Jaya Santoso, Darmaji Darmaji, Ana Muliyana
et al.
This paper investigates the metric dimension of a class of graphs known as cycle books, denoted , which feature a shared path across multiple cycles. We focus on characterizing the minimum number of vertex subsets required so that each vertex in the graph can be uniquely identified by its distances to those subsets. To support our analysis, we present two propositions and a general theorem that establish the metric dimension for various configurations of cycle book graphs. Specifically, we prove that for , and for , while for . Furthermore, we provide a general result for : the metric dimension is when is odd and , or when is even and ; and when is odd and . These findings contribute to the growing body of knowledge on metric properties in graph theory, particularly in structured and cyclic graph families.This paper investigates the metric dimension of a class of graphs known as cycle books, denoted , which feature a shared path across multiple cycles. We focus on characterizing the minimum number of vertex subsets required so that each vertex in the graph can be uniquely identified by its distances to those subsets. To support our analysis, we present two propositions and a general theorem that establish the metric dimension for various configurations of cycle book graphs. Specifically, we prove that for , and for , while for . Furthermore, we provide a general result for : the metric dimension is when is odd and , or when is even and ; and when is odd and . These findings contribute to the growing body of knowledge on metric properties in graph theory, particularly in structured and cyclic graph families.This paper investigates the metric dimension of a class of graphs known as cycle books, denoted , which feature a shared path across multiple cycles. We focus on characterizing the minimum number of vertex subsets required so that each vertex in the graph can be uniquely identified by its distances to those subsets. To support our analysis, we present two propositions and a general theorem that establish the metric dimension for various configurations of cycle book graphs. Specifically, we prove that for , and for , while for . Furthermore, we provide a general result for : the metric dimension is when is odd and , or when is even and ; and when is odd and . These findings contribute to the growing body of knowledge on metric properties in graph theory, particularly in structured and cyclic graph families.
The growth of the Hankel determinant whose elements are logarithmic coefficients for different subclasses of univalent functions has recently attracted considerable interest. In this paper, we obtain the bounds for the first four initial logarithmic coefficients for the subclass of starlike functions with respect to conjugate points in an open unit disk. Furthermore, we determine the upper bounds of the second Hankel determinant of logarithmic coefficients for this subclass. We also present some new consequences of our results.
Mrs. Nalini Jagtap, Mrs. P. P. Shevatekar, Mr. Nareshkumar Mustary
- Huge amount of data is getting generated every second. These data is needed to be analyzed. Classification in data mining is a technique based on machine learning algorithms which uses mathematics, statistics, probability distributions and artificial intelligence. Classification predicts the group membership for data items or it represents descriptive analysis of data items for effective decision making.Now a day’s data mining is touching every aspect of individual life includes Data Mining for Financial Data Analysis, Data Mining for the Telecommunications Industry Data Analysis, Data Mining for the Retail Industry Data Analysis, Data Mining in Healthcare and Biomedical Research Data Analysis, and Data Mining in Science and Engineering Data Analysis, etc. The goal of this survey is to provide a comprehensive review of different classification techniques in data mining based on decision tree, rule based Algorithms, neural networks, support vector machines, Bayesian networks
A.T. Kasimov, G.A. Yessenbayeva, B.A. Kasimov
et al.
In the article, an analytical and numerical study based on one modified refined bending theory is presented. By the finite difference method, a general numerical calculation algorithm is developed. The solution obtained by the proposed method is compared with the results of known solutions, namely, with the solution of the classical theory, the exact solution, the solution in trigonometric series, as well as with experimental data. Comparison of the results obtained by the method given in the article with the solutions determined by other methods shows sufficient accuracy, which indicates the reliability of the proposed method based on one option of the modified refined bending theory. Classical theory is not applicable to such problems under consideration.
Olawale O. Kehinde, Justin B. Munyakazi, Appanah R. Appadu
Despite the availability of an abundant literature on singularly perturbed problems, interest toward non-linear problems has been limited. In particular, parameter-uniform methods for singularly perturbed semilinear problems are quasi-non-existent. In this article, we study a two-dimensional semilinear singularly perturbed convection-diffusion problems. Our approach requires linearization of the continuous semilinear problem using the quasilinearization technique. We then discretize the resulting linear problems in the framework of non-standard finite difference methods. A rigorous convergence analysis is conducted showing that the proposed method is first-order parameter-uniform convergent. Finally, two test examples are used to validate the theoretical findings.
Poisson Log-Normal Model is one of the hierarchical mixed models that can be used for count data. Several estimation methods can be used to estimate the model parameters. The first objective of this study was to examine the performance of the parameter estimator and model built using the Hierarchical Bayes method via Markov Chain Monte Carlo (MCMC) with simulation. The second objective was applied the Poisson Log-Normal model to the West Java illiteracy Cases data which is sourced from the Susenas data on March 2019. In 2019, the incidence of illiteracy is a very rare occurrence in West Java Province. So that, it is suitable as an application case in this study. The simulation results showed that the Hierarchical Bayes parameter estimator through MCMC has the smallest Root Mean Squared Error of Prediction (RMSEP) value and the absolute bias is relatively mostly similar when compared to the Maximum Likelihood (ML) and Penalized Quasi-Likelihood (PQL) methods. Meanwhile, the empirical results showed that the fixed variable is the number of respondents who have a maximum education of elementary school have the greatest risk of illiteracy. Also, the diversity of census blocks significantly affects illiteracy cases in West Java 2019.
Data science is collaborative and its students should learn teamwork and collaboration. Yet it can be a challenge to fit the teaching of such skills into the data science curriculum. Team-Based Learning (TBL) is a pedagogical strategy that can help educators teach data science better by flipping the classroom to employ small-group collaborative learning to actively engage students in doing data science. A consequence of this teaching method is helping students achieve the workforce-relevant data science learning goals of effective communication, teamwork, and collaboration. We describe the essential elements of TBL: accountability structures and feedback mechanisms to support students collaborating within permanent teams on well-designed application exercises to do data science. The results of our case study of using TBL to teach a modern, introductory data science course indicate that the course effectively taught reproducible data science workflows, beginning R programming, and communication and collaboration. Students also reported much room for improvement in their learning of statistical thinking and advanced R concepts. To help the data science education community adopt this appealing pedagogical strategy, we outline steps for deciding on using TBL, preparing and planning for it, and overcoming potential pitfalls when using TBL to teach data science.
Probabilities. Mathematical statistics, Special aspects of education
Matthew D. Beckman, Mine Çetinkaya-Rundel, Nicholas J. Horton
et al.
A version control system records changes to a file or set of files over time so that changes can be tracked and specific versions of a file can be recalled later. As such, it is an essential element of a reproducible workflow that deserves due consideration among the learning objectives of statistics courses. This article describes experiences and implementation decisions of four contributing faculty who are teaching different courses at a variety of institutions. Each of these faculty has set version control as a learning objective and successfully integrated one such system (Git) into one or more statistics courses. The various approaches described in the article span different implementation strategies to suit student background, course type, software choices, and assessment practices. By presenting a wide range of approaches to teaching Git, the article aims to serve as a resource for statistics and data science instructors teaching courses at any level within an undergraduate or graduate curriculum.
Probabilities. Mathematical statistics, Special aspects of education
Nor Hayati Binti Shafii, Rohana Alias, Nur Fithrinnissaa Zamani
et al.
Air pollution is a current monitored problem in areas with high population density such as big cities. Many regions in Malaysia are facing extreme air quality issues. This situation is caused by several factors such as human behavior, environmental awareness and technological development. Accessing the air pollution index (API) accurately is very important to control its impact on environmental and human health. The work presented here aims to access air pollution index of PM2.5 using Support Vector Machine (SVM) and to compare the accuracy of four different types of the kernel function in Support Vector Machine (SVM). The data used is provided by the Department of Environment (DOE) and it is recorded from two Continuous Air Quality Monitoring Stations (CAQM) located at Tanah Merah and Kota Bharu. The results are analyzed using mean absolute error (MAE) and root mean squared error (RMSE). It is found that the proposed model using Radial Basis Function (RBF) with its parameters of cost and gamma equal to 100 can effectively and accurately forecast the air pollution index with Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) of 0.03868583 and 0.06251793 respectively for API in Kota Bharu and 0.03857308 (MAE) and 0.05895648 (RMSE) for API in Tanah Merah.
Ibrahim Sule, Sani Ibrahim Doguwa, Audu Isah
et al.
Background: In the last few years, statisticians have introduced new generated families of univariate distributions. These new generators are obtained by adding one or more extra shape parameters to the underlying distribution to get more flexibility in fitting data in different areas such as medical sciences, economics, finance and environmental sciences. The addition of parameter(s) has been proven useful in exploring tail properties and also for improving the goodness-of-fit of the family of distributions under study.
Methods: A new three parameter family of distributions was introduced by using the idea of T-X methodology. Some statistical properties of the new family were derived and studied.
Results: A new Topp Leone Kumaraswamy-G family of distributions was introduced. Two special sub-models, that is, the Topp Leone Kumaraswamy exponential distribution and Topp Leone Kumaraswamy log-logistic distribution were investigated. Two real data sets were used to assess the flexibility of the sub-models.
Conclusion: The results suggest that the two sub-models performed better than their competitors.
There has been increasing interest in recent years in the development of approaches to estimate causal effects when the number of potential confounders is prohibitively large. This growth in interest has led to a number of potential estimators one could use in this setting. Each of these estimators has different operating characteristics, and it is unlikely that one estimator will outperform all others across all possible scenarios. Coupling this with the fact that an analyst can never know which approach is best for their particular data, we propose a synthetic estimator that averages over a set of candidate estimators. Averaging is widely used in statistics for problems such as prediction, where there are many possible models, and averaging can improve performance and increase robustness to using incorrect models. We show that these ideas carry over into the estimation of causal effects in high-dimensional scenarios. We show theoretically that averaging provides robustness against choosing a bad model, and show empirically via simulation that the averaging estimator performs quite well, and in most cases nearly as well as the best among all possible candidate estimators. Finally, we illustrate these ideas in an environmental wide association study and see that averaging provides the largest benefit in the more difficult scenarios that have large numbers of confounders.
Lakhdar Riabi, Kacem Belghaba, Mountassir Hamdi Cherif
et al.
<p>The idea proposed in this work is to extend the ZZ transform method to resolve the nonlinear fractional partial differential equations by combining them with the so-called homotopy perturbation method (HPM). We apply this technique to solve some nonlinear fractional equations as: nonlinear time-fractional Fokker-Planck equation, the cubic nonlinear time-fractional Schrodinger equation and the nonlinear timefractional KdV equation. The fractional derivative is described in the Caputo sense. The results show that this is the appropriate method to solve somme models of nonlinear partial differential equations with time-fractional derivative.</p>
People in this new era of modernization nowadays take Internet as one of the vital thing for daily activities.
Internet is not only for adults, it is also a needs for people of all ages. However, network vulnerabilities
exist in all network that are connec
ted to the Internet. The network mostly are exposed to the malicious
software or mostly known as malware. In fact, this malware is growing rapidly and giving a bad impact to
the human intervention. The number of attack are increasing rapidly and it comes i
n various way just to
exploit the victims. There are various type of malware attack. For instance, viruses, worms, spyware,
rootkits, Trojan horse and botnet are considered as noteworthy threat for the computer network. Some
people giving full confidence on the security of data transmission to the network. However, other can access
the personal information without them realizing it. The objective of this paper is to detect malware attack
using honeypot Dionaea. Malicious file launched was detected by the honeypot and the file was analyzed
by using the sandbox tool, Virus Total. This paper found that honeypot Dionaea is helpful in detecting
various types of malware attack.