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Food is essential for all the human being. In the food we are gained the more number of energy to our body. But now days, Lot of chemical substances that are added in the food. That makes human being to be unhealthy person. Children are also affected due to unhealthy food. In our proposal, we used to find the quality of food using the pH sensor. Th...
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Aim/Purpose: Teachers are being asked to integrate mobile technologies into their content creation and distribution tasks. This research aims to provide an understanding of teachers taking on this process and whether the use of technology has influenced their content creation and distribution in the classroom. Background: Many claim that the use of...
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... Prescriptions classify labels into medicine and infection groups, with each group containing brand extension such as drug name, dose, administration path, adverse drug occurrence, warning, and treatment response. They test a variety of RNN-based classification algorithms, including RNNs and GRUs, bidirectional LSTMs (Bi-LSTM), and different kinds of LSTMs with sparse representation [22]. They discovered that all RNN variations outperformed the sparse baselines by a large margin, especially when it came to identifying more complex attributes like medication length and intensity as well as clinical manifestations [23]. ...
Due to the advances in machine learning and deep learning models, their use in medical decision frameworks is still minimal. However, the rapid digitization of health information has created an excellent forum for evaluating the effectiveness of such strategies in healthcare. H s a result, an increasing number of research studies are appearing in the fields that use deep learning on Electronic health Records (EHR) for customized threat and health trajectory prediction. Predictive analytics in machine learning is primarily used in the healthcare sector for disease diagnosis. Machine learning approaches aid in the prediction of relationships in EHR results. The health system recommendations in the corpus could be one of the sources, and different healthcare criteria could be element sized, resulting in a structure that has a meaningful effect on healthcare decision-making. Although this is a consistent growth, the field's potential to analyse and differentiate such frameworks for a given scenario has been hindered by considerable paper-to-paper heterogeneity (from sources of data and frameworks used to the research situation they seek to respond).As a result, the objective of this study is to include Machine Learning and Deep Learning Applications for preserving electronic health records. We also hope to: (1) develop and utilize one of the globe's greatest and more important associated primary healthcare EHR datasets as an unique resource for educating such information models; (2) include a guidelines for dealing with EHR data for machine learning; (3) provide a few of the quality standards for evaluating the "awesomeness" of deep learning models in improved clinical forecasting; and (4) suggest future studies for making deep learning models more suitable for EHR data. Our findings identify the importance of interacting with extremely imbalanced records and suggest that concurrent deep learning frameworks like RNN might be better suited to dealing with EHR's spatial design.
