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Intelligent System for the Dissemination and Distribution of Content in Social Networks Using Process Automation by Means of Software-Based Robots (RPA)

Intelligent System for the Dissemination and Distribution of...

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International Conference on Management, Tourism and Technologies, ICMTT 2024

作者： Quisi-Peralta, Diego F. Salgado-Guerrero, Juan P. Murillo-Valarezo, Jordan F. Pulla-Sanchez, Daniel J. Paguay-Palaguachi, Luis D. Angamarca-Deleg, Verónica E. Research Group on Digital Transformation and Data Science Universidad Politécnica Salesiana Cuenca Azuay010102 Ecuador

ISBN: (纸本)9783031748271

Social media content dissemination systems have become especially important nowadays, due to the growing popularity of social networks as a means of communication and promotion. In this context a social media content dissemination system that uses process automation techniques. The system allows users to create content using templates and distribute it on various social network platforms in an automated and personalized way. Algorithms are used to analyze user interaction patterns with the content to optimize the dissemination strategy. In addition, an intuitive and easy-to-use user interface is implemented so that users can interact with the system effectively. The results see significant improvement in the interaction rate and effectiveness of content dissemination in social networks. This system represents an innovative solution for the dissemination of content on social networks, optimizing the publication time by 50% and the generation of content by 25%, since it is all done from an integrated system that includes templates. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Chatbots

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Machine Learning Models for Early Prediction of COVID-19 Infections Based on Clinical Signs

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SN Computer science 2024年第1期5卷 1-10页

作者： Ben Ammar, Boulbaba Salem, Ali Ben Said, Mouna Ben Aouicha, Mohamed Data Engineering and Semantics Research Unit Faculty of Science University of Sfax Sfax Tunisia Digital Research Center of Sfax Ministry of Higher Education and Scientific Research Sfax Tunisia Signals systeMs aRtificial intelligence and neTworkS Laboratory (SM@RTS) Digital Research Center of Sfax Sfax Tunisia

Nowadays, the appearance of common symptoms, such as cough, fever, and loss of smell and taste, is the starting point of a battle against the coronavirus. The first standard method of COVID-19 infection assertion has become the RT-PCR test, which is however an uncomfortable solution for both patients and medical staff due to its high cost, timeliness, and false-negative result issue. This has raised the need for reliable automatic detection systems that aid in the early prediction of the COVID-19 infections with a lower cost. In this work, we aim at profiting from the Machine Learning (ML) advances to provide a reliable and low-cost COVID-19 prediction system. This system is based on the disease starting point, which is the patients’ clinical symptoms, that are still under-explored. We developed seven predictive models using traditional ML classification algorithms using a public dataset of obvious high-risk factors from patients’ clinical signs. The dataset has first undergone a pre-processing phase consisting of feature engineering and dataset resampling to deal with imbalanced dataset issue. Our best classification model is able to detect true positives and true negatives and weed out false positive and false negatives with an accuracy of 93%. © 2024, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Clinical signs COVID-19 Early diagnosis Machine learning

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Cloud-magnetic resonance imaging system:In the era of 6G and artificial intelligence

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Magnetic Resonance Letters 2025年第1期5卷 52-63页

作者： Yirong Zhou Yanhuang Wu Yuhan Su Jing Li Jianyu Cai Yongfu You Jianjun Zhou Di Guo Xiaobo Qu Department of Electronic Science Intelligent Medical Imaging R&D CenterFujian Provincial Key Laboratory of Plasma and Magnetic ResonanceNational Institute for Data Science in Health and MedicineXiamen UniversityXiamen361104China Department of Electronic Science Key Laboratory of Digital Fujian on IoT CommunicationXiamen UniversityXiamen361104China Shanghai Electric Group Co. LtdShanghai200002China China Telecom Group Quanzhou362018China China Mobile Group Xiamen361009China Department of Radiology Zhongshan Hospital(Xiamen)Fudan UniversityXiamen Municipal Clinical Research Center for Medical ImagingFujian Province Key Clinical Specialty Construction Project(Medical Imaging Department)Xiamen Key Laboratory of Clinical Transformation of Imaging Big Data and Artificial IntelligenceXiamen361006China School of Computer and Information Engineering Xiamen University of TechnologyXiamen361024China

Magnetic resonance imaging(MRI)plays an important role in medical diagnosis,generating petabytes of image data annually in large *** voluminous data stream requires a significant amount of network bandwidth and extensive storage ***,local data processing demands substantial manpower and hardware *** isolation across different healthcare institutions hinders crossinstitutional collaboration in clinics and *** this work,we anticipate an innovative MRI system and its four generations that integrate emerging distributed cloud computing,6G bandwidth,edge computing,federated learning,and blockchain *** system is called Cloud-MRI,aiming at solving the problems of MRI data storage security,transmission speed,artificial intelligence(AI)algorithm maintenance,hardware upgrading,and collaborative *** workflow commences with the transformation of k-space raw data into the standardized Imaging Society for Magnetic Resonance in Medicine Raw data(ISMRMRD)***,the data are uploaded to the cloud or edge nodes for fast image reconstruction,neural network training,and automatic ***,the outcomes are seamlessly transmitted to clinics or research institutes for diagnosis and other *** Cloud-MRI system will save the raw imaging data,reduce the risk of data loss,facilitate inter-institutional medical collaboration,and finally improve diagnostic accuracy and work efficiency.

关键词： Magnetic resonance imaging Cloud computing 6G bandwidth Artificial intelligence Edge computing Federated learning Blockchain

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Monitoring metrics over time: Why clinical trialists need to systematically collect site performance metrics

Research methods in medicine & health sciences

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research methods in medicine & health sciences 2023年第4期4卷 124-135页

作者： Victoria Yorke-Edwards Carlos Diaz-Montana Macey L Murray Matthew R Sydes Sharon B Love MRC Clinical Trials Unit at UCL Institute of Clinical Trials and Methodology University College London London UK. Health Data Research UK London UK. NHS DigiTrials Data Services Directorate NHS Digital Leeds UK. British Heart Foundation Data Science Centre Health Data Research UK London UK.

Background:Over the last decade, there has been an increasing interest in risk-based monitoring (RBM) in clinical trials, resulting in a number of guidelines from regulators and its inclusion in ICH GCP. However, there is a lack of detail on how to approach RBM from a practical perspective, and insufficient understanding of best practice. Purpose:We present a method for clinical trials units to track their metrics within clinical trials using descriptive statistics and visualisations. research Design:We suggest descriptive statistics and visualisations within a SWAT methodology. Study Sample:We illustrate this method using the metrics from TEMPER, a monitoring study carried out in three trials at the MRC Clinical Trials unit at UCL. data Collection:The data collection for TEMPER is described in . Results:We show the results and discuss a protocol for a Study-Within-A-Trial (SWAT 167) for those wishing to use the method. Conclusions:The potential benefits metric tracking brings to clinical trials include enhanced assessment of sites for potential corrective action, improved evaluation and contextualisation of the influence of metrics and their thresholds, and the establishment of best practice in RBM. The standardisation of the collection of such monitoring data would benefit both individual trials and the clinical trials community.

关键词： Risk-based monitoring Study-Within-A-Trial (SWAT) central monitoring centralised monitoring clinical trials

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NFDI4Energy Task Area 4: FAIR data for Energy System research

NFDI4Energy Task Area 4: FAIR Data for Energy System Researc...

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2023 Designing Futures: Zukunfte gestalten, INFORMATIK 2023 - 2023 Designing Futures: Shaping the Future, INFORMATIK 2023

作者： Wein, Amanda Reinkensmeier, Jan Weidlich, Anke Lilliestam, Johan Hagenmeyer, Veit Richter, Mascha Auer, Sören Nieße, Astrid Lehnhoff, Sebastian OFFIS E.V. RandD Division Energy Escherweg 2 Oldenburg26121 Germany Friedrichstr. 39 Freiburg79110 Germany Friedrich-Alexander-Universität Erlangen-Nörnberg Lehrstuhl for Sustainability Transition Policy Lange Gasse 20 Nörnberg90403 Germany Karlsruhe Institute of Technology Institute for Automation and Applied Informatics Kaiserstr. 89 Karlsruhe76131 Germany Reiner Lemoine Institut GGmbH Research Unit Transformation of Energy Systems Rudower Chaussee 12 Berlin12489 Germany Technische Informationsbibliothek Data Science and Digital Libraries Research Group Welfengarten 1 B Hannover30167 Germany Carl von Ossietzky Universität Oldenburg Department for Informatik Ammerländer Heerstr. 114-118 Oldenburg26129 Germany

ISBN: (纸本)9783885797319

The NFDI4Energy consortium will create a research data infrastructure for energy system research, emphasizing the openness and FAIRness of data and models in this research domain. Within the consortium, Task Area 4 focuses on the development of resources and services that will provide a semantic layer for the overall platform built by NFDI4Energy. The team of this Task Area will produce artifacts including a domain ontology, metadata standards, a knowledge graph, a Persistent Identifier service, and integration infrastructure to join these artifacts to the NFDI4Energy platform. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Metadata

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Teaching accounting principles using augmented reality and artificial intelligence-generated IsiZulu language translations

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International Journal of Educational research Open 2025年 8卷

作者： Ade-Ibijola, Abejide Sukhari, Aneetha Oyelere, Solomon Sunday Research Group on Data Artificial Intelligence and Innovations for Digital Transformation Johannesburg Business School University of Johannesburg Johannesburg South Africa College of Accounting Sciences University of South Africa Pretoria South Africa Department of Computer Science University of Exeter EX4 4PY Exeter United Kingdom & Johannesburg Business School University of Johannesburg Johannesburg South Africa

One of the sustainable development goals identified by the united Nations (UN) aims to ensure inclusive, equitable, quality education and promote lifelong learning opportunities for all. The report issued by the UN in 2022 highlighted that the global education crisis for vulnerable groups has worsened due to the COVID-19 pandemic, war, lack of infrastructure, and little or no psychosocial support. The prevalence of English as the medium of instruction in higher education, is a challenge for students who speak English as a second language. In South Africa, and Africa as a whole, the majority of the population speak in their mother tongue as a first language, which is problematic for students enrolled at English medium universities. This study focuses on accounting education from a South African perspective. Earlier research found that the number of African chartered accountants in South Africa are inadequately represented and cited one of the reasons as being a language barrier. We propose that the translation of accounting concepts into indigenous African languages can improve the understanding of accounting principles which will allow for inclusive, equitable, and quality education for all. In doing so, we develop a prototype application, which encompasses augmented reality and artificial intelligence-generated language translation functionalities to teach accounting principles in an indigenous African language, namely, IsiZulu. The application, named Zulu Augmented Reality Translation (ZART), aids the learning process of accounting principles, by providing an engaging and interactive learning experience in IsiZulu. The paper commences with a literature review on the teaching pedagogy and challenges in accounting education. Thereafter, the design science research applied in the development of ZART is discussed. Using questionnaires, an experiment is conducted to evaluate the usability of ZART, students’ perceptions and pedagogical experiences of using ZART. The res

关键词： Accounting education Artificial intelligence-generated language translations Augmented reality Design science research IsiZulu Sustainable development goal 4

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Making Epidemiological and Clinical Studies FAIR Using the Example of COVID-19

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Datenbank-Spektrum 2024年第2期24卷 117-128页

作者： Pigeot, Iris Ahrens, Wolfgang Darms, Johannes Fluck, Juliane Golebiewski, Martin Hahn, Horst K. Hu, Xiaoming Intemann, Timm Kasbohm, Elisa Kirsten, Toralf Klammt, Sebastian Klopfenstein, Sophie Anne Ines Lassen-Schmidt, Bianca Peters, Manuela Sax, Ulrich Waltemath, Dagmar Schmidt, Carsten Oliver Leibniz Institute for Prevention Research and Epidemiology—BIPS Bremen Germany University Bremen Department of Mathematics and Informatics Bremen Germany —Information Centre for Life Sciences Bonn Germany University of Bonn Institute of Geodesy and Geoinformation Bonn Germany Heidelberg Germany Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany Greifswald University Medicine Institute for Community Medicine Greifswald Germany University of Leipzig Medical Center Institute for Medical Data Science Leipzig Germany —KKS-Network e. V Berlin Germany Berlin Institute of Health at Charité—Universitätsmedizin Berlin Core Unit eHealth and Interoperability Berlin Germany University Medical Center Göttingen Department of Medical Informatics Göttingen Germany Greifswald University Medicine Department of Medical Informatics Greifswald Germany

FAIRification of personal health data is of utmost importance to improve health research and political as well as medical decision-making, which ultimately contributes to a better health of the general population. Despite the many advances in information technology, several obstacles such as interoperability problems remain and relevant research on the health topic of interest is likely to be missed out due to time-consuming search and access processes. A recent example is the COVID-19 pandemic, where a better understanding of the virus’ transmission dynamics as well as preventive and therapeutic options would have improved public health and medical decision-making. Consequently, the NFDI4Health Task Force COVID-19 was established to foster the FAIRification of German COVID-19 studies. This paper describes the various steps that have been taken to create low barrier workflows for scientists in finding and accessing German COVID-19 research. It provides an overview on the building blocks for FAIR health research within the Task Force COVID-19 and how this initial work was subsequently expanded by the German consortium National research data Infrastructure for Personal Health data (NFDI4Health) to cover a wider range of studies and research areas in epidemiological, public health and clinical research. Lessons learned from the Task Force helped to improve the respective tasks of NFDI4Health. © The Author(s) 2024.

关键词： COVID-19

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Representing Multimorbid Disease Progressions Using Directed Hypergraphs

SSRN

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SSRN 2023年

作者： Burke, Jamie Bailey, Rowena Akbari, Ashley Fasusi, Kevin Lyons, Ronan A. Pearson, Jonathan Rafferty, James Schofield, Daniel School of Mathematics College of Science and Engineering University of Edinburgh EdinburghEH9 3FD United Kingdom Population Data Science Medical School Faculty of Medicine Health & Life Science Swansea University SwanseaSA1 8PP United Kingdom Swansea Trials Unit Medical School Faculty of Medicine Health & Life Science Swansea University SwanseaSA1 8PP United Kingdom Digital Analytics and Research Team Transformation Directorate NHS England and NHS Improvement LeedsLS2 7UE United Kingdom

Objective: To introduce directed hypergraphs as a novel tool for assessing the temporal relationships between coincident diseases, addressing the need for a more accurate representation of multimorbidity and leveraging the growing availability of electronic healthcare databases and improved computational ***: Directed hypergraphs offer a high-order analytical framework that goes beyond the limitations of directed graphs in representing complex relationships such as multimorbidity. We apply this approach to multimorbid disease progressions observed from two multimorbidity sub-cohorts of the SAIL databank, after having been filtered according to the Charlson and Elixhauser comorbidity indices, respectively. After constructing a novel weighting scheme based on disease prevalence, we demonstrate the power of these higher-order models through the use of PageRank centrality to detect and classify the temporal nature of conditions within the two comorbidity ***: In the Charlson population, we found that chronic pulmonary disease (CPD), cancer and diabetes were conditions observed early in a patient's disease progression (predecessors), with stroke and dementia appearing later on (successors) and myocardial infarction acting as a transitive condition to renal failure and congestive heart failure. In Elixhauser, we found renal failure, neurological disorders and arrhythmia were classed as successors and hypertension, depression, CPD and cancer as predecessors, with diabetes becoming a transitive condition in the presence of obesity and alcohol abuse. The dynamics of these and other conditions changed across age and sex but not across deprivation. Unlike the directed graph, the directed hypergraph could model higher-order disease relationships, which translated into stronger classifications between successor and predecessor conditions, alongside the removal of spurious ***: This study underscores the utility of directed hypergraphs as

关键词： Directed graphs

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NEC's Solutions for Venues in the New Normal Era

NEC Technical Journal

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NEC Technical Journal 2021年第1期15卷 109-113页

作者： Kazufumi, Hoshi Masayuki, Ariyoshi Seigo, Miyawaki Tsubasa, Nabetani Gaku, Nakano Trading and Service Industries Solutions Division Japan Data Science Research Laboratories Japan PS Network Business Promotion Division Japan Digital Business Platform Unit Japan Biometrics Research Laboratories Japan

The impact of the novel coronavirus (COVID-19) pandemic is unprecedented in recent history. That impact has been especially severe for venues that attract mass gatherings, such as at sports events and concerts. Although remote viewing is becoming increasingly popular, there is still great demand for the shared feeling and crowds that can be experienced through physical viewings. In this paper, we will discuss various solutions now under development at NEC, which are intended to make it possible for people to visit these venues in person and enjoy their experience safely and securely in the new normal era. Those solutions include touchless, walk-through security check, entry confirmation and on-site monitoring of visitors, enhanced on-site entertainment using smartphone apps, visitor density visualization to avoid overcrowding, and social distancing measurement technology to confirm that visitors are maintaining a safe distance between one another. © 2021 NEC Mediaproducts. All rights reserved.

关键词： Face recognition

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data provenance and integrity of health-care systems data for clinical trials

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The Lancet digital Health 2022年第8期4卷 e567-e568页

作者： Murray, Macey L Love, Sharon B Carpenter, James R Hartley, Suzanne Landray, Martin J Mafham, Marion Parmar, Mahesh K B Pinches, Heather Sydes, Matthew R Institute of Clinical Trials and Methodology University College London LondonWC1V 6LJ United Kingdom Health Data Research UK London United Kingdom NHS DigiTrials Programme NHS Digital Leeds United Kingdom Data Services Directorate NHS Digital Leeds United Kingdom Medical Statistics London School of Hygiene and Tropical Medicine University of London London United Kingdom Clinical Trial Service Unit and Epidemiological Studies Unit University of Oxford Oxford United Kingdom Nuffield Department of Population Health University of Oxford Oxford United Kingdom British Heart Foundation Data Science Centre Health Data Research UK London United Kingdom

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