• Home
  • Ghasem Tohidi
  • OpenAccess
    • List of Articles Ghasem Tohidi

      • Open Access Article

        1 - Improved NARX-ANFIS Network structure with Genetic Algorithm to optimizing Cash Flow of ATM Model
        Neda kiani Ghasem Tohidi Shabnam Razavyan Nosratallah Shadnoosh Masood Sanei
        Nowadays, the rapid growth of data in organizations has caused managers to look for a way to analyze them. Extracting useful knowledge from aggregation data can lead to appropriate strategic decision-making for the organization. This paper suggests an application of hyb More
        Nowadays, the rapid growth of data in organizations has caused managers to look for a way to analyze them. Extracting useful knowledge from aggregation data can lead to appropriate strategic decision-making for the organization. This paper suggests an application of hybrid network based on amount month demand in every ATM device based on transaction mean of 9 months for 1377 devices to obtain customer behavior patterns, to do so, first designed a basic model based on an auto-regressive with exogenous input network (NARX) then, the optimization of the weight and bias of the designed network is made by the genetic algorithm (GA). As a result, finding the weights of the network represents a nonlinear optimization problem that is solved by the genetic algorithm. Paper results show that the NARX-ANFIS Hybrid network using GA for the learning of rules and to optimize the network weights and weights of the network and the fixed threshold can improve the accuracy of the prediction model. Also, classic models are more efficient and increased benefits and lower financing costs and more rational inventory cash control. As well, the designed model can lead to increase benefits and decrease costs in the bank so that, exact forecast and optimal cash upload in ATMs will lead to increase funds on the bank and rise customers and popularity the brand of the bank. Manuscript profile
      • Open Access Article

        2 - Measuring the Interval industry cost efficiency score in DEA
        Ghasem Tohidi Simin Tohidnia
        In this paper we extend the concept of "cost minimizing industry structure" and develop two DEA models for dealing with imprecise data. The main aim of this study is to propose an approach to compute the industry cost efficiency measure in the presence of interval data. More
        In this paper we extend the concept of "cost minimizing industry structure" and develop two DEA models for dealing with imprecise data. The main aim of this study is to propose an approach to compute the industry cost efficiency measure in the presence of interval data. We will see that the value obtained by the proposed approach is an interval value. The lower bound and upper bound of the interval industry cost efficiency measure are computed and then decomposed into three components to examine the relationship between them and the lower and upper bounds of the individual interval cost efficiency measures. We also define the cost efficient organization of the industry as a set of DMUs, which minimizes the total cost of producing the interval industry output vector. In fact, this paper determines the optimal number of DMUs and the reallocation of the industry observed outputs among them. We hereby determine the effects of the optimal number of DMUs and the reallocation of outputs among them on the interval industry cost efficiency measure. Finally, a numerical example will be presented to illustrate the proposed approach. Manuscript profile