Journal of Web Engineering最新文献_第5页

Semantically Enriched Keyword Prefetching Based on Usage and Domain Knowledge 基于使用和领域知识的语义丰富关键词预取

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-03-01 DOI: 10.13052/jwe1540-9589.2332

Sonia Setia;Jyoti;Neelam Duhan;Aman Anand;Nikita Verma

{"title":"Semantically Enriched Keyword Prefetching Based on Usage and Domain Knowledge","authors":"Sonia Setia;Jyoti;Neelam Duhan;Aman Anand;Nikita Verma","doi":"10.13052/jwe1540-9589.2332","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2332","url":null,"abstract":"In intelligent web systems [2], web prefetching [27] plays a crucial role. In order to make accurate predictions for web prefetching, it is important but challenging to uncover valuable information from web use statistics [16]. Using statistics and domain expertise, this study presents a new approach dubbed SPUDK for efficient prefetching. In this paper, it is shown how web access logs can be used efficiently for browsing prediction. Our main focus is on the technique needed to manage the queries found in web access logs so that valuable information can be attained. We further process these access logs using a taxonomy and a thesaurus, WordNet, to find the semantics of queries. SPUDK, a system that organises use data into semantic clusters, is one example of this approach. Our contributions in this paper are as follows: (1) A technique to exploit query keywords from access logs. (2) An approach to enrich queries with semantic information. (3) A new similarity measure for finding similarity among URLs present in access logs. (4) A novel clustering technique to find semantic clusters of URLs. (5) Experimental evaluation of the proposed system. The proposed SPUDK system is evaluated using American Online (AOL) logs, which gives improvement of 39% in precision of prediction, 35% in hit ratio and reduction of 50.6% in latency on average as compared to other prediction techniques in the literature.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 3","pages":"341-375"},"PeriodicalIF":0.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10547277","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141251081","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

Leveraging the Synergy of IPv6, Generative AI, and Web Engineering to Create a Big Data-Driven Education Platform 利用 IPv6、生成式人工智能和网络工程的协同作用创建大数据驱动的教育平台

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-03-01 DOI: 10.13052/jwe1540-9589.2321

Gao Yongli;Dong Qi;Chen Zhipeng

{"title":"Leveraging the Synergy of IPv6, Generative AI, and Web Engineering to Create a Big Data-Driven Education Platform","authors":"Gao Yongli;Dong Qi;Chen Zhipeng","doi":"10.13052/jwe1540-9589.2321","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2321","url":null,"abstract":"The rapid advancement of network technology in China has significantly accelerated the implementation of information technology in higher education. Through the utilization of computer technology, multimedia technology, big data technology, artificial intelligence technology, and network communication technology, the integration of these technologies in university teaching has become widespread. This paper presents an analysis and discussion on the utilization of the latest IPv6 network transmission protocol technology to enhance the application of data collection in university education, with a specific focus on gathering information related to university faculties. By leveraging web engineering and multimedia technology as fundamental components, the network facilitates the sharing of educational resources among students, thereby enabling the reform of management approaches, fostering educational progress in China, and establishing a comprehensive big data-driven education platform specifically tailored to colleges and universities. Additionally, the incorporation of big data visualization and analysis tools allows for easy retrieval of existing university educational information, facilitates the creation of data charts, and expedites the utilization of data for its inherent value. Finally, the proposed approach employs generative AI to collect and analyze feedback from students and educators, followed by the application of web engineering techniques to continuously enhance the online education platform based on this feedback.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 2","pages":"197-226"},"PeriodicalIF":0.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606069","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

Flight Price Prediction Web-Based Platform: Leveraging Generative AI for Real-Time Airfare Forecasting 航班价格预测网络平台：利用生成式人工智能进行实时机票价格预测

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-03-01 DOI: 10.13052/jwe1540-9589.2325

Yuanyuan Guan

{"title":"Flight Price Prediction Web-Based Platform: Leveraging Generative AI for Real-Time Airfare Forecasting","authors":"Yuanyuan Guan","doi":"10.13052/jwe1540-9589.2325","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2325","url":null,"abstract":"The aviation business encounters difficulties in correctly and swiftly predicting flight fares due to the dynamic nature of the sector. Factors such as variations in demand, fuel costs, and the intricacies of various routes have an impact on this. This work presents a new method to tackle this issue by utilizing generative artificial intelligence (GAI) approaches to accurately forecast airfares in real-time. This paper presents a novel framework that integrates generative models, deep learning architectures, and historical pricing data to improve the precision of future flight price predictions. The study employs a GAI within a cutting-edge web engineering framework. This approach is designed primarily to gather knowledge about complex patterns and relationships present in historical airline data. Through the utilization of this methodology, the model is able to accurately perceive complex connections and adjust to ever-changing market conditions. Our model utilizes deep neural networks to effectively handle various circumstances and extract vital information, so facilitating a comprehensive comprehension of the intricate elements that impact flight cost. Moreover, the suggested approach places significant emphasis on precisely predicting upcoming occurrences in real-time, facilitating prompt reactions to market volatility and offering a valuable resource for airlines, travel agents, and customers alike. In order to enhance the accuracy of real-time forecasts, we utilize a web-based platform that allows for smooth interaction with live data streams and guarantees swift updates. The results demonstrate the model's capacity to adjust to dynamic market conditions, rendering it an attractive option for stakeholders in search of precise and current forecasts of flight prices.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 2","pages":"299-314"},"PeriodicalIF":0.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10504110","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140606117","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

Contextualized Satire Detection in Short Texts Using Deep Learning Techniques 利用深度学习技术在短文中进行语境化讽刺检测

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-01-01 DOI: 10.13052/jwe1540-9589.2312

Ashraf Kamal;Muhammad Abulaish;Jahiruddin

{"title":"Contextualized Satire Detection in Short Texts Using Deep Learning Techniques","authors":"Ashraf Kamal;Muhammad Abulaish;Jahiruddin","doi":"10.13052/jwe1540-9589.2312","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2312","url":null,"abstract":"Satire is prominent in user-generated content on various online platforms in the form of satirical news, customer reviews, blogs, articles, and short messages that are typically of an informal nature. As satire is also used to disseminate false information on the Internet, its computational detection has become a well-known issue. Existing work focuses primarily on formal document- or sentence-level textual data, whereas informal short texts have gotten less attention for satire detection. This paper presents a new model called BiLSTM self-attention (BiSAT) for detecting satire in informal short texts. It consists of various components such as input, embedding, self-attention, and two bi-directional long short-term memory (BiLSTM) layers for learning crucial contextual information pertaining to the satire present in the texts. The input layer uses the text as input to create an input vector, which is then given to the embedding layer to create the appropriate numeric vector. The output of the embedding layer is passed on to the first BiLSTM layer, which extracts contextual information-based sequences in the opposite direction. Between the first and second BiLSTM layers, a self-attention layer is employed to draw attention to the important satirical information that is acquired by the hidden layer of the first BiLSTM. The BiSAT model also takes a classic feature engineering approach, employing a 13-dimensional auxiliary feature vector comprised of features from four separate feature categories: sentiment, punctuation, hyperbole, and affective. The proposed BiSAT model is empirically evaluated on two benchmark datasets and a newly created dataset called Satire-280. It outperforms existing research and baseline methods by a significant margin. The Satire-280 dataset along with code can be downloaded from GitHub repository: https://github.com/Ashraf-Kamal/Satire-Detection.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 1","pages":"27-52"},"PeriodicalIF":0.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488438","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342678","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

Generative Architecture for Data Imputation in Secure Blockchain-Enabled Spatiotemporal Data Management 安全区块链时空数据管理中的数据推算生成架构

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-01-01 DOI: 10.13052/jwe1540-9589.2315

Song Li;WenFen Liu;Yan Wu;Jie Zhao

{"title":"Generative Architecture for Data Imputation in Secure Blockchain-Enabled Spatiotemporal Data Management","authors":"Song Li;WenFen Liu;Yan Wu;Jie Zhao","doi":"10.13052/jwe1540-9589.2315","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2315","url":null,"abstract":"In the era of big data, one of the most critical challenges is ensuring secure access, retrieval, and sharing of linked spatiotemporal data. To address this challenge, this paper introduces a groundbreaking blockchain-enabled evolutionary indirect feedback graph algorithm for the secure management of interconnected spatiotemporal datasets. The algorithm utilizes a generative neural network model for data imputation, predicting and generating plausible values to improve dataset completeness and integrity. The core architecture utilizes blockchain technology to optimize data retrieval efficiency and uphold robust access control mechanisms. The algorithm incorporates indirect feedback mechanisms, allowing users to provide implicit feedback through their interactions, enhancing the relevance and efficiency of data retrieval. In addition. sophisticated graph-based techniques are used to model intricate relationships between data entities, facilitating seamless data retrieval and sharing in interwoven datasets. The algorithm's data security approach includes comprehensive access control mechanisms, encryption, and authentication mechanisms, safeguarding data confidentiality and integrity. Extensive evaluations show significant enhancements in retrieval performance and access control precision, making the proposed model a promising solution for the secure management of expansive interconnected spatiotemporal data.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 1","pages":"111-163"},"PeriodicalIF":0.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488442","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342745","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

Increased Productivity and Reduced Waste with Robotic Process Automation and Generative AI-Powered IoE Services 利用机器人流程自动化和生成性人工智能驱动的物联网服务提高生产率并减少浪费

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-01-01 DOI: 10.13052/jwe1540-9589.2313

Wei Lo;Chun-Ming Yang;Qiansha Zhang;Mingyuan Li

{"title":"Increased Productivity and Reduced Waste with Robotic Process Automation and Generative AI-Powered IoE Services","authors":"Wei Lo;Chun-Ming Yang;Qiansha Zhang;Mingyuan Li","doi":"10.13052/jwe1540-9589.2313","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2313","url":null,"abstract":"The convergence of robotic process automation (RPA) and generative AI (GAI) within the context of Internet of Everything (IoE) services represents a profound paradigm shift. This fusion of technologies not only streamlines routine tasks but also catalyzes innovation while harnessing the potential of interconnected devices. Such integration empowers organizations to achieve remarkable gains in efficiency and sustainability. This paper embarks on an exploration of these transformative services, designed to elevate productivity, and curtail wasteful practices in contemporary industries. By closely examining intricate case studies, we illuminate the multifaceted advantages of this integrated approach. Our investigation demonstrates how RPA accelerates the execution of repetitive processes, substantially diminishing the margin for human error and amplifying operational efficiency. In contrast, generative AI introduces a disruptive force, generating fresh ideas, designs, and solutions, thereby elevating the quality of products and services. The infusion of these cutting-edge technologies into the fabric of IoE services paves the way for organizations to attain unprecedented levels of automation, intelligence, and connectivity. Furthermore, this paper comprehensively addresses the intricate challenges and considerations associated with the proposed implementation. We delve into ethical concerns, security implications, and the necessary workforce adaptation to offer a balanced perspective on the adoption of these technologies. Additionally, we navigate through potential limitations and constraints, underscoring the imperative need for strategic planning and robust governance.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 1","pages":"53-87"},"PeriodicalIF":0.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488437","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342720","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

Introducing Students to Web Engineering Topics by Teaching Web Augmentation 通过网络增强教学向学生介绍网络工程主题

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-01-01 DOI: 10.13052/jwe1540-9589.2311

Iñigo Aldalur

{"title":"Introducing Students to Web Engineering Topics by Teaching Web Augmentation","authors":"Iñigo Aldalur","doi":"10.13052/jwe1540-9589.2311","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2311","url":null,"abstract":"Web augmentation has gained prominence as a promising approach to elevate the user experience by tailoring web content to meet diverse user needs and preferences. It offers the potential to enhance accessibility and personalization, making web experiences more engaging and inclusive. This research study examines the use of web augmentation in the context of web engineering education and its influence on student motivation. The study investigates how the integration of web augmentation techniques motivates students and enhances their learning experience. A questionnaire was administered to gather data on student perceptions and motivation levels following their exposure to web augmentation in the web engineering subject. The findings reveal that the implementation of web augmentation in the web engineering subject has positively motivated students. Students express a strong preference for web augmentation as a learning tool, citing increased engagement and a more interactive learning environment. The findings support the adoption of web augmentation techniques as a valuable pedagogical tool, enhancing the learning experience and fostering student engagement. This study contributes to the field by addressing the gap in related work that focuses on the use of web augmentation specifically in educational settings.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 1","pages":"1-26"},"PeriodicalIF":0.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488440","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342721","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

A Hypersensitive Intelligent Filter for Detecting Explicit Content in Learning Environments 用于检测学习环境中明确内容的超灵敏智能过滤器

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-01-01 DOI: 10.13052/jwe1540-9589.2314

Yong Yu;Xiaoguo Yin

引用次数: 0

An Intelligent Web-Based Energy Management System for Distributed Energy Resources Integration and Optimization 基于网络的分布式能源资源整合与优化智能能源管理系统

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2024-01-01 DOI: 10.13052/jwe1540-9589.2316

Lijun Zhao;Qingsheng Li;Guanhua Ding

{"title":"An Intelligent Web-Based Energy Management System for Distributed Energy Resources Integration and Optimization","authors":"Lijun Zhao;Qingsheng Li;Guanhua Ding","doi":"10.13052/jwe1540-9589.2316","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2316","url":null,"abstract":"The integration of renewable energy sources into power distribution systems frequently presents challenges for conventional energy management systems (EMS) due to the unpredictable and unstable characteristics of such energy sources. As a result, novel and cutting-edge solutions are required. This paper presents an intelligent web-based energy management system (iW-EMS) specifically designed to address the integration and optimization of distributed energy resources, as outlined in the proposed approach. The system incorporates a hybrid novel optimization approach that integrates simulated annealing and cone programming to effectively manage the distribution of energy resources and attain optimal outcomes from the proposed EMS. Additionally, it leverages generative AI services to create optimal scenarios based on historical data and real-time information, ensuring adaptability to the dynamic nature of renewable energy generation, providing a user-friendly and flexible web environment for scenario planning. The proposed framework facilitates seamless communication and collaboration among stakeholders involved in renewable energy integration, while also enabling the incorporation of real-world data sources such as weather forecasts and energy consumption patterns into the planning process.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"23 1","pages":"165-195"},"PeriodicalIF":0.8,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10488436","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140342743","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

A Study on Performance Improvement of Prompt Engineering for Generative AI with a Large Language Model 利用大型语言模型提高生成式人工智能提示工程性能的研究

IF 0.8 4区计算机科学

Journal of Web Engineering Pub Date : 2023-11-01 DOI: 10.13052/jwe1540-9589.2285

Daeseung Park;Gi-taek An;Chayapol Kamyod;Cheong Ghil Kim

{"title":"A Study on Performance Improvement of Prompt Engineering for Generative AI with a Large Language Model","authors":"Daeseung Park;Gi-taek An;Chayapol Kamyod;Cheong Ghil Kim","doi":"10.13052/jwe1540-9589.2285","DOIUrl":"https://doi.org/10.13052/jwe1540-9589.2285","url":null,"abstract":"In the realm of Generative AI, where various models are introduced, prompt engineering emerges as a significant technique within natural language processing-based Generative AI. Its primary function lies in effectively enhancing the results of sentence generation by large language models (LLMs). Notably, prompt engineering has gained attention as a method capable of improving LLM performance by modifying the structure of input prompts alone. In this study, we apply prompt engineering to Korean-based LLMs, presenting an efficient approach for generating specific conversational responses with less data. We achieve this through the utilization of the query transformation module (QTM). Our proposed QTM transforms input prompt sentences into three distinct query methods, breaking them down into objectives and key points, making them more comprehensible for LLMs. For performance validation, we employ Korean versions of LLMs, specifically SKT GPT-2 and Kakaobrain KoGPT-3. We compare four different query methods, including the original unmodified query, using Google SSA to assess the naturalness and specificity of generated sentences. The results demonstrate an average improvement of 11.46% when compared to the unmodified query, underscoring the efficacy of the proposed QTM in achieving enhanced performance.","PeriodicalId":49952,"journal":{"name":"Journal of Web Engineering","volume":"22 8","pages":"1187-1206"},"PeriodicalIF":0.8,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10452390","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139987275","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