Measuring performance solely by relying on returns is probably not enough, it is important to consider both returns and risks. Some measurement methods that consider both of these factors are the Sharpe Ratio index, Treynor Ratio, Jensen Alpha, and Information Ratio. Risk analysis using Value at Risk Monte Carlo simulation is also important to determine the potential for extreme risks. The purpose of this study is to provide a good understanding of the performance and risk of mutual fund investments. Based on the performance results, Schroder is the most superior mutual fund, with the highest Information Ratio, Sharpe Ratio, and Jensen Ratio, indicating that they are able to generate good returns considering the risks taken. However, Schroder also has the highest VaR, meaning it has the potential for large losses in the worst market conditions. On the other hand, MNC is at the bottom in almost all performance methods, indicating poor performance with low returns and lower risks.

Image classification is widely used in everyday life such as in car steering, closed-circuit television (CCTV), traffic cameras, etc. The implementation of image classification can be done using several methods, including neural network and support vector machine models. The neural network method is able to find the right weights that allow the network to show the desired behaviour while the support vector machine method has many dimensions and can overcome linear and non-linear data. In this research, feature extraction was carried out using VGG16 to increase accuracy. This research aims to find out how to implement the neural network and SVM algorithms to classify images and determine the results of analyzing the performance of the two methods. The data used in this study is secondary data consisting of 10 types of large wild cats with a total of 2339 training image datasets and 50 testing image datasets. The research stages consist of data augmentation, model design, model training, and model evaluation. Classification with the neural network model produced an accuracy of 96% and the support vector machine model produced an accuracy of 96%, which means that in a consistent training environment, the two models have the same performance.

There are many patterns of breast cancer change that make it a global health challenge. The research aims to propose an expanded gamma distribution with parameters and apply it to data for (103) patients with breast cancer. Data normality tests were used, such as the Kolmigrov-Smirnov test, the Anderson-Darling test, and the Chi-square test, to fit the real data. The parameters of the proposed distribution were estimated using the maximum likelihood method. It was found that there is a large difference in the real data with positive Skewness in it. The maximum likelihood estimates reflected the suitability of the data to the proposed distribution, which indicates the accuracy of the obtained results.

The aim of this research is to give a new definition and study a new notion of fuzzy ??g-topological R-module spaces using fuzzy ??g-open sets. The concept of fuzzy ??g-topological R-modules is introduced and study them to provide a mathematical basis. Traditional cautiousness for F bisected by ??g-tautologies reigns furtive-space yield we assay different fuzzy ??g-separation axioms such as  where  in the context of these spaces.  Also, we study fuzzy -separation axiom  in fuzzy  - Topological. R-Module space. The results indicate that throughout the  domain of a fuzzy topological R-module space  the following fuzzy separation axioms  and are valid.  Also, we also show that possessing fuzzy ??g-open neighborhoods has further consequences to the structure and continuity of fuzzy topological spaces. The main conclusion of our research is that the mysterious ??g topological R-unit spaces are a new and interesting area for further study. The terms of their operations in addition to providing important information about the interaction between fuzziness and topological properties that may be useful in many mathematical and applied branches.

This paper will employ a novel approach that builds upon the lasso method, utilizing it in two stages.   The first stage applies to the principal components to select the important principal component and exclude the unimportant ones. This technique is effective in identifying significant principal components while attempting to eliminate bias in selecting these components over others. Additionally, it removes the ranking in determining the principal components compared to classical methods.  Moreover, the second stage involves determining the effective importance within each component by zeroing out the scores loading values within each component. To compare the performance of the proposed method in principal component analysis, a simulation approach can be used. Subsequently, the performance of the proposed method is tested using real data.

This Article Review for aims to analyze the effectiveness of dynamic programming as a tool to improve robust queue management strategies in service systems. Dynamic programming is an optimization technique used to determine the optimal solution to problems that can be broken down into smaller problems. Explore how dynamic programming can be used to improve queue management strategies, including reducing wait times, improving resource allocation, and increasing system efficiency. The research is based on an analytical model that combines dynamic programming with row theory Immune-waiting, includes mathematical and experimental analysis to evaluate the effectiveness of these strategies in different applied contexts. The research aims to provide practical insights on how dynamic programming can be used to improve the performance of SOA systems and to provide recommendations for improving management strategies.

This research delves into the crucial aspect of diversity within generative models, exploring both its understanding and potential enhancement. Diversity in generative models refers to the ability of the model to produce a wide range of outputs that cover the variability present in the underlying data distribution. Understanding diversity is fundamental for assessing the quality and applicability of generative models across various domains, including natural language processing, computer vision, and creative arts. We discusses existing methods and metrics for evaluating diversity in generative models and highlights the importance of diversity in promoting fairness, robustness, and creativity. It explores strategies for enhancing diversity in generative models, such as regularization techniques, diversity-promoting objectives, and novel architectures. By advancing our understanding of diversity and implementing techniques to enhance it, generative models can better capture the complexity and richness of real-world data, leading to improved performance and broader applicability.