Pinned Repositories
Comparative-Study-of-Machine-Learning-Techniques-for-the-Early-Detection-of-Heart-Disease-in-IoT-Env
Decision Tree (DT), Naïve Bayes (NB), K-Nearest Neighbor (KNN), Random Forest (RF) and Support Vector Machine (SVM) are compared with Multilayer Perceptron (MLP) Neural Network model for early predictions of heart disease in IoT environments using four different datasets.
covid-19-data
Data on COVID-19 (coronavirus) confirmed cases, deaths, and tests • All countries • Updated daily by Our World in Data
COVID-19-Worldwide
Neural Network Prediction Model for Worldwide COVID-19 Mortality Rate based on Country's Socioeconomic data
Deep-learning-for-intrusion-detection-using-Recurrent-Neural-network-RNN
Deep Learning techniques can be implemented in the field of cybersecurity to handle the issues related to intrusion just as they have been successfully implemented in the areas such as computer vision and natural language processing (NLP). RNN model is compared with J48, Artificial Neural Network, Random Forest, Support Vector Machine and other machine learning techniques to detect malicious attacks in terms of binary and multiclass classifications.
Heart_Disease_detection
In this project we are going to predict heart disease problems. Machine learning plays a vital role in the area therefore we have used various machine learning techniques such as Decision Tree, Naiive bayes, K nearest neighbors, random forest and support vector machine (SVM) are compared together and an Ensemble model is developed for more comprehensive comparison. . Four datasets are used for this experiment. These records have categorical, integer, and numerical attribute values.
Ahamasaleh's Repositories
Ahamasaleh/Deep-learning-for-intrusion-detection-using-Recurrent-Neural-network-RNN
Deep Learning techniques can be implemented in the field of cybersecurity to handle the issues related to intrusion just as they have been successfully implemented in the areas such as computer vision and natural language processing (NLP). RNN model is compared with J48, Artificial Neural Network, Random Forest, Support Vector Machine and other machine learning techniques to detect malicious attacks in terms of binary and multiclass classifications.
Ahamasaleh/Comparative-Study-of-Machine-Learning-Techniques-for-the-Early-Detection-of-Heart-Disease-in-IoT-Env
Decision Tree (DT), Naïve Bayes (NB), K-Nearest Neighbor (KNN), Random Forest (RF) and Support Vector Machine (SVM) are compared with Multilayer Perceptron (MLP) Neural Network model for early predictions of heart disease in IoT environments using four different datasets.
Ahamasaleh/covid-19-data
Data on COVID-19 (coronavirus) confirmed cases, deaths, and tests • All countries • Updated daily by Our World in Data
Ahamasaleh/COVID-19-Worldwide
Neural Network Prediction Model for Worldwide COVID-19 Mortality Rate based on Country's Socioeconomic data
Ahamasaleh/Heart_Disease_detection
In this project we are going to predict heart disease problems. Machine learning plays a vital role in the area therefore we have used various machine learning techniques such as Decision Tree, Naiive bayes, K nearest neighbors, random forest and support vector machine (SVM) are compared together and an Ensemble model is developed for more comprehensive comparison. . Four datasets are used for this experiment. These records have categorical, integer, and numerical attribute values.