Context:Postoperative sore throat (POST) is a very common complaint following tracheal intubation. Although it resolves spontaneously, efforts must be taken to reduce it.Aims:This study aims to compare the effect of cuff inflation using manometer versus conventional technique on the incidence of POST. Secondary objectives were to assess the incidence postoperative hoarseness and cough.Settings and Design:A total of 120 patients were included in this prospective observational comparative study.Subjects and Methods:After approval from the hospital ethics committee, consenting American Society of Anesthesiologists physical status Class I and II patients, scheduled for gynecologic laparoscopic surgery under general anesthesia, were included. They were randomly allocated by closed envelope technique to either Group A where the cuff pressure was adjusted to 25 cmH2O using a manometer or Group B where cuff inflation was guided clinically. Patients were monitored for sore throat, hoarseness of voice, and cough postoperatively.Statistical Analysis Used:To calculate the incidence of sore throat, hoarseness, and cough, descriptive statistics were applied. For checking association of sore throat and cuff pressure, Chi-square test and for comparing numerical values independent sample t-test were applied.Results:The incidence of POST was significantly less in Group A than in B (P < 0.001) up to 24 h. Incidence of hoarseness was less in Group A and incidence of cough was higher in Group B, but these differences were not statistically significant.Conclusion:Cuff inflation guided by manometer significantly reduces the incidence of POST.

In this paper, we concentrate on the identification of skin cancer. The skin images are taken from a medical database which is a pre-processed image, which is given as input for different machine learning algorithm. The algorithm used is KNN classifier, SVM classifier, and CNN model. where these classifiers will classify whether a given image is cancerous or non-cancerous image. In case of the KNN and SVM the output is 80%, hence in CNN model substantial improvement in accuracy of cancer detection is obtained & it can classify the cancerous & Non-cancerous images efficiently. The process was conducted for test data, training data and validation data using different-images. The training dataset was trained with 100 epochs. The process obtained the accuracy of 97% in training result. in testing result obtained is 95% of accuracy and 96% for validation testing.

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