Journal of Advances in Computer Engineering and Technology
Volume:2 Issue: 4, Autumn 2016

One of the most important issues related to knowledge discovery is the field of comment mining. Opinion mining is a tool through which the opinions of people who comment about a specific issue can be evaluated in order to achieve some interesting results. This is a subset of data mining. Opinion mining can be improved using the data mining algorithms. One of the important parts of opinion mining is the sentiment analysis in social networks. Today, the social networks contain billions of users' comments about different issues. In previous researches in this area, various methods have been used for Persian comments analysis. In these studies, preprocessing is one of the most important parts. It arranges the data set for analysis in a standard form. The number of hashtags selected for analysis is limited. To detect the positive and negative comments, knowledge extraction or neural network techniques have been used. The current research presents a method of analysis which can analyze any hashtag for each group of users and has no limitations in this regard. Type of hashtag, the number of likes, type of user and type of positive and negative sentences can be analyzed by this method. The results of simulation and comparison of divorce data set show that the proposed method has an acceptable performance.

predicting the effort of a successful project has been a major problem for software engineers the significance of which has led to extensive investigation in this area. One of the main objectives of software engineering society is the development of useful models to predict the costs of software product development. The absence of these activities before starting the project will lead to various problems. Researchers focus their attention on determining techniques with the highest effort prediction accuracy or on suggesting new combinatory techniques for providing better estimates. Despite providing various methods for the estimation of effort in software projects, compatibility and accuracy of the existing methods is not yet satisfactory. In this article, a new method has been presented in order to increase the accuracy of effort estimation. This model is based on the type-2 fuzzy logic in which the gradient descend algorithm and the neuro-fuzzy-genetic hybrid approach have been used in order to teach the type-2 fuzzy system. In order to evaluate the proposed algorithm, three databases have been used. The results of the proposed model have been compared with neuro-fuzzy and type-1 fuzzy system. This comparison reveals that the results of the proposed model have been more favorable than those of the other two models.

Hadoop MapReduce framework is an important distributed processing model for large-scale data intensive applications. The current Hadoop and the existing Hadoop distributed file system’s rack-aware data placement strategy in MapReduce in the homogeneous Hadoop cluster assume that each node in a cluster has the same computing capacity and a same workload is assigned to each node. Default Hadoop doesn’t consider load state of each node in distribution input data blocks, which may cause inappropriate overhead and reduce Hadoop performance, but in practice, such data placement policy can noticeably reduce MapReduce performance and may increase extra energy dissipation in heterogeneous environments. This paper proposes a resource aware adaptive dynamic data placement algorithm (ADDP) .With ADDP algorithm, we can resolve the unbalanced node workload problem based on node load status. The proposed method can dynamically adapt and balance data stored on each node based on node load status in a heterogeneous Hadoop cluster. Experimental results show that data transfer overhead decreases in comparison with DDP and traditional Hadoop algorithms. Moreover, the proposed method can decrease the execution time and improve the system’s throughput by increasing resource utilization

In recent years, utilization of feature selection techniques has become an essential requirement for processing and model construction in different scientific areas. In the field of software project effort estimation, the need to apply dimensionality reduction and feature selection methods has become an inevitable demand. The high volumes of data, costs, and time necessary for gathering data , and also the complexity of the models used for effort estimation are all reasons to use the methods mentioned. Therefore, in this article, a genetic algorithm has been used for feature selection in the field of software project effort estimation. This technique has been tested on well-known data sets. Implementation results indicate that the resulting subset, compared to the original data set, has produced better outcomes in terms of effort estimation accuracy. This article showed that genetic algorithms are ideal methods for selecting a subset of features and improving effort estimation accuracy.

In this paper, a new extended method of multi criteria decision making based on fuzzy-Topsis theory is introduced. fuzzy mcdm algorithm for determining the best choice among all possible choices when the data are fuzzy is also presented. Using a new index leads to procedure for choosing fuzzy ideal and negative ideal solutions directly from the fuzzy data observed alternatives.in this algorithm we used triangular fuzzy number. Mostly, it is not possible to gather precise data, so decision making based on these data loses its efficiency. The fuzzy theory has been used to overcome this draw back. In multi-criteria decision making, criteria can correlate with each other, most of which are ignored in classic MCDM. In this paper, correlation coefficient of fuzzy criteria has been studied to adapt the interrelation between criteria and a new algorithm is proposed to obtain decision making. Finally the efficiency of suggested method is demonstrated with an example..

the telecommunications industry plays an important role in providing ICT services to a wide range of customers. In addition to individual customers, corporate customers also are user of these services and have an important role to make return on investment for telecom companies (Telcos). Therefore, this group of customers should not be ignored by any reason. This is where the Telecom Companies provide special services that named B2B to these customers. The Business Process Framework eTOM is proposed as a telecom. Framework to standardize and mature B2B processes by a separate section called Engaged Parties. In this paper, by using the ITSM Reference Model, we aim to improve the B2B processes in the business process framework already named item. Hereby, considering the ever-increasing Demands and needs of customers (in this paper customers mostly are Enterprises and Companies), and declaring the power inherent while using Customer Relationship Processes of ITSM Reference Model, we aim to complete B2B processes of the eTOM framework while focusing on Telcos.