role in understanding the stable state of buildings and structures. The stability and deformation of monitoring points were analysed, and time-series data of monitoring points were denoised with wavelet analysis and Kalman filtering, and exponent function and periodic function were used to get the ideal deformation trend model of monitoring points. Through calculating the monitoring data obtained, analyzing the deformation trend, and cognizing the deformation regularity, it can better service mine safety production and decision-making.展开更多
This work evaluates a recently developed multivariate statistical method based on the creation of pseudo or latent variables using principal component analysis （PCA）. The application is the data mining of gene expre...This work evaluates a recently developed multivariate statistical method based on the creation of pseudo or latent variables using principal component analysis （PCA）. The application is the data mining of gene expression data to find a small subset of the most important genes in a set of thousand or tens of thousands of genes from a relatively small number of experimental runs. The method was previously developed and evaluated on artificially generated data and real data sets. Its evaluations consisted of its ability to rank the genes against known truth in simulated data studies and to identify known important genes in real data studies. The purpose of the work described here is to identify a ranked set of genes in an experimental study and then for a few of the most highly ranked unverified genes, experimentally verify their importance.This method was evaluated using the transcriptional response of Escherichia coli to treatment with four distinct inhibitory compounds： nitric oxide, S-nitrosoglutathione, serine hydroxamate and potassium cyanide. Our analysis identified genes previously recognized in the response to these compounds and also identified new genes.Three of these new genes, ycbR, yJhA and yahN, were found to significantly （p-values〈0.002） affect the sensitivityofE, coli to nitric oxide-mediated growth inhibition. Given that the three genes were not highly ranked in the selected ranked set （RS）, these results support strong sensitivity in the ability of the method to successfully identify genes related to challenge by NO and GSNO. This ability to identify genes related to the response to an inhibitory compound is important for engineering tolerance to inhibitory metabolic products, such as biofuels, and utilization of cheap sugar streams, such as biomass-derived sugars or hydrolysate.展开更多
China＇s capital market is different from that of the US in economic, political, and socio-cultural ways. China＇s dynamic and fast growing economy for the past decade entails some structural changes and weaknesses an...China＇s capital market is different from that of the US in economic, political, and socio-cultural ways. China＇s dynamic and fast growing economy for the past decade entails some structural changes and weaknesses and as a consequence, there are some business failures. We propose bankruptcy prediction models using Chinese firm data via several data mining tools and traditional logit analysis. We used Chinese firm data one year prior to bankruptcy and our results suggest that the financial variables developed by Altman （1968） and Ohlson （1980） perform reasonably well in determining business failures of Chinese firms, but the overall prediction rate is low compared with those of the US or other countries＇ studies. The reasons for this low prediction rate may be structural weaknesses resulting from China＇s fast growth and immature capital market.展开更多
Clinical databases have accumulated large quantities of information about patients and their medical conditions. Current challenges in biomedical research and clinical practice include information overload and the nee...Clinical databases have accumulated large quantities of information about patients and their medical conditions. Current challenges in biomedical research and clinical practice include information overload and the need to optimize workflows, processes and guidelines, to increase capacity while reducing costs and improving efficiency. There is an urgent need for integrative and interactive machine learning solutions, because no medical doctor or biomedical researcher can keep pace today with the increasingly large and complex data sets – often called "Big Data".展开更多
To serve as a reference for future foreign tourism study,relevant tourist sectors have done in-depth investigations on foreign tourism both domestically and internationally.A study of outbound tourism activities from ...To serve as a reference for future foreign tourism study,relevant tourist sectors have done in-depth investigations on foreign tourism both domestically and internationally.A study of outbound tourism activities from the viewpoint of tourists can examine its development law and create successful marketing tactics based on the rise in the number of foreign tourists.Based on this,this study suggests a data mining technique to examine the variations in travel needs and marketing tactics among various consumer groups.The combined example analysis demonstrates how logical and useful our data mining analysis is.Our data tests demonstrate that the tourism strategy outlined in this paper can enhance the number of tourists by piquing their interest based on the rise in the number of international travellers travelling overseas.展开更多
In the era of big data, the application value of information has increased. Besides the efficient transmission of information, the quality of in-depth mining and the reasonable data analysis method should be ensured. ...In the era of big data, the application value of information has increased. Besides the efficient transmission of information, the quality of in-depth mining and the reasonable data analysis method should be ensured. These are the prerequisites for data application and cannot be ignored. Against this background, Python was popularized. Python belongs to a high-level and relatively complete programming language. Its application advantages are obvious, especially in the field of big data mining and data analysis. This paper will study the practical application of Python in order to further improve the data mining ability and ensure the quality of data analysis.展开更多
Background:The purpose of this study was to identify the characteristics and principles of acupoints applied for treating chronic hepatitis B infection.Methods:The published clinical studies on acupuncture for the tre...Background:The purpose of this study was to identify the characteristics and principles of acupoints applied for treating chronic hepatitis B infection.Methods:The published clinical studies on acupuncture for the treatment of chronic hepatitis B infection were gathered from various databases,including SinoMed,Chongqing Vip,China National Knowledge Infrastructure,Wanfang,the Cochrane Library,PubMed,Web of Science and Embase.Excel 2019 was utilized to establish a database of acupuncture prescriptions and conduct statistics on the frequency,meridian application,distribution and specific points,as well as SPSS Modeler 18.0 and SPSS Statistics 26.0 to conduct association rule analysis and cluster analysis to investigate the characteristics and patterns of acupoint selection.Results:A total of 42 studies containing 47 acupoints were included,with a total frequency of 286 acupoints.The top five acupoints used were Zusanli(ST36),Ganshu(BL18),Yanglingquan(GB34),Sanyinjiao(SP6)and Taichong(LR3),and the most commonly used meridians was the Bladder Meridian of Foot-Taiyang.The majority of acupuncture points are located in the lower limbs,back,and lumbar regions,with a significant percentage of them being Five-Shu acupoints.The strongest acupoint combination identified was Zusanli(ST36)–Ganshu(BL18),in addition to which 13 association rules and 4 valid clusters were obtained.Conclusion:Zusanli(ST36)–Ganshu(BL18)could be considered a relatively reasonable prescription for treating chronic hepatitis B infection in clinical practice.However,further high-quality studies are needed.展开更多
The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain facto...The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain factor conditions into quantitative values with the uncertain illation based on cloud model, and then, inte- grating correlation analysis, a new way of figuring out the weight of land evaluation factors is proposed. It may solve the limitations of the conventional ways.展开更多
This study sought to conduct a bibliometric analysis of acupuncture studies focusing on heart rate variability(HRV)and to investigate the correlation between various acupoints and their effects on HRV by utilizing ass...This study sought to conduct a bibliometric analysis of acupuncture studies focusing on heart rate variability(HRV)and to investigate the correlation between various acupoints and their effects on HRV by utilizing association rule mining and network analysis.A total of 536 publications on the topic of acupuncture studies based on HRV.The disease keyword analysis revealed that HRV-related acupuncture studies were mainly related to pain,inflammation,emotional disorders,gastrointestinal function,and hypertension.A separate analysis was conducted on acupuncture prescriptions,and Neiguan(PC6)and Zusanli(ST36)were the most frequently used acupoints.The core acupoints for HRV regulation were identified as PC6,ST36,Shenmen(HT7),Hegu(LI4),Sanyinjiao(SP6),Jianshi(PC5),Taichong(LR3),Quchi(LI11),Guanyuan(CV4),Baihui(GV20),and Taixi(KI3).Additionally,the research encompassed 46 reports on acupuncture animal experiments conducted on HRV,with ST36 being the most frequently utilized acupoint.The research presented in this study offers valuable insights into the global research trend and hotspots in acupuncture-based HRV studies,as well as identifying frequently used combinations of acupoints.The findings may be helpful for further research in this field and provide valuable information about the potential use of acupuncture for improving HRV in both humans and animals.展开更多
The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibi...The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.展开更多
As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain ...As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.展开更多
The outbreak of the pandemic,caused by Coronavirus Disease 2019(COVID-19),has affected the daily activities of people across the globe.During COVID-19 outbreak and the successive lockdowns,Twitter was heavily used and...The outbreak of the pandemic,caused by Coronavirus Disease 2019(COVID-19),has affected the daily activities of people across the globe.During COVID-19 outbreak and the successive lockdowns,Twitter was heavily used and the number of tweets regarding COVID-19 increased tremendously.Several studies used Sentiment Analysis(SA)to analyze the emotions expressed through tweets upon COVID-19.Therefore,in current study,a new Artificial Bee Colony(ABC)with Machine Learning-driven SA(ABCMLSA)model is developed for conducting Sentiment Analysis of COVID-19 Twitter data.The prime focus of the presented ABCML-SA model is to recognize the sentiments expressed in tweets made uponCOVID-19.It involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature vectors.For identification and classification of the sentiments,the Support Vector Machine(SVM)model is exploited.At last,the ABC algorithm is applied to fine tune the parameters involved in SVM.To demonstrate the improved performance of the proposed ABCML-SA model,a sequence of simulations was conducted.The comparative assessment results confirmed the effectual performance of the proposed ABCML-SA model over other approaches.展开更多
It is difficult to detect the anomalies whose matching relationship among some data attributes is very different from others’ in a dataset. Aiming at this problem, an approach based on wavelet analysis for detecting ...It is difficult to detect the anomalies whose matching relationship among some data attributes is very different from others’ in a dataset. Aiming at this problem, an approach based on wavelet analysis for detecting and amending anomalous samples was proposed. Taking full advantage of wavelet analysis’ properties of multi-resolution and local analysis, this approach is able to detect and amend anomalous samples effectively. To realize the rapid numeric computation of wavelet translation for a discrete sequence, a modified algorithm based on Newton-Cores formula was also proposed. The experimental result shows that the approach is feasible with good result and good practicality.展开更多
We used both correlation and covariance-principal component analysis (PCA) to classify the same absorption-reflectance data collected from 13 different polymeric fabric materials that was obtained using Attenuated Tot...We used both correlation and covariance-principal component analysis (PCA) to classify the same absorption-reflectance data collected from 13 different polymeric fabric materials that was obtained using Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR). The application of the two techniques, though similar, yielded results that represent different chemical properties of the polymeric substances. On one hand, correlation-PCA enabled the classification of the fabric materials according to the organic functional groups of their repeating monomer units. On the other hand, covariance-PCA was used to classify the fabric materials primarily according to their origins;natural (animal or plant) or synthetic. Hence besides major chemical functional groups of the repeat units, it appears covariance-PCA is also sensitive to other characteristic chemical (inorganic and/or organic) or biochemical material inclusions that are found in different samples. We therefore recommend the application of both covariance-PCA and correlation-PCA on datasets, whenever applicable, to enable a broader classification of spectroscopic information through data mining and exploration.展开更多
Social media is an essential component of our personal and professional lives. We use it extensively to share various things, including our opinions on daily topics and feelings about different subjects. This sharing ...Social media is an essential component of our personal and professional lives. We use it extensively to share various things, including our opinions on daily topics and feelings about different subjects. This sharing of posts provides insights into someone’s current emotions. In artificial intelligence (AI) and deep learning (DL), researchers emphasize opinion mining and analysis of sentiment, particularly on social media platforms such as Twitter (currently known as X), which has a global user base. This research work revolves explicitly around a comparison between two popular approaches: Lexicon-based and Deep learning-based Approaches. To conduct this study, this study has used a Twitter dataset called sentiment140, which contains over 1.5 million data points. The primary focus was the Long Short-Term Memory (LSTM) deep learning sequence model. In the beginning, we used particular techniques to preprocess the data. The dataset is divided into training and test data. We evaluated the performance of our model using the test data. Simultaneously, we have applied the lexicon-based approach to the same test data and recorded the outputs. Finally, we compared the two approaches by creating confusion matrices based on their respective outputs. This allows us to assess their precision, recall, and F1-Score, enabling us to determine which approach yields better accuracy. This research achieved 98% model accuracy for deep learning algorithms and 95% model accuracy for the lexicon-based approach.展开更多
:Strabismus is a medical condition that is defined as the lack of coordination between the eyes.When Strabismus is detected at an early age,the chances of curing it are higher.The methods used to detect strabismus and...:Strabismus is a medical condition that is defined as the lack of coordination between the eyes.When Strabismus is detected at an early age,the chances of curing it are higher.The methods used to detect strabismus and measure its degree of deviation are complex and time-consuming,and they always require the presence of a physician.In this paper,we present a method of detecting strabismus and measuring its degree of deviation using videos of the patient’s eye region under a cover test.Our method involves extracting features from a set of training videos(training corpora)and using them to build a classifier.A decision tree(ID3)is built using labeled cases from actual strabismus diagnosis.Patterns are extracted from the corresponding videos of patients,and an association between the extracted features and actual diagnoses is established.Matching Rules from the correlation plot are used to predict diagnoses for future patients.The classifier was tested using a set of testing videos(testing corpora).The results showed 95.9%accuracy,4.1%were light cases and could not be detected correctly from the videos,half of them were false positive and the other half was false negative.展开更多
文摘By using CiteSpace software to create a knowledge map of authors,institutions and keywords,the literature on the spatio-temporal behavior of Chinese residents based on big data in the architectural planning discipline published in the China Academic Network Publishing Database(CNKI)was analyzed and discussed.It is found that there was a lack of communication and cooperation among research institutions and scholars;the research hotspots involved four main areas,including“application in tourism research”,“application in traffic travel research”,“application in work-housing relationship research”,and“application in personal family life research”.
文摘By analyzing the correlation between courses in students’grades,we can provide a decision-making basis for the revision of courses and syllabi,rationally optimize courses,and further improve teaching effects.With the help of IBM SPSS Modeler data mining software,this paper uses Apriori algorithm for association rule mining to conduct an in-depth analysis of the grades of nursing students in Shandong College of Traditional Chinese Medicine,and to explore the correlation between professional basic courses and professional core courses.Lastly,according to the detailed analysis of the mining results,valuable curriculum information will be found from the actual teaching data.
基金Project 70771105 supported by the National Natural Science Foundation of China
文摘In recent years improper allocation of safety input has prevailed in coal mines in China, which resulted in the frequent accidents in coal mining operation. A comprehensive assessment of the input efficiency of coal mine safety should lead to improved efficiency in the use of funds and management resources. This helps government and enterprise managers better understand how safety inputs are used and to optimize allocation of resources. Study on coal mine＇s efficiency assessment of safety input was con- ducted in this paper. A C^2R model with non-Archimedean infinitesimal vector based on output is established after consideration of the input characteristics and the model properties. An assessment of an operating mine was done using a specific set of input and output criteria. It is found that the safety input was efficient in 2002 and 2005 and was weakly efficient in 2003. However, the efficiency was relatively low in both 2001 and 2004. The safety input resources can be optimized and adjusted by means of projection theory. Such analysis shows that, on average in 2001 and 2004, 45% of the expended funds could have been saved. Likewise, 10% of the safety management and technical staff could have been eliminated and working hours devoted to safety could have been reduced by 12%. These conditions could have Riven the same results.
基金the Shanghai Post-Phosphor Plan ( No.0 1QMH14 11)
文摘Springback of sheet metal induced by elastic recovery is one of major defects in sheet metal forming processed. Springback is influenced by many factors including properties of the sheet material and processing conditions. In this paper, a springback simulation was conducted and comparisons between the results based on different processing variables were illustrated. The discovery of knowledge of the effects of geometry and process parameters on springback from FEM results becomes increasingly important, as the number of numerical simulation has grown exponentially. Data mining is an effective tool to realize knowledge discovery in simulation results. A data-mining algorithm, rough sets theory (RST), was applied to analyze the effects of process parameters on springback in U-bending.
基金Project(40771175)supported by the National Nature Science Foundation of China
文摘The buildings and structures of mines were monitored automatically using modern surveying technology. Through the analysis of the monitoring data, the deformation characteristics were found out from three aspects containing points, lines and regions, which play an important role in understanding the stable state of buildings and structures. The stability and deformation of monitoring points were analysed, and time-series data of monitoring points were denoised with wavelet analysis and Kalman filtering, and exponent function and periodic function were used to get the ideal deformation trend model of monitoring points. Through calculating the monitoring data obtained, analyzing the deformation trend, and cognizing the deformation regularity, it can better service mine safety production and decision-making.
文摘This work evaluates a recently developed multivariate statistical method based on the creation of pseudo or latent variables using principal component analysis （PCA）. The application is the data mining of gene expression data to find a small subset of the most important genes in a set of thousand or tens of thousands of genes from a relatively small number of experimental runs. The method was previously developed and evaluated on artificially generated data and real data sets. Its evaluations consisted of its ability to rank the genes against known truth in simulated data studies and to identify known important genes in real data studies. The purpose of the work described here is to identify a ranked set of genes in an experimental study and then for a few of the most highly ranked unverified genes, experimentally verify their importance.This method was evaluated using the transcriptional response of Escherichia coli to treatment with four distinct inhibitory compounds： nitric oxide, S-nitrosoglutathione, serine hydroxamate and potassium cyanide. Our analysis identified genes previously recognized in the response to these compounds and also identified new genes.Three of these new genes, ycbR, yJhA and yahN, were found to significantly （p-values〈0.002） affect the sensitivityofE, coli to nitric oxide-mediated growth inhibition. Given that the three genes were not highly ranked in the selected ranked set （RS）, these results support strong sensitivity in the ability of the method to successfully identify genes related to challenge by NO and GSNO. This ability to identify genes related to the response to an inhibitory compound is important for engineering tolerance to inhibitory metabolic products, such as biofuels, and utilization of cheap sugar streams, such as biomass-derived sugars or hydrolysate.
文摘China＇s capital market is different from that of the US in economic, political, and socio-cultural ways. China＇s dynamic and fast growing economy for the past decade entails some structural changes and weaknesses and as a consequence, there are some business failures. We propose bankruptcy prediction models using Chinese firm data via several data mining tools and traditional logit analysis. We used Chinese firm data one year prior to bankruptcy and our results suggest that the financial variables developed by Altman （1968） and Ohlson （1980） perform reasonably well in determining business failures of Chinese firms, but the overall prediction rate is low compared with those of the US or other countries＇ studies. The reasons for this low prediction rate may be structural weaknesses resulting from China＇s fast growth and immature capital market.
文摘Clinical databases have accumulated large quantities of information about patients and their medical conditions. Current challenges in biomedical research and clinical practice include information overload and the need to optimize workflows, processes and guidelines, to increase capacity while reducing costs and improving efficiency. There is an urgent need for integrative and interactive machine learning solutions, because no medical doctor or biomedical researcher can keep pace today with the increasingly large and complex data sets – often called "Big Data".
基金2021 Youth Innovation Talents Project of Universities in Guangdong Province“Cause Analysis and Countermeasure Research on the Difference of Tourism Resources Development and Marketing Weakening in Underdeveloped Regions of Western Guangdong”(Project No.2021WQNCX241).
文摘To serve as a reference for future foreign tourism study,relevant tourist sectors have done in-depth investigations on foreign tourism both domestically and internationally.A study of outbound tourism activities from the viewpoint of tourists can examine its development law and create successful marketing tactics based on the rise in the number of foreign tourists.Based on this,this study suggests a data mining technique to examine the variations in travel needs and marketing tactics among various consumer groups.The combined example analysis demonstrates how logical and useful our data mining analysis is.Our data tests demonstrate that the tourism strategy outlined in this paper can enhance the number of tourists by piquing their interest based on the rise in the number of international travellers travelling overseas.
文摘In the era of big data, the application value of information has increased. Besides the efficient transmission of information, the quality of in-depth mining and the reasonable data analysis method should be ensured. These are the prerequisites for data application and cannot be ignored. Against this background, Python was popularized. Python belongs to a high-level and relatively complete programming language. Its application advantages are obvious, especially in the field of big data mining and data analysis. This paper will study the practical application of Python in order to further improve the data mining ability and ensure the quality of data analysis.
基金supported by Chongqing Municipal Health and Family Planning Commission and Chongqing Municipal Science and Technology Commission Jointly Funded Key Research Projects in Traditional Chinese Medicine(ZY201801007).
文摘Background:The purpose of this study was to identify the characteristics and principles of acupoints applied for treating chronic hepatitis B infection.Methods:The published clinical studies on acupuncture for the treatment of chronic hepatitis B infection were gathered from various databases,including SinoMed,Chongqing Vip,China National Knowledge Infrastructure,Wanfang,the Cochrane Library,PubMed,Web of Science and Embase.Excel 2019 was utilized to establish a database of acupuncture prescriptions and conduct statistics on the frequency,meridian application,distribution and specific points,as well as SPSS Modeler 18.0 and SPSS Statistics 26.0 to conduct association rule analysis and cluster analysis to investigate the characteristics and patterns of acupoint selection.Results:A total of 42 studies containing 47 acupoints were included,with a total frequency of 286 acupoints.The top five acupoints used were Zusanli(ST36),Ganshu(BL18),Yanglingquan(GB34),Sanyinjiao(SP6)and Taichong(LR3),and the most commonly used meridians was the Bladder Meridian of Foot-Taiyang.The majority of acupuncture points are located in the lower limbs,back,and lumbar regions,with a significant percentage of them being Five-Shu acupoints.The strongest acupoint combination identified was Zusanli(ST36)–Ganshu(BL18),in addition to which 13 association rules and 4 valid clusters were obtained.Conclusion:Zusanli(ST36)–Ganshu(BL18)could be considered a relatively reasonable prescription for treating chronic hepatitis B infection in clinical practice.However,further high-quality studies are needed.
文摘The veracity of land evaluation is tightly related to the reasonable weights of land evaluation fac- tors. By mapping qualitative linguistic words into a fine-changeable cloud drops and translating the uncertain factor conditions into quantitative values with the uncertain illation based on cloud model, and then, inte- grating correlation analysis, a new way of figuring out the weight of land evaluation factors is proposed. It may solve the limitations of the conventional ways.
基金supported by the Natural Science Foundation of Sichuan Province(2023NSFSC1799)the Science and Technology Development Fund of the Affiliated Hospital of Chengdu University of Traditional Chinese Medicine(21ZS05,23YY07)Chengdu University of Traditional Chinese Medicine Xinglin Scholar Postdoctoral Program BSH2023010.
文摘This study sought to conduct a bibliometric analysis of acupuncture studies focusing on heart rate variability(HRV)and to investigate the correlation between various acupoints and their effects on HRV by utilizing association rule mining and network analysis.A total of 536 publications on the topic of acupuncture studies based on HRV.The disease keyword analysis revealed that HRV-related acupuncture studies were mainly related to pain,inflammation,emotional disorders,gastrointestinal function,and hypertension.A separate analysis was conducted on acupuncture prescriptions,and Neiguan(PC6)and Zusanli(ST36)were the most frequently used acupoints.The core acupoints for HRV regulation were identified as PC6,ST36,Shenmen(HT7),Hegu(LI4),Sanyinjiao(SP6),Jianshi(PC5),Taichong(LR3),Quchi(LI11),Guanyuan(CV4),Baihui(GV20),and Taixi(KI3).Additionally,the research encompassed 46 reports on acupuncture animal experiments conducted on HRV,with ST36 being the most frequently utilized acupoint.The research presented in this study offers valuable insights into the global research trend and hotspots in acupuncture-based HRV studies,as well as identifying frequently used combinations of acupoints.The findings may be helpful for further research in this field and provide valuable information about the potential use of acupuncture for improving HRV in both humans and animals.
文摘The technological evolution emerges a unified (Industrial) Internet of Things network, where loosely coupled smart manufacturing devices build smart manufacturing systems and enable comprehensive collaboration possibilities that increase the dynamic and volatility of their ecosystems. On the one hand, this evolution generates a huge field for exploitation, but on the other hand also increases complexity including new challenges and requirements demanding for new approaches in several issues. One challenge is the analysis of such systems that generate huge amounts of (continuously generated) data, potentially containing valuable information useful for several use cases, such as knowledge generation, key performance indicator (KPI) optimization, diagnosis, predication, feedback to design or decision support. This work presents a review of Big Data analysis in smart manufacturing systems. It includes the status quo in research, innovation and development, next challenges, and a comprehensive list of potential use cases and exploitation possibilities.
文摘As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.
基金The Deanship of ScientificResearch (DSR)at King Abdulaziz University,Jeddah,Saudi Arabia has funded this project,under Grant No. (FP-205-43).
文摘The outbreak of the pandemic,caused by Coronavirus Disease 2019(COVID-19),has affected the daily activities of people across the globe.During COVID-19 outbreak and the successive lockdowns,Twitter was heavily used and the number of tweets regarding COVID-19 increased tremendously.Several studies used Sentiment Analysis(SA)to analyze the emotions expressed through tweets upon COVID-19.Therefore,in current study,a new Artificial Bee Colony(ABC)with Machine Learning-driven SA(ABCMLSA)model is developed for conducting Sentiment Analysis of COVID-19 Twitter data.The prime focus of the presented ABCML-SA model is to recognize the sentiments expressed in tweets made uponCOVID-19.It involves data pre-processing at the initial stage followed by n-gram based feature extraction to derive the feature vectors.For identification and classification of the sentiments,the Support Vector Machine(SVM)model is exploited.At last,the ABC algorithm is applied to fine tune the parameters involved in SVM.To demonstrate the improved performance of the proposed ABCML-SA model,a sequence of simulations was conducted.The comparative assessment results confirmed the effectual performance of the proposed ABCML-SA model over other approaches.
基金Project(50374079) supported by the National Natural Science Foundation of China
文摘It is difficult to detect the anomalies whose matching relationship among some data attributes is very different from others’ in a dataset. Aiming at this problem, an approach based on wavelet analysis for detecting and amending anomalous samples was proposed. Taking full advantage of wavelet analysis’ properties of multi-resolution and local analysis, this approach is able to detect and amend anomalous samples effectively. To realize the rapid numeric computation of wavelet translation for a discrete sequence, a modified algorithm based on Newton-Cores formula was also proposed. The experimental result shows that the approach is feasible with good result and good practicality.
文摘We used both correlation and covariance-principal component analysis (PCA) to classify the same absorption-reflectance data collected from 13 different polymeric fabric materials that was obtained using Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR). The application of the two techniques, though similar, yielded results that represent different chemical properties of the polymeric substances. On one hand, correlation-PCA enabled the classification of the fabric materials according to the organic functional groups of their repeating monomer units. On the other hand, covariance-PCA was used to classify the fabric materials primarily according to their origins;natural (animal or plant) or synthetic. Hence besides major chemical functional groups of the repeat units, it appears covariance-PCA is also sensitive to other characteristic chemical (inorganic and/or organic) or biochemical material inclusions that are found in different samples. We therefore recommend the application of both covariance-PCA and correlation-PCA on datasets, whenever applicable, to enable a broader classification of spectroscopic information through data mining and exploration.
文摘Social media is an essential component of our personal and professional lives. We use it extensively to share various things, including our opinions on daily topics and feelings about different subjects. This sharing of posts provides insights into someone’s current emotions. In artificial intelligence (AI) and deep learning (DL), researchers emphasize opinion mining and analysis of sentiment, particularly on social media platforms such as Twitter (currently known as X), which has a global user base. This research work revolves explicitly around a comparison between two popular approaches: Lexicon-based and Deep learning-based Approaches. To conduct this study, this study has used a Twitter dataset called sentiment140, which contains over 1.5 million data points. The primary focus was the Long Short-Term Memory (LSTM) deep learning sequence model. In the beginning, we used particular techniques to preprocess the data. The dataset is divided into training and test data. We evaluated the performance of our model using the test data. Simultaneously, we have applied the lexicon-based approach to the same test data and recorded the outputs. Finally, we compared the two approaches by creating confusion matrices based on their respective outputs. This allows us to assess their precision, recall, and F1-Score, enabling us to determine which approach yields better accuracy. This research achieved 98% model accuracy for deep learning algorithms and 95% model accuracy for the lexicon-based approach.
基金funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University,through the Research Funding Program(Grand No.FRP-1440-32).
文摘:Strabismus is a medical condition that is defined as the lack of coordination between the eyes.When Strabismus is detected at an early age,the chances of curing it are higher.The methods used to detect strabismus and measure its degree of deviation are complex and time-consuming,and they always require the presence of a physician.In this paper,we present a method of detecting strabismus and measuring its degree of deviation using videos of the patient’s eye region under a cover test.Our method involves extracting features from a set of training videos(training corpora)and using them to build a classifier.A decision tree(ID3)is built using labeled cases from actual strabismus diagnosis.Patterns are extracted from the corresponding videos of patients,and an association between the extracted features and actual diagnoses is established.Matching Rules from the correlation plot are used to predict diagnoses for future patients.The classifier was tested using a set of testing videos(testing corpora).The results showed 95.9%accuracy,4.1%were light cases and could not be detected correctly from the videos,half of them were false positive and the other half was false negative.
