Chronic Kidney Disease (CKD) is a major global health issue that can lead to serious complications and long-term medical care. This study aims to identify key clinical factors associated with CKD status using binary logistic regression analysis. The dataset, obtained from Kaggle, contains 400 patient records with various clinical and demographic attributes. The dependent variable is CKD status (positive or negative), while the independent variables include age, blood pressure, hemoglobin level, urine albumin level, and serum creatinine. Initial analysis involved descriptive statistics and multicollinearity checks, followed by model estimation and evaluation using likelihood ratio and Wald tests. The final model identified four significant predictors: blood pressure, hemoglobin, urine albumin, and serum creatinine. The model achieved a high classification accuracy of 95.50% and an Area Under the ROC Curve (AUC) of 98.78%, indicating excellent predictive performance. These results highlight the importance of these clinical indicators in early CKD detection and support their use in risk assessment models for kidney disease screening
Keywords: Chronic Kidney Disease, Binary Logistic Regression, Likelihood Ratio Test, Wald Test, Classification Accuracy

This study aims to determine the level, attribute, and stimulus most considered by respondents in choosing expedition services. The method used is conjoint analysis by applying the conjoint analysis full-profile method to design the stimulus. The attributes and levels used are delivery type (ECO, REG, express, and same day), payment methods (cash and online transfers), service promotions (cost discounts and goods pick-up), and service responsiveness (responses to damage or loss of goods and responses to late delivery). The results of research conducted on 150 respondents showed that delivery type is the most preferred attribute with a value of 0,324. The levels with the largest part-worth of each attribute are REG (0.335), online transfers (0.210), cost discounts (0.270), and response to damage or loss of goods (0.250). The most popular stimulus is expedition services with standard shipping (REG), online transfers, cost discounts, and responses to damage or loss of goods.

Diabetes is one of the non-communicable diseases that is considered dangerous due to its susceptibility to complications. This disease is caused by high blood sugar levels in a person's body, which makes the blood more alkaline and slows down the metabolic process. In this study, we observed 8 variables that are considered influential in diabetes and will build a regression model that can predict the response variable (y) through Logistic Regression Analysis. Logistic Regression Analysis is a statistical analysis method used to describe the relationship between a dependent variable with two or more categories and one or more independent variables that are categorical or continuous. Based on the results, the logistic regression model for factors influencing diabetes in the Indian Pima tribe includes variables such as number of pregnancies, glucose level, blood pressure, body mass index, and diabetes pedigree function

Determining the price of option contracts is a crucial aspect of financial markets, particularly for investors aiming to manage risk and make informed investment decisions. In this study, the price of an Asian call option is calculated using the Monte Carlo Stratified Sampling method based on the stock price data of Tesla, Inc. (TSLA) from January 2021 to December 2023. This method has been proven to reduce variance compared to the Standard Monte Carlo simulation, leading to faster price convergence and more efficient results. The parameters used in the simulation include the initial stock price  (S_0), number of simulations (N), maturity time  (T)dividend = 0, risk-free rate (r), strike price ( K), and volatility

This study compares different machine learning models for time series forecasting in financial data analysis. Models including ARIMA, LSTM, and GRU are applied to predict stock price movements. We measure the accuracy and computational efficiency of each model on various datasets and discuss their strengths and weaknesses in financial forecasting contexts. The findings suggest that deep learning models show significant improvement in capturing complex temporal patterns over traditional methods.

This paper investigates the stability characteristics of fractional order differential equations (FODEs) incorporating time delays. Using the Lyapunov-Krasovskii method, we derive sufficient conditions for the stability of solutions to these delayed fractional systems. The theoretical findings are applied to several examples, including models of population dynamics and engineering systems. Numerical simulations validate the theoretical results, demonstrating the role of time delays in system behavior and stability.

The student study period is one of the important aspects to measure the quality of a higher education institution. The length of the student study period can be assumed to come from internal factors and external factors, so it is necessary to conduct research that aims to identify and model the factors that affect the student study period. The method used in this research is logistic regression and the data used is primary data obtained from distributing questionnaires. The target of this research is aimed at alumni students from the Mathematics Study Programme of Udayana University from 2011 to 2019. In this study, the best model produced has a classification accuracy of 98.17% and the independent variables that have a significant effect on the study period are gender, tuition fees and interest in majors.

When making investment decisions, it is crucial for investors to consider various risks that may arise, both in the short and long term. One method to measure risk is through volatility. Volatility represents a statistical measurement of the degree of price variation over a specific period, expressed as volatility (?) (Aklimawati & Wahyudi, 2013). This study aims to discuss the pricing of European option contracts using Conditional Monte Carlo simulation and the Black-Scholes method. The data used in this study is secondary data obtained from Yahoo Finance. The data consists of quantitative information, namely the monthly closing prices of Toyota Motor Corporation (TM) stock, spanning 5 years from July 1, 2019, to July 1, 2024, yielding 60 data points. In this research, the pricing of European call option contracts was calculated using Conditional Monte Carlo simulation and the Black-Scholes method. The study concludes that European option contract pricing can be determined using two methods: Conditional Monte Carlo simulation and the Black-Scholes method. Conditional Monte Carlo simulation can be employed to calculate European option prices in a structured manner, utilizing stochastic volatility estimated through the Ordinary Least Squares (OLS) method. The two methods yield differing option prices; Conditional Monte Carlo simulation produces lower option price estimates with relatively lower error values compared to the Black-Scholes method at every strike price. The lower estimates from Conditional Monte Carlo simulation are due to its consideration of stochastic changes in volatility, whereas the Black-Scholes method results in higher prices due to its assumption of constant volatility. The comparison demonstrates that Conditional Monte Carlo simulation provides cheaper price estimates under market conditions with non-constant volatility, despite requiring higher computational time compared to the Black-Scholes method.
 
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This paper explores the use of genetic algorithms (GAs) for optimizing nonlinear systems in resource allocation. By simulating various allocation scenarios, we demonstrate the efficiency of GAs in finding near-optimal solutions in complex environments. The study provides a comparison of GA performance against traditional optimization methods and identifies scenarios where GAs outperform. The results emphasize the utility of GAs in real-world applications, especially when conventional approaches struggle with large solution spaces.

This research presents a numerical investigation into heat transfer in nanofluid flow using an advanced lattice Boltzmann method (LBM). The study modifies the standard LBM to incorporate the unique properties of nanofluids, such as enhanced thermal conductivity. We simulate convective heat transfer in a pipe with varying nanoparticle concentrations, assessing the effects on heat transfer rates. Results show that nanofluids significantly improve heat transfer efficiency, offering valuable insights for engineering applications in cooling systems.

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.