National forest inventories (NFIs) have traditionally been designed to assess the production value of forests as well as forest biodiversity. However, in this study, the aim is to show a new application of NFIs, namely the estimation of the landscape metric contagion. This metric is commonly calculated on raster-based land cover/use maps. In this study, a sample-based dataset from the Swedish NFI was used. The estimated contagion metric is based on a distance-dependent function so that the value of the metric is small for longer distances, whereas the corresponding estimated variance is large for longer distances. With this procedure, comparisons can be made for different landscapes at a given time and or to compare any given landscape over time. The main advantages are that the approach can be applied where raster-based land cover/use maps of the landscape are not available and that the data obtained from NFIs (e.g., land cover type) typically are of high quality in comparison with remotely sensed data due to being based on direct observation in the field survey. The procedure applied here accommodates both the patch-mosaic and the gradient-based model approach to landscape structure.

Cloud computing is an important infrastructure for distributed systems with the main objective of reducing the use of resources. In a cloud environment, users may face thousands of resources to run each task. However, allocation of resources to tasks by the user is an impossible endeavor. Accurate scheduling of system resources results in their optimal use as well as an increase in the reliability of cloud computing. This study designed a system based on fuzzy logic and followed by an introduction of an efficient and precise algorithm for scheduling resources for improving the reliability of cloud computing. Waiting and turnaround times of the proposed method were compared to those of previous works. In the proposed method, the waiting time is equal to 26.99 and the turnaround time is equal to 82.99. According to the results, the proposed method outperforms other methods in terms of waiting time and turnaround time as well as accuracy.

Abstract-Software maintenance mainly refers to the process of correcting software after delivery. Maintenance process is usually a high percentage of Organizational effort to the whole process of software programs. As a result, the effectiveness of the entire production process and customer satisfaction is dependent on the effectiveness of maintenance activities. Because many factors including type of service, type of product and human factors is dependent on the maintenance process, And the imprecise nature of qualitative factors and sub-criteria leading software maintenance, accurate assessment can be maintained in order to measure the effectiveness of programs seem highly desirable. In this paper, using adaptive fuzzy neural network to provide a method for evaluating the capability of software maintenance conducted after the tests, the root mean square error of the proposed method was equal to 0.34331. The results show that the method is based on adaptive fuzzy neural, maintainability software performance evaluation is appropriate.

In fact, reliability as the qualities metric is the probability success or the probability that a system or a set of tasks will work without failure for a specified constraints of time and space, as specified in the design and operating conditions specified temperature, humidity, vibration and action. A relatively new methodologies for developing complex software systems engineering is an aspect oriented software systems, that provides the new methods for the separation of concerns multiple module configuration or intervention and automatic integration them with a system. In this paper, a method using fuzzy logic to measure software reliability based on the above aspects is presented. The proposed approach regarding the use of appropriate metrics and low errors in the estimation of reliability has a better performance than other methods.

In fact, Reliability as the qualities metric is the probability success or The probability that a system or set of tasks without failure for a specified constraints of time and space, as specified in the design and operating conditions specified temperature, humidity, vibration and action. A relatively new methodologies for developing complex software systems engineering is an aspectoriented software systems, that provides the new methods for the separation of concerns multiple module configuration or intervention and automatic integration them with a system. In this paper, using MLP artificial neural networks and adaptive fuzzy neural network assess the reliability of the aspect oriented software and at the end, two methods were compared with each other. After examination, the root means square error method based on artificial neural networks, fuzzy neural network-based method of 0.024262 and 0.021874 to be adaptive. The results show that the method is based on adaptive fuzzy neural networks with low error in the estimation of reliability, performance is better than the MLP artificial neural network approach.