271 results
Search Results
2. Machine-Learning Holistic Review in Tourism and Hospitality
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Ashqar, Rashed Isam, Ramos, Célia M. Q., Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, M. A. Musleh Al-Sartawi, Abdalmuttaleb, editor, Helmy Abd Wahab, Mohd, editor, and Hussainey, Khaled, editor
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- 2024
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3. The Technique of Processing Non-Gaussian Data Based on Artificial Intelligence
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Gnatuk, Viktor, Kivchun, Oleg, Mozhaeva, Sofia, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, and Gibadullin, Arthur, editor
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- 2024
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4. Exploration and Practice of Predictive Maintenance Technology in Automobile Factories
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Hao, Zhiqiang, Li, Guojun, Chen, Lei, Niu, Jingcheng, China Society of Automotive Engineers, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Oneto, Luca, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, and Tan, Kay Chen, Series Editor
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- 2024
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5. A guide to understanding big data for the nurse scientist: A discursive paper.
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Duah, Henry Ofori, Boch, Samantha, Arter, Sara, Nidey, Nichole, and Lambert, Joshua
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MEDICAL protocols , *DATA security , *DATABASE management , *ARTIFICIAL intelligence , *POPULATION health , *HEALTH , *DATA analytics , *NURSING education , *CODES of ethics , *INFORMATION resources , *PHILOSOPHY of nursing , *NURSING research , *NURSING practice , *PUBLIC health , *DATA quality , *DATA analysis software , *HEALTH equity , *MEDICAL ethics - Abstract
Big data refers to extremely large data generated at high volume, velocity, variety, and veracity. The nurse scientist is uniquely positioned to leverage big data to suggest novel hypotheses on patient care and the healthcare system. The purpose of this paper is to provide an introductory guide to understanding the use and capability of big data for nurse scientists. Herein, we discuss the practical, ethical, social, and educational implications of using big data in nursing research. Some practical challenges with the use of big data include data accessibility, data quality, missing data, variable data standards, fragmentation of health data, and software considerations. Opposing ethical positions arise with the use of big data, and arguments for and against the use of big data are underpinned by concerns about confidentiality, anonymity, and autonomy. The use of big data has health equity dimensions and addressing equity in data is an ethical imperative. There is a need to incorporate competencies needed to leverage big data for nursing research into advanced nursing educational curricula. Nursing science has a great opportunity to evolve and embrace the potential of big data. Nurse scientists should not be spectators but collaborators and drivers of policy change to better leverage and harness the potential of big data. [ABSTRACT FROM AUTHOR]
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- 2024
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6. A Review Paper on Exploring the Concept of Data Science: A Comprehensive Analysis.
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Gaikwad, Samiksha, Chaudhari, Parimal, Jadhav, Dipali, Bodade, Punam, and Shirbhate, Dhiraj
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DATA science ,TECHNOLOGICAL innovations ,ARTIFICIAL intelligence ,BLOCKCHAINS ,EDGE computing ,MACHINE learning - Abstract
Data science is a rapidly growing technology in the technical world that fulfills the requirements for data and various data aspects. The core of all emerging technologies is data science, which includes machine learning, artificial intelligence, robotics, edge computing, and blockchain technology. In this review paper, we consider the detailed concept on data science, such as where the data is generated, the skills to handle data, its growth, how it works, and the impact of data science on other technologies. The basic aim of this review paper is to provide a basic summary of data science that everyone easily understands. [ABSTRACT FROM AUTHOR]
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- 2024
7. Artificial intelligence in entrepreneurship education: a scoping review
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Chen, Li, Ifenthaler, Dirk, Yau, Jane Yin-Kim, and Sun, Wenting
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- 2024
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8. Artificial intelligent housekeeper based on consumer purchase decision: a case study of online E-commerce
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Guo, Yan, Tang, Qichao, Wang, Haoran, Jia, Mengjing, and Wang, Wei
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- 2024
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9. Big data, machine learning and uncertainty in foresight studies
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Muraro, Vinicius and Salles-Filho, Sergio
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- 2024
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10. Research on Smart City Platform Construction Technology for Digital Twins.
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Jianxiong Zhang, Wuqi Gao, and Shiqian Wang
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DIGITAL twins ,SMART cities ,CONSTRUCTION industry ,BIG data ,ARTIFICIAL intelligence - Abstract
Urban digital twin is a key step to build a smart city, digital twin is an important application scenario for smart city platform, and the relationship between the two are both current research hotspots. In this paper, we will start from the demand of digital twin on smart city platform, study the architecture method and key technology of smart city platform, in this paper's platform construction method compared to the traditional construction method, reduces the difficulty of digital twin smart city construction, and also reduces the coupling degree between smart city platform modules, and use a smart city platform for engineering verification. Finally compared with the traditional smart platform construction techniques, the techniques in this paper are better than the traditional ones in terms of coupling, difficulty and cost. Through engineering verification and experimental results show that this paper on the digital twin-oriented smart city construction technology, the coupling degree of each module is the lowest, and in the development efficiency experiments, this paper by comparing with the traditional technology, the experimental development cycle compared to the traditional technology can be shortened by 61.7% of the development cycle, greatly reducing the development cost and improving the construction efficiency. [ABSTRACT FROM AUTHOR]
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- 2024
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11. Incorporation of artificial intelligence, Big Data, and Internet of Things (IoT): an insight into the technological implementations in business success.
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Hamdan, Allam, Alareeni, Bahaaeddin, Hamdan, Reem, and Dahlan, Mohanad A.
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ARTIFICIAL intelligence ,BUSINESS success ,BIG data ,INTERNET of things ,TECHNOLOGICAL innovations - Abstract
New technology refers to the use of computing machines, AI, Big Data, IoT, deep learning, IT, MIS, AIS, knowledge management, capture, manipulate, and retrieve shared knowledge. Therefore, the integration of modern technology, entrepreneurship, and business should be well managed to provide a wide range of high-quality and competitive products and services in societies. The aim of this special issue is to highlight the latest features that blend AI, Big Data, and IoT facilitated and employ them to support the successful growth of businesses. The target of this special issue is to accept high-quality scientific articles that express theory and practical conceptualizations of ideas and critical surveys that cover all aspects pertaining to IoT, AI, and Big Data and their relationship to business success. The special issue received 36 papers, some of which were presented in ICBT'2021 and CBF'2022. All of them were desk evaluated by the editors, followed by at least two blind reviews. [ABSTRACT FROM AUTHOR]
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- 2024
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12. 'This is NOT human services': Counter-mapping automated decision-making in social services in Australia.
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Sleep, Lyndal
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ARTIFICIAL intelligence ,SOCIAL services ,COST control ,WELFARE state ,BIG data - Abstract
This paper offers a counter-map of automation in social services decision-making in Australia. It aims to amplify alternative discourses that are often obscured by power inequalities and disadvantage. Redden (2005) has used counter-mapping to frame an analysis of big data in government in Canada, contrasting with 'dominant outward facing government discourses about big data applications' to focus on how data practices are both socially shaped and shaping. This paper reports on a counter-mapping project undertaken in Australia using a mixed methods approach incorporating document analysis, interviews and web scraping to amplify divergent discourses about automated decision-making. It demonstrates that when the focus of analysis moves beyond dominant discourses of neoliberal efficiency, cost cutting, accuracy and industriousness, alternative discourses of service users' experiences of automated decision-making as oppressive, harmful, punitive and inhuman(e) can be located. [ABSTRACT FROM AUTHOR]
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- 2024
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13. Understanding Digital Turn in Urban Research: A Bibliometric Analysis of Contemporary Global Urban Literature.
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Sayın, Özgür
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URBAN research ,LITERATURE reviews ,TECHNOLOGICAL innovations ,ARTIFICIAL intelligence ,INTERNET of things ,CITATION indexes ,HONESTY - Abstract
Copyright of Urban Academy/ Kent Akademisi is the property of ICAM NETWORK and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2024
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14. How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences.
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Jiang, Shijie, Sweet, Lily‐belle, Blougouras, Georgios, Brenning, Alexander, Li, Wantong, Reichstein, Markus, Denzler, Joachim, Shangguan, Wei, Yu, Guo, Huang, Feini, and Zscheischler, Jakob
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MACHINE learning ,ARTIFICIAL intelligence ,EARTH currents ,ARTIFICIAL languages ,RESEARCH questions - Abstract
Interpretable Machine Learning (IML) has rapidly advanced in recent years, offering new opportunities to improve our understanding of the complex Earth system. IML goes beyond conventional machine learning by not only making predictions but also seeking to elucidate the reasoning behind those predictions. The combination of predictive power and enhanced transparency makes IML a promising approach for uncovering relationships in data that may be overlooked by traditional analysis. Despite its potential, the broader implications for the field have yet to be fully appreciated. Meanwhile, the rapid proliferation of IML, still in its early stages, has been accompanied by instances of careless application. In response to these challenges, this paper focuses on how IML can effectively and appropriately aid geoscientists in advancing process understanding—areas that are often underexplored in more technical discussions of IML. Specifically, we identify pragmatic application scenarios for IML in typical geoscientific studies, such as quantifying relationships in specific contexts, generating hypotheses about potential mechanisms, and evaluating process‐based models. Moreover, we present a general and practical workflow for using IML to address specific research questions. In particular, we identify several critical and common pitfalls in the use of IML that can lead to misleading conclusions, and propose corresponding good practices. Our goal is to facilitate a broader, yet more careful and thoughtful integration of IML into Earth science research, positioning it as a valuable data science tool capable of enhancing our current understanding of the Earth system. Plain Language Summary: Artificial Intelligence is a rapidly advancing field, in which Interpretable Machine Learning (IML) is seen as having the potential to significantly improve our understanding of Earth's complex environmental systems. IML goes beyond the predictive power of machine learning models, focusing instead on uncovering the relationships within the data that are revealed by the model's learning process. However, there is still a lack of straightforward, practical domain‐specific guidelines for geoscientists that facilitate both broader and more careful application in the field. In this paper, we aim to demonstrate the real‐world benefits of IML in typical geoscientific analysis. We provide a clear, step‐by‐step workflow that shows how IML can be used to address specific questions. We also point out some common pitfalls in using IML and offer solutions to avoid them. Our goal is to make IML more accessible and useful to a wider range of geoscientists, and we believe that IML, if used properly and thoughtfully, can become an essential and valuable tool to advance our understanding of complex Earth systems. Key Points: We demonstrate the broader relevance of Interpretable Machine Learning (IML) to most geoscientists and underexplored opportunities for its useWe describe a workflow for the effective use of IML while cautioning against potential and common pitfallsWe suggest good practices for its adoption and advocate for more careful application to ensure reliable and robust insights for the field [ABSTRACT FROM AUTHOR]
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- 2024
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15. Data and Energy Impacts of Intelligent Transportation—A Review.
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Rajashekara, Kaushik and Koppera, Sharon
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ARTIFICIAL intelligence ,AUTONOMOUS vehicles ,ENERGY consumption ,CITIES & towns ,ELECTRIC automobiles ,ELECTRIC vehicles ,ELECTRONIC data processing - Abstract
The deployment of intelligent transportation is still in its early stages and there are many challenges that need to be addressed before it can be widely adopted. Autonomous vehicles are a class of intelligent transportation that is rapidly developing, and they are being deployed in selected cities. A combination of advanced sensors, machine learning algorithms, and artificial intelligence are being used in these vehicles to perceive their environment, navigate, and make the right decisions. These vehicles leverage extensive data sourced from various sensors and computers integrated into the vehicle. Hence, massive computational power is required to process the information from various built-in sensors in milliseconds to make the right decision. The power required by the sensors and the use of additional computational power increases the energy consumption, and, hence, could reduce the range of the autonomous electric vehicle relative to a standard electric car and lead to additional emissions. A number of review papers have highlighted the environmental benefits of autonomous vehicles, focusing on aspects like optimized driving, improved route selection, fewer stops, and platooning. However, these reviews often overlook the significant energy demands of the hardware systems—such as sensors, computers, and cameras—necessary for full autonomy, which can decrease the driving range of electric autonomous vehicles. Additionally, previous studies have not thoroughly examined the data processing requirements in these vehicles. This paper provides a more detailed review of the volume of data and energy usage by various sensors and computers integral to autonomous features in electric vehicles. It also discusses the effects of these factors on vehicle range and emissions. Furthermore, the paper explores advanced technologies currently being developed by various industries to enhance processing speeds and reduce energy consumption in autonomous vehicles. [ABSTRACT FROM AUTHOR]
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- 2024
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16. Evolutionary computation-based self-supervised learning for image processing: a big data-driven approach to feature extraction and fusion for multispectral object detection.
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Shen, Xiaoyang, Li, Haibin, Shankar, Achyut, Viriyasitavat, Wattana, and Chamola, Vinay
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OBJECT recognition (Computer vision) ,MACHINE learning ,ARTIFICIAL intelligence ,FEATURE extraction ,IMAGE processing ,EVOLUTIONARY computation ,MULTISPECTRAL imaging - Abstract
The image object recognition and detection technology are widely used in many scenarios. In recent years, big data has become increasingly abundant, and big data-driven artificial intelligence models have attracted more and more attention. Evolutionary computation has also provided a powerful driving force for the optimization and improvement of deep learning models. In this paper, we propose an image object detection method based on self-supervised and data-driven learning. Differ from other methods, our approach stands out due to its innovative use of multispectral data fusion and evolutionary computation for model optimization. Specifically, our method uniquely combines visible light images and infrared images to detect and identify image targets. Firstly, we utilize a self-supervised learning method and the AutoEncoder model to perform high-dimensional feature extraction on the two types of images. Secondly, we fuse the extracted features from the visible light and infrared images to detect and identify objects. Thirdly, we introduce a model parameter optimization method using evolutionary learning algorithms to enhance model performance. Validation on public datasets shows that our method achieves comparable or superior performance to existing methods. [ABSTRACT FROM AUTHOR]
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- 2024
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17. 大数据驱动的生成式 AI 在服装设计中的应用 —以 Midjourney 为例.
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于家蓓 and 朱伟明
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Copyright of Journal of Silk is the property of Zhejiang Sci-Tech University Magazines and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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- 2024
- Full Text
- View/download PDF
18. The integration strategy of information system based on artificial intelligence big data technology in metaverse environment.
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Lin, Yechuan and Liu, Shixing
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SYSTEM integration ,INFORMATION technology ,ARTIFICIAL intelligence ,INFORMATION storage & retrieval systems ,SHARED virtual environments - Abstract
The concept of the meta-universe is still in its early stages, but many leading tech companies have invested heavily in research and development for this technology. The development of meta-smart cities is a significant trend. In the meta-universe environment, integrating information systems is crucial for analyzing AI big data. Establishing an integrated platform for medical information systems is key to advancing information technology. In the context of the meta-universe, creating an efficient and unified integration platform to eliminate medical information silos and reduce system integration costs has become a pressing issue in medical informatization. This paper proposes a medical information system integration method based on an integration platform and utilizing cloud computing technology as a data center. The core business layer uses the integration software "Ensemble" as the integration platform. The underlying data center employs a Hadoop storage cluster with distributed data storage and parallel computing technology, and the existing scheduling algorithm is studied and analyzed to enhance the resource scheduling algorithm for medical small file data. The effectiveness of the algorithm is simulated and verified on an experimental platform, demonstrating improved efficiency in resource scheduling. [ABSTRACT FROM AUTHOR]
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- 2024
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19. Leveraging Visualization and Machine Learning Techniques in Education: A Case Study of K-12 State Assessment Data.
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Taylor, Loni, Gupta, Vibhuti, and Jung, Kwanghee
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DATA-based decision making in education ,ARTIFICIAL intelligence ,DATA visualization ,MACHINE learning ,MICROSOFT Azure (Computing platform) ,INDIVIDUALIZED instruction - Abstract
As data-driven models gain importance in driving decisions and processes, recently, it has become increasingly important to visualize the data with both speed and accuracy. A massive volume of data is presently generated in the educational sphere from various learning platforms, tools, and institutions. The visual analytics of educational big data has the capability to improve student learning, develop strategies for personalized learning, and improve faculty productivity. However, there are limited advancements in the education domain for data-driven decision making leveraging the recent advancements in the field of machine learning. Some of the recent tools such as Tableau, Power BI, Microsoft Azure suite, Sisense, etc., leverage artificial intelligence and machine learning techniques to visualize data and generate insights from them; however, their applicability in educational advances is limited. This paper focuses on leveraging machine learning and visualization techniques to demonstrate their utility through a practical implementation using K-12 state assessment data compiled from the institutional websites of the States of Texas and Louisiana. Effective modeling and predictive analytics are the focus of the sample use case presented in this research. Our approach demonstrates the applicability of web technology in conjunction with machine learning to provide a cost-effective and timely solution to visualize and analyze big educational data. Additionally, ad hoc visualization provides contextual analysis in areas of concern for education agencies (EAs). [ABSTRACT FROM AUTHOR]
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- 2024
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20. Agile Project Management in the Age of Digital Transformation: Exploring Emerging Trends.
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Gorski, Hortensia, Gligorea, Ilie, Brudan, Adrian, and Oancea, Romana
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PROJECT management ,DIGITAL transformation ,DIGITAL technology ,COMPUTER software development ,ARTIFICIAL intelligence - Abstract
In the context of today's dynamic environment, agility and speed are two essential characteristics that apply to project management in the software development industry, as well as in many other industries. In order to meet the complex and continuous challenges of the digital age, the principles, techniques and methods of Agile Project Management and Scrum are expected to become more widespread, especially in software development, replacing or augmenting the traditional ones. This paper aims to identify trends in project management related to digital transformation and diffusion of Industry 4.0 technologies. A bibliometric analysis was carried out by searching the WOS database. The resulting documents were exported and processed in VOSviewer to fulfil the scope. The research revealed that, in the context of digital transformation, information technology supports the agile approach, agile transformation and agile project management. Furthermore, emerging technologies specific to Industry 4.0, especially artificial intelligence, and big data, can contribute significantly to all project phases. These emerging technologies can improve data processing and analysis, project forecasting, and risks prediction, can support decision making thus contributing to the success of the project. [ABSTRACT FROM AUTHOR]
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- 2024
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21. Data sovereignty requirements for patient-oriented AI-driven clinical research in Germany
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Radic, Marija, Busch-Casler, Julia, Vosen, Agnes, Herrmann, Philipp, Appenzeller, Arno, Mucha, Henrik, Philipp, Patrick, Frank, Kevin, Dauth, Stephanie, Köhm, Michaela, Orak, Berna, Spiecker genannt Döhmann, Indra, and Böhm, Peter
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- 2024
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22. Accounting of the Future: Technological Impact.
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Silva, Adriana and Proença, Catarina
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ACCOUNTING ,TECHNOLOGICAL innovations ,DIGITAL technology ,BUSINESS models ,PROFESSIONALISM - Abstract
Purpose: This paper explores the revolution in accounting driven by technological innovations, highlighting the transition of digitalization from a mere choice to a vital business necessity. The dynamic environment requires accountants to understand and incorporate new technologies to thrive in an ever-evolving business environment. The so-called "new" technologies promise to perform tasks more agile and efficient, thus providing a faster and more cost-effective approach in various sectors (White et al., 2017). In turn, companies' success is intrinsically linked to the ability to invest, use, and exploit these technological innovations effectively (Cong et al., 2018). With the continuous advancement of increasingly sophisticated technologies with disruptive capabilities, new companies have emerged to adopt innovative business models (Watson, 2017), while some established institutions have reinvented their business models. As a result of this dynamic context, the accounting professions are under pressure, as indicated by several studies (e.g., Cai & Singh, 2019; Kaya et al., 2019; Kokina & Blanchette, 2019; Kruskopf et al., 2019; Marshall & Lambert, 2018). At the same time, there is a permanent need to reinvent accounting education (Alderman, 2019; Kokina & Blanchette, 2019; Pan & Seow, 2016), to keep up with current changes. Thus, this paper aims to provide a concise overview of emerging technologies in accounting, highlighting findings that contribute to the up-to-date understanding of the transformative role of these technologies in contemporary accounting practice. Methodology: In this work, a literature review was carried out, looking for articles related to emerging technologies in accounting and published in important and quality scientific journals. The research was conducted in Web of Science (WoS), Scopus and Google Scholar on emerging technologies in accounting (search terms: "emerg* technolog*" and "accounting"), in the period between 2003-2023. Subsequently, the articles to be considered were manually selected, as well as the relevant information in each of them. More precisely, the search was restricted to documents presumed to be scientific articles written in English. Subsequently, through a verification process, we manually analyzed all keywords, titles, and abstracts of the articles, and when necessary, the entire content of each article included in the database was reviewed. Results: The rapid digital transformation that we are witnessing is transversal to all areas and accounting and auditing has undergone several changes in recent years, with a transformation of business models (Tiron-Tudor et al., 2024). As artificial intelligence, blockchain, big data analytics, robotics, cybersecurity, and other advanced technologies permeate the accounting field, we are witnessing not only the modernization of processes, but also the training of professionals to become strategic and analytical agents. These innovations are not simply additions to accounting practice, but transformations that transcend how we understand and perform accounting work. The literature has recognised that the combination of various technologies such as robotic process automation (RPA) and artificial intelligence, blockchain, and big data analytics can be an asset for solving problems in the areas of accounting and auditing (Asatiani et al. 2020; Cooper et al. 2020; Ribeiro et al. 2021). The introduction of new technologies is a complex, time-consuming and, in certain circumstances, costly process. This can be a challenge, particularly for smaller companies, which may not be able to implement new technologies on a scale comparable to larger companies. Consequently, these companies may face difficulties adapting to technological changes (Marr, 2016). The continued advancement of AI and other emerging technologies, combined with an increasing ability to analyze large volumes of data, has increased the threat of significant automation of many jobs in the future (Brynjolfsson & McAfee, 2014). However, it is crucial to remember that even with the extinction of some jobs, new job opportunities will emerge (Marr, 2016). Originality: The theme of accounting transformation driven by emerging technologies is both original and crucial. It originally highlights how traditional accounting practices are reinvented by rapid technological evolution. It's important because companies that don't keep up with these changes risk being left behind in a competitive market. The importance also lies in the changing role of accounting professionals who use advanced technologies to aid decisionmaking. In short, this theme highlights the need for organizations to understand and adopt emerging technologies to ensure their sustainability and growth in a dynamic business environment. [ABSTRACT FROM AUTHOR]
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- 2024
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23. Current Trends in Artificial Intelligence and Bovine Mastitis Research: A Bibliometric Review Approach.
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Mitsunaga, Thatiane Mendes, Nery Garcia, Breno Luis, Pereira, Ligia Beatriz Rizzanti, Costa, Yuri Campos Braga, da Silva, Roberto Fray, Delbem, Alexandre Cláudio Botazzo, and dos Santos, Marcos Veiga
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ARTIFICIAL neural networks ,BOVINE mastitis ,ARTIFICIAL intelligence ,MACHINE learning ,DATABASES ,MILK quality - Abstract
Simple Summary: Artificial intelligence has become essential for aiding in different knowledge domains by improving knowledge extraction from raw data and process automation. In dairy production, artificial intelligence offers promising applications in detecting and managing bovine mastitis, the most critical disease affecting the mammary gland in dairy cows, impacting milk production and profitability in dairy farms. This research evaluated the evolution of artificial intelligence applications in bovine mastitis between 2011 and 2021 using the Scopus database and the frequency of terms cited in titles, abstracts, and keywords. We selected the 62 papers that were the most relevant according to their citation index. Our results pointed out that the terms "machine learning" and "mastitis" were the most cited, with a significant increase between 2018 and 2021. There was an increase in artificial intelligence applications for bovine mastitis per country, showing applications primarily aimed at improving the current mastitis detection systems. The most cited model was artificial neural networks. We concluded that using artificial intelligence in bovine mastitis was related to mastitis detection as a vital tool to prevent this disease, considering its major impacts on dairy production and economic return. Mastitis, an important disease in dairy cows, causes significant losses in herd profitability. Accurate diagnosis is crucial for adequate control. Studies using artificial intelligence (AI) models to classify, identify, predict, and diagnose mastitis show promise in improving mastitis control. This bibliometric review aimed to evaluate AI and bovine mastitis terms in the most relevant Scopus-indexed papers from 2011 to 2021. Sixty-two documents were analyzed, revealing key terms, prominent researchers, relevant publications, main themes, and keyword clusters. "Mastitis" and "machine learning" were the most cited terms, with an increasing trend from 2018 to 2021. Other terms, such as "sensors" and "mastitis detection", also emerged. The United States was the most cited country and presented the largest collaboration network. Publications on mastitis and AI models notably increased from 2016 to 2021, indicating growing interest. However, few studies utilized AI for bovine mastitis detection, primarily employing artificial neural network models. This suggests a clear potential for further research in this area. [ABSTRACT FROM AUTHOR]
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- 2024
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24. Trends and Challenges towards Effective Data-Driven Decision Making in UK Small and Medium-Sized Enterprises: Case Studies and Lessons Learnt from the Analysis of 85 Small and Medium-Sized Enterprises.
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Tawil, Abdel-Rahman H., Mohamed, Muhidin, Schmoor, Xavier, Vlachos, Konstantinos, and Haidar, Diana
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SMALL business ,ARTIFICIAL intelligence ,DATA science ,BIG data ,RESEARCH questions ,DIGITAL technology - Abstract
The adoption of data science brings vast benefits to Small and Medium-sized Enterprises (SMEs) including business productivity, economic growth, innovation and job creation. Data science can support SMEs to optimise production processes, anticipate customers' needs, predict machinery failures and deliver efficient smart services. Businesses can also harness the power of artificial intelligence (AI) and big data, and the smart use of digital technologies to enhance productivity and performance, paving the way for innovation. However, integrating data science decisions into an SME requires both skills and IT investments. In most cases, such expenses are beyond the means of SMEs due to their limited resources and restricted access to financing. This paper presents trends and challenges towards effective data-driven decision making for organisations based on a 3-year long study which covered more than 85 UK SMEs, mostly from the West Midlands region of England. In particular, this study attempts to find answers to several key research questions around data science and AI adoption among UK SMEs, and the advantages of digitalisation and data-driven decision making, as well as the challenges hindering their effective utilisation of these technologies. We also present two case studies that demonstrate the potential of digitisation and data science, and use these as examples to unveil challenges and showcase the wealth of currently available opportunities for SMEs. [ABSTRACT FROM AUTHOR]
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- 2024
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25. FILM AND TELEVISION SPECIAL EFFECTS AI SYSTEM INTEGRATING COMPUTER ARTIFICIAL INTELLIGENCE AND BIG DATA TECHNOLOGY.
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YAO JU and GUOBIN WEI
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PARTICLE swarm optimization ,ARTIFICIAL intelligence ,BIG data ,COMPUTER systems ,3-D films ,EXTREME value theory - Abstract
Particle systems can achieve many scenarios that are difficult to achieve in the field or expensive in reality. In this paper, the requirements of 3D film special effects and the design process of particle systems are studied. Unity3D engine was used to simulate 3D movie special effects. Then, the motion trajectory planning of 3D video group animation characters based on particle swarm optimization is proposed. Then, the system models the animated characters' moving track to achieve the realism's dynamic effect. This project intends to use the gravity optimization method for particle swarm optimization. The aim is to overcome the optimization difficulty caused by particle swarm optimization, which is easy to fall into local extreme values. Finally, the generated trajectory information is input into the 3D simulation system for conflict detection and clustering tests. Experiments show that the proposed algorithm can effectively render memorable scenes such as movies and TV. The picture has a high real-time frame rate and is realistic. [ABSTRACT FROM AUTHOR]
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- 2024
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26. Revolutionizing Cardiology through Artificial Intelligence—Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment—A Comprehensive Review of the Past 5 Years.
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Stamate, Elena, Piraianu, Alin-Ionut, Ciobotaru, Oana Roxana, Crassas, Rodica, Duca, Oana, Fulga, Ana, Grigore, Ionica, Vintila, Vlad, Fulga, Iuliu, and Ciobotaru, Octavian Catalin
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MACHINE learning ,ARTIFICIAL intelligence ,PULMONARY embolism ,CARDIAC pacing ,SCIENTIFIC literature ,BIG data - Abstract
Background: Artificial intelligence (AI) can radically change almost every aspect of the human experience. In the medical field, there are numerous applications of AI and subsequently, in a relatively short time, significant progress has been made. Cardiology is not immune to this trend, this fact being supported by the exponential increase in the number of publications in which the algorithms play an important role in data analysis, pattern discovery, identification of anomalies, and therapeutic decision making. Furthermore, with technological development, there have appeared new models of machine learning (ML) and deep learning (DP) that are capable of exploring various applications of AI in cardiology, including areas such as prevention, cardiovascular imaging, electrophysiology, interventional cardiology, and many others. In this sense, the present article aims to provide a general vision of the current state of AI use in cardiology. Results: We identified and included a subset of 200 papers directly relevant to the current research covering a wide range of applications. Thus, this paper presents AI applications in cardiovascular imaging, arithmology, clinical or emergency cardiology, cardiovascular prevention, and interventional procedures in a summarized manner. Recent studies from the highly scientific literature demonstrate the feasibility and advantages of using AI in different branches of cardiology. Conclusions: The integration of AI in cardiology offers promising perspectives for increasing accuracy by decreasing the error rate and increasing efficiency in cardiovascular practice. From predicting the risk of sudden death or the ability to respond to cardiac resynchronization therapy to the diagnosis of pulmonary embolism or the early detection of valvular diseases, AI algorithms have shown their potential to mitigate human error and provide feasible solutions. At the same time, limits imposed by the small samples studied are highlighted alongside the challenges presented by ethical implementation; these relate to legal implications regarding responsibility and decision making processes, ensuring patient confidentiality and data security. All these constitute future research directions that will allow the integration of AI in the progress of cardiology. [ABSTRACT FROM AUTHOR]
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- 2024
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27. The autonomous choice architect
- Author
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Mills, Stuart and Sætra, Henrik Skaug
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- 2024
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28. Recent Trends in Intelligence Enabled Research : Selected Papers of Fifth Doctoral Symposium, DoSIER 2023
- Author
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Siddhartha Bhattacharyya, Gautam Das, Sourav De, Leo Mrsic, Siddhartha Bhattacharyya, Gautam Das, Sourav De, and Leo Mrsic
- Subjects
- Computational intelligence, Artificial intelligence, Internet of things, Big data
- Abstract
This book gathers extended versions of papers presented at DoSIER 2023 (Fifth Doctoral Symposium on Intelligence Enabled Research, held at Cooch Behar Government Engineering College, West Bengal, India, during December 20–21, 2023). The papers address the rapidly expanding research area of computational intelligence, which, no longer limited to specific computational fields, has since made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design, to name but a few. Presenting chapters written by experts active in these areas, the book offers a valuable reference guide for researchers and industrial practitioners alike and inspires future studies.
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- 2024
29. An Innovative K-Anonymity Privacy-Preserving Algorithm to Improve Data Availability in the Context of Big Data.
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Linlin Yuan, Tiantian Zhang, Yuling Chen, Yuxiang Yang, and Huang Li
- Subjects
BIG data ,GREEDY algorithms ,INFORMATION theory ,ALGORITHMS ,ARTIFICIAL intelligence ,STATISTICS ,BLOCKCHAINS - Abstract
The development of technologies such as big data and blockchain has brought convenience to life, but at the same time, privacy and security issues are becoming more and more prominent. The K-anonymity algorithm is an effective and low computational complexity privacy-preserving algorithm that can safeguard users' privacy by anonymizing big data. However, the algorithm currently suffers from the problem of focusing only on improving user privacy while ignoring data availability. In addition, ignoring the impact of quasi-identified attributes on sensitive attributes causes the usability of the processed data on statistical analysis to be reduced. Based on this, we propose a new K-anonymity algorithm to solve the privacy security problem in the context of big data, while guaranteeing improved data usability. Specifically, we construct a new information loss function based on the information quantity theory. Considering that different quasi-identification attributes have different impacts on sensitive attributes, we set weights for each quasi-identification attribute when designing the information loss function. In addition, to reduce information loss, we improve K-anonymity in two ways. First, we make the loss of information smaller than in the original table while guaranteeing privacy based on common artificial intelligence algorithms, i.e., greedy algorithm and 2-means clustering algorithm. In addition, we improve the 2-means clustering algorithm by designing a mean-center method to select the initial center of mass. Meanwhile, we design the K-anonymity algorithm of this scheme based on the constructed information loss function, the improved 2-means clustering algorithm, and the greedy algorithm, which reduces the information loss. Finally, we experimentally demonstrate the effectiveness of the algorithm in improving the effect of 2-means clustering and reducing information loss. [ABSTRACT FROM AUTHOR]
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- 2024
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30. AUTOMATION AND ROBOTICS IN WASTE MANAGEMENT: A STEP TOWARDS IN INDUSTRY4.0.
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SULAIMAN, PESHRAW
- Subjects
AUTOMATION ,ARTIFICIAL intelligence ,INDUSTRY 4.0 ,WASTE management ,SUSTAINABILITY - Abstract
This paper shows the critical role of robotics and automation in waste management, presenting a broad analysis of their integration as a transformative step towards Industry 4.0. However, focusing on the challenges faced by growing cities in efficiently handling waste, the study emphasizes smart waste management solutions and the growing demand for innovative. Key components of Industry 4.0, including Artificial Intelligence (AI), Big Data, the Internet of Things (IoT) and Robotics, are explored for their potential to revolutionize waste management practices. The discussion involves the multidimensional impact of these technologies on waste process such as collection, sorting, and disposal processes. Examples such as the Pneumatic Waste Collection System 4.0 (PWC 4.0) and swarm robotics illustrate practical applications, highlighting their involvement to efficiency, sustainability, and inclusivity. By delving into the soft aspects of smart cities and the domains defined by Professor Dr. Rudolf Giffinger, the paper highlights the broader implications of Industry 4.0 in enhancing the quality of life for citizens. The integration of digital technologies into waste management processes aligns with the global agenda of sustainable development and environmental conservation, positioning it as a significant stride towards smarter and more environmentally conscious cities. [ABSTRACT FROM AUTHOR]
- Published
- 2024
31. Interdisciplinarity of information science: an evolutionary perspective of theory application.
- Author
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Zhang, Chao, Wang, Fang, Huang, Yi, and Chang, Le
- Subjects
INFORMATION science ,CITATION indexes ,EVOLUTIONARY theories ,ARTIFICIAL intelligence ,BIG data - Abstract
Purpose: This paper aims to reveal the interdisciplinarity of information science (IS) from the perspective of the evolution of theory application. Design/methodology/approach: Select eight representative IS journals as data sources, extract the theories mentioned in the full texts of the research papers and then measure annual interdisciplinarity of IS by conducting theory co-occurrence network analysis, diversity measure and evolution analysis. Findings: As a young and vibrant discipline, IS has been continuously absorbing and internalizing external theoretical knowledge and thus formed a high degree of interdisciplinarity. With the continuous application of some kernel theories, the interdisciplinarity of IS appears to be decreasing and gradually converging into a few neighboring disciplines. Influenced by big data and artificial intelligence, the research paradigm of IS is shifting from a theory centered one to a technology centered one. Research limitations/implications: This study helps to understand the evolution of the interdisciplinarity of IS in the past 21 years. The main limitation is that the data were collected from eight journals indexed by the Social Sciences Citation Index and a small amount of theories might have been omitted. Originality/value: This study identifies the kernel theories in IS research, measures the interdisciplinarity of IS based on the evolution of the co-occurrence network of theory source disciplines and reveals the paradigm shift being happening in IS. [ABSTRACT FROM AUTHOR]
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- 2024
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32. The future of platforms, big data and new forms of capital accumulation.
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Nayak, Bhabani Shankar and Walton, Nigel
- Subjects
BIG data ,DISCOURSE analysis ,ELECTRONIC commerce ,MARXIST philosophy ,DIGITAL technology ,ARTIFICIAL intelligence ,MANUFACTURING processes - Abstract
Purpose: The paper argues that the classical Marxist theory of capitalist accumulation is inadequate to understand new forms of capitalism and their accumulation processes determined by "platforms" and "big data". Big data platforms are shaping the processes of production, labour, the price of products and market conditions. "Digital platforms" and "big data" have become an integral part of the processes of production, distribution and exchange relations. These twin pillars are central to the capitalist accumulation processes. The article argues that the classical Marxist theory of capitalist accumulation is inadequate to understand new forms of capitalism and their accumulation processes determined by "platforms" and "big data". Design/methodology/approach: As a conceptual paper, this paper follows critical methodological lineages and traditions based on non-linear historical narratives around the conceptualisation, construction and transition of the "Marxist theory of capital accumulation" in the age of platform economy. This paper follows a discourse analysis (Fairclough, 2003) to locate the way in which an artificial intelligence (AI)-led platform economy helps identify and conceptualise new forms of capitalist accumulation. It engages with Jørgensen and Phillips' (2002) contextual and empirical discursive traditions to undertake a qualitative comparative analysis by exploring a broad range of complex factors with case studies and examples from leading firms within the platform economy. Finally, it adopts two steps of "Theory Synthesis and Theory Adaptation" as outlined by Jaakkola (2020) to synthesise, adopt and expand the Marxist theory of capital accumulation under platform capitalism. Findings: This article identifies new trends and forms of data driven capitalist accumulation processes within the platform capitalism. The findings suggest that an AI led platform economy creates new forms of capitalist accumulation. The article helps to develop theoretical understanding and conceptual frameworks to understand and explain these new forms of capital accumulation. Originality/value: This study builds upon the limited theorisation on the AI and new capitalist accumulation processes. This article identifies new trends and forms of data driven capitalist accumulation processes within platform capitalism. The article helps to understand digital and platform capitalisms in the lens of digital labour and expands the theory of capitalist accumulation and its new forms in the age of datafication. While critiquing the Marxist theory of capitalist accumulation, the article offers alternative approaches for the future. [ABSTRACT FROM AUTHOR]
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- 2024
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33. Building digital patient pathways for the management and treatment of multiple sclerosis.
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Wenk, Judith, Voigt, Isabel, Inojosa, Hernan, Schlieter, Hannes, and Ziemssen, Tjalf
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MULTIPLE sclerosis ,DIGITAL twins ,ARTIFICIAL intelligence ,ELECTRONIC equipment ,BIG data - Abstract
Recent advances in the field of artificial intelligence (AI) could yield new insights into the potential causes of multiple sclerosis (MS) and factors influencing its course as the use of AI opens new possibilities regarding the interpretation and use of big data from not only a cross-sectional, but also a longitudinal perspective. For each patient with MS, there is a vast amount of multimodal data being accumulated over time. But for the application of AI and related technologies, these data need to be available in a machine-readable format and need to be collected in a standardized and structured manner. Through the use of mobile electronic devices and the internet it has also become possible to provide healthcare services from remote and collect information on a patient's state of health outside of regular check-ups on site. Against this background, we argue that the concept of pathways in healthcare now could be applied to structure the collection of information across multiple devices and stakeholders in the virtual sphere, enabling us to exploit the full potential of AI technology by e.g., building digital twins. By going digital and using pathways, we can virtually link patients and their caregivers. Stakeholders then could rely on digital pathways for evidence-based guidance in the sequence of procedures and selection of therapy options based on advanced analytics supported by AI as well as for communication and education purposes. As far as we aware of, however, pathway modelling with respect to MS management and treatment has not been thoroughly investigated yet and still needs to be discussed. In this paper, we thus present our ideas for a modular-integrative framework for the development of digital patient pathways for MS treatment. [ABSTRACT FROM AUTHOR]
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- 2024
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34. The scheduling techniques in the Hadoop and Spark of smart cities environment: a systematic review.
- Author
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Mirza, Nada Masood, Ali, Adnan, and Ishak, Mohamad Khairi
- Subjects
SMART cities ,REAL-time computing ,BIG data ,SCHEDULING ,MUNICIPAL services ,AMBIENT intelligence ,ARTIFICIAL intelligence - Abstract
Processing extensive and diverse data in real-time is a significant challenge in the context of smart cities. Timely access to information and efficient analytics is essential for smart city services to make data-driven decisions and enhance urban living. Scheduling algorithms play a crucial role in ensuring the prompt delivery of services and efficient task completion. This paper explores various scheduling techniques, including static, dynamic, and hybrid schedulers, and compares their objectives and performance. Additionally, the study examines two prominent data processing frameworks, Hadoop and Spark, and compares their capabilities in handling big data in smart cities. With its ability to process large amounts of data quickly and efficiently, Spark has shown superiority over Hadoop in realtime data processing and performance optimization. The paper concludes by highlighting the strengths and limitations of each framework. It discusses the need for further research in optimizing scheduling techniques and exploring hybrid artificial intelligence scheduling for Spark. Overall, the findings contribute to a better understanding of data processing in real-time and provide insights for researchers and practitioners in smart cities. [ABSTRACT FROM AUTHOR]
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- 2024
- Full Text
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35. Net Zero Dairy Farming—Advancing Climate Goals with Big Data and Artificial Intelligence.
- Author
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Neethirajan, Suresh
- Subjects
ARTIFICIAL intelligence ,DAIRY farming ,SUSTAINABLE agriculture ,SUSTAINABILITY ,BIG data ,DIGITAL technology ,AGRICULTURAL technology - Abstract
This paper explores the transformative potential of Big Data and Artificial Intelligence (AI) in propelling the dairy industry toward net zero emissions, a critical objective in the global fight against climate change. Employing the Canadian dairy sector as a case study, the study extrapolates its findings to demonstrate the global applicability of these technologies in enhancing environmental sustainability across the agricultural spectrum. We begin by delineating the environmental challenges confronting the dairy industry worldwide, with an emphasis on greenhouse gas (GHG) emissions, including methane from enteric fermentation and nitrous oxide from manure management. The pressing need for innovative approaches in light of the accelerating climate crisis forms the crux of our argument. Our analysis delves into the role of Big Data and AI in revolutionizing emission management in dairy farming. This includes applications in optimizing feed efficiency, refining manure management, and improving energy utilization. Technological solutions such as predictive analytics for feed optimization, AI in herd health management, and sensor networks for real-time monitoring are thoroughly examined. Crucially, the paper addresses the wider implications of integrating these technologies in dairy farming. We discuss the development of benchmarking standards for emissions, the importance of data privacy, and the essential role of policy in promoting sustainable practices. These aspects are vital in supporting the adoption of technology, ensuring ethical use, and aligning with international climate commitments. Concluding, our comprehensive study not only suggests a pathway for the dairy industry towards environmental sustainability but also provides insights into the role of digital technologies in broader agricultural practices, aligning with global environmental sustainability efforts. [ABSTRACT FROM AUTHOR]
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- 2024
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36. Artificial Intelligence-Driven Facial Image Analysis for the Early Detection of Rare Diseases: Legal, Ethical, Forensic, and Cybersecurity Considerations.
- Author
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Kováč, Peter, Jackuliak, Peter, Bražinová, Alexandra, Varga, Ivan, Aláč, Michal, Smatana, Martin, Lovich, Dušan, and Thurzo, Andrej
- Abstract
This narrative review explores the potential, complexities, and consequences of using artificial intelligence (AI) to screen large government-held facial image databases for the early detection of rare genetic diseases. Government-held facial image databases, combined with the power of artificial intelligence, offer the potential to revolutionize the early diagnosis of rare genetic diseases. AI-powered phenotyping, as exemplified by the Face2Gene app, enables highly accurate genetic assessments from simple photographs. This and similar breakthrough technologies raise significant privacy and ethical concerns about potential government overreach augmented with the power of AI. This paper explores the concept, methods, and legal complexities of AI-based phenotyping within the EU. It highlights the transformative potential of such tools for public health while emphasizing the critical need to balance innovation with the protection of individual privacy and ethical boundaries. This comprehensive overview underscores the urgent need to develop robust safeguards around individual rights while responsibly utilizing AI's potential for improved healthcare outcomes, including within a forensic context. Furthermore, the intersection of AI and sensitive genetic data necessitates proactive cybersecurity measures. Current and future developments must focus on securing AI models against attacks, ensuring data integrity, and safeguarding the privacy of individuals within this technological landscape. [ABSTRACT FROM AUTHOR]
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- 2024
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37. Digital Transformation and Innovation: The Influence of Digital Technologies on Turnover from Innovation Activities and Types of Innovation.
- Author
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Vărzaru, Anca Antoaneta and Bocean, Claudiu George
- Abstract
In today's competitive and globalized world, innovation is essential for organizational survival, offering a means for companies to address environmental impacts and social challenges. As innovation processes accelerate, managers need to rethink the entire value-creation chain, with digital transformation emerging as a continuous process of organizational adaptation to the evolving societal landscape. The research question focuses on how digital technologies—such as artificial intelligence, Big Data, cloud computing, industrial and service robots, and the Internet of Things—influence innovation-driven revenues among enterprises within the European Union (EU). The paper examines, using neural network analysis, the specific impact of each digital technology on innovation revenues while exploring how these technologies affect various types of social innovation within organizations. Through cluster analysis, the study identifies patterns among EU countries based on their digital technology adoption, innovation expenditures, and revenues and the proportion of enterprises engaged in innovation activities. The findings highlight the central role of digital technologies in enhancing innovation and competitiveness, with significant implications for managers and policymakers. These results underscore the necessity for companies to strategically integrate digital technologies to sustain long-term competitiveness in the rapidly evolving digital landscape of the EU. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
38. A Comprehensive Survey on the Societal Aspects of Smart Cities.
- Author
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Bastos, David, Costa, Nuno, Rocha, Nelson Pacheco, Fernández-Caballero, Antonio, and Pereira, António
- Subjects
SMART cities ,INTERNET of things ,ARCHITECTURAL design ,ARTIFICIAL intelligence ,NETWORK neutrality - Abstract
Smart cities and information and communications technology is a rapidly growing field in both research and real-world implementation, but it is one that is still new and with many different ideas. Unfortunately, there is less cooperation and knowledge sharing across the field, and research often fails to move into real-world applications, which holds it back from becoming fully realized. This paper aims to provide an overview of the current state of smart cities, its definitions, technologies, and technical dimensions, from architectural design to standards and data handling, and how they are handled in the real world and its impact on society. Additionally, it examines important smart city projects, their applications, and ranking systems. This text aims to forecast the future of the field, its impact, the challenges it faces, and what should be addressed to help it reach its full potential. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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39. AI governance in India – law, policy and political economy.
- Author
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Joshi, Divij
- Subjects
ARTIFICIAL intelligence ,INFRASTRUCTURE (Economics) ,MARKET design & structure (Economics) ,BIG data ,DATA analysis - Abstract
Artificial Intelligence technologies have elicited a range of policy responses in India, particularly as the Government of India attempts to position and project the country as a global leader in the production of AI technologies. Policy responses have ranged from providing public infrastructure to enable market-led AI production, to nationalising datasets in an effort to enable Big Data analysis through AI. This paper examines the recent history of AI policy in India from a critical political economy perspective, and argues that AI policy and governance in India constructs and legitimises a globally-dominant paradigm of informational capitalism, based on the construction of data as a productive resource for an information-based economic production, and encouraging self-regulation of harmful impacts by firms, even as it attempts to secure a strong hand for the state to determine, both through law and infrastructure, how such a market is structured and to what ends. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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40. Enhancing Resilience via Exponential Technologies: Analysing Trends, Focus and Contributions.
- Author
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Arora, Manpreet, Kumar, Jeetesh, Dhiman, Vaishali, Rathore, Sunaina, Singh, Swati, and Chandel, Monika
- Subjects
ARTIFICIAL intelligence ,BIBLIOMETRICS ,BIG data ,CLOUD computing ,BLOCKCHAINS - Abstract
This article seeks to conduct a bibliometric analysis focusing on exponential technologies such as big data, internet of thing (IoT), artificial intelligence (AI), blockchain and cloud computing. It aims to outline research trends in this domain and explore their correlation with resilience. The study aims to track the evolution of research trends in this field over time and identify less explored dimensions of exponential technologies. Leveraging performance analysis and science mapping techniques, the paper highlights the significant growth and potential in these areas, considering them as pivotal agendas of the twenty-first century. By examining scientific productivity metrics such as publications, authors, institutions, countries and keywords, the article offers insights into emerging areas within exponential technologies. As the first comprehensive study of its kind, it provides a broad overview of the main trends and patterns in resilience research encompassing big data, IoT, AI, blockchain and cloud computing, consolidating them into a single cohesive narrative. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
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41. An eXplainable Artificial Intelligence Methodology on Big Data Architecture.
- Author
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La Gatta, Valerio, Moscato, Vincenzo, Postiglione, Marco, and Sperlì, Giancarlo
- Abstract
Although artificial intelligence has become part of everyone's real life, a trust crisis against such systems is occurring, thus increasing the need to explain black-box predictions, especially in the military, medical, and financial domains. Modern eXplainable Artificial Intelligence (XAI) techniques focus on benchmark datasets, but the cognitive applicability of such solutions under big data settings is still unclear due to memory or computation constraints. In this paper, we extend a model-agnostic XAI methodology, named Cluster-Aided Space Transformation for Local Explanation (CASTLE), to be able to deal with high-volume datasets. CASTLE aims to explain the black-box behavior of predictive models by combining both local (i.e., based on the input sample) and global (i.e., based on the whole scope for action of the model) information. In particular, the local explanation provides a rule-based explanation for the prediction of a target instance as well as the directions to update the likelihood of the predicted class. Our extension leverages modern big data technologies (e.g., Apache Spark) to handle the high volume, variety, and velocity of huge datasets. We have evaluated the framework on five datasets, in terms of temporal efficiency, explanation quality, and model significance. Our results indicate that the proposed approach retains the high-quality explanations associated with CASTLE while efficiently handling large datasets. Importantly, it exhibits a sub-linear, rather than exponential, dependence on dataset size, making it a scalable solution for massive datasets or in any big data scenario. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
42. Intelligent Interconnected Healthcare System: Integrating IoT and Big Data for Personalized Patient Care.
- Author
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Abatal, Ahmed, Mzili, Mourad, Mzili, Toufik, Cherrat, Khaoula, Yassine, Asmae, and Abualigah, Laith
- Subjects
ARTIFICIAL intelligence ,INDIVIDUALIZED medicine ,PUBLIC health infrastructure ,LENGTH of stay in hospitals ,HOSPITAL supplies - Abstract
This paper introduces the intelligent interconnected healthcare system (IIHS), an innovative fusion of the Internet of Things (IoT) and big data analytics technologies designed to revolutionize proactive and personalized healthcare. IIHS facilitates the integration of real-time data from various devices, ambient sensors, and hospital equipment, creating a continuous stream of comprehensive healthcare data. Leveraging advanced data analysis, IIHS offers actionable insights for ongoing patient health monitoring, trend prediction through machine learning, and rapid information access via a user-friendly interface. The system architecture features a combination of centralized cloud storage and edge storage at healthcare facilities, enhancing both efficiency and security in data management. The effectiveness of IIHS has been demonstrated in two healthcare facilities, which reported significant reductions in patient length of stay and readmission rates. This indicates the system's potential to improve patient care while seamlessly integrating with existing healthcare infrastructures. IIHS represents the future of digital and personalized medicine, offering a scalable, patient-centric solution that supports the ongoing transformation towards data-driven healthcare. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
43. Advances in Tourism, Technology and Systems : Selected Papers From ICOTTS 2023, Volume 2
- Author
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João Vidal Carvalho, António Abreu, Dália Liberato, José Angel Díaz Rebolledo, João Vidal Carvalho, António Abreu, Dália Liberato, and José Angel Díaz Rebolledo
- Subjects
- Computational intelligence, Artificial intelligence, Tourism, Management, Big data
- Abstract
This book features a collection of high-quality research papers presented at the International Conference on Tourism, Technology and Systems (ICOTTS 2023), held at Anáhuac University, Bacalar, Mexico, from 2 to 4 November 2023. The book is divided into two volumes, and it covers the areas of technology in tourism and the tourist experience, generations and technology in tourism, digital marketing applied to tourism and travel, mobile technologies applied to sustainable tourism, information technologies in tourism, digital transformation of tourism business, e-tourism and tourism 2.0, big data and management for travel and tourism, geotagging and tourist mobility, smart destinations, robotics in tourism, and information systems and technologies.
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- 2024
44. Advances in Tourism, Technology and Systems : Selected Papers From ICOTTS 2023, Volume 1
- Author
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António Abreu, João Vidal Carvalho, Pedro Liberato, Hazael Cerón Monroy, António Abreu, João Vidal Carvalho, Pedro Liberato, and Hazael Cerón Monroy
- Subjects
- Computational intelligence, Artificial intelligence, Tourism, Management, Big data
- Abstract
This book features a collection of high-quality research papers presented at the International Conference on Tourism, Technology and Systems (ICOTTS 2023), held at Anáhuac University, Bacalar, Mexico, from 2 to 4 November 2023. The book is divided into two volumes, and it covers the areas of technology in tourism and the tourist experience, generations and technology in tourism, digital marketing applied to tourism and travel, mobile technologies applied to sustainable tourism, information technologies in tourism, digital transformation of tourism business, e-tourism and tourism 2.0, big data and management for travel and tourism, geotagging and tourist mobility, smart destinations, robotics in tourism, and information systems and technologies.
- Published
- 2024
45. Artificial Intelligence Tools and Applications in Embedded and Mobile Systems : Selected Papers From the First International Conference on Embedded and Mobile Systems (ICTA-EMOS), 24-25 November 2022, Arusha, Tanzania
- Author
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Jorge Marx Gómez, Anael Elikana Sam, Devotha Godfrey Nyambo, Jorge Marx Gómez, Anael Elikana Sam, and Devotha Godfrey Nyambo
- Subjects
- Information technology—Management, Artificial intelligence, Big data, Computers, Special purpose, Business information services
- Abstract
The emergence of Artificial Intelligence (AI) has had a tremendous impact on embedded and mobile systems. This book presents a diverse collection of papers that showcase cutting-edge research and practical applications of AI in this field. The peer-reviewed research articles stem from the First International Conference on Embedded and Mobile Systems (ICTA-EMOS), which was held on November 24th – 25th, 2022, in Arusha, Tanzania, East Africa. They demonstrate the breadth and depth of AI's impact across various domains, exploring topics such as healthcare advances, transportation optimization, sustainable solutions, and business and process optimization.
- Published
- 2024
46. Using artificial intelligence to enhance patient autonomy in healthcare decision-making
- Author
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Guerrero Quiñones, Jose Luis
- Published
- 2024
- Full Text
- View/download PDF
47. Advancing Arctic sea ice remote sensing with AI and deep learning: now and future.
- Author
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Li, Wenwen, Hsu, Chia-Yu, and Tedesco, Marco
- Subjects
DEEP learning ,SEA ice ,REMOTE sensing ,ARTIFICIAL intelligence ,BIG data - Abstract
The revolutionary advances of Artificial Intelligence (AI) in the past decade have brought transformative innovation across science and engineering disciplines. Also in the field of Arctic science, we have witnessed an increasing trend in the adoption of AI, especially deep learning, to support the analysis of Arctic big data and facilitate new discoveries. In this paper, we provide a comprehensive review of the applications of deep learning in sea ice remote sensing domains, focusing on problems such as sea ice lead detection, thickness estimation, concentration, sea ice extent forecasting and motion detection as well as sea ice type classification. In addition to discussing these applications, we also summarize technological advances that provide customized deep learning solutions, including new loss functions and learning strategies to better understand sea ice dynamics. To promote the growth of this exciting interdisciplinary field, we further explore several research areas where the Arctic sea ice community can benefit from cutting-edge AI technology. These areas include improving multi-modal deep learning capabilities, enhancing model accuracy in measuring prediction uncertainty, better leveraging AI foundation models, and deepening the integration with physics-based models. We hope that this paper can serve as a cornerstone in the progress of Arctic sea ice research using AI and inspire further advances in this field. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
48. Improved adaptive-phase fuzzy high utility pattern mining algorithm based on tree-list structure for intelligent decision systems.
- Author
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Chen, Jing, Liu, Aijun, Zhang, Hongjun, Yang, Shengyi, Zheng, Hui, Zhou, Ning, and Li, Peng
- Subjects
ARTIFICIAL intelligence ,SMART structures ,ALGORITHMS ,DATA mining ,BIG data - Abstract
With the rapid development of AI and big data mining technologies, computerized medical decision-making has become increasingly prominent. The aim of high-utility pattern mining (HUPM) is to discover meaningful patterns in medical databases that contribute to maximizing the utility from the perspective of diagnosis. However, HUPM pays less attention to the interpretability and explainability of these patterns in medical decision-making scenarios. This paper proposes a novel algorithm called the Improved fuzzy high-utility pattern mining (IF-HUPM) to address this problem. First, the paper applies a fuzzy preprocessing method to divide the fuzzy intervals of a medical quantitative data set, which enhances the fuzziness and interpretability of the data. Next, in the process of IF-HUPM, both fuzzy tree and list structures are employed to calculate fuzzy high-utility values. By combining the characteristics of the one-stage and two-stage algorithms of HUPM, an adaptive-phase Fuzzy HUPM hybrid frame is proposed. The experimental results demonstrate that the proposed IF-HUPM algorithm enhances both accuracy and efficiency and the mining process requires less time and space on average. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
49. 大数据与计算模型.
- Author
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李国杰
- Abstract
Copyright of Big Data Research (2096-0271) is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Published
- 2024
- Full Text
- View/download PDF
50. EDITORS' NOTE: INTRODUCTION TO THE THEMATIC ISSUE ON RESPONSIBLE ARTIFICIAL INTELLIGENCE AND PLATFORM LABOUR.
- Author
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Adeli, Hojjat, Makó, Csaba, Kis, Norbert, and Török, Bernát
- Subjects
BIG data ,ARTIFICIAL intelligence ,MACHINE learning ,INDUSTRIAL applications ,SOCIAL sciences - Published
- 2024
- Full Text
- View/download PDF
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