Short-Term-Wind-Speed-Prediction-based-on-Deep-Learning

LSTM neural network realizes the prediction of wind speed through the learning of various parameters. It can provide important support for the smooth operation of power system and the optimization of control strategy. The fuzzy rough set theory is used to reduce many factors that affect wind speed. It simplifies the input of the neural network prediction model and improves the accuracy and speed. Compared with the traditional neural network prediction method, MAE and MAPE in FRS-LSTM wind speed forecasting model have decreased and the accuracy has been improved greatly.

Feature-Selection-Using-Mutual-Information-F-classification-T-test-Sequential-Forward-Selection-a

The study of gene expression of cells and tissue is one of the major ways for discovery in medicines. The main challenge of such gene data is high input dimensionality, heterogeneity in the data with very low sample size. To overcome this, gene subset selection/Feature Selection has become a crucial and essential step. This is solved using: A. Applying the three filter methods( 1. Mutual Info[f1] 2. F Classif[f2] and 3.T-Test[f3] ) on the three datasets to get important features. B. Select the most important N/3 features from each of these three filter methods(f1,f2,f3).F = { f1 U f2 U f3) C. Now apply feature selection in a cascaded manner. a. F1( N features ) → F2( 2N/3 features out of selected features from F1) → F3(N/3 features out of selected features from F2) b. F2 → F3 → F1 c. F3 → F1 → F2 D. Classifying the test data using wrapper methods(Sequential Forward Search and Sequential Backward Search ) with N features. Using KNN and SVM for Classification. Reporting Accuray, F-Score, and Confusion Matrix.

Heart-Disease-Prediction-Using-Machine-Learning

“Disease Prediction” system based on predictive modeling predicts the disease of the user on the basis of the symptoms that user provides as an input to the system. The system analyzes the symptoms provided by the user as input and gives the probability of the disease as an output. Disease Prediction is done by implementing 7 techniques such as Naïve Bayes, KNN, Decision Tree, Linear Regression, SVM, Artificial Neural Network and Random Forest Algorithms. These techniques calculate the probability of the disease. Therefore, maximum prediction accuracy probability 91.80% is obtained.