Journal of Advances in Computer Engineering and Technology
Volume:3 Issue: 2, Spring 2017

E-readiness is one of the major prerequisites for effective implementation of e-government. For the correct implementation of e-government, it is needed to accurately assess the state of e-readiness in desired community. In this regard, there are models to assess, but the correct choice of model is one of the most important challenges in this area. The process of evaluating and selecting the appropriate options in the implementation of e-government due to the involvement of different groups of decision-makers, existence of interrelationships between technology and desired community as well as existing platforms is a complex process. In recent decades, with access to computational methods and powerful decision making systems selecting more accurate options, effective analysis of qualitative and quantitative characteristic and studying the interaction between them are provided. This article tries to examine the performance of e-readiness assessment models and multi criteria decision making methods and introduces the best selection of the e-readiness model for effective implementation of e-government. In order to reach this purpose, we introduced a layered architecture based on multi-criteria decision making methods and SWOT Analysis. The proposed layered architecture, reduces decision making errors and increases the accuracy in choosing the appropriate e-readiness assessment model.

High-performance computing and vast storage are two key factors required for executing data-intensive applications. In comparison with traditional distributed systems like data grid, cloud computing provides these factors in a more affordable, scalable and elastic platform. Furthermore, accessing data files is critical for performing such applications. Sometimes accessing data becomes a bottleneck for the whole cloud workflow system and decreases the performance of the system dramatically. Job scheduling and data replication are two important techniques which can enhance the performance of data-intensive applications. It is wise to integrate these techniques into one framework for achieving a single objective. In this paper, we integrate data replication and job scheduling with the aim of reducing response time by reduction of data access time in cloud computing environment. This is called data replication-based scheduling (DRBS). Simulation results show the effectiveness of our algorithm in comparison with well-known algorithms such as random and round-robin.

There are several different methods to make an efficient strategy for steganalysis of digital images. A very powerful method in this area is rich model consisting of a large number of diverse sub-models in both spatial and transform domain that should be utilized. However, the extraction of a various types of features from an image is so time consuming in some steps, especially for training phase with a large number of high resolution images that consist of two steps: train and test. Multithread programming is a near solution to decreasing the required time but it’s limited and it ‘snot so scalable too. In this paper, we present a CUDA based approach for data-parallelization and optimization of sub-model extraction process. Also, construction of the rich model is analyzed in detailed, presenting more efficient solution. Further, some optimization techniques are employed to reduce the total number of GPU memory accesses. Compared to single-thread and multi-threaded CPU processing, 10x-12x and 3x-4x speedups are achieved with implementing our CUDA-based parallel program on GT 540M and it can be scaled with several CUDA cards to achieve better speedups.

Peer-to-peer applications (P2P) are no longer limited to home users, and start being accepted in academic and corporate environments. While file sharing and instant messaging applications are the most traditional examples, they are no longer the only ones benefiting from the potential advantages of P2P networks. For example, network file storage, data transmission, distributed computing, and collaboration systems have also taken advantage of such networks.The reasons why this model of computing is attractive unfold in three. First, P2P networks are scalable, i.e., deal well (efficiently) with both small groups and with large groups of participants. In this paper, we will present a summary of the main safety aspects to be considered in P2P networks, highlighting its importance for the development of P2P applications and systems on the Internet and deployment of enterprise applications with more critical needs in terms of security. P2P systems are no longer limited to home users, and start being accepted in academic and corporate environments.

Nowadays, effort estimation in software projects is turned to one of the key concerns for project managers. In fact, accurately estimating of essential effort to produce and improve a software product is effective in software projects success or fail, which is considered as a vital factor. Lack of access to satisfying accuracy and little flexibility in existing estimation models have attracted the researchers’ attention to this area in last few years. One of the existing effort estimation methods is COCOMO (Constructive Cost Model) which has been taken importantly as an appropriate method for software projects. Although COCOMO has been invented some years ago, it has still got effort estimation ability in software projects. Many researchers have attempted to improve effort estimation ability in this model by improving COCOMO operation; but despite many efforts, COCOMO results are not satisfying yet. In this research, a new compound method is presented to increase COCOMO estimation accuracy. In the proposed method, much better factors are gained using combination of invasive weed optimization and COCOMO estimation method in contrast with basic COCOMO. With the best factors, the proposed model’s optimality will be maximized. In this method, a real data set is used for evaluating and its operation is analyzed in contrast to other models. Operational parameters improvement is affirmed by this model’s estimation results.

Speech is one of the most opulent and instant methods to express emotional characteristics of human beings, which conveys the cognitive and semantic concepts among humans. In this study, a statistical-based method for emotional recognition of speech signals is proposed, and a learning approach is introduced, which is based on the statistical model to classify internal feelings of the utterance. This approach analyzes and tracks the emotional state changes trend of speaker during the speech. The proposed method classifies utterance emotions in six standard classes including, boredom, fear, anger, neutral, disgust and sadness. For this purpose, it is applied the renowned speech corpus database, EmoDB, for training phase of the proposed approach. In this process, once the pre-processing tasks are done, the meaningful speech patterns and attributes are extracted by MFCC method, and meticulously selected by SFS method. Then, a statistical classification approach is called and altered to employ as a part of the method. This approach is entitled as the LGMM, which is used to categorize obtained features. Aftermath, with the help of the classification results, it is illustrated the emotional states changes trend to reveal speaker feelings. The proposed model also has been compared with some recent models of emotional speech classification, in which have been used similar methods and materials. Experimental results show an admissible overall recognition rate and stability in classifying the uttered speech in six emotional states, and also the proposed algorithm outperforms the other similar models in classification accuracy rates.