Genetic Relationship of Minerals and Carbonate Units of Stratigraphy Sequence in the Ahmadabad Deposit (NE of Bafq) Using Data Related to Rare Earth Elements

Ahmadabad Pb-Zn ore deposit is located in the mineral area of Bahabad in Central Iran Zone. This ore deposit like other metallogenic areas in Bahabad is found in Triassic carbonate rocks. Carbonate rocks in Shotori formation have the highest frequency in the regional sequence stratigraphy. This formation is composed of TRSh1, TRSh2, TRSh3 and TRSh4 units. The TRSh3 unit hosts minerals in ore deposit Ahmadabad. Microcrystalline particles are the main constituent of these rocks. The most important minerals in this ore deposit include calamine Celestine, Cerussite and Wulfenite. The comparison of normalized ore patterns and carbonate sequence indicates that they have a specified genetic relationship. Here the TRSh2 unit is more similar to minerals.

FICA: fuzzy imperialist competitive algorithm

Despite the success of the imperialist competitive algorithm(ICA)in solving optimization problems,it still suffers from frequently falling into local minima and low convergence speed.In this paper,a fuzzy version of this algorithm is proposed to address these issues.In contrast to the standard version of ICA,in the proposed algorithm,powerful countries are chosen as imperialists in each step;according to a fuzzy membership function,other countries become colonies of all the empires.In absorption policy,based on the fuzzy membership function,colonies move toward the resulting vector of all imperialists.In this algorithm,no empire will be eliminated;instead,during the execution of the algorithm,empires move toward one point.Other steps of the algorithm are similar to the standard ICA.In experiments,the proposed algorithm has been used to solve the real world optimization problems presented for IEEE-CEC 2011 evolutionary algorithm competition.Results of experiments confirm the performance of the algorithm.

An ensemble method for data stream classification in the presence of concept drift

One recent area of interest in computer science is data stream management and processing. By ‘data stream', we refer to continuous and rapidly generated packages of data. Specific features of data streams are immense volume, high production rate, limited data processing time, and data concept drift; these features differentiate the data stream from standard types of data. An issue for the data stream is classification of input data. A novel ensemble classifier is proposed in this paper. The classifier uses base classifiers of two weighting functions under different data input conditions. In addition, a new method is used to determine drift, which emphasizes the precision of the algorithm. Another characteristic of the proposed method is removal of different numbers of the base classifiers based on their quality. Implementation of a weighting mechanism to the base classifiers at the decision-making stage is another advantage of the algorithm. This facilitates adaptability when drifts take place, which leads to classifiers with higher efficiency. Furthermore, the proposed method is tested on a set of standard data and the results confirm higher accuracy compared to available ensemble classifiers and single classifiers. In addition, in some cases the proposed classifier is faster and needs less storage space.