Journal of Artificial Intelligence and Data Mining
Volume:4 Issue: 2, Summer-Autumn 2016

With due respect to the authors’ rights, plagiarism detection, is one of the critical problems in the field of text-mining that many researchers are interested in. This issue is considered as a serious one in high academic institutions. There exist language-free tools which do not yield any reliable results since the special features of every language are ignored in them. Considering the paucity of works in the field of Persian language due to lack of reliable plagiarism checkers in Persian there is a need for a method to improve the accuracy of detecting plagiarized Persian phrases. Attempt is made in the article to present the PCP solution. This solution is a combinational method that in addition to meaning and stem of words, synonyms and pluralization is dealt with by applying the document tree representation based on manner fingerprinting the text in the 3-grams words. The obtained grams are eliminated from the text, hashed through the BKDR hash function, and stored as the fingerprint of a document in fingerprints of reference documents repository, for checking suspicious documents. The PCP proposed method here is evaluated by eight experiments on seven different sets, which include suspicions document and the reference document, from the Hamshahri newspaper website. The results indicate that accuracy of this proposed method in detection of similar texts in comparison with "Winnowing" localized method has 21.15 percent is improvement average. The accuracy of the PCP method in detecting the similarity in comparison with the language-free tool reveals 31.65 percent improvement average.

This paper presents an application of design of experiments techniques to determine the optimized parameters of artificial neural network (ANN), which are used to estimate force from Electromyogram (sEMG) signals. The accuracy of ANN model is highly dependent on the network parameters settings. There are plenty of algorithms that are used to obtain the optimal ANN setting. However, to the best of our knowledge they did not use regression analysis to model the effect of each parameter as well as present the percent contribution and significance level of the ANN parameters for force estimation. In this paper, sEMG experimental data are collected and the ANN parameters based on an orthogonal array design table are regulated to train the ANN. Taguchi help us to find the optimal parameters settings. Next, analysis of variance (ANOVA) technique is used to obtain significance level as well as contribution percentage of each parameter to optimize ANN’s modeling in human force estimation. The results indicated that design of experiments is a promising solution to estimate the human force from sEMG signals.

Technology assessment and selection has a substantial impact on organizations procedures in regards to technology transfer. Technological decisions are usually made by a group of experts, and whereby integrity of these viewpoints to a single decision can be quite complex. Today, operational databases and data warehouses exist to manage and organize data with specific features and henceforth, the need for a decision-aid approach is essential. The process of developing data warehouses involves time consuming steps, complex queries, slow query response rates and limited functions, which is also true for operational databases. In this regards, Fuzzy multi-criteria procedures in choosing efficient data sources (data warehouse and traditional relational databases) based on organization requirements is addressed in this paper. In proposing an appropriate selection framework the paper compares a Triangular Fuzzy Numbers (TFN) based framework and Fuzzy Analytical Hierarchy Process (AHP), based on data sources models, business logic, data access, storage and security. Results show that two procedures rank data sources in a similar manner and due to the accurate decision-making.

Software project management is one of the significant activates in the software development process. Software Development Effort Estimation (SDEE) is a challenging task in the software project management. SDEE is an old activity in computer industry from 1940s and has been reviewed several times. A SDEE model is appropriate if it provides the accuracy and confidence simultaneously before software project contract. Due to the uncertain nature of development estimates and in order to increase the accuracy, researchers recently have focused on machine learning techniques. Choosing the most effective features to achieve higher accuracy in machine learning is crucial. In this paper, for narrowing the semantic gap in SDEE, a hierarchical method of filter and wrapper Feature Selection (FS) techniques and a fused measurement criteria are developed in a two-phase approach. In the first phase, two stage filter FS methods provide start sets for wrapper FS techniques. In the second phase, a fused criterion is proposed for measuring accuracy in wrapper FS techniques. Experimental results show the validity and efficiency of the proposed approach for SDEE over a variety of standard datasets.

A new approach for pole placement of nonlinear systems using state feedback and fuzzy system is proposed. We use a new online fuzzy training method to identify and to obtain a fuzzy model for the unknown nonlinear system using only the system input and output. Then, we linearized this identified model at each sampling time to have an approximate linear time varying system. In order to stabilize the obtained linear system, we first choose the desired time invariant closed loop matrix and then a time varying state feedback is used. Then, the behavior of the closed loop nonlinear system will be as a linear time invariant (LTI) system. Therefore, the advantage of proposed method is global asymptotical exponential stability of unknown nonlinear system. Because of the high speed convergence of proposed adaptive fuzzy training method, the closed loop system is robust against uncertainty in system parameters. Finally the comparison has been done with the boundary layer sliding mode control (SMC).

Nowadays organizations require an effective governance framework for their service-oriented architecture (SOA) in order to enable them to use a framework to evaluate their current state governance and determine the governance requirements, and then to offer a suitable model for their governance. Various frameworks have been developed to evaluate the SOA governance. In this paper, a brief introduction to the internal control framework COBIT is described, and it is used to show how to develop a framework to evaluate the SOA governance within an organization. The SOA and information technology expert surveys are carried out to evaluate the proposed framework. The results of this survey verify the proposed framework.

Video-based face recognition has attracted significant attention in many applications such as media technology, network security, human-machine interfaces, and automatic access control system in the past decade. The usual way for face recognition is based upon the grayscale image produced by combining the three color component images. In this work, we consider grayscale image as well as color space in the recognition process. For key frame extractions from a video sequence, the input video is converted to a number of clusters, each of which acts as a linear subspace. The center of each cluster is considered as the cluster representative. Also in this work, for comparing the key frames, the three popular color spaces RGB, YCbCr, and HSV are used for mathematical representation, and the graph-based discriminant analysis is applied for the recognition process. It is also shown that by introducing the intra-class and inter-class similarity graphs to the color space, the problem is changed to determining the color component combination vector and mapping matrix. We introduce an iterative algorithm to simultaneously determine the optimum above vector and matrix. Finally, the results of the three color spaces and grayscale image are compared with those obtained from other available methods. Our experimental results demonstrate the effectiveness of the proposed approach.

Extracting biomedical relations such as drug-drug interaction (DDI) from text is an important task in biomedical NLP. Due to the large number of complex sentences in biomedical literature, researchers have employed some sentence simplification techniques to improve the performance of the relation extraction methods. However, due to difficulty of the task, there is no noteworthy improvement in the research literature. This paper aims to explore clause dependency related features alongside to linguistic-based negation scope and cues to overcome complexity of the sentences. The results show by employing the proposed features combined with a bag of words kernel, the performance of the used kernel methods improves. Moreover, experiments show the enhanced local context kernel outperforms other methods. The proposed method can be used as an alternative approach for sentence simplification techniques in biomedical area which is an error-prone task.

This paper presents a new hidden Markov model-based (HMM-based) speech enhancement framework based on the independent component analysis (ICA). We propose analytical procedures for training clean speech and noise models by the Baum re-estimation algorithm and present a Maximum a posterior (MAP) estimator based on Laplace-Gaussian (for clean speech and noise respectively) combination in the HMM framework, namely sparse code shrinkage-HMM (SCS-HMM).
The proposed method on TIMIT database in the presence of three noise types at three SNR levels in terms of PESQ and SNR are evaluated and compared with Auto-Regressive HMM (AR-HMM) and speech enhancement based on HMM with discrete cosine transform (DCT) coefficients using Laplace and Gaussian distributions (LaGa-HMMDCT). The results confirm the superiority of SCS-HMM method in presence of non-stationary noises compared to LaGa-HMMDCT. The results of SCS-HMM method represent better performance of this method compared to AR-HMM in presence of white noise based on PESQ measure.

Due to the rapid growth of data mining technology, obtaining private data on users through this technology becomes easier. Association Rules Mining is one of the data mining techniques to extract useful patterns in the form of association rules. One of the main problems in applying this technique on databases is the disclosure of sensitive data by endangering security and privacy. Hiding the association rules is one of the methods to preserve privacy and it is a main subject in the field of data mining and database security, for which several algorithms with different approaches are presented so far. An algorithm to hide sensitive association rules with a heuristic approach is presented in this article, where the Perturb technique based on reducing confidence or support rules is applied with the attempt to remove the considered item from a transaction with the highest weight by allocating weight to the items and transactions. Efficiency is measured by the failure criteria of hiding, number of lost rules and ghost rules, and execution time. The obtained results of this study are assessed and compared with two known FHSAR and RRLR algorithms, based on two real databases (dense and sparse). The results indicate that the number of lost rules in all experiments are reduced by 47% in comparison with RRLR and reduced by 23% in comparison with FHSAR. Moreover, the other undesirable side effects, in this proposed algorithm in the worst case are equal to that of the base algorithms.

Uniaxial compressive strength (UCS) and internal friction coefficient (µ) are the most important strength parameters of rock. They could be determined either by laboratory tests or from empirical correlations. The laboratory analysis sometimes is not possible for many reasons. On the other hand, Due to changes in rock compositions and properties, none of the correlations could be applied as an exact universal correlation. In such conditions, the artificial intelligence could be an appropriate candidate method for estimation of the strength parameters. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) which is one of the artificial intelligence techniques was used as dominant tool to predict the strength parameters in one of the Iranian southwest oil fields. A total of 655 data sets (including depth, compressional wave velocity and density data) were used. 436 and 219 data sets were randomly selected among the data for constructing and verification of the intelligent model, respectively.
To evaluate the performance of the model, root mean square error (RMSE) and correlation coefficient (R2) between the reported values from the drilling site and estimated values was computed. A comparison between the RMSE of the proposed model and recently intelligent models shows that the proposed model is more accurate than others. Acceptable accuracy and using conventional well logging data are the highlight advantages of the proposed intelligent model.

Metal oxide surge arrester accurate modeling and its parameter identification are very important for insulation coordination studies, arrester allocation and system reliability. Since quality and reliability of lightning performance studies can be improved with the more efficient representation of the arresters´ dynamic behavior. In this paper, Big Bang – Big Crunch and Hybrid Big Bang – Big Crunch optimization algorithms are used to selects optimum surge arrester model equivalent circuit parameters values, minimizing the error between the simulated peak residual voltage value and this given by the manufacturer.The proposed algorithms are applied to a 63 kV and 230 kV metal oxide surge arrester. The obtained results show that using this method the maximum percentage error is below 1.5 percent.

This paper presents an indirect adaptive system based on neuro-fuzzy approximators for the speed control of induction motors. The uncertainty including parametric variations, the external load disturbance and unmodeled dynamics is estimated and compensated by designing neuro-fuzzy systems. The contribution of this paper is presenting a stability analysis for neuro-fuzzy speed control of induction motors. The online training of the neuro-fuzzy systems is based on the Lyapunov stability analysis and the reconstruction errors of the neuro-fuzzy systems are compensated in order to guarantee the asymptotic convergence of the speed tracking error. Moreover, to improve the control system performance and reduce the chattering, a PI structure is used to produce the input of the neuro-fuzzy systems. Finally, simulation results verify high performance characteristics and robustness of the proposed control system against plant parameter variation, external load and input voltage disturbance.