In dynamic e-commerce environments, researchers strive to understand users' interests and behaviors to enhance personalized product recommendations. Traditional collaborative filtering (CF) algorithms have encountered computational challenges such as similarity errors and user rating habits. This research addresses these issues by emphasizing user profiling techniques. This article proposes an innovative user profile updating technique that explores the key components of user profile (basic information, behavior, and domain knowledge). An enhanced kernel fuzzy mean clustering algorithm constructs a dynamic user portrait based on domain knowledge mapping. This dynamic portrait is combined with e-commerce personalized recommendation to improve the accuracy of inferring user interests, thus facilitating accurate recommendation on the platform. The method proposed in this article greatly improves the overall performance and provides strong support for developing smarter and more personalized e-commerce product recommendation systems.
{"title":"Personalized Recommendation Method of E-Commerce Products Based on In-Depth User Interest Portraits","authors":"Jingyi Li, Shaowu Bao","doi":"10.4018/ijitwe.335123","DOIUrl":"https://doi.org/10.4018/ijitwe.335123","url":null,"abstract":"In dynamic e-commerce environments, researchers strive to understand users' interests and behaviors to enhance personalized product recommendations. Traditional collaborative filtering (CF) algorithms have encountered computational challenges such as similarity errors and user rating habits. This research addresses these issues by emphasizing user profiling techniques. This article proposes an innovative user profile updating technique that explores the key components of user profile (basic information, behavior, and domain knowledge). An enhanced kernel fuzzy mean clustering algorithm constructs a dynamic user portrait based on domain knowledge mapping. This dynamic portrait is combined with e-commerce personalized recommendation to improve the accuracy of inferring user interests, thus facilitating accurate recommendation on the platform. The method proposed in this article greatly improves the overall performance and provides strong support for developing smarter and more personalized e-commerce product recommendation systems.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139144885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The research in this article aims to consider low-carbon factors, through reasonable vehicle allocation and optimization of distribution routes, to achieve high satisfaction and low total cost, and to provide an optimized solution for fresh food distribution companies. In this article, cargo damage cost, energy cost, and carbon emission cost are added to the total cost, and customer satisfaction constraints based on time and quality are added, respectively, to construct a multi-vehicle cold chain VRP model under the low-carbon perspective. In order to obtain a good initial path method, a good chromosome is generated and added to the initial chromosome population according to the constraints of the vehicle type and time window, and the local elite retention strategy is combined to speed up the population convergence. Finally, taking the data of A Fresh Food Company as an example, the MATLAB software is used to realize the programming, which verifies the validity and superiority of the multi-vehicle cold chain VRP model under the low-carbon perspective.
本文的研究旨在考虑低碳因素，通过合理配置车辆和优化配送路线，实现高满意度和低总成本，为生鲜食品配送企业提供优化方案。本文在总成本中加入了货损成本、能源成本和碳排放成本，并分别加入了基于时间和质量的客户满意度约束，构建了低碳视角下的多车辆冷链 VRP 模型。为了获得良好的初始路径方法，根据车辆类型和时间窗的约束条件，生成良好的染色体并添加到初始染色体群中，同时结合局部精英保留策略，加快群体收敛速度。最后，以 A 生鲜食品公司的数据为例，利用 MATLAB 软件实现编程，验证了低碳视角下多车冷链 VRP 模型的有效性和优越性。
{"title":"Research on VRP Model Optimization of Cold Chain Logistics Under Low-Carbon Constraints","authors":"Ruixue Ma, Qiang Zhu","doi":"10.4018/ijitwe.335036","DOIUrl":"https://doi.org/10.4018/ijitwe.335036","url":null,"abstract":"The research in this article aims to consider low-carbon factors, through reasonable vehicle allocation and optimization of distribution routes, to achieve high satisfaction and low total cost, and to provide an optimized solution for fresh food distribution companies. In this article, cargo damage cost, energy cost, and carbon emission cost are added to the total cost, and customer satisfaction constraints based on time and quality are added, respectively, to construct a multi-vehicle cold chain VRP model under the low-carbon perspective. In order to obtain a good initial path method, a good chromosome is generated and added to the initial chromosome population according to the constraints of the vehicle type and time window, and the local elite retention strategy is combined to speed up the population convergence. Finally, taking the data of A Fresh Food Company as an example, the MATLAB software is used to realize the programming, which verifies the validity and superiority of the multi-vehicle cold chain VRP model under the low-carbon perspective.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138964388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
To overcome limitations in existing methods for sentiment analysis of tourism reviews, the authors propose an image-text multimodal sentiment analysis method (TBGAV). It consists of three modules: image sentiment extraction, text sentiment extraction, and image-text fusion. The image sentiment extraction module employs a pre-trained VGG19 model to capture sentiment features. The text sentiment extraction module utilizes the tiny bidirectional encoder representations from transformers (TinyBERT) model, incorporating the bidirectional recurrent neural network and attention (BiGRU-Attention) module for deeper sentiment semantics. The image-text fusion module employs the dual linear fusion approach to correlate image-text links and the maximum decision-making approach for high-precision sentiment prediction. TBGAV achieves superior performance on the Yelp dataset with accuracy, recall rates, and F1 scores of 77.51%, 78.01%, and 78.34%, respectively, outperforming existing methods. Accordingly, TBGAV is expected to help improve travel-related recommender systems and marketing strategies.
{"title":"A TBGAV-Based Image-Text Multimodal Sentiment Analysis Method for Tourism Reviews","authors":"Ke Zhang, Shunmin Wang, Yuyuan Yu","doi":"10.4018/ijitwe.334595","DOIUrl":"https://doi.org/10.4018/ijitwe.334595","url":null,"abstract":"To overcome limitations in existing methods for sentiment analysis of tourism reviews, the authors propose an image-text multimodal sentiment analysis method (TBGAV). It consists of three modules: image sentiment extraction, text sentiment extraction, and image-text fusion. The image sentiment extraction module employs a pre-trained VGG19 model to capture sentiment features. The text sentiment extraction module utilizes the tiny bidirectional encoder representations from transformers (TinyBERT) model, incorporating the bidirectional recurrent neural network and attention (BiGRU-Attention) module for deeper sentiment semantics. The image-text fusion module employs the dual linear fusion approach to correlate image-text links and the maximum decision-making approach for high-precision sentiment prediction. TBGAV achieves superior performance on the Yelp dataset with accuracy, recall rates, and F1 scores of 77.51%, 78.01%, and 78.34%, respectively, outperforming existing methods. Accordingly, TBGAV is expected to help improve travel-related recommender systems and marketing strategies.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138594198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The combination of traditional basketball footwork mobile teaching and AI will become a hot spot in basketball footwork research. This article used a deep learning (DL) unsupervised transfer method: Convolutional neural networks are used to extract source and target domain samples for transfer learning. Feature extraction is performed on the data, and the impending action of a basketball player is predicted. Meanwhile, the unsupervised human action transfer method is studied to provide new ideas for basketball footwork action series data modeling. Finally, the theoretical framework of DL unsupervised transfer learning is reviewed. Its principle is explored and applied in the teaching of basketball footwork. The results show that convolutional neural networks can predict players' movement trajectories, unsupervised training using network data dramatically increases the variety of actions during training. The classification accuracy of the transfer learning method is high, and it can be used for the different basketball footwork in the corresponding stage of the court.
{"title":"Basketball Footwork and Application Supported by Deep Learning Unsupervised Transfer Method","authors":"Yu Feng, Hui Sun","doi":"10.4018/ijitwe.334365","DOIUrl":"https://doi.org/10.4018/ijitwe.334365","url":null,"abstract":"The combination of traditional basketball footwork mobile teaching and AI will become a hot spot in basketball footwork research. This article used a deep learning (DL) unsupervised transfer method: Convolutional neural networks are used to extract source and target domain samples for transfer learning. Feature extraction is performed on the data, and the impending action of a basketball player is predicted. Meanwhile, the unsupervised human action transfer method is studied to provide new ideas for basketball footwork action series data modeling. Finally, the theoretical framework of DL unsupervised transfer learning is reviewed. Its principle is explored and applied in the teaching of basketball footwork. The results show that convolutional neural networks can predict players' movement trajectories, unsupervised training using network data dramatically increases the variety of actions during training. The classification accuracy of the transfer learning method is high, and it can be used for the different basketball footwork in the corresponding stage of the court.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138617584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of science and technology, edge computing and big data privacy protection are more and more widely used in various fields. The application of big data privacy protection based on edge computing in the prediction of sport trajectories for martial arts training shows good performance and privacy protection. Edge computing can process and analyze data in real time to improve the accuracy and efficiency of sport trajectory prediction. Big data privacy protection can ensure the security of athletes' personal information and training data and prevent data leakage and misuse. However, existing related works still have some deficiencies in data processing speed, accuracy, and privacy protection. In this paper, the authors address these issues and propose an edge computing-based big data privacy protection method to improve the accuracy and security of sport trajectory prediction for martial arts training.
{"title":"Application of Big Data Privacy Protection Based on Edge Computing in the Prediction of Martial Arts Training Movement Trajectory","authors":"Xing Li, ZhiYing Cui, FeiFei Zhang, Li Li","doi":"10.4018/ijitwe.334226","DOIUrl":"https://doi.org/10.4018/ijitwe.334226","url":null,"abstract":"With the development of science and technology, edge computing and big data privacy protection are more and more widely used in various fields. The application of big data privacy protection based on edge computing in the prediction of sport trajectories for martial arts training shows good performance and privacy protection. Edge computing can process and analyze data in real time to improve the accuracy and efficiency of sport trajectory prediction. Big data privacy protection can ensure the security of athletes' personal information and training data and prevent data leakage and misuse. However, existing related works still have some deficiencies in data processing speed, accuracy, and privacy protection. In this paper, the authors address these issues and propose an edge computing-based big data privacy protection method to improve the accuracy and security of sport trajectory prediction for martial arts training.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139224230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the development of society, the education goal of art and design education for students is constantly changing, focusing more on students' professional and practical abilities, but the traditional teaching methods cannot provide the conditions needed for teaching. In this article, the three parts of art design classroom atmosphere before, during and after class, and according to the characteristics and needs of each part into artificial intelligence technology to build a smart classroom system. The experimental results show that AI technologies can improve the efficiency of classroom management, and the information obtained from the emotional score data can help teachers understand and master the classroom teaching situation, and analyze the shortcomings of classroom teaching content based on the comprehensive classroom emotional score. In addition, AI technology can also enrich classroom content, diversify teaching modes, increase the interaction space between teachers and students and students, and improve the teaching quality of art design classrooms.
{"title":"Classroom Design and Application of Art Design Education Based on Artificial Intelligence","authors":"Yawen Zhao, Licheng Gao","doi":"10.4018/ijitwe.334008","DOIUrl":"https://doi.org/10.4018/ijitwe.334008","url":null,"abstract":"With the development of society, the education goal of art and design education for students is constantly changing, focusing more on students' professional and practical abilities, but the traditional teaching methods cannot provide the conditions needed for teaching. In this article, the three parts of art design classroom atmosphere before, during and after class, and according to the characteristics and needs of each part into artificial intelligence technology to build a smart classroom system. The experimental results show that AI technologies can improve the efficiency of classroom management, and the information obtained from the emotional score data can help teachers understand and master the classroom teaching situation, and analyze the shortcomings of classroom teaching content based on the comprehensive classroom emotional score. In addition, AI technology can also enrich classroom content, diversify teaching modes, increase the interaction space between teachers and students and students, and improve the teaching quality of art design classrooms.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139248998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to consolidate the poverty alleviation achievements of impoverished counties, villages, and households, it is necessary to establish and improve stable poverty alleviation mechanisms. This article takes the Chengde region as the research object, and based on a large number of domestic and foreign poverty alleviation literature, combined with relevant poverty alleviation theories, uses fuzzy algorithms under big data to study and analyze the long-term mechanism of poverty alleviation and return prevention in the Chengde region. A multi classifier model with limited fuzzy rules is proposed to address the issues of low efficiency and long modeling time in existing fuzzy rule classification algorithms. When minimizing the cost function during model training, the cost function is fuzzy, thereby improving efficiency. The results indicate that the long-term poverty alleviation mechanism in Chengde from the perspective of fuzzy algorithm big data has profound strategic and theoretical significance for poverty alleviation.
{"title":"Application of Long-Term Poverty Alleviation Mechanism in Chengde From the Perspective of Big Data Based on Computational Neural Model Fuzzy Algorithm","authors":"Yanjie Zhu, Chunzheng Fu","doi":"10.4018/ijitwe.333897","DOIUrl":"https://doi.org/10.4018/ijitwe.333897","url":null,"abstract":"In order to consolidate the poverty alleviation achievements of impoverished counties, villages, and households, it is necessary to establish and improve stable poverty alleviation mechanisms. This article takes the Chengde region as the research object, and based on a large number of domestic and foreign poverty alleviation literature, combined with relevant poverty alleviation theories, uses fuzzy algorithms under big data to study and analyze the long-term mechanism of poverty alleviation and return prevention in the Chengde region. A multi classifier model with limited fuzzy rules is proposed to address the issues of low efficiency and long modeling time in existing fuzzy rule classification algorithms. When minimizing the cost function during model training, the cost function is fuzzy, thereby improving efficiency. The results indicate that the long-term poverty alleviation mechanism in Chengde from the perspective of fuzzy algorithm big data has profound strategic and theoretical significance for poverty alleviation.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139247968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Basketball, as an offensive and defensive game centered around high altitude, has become an international mass competitive sport. Traditional methods cannot comprehensively evaluate the future potential of players, nor can they simply add up individual competitive abilities to judge the overall competitive performance of a team. To address these issues, this article proposes a video-based RBF neural network competitive scoring method, which analyzes players' past sports behavior, captures every subtle difference in their abilities, and achieves objective evaluation of players' competitive performance. Through comparative experiments, the accuracy of the test results is improved by about 5% compared to conventional RBF methods. This indicates that the improved RBF neural network designed in this article has significantly better prediction performance than traditional convolutional neural networks. This study provides a new method for evaluating the competitive performance of basketball players and has important guiding significance for basketball training and skill enhancement.
{"title":"The Rating of Basketball Players' Competitive Performance Based on RBF-EVA Method","authors":"Jian Jia, Hua Chen","doi":"10.4018/ijitwe.334018","DOIUrl":"https://doi.org/10.4018/ijitwe.334018","url":null,"abstract":"Basketball, as an offensive and defensive game centered around high altitude, has become an international mass competitive sport. Traditional methods cannot comprehensively evaluate the future potential of players, nor can they simply add up individual competitive abilities to judge the overall competitive performance of a team. To address these issues, this article proposes a video-based RBF neural network competitive scoring method, which analyzes players' past sports behavior, captures every subtle difference in their abilities, and achieves objective evaluation of players' competitive performance. Through comparative experiments, the accuracy of the test results is improved by about 5% compared to conventional RBF methods. This indicates that the improved RBF neural network designed in this article has significantly better prediction performance than traditional convolutional neural networks. This study provides a new method for evaluating the competitive performance of basketball players and has important guiding significance for basketball training and skill enhancement.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139251393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The traditional grid did not consider the dynamic characteristics of the big data of innovation and entrepreneurship education. The grid based quantitative evaluation model of analytical AI teaching information based on adaptive identification and weighting algorithm is gradually applied to the daily operating system of innovation and entrepreneurship education. This article studies the application of adaptive recognition weighting algorithm in grid analysis of innovation and entrepreneurship education in domestic vocational colleges, and proposes an AI teaching model of grid analysis based on adaptive recognition weighting algorithm and online analysis of innovation and entrepreneurship education intelligence in colleges and universities. The results show that the innovation and entrepreneurship education model in colleges and universities based on grid analysis network teaching and adaptive recognition weighting algorithm can efficiently and intelligently diagnose students' teaching data, and achieve the innovation of big data analysis technology in colleges and universities.
{"title":"Application of Big Data in Entrepreneurship and Innovation Education for Higher Vocational Teaching","authors":"Long Chen, Jiang He","doi":"10.4018/ijitwe.333898","DOIUrl":"https://doi.org/10.4018/ijitwe.333898","url":null,"abstract":"The traditional grid did not consider the dynamic characteristics of the big data of innovation and entrepreneurship education. The grid based quantitative evaluation model of analytical AI teaching information based on adaptive identification and weighting algorithm is gradually applied to the daily operating system of innovation and entrepreneurship education. This article studies the application of adaptive recognition weighting algorithm in grid analysis of innovation and entrepreneurship education in domestic vocational colleges, and proposes an AI teaching model of grid analysis based on adaptive recognition weighting algorithm and online analysis of innovation and entrepreneurship education intelligence in colleges and universities. The results show that the innovation and entrepreneurship education model in colleges and universities based on grid analysis network teaching and adaptive recognition weighting algorithm can efficiently and intelligently diagnose students' teaching data, and achieve the innovation of big data analysis technology in colleges and universities.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139253964","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study introduces the ‘SARIMA Improved Model + Pearson Correlation Coefficient' approach to predict the demand for big data jobs in Jiangsu Province schools from January 2016 to December 2019. It also explores the matching between demand and supply in universities. The model is fault-tolerant, offers fast predictions, and addresses the disconnect between college talent training and teacher demand. The SARIMA-BP model predicts the trend of big data teacher demand in Jiangsu Province. The model, though untested in recruitment data prediction, with a large database, achieves root mean square error of 7.66, indicating high precision and reliability. Based on matching research and the local big data education industry in Jiangsu Province, countermeasures and suggestions are presented under the “one body, two wings, and one tail” framework. This concise summary highlights the research's core components and objectives.
{"title":"Matching Prediction of Teacher Demand and Training Based on SARIMA Model Based on Neural Network","authors":"Jianliu Zhu","doi":"10.4018/ijitwe.333637","DOIUrl":"https://doi.org/10.4018/ijitwe.333637","url":null,"abstract":"This study introduces the ‘SARIMA Improved Model + Pearson Correlation Coefficient' approach to predict the demand for big data jobs in Jiangsu Province schools from January 2016 to December 2019. It also explores the matching between demand and supply in universities. The model is fault-tolerant, offers fast predictions, and addresses the disconnect between college talent training and teacher demand. The SARIMA-BP model predicts the trend of big data teacher demand in Jiangsu Province. The model, though untested in recruitment data prediction, with a large database, achieves root mean square error of 7.66, indicating high precision and reliability. Based on matching research and the local big data education industry in Jiangsu Province, countermeasures and suggestions are presented under the “one body, two wings, and one tail” framework. This concise summary highlights the research's core components and objectives.","PeriodicalId":51925,"journal":{"name":"International Journal of Information Technology and Web Engineering","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2023-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139271524","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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