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.

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.

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.

SummaryThe aim of this paper is to present a new ALGORITHM to determine ultimate pit outline and mining sequence simultaneously based on the MAXIMIZATION of the net present value (NPV). For this purpose, a nonlinear binary mathematical model was established and then a heuristic ALGORITHM was developed to solve this NP-Hard problem. IntroductionThe ultimate pit limit is an important problem which is determined by MAXIMIZATION of undiscounted profit or NPV. The floating cone ALGORITHM and its modified versions, Korobov ALGORITHM, Lerchs-Grossman method and maximal flow ALGORITHM were developed to generate ultimate pit limit based on the MAXIMIZATION of the undiscounted profit. Nevertheless, it is better to determine the pit outline based on the MAXIMIZATION of NPV. To achieve this goal some ALGORITHMs like Wang-Sevim, Latorre-Golosinski and Roman were established. Methodology and ApproachesThe binary and nonlinear mathematical model to determine the ultimate pit limit on the basis of maximizing NPV and a few suggestions for its linearization were presented. Afterwards, by defining the concepts of downward cone, positional weight and nearest ore index, a heuristic ALGORITHM was developed to determine the ultimate pit limit and mining sequence all together. Results and ConclusionsThe ALGORITHM was applied for 2D and 3D block models and the results showed that it is able to produce optimum outcome. Complexity of the ALGORITHM is low and easy to use and as well as for education purpose. It is also able to consider variable slopes and grade-based constraints for production planning in the ALGORITHM.

Production strategy from a hydrocarbon reservoir plays an important role in optimal field development in the sense of maximizing oil recovery and economic profits. To this end, self-adapting optimization ALGORITHMs are necessary due to the great number of variables and the excessive time required for exhaustive simulation runs. Thus, this paper utilizes genetic ALGORITHM (GA), and the objective function is defined as net present value (NPV). After developing a suitable program code and coupling it with a commercial simulator, the accuracy of the code was ensured using a synthetic reservoir. Afterward, the program was applied to an Iranian southwest oil reservoir in order to attain the optimum scenario for primary and secondary production. Different hybrid water/gas injection scenarios were studied, and the type of wells, the number of wells, well coordination/location, and the flow rate (production/injection) of each well were optimized. The results from these scenarios were compared, and simultaneous water and gas (SWAG) injection was found to have the highest overall profit representing an NPV of about 28. 1 billion dollars. The application of automated optimization procedures gives rise to the possibility of including additional decision variables with less time consumption, and thus pushing the scopes of optimization projects even further.

We address the throughput MAXIMIZATION problem for downlink transmission in DF-relay-assisted cognitive radio networks (CRNs) based on simultaneous wireless information and power transfer (SWIPT) capability. In this envisioned network, multiple-input multiple-output (MIMO) relay and secondary user (SU) equipment are designed to handle both radio frequency (RF) signal energy harvesting and SWIPT functional tasks. Additionally, the cognitive base station (CBS) communicates with the SU only via the MIMO relay. Based on the considered network model, several combined constraints of the main problem complicate the solution. Therefore, in this paper, we apply heuristic guidelines within the convex optimization framework to handle this complexity. First, consider the problem of maximizing throughput on both sides of the relay separately. Second, each side progresses to solve the complex problem optimally by adopting strategies for solving sub-problems. Finally, these optimal solutions are synthesized by proposing a heuristic iterative power allocation ALGORITHM that satisfies the combinatorial constraints with short convergence times. The performance of the optimal proposed ALGORITHM (OPA) is evaluated against benchmark ALGORITHMs via numerical results on optimality, convergence time, constraints’ compliance, and imperfect channel state information (CSI) on the CBS-PU link.