Data mining and knowledge discovery are important technologies for business and research. Despite their benefits in various areas such as marketing, business and medical analysis, the use of data mining techniques can also result in new threats to privacy and information security. Therefore, a new class of data mining methods called privacy preserving data mining (PPDM) has been developed. The aim of researches in this field is to develop techniques those could be applied to databases without violating the privacy of individuals. In this work we introduce a new approach to preserve sensitive information in databases with both numerical and categorical attributes using fuzzy logic. We map a database into a new one that conceals private information while preserving mining benefits. In our proposed method, we use fuzzy membership functions (MFs) such as Gaussian, P-shaped, Sigmoid, S-shaped and Z-shaped for private data. Then we cluster modified datasets by EXPECTATION MAXIMIZATION (EM) ALGORITHM. Our experimental results show that using fuzzy logic for preserving data privacy guarantees valid data clustering results while protecting sensitive information. The accuracy of the clustering ALGORITHM using fuzzy data is approximately equivalent to original data and is better than the state of the art methods in this field.

Multilevel image thresholding is essential for segmenting images. EXPECTATION MAXIMIZATION (EM) is effective for finding thresholds; but, it is sensitive to starting points. The Grey Wolf Optimizer (GWO) is fast at finding thresholds but can get stuck in local optima. This paper presents a new ALGORITHM, EM+GWO, combining both methods to improve segmentation. EM estimates Gaussian Mixture Model (GMM) coefficients, while GWO finds better solutions when EM is stuck. GWO adjusts GMM parameters using Root Mean Square Error (RMSE) for the best fit. The ALGORITHM was tested on nine standard images, evaluating global fitness, PSNR, SSIM, FSIM, and computational time. The results show that EM+GWO significantly enhances image segmentation effectiveness. Statistical tools indicate that RCG achieves the best RMSE and PSNR in 7 out of 9 test images, and it holds the highest rank in both SSIM and FSIM. The average execution time of each ALGORITHM was calculated, showing that EM+GWO has an acceptable running time compared to EM and GWO. This balance between computational efficiency and improved segmentation performance makes the proposed EM+GWO ALGORITHM a robust and effective solution for image segmentation tasks. Overall, the combination of EM and GWO methods provides a more reliable and accurate approach to optimizing image segmentation, avoiding local optima, and enhancing overall performance.

Background: Leukemia is a kind of malignancy blood system which leads to death of human beings in a very short period of time. In this paper, the effective factors on survival time of the acute lymphoblastic leukemia (ALL) patients have been considered to achieve a linear regression model show the relation between the life-time after diagnosis and some explanatory factors.Methods: In this study, the data of 52 patients died from ALL was used. The designed model contained three variables, hemoglobin, large undifferentiated cell (LUC) and age. According to the data suggesting, a kind of mixture distribution, we considered a mixture model for survival time. Applying the EM-ALGORITHM, we have found the maximum likelihood estimate of mean survival time and the Bayesian estimate of the mean survival time by Monte Carlo Markov Chain method.Findings: Based on the obtained estimating survival function, we can predict the survival time of the patients and decide about their treatment protocol.Conclusion: It is suggested that by conducting larger studies and statistical analysis used in this paper, a correlative can be found between clinical & Para clinical findings and the survival time. This model can be used in often kinds of diseases for determining the prognosis.

The influence MAXIMIZATION problem in social networks aims to find a minimal set of individuals in order to produce the highest influence on the other individuals in the network. In the last two decades, a lot of ALGORITHMs have been proposed to solve the time efficiency and effectiveness challenges of this NP-Hard problem. Undoubtedly, the CELF ALGORITHM (besides the naive greedy ALGORITHM) has the highest effectiveness among them. Of course, the CELF ALGORITHM is faster than the naive greedy ALGORITHM (about 700 times). This superiority has led many researchers to make extensive use of the CELF ALGORITHM in their innovative approaches. However, the main drawback of the CELF ALGORITHM is the very long running time of its first iteration since it has to estimate the influence spread for all nodes by the expensive Monte-Carlo simulations, similar to the naive greedy ALGORITHM. In this paper, a heuristic approach is proposed, namely optimized-CELF ALGORITHM, in order to improve this drawback of the CELF ALGORITHM by avoiding the unnecessary Monte-Carlo simulations. It is found that the proposed ALGORITHM reduces the CELF running time, and subsequently, improves the time efficiency of the other ALGORITHMs that have employed CELF as a base ALGORITHM. The experimental results on the wide spectral of real datasets show that the optimized-CELF ALGORITHM provides a better running time gain, about 88-99% and 56-98% for k=1 and k=50, respectively, compared to the CELF ALGORITHM without missing effectiveness.

During the very last decade, people have been spending lots of time working with social networks to interact with friends and to share information, thoughts, news, and etc. These social networks comprise a very important part of our daily lives. Along with the exploitation of the development of social networks, finding influential individuals in a social network has many practical functions in marketing, politics, and even control of the diseases. In the present research, a novel method called the dynamic generalized vulture ALGORITHM has been proposed to solve influence MAXIMIZATION problems. Regarding the fact that in real world social networks own very dynamic and scalable nature, through our proposed ALGORITHM, we have considered two important criteria which have been rarely taken into consideration in previous projects. The first criterion is due to the network structure change during time pass and the other refers to scalability. The suggested ALGORITHM was measured considering standard data sets. The results showed that the proposed ALGORITHM has been more scalable and has had higher precision in locating the most influential tops in such networks compared with other ALGORITHMs due to the reduction of search area and using several different mechanisms during navigation and optimization, balance creation and moving through these stages.

In this paper, we discuss the calibration of the geometric Brownian motion model equipped with Markov-switching factor. Since the motivation for this research comes from a recent stream of literature in stock economics, we propose an efficient estimation method to sample a series of stock prices based on the EXPECTATION-MAXIMIZATION ALGORITHM. We also implement an empirical application to evaluate the performance of the suggested model. Numerical results through the classification of the data set show that the proposed Markov-switching model fits the actual stock prices and reflects the main stylized facts of market dynamics. Since the motivation for this research comes from a recent stream of literature in stock economics, we propose an efficient estimation method to sample a series of stock prices based on the EXPECTATION-MAXIMIZATION ALGORITHM. Numerical results through the classification of the data set show that the proposed Markov-switching model fits the actual stock prices and reflects the main stylized facts of market dynamics. Since the motivation for this research comes from a recent stream of literature in stock economics, we propose an efficient estimation method to sample a series of stock prices based on the EXPECTATION-MAXIMIZATION ALGORITHM.