Considering the concept of clustering, the main idea of the present study is based on the fact that all stocks for choosing and ranking will not be necessarily in one cluster. Taking the mentioned point into account, this study aims at offering a new methodology for making decisions concerning the formation of a portfolio of stocks in the stock market. To meet this end, Multiple-Criteria Decisi...

abstract the present study was carried out for developing and evaluating of the optimization model of cropping pattern in irrigation networks using analytical hierarchy process (ahp). the model was programmed into two sub-models. the objective of the first sub-model was ranking of crop appropriately, and the objective of the second sub-model was ranking of cropping area of each crop. ten criter...

The main aim of this paper is to present a novel method for ranking hesitant fuzzy sets (HFSs) based on transforming HFSs into fuzzy sets (FSs). The idea behind the method is an interesting HFS decomposition which is referred here to as the horizontal representation in the current study. To show the validity of the proposed ranking method, we apply it to solve a multi-attribute decision-making ...

Search engines are among the most important applications or services on the web. Most existing successful search engines use a centralized architecture and global ranking algorithms to generate the ranking of documents crawled in their databases, for example, Google's PageRank. However, global ranking of documents has two potential problems: high computation cost, and potentially poor rankings....

Data envelopment analysis (DEA) is a useful mathematical tool for evaluating the performance of production units and ranking their relative efficiency. In many real-world applications, belong to several separate groups also consist sub-units. this paper, we introduce new method group efficiency two-stage systems. To end, some DEA models are introduced systems based on average weakest criteria. ...

We present an algorithm for incremental statistical parsing with Parallel Multiple Context-Free Grammars (PMCFG). This is an extension of the algorithm by Angelov (2009) to which we added statistical ranking. We show that the new algorithm is several times faster than other statistical PMCFG parsing algorithms on real-sized grammars. At the same time the algorithm is more general since it suppo...

Learning programs is a timely and interesting challenge. In Programming by Example (PBE), a system attempts to infer a program from input and output examples alone, by searching for a composition of some set of base functions. We show how machine learning can be used to speed up this seemingly hopeless search problem, by learning weights that relate textual features describing the provided inpu...