Stock market is one of the options available to invest in liquidity. Investors in this area used a variety of approaches to predict stock prices. But due to the nonlinear relationship between variables affecting stock prices, Artificial Neural Networks are one of the most suitable approaches for this work. These networks, through different search optimization Algorithms, try to identify the relationships between these variables. The higher the Algorithms used, the higher the efficiency of the Algorithms, the more accurate the identification of the relationships between the variables. In this paper, an attempt has been made to combine chaotic maps and Colonial Competition Algorithms with the reform movement angle to the Colonial colonies so that we can deal with the possibility of being trapped in local optimum to reduce as much as possible. Therefore, using this approach, it is tried to predict the stock price of Iran Khodro Company. To evaluate the performance of the proposed approach to other conventional approaches of neural network education, three perspectives: the degree of accuracy of prediction, the amount of memory used and the time of execution were used. The results show that the proposed approach has a better performance than other approaches.

A Hyper Spherical Search (HSS) optimization Algorithm based on Chaos Theory is proposed that resolves the weakness of the standard HSS optimization Algorithm including the speed of convergence and the sequential increment in the number of Algorithm iterations to achieve the optimal solution. For this, in the particle initiation and search steps of the proposed Algorithm, random values used in the standard Algorithm are replaced with the values of two mappings, Chebyshev and Liebovitch, that makes the results of the proposed Algorithm definite and decreases their standard deviation. The simulation results on the standard benchmark functions show that the proposed Algorithm not only has faster convergence, but also acts as a more accurate search Algorithm to find the optimal solution in comparison to standard hyper spherical search Algorithm and some other optimization Algorithms such as genetic, particle swarm, and harmony search Algorithm.

A hybrid optimization Algorithm based on genetic Algorithm and choatic hyper spherical search method is proposed. In the proposed method, in order to increase the efficiency of searching the optimal solution, Chaos Theory along with genetic operators have been used. This, not only makes the results of the proposed Algorithm definite and decreases their standard deviation, but also resolves the weakness of the hyper spherical search optimization Algorithm based on Chaos Theory including the speed of convergence and the weak performance in some benchmark functions. The simulation results on the standard benchmark functions show that the proposed Algorithm not only has faster convergence, but also acts as a more accurate search Algorithm to find the optimal solution in comparison to the standard hyper spherical search Algorithm, chaotic hyper sherical search Algorithm, and some other optimization Algorithms such as genetic, particle swarm, and harmony search Algorithm.

The intriguing characteristics of chaotic maps have prompted researchers to use these sequences in watermarking systems to good effect. In this paper we aim to use a tent map to encrypt the binary logo to achieve a like-noise signal.This approach makes extraction of the watermark signal by potential attacker very hard. Embedding locations are selected based on certain principles.Experimental results demonstrate that our proposed watermarking method is highly superior to other techniques reported in literature and readily achieves the desired robustness and security level.

Increasing the dimension of the databases have involved the attribute reduction as a critical issue in data mining that it searches to find a subset of attributes with the most effectiveness on the hidden patterns. In the current years, the rough set Theory has been considered by researchers as one of the most effective and efficient tools to the reduction. In this paper, the soccer league Competition Algorithm is modified and adopted to solve the attribute reduction problem for the first time. The ability to escape the local optimal, the ability to use the information distributed by players in the search space, the rapid convergence to the optimal solutions, and the low Algorithm’ s parameters were the motivation of considering the Algorithm in the current research. The proposed ideas to modify the Algorithm consist of utilizing the total power of fixed and saved players in calculating the power of each team, considering the combination of continuous and discrete structures for each player, proposing a novel discretization method, providing a hydraulic analysis appropriate to the research problem for evaluating each player, designing correction in Imitation and Provocation operators based on the challenges in their original version. The proposed ideas are performed on small, medium and large data sets from UCI and the experimental results are compared with the state-of-the-art Algorithms. This comparison shows that the competitive advantages of the proposed Algorithm over the investigated Algorithms.

Diabetes is the costliest gland disease in the world. Given the high rates of diabetic people, the necessity of reducing the costs of early re-hospitalization and increasing re-admissions within 30 days after discharge have drawn the attention of researchers and other health sector authorities to find ways to reduce potential and preventable hospital re-admissions. The objective of this paper is to estimate the risk of re-hospitalization of diabetic patients. In order to achieve this goal, the data were first pre-processed, and then, radial base function neural network combined with Colonial Competition optimization Algorithm was used to estimate the risk of re-hospitalization of diabetic patients. Moreover, this risk was estimated using back propagation neural network Algorithm and the radial base function neural network Algorithm. The accuracy of the proposed method is 99.91. This method shows higher performance compared to radial base function neural network method and back propagation neural network without feature selection.

2The current research was conducted with the aim of compiling and validating the educational package of the 10th grade religion and life lesson based on Choice Theory. The research method for the stage of designing and compiling the educational package was descriptive-analytical method mixed . The main themes of the training package and the time required for training were taken from written sources such as the model of G schools and internal and external research projects. To validate the package, while using the face validity method, the content validity method was used in a qualitative and quantitative way. It was used with a panel of 14 experts. 14 experts were considered as the statistical population, 10 of them were in the field of psychology, two were in the field of curriculum planning, and two were in the field of education. In addition, two relative coefficients of content validity and content validity index were used. The minimum and maximum relative content validity coefficients for each item of the package were calculated as 1 and 0.71, and the content validity index in most sessions reached the number (0.85) for all goals in the educational protocol. The results revealed that the educational package of the Religion and Life lesson based on Choice Theory for 10th grade students have good formal and content validity and has the necessary validity for educational applications and research use.

This study focuses on the investigation of intelligent form-finding and vibration analysis of a triangular polyhedral tensegrity that is enclosed within a sphere and subjected to external loads. The nonlinear dynamic equations of the system are derived using the Lagrangian approach and the finite element method. The proposed form-finding approach, which is based on a basic genetic Algorithm, can determine regular or irregular tensegrity shapes without dimensional constraints. Stable tensegrity structures are generated from random configurations and based on defined constraints (nodes located on the sphere, parallelism, and area of upper and lower surfaces), and shape finding is performed using the fitness function of the genetic Algorithm and multi-objective optimization goals. The genetic Algorithm's efficacy in determining the shape of structures with unpredictable configurations is evaluated in two distinct scenarios: one involving a known connection matrix and the other involving fixed or random member positions (struts and cables). The shapes obtained from the Algorithm suggested in this study are validated using the force density approach, and their vibration characteristics are examined. The findings of the comparative study demonstrate the efficacy of the proposed methodology in determining the vibrational behavior of tensegrity structures through the utilization of intelligent shape seeking techniques.

Over the past decade, municipal waste production has increased dramatically due to increasing global population and the rate urbanization. Therefore, one of the important issues that has been used in recent years and has been discussed to increase the efficiency and productivity of transportation systems is the discussion of routing of waste transportation machines. In addition, poor management and inappropriate collection and storage since it creates environmental problems in the urban ecosystem is considered a global challenge, as the presentation of these urban services has been significantly neglected due to lack of sufficient data and information. The aim of this study was to provide an appropriate solution for proper collection of waste in 40 neighborhoods of Zabul city to minimize the time of mission and increase the satisfaction of citizens of such urban services. The research method is descriptive and applied studies based on attribution algenomy and field studies. In order to conduct this study, metaheuristic Algorithm of Colonial Competition and Arc GIS software was used and all the related programming was done in MATLAB software environment. The results indicated that Colonial competitive Algorithm with 20 replications at 15 replications with a time equal to 4. 11 seconds with 71% confidence coefficient was obtained in the best possible state and showed the order of the best intersections. Confidence coefficient was obtained in the best possible state and showed the order of the best intersections. Confidence coefficient was obtained in the best possible state and showed the order of the best intersections.