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Artificial Intelligence - DOAJ

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Last Updated: 10 September 2022

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An Approach to Collecting School District Level COVID-19 Mask Mandate Information in the United States form the Web using Tools Powered by Artificial Intelligence.

This study was designed to gather online evidence about COVID-19 mask mandates in United States school districts using artificial intelligence to verify and submit official data published by U. S. Department of Education and connecting to government websites in the United States. As a pilot project, the authors were able to successfully build the automated Google search engine to query and retrieve school district level mask mandate information for the state of Ohio. The results, which were predicted by the algorithm, were used to verify the same data collected by ED through monthly questionnaires of public schools, and would help determine masking policy results for states with low response rates to the ED survey. These statistics will also be linked to statistics on the number of COVID-19 cases in schools reported by state governments in order to determine the effectiveness of masking efforts in lowering COVID-19 case counts.

Source link: https://doi.org/10.23889/ijpds.v7i3.2016


Use of “Hidden in Plain Sight” de-identification methodology in electronic healthcare data provides minimal risk of misidentification: Results from the iCAIRD Safe Haven Artificial Intelligence Platform.

Objectives In Plain Sight, U201d Objectives When using a u201cHidden de-identification system applied to Scottish healthcare results, The Industrial Centre for Artificial Intelligence Research in Opticities (IDG) Safe Haven Artificial Intelligence Platform aims to determine the possibility of misidentification when using a u201d Identification scheme used to identify misidentification. Methodology: Our HIPS program utilizes a NER u201cfind and replaceu201d system to de-identification that preserves the text's original text structure, rather than the traditional redaction of potentially identifiable information in routinely collected healthcare records. This ensures that context is preserved, which is vital to the interpretation of free text and future Artificial Intelligence applications. As part of the iCAIRD program, we therefore conducted an analysis of this option in terms of the possibility of misidentification using HIPS on structured Scottish data deployed in SHAIP. Among the 169,964 patients, there were only three cases of Forename/Surname/DOB in the five cohorts, and five cases of Forename/Surname/DOB in particular, with five others reporting misidentification.

Source link: https://doi.org/10.23889/ijpds.v7i3.2023


Artificial intelligence (AI) applications for marketing: A literature-based study

Artificial Intelligence has a huge market potential. AI is changing the way brands and customers communicate with one another. When AI is used to personalize their experiences, users feel relaxed and are more likely to buy what is offered. AI tools can also be used to analyze the effectiveness of a competitor's campaigns and reveal their customers' needs. Machine Learning is a subset of AI that helps computers analyze and interpret data without being explicitly programmed. Its subset of AI that can help computers analyze and interpret data without being explicitly programmed. In addition, ML aids humans in solving problems in a more effective manner. From Scopus, Google scholar, studyGate, and other websites, relevant articles on AI in marketing were uncovered for this study. For this study, relevant articles on AI in marketing have been found from Scopus, Google scholar, researchGate, and other websites. This paper explores the role of AI in marketing. The precise applications of AI in various market segments and their transformations for marketing industries are investigated. Finally, the most useful uses of AI for marketing are acknowledged and discussed.

Source link: https://doi.org/10.1016/j.ijin.2022.08.005


Empirical approach for prediction of bearing pressure of spread footings on clayey soil using artificial intelligence (AI) techniques

Based on plate load test results, this paper investigates the use of two artificial intelligence techniques, specifically support vector regression and artificial neural network, in the estimation of the bearing pressure of spread footings on clay soils based on plate load test results. From the literature, a data set consisting of 576 numbers of data points from 58 numbers of full-scale and small-scale PLTs was assembled. In the analysis, three SVR models, including the polynomial kernel function, radial basis kernel function, and exponential radial basis kernel function, were used, as well as one ANN model with the Bayesian Regularization learning algorithm.

Source link: https://doi.org/10.1016/j.rineng.2022.100489


Artificial intelligence in the pediatric echocardiography laboratory: Automation, physiology, and outcomes

In this state-of-the-art review, we highlight the significant literature on machine learning in echocardiography and its use in the pediatric echocardiography lab, as well as the use of echo measurements to help analyze pediatric cardiovascular disease and outcomes.

Source link: https://doi.org/10.3389/fradi.2022.881777


Explainable Artificial Intelligence for Tabular Data: A Survey

Machine learning methods are quickly adopted in a variety of fields, research and industry, and industry. Sadly, many of these strategies are not appropriate for tabular data, which is surprising considering the use and widespread use of tabular data in critical industries such as finance, healthcare, and criminal justice. Despite the abundant literature on XAI, there are still no survey papers that focus on tabular results to date. As a result of the existing survey papers, which cover a variety of XAI methods, it is also difficult for researchers working on tabular data to go through all of these surveys and find the methods that are appropriate for their analysis. Our paper is the first to offer researchers with a map that aids them in navigating the XAI literature in the context of tabular data.

Source link: https://doi.org/10.1109/ACCESS.2021.3116481


Optimization of the Medical Service Consultation System Based on the Artificial Intelligence of the Internet of Things

Medical assistant diagnosis system applications can be implemented by knowing how to encourage doctors to accept and use artificial intelligence medical assistant diagnostic devices can increase the adoption of artificial intelligence medical assistant diagnosis software. This paper examines a company operating model based on multi-party participation and sharing of medical consultation data, despite the current challenges faced by the Internet of Things medical consultation services. We developed the information exchange, overall logic, and service implementation process of the service model, as well as the construction of the artificial intelligence medical services model. We use IoT technologies to create a vital signs monitoring environment and describe how to use IoT devices. There is no significant difference in the contribution of various samples to the weight change in the error backpropagation scheme, which makes the adjustment of network parameters not particularly relevant by difficult medical consultation samples, therefore weakening the effect of network medical consultation.

Source link: https://doi.org/10.1109/ACCESS.2021.3096188


Artificial Intelligence Applied to Stock Market Trading: A Review

Artificial Intelligence is a research field that has piqued a lot of attention since the 1990s, when the personal computer's widespread technological growth and popularization of the personal computer has heightened. Thousands of new strategies have been devised to cope with the stock market's price prediction issue since then. Based on a sample of 2326 papers from the Scopus website between 1995 and 2019, this paper presents a systematic review of the science on Artificial Intelligence in the stock market.

Source link: https://doi.org/10.1109/ACCESS.2021.3058133

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions

* Please keep in mind that all text is summarized by machine, we do not bear any responsibility, and you should always check original source before taking any actions