FreeFlow: A framework for server-driven mobile apps by Nabhya Sharma, Ravindra Nath Tripathi, Vikas Tripathi

“…Upon app initialization, a single HTTP POST request is sent from the application to the server, retrieving both data and UI code. …”
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Assessing Data Completeness in Emergency Medical Team Reports: Analysis of the Response to Cyclone Idai in Mozambique using the WHO Minimum Data Set by Odgerel Chimed-Ochir, Inn-Kynn Khaing, Ami Fukunaga, Takahito Yoshida, Yuki Takamura, Yui Yumiya, Matchecane Cossa, Isse Ussene, Salio Flavio, Ryoma Kayano, Tatsuhiko Kubo

“…Conclusion: This study underscores the need for standardized training, and the data collection applications that enable the automatic inclusion of information such as geotags. …”
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Multimodal Data Fusion for Tabular and Textual Data: Zero-Shot, Few-Shot, and Fine-Tuning of Generative Pre-Trained Transformer Models by Shadi Jaradat, Mohammed Elhenawy, Richi Nayak, Alexander Paz, Huthaifa I. Ashqar, Sebastien Glaser

“…The MDF framework’s success demonstrates its potential for broader applications beyond traffic crash analysis, particularly in domains where labeled data are scarce and predictive modeling is essential.…”
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A comprehensive review on application of drone, virtual reality and augmented reality with their application in dragline excavation monitoring in surface mines by Piyush Singh, Vmsr Murthy, Dheeraj Kumar, Simit Raval

“…Furthermore, it discusses the current applications of AI-driven 3D computer vision techniques in automating data analytics with point clouds for the extraction of terrain parameters and plan efficient dump disposal strategies by proper positioning of dragline in a large surface mine.…”
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Application of artificial neural networks for predicting soil settlement in geotechnical applications with plastic waste reinforcement above buried pipes by Sinan A. Al-Haddad, Hala Adnan Abbas, Luttfi A. Al-Haddad, Mustafa I. Al-Karkhi

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<i>Coherence in Natural Language: Data Stuctures and Applications</i> Florian Wolf and Edward Gibson (Massachusetts Institute of Technology) The MIT Press, 2006, vii+137 pp; hardbound, ISBN 0-262-23251-0, $36.00 by Alistair Knott

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Implementation strategy of telecom operators’ data lake technology by Junjun HU, Xiaojun XIE, Yanbin SHI, Qi YU

“…Firstly,the traditional data architecture of telecom operators was analyzed and the problems such as low quality,lack of real-time,lack of flexibility and mutual constraints of storage and application were identified.Then,the feasibility of introducing data lake technology was discussed,the storage scale and low cost of data lake,the originality of data and ease of use,and the application of on-demand modeling were clarified,which could provide great benefits for telecom operators’ data architecture optimization.Finally,the data lake construction plan of unified storage,unified standards,near-source acquisition,and application separation was proposed,and key points such as data lake data partitioning,deployment,lake entry and application data dynamic loading,and unified data management were clearly stated.…”
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Implementation strategy of telecom operators’ data lake technology by Junjun HU, Xiaojun XIE, Yanbin SHI, Qi YU

“…Firstly,the traditional data architecture of telecom operators was analyzed and the problems such as low quality,lack of real-time,lack of flexibility and mutual constraints of storage and application were identified.Then,the feasibility of introducing data lake technology was discussed,the storage scale and low cost of data lake,the originality of data and ease of use,and the application of on-demand modeling were clarified,which could provide great benefits for telecom operators’ data architecture optimization.Finally,the data lake construction plan of unified storage,unified standards,near-source acquisition,and application separation was proposed,and key points such as data lake data partitioning,deployment,lake entry and application data dynamic loading,and unified data management were clearly stated.…”
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Energy Management for Distributed Carbon-Neutral Data Centers by Wenting Chang, Chuyi Liu, Guanyu Ren, Jianxiong Wan

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Artificial neural networks in cardiology: analysis of graphic data by P. S. Onishchenko, K. Yu. Klyshnikov, E. A. Ovcharenko

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Construction of big data open platform for telecom operators by Haifeng ZHANG, Zhao DONG, Juan LI, Lei QI, Huiqiong GU

“…Big data is accelerating the application across various industries,the opening of the data will help operators in the internal aggregation and sharing,and help to serve the ever-changing application scenarios,this is important for operators to develop new business.The background of combining big data with cloud computing was analyzed,overall architecture of big data platform for cloud computing was proposed,then the big data open platform implementation scheme was focused on,the platform planning and the problems to be solved in the project were carried out,and the reference for the related researchers were provided.…”
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A Fine-Grained Mobile Data Security Model by Yubin Yang, Liming Cheng

“…A kind of fine-grained security model for mobile data was proposed, tagged the mobile data and mobile applications in the intelligent terminal and the mobile access gateway/server, to control the operation of mobile data, and achieve the purpose of mobile data security isolation. …”
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Building an Associative Classification Data Model Based on the Apriori Method by K. V. Mulyukova, V. M. Kureichik

“…Also, an example showing an approach to the mining of large web data was implemented. Based on the understanding of Web Mining technology and the described difficulties of analyzing web data in the PHP language, methods for effectively solving the practical problem of analyzing web data based on transactions committed in a dynamic web application have been proposed. …”
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Data Mining for the Internet of Things: Literature Review and Challenges by Feng Chen, Pan Deng, Jiafu Wan, Daqiang Zhang, Athanasios V. Vasilakos, Xiaohui Rong

“…And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. …”
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Emergent Technologies in Big Data Sensing: A Survey by Ting Zhu, Sheng Xiao, Qingquan Zhang, Yu Gu, Ping Yi, Yanhua Li

“…When the number of data generating sensors increases and the amount of sensing data grows to a scale that traditional methods cannot handle, big data methods are needed for sensing applications. …”
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Cross-Company Data Sharing Using Distributed Analytics by Soo-Yon Kim, Stefanie Berninger, Max Kocher, Martin Perau, Sandra Geisler

“…Decision making in modern supply chain management relies heavily on data-driven decision support. Companies show a growing interest in building insights not only on data from within the company’s own boundaries, but also from collaborators and other actors in the market. …”
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Machine learning based on laboratory data for disease prediction by A. V. Gusev, R. E. Novitskiy, A. A. Ivshin, A. A. Alekseev

“…An analysis of sources that describe the application of artificial intelligence techniques to obtain predictive analytics, taking into account information about patients, such as demographic, anamnestic, and laboratory data, the data of instrumental studies, information about existing and former diseases available in MIS, was performed. …”
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Enhancing healthcare data privacy and interoperability with federated learning by Adil Akhmetov, Zohaib Latif, Benjamin Tyler, Adnan Yazici

“…Despite advances in electronic medical records, mobile health applications, and wearable sensors, current digital health cannot fully exploit these data due to the lack of data analysis and exchange between heterogeneous systems. …”
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A data matching algorithm based on instruction pipeline by Yang Jiajia, Li Zheng, Zheng Er, Zhao Jing, Yan Wei, Liu Jin

“…The data matching technology based on regular expressions has significant application value in basic data governance and cleaning. …”
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Statistical data analysis methods in Brillouin spectroscopy: Tutorial by Christopher G. Poulton, Hadi Mahmodi, Matthew D. Arnold, Luke McAlary, Lezanne Ooi, Irina Kabakova

“…In this tutorial, we discuss in detail the application of the two unsupervised machine learning methods, principal component analysis and vertex component analysis, which can be used to retrieve information from the Brillouin microscopy data of heterogeneous samples. …”
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