In the Indian population, a larger part is under the subsistence level. Most of the people are living in areas of poor sanitation and have very little access to good medical facilities. From time to time, they don’t have the notice to go to a physician at the absolute time. The condition has been defined as a skin disorder or disease wherever there is a failure to induce the right identification and treatment in time typically ends up in advanced stages. Skin diseases tend to be itchy and cover the body easily. Among them, Psoriasis exists as a chronic inflammatory disease characterized by scaly patches on the skin. The proposed system focuses on SVM segmentation and scaling of 2D processed skin pore images of Psoriasis. The Feature Scaling Technique uses color, contrast, and image texture along with a combination of SVM classification features to diagnose and come up with a treatment solution. This computer-assisted image processing system removes erythematous from the psoriasis image for analysis and determination of growth rate. Therefore, earlier identification cuts back the symptoms of the illness and helps in developing a condition that indulges the strategies to live along with the disease condition called Psoriasis.
This paper describes the submission of the team Amrita_CEN to the shared task on iSarcasm Eval: Intended Sarcasm Detection in English and Arabic at SemEval 2022. The sarcasm detection task was formulated as a classification problem and modelled using machine learning classifiers. We used K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes, Logistic Regression, Decision Tree, and the Random Forest ensemble method. In addition, the class imbalance problem in the dataset was addressed using a feature engineering technique. We submitted the predictions by SVM, Logistic Regression and Random Forest ensemble based on the performance during training.
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