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.

In recent years, rough set theory has been considered as a strong solution to solve artificial intelligence problem such as data mining. But, the classic rough set theory is not effective in the case of attribute reduction in incomplete information systems. Since there are null values for some of attributes in a data set, an incomplete information system is created. In this paper, a novel method proposed to solve attribute reduction in incomplete information system based on rough set theory by combining and modifying imperialist competitive algorithm with fuzzy logic. Utilizing the fuzzy logic to control the parameters of the algorithm was useful and generated better solutions compared to its classic draft. In this research, no changes imposed on incomplete data, and it was just considered as a complete systems. The fuzzy imperialist competitive algorithm acted intelligently to reduce the number of attribute in incomplete information system, providing appropriate results that is worthy of attention.

This article discusses about the different definitions of the category of quantity. From these definitions, the second and the third are zoomed on more. Because number is not a secondary philosophical intelligible. It is not also from the genus of existence. On the contrary, it exists in its object existence and it is a first intelligible. We demonstrate that number is of nature and the thing, which actually is divided, is object not number. Number is a thing although it can be divided. It also deduces that different numbers are species. We argue that the difference between connected quantity and separate quantity is not in defect division, but in having common component. We also criticize the argument that declares straight lines and curves are species.

The purpose of this research is to identify effective criteria for measuring competitive position of certain products of Shahdiran company, a regional major food and juice producer in Iran. To determine the criteria and their relative importance, Analytical Hierarchy Process (AHP) was used by organizing a decision team consisting of 6 top managers of the company. Four major competitors (based on market share) were selected for comparison purposes. Cook and Seiford’s developed model which consists of 19 successive steps was used to determine the company’s competitive position. For that, questionnaires based on Saaty scale, were designed to determine; effective criteria, relative importance of each criterion, and company’s rank from retailer’s point of view.A survey research was also conducted to identify the position of other food product retailers in Mashhad as competitors. As a result, company's position in comparison with its four major competitors was determined through using 20 known criteria and market mix. The research further revealed that Alpha totally holds the first rank among its nearest competitors in the regional market while it holds rank 1 for product, 3 for price, 1 for place and finally the second rank for promotion.

Inertial Navigation System (INS) is one of the navigation systems widely used in various land-based, aerial, and marine applications. Among all types of INS, Microelectromechanical System (MEMS)-based INS can be widely utilized, owing to their low cost, lightweight, and small size. However, due to the manufacturing technology, MEMS-based INS suffers from deterministic and stochastic errors, which increase positioning errors over time. In this paper, a new effective noise reduction method is proposed that can provide more accurate outputs of MEMS-based inertial sensors. The intelligent method in this paper is a combined denoising method that combines Wavelet Transform (WT), Permutation Entropy (PE), Support Vector Regression (SVR), and Genetic Algorithm (GA). Firstly, WT is employed to obtain a time-frequency representation of raw data. Secondly, a four-element feature vector is formed. These four features are (1) amplitude of frequency, (2) its ratio to mean of amplitudes of all frequencies, (3) location of frequency in time-frequency representation, and (4) judgment on behaviors of frequency that is obtained by utilizing PE. Thirdly, based on the feature vector, the GA-SVR algorithm predicts amplitudes of all frequencies in the time-frequency representation of the denoised signal. Finally, by employing inverse WT the denoised signal is obtained. In this work, the outputs of the Inertial Measurement Unit (IMU) in ADIS16407 sensor, as a low-cost and MEMS-based INS, have been utilized for data collection. The proposed method has been compared with other noise reduction methods and the achieved results verify superior improvement than other methods.

Reliable access control is a major challenge of cloud storage services. This paper presents a cloud-based file-sharing architecture with ciphertext-policy attribute-based encryption (CP-ABE) access control mechanism. In CP-ABE, the data owner can specify the ciphertext access structure, and if the user key satisfies this access structure, the user can decrypt the ciphertext. The trusted authority embeds the private key of each attribute in a so-called attribute access polynomial and stores its coefficients publicly on the cloud. By means of the access polynomial, each authorized user will be able to retrieve the private key of the attribute by using her/his owned pre-shard key. In contrast, the data owner encrypts the file with a randomly selected key, namely the cipher key. The data owner encrypts the cipher key by CP-ABE scheme with the desired policies. Further, the data owner can create a different polynomial called query access polynomial for multi-keyword searching. Finally, the data owner places the encrypted file along the encrypted cipher key and query access polynomial in the cloud. The proposed scheme supports fast attribute revocation using updating the corresponding access polynomial and re-encrypting the affected cipher keys by the cloud server. Moreover, most of the calculations at the decryption and searching phases are outsourced to the cloud server, thereby allowing the lightweight nodes with limited resources to act as data users. Our analysis shows that the proposed scheme is both secure and efficient.

Coherency attribute is one of the proper tools in interpretation of structural discontinuities and stratigraphy features in 3-D seismic data. Coherency measurements in three dimensions discuss trace-to-trace similarity and therefore represent interpretable changes in these cases. The similar traces are mapped with high coherence coefficients while anomalies and discontinuities have low coherence coefficients. Coherency attribute shows evaluation criterion of lateral changes in the seismic response, caused by variation in structure, stratigraphy, lithology, porosity and the presence of hydrocarbon. Output of this attribute is a coherence cube which illustrates structural discontinuities and stratigraphy features with higher resolution. In this paper, the application of two conventional coherency attributes based on eigenstructure and crosscorrelation for detection of faults in 3-D synthetic seismic data and actual seismic data is presented.Considering the experimental results, this method has an appropriate response to low SNR for 3-D synthetic models and 3-D actual data. In addition, the comparison of eigenstructure -based coherency attribute method with crosscorrelation-based coherency attribute method indicates the former has higher resolution for detection faults than the latter.