Renewable energy solutions are appropriate for on-grid and off-grid applications, acting as a supporter for the utility network or rural locations without the need to develop or extend costly and difficult grid infrastructure. As a result, hybrid renewable energy sources have become a popular option for grid-connected or standalone systems. This paper examines hybrid renewable energy power production systems with a focus on energy sustainability, reliability due to irregularities, techno-economic feasibility, and being environmentally friendly. In attaining a reliable, clean, and cost-effective system, sizing optimal hybrid renewable energy sources (HRES) is a crucial challenge. The presenters went further to outline the best sizing approach that can be used in HRES, taking into consideration the key components, parameters, methods, and data. Moreover, the goal functions, constraints from design, system components, optimization software tools, and meta-heuristic algorithm methodologies were highlighted for the available studies in this timely synopsis of the state of the art. Additionally, current issues resulting from scaling HRES were also identified and discussed. The latest trends and advances in planning problems were thoroughly addressed. Finally, this paper provides suggestions for further research into the appropriate component sizing in HRES.
Breast cancer is one of the most common invasive cancers in women and it continues to be a worldwide medical problem since the number of cases has significantly increased over the past decade. Breast cancer is the second leading cause of death from cancer in women. The early detection of breast cancer can save human life but the traditional approach for detecting breast cancer disease needs various laboratory tests involving medical experts. To reduce human error and speed up breast cancer detection, an automatic system is required that would perform the diagnosis accurately and timely. Despite the research efforts for automated systems for cancer detection, a wide gap exists between the desired and provided accuracy of current approaches. To overcome this issue, this research proposes an approach for breast cancer prediction by selecting the best fine needle aspiration features. To enhance the prediction accuracy, several feature selection techniques are applied to analyze their efficacy, such as principal component analysis, singular vector decomposition, and chi-square (Chi2). Extensive experiments are performed with different features and different set sizes of features to investigate the optimal feature set. Additionally, the influence of imbalanced and balanced data using the SMOTE approach is investigated. Six classifiers including random forest, support vector machine, gradient boosting machine, logistic regression, multilayer perceptron, and K-nearest neighbors (KNN) are tuned to achieve increased classification accuracy. Results indicate that KNN outperforms all other classifiers on the used dataset with 20 features using SVD and with the 15 most important features using a PCA with a 100% accuracy score.
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