<b><i>Introduction:</i></b> Dysphagia as a consequence of multiple sclerosis (MS) puts individuals at higher risk of dehydration, malnutrition, and aspiration pneumonia. This study intended to investigate the effects of a combined program of neuromuscular electrical stimulation (NMES) and conventional swallowing therapy to improve swallow safety and efficiency, oral intake, and physical, emotional, and functional impacts of dysphagia in people with dysphagia and MS. <b><i>Methods:</i></b> In this single-case experimental study with ABA design, two participants with dysphagia caused by MS underwent 12 sessions therapy during 6 weeks following a baseline of 4 evaluation sessions. They were evaluated 4 more times in the follow-up phase after therapy sessions. Scores of Mann Assessment of Swallowing Ability (MASA), DYsphagia in MUltiple Sclerosis (DYMUS), and timed test of swallowing capacity were obtained at baseline, during treatment, and in the follow-up phases. The Dysphagia Outcome and Severity Scale (DOSS) based on videofluoroscopic swallow studies, Persian-Dysphagia Handicap Index (Persian-DHI), and Functional Oral Intake Scale (FOIS) were also completed before and after treatment. Visual analysis and percentage of nonoverlapping data were calculated. <b><i>Results:</i></b> MASA, DYMUS, FOIS, and DHI scores indicated significant improvement in both participants. Although the scores of the timed test of swallowing capacity in participant 1 (B.N.) and DOSS in participant 2 (M.A.) showed no changes, considerable improvements including reducing the amount of residue and the number of swallows required to clear bolus were seen in the posttreatment videofluoroscopic records of both participants. <b><i>Conclusion:</i></b> NMES in conjunction with conventional dysphagia therapy based on motor learning principles could improve the swallowing function and decrease disabling effects of dysphagia on different aspects of life in participants with dysphagia caused by MS.
Applications require the ability to perceive others' opinions as one of the most outstanding parts of knowledge. Finding the positive or negative feelings in sentences is called sentiment analysis (SA). Businesses use it to understand customer sentiment in comments on websites or social media. An optimized loss function and novel data augmentation methods are proposed for this study, based on Bidirectional Encoder Representations from Transformers (BERT). First, a crawled dataset from Persian movie comments on various sites has been prepared. Then, balancing and augmentation techniques are accomplished on the dataset. Next, some deep models and the proposed BERT are applied to the dataset. We focus on customizing the loss function, which achieves an overall accuracy of 94.06 for multi-label (positive, negative, neutral) sentences. And the comparative experiments are conducted on the dataset, where the results reveal the performance of the proposed model is significantly superior compared with other models.
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