Journal of Web Engineering最新文献_第4页

Integration of an Open Source Identity Management System in Educational Platforms 在教育平台中整合开源身份管理系统

IF 0.7 4区计算机科学

Journal of Web Engineering Pub Date : 2024-06-01 DOI: 10.13052/jwe1540-9589.2345

Enrique Barra;Alejandro Pozo;Sonsoles López-Pernas;Alvaro Alonso;Aldo Gordillo

引用次数: 0

Classification of Firewall Log Files with Different Algorithms and Performance Analysis of These Algorithms 使用不同算法对防火墙日志文件进行分类，并对这些算法进行性能分析

IF 0.7 4区计算机科学

Journal of Web Engineering Pub Date : 2024-06-01 DOI: 10.13052/jwe1540-9589.2344

Ebru Efeoğlu;Gurkan Tuna

{"title":"Classification of Firewall Log Files with Different Algorithms and Performance Analysis of These Algorithms","authors":"Ebru Efeoğlu;Gurkan Tuna","doi":"10.13052/jwe1540-9589.2344","DOIUrl":"10.13052/jwe1540-9589.2344","url":null,"abstract":"Classifying firewall log files allows analysing potential threats and deciding on appropriate rules to prevent them. Therefore, in this study, firewall log files are classified using different classification algorithms and the performance of the algorithms are evaluated using performance metrics. The dataset was prepared using the log files of a firewall. It was filtered to make it free from any personal data and consisted of 12 attributes in total and from these attributes the action attribute was selected as the class. In the performance evaluation, Simple Cart and NB tree algorithms made the best predictions, achieving an accuracy rate of 99.84%. Decision Stump had the worst prediction performance, achieving an accuracy rate of 79.68%. As the total number of instances belonging to each of the classes in the dataset was not equal, the Matthews correlation coefficient was also used as a performance metric in the evaluations. The Simple Cart, BF tree, FT tree, J48 and NB Tree algorithms achieved the highest average values. However, although the reset-both class was not predicted successfully by the others, the Simple Cart algorithm made the best predictions for it. The values of other performance metrics used in this study also support this conclusion. Therefore, the Simple Cart algorithm is recommended for use in classifying firewall log files. However, there is a need to develop a prefiltering and parsing approach to process different log files as each firewall brand creates and maintains log files in its own format. Therefore, in this study, a novel prefiltering and parsing approach has been proposed to process log files with different structures and create structured datasets using them.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 4","pages":"561-593"},"PeriodicalIF":0.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10634590","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Risk Score Computation for Android Mobile Applications Using the Twin k-NN Approach 使用双 k-NN 方法计算安卓移动应用的风险得分

IF 0.7 4区计算机科学

Journal of Web Engineering Pub Date : 2024-06-01 DOI: 10.13052/jwe1540-9589.2343

Mahmood Deypir;Toktam Zoughi

{"title":"Risk Score Computation for Android Mobile Applications Using the Twin k-NN Approach","authors":"Mahmood Deypir;Toktam Zoughi","doi":"10.13052/jwe1540-9589.2343","DOIUrl":"10.13052/jwe1540-9589.2343","url":null,"abstract":"The Android operating system has a dominant market for use within a wide range of devices. Along with the widespread growth of the use of the Android system and the development of a huge number of apps for this operating system, new malicious apps are released daily by adversaries, which are difficult to identify and deal with. This is due to them using sophisticated techniques and strikes. Although there are a diverse range of classification models and risk estimation metrics for identifying malware in this operating system, there is still a requirement for more effective approaches in this context. In this paper, we present a new algorithm to calculate the security risk score of Android apps, which can be used to identify malicious apps from benign ones. This algorithm uses a novel technique named twin \u0000<tex>$k$</tex>\u0000-nearest neighbor. In this technique, to estimate the security risk of an unknown app, its nearest neighbors to malicious apps and its nearest neighbors to normal apps are computed separately using an appropriate distance formula. Then, the security risk of the input app can be computed using a simple formulation. In this formulation, the average distances of both \u0000<tex>$k$</tex>\u0000-nearest malicious apps and \u0000<tex>$k$</tex>\u0000-nearest non-malicious apps to the input app are used. In this way, the proposed method can calculate a high security risk for malware and a lower security risk for goodware. Experimental evaluations on real datasets show that the proposed algorithm has better performance over the previously proposed ones in terms of detection rate, precision, recall, and f1-score.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 4","pages":"535-559"},"PeriodicalIF":0.7,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10634593","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141928642","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transformative Technologies in the Evaluation of a Vocational Education System 职业教育系统评估中的变革技术

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-03-01 DOI: 10.13052/jwe1540-9589.2324

Yanjun Zhang;Xiaoyu Sun;Jiangde Yu

{"title":"Transformative Technologies in the Evaluation of a Vocational Education System","authors":"Yanjun Zhang;Xiaoyu Sun;Jiangde Yu","doi":"10.13052/jwe1540-9589.2324","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2324","url":null,"abstract":"The increasing demand for vocational education has necessitated the presence of highly skilled teachers. This study presents a novel framework for the effective management of vocational college instructors' professional development through the utilization of advanced technologies. The system utilizes deep learning technology to analyze many data points, including academic achievements, teaching experience, student comments, and professional activities, in order to assess the performance and potential of teachers. The system evaluates both the positive and negative aspects, offers customized training programs, and enhances the delivery of instruction through the utilization of a generative language model. The effectiveness of the system is supported by a case study, which demonstrates enhancements in talent management, professional development, teaching quality, and student happiness. This proposed solution aims to improve vocational education by empowering educators and transforming the processes of evaluation, support, and guidance throughout their professional trajectories.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 2","pages":"275-298"},"PeriodicalIF":0.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504111","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SPARQL Optimization Using Re-ordering Joining Patterns with Surrogate Key Concept and Subset Patterns 使用重排序连接模式与代理关键概念和子集模式优化 SPARQL

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-03-01 DOI: 10.13052/jwe1540-9589.2334

Rupal Gupta;Sanjay Kumar Malik

{"title":"SPARQL Optimization Using Re-ordering Joining Patterns with Surrogate Key Concept and Subset Patterns","authors":"Rupal Gupta;Sanjay Kumar Malik","doi":"10.13052/jwe1540-9589.2334","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2334","url":null,"abstract":"Semantic web data resides on the web in the form of knowledge graphs known as RDF graphs and searching around the web has been always a crucial task. For the data retrieval of RDF data of the semantic web, SPARQL query language has been used which in turn is based on triple patterns and joins. Optimization of SPARQL query has been a problematic concern for decades due to the large amount of triple patterns associated with RDF data. Although several researchers have put a lot of effort into the optimization of SPARQL query, it is difficult to understand the concept from scratch due to its diversified nature. This paper analyses various optimization techniques for the SPARQL query used with the semantic web to process knowledge graphs. These techniques include join-based, heuristic-based, rule-based, and indexing-based approaches for optimization. This paper will help researchers in this domain to easily get into the core concept of SPARQL execution along with various optimization approaches used for query processing, which can help in various other domains like linked open data and information retrieval. In this paper, an optimization algorithm HSOA (hybrid SPARQL optimization algorithm) has been proposed, which comprises the features of index-based, cost-based, and triple reordering-based optimization approaches. The proposed hybrid algorithm has been designed specifically for n-triple RDF data, which comprises subset patterns, and surrogate key concepts. The results produced by the proposed algorithm are encouraging and have also been tested and compared with the benchmark dataset and SPARQL queries like LUBM, BSBM, and SP2Bench.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 3","pages":"393-430"},"PeriodicalIF":0.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547280","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Music Curriculum Research Using a Large Language Model, Cloud Computing and Data Mining Technologies 利用大语言模型、云计算和数据挖掘技术开展音乐课程研究

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-03-01 DOI: 10.13052/jwe1540-9589.2323

Yuting Shang

{"title":"Music Curriculum Research Using a Large Language Model, Cloud Computing and Data Mining Technologies","authors":"Yuting Shang","doi":"10.13052/jwe1540-9589.2323","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2323","url":null,"abstract":"This paper presents a method to enhance the scientific nature of the music curriculum model by integrating a large language model, cloud computing and data mining technology for the analysis of the music teaching curriculum model. To maintain the integrity of the mixing matrix while employing the frequency hopping frequency, the paper suggests dividing the mixing matrix into a series of sub-matrices along the vertical time axis. This approach transforms wideband music signal processing into a narrowband processing problem. Additionally, two hybrid matrix estimation algorithms are proposed in this paper using underdetermined conditions. Furthermore, utilizing the estimated mixing matrix and the detected time-frequency support domain, the paper employs the subspace projection algorithm for underdetermined blind separation of music signals in the time-frequency domain. This procedure, along with the integration of the estimated direction of arrival (DoA), enables the completion of frequency-hopping network station music signal sorting. Extensive simulation teaching demonstrates that the music curriculum model proposed in this paper, based on a large language model, cloud computing and data mining technologies, significantly enhances the quality of modern music teaching.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 2","pages":"251-273"},"PeriodicalIF":0.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504109","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of an Improved Convolutional Neural Network Algorithm in Text Classification 改进的卷积神经网络算法在文本分类中的应用

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-03-01 DOI: 10.13052/jwe1540-9589.2331

Jing Peng;Shuquan Huo

引用次数: 0

Data Lake Conceptualized Web Platform for Food Research Data Collection 数据湖概念化食品研究数据收集网络平台

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-03-01 DOI: 10.13052/jwe1540-9589.2333

Gi-taek An;Seyoung Oh;Eunhye Kim;Jung-min Park

{"title":"Data Lake Conceptualized Web Platform for Food Research Data Collection","authors":"Gi-taek An;Seyoung Oh;Eunhye Kim;Jung-min Park","doi":"10.13052/jwe1540-9589.2333","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2333","url":null,"abstract":"Food research is uniquely intertwined with everyday life and necessitates the utilization of big data. Within this domain, the research data consist of various forms and formats, encompassing biological experiment results, chemical analysis data, nutritional information, microbiological data, sensor data, images, and videos. This diversity stems from the integration of data from various subdomains within the larger field. With recent advancements in deep learning technology, the importance of data has grown significantly, resulting in increased reliance on data-driven research. Although specialized platforms for sharing and utilizing data have been established at the national level, particularly in the bioscience field, food research lacks a dedicated infrastructure and specialized data-sharing platforms. In this study, we develop a platform that leverages Hadoop-based distributed file systems to create a data lake. This platform enables data storage and sharing through a web-based interface. The distributed file system supports scalability by adding data nodes, making it an effective solution for capacity expansion. In addition, the web-based platform ensures high accessibility, allowing users access from anywhere, at any time, using any device. Finally, we introduce the establishment of a 1.8 PB Hadoop-based physical storage system and present an approach for building a highly accessible web platform with substantial utility.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 3","pages":"377-392"},"PeriodicalIF":0.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547279","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing English Language Education Through Big Data Analytics and Generative AI 通过大数据分析和生成式人工智能加强英语教育

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-03-01 DOI: 10.13052/jwe1540-9589.2322

Jianhua Liu

{"title":"Enhancing English Language Education Through Big Data Analytics and Generative AI","authors":"Jianhua Liu","doi":"10.13052/jwe1540-9589.2322","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2322","url":null,"abstract":"This research paper provides a comprehensive examination of the significant impact of big data analytics and generative artificial intelligence (GAI) on the field of English language education. Utilizing a meticulous framework rooted in the evolutionary network influence of big data, our study critically analyzes several aspects of student engagement, learning motivation, self-efficacy, and the existing disparities among learners. Our primary objective is to enhance students' active participation, intrinsic interest, and self-confidence in the context of English language learning, thus advancing their overall linguistic competence. To achieve these objectives, our study systematically integrates the concept of practice education with a multidisciplinary approach, leveraging the power of big data analysis and GAI, and reveals profound insights into student learning behaviors, preferences, and personalized educational needs. We employ advanced techniques for meticulous data processing and interpretation, empowering educators to make data-informed decisions and tailor pedagogical strategies to meet the unique requirements of each student. This data-driven pedagogical approach not only facilitates the implementation of effective teaching methodologies but also effectively addresses the disparities stemming from diverse student backgrounds, thereby fostering a more inclusive and personalized learning environment.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 2","pages":"227-249"},"PeriodicalIF":0.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504108","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches 通过集成信息检索和深度学习方法加强在线用户评论中的建议检测

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-03-01 DOI: 10.13052/jwe1540-9589.2335

Zahra Hadizadeh;Amin Nazari;Muharram Mansoorizadeh

{"title":"Enhancing Suggestion Detection in Online User Reviews through Integrated Information Retrieval and Deep Learning Approaches","authors":"Zahra Hadizadeh;Amin Nazari;Muharram Mansoorizadeh","doi":"10.13052/jwe1540-9589.2335","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2335","url":null,"abstract":"In the aftermath of the COVID-19 pandemic, using web platforms as a communication medium and decision-making tool in online commerce has become widely acknowledged. User-generated comments, reflecting positive and negative sentiments towards specific items, serve as invaluable indicators, offering recommendations for product and organizational improvements. Consequently, the extraction of suggestions from mined opinions can enhance the efficacy of companies and organizations in this domain. Prevailing research in suggestion mining predominantly employs rule-based methodologies and statistical classifiers, relying on manually identified features. However, a recent trend has emerged wherein researchers explore solutions grounded in deep learning tools and techniques. This study aims to employ information retrieval techniques for the automated identification of suggestions. To this end, various methodologies, including distance measurement approaches, multilayer perceptron neural networks, support vector machines, regression logistics, convolutional neural networks utilizing TF-IDF, Bag of Words (BOW), and Word2Vec vectors, along with keyword extraction, have been integrated. The proposed approach is assessed using the SemEval2019 dataset to extract suggestions from the textual content of online user reviews. The obtained results demonstrate a notable enhancement in the F\u0000<inf>1</inf>\u0000 score, reaching 0.76 compared to prior research. The experiments further suggest that information retrieval-based approaches exhibit promising potential for this specific task.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 3","pages":"431-463"},"PeriodicalIF":0.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547281","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251179","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0