Enterprise Information Systems最新文献

{"title":"Applied sentiment analysis on a real estate advertisement recommendation model","authors":"Regina Fang-Ying Lin, Jiesheng Wu, K. Tseng, Y. Tang, Lu Liu","doi":"10.1080/17517575.2022.2037158","DOIUrl":"https://doi.org/10.1080/17517575.2022.2037158","url":null,"abstract":"ABSTRACT Recently, the data generated are exploding in the information age. In the post-COVID-19 era, some real estate contracts have been signed online, and online advertisement recommendation has become a new way to reduce the searching cost. Therefore, the model in which real estate online recommendations can be made suitable without user preferences has become a tricky problem. This study uses sentiment and economic data to predict real estate sales and then made an advertisement recommendation from the forecast results. The 2SA-RERec (Two Sentiment Analysis of Real Estate Recommendation) model is proposed, which shows the highest accuracy among the others.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49089002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Security and privacy of smart-X systems","authors":"S. Tsai, D. Agrawal, Yong Deng","doi":"10.1080/17517575.2021.1923066","DOIUrl":"https://doi.org/10.1080/17517575.2021.1923066","url":null,"abstract":"Smart-X is a combination of existing smart technologies that can solve various real-time problems. It is also an emerging trend that can be viewed to be slightly disruptive. There are several technological and regulatory challenges to be addressed. The most important of them are data ownership, security, privacy, and information sharing. The security and privacy issues of the underlying technologies used are carried over to Smart-X systems (Fabisiak 2018). Privacy is a serious concern with IoT technologies. WSN systems have their fair share of flaws in the security aspect. These chinks in the armour have to be ironed out before the technologies can be reliably used as a part of smart X systems. Conclusively, one can confidently state that Smart X is still striding away from being implementable in smart cities and smart grids as a standalone system (Khatwani and Srivastava 2018). These motivations have attracted significant attention recently. In this special issue, we focus on the Security and Privacy of Smart-X Systems, addressing both original algorithmic development and new applications (Zhang, Mouritsen, and Miller 2019). By bringing together original contributions of leading researchers and practitioners from academia as well as industry, we have solicited seven papers for publication after a rigorous peerreview process. They address a wide range of theoretical and application issues in this domain. An integrative perspective of this special issue is provided here by summarising each contribution contained therein. As a massive coordinated attack on the availability of services through many compromised systems, distributed denial of service (DDoS) flooding attack has become one of the critical challenges to security professionals. To defend against DDoS attack along with maintaining Quality of Service (QoS) for legitimate users, a policy-based network management (PBNM) strategy has been proposed by Dahiya and Gupta. The strategy is used to enable users to negotiate with the service provider dynamically on cost and types of services. With the expanding range of channels for customers to acquire products, the Smart-X system plays an essential role in pricing strategies in that fairness concerns could significantly impact the price. If customers in the BM store have concerns, the firm is induced to lower product prices in stores, leading to total profit reduction. Meanwhile, it is better for the firm to operate in dual channels when customers do not have fairness concerns. Through comparison among three models with or without customers’ fairness concerns, security and privacy concerns, L. Yang et al. researched how to examine the impacts of customers’ fairness concerns on the firm’s optimal pricing strategy as well as total profit. It is also vital to investigate the impact of privacy concerns on purchasing decisions of customers and pricing strategies of service providers in Smart-X systems. S. Dong et al. develop profit maximisation models to","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":"16 1","pages":"403 - 405"},"PeriodicalIF":4.4,"publicationDate":"2022-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45407126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Deep Learning-based Sentiment Analysis Method Enhanced with Emojis in Microblog Social Networks","authors":"Xianyong Li, Jiabo Zhang, Yajun Du, Jian Zhu, Yongquan Fan, Xiaoliang Chen","doi":"10.1080/17517575.2022.2037160","DOIUrl":"https://doi.org/10.1080/17517575.2022.2037160","url":null,"abstract":"ABSTRACT To exactly classify sentiments of microblog reviews with emojis in microblog social networks, this paper first proposes an emoji vectorisation method to achieve emoji vectors. Then, an emoji-text integrated bidirectional LSTM (ET-BiLSTM) model for sentiment analysis is proposed. In this model, review text-based sentence representations are extracted by a bidirectional LSTM network. Emoji-based auxiliary representations are obtained by a new attention mechanism. The two representations are further integrated into final review representation vectors. Finally, experimental results indicate that the proposed ET-BiLSTM model improves the performance of sentiment classification evaluated by macro-P, macro-R and macro-F1 scores in microblog social networks.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":"17 1","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42714142","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building trust of Blockchain-based Internet-of-Thing services using public key infrastructure","authors":"W. Viriyasitavat, Lida Xu, Assadaporn Sapsomboon, G. Dhiman, D. Hoonsopon","doi":"10.1080/17517575.2022.2037162","DOIUrl":"https://doi.org/10.1080/17517575.2022.2037162","url":null,"abstract":"ABSTRACT The advancement of hardware, software, and Internet infrastructure leads to the increasing quantities of smart Internet of Things (IoT) devices. Meanwhile, security issues have increasingly brought to us the concerns due to the evolving IoT scope and mass communications. Trusting service vendors depends on their devices that generate information and provide executions. Blockchain becomes an attractive choice, as evidenced by its wide adoptions. However, trusting IoT-based services becomes an important issue since the implementation of Blockchain-based IoT (BIoT) services is proprietary and independent. This paper introduces a generic architecture design that incorporates Public Key Infrastructure (PKI) to establish trust of BIoT services. This can potentially solve the trust problem and based on our experiment it can be scaled well. We also demonstrate how specification languages can be useful to express requirements. It decouples users from Blockchain and thus they can specify qualities of BIoT services without deep knowledge to work with Blockchain.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43116489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intelligent resource management at the network edge using content delivery networks","authors":"Mahdi Abbasi, M. Khosravi, A. Ramezani","doi":"10.1080/17517575.2022.2037159","DOIUrl":"https://doi.org/10.1080/17517575.2022.2037159","url":null,"abstract":"ABSTRACT In a distributed denial-of-service attack, a large volume of packets is sent to the victim server to prevent service delivery and make it unavailable to valid users and clients at the network edge. In this paper, we use content delivery networks to propose a new intelligent solution that protects the main servers at the network edge by using alternative edge servers and agents. Thus, Internet attacks on the main servers could be prevented. The simulation results show that, the proposed mechanism has considerably increased the processing efficiency at the edge servers, and simultaneously provided the required security against internet attacks.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44326900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning and intelligent system towards smart manufacturing","authors":"Mu-Yen Chen, E. Lughofer, E. Eğrioğlu","doi":"10.1080/17517575.2021.1898050","DOIUrl":"https://doi.org/10.1080/17517575.2021.1898050","url":null,"abstract":"Machine learning has been applied to solve complex problems in human society for years. The success of machine learning is because of the support of computing capabilities as well as sensing technology. An evolution of artificial intelligence and data-driven approaches will soon cause considerable impacts on the field. Search engines, image recognition, biometrics, speech and handwriting recognition, natural language processing, and even medical diagnostics and financial credit ratings are all common examples. It is clear that many challenges will be brought to public as artificial intelligence infiltrates our world, and more specifically, our lives. With the integration and extensive applications of the new generation of information technologies (such as cloud computing, IoT, big data, deep learning, AVG) in manufacturing industry, a number of countries have put forward their national advanced manufacturing development strategies, such as Industry 4.0 in Germany, Industrial Internet and manufacturing system based on CPS (Cyber-Physical Systems) in the USA, as well as Made in China 2025 and Internet Plus Manufacturing in China. Smart Manufacturing and the Smart Factory enables all information about the manufacturing process to be available when and where it is needed across entire manufacturing supply chains and product lifecycles. Smart Manufacturing is being predicted as the next Industrial Revolution or Industry 4.0. And, as with many other advances throughout recent years, it all has to do with technology connectivity and the advances in the contextualisation of data. However, with neither the intelligent system support nor the support of data science technology, ‘smart’ cannot be achieved.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":"16 1","pages":"189 - 192"},"PeriodicalIF":4.4,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45282105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A data analytic-based logistics modelling framework for E-commerce enterprise","authors":"Abhishek Verma, Y. Kuo, M. Kumar, S. Pratap, Velvet Chen","doi":"10.1080/17517575.2022.2028195","DOIUrl":"https://doi.org/10.1080/17517575.2022.2028195","url":null,"abstract":"ABSTRACT Data-driven approaches have noteworthy significance in managing and improving logistics in E-commerce enterprises. This study focuses on the development of an integrated framework to analyse the Brazilian E-Commerce enterprise public dataset. From the analysis, it is found that sellers of Ibitinga city of SP state had the most count of late deliveries where 42 sellers are under-performing in terms of estimated delivery time. Locations of customers and sellers were spotted on a map to get a geographical representation. The proposed framework may help E-Commerce enterprise owners and retail merchants to make better decisions related to sales and E-Commerce enterprise logistics.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45101347","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intellectual structure of cybersecurity research in enterprise information systems","authors":"Nitin Singh, Venkataraghavan Krishnaswamy, Z. Zhang","doi":"10.1080/17517575.2022.2025545","DOIUrl":"https://doi.org/10.1080/17517575.2022.2025545","url":null,"abstract":"ABSTRACT Enterprises aspire for ongoing and effective information systems security. Cybersecurity frameworks ensure the availability, confidentiality, and integrity of information. Inspired by the omnipresent challenges and ever-increasing spending by enterprises, we identify the state of research on cybersecurity in enterprises. We employ citation, co-citation, centrality, and citation-path analysis to uncover its intellectual core. Our study reveals five core themes of cybersecurity research: (a) artificial intelligence in cybersecurity, (b) grids, networks, and platform security, (c) algorithms & methods, (d) optimisation & modelling, and (e) cybersecurity management. We discuss the implications for EIS and opportunities for research in each of these themes.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47996575","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comprehensive survey on machine learning approaches for malware detection in IoT-based enterprise information system","authors":"Akshat Gaurav, B. Gupta, P. Panigrahi","doi":"10.1080/17517575.2021.2023764","DOIUrl":"https://doi.org/10.1080/17517575.2021.2023764","url":null,"abstract":"ABSTRACT The Internet of Things (IoT) is a relatively new technology that has piqued academics’ and business information systems’ attention in recent years. The Internet of Things establishes a network that enables smart devices in an organisational information system to connect to one another and exchange data with the central storage. Android apps are placed on Android apps to enhance the user-friendliness of IoT devices in business information systems, making them more interactive and user-friendly. However, the usage of Android apps makes IoT devices susceptible to all forms of malware attacks, including those that attempt to hack into IoT devices and get access to sensitive information stored in the corporate information system. The researchers offered a variety of attack mitigation approaches for detecting harmful malware embedded in an Android application operating on an IoT device. In this context, machine learning offered the most promising strategies to detect malware attacks in IoT-based enterprise information systems because of its better accuracy and precision. Its capacity to adapt to new forms of malware attacks is a result of its learning capabilities. Therefore, we conduct a detailed survey, which discusses emerging machine learning algorithms for detecting malware in business information systems powered by the Internet of Things. This article reviews all available research on malware detection, including static malware detection, dynamic malware detection, promoted malware detection and hybrid malware detection.","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":" ","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48554892","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enterprise Information Systems: 23rd International Conference, ICEIS 2021, Virtual Event, April 26–28, 2021, Revised Selected Papers","authors":"","doi":"10.1007/978-3-031-08965-7","DOIUrl":"https://doi.org/10.1007/978-3-031-08965-7","url":null,"abstract":"","PeriodicalId":11750,"journal":{"name":"Enterprise Information Systems","volume":"1 3","pages":""},"PeriodicalIF":4.4,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50986471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}