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The primary goal of this study was to assess the prevalence of oral involvement and, secondarily, the likely variables in patients with confirmed COVID-19 accompanied by mucormycosis infection. The study design was a cross-sectional descriptive sort that was performed at a tertiary centre. The non-probability convenience sampling approach was used to determine the sample size. Between May 2021 and July 2021, all patients who presented to our tertiary care centre with suspected mucormycosis were considered for the investigation. The research only included individuals with proven mucormycosis after COVID-19. The features of the patients, the frequency of intraoral signs/symptoms, and the possible variables were all noted. Of the 333 COVID-19-infected patients, 47 (14%) were diagnosed with confirmed mucormycosis. The mean (SD) age of the patients was 59.7 (11.9) years. Of the 47 patients with confirmed mucormycosis, 34% showed sudden tooth mobility, 34% expressed toothache, 8.5% reported palatal eschar, 34% presented with jaw pain, 8.5% had tongue discoloration, and 17% had temporomandibular pain. About 53% of the patients were known cases of type 2 diabetes mellitus, 89% of patients had a history of hospitalization due to COVID-19 infection, 89.3% underwent oxygen support therapy, and 89.3% were administered intravenous steroids during hospitalization due to COVID-19 infection. About 14% of the suspected cases attending the mucormycosis out-patient department (OPD) had been confirmed with definite mucormycosis. Oral involvement was seen in 45% of cases of CAM (COVID-associated mucormycosis). The most frequent oral symptoms presented in CAM were sudden tooth mobility and toothache. Diabetes and steroids were the likely contributing factors associated with CAM.

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The weld defects are formed due to the incorrect welding patterns or wrong welding process. The defects in the weld may vary from size, shape and their projected quality. The most common weld defects occur during welding process is slag inclusions, porosity, lack of fusion and incomplete penetration. In this study, an effective method for weld defect classification using machine learning algorithm is presented. The system uses Speeded-up Robust Features (SURF) for feature extraction and one of the machine learning algorithms called Auto-Encoder Classifier (AEC) for classification. Initially, the features that distinguish weld defects and no defects in the weld image are extracted by SURF. Then, AEC is analyzed for weld image classification using different number of neurons in different hidden layers (2 and 3 hidden layers). The performance of the system is evaluated by GD X-ray weld image database. The results show that the weld images are correctly classified with 98% accuracy using SURF and AEC.