Showing 2 results for Random Forest (rf)
Jayati Vaish, Anil Kumar Tiwari, Seethalekshmi K.,
Volume 19, Issue 4 (12-2023)
Abstract
In recent years, Microgrids in integration with Distributed Energy Resources (DERs) are playing as one of the key models for resolving the current energy problem by offering sustainable and clean electricity. Selecting the best DER cost and corresponding energy storage size is essential for the reliable, cost-effective, and efficient operation of the electric power system. In this paper, the real-time load data of Bengaluru city (Karnataka, India) for different seasons is taken for optimization of a grid-connected DERs-based Microgrid system. This paper presents an optimal sizing of the battery, minimum operating cost and, reduction in battery charging cost to meet the overall load demand. The optimization and analysis are done using meta-heuristic, Artificial Intelligence (AI), and Ensemble Learning-based techniques such as Particle Swarm Optimization (PSO), Artificial Neural Network (ANN), and Random Forest (RF) model for different seasons i.e., winter, spring & autumn, summer and monsoon considering three different cases. The outcome shows that the ensemble learning-based Random Forest (RF) model gives maximum savings as compared to other optimization techniques.
Biswapriyo Sen, Maharishi Kashyap, Jitendra Singh Tamang, Sital Sharma, Rijhi Dey,
Volume 20, Issue 2 (6-2024)
Abstract
Cardiovascular arrhythmia is indeed one of the most prevalent cardiac issues globally. In this paper, the primary objective was to develop and evaluate an automated classification system. This system utilizes a comprehensive database of electro- cardiogram (ECG) data, with a particular focus on improving the detection of minority arrhythmia classes.
In this study, the focus was on investigating the performance of three different supervised machine learning models in the context of arrhythmia detection. These models included Support Vector Machine (SVM), Logistic Regression (LR) and Random Forest (RF). An analysis was conducted using real inter-patient electrocardiogram (ECG) records, which is a more realistic scenario in a clinical environment where ECG data comes from various patients.
The study evaluated the models’ performances based on four important metrics: accuracy, precision, recall, and f1-score. After thorough experimentation, the results highlighted that the Random Forest (RF) classifier outperformed the other methods in all of the metrics used in the experiments. This classifier achieved an impressive accuracy of 0.94, indicating its effectiveness in accurately detecting arrhythmia in diverse ECG signals collected from different patients.
