rapid technological and economic growth over the last several decades has changed human lives and made modern society face complex decision making problems. these kinds of problems are characterized by incommensurate and conflicting criteria or objectives such as cost, reliability, performance, safety and productivity. multi criteria decision making is an approach that can be used to deal compl...

Most of data in Multi-attribute decision making (MADM) problems are changeable rather than constant and stable. Therefore, sensitivity analysis after problem solving can effectively contribute to making accurate decisions. In this paper, we offer a new method for sensitivity analysis in multi-attribute decision making problems in which if the weights of one attribute changes, then we can dete...

This paper introduces modiication of two existing rule-inducing ML algorithms; they are now capable of processing continuous classes. The rst one is the CN4 algorithm, a large extension of the well-known CN2. It invokes its discretization procedure and also deals with continuous classes within the inductive process itself, i.e., 'on-line'. The other ML system, KEX (Knowledge EXplorer), treats n...

the electronic industry suffers a rapid changing and highly rival environment. thus, firms have an essential need to strive for acquiring the competitive advantage. strategy organizational agility (soa) is a tool which enables to assist firms to attain the competitive advantage. therefore, this study benchmarks the core competencies from a case study within the supply chain network and establis...

Numerical simulation of structural response is a challenging issue in earthquake engineering and there has been remarkable progress in this area in the last decade. Endurance Time (ET) method is a new response history based analysis procedure for seismic assessment and structural design in which structures are subjected to a gradually intensifying dynamic excitation and their seismic performanc...

Prediction is a well-researched area for Machine Learning applications. In these tasks, the aim is to predict the value for some unseen characteristic based upon the values of other, seen, characteristics for a given example. Machine learning has been extensively applied to these types of tasks by automating the derivation of a predictive function or a set of predictive rules. This predictive f...