Early detection of mental health issues allows specialists to treat them more effectively and it improves patient’s quality of life. Mental health is about one’s psychological, emotional, and social well-being. It affects the way how one thinks, feels, and acts. Mental health is very important at every stage of life, from childhood and adolescence through adulthood. This study identified five machine learning techniques and assessed their accuracy in identifying mental health issues using several accuracy criteria. The five machine learning techniques are Logistic Regression, K-NN Classifier, Decision Tree Classifier, Random Forest, and Stacking. We have compared these techniques and implemented them and also obtained the most accurate one in Stacking technique based with an accuracy of prediction 81.75%.
Recently the novel coronavirus disease pushed the world into the dramatic situation. The tough thing to deal with novel corona virus is the prediction. In the beginning RT PCR test is the golden standard test for the prediction of COVID, which takes more time, more licensed laboratories, trained personnel and prediction accuracy will be not fruitful. In our System, We used current technology for the prediction, which involves: An Efficient Random Forest, a machine learning classification model which predicts whether the person is Corona affected or not using routine blood reports and a deep learning model, Modified DenseNet121 which was pre-trained to predict theCovid using CT scan images. To analyze the machine learning model performance, 5744 blood report samples have been collected from Kagglerepository;similarly, 2482 CT scan samples have been collected from the Kaggle repository, for prediction using Random Forest and DenseNet121 model. The proposed model which is developed using machine and deep learning techniques can be deployed easily and can be used for rapid and accurate prediction of Covid19.
Relevance Feedback is an important tool for grasping user's need in Interactive Content Based Image Retrieval (CBIR). Keeping this in mind, we have build up a framework using Relevance Vector Machine Classifier in interactive framework where user labels images as appropriate and inappropriate. The refinement of the images shown to the user is done using a few rounds of relevance feedback. This appropriate and inappropriate set then provides the training set for the RVM for each of these rounds. The method uses Histogram Intersection kernel with this interactive RVM (IKRVM). It has a retrieval component on top of this which searches for those images for retrieving which falls in the nearest neighbor set of the query image on the basis of histogram intersection based identical ranking (HIIR). The experimental results shows that the proposed framework shows better precision when compared with Active learning based RVMActive implemented with Radial Basis or Polynomial Kernels.
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