Comparative Analysis of Selected Machine Learning Algorithms Based On Generated Smart Home Dataset (Published)
There has being recent interest in applying machine learning techniques in smart homes for the purpose of securing the home. This paper presents the comparative study on six classification algorithms based on generated smart home datasets. These includes Logistics Regression, Support vector machine, Random forest, K-Nearest Neighbor, Decision Tree and Gaussian Naïve Bayes. Two different smart home datasets were generated and used to train and test the algorithms. The confusion matrix was used to evaluate the outputs of the classifiers. From the confusion matrix, Prediction Accuracy, Precision, Recall and F1-Score of the models were calculated. The Support Vector Machine (SVM) outperformed the other algorithms in terms of accuracy on both datasets with values of 67.89 and 88.56 respectively. The SVM and Logistics Regression also maintained the highest precision of 100.0 as compared to the other algorithms.
Keywords: classification algorithms, smart home, support vector machine
Comparative Analysis of Selected Machine Learning Algorithms Based on Generated Smart Home Dataset (Published)
There has being recent interest in applying machine learning techniques in smart homes for the purpose of securing the home. This paper presents the comparative study on six classification algorithms based on generated smart home datasets. These includes Logistics Regression, Support vector machine, Random forest, K-Nearest Neighbor, Decision Tree and Gaussian Naïve Bayes. Two different smart home datasets were generated and used to train and test the algorithms. The confusion matrix was used to evaluate the outputs of the classifiers. From the confusion matrix, Prediction Accuracy, Precision, Recall and F1-Score of the models were calculated. The Support Vector Machine (SVM) outperformed the other algorithms in terms of accuracy on both datasets with values of 67.89 and 88.56 respectively. The SVM and Logistics Regression also maintained the highest precision of 100.0 as compared to the other algorithms.
Musa Martha Ozohu, and Oghenekaro Linda Uchenna (2022) Comparative Analysis of Selected Machine Learning Algorithms Based on Generated Smart Home Dataset, European Journal of Computer Science and Information Technology, Vol.10, No.3, pp.59-70,
Keywords: classification algorithms, smart home, support vector machine
Comparative Analysis of Selected Machine Learning Algorithms Based on Generated Smart Home Dataset (Published)
There has being recent interest in applying machine learning techniques in smart homes for the purpose of securing the home. This paper presents the comparative study on six classification algorithms based on generated smart home datasets. These includes Logistics Regression, Support vector machine, Random forest, K-Nearest Neighbor, Decision Tree and Gaussian Naïve Bayes. Two different smart home datasets were generated and used to train and test the algorithms. The confusion matrix was used to evaluate the outputs of the classifiers. From the confusion matrix, Prediction Accuracy, Precision, Recall and F1-Score of the models were calculated. The Support Vector Machine (SVM) outperformed the other algorithms in terms of accuracy on both datasets with values of 67.89 and 88.56 respectively. The SVM and Logistics Regression also maintained the highest precision of 100.0 as compared to the other algorithms.
Citation: Musa Martha Ozohu and Oghenekaro Linda Uchenna (2021) Comparative Analysis of Selected Machine Learning Algorithms Based on Generated Smart Home Dataset, European Journal of Computer Science and Information Technology, Vol.9, No.4, pp.1-15, 2021
Keywords: classification algorithms, smart home, support vector machine
Analysis and forecasting the outbreak of Covid-19 in Ethiopia using machine learning (Published)
Coronavirus outbreaks affect human beings as a whole and can be a cause of serious illness and death. Machine learning (ML) models are the most significant function in disease prediction, such as the Covid-19 pandemic, in high-performance forecasting and used to help decision-makers understand future situations. ML algorithms have been used for a long time in many application areas that include recognition and prioritization for certain treatments. Too many ML furcating models are used to deal with problems. In this study, predict a pandemic outbreak using the ML forecasting models. The models are designed to predict Covid-19, depending on the number of confirmed cases, recovered cases and death cases, based on the available dataset. Support Vector Machine (SVM) and Polynomial Regression (PR) models were used for this study to predict Covid-19 ‘s aggressive risk. All three cases, such as confirmed, recovered and death, models predict death in Ethiopia over the next 30 days. The experimental result showed that SVM is doing better than PR to predict the Covid-19 pandemic. According to this report, the pandemic in Ethiopia increased by half between the mid of July 2020. Then Ethiopia will face a number of hospital shortages, and quarantine place.
Keywords: COVID-19, Forecasting, coronavirus, machine learning, polynomial regressing, support vector machine