**1. Introduction**

Today, it is important to predict the cost of information technology in each organization for cost-effective purposes, and it is possible with the correct strategy and new modeling help technology managers to estimate the implementation costs and also intangible IT costs of the organization. One of the main issues related to the cost of information technology is the determination of costs, especially those that are of an indirect nature [1].

Terminating amount of investment and budgeting for each year, especially in new technologies including IT, is heavily complicated. IT creates a lot of cost for organization if IT managers do not have enough knowledge and information related to. Therefore, correct recognition of cost factors and affecting factors can be associated to cost prediction in the area and having great support to the cost-effectiveness of any enterprise. IT investment has a positive effect on technical effects on the firms' production process. The increasing dependence of many businesses on IT and the high percentage of IT investment in all invested capitals in business environment [2] are essential functions that should be considered within every organization, providing possible solution and services expected to assess the

achievement of business objective. The first step in IT investment is to know exactly what that investment is and measuring and tracking this expenditure over time against a convenient base [3]. If the cost follows an industrial standard, it enables the organization to have right understanding for enough investment in the area of IT in a specific period. The growth of revenue via offered technology solution enables organization to achieve strategy business goals and level of competitiveness. In order to assess achievement level related to such expectation, mechanism has to be determined. Researchers and practitioners have expressed concern about cost, benefit, and quality of software documentation in practice. In this chapter, IT cost is estimated for system maintenance phase by quantifying IT practical services tangible by users. Having available information enables organization to determine how much maintenance cost has been spent for IT services with consideration of their sub-components. To achieve these goals, it is necessary to develop a model based on experimental and historical data utilizing ANFIS modeling. In the past decades, numerous studies have been published on software cost estimation method including expert judgment, parametric models, and at least machine learning [4]. The results of modeling are shown in which the ANFIS is used for quantifying generated consumable services of IT to show the level of effectiveness of IT activities and also their maintenance cost forecasting. Neuro-fuzzy inference system adapted to the Takagi-Sugeno known as a fuzzy model was used for these models. ANFIS is trained with a volume of data to quantify the services with fuzzy data. Four fundamental components utilized and applied in maintenance channel and system lost include human resource, technology/infrastructure, and process and system downtime considered as IT cost factors and normally tracked in any organization and feed to model. All four variables include some components with diversity and difference in each organization related to their activity.

This model can be suggested and designed as a new module implemented in monitoring systems of the use case organization considered as significant innovation with incorporation of IT services quantification and cost estimation, concurrently practical in IT field. The reason that authors used ANFIS is that it not only includes the characteristics of both methods but also eliminates some disadvantage of their lonely use case. In this chapter, backpropagation learning in neural section is applied. Output variables obtained by applying fuzzy rule to fuzzy set of input variables in Takagi-Sugeno inference system. The results showed that the proposed model is a powerful technique and valuable tool for forecasting variables from known and achieved knowledge that is not easily measured.
