Forthcoming and Online First Articles

International Journal of Information Technology and Management

International Journal of Information Technology and Management (IJITM)

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International Journal of Information Technology and Management (26 papers in press)

Regular Issues

  • Multilevel secure storage method of electronic documents based on Hash function   Order a copy of this article
    by Runhua Miao 
    Abstract: In order to improve the storage security and efficiency of electronic documents, a new multi-level secure storage method of electronic documents based on Hash function is proposed in this paper. Firstly, the vector space model is constructed, and the electronic document data is divided into fixed size data blocks by using the idea of linear segmentation. Then, after normalisation, Hash function is used to encrypt each data block. Finally, according to the encryption results, the compressed sensing method is used for multi-level secure storage of electronic documents. The experimental results show that, compared with the traditional storage methods, this method has strong encryption performance, and the maximum intrusion rate is no more than 0.5%, which can ensure the security of electronic documents. This method can improve the storage efficiency of electronic documents.
    Keywords: Hash function; electronic document; multi-level security storage; data segmentation.
    DOI: 10.1504/IJITM.2022.10051753
     
  • An Enterprise financial credit risk measurement method based on differential evolution algorithm   Order a copy of this article
    by Lixia Du, Xin An 
    Abstract: In order to reduce the time cost and risk misjudgement rate of financial information risk measurement, this paper proposes a new enterprise financial credit risk measurement method based on differential evolution algorithm. Firstly, after preprocessing the enterprise financial credit risk data and determining the location of the clustering centre, a differential evolution automatic clustering model is constructed. Secondly, according to the clustering results, the differential evolution algorithm is used to measure the basic process of enterprise financial credit risk. Finally, the improved differential evolution algorithm is used for iterative measurement to achieve enterprise financial credit risk data measurement. The experimental results show that the time cost of the proposed method for enterprise financial credit risk measurement can be controlled within 0.4 s, and the error rate is not more than 1% under the condition of 1,000 data.
    Keywords: differential evolution algorithm; corporate finance; credit risks; measurement method.
    DOI: 10.1504/IJITM.2022.10051754
     
  • Performance evaluation method of human resource management based on Chaotic Algorithm.   Order a copy of this article
    by Lina Si, Zhanlei Shang 
    Abstract: This paper proposes a new method of HR management performance evaluation based on chaotic algorithm. First of all, to improve the evaluation performance as the research goal, design the guiding principles of evaluation. Second, under the guidance of the principle, the multi type data of HR management are collected, and the chaotic algorithm is used to extract the characteristics of the data. Finally, after constructing the index system, calculate the weight and construct the evaluation function to complete the overall management performance evaluation. The test results show that the evaluation performance of the method has been improved. On the basis of shortening the evaluation time, the evaluation accuracy has been improved to 97.68%.
    Keywords: chaotic algorithm; human resources; management performance evaluation; index system.
    DOI: 10.1504/IJITM.2022.10051755
     
  • A data integrity detection method for accounting informatization based on homomorphic hash function   Order a copy of this article
    by Zhao Guang, Zhi Li 
    Abstract: In order to solve the problems of low data detection accuracy and high detection time overhead, this paper proposes an accounting information data integrity detection method based on homomorphic hash function. First, the accounting data is collected by data mining method and the strong relevance of the data is determined by association rules. Then, set the distance matrix to determine the data key points, match the niche factor between the data key points, and complete the feature extraction. Finally, the binary code is used to mark the accounting information data, and the anti-collision of homomorphic hash function is used to complete the projection of accounting data, so as to realise the data integrity detection. The results show that the detection accuracy of this method is up to 98%, and the detection time overhead is within 4S, which shows that this method can effectively improve the integrity detection effect.
    Keywords: homomorphic hash function; accounting informatisation; data detection; integrity: association rules.
    DOI: 10.1504/IJITM.2022.10051756
     
  • New business management model of enterprises based on data-driven   Order a copy of this article
    by Xiaofeng Zhang 
    Abstract: In the current enterprise management mode, there are problems such as low efficiency of enterprise management data processing and reducing the economic benefits of enterprises, which affect the rapid development of enterprises. In order to solve this problem, this paper studies the new business management model of enterprises based on data-driven. Build a data-driven enterprise management mode framework, integrate enterprise management data with KNN algorithm, and calculate user access trust and reliability values with trust management model to improve data processing efficiency and data security. Based on the digital processing of enterprise management data, the development strategy of new business management mode is given. The experimental results show that after applying the management mode designed in this paper, the maximum profit of the enterprise can reach 20.5 million yuan, and the maximum value of the enterprise data processing time is only 6.03 s, which proves that the designed management mode is more efficient for the enterprise management data processing, and can effectively improve the enterprise economic income, and has certain practical application value.
    Keywords: data-driven; business management model; data integration; KNN algorithm; trust management model; development strategy.
    DOI: 10.1504/IJITM.2022.10051929
     
  • A Recognition method of abnormal learning behavior in MOOC online education based on background subtraction   Order a copy of this article
    by Hongmei Wan 
    Abstract: In order to overcome the problems of high time-consuming and poor recognition accuracy of learning behaviour recognition, this paper proposes an abnormal learning behaviour recognition method for MOOC online education based on background subtraction. Firstly, the characteristics of students’ abnormal learning behaviours are collected and extracted. Then, the background difference algorithm is used to obtain the foreground object and background of the learning image, and the image pixels are classified. Finally, the mean background method is used to obtain the learning background, the abnormal behaviour recognition classifier is designed, and the background subtraction method is used to realise the abnormal learning behaviour recognition. The results show that the recognition accuracy of this method is as high as 98.32%, the recognition time is only 0.52 s, and the recognition recall rate is as high as 96.7%, indicating that this method can improve the recognition effect of abnormal learning behaviour.
    Keywords: background subtraction; binarisation treatment; mean background method; background difference method; online education.
    DOI: 10.1504/IJITM.2022.10051930
     
  • An evaluation method of government digital service quality based on big data   Order a copy of this article
    by Siguang Dai, Zhongming Tang, Ling Zhou 
    Abstract: Aiming at the problems of low accuracy and poor reliability of government service quality evaluation, this paper proposes a government digital service quality evaluation method based on big data. Firstly, the tree structure of index selection was obtained by big data analysis method, and the service quality evaluation index was selected. Secondly, the factor analysis method is used to analyse the reasonability of each evaluation factor and construct the evaluation system. Secondly, it obtains the government digital service data, and finally calculates the weight of service quality evaluation index. According to the clustering of big data, the fuzzy language is integrated and processed to realise the evaluation of government digital service quality. The results show that the evaluation time of the proposed method is not more than 29 s, and the consistency coefficient value of quality evaluation accuracy is 0.79. It has high evaluation efficiency and accuracy.
    Keywords: big data; government digital service; quality evaluation; evaluation index; fuzzy logic.
    DOI: 10.1504/IJITM.2022.10051931
     
  • A Performance evaluation method of new business model based on grey correlation algorithm   Order a copy of this article
    by Yan Wang 
    Abstract: A new business model performance evaluation method based on grey correlation algorithm is designed to solve the problems of large evaluation error and low key of screening indicators in the new business model performance evaluation. First, analyse the new business model and screen the performance evaluation indicators of the new business model; then, the clustering algorithm is used to determine the cluster family of each index, extract the performance evaluation index characteristics of the new business model, and construct the performance evaluation index system. Finally, the grey correlation algorithm is used to determine the grey correlation degree between the indicators, quantify the evaluation indicators, build a grey correlation model for the performance evaluation of the new business model, and realise the performance evaluation. The experimental results show that the evaluation error of the proposed evaluation method is only 2%, and the key degree of the selected index is higher than 90%, which is increased by more than 5%. This method has higher practical application value.
    Keywords: grey correlation algorithm; new business model; performance evaluation; clustering algorithm; main sequence; correlation sequence; consistency check.
    DOI: 10.1504/IJITM.2022.10052229
     
  • Research on digital English teaching materials recommendation based on improved machine learning   Order a copy of this article
    by Miao Ma 
    Abstract: In order to overcome the problems of low accuracy, time-consuming and low user satisfaction in traditional methods, a digital English teaching materials recommendation method based on improved machine learning is proposed. Firstly, use web crawlers to obtain the data of digital English teaching platform, and use Word2vec model data for training to obtain the data feature vector. Secondly, K-means algorithm is used to cluster users according to feature vectors, and multi-Markov chains are used to predict user interest. Finally, the decision tree algorithm in machine learning is improved on the gradient boosting framework, and the digital English teaching materials are recommended by using the improved algorithm and the user interest prediction results. The experimental results show that the accuracy of this method is more than 96%, the average time of digital English teaching materials recommendation is 76.1 ms, and the average user satisfaction is 96.6.
    Keywords: improved machine learning; digitisation; English teaching; data recommendation; multiple Markov chains; decision tree.
    DOI: 10.1504/IJITM.2022.10052230
     
  • The personalized classification of brand promotion information based on K-means algorithm   Order a copy of this article
    by Xi Li  
    Abstract: In order to improve the efficiency of personalised classification of brand promotion information and shorten the time of personalised classification, this paper proposes a personalised classification method of brand promotion information based on K-means algorithm. First, collect brand promotion information, and calculate the text relevance of brand promotion information through weighting factors; Secondly, the attribute division of extension information is carried out by using the three branch decision-making theory; then, the information features of brand promotion are extracted by capsule network; Finally, calculate the similarity between different brand promotion information, obtain the brand promotion information classification function, and realise the personalised classification of brand promotion information through k-means algorithm. The experimental results show that the classification accuracy of this method is 98.08%, and the time of personalised information classification is only 1.20 s, indicating that this method can effectively improve the efficiency of personalised classification of brand promotion information.
    Keywords: k-means algorithm; information attribute division; feature extraction; personalised classification of information.
    DOI: 10.1504/IJITM.2022.10052258
     
  • Study on marketing strategy innovation of mobile payment service under internet environment   Order a copy of this article
    by Xin Liu 
    Abstract: In order to overcome the problems of low efficiency, low user satisfaction and poor customer growth rate under the traditional marketing strategy, this paper studies the innovative strategy of mobile payment business marketing strategy under the internet environment. First of all, study the status quo of mobile payment business marketing in the internet environment, obtain mobile payment business data through questionnaire survey, and analyse the problems in mobile payment business marketing. Secondly, build a user profile of mobile payment business marketing, and classify user attributes, consumption characteristics and user activity through K-means clustering method; Finally, the marketing strategy is innovated from three aspects: product marketing, pricing marketing and channel marketing. The results show that the marketing benefit after the application of this strategy is 19.52 million yuan, the user satisfaction can reach 98.9%, and the customer growth rate can reach 21.3%, improving the marketing benefit of mobile payment business.
    Keywords: questionnaire survey; marketing strategy; user portrait; mobile payment service; user classification.
    DOI: 10.1504/IJITM.2023.10055226
     
  • A Data Mining Method Based on Label Mapping for Long-term and Short-term Browsing Behavior of Network Users   Order a copy of this article
    by Liu Xiangyuan 
    Abstract: In order to improve the speedup and recognition accuracy of the recognition process, this paper designs a data mining method based on label mapping for long-term and short-term browsing behaviour of network users. First, after removing the noise information in the behaviour sequence, calculate the similarity of behaviour characteristics. Then, multi-source behaviour data is mapped to the same dimension, and a behaviour label mapping layer and a behaviour data mining layer are established. Finally, the similarity of the tag matrix is calculated based on the similarity calculation results, and the mining results are output using SVM binary classification process. Experimental results show that the acceleration ratio of this method exceeds 0.9; area under curve receiver operating characteristic curve (AUC-ROC) value increases rapidly in a short time, and the maximum value can reach 0.95, indicating that the mining precision of this method is high.
    Keywords: long-term and short-term browsing behaviour; data mining; lifting small transformation method; data mapping; label mapping; SVM secondary classification.
    DOI: 10.1504/IJITM.2023.10055227
     
  • An evaluation of customer trust in e-commerce market based on entropy weight analytic hierarchy process   Order a copy of this article
    by Yan Liu 
    Abstract: In order to solve the problems of large generalisation error, low recall rate and low retrieval accuracy of customer evaluation information in traditional trust evaluation methods, an evaluation method of customer trust in e-commerce market based on entropy weight analytic hierarchy process was designed. Firstly, build an evaluation index system of customer trust in e-commerce market. Secondly, the customer trust matrix is established, and the index weight is calculated by using the analytic hierarchy process and entropy weight method. Finally, five-scale Likert method is used to analyse the indicator factors and establish a comment set, and the trust evaluation value is obtained by combining the indicator membership. The experiment shows that the maximum generalisation error of this method is only 0.029, the recall rate is 97.5%, and the retrieval accuracy of customer evaluation information is closer to 1.
    Keywords: e-commerce market; customer trust; trust evaluation; entropy weight method; analytic hierarchy process; index weight; degree of membership; commentary.
    DOI: 10.1504/IJITM.2023.10055229
     
  • Student’s classroom behavior recognition method based on abstract Hidden Markov model   Order a copy of this article
    by Guojuan Li 
    Abstract: In order to improve the standardisation of mutual information index, accuracy rate and recall rate of student classroom behaviour recognition method, this paper proposes a student’s classroom behaviour recognition method based on abstract hidden Markov model (HMM). After cleaning the students’ classroom behaviour data, improve the data quality through interpolation and standardisation, and then divide the types of students’ classroom behaviour. Then, in support vector machine, abstract HMM is used to calculate the output probability density of support vector machine. Finally, according to the characteristic interval of classroom behaviour, we can judge the category of behaviour characteristics. The experiment shows that normalised mutual information (NMI) index of this method is closer to one, and the maximum AUC-PR index can reach 0.82, which shows that this method can identify students’ classroom behaviour more effectively and reliably.
    Keywords: classroom behaviour; hidden Markov model; HMM; abstract space; behaviour recognition; probability density; behaviour category.
    DOI: 10.1504/IJITM.2023.10055231
     
  • Research on evaluation method of e-commerce platform customer relationship based on decision tree algorithm   Order a copy of this article
    by Quan Zhang, Bohan Liu 
    Abstract: In order to overcome the problems of poor evaluation accuracy and long evaluation time in traditional customer relationship evaluation methods, this study proposes a new customer relationship evaluation method for e-commerce platform based on decision tree algorithm. Firstly, analyse the connotation and characteristics of customer relationship; secondly, the importance of customer relationship in e-commerce platform is determined by using decision tree algorithm by selecting and dividing attributes according to the information gain results. Finally, the decision tree algorithm is used to design the classifier, the weighted sampling method is used to obtain the training samples of the base classifier, and the multi-period excess income method is used to construct the customer relationship evaluation function to achieve customer relationship evaluation. The experimental results show that the accuracy of the customer relationship evaluation results of this method is 99.8%, and the evaluation time is only 51 minutes.
    Keywords: decision tree algorithm; electronic commerce; customer relationship; multi-period excess income method; weighted sampling.
    DOI: 10.1504/IJITM.2023.10055319
     
  • Evaluation method of cross-border e-commerce supply chain innovation mode based on blockchain technology   Order a copy of this article
    by Jinxin Wang, Mingli Sun 
    Abstract: In view of the low evaluation accuracy of the effectiveness of cross-border e-commerce supply chain innovation model and the low correlation coefficient of innovation model influencing factors, the evaluation method of cross-border e-commerce supply chain innovation model based on blockchain technology is studied. First, analyse the operation mode of cross-border e-commerce supply chain, and determine the key factors affecting the innovation mode; Then, the comprehensive integration weighting method is used to analyse the factors affecting innovation and calculate the weight value; Finally, the blockchain technology is introduced to build an evaluation model for the supply chain innovation model and realise the evaluation of the cross-border e-commerce supply chain innovation model. The experimental results show that the evaluation accuracy of the proposed method is high, and the highest correlation coefficient of the influencing factors of innovation mode is about 0.99, which is feasible.
    Keywords: blockchain; cross border e-commerce; supply chain; innovation mode; inverse function; trust relationship.
    DOI: 10.1504/IJITM.2023.10055320
     
  • Quantitative evaluation method of ideological and political teaching achievements based on collaborative filtering algorithm   Order a copy of this article
    by Hua Wang 
    Abstract: In order to overcome the problems of large error, low evaluation accuracy and long evaluation time in traditional evaluation methods of ideological and political education, this paper designs a quantitative evaluation method of ideological and political education achievements based on collaborative filtering algorithm. First, the evaluation index system is constructed to divide the teaching achievement evaluation index data in a small scale; then, the quantised dataset is determined and the quantised index weight is calculated; finally, the collaborative filtering algorithm is used to generate a set with high similarity, construct a target index recommendation list, construct a quantitative evaluation function and solve the function value to complete the quantitative evaluation of teaching achievements. The results show that the evaluation error of this method is only 1.75%, the accuracy can reach 98%, and the time consumption is only 2.0 s, which shows that this method can improve the quantitative evaluation effect.
    Keywords: collaborative filtering; ideological and political education; quantitative analysis; Jaccard coefficient; neighbour set.
    DOI: 10.1504/IJITM.2023.10055321
     
  • The performance evaluation of teaching reform based on hierarchical multi task deep learning   Order a copy of this article
    by Jianlei Zhang  
    Abstract: The research goal is to solve the problems of low accuracy and long time existing in traditional teaching reform performance evaluation methods, a performance evaluation method of teaching reform based on hierarchical multi-task deep learning is proposed. Under the principle of constructing the evaluation index system, the evaluation indicator system should be constructed. The weight of the evaluation index is calculated through the analytic hierarchy process, and the calculation result of the evaluation weight is taken as the model input sample. A hierarchical multi-task deep learning model for teaching reform performance evaluation is built, and the final teaching reform performance score is obtained. Through relevant experiments, it is proved that compared with the experimental comparison method, this method has the advantages of high evaluation accuracy and short time, and can be further applied in relevant fields.
    Keywords: hierarchical multi-task deep learning; reform in education; performance evaluation; analytic hierarchy process; loss function.
    DOI: 10.1504/IJITM.2023.10055322
     
  • A Risk Identification Method for Abnormal Accounting Data Based on Weighted Random Forest   Order a copy of this article
    by Yan Shi  
    Abstract: In order to improve the identification accuracy, accuracy and time-consuming of traditional financial risk identification methods, this paper proposes a risk identification method for financial abnormal data based on weighted random forest. Firstly, SMOTE algorithm is used to collect abnormal financial data; secondly, the original accounting data is decomposed into features, and the features of abnormal data are extracted through random forests; then, the index weight is calculated according to the entropy weight method; finally, the negative gradient fitting is used to determine the loss function, and the weighted random forest method is used to solve the loss function value, and the recognition result is obtained. The results show that the identification accuracy of this method can reach 99.9%, the accuracy rate can reach 96.06%, and the time consumption is only 6.8 seconds, indicating that the risk identification effect of this method is good.
    Keywords: SMOTE algorithm; weighted random forest; loss function; negative gradient fitting.
    DOI: 10.1504/IJITM.2023.10055323
     
  • Risk assessment method of power grid construction project investment based on grey relational analysis   Order a copy of this article
    by Fulei Chen, Mingzhu Sun, Lei Shen 
    Abstract: In view of the problems of low accuracy, long time consuming and low efficiency of the existing engineering investment risk assessment method; this paper puts forward the investment risk assessment method of power grid construction project based on grey correlation analysis. Firstly, classify the risks of power grid construction project; secondly, determine the primary index and secondary index of investment risk assessment of power grid construction project; then construct the correlation coefficient matrix of power grid project investment risk to calculate the correlation degree and weight of investment risk index; finally, adopt the grey correlation analysis method to construct investment risk assessment function to realise investment risk assessment. The experimental results show that the average accuracy of evaluating the investment risk of power grid construction projects using the method is 95.08%, and the maximum time consuming is 49 s, which proves that the method has high accuracy, short time consuming and high evaluation efficiency.
    Keywords: relevance; grey correlation analysis; forward backward algorithm; correlation matrix; weight calculation.
    DOI: 10.1504/IJITM.2023.10055325
     
  • Online allocation of teaching resources for ideological and political courses in colleges and universities based on differential search algorithm   Order a copy of this article
    by Mingli Sun, Jinxin Wang 
    Abstract: In order to improve the classification accuracy and online allocation accuracy of teaching resources and shorten the allocation time, this paper proposes a new online allocation method of college ideological and political curriculum teaching resources based on differential search algorithm. Firstly, the feedback parameter model of teaching resources cleaning is constructed to complete the cleaning of teaching resources. Secondly, according to the results of anti-interference consideration, the linear feature extraction of ideological and political curriculum teaching resources is carried out. Finally, the online allocation objective function of teaching resources for ideological and political courses is constructed, and the differential search algorithm is used to optimise the objective function to complete the online allocation of resources. The experimental results show that this method can accurately classify the teaching resources of ideological and political courses, and can shorten the allocation time, with the highest allocation accuracy of 97%.
    Keywords: differential search algorithm; college ideological and political course; teaching resources; online allocation.
    DOI: 10.1504/IJITM.2023.10055326
     
  • Research on Construction of Police Online Teaching Platform based on Blockchain and IPFS Technology   Order a copy of this article
    by Qilei Wang 
    Abstract: Under the current framework of police online teaching, in order to effectively solve the lack of high-quality resources of the traditional platform, backward sharing technology, poor performance of the digital platform and the privacy problems faced by each subject in sharing. This paper designs and implements the online teaching platform based on blockchain and Interplanetary File System (IPFS). Based on the architecture of a "decentralized" police online teaching platform, the platform uses blockchain to store hashes of encrypted private information and user-set access control policies, while the real private information is stored in IPFS after encryption. In the realization of privacy information security storage at the same time to ensure the effective control of the user's own information. In order to achieve flexible rights management, the system classifies private information. In addition, the difficulties of police online teaching and training reform in the era of big data are solved one by one from the aspects of communication mode, storage facilities, incentive mechanism and confidentiality system, which effectively improves the stability and security of police online teaching.
    Keywords: Blockchain; IPFS Technology; Police; Online Teaching.
    DOI: 10.1504/IJITM.2023.10055945
     
  • An evaluation of English distance information teaching quality based on decision tree classification algorithm   Order a copy of this article
    by Xueqi Liu 
    Abstract: In order to overcome the problems of low evaluation accuracy and long evaluation time in traditional teaching quality evaluation methods, a method of English distance information teaching quality evaluation based on decision tree classification algorithm is proposed. Firstly, construct teaching quality evaluation indicators under different roles. Secondly, the information gain theory in decision tree classification algorithm is used to divide the attributes of teaching resources. Finally, the rough set theory is used to calculate the index weight and establish the risk evaluation index factor set. The result of teaching quality evaluation is obtained through fuzzy comprehensive evaluation method. The experimental results show that the accuracy rate of the teaching quality evaluation of this method can reach 99.2%, the recall rate of the English information teaching quality evaluation is 99%, and the time used for the English distance information teaching quality evaluation of this method is only 8.9 seconds.
    Keywords: Decision Tree Classification; Information gain theory; Rough set theory; Index weight; Membership Matrix.
    DOI: 10.1504/IJITM.2023.10056622
     
  • Research on fast mining of enterprise marketing investment data based on improved association rules   Order a copy of this article
    by Yinghui Liu, Xiaosi Xu, Qixing Yin 
    Abstract: Because of the problems of low mining precision and slow mining speed in traditional enterprise marketing investment data mining methods, a fast mining method for enterprise marketing investment databased on improved association rules is proposed. First, the enterprise marketing investment data is collected through the crawler framework, and then the collected data is cleaned. Then, the cleaned data features are extracted, and the correlation degree between features is calculated. Finally, according to the calculation results, all data items are used as constraints to reduce the number of frequent itemsets. A pruning strategy is designed in advance. Combined with the constraints, the Apriori algorithm of association rules is improved, and the improved algorithm is used to calculate all frequent itemsets, Obtain fast mining results of enterprise marketing investment data. The experimental results show that the proposed method is fast and accurate in data mining of enterprise marketing investment.
    Keywords: improve association rules; enterprise marketing investment; Crawler framework; correlation degree; Apriori algorithm.
    DOI: 10.1504/IJITM.2023.10057265
     
  • Users' satisfaction evaluation based on ISO standards for tourism and travel mobile applications   Order a copy of this article
    by Carolina Almeida, Bráulio Alturas 
    Abstract: This study is based on the results of a survey applied to a sample of 201 users which gave them an opportunity to identify points of improvement that will be used in a proposal for a new application. In addition, it aimed to obtain data that allows analysis metrics to be applied to international standard organisation (ISO) standards and attempts to understand why individuals use these applications, based on the technology acceptance model (TAM). From the results obtained, it was concluded that there was an average level of satisfaction with the applications of 4.22, on a scale ranging from 1 to 5. Based on the opinions of the respondents, it was also possible to select future improvements for the applications that could contribute to a new tourism and travel application. Finally, it was concluded that there is a direct link between content acceptance and quality.
    Keywords: application; user satisfaction; travel; tourism; rating; TAM; ISO standards; mobile applications.
    DOI: 10.1504/IJITM.2023.10062400
     

Special Issue on: LISS 2017 Emerging Trends, Issues and Challenges in Big Data and Its Implementation

  • Evolution of intellectual structure of data mining research based on keywords   Order a copy of this article
    by Yue Huang 
    Abstract: Data mining has made rapid progress in the past decade and detecting intellectual structure of data mining research is of great help to researchers. We retrieved 5380 papers, published in 11 leading journals of data mining defined by Google Scholar, from SCIE under Web of Science and Scopus databases between 2007 and 2016 to carry out bibliometric analysis. As indicated by the analysis on the evolution of keyword frequency, the research focus of data mining has shifted from such topics as association rule mining to large-scale complex networks. Matrices of high-frequency keywords were also built for different time periods, namely 2007 to 2016 for the whole picture during these years, 2007 to 2011 and 2012 to 2016 for two periods. Clustering results show there are four main data mining topics and the attention has been paid more to graph data mining and complex network analysis in the past 5 years.
    Keywords: data mining; intellectual structure; co-word analysis; clustering; evolution analysis.