Titlebook: Advances in Computing and Data Sciences; 6th International Co Mayank Singh,Vipin Tyagi,Tuncer ?ren Conference proceedings 2022 The Editor(s

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Gideon Walter Mutanda,Antony W. Pepelars which enables them to create the right edits and correct the claim before going out to the payer. This in turn helps the healthcare provider dramatically improve both net patient revenue and cash flow. They can also put a check on their costs, as fewer denials mean less rework, resources, and tim

耐寒

Gideon Walter Mutanda,Antony W. Pepelaconcentrates on the experimental aspect, particularly the DNN architecture model has employed for training the dataset. This design employs the adamax optimizer, tanh as an activation function, and four hidden layers with a learning rate of 0.01 to get the highest accuracy with the minimum loss. We

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https://doi.org/10.1007/978-3-031-41669-9ormative minority samples that are appropriate for over-sampling. The process is in two way 1.) it identify and remove the noisy and overlapping samples from borderline minority instances based on the sampling seeds, and 2) synthetic samples are generated from the informative minority samples. Exper

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https://doi.org/10.1007/978-3-031-15889-6ed 2D-CNN model has been achieved from the task-evoked fMRI data with classification accuracy of 85.3%, sensitivity of 89.5%, and F1-Score of 87.2%. The experimental results shows that the proposed model effectively distinguishes the neuronal response under the task evoked stimuli.

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