@article{ author = {Minaei-Bidgoli, Behrouz and Faili, Heshaam and Aminian, Maryam}, title = {Unsupervised Persian Verb Valency Induction}, abstract ={Valency is the key concept in dependency grammar. Among all word categories, verbs are the most important categories with a key role in syntax and semantics. Verb is the central role in a sentence and acts as the main semantic component in the dependency grammar. In this paper, after studying several methods for unsupervised discovery of Persian verb valency, the ambiguities are studied. Among all methods, EM gained the best results i.e. two time more F-score that binomial hypothesis testing}, Keywords = {Dependency grammar, verb valency, Persian language, unsupervised induction, EM algorithm}, volume = {9}, Number = {2}, pages = {3-12}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-102-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-102-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2013} } @article{ author = {}, title = {}, abstract ={}, Keywords = {}, volume = {9}, Number = {2}, pages = {13-22}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-97-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-97-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2013} } @article{ author = {Hourali, Maryam}, title = {An Intelligent Ontology Construction System Using Hybrid ART Neural Network and C-value Method}, abstract ={In recent years, many efforts have been done to design ontology learning methods and automate ontology construction process. The ontology construction process is a time-consuming and costly procedure for almost all domains/applications, so automating this process is a solution to overcome the knowledge acquisition bottleneck in information systems and reduce the construction cost. In this article a novel intelligent ontology learning method is proposed which can be used in many domains and applications. The proposed learning system has no need for initial common or specialized input ontologies or predefined semantic terms indeed, the initial database anonly consists of input texts sets. The proposed learning system could extract associated ontologies of various domains using combined methods. To do this, a combination of linguistic, statisticaland machine learning methods based on the C-value method, the TF-IDF one, the neural network, and co occurance analysis are applied. So, first domain-related documents were collected. Then natural language processing methods such as C-value method were implemented for extracting meaningful terms from documents. Next, ART (Adaptive Resonance Theory) neural network was used to cluster documents and associated weight of terms was calculated by TF–IDF method in order to find candidate keyword for each cluster. Finally, co-occurrence analysis was used to construct concept hierarchy and complete the ontology. Results show that the proposed ontology learning method has a high precision comparing to similar studies}, Keywords = {Ontology, ART Neural Network, TF-IDF, C-value }, volume = {9}, Number = {2}, pages = {23-36}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-130-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-130-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2013} } @article{ author = {Mehrnam, Amir hossein and MotiNasrabadi, Ali and Ghodousi, Mahrad and Mohamadian, Amin and Torabi, Shahl}, title = {Detection of Guilty Knowledge, Using Single Trial ERPs, and Based on Recurrence plots Nonlinear Method}, abstract ={In this study, Recurrence Plots (RPs) has been adapted as a nonlinear approach in order to detection of guilty subjects’ knowledge based on their single-trial ERPs. The dataset were acquired from 49 human subjects who were participated in a Concealed Information Test (CIT). According to the test protocol, guilty subjects denied their information about familiar faces, so the aim was to detect the concealed faces in these subjects. Recurrence quantifiers were employed in feature extraction stage. Chaotic dynamic of brain’s signals and figuring out the trajectories in phase space are two important issues that can be indicated in these quantifiers. Results demonstrate, that the appearance of P300 signals in guilty subjects (because of denying a familiar face), increase the determinism and predictability of their brain’s signals. Also using Genetic Algorithm (GA) in feature selection level together with Linear Discriminant Analysis (LDA) classifier and a new method named: “inconstant threshold detection”, we achieved an accuracy about 89.7% on combining information of Fz, Cz and Pz channels.}, Keywords = {Recurrence Plots Nonlinear Method, Guilty Knowledge Test, Single Trial ERPs }, volume = {9}, Number = {2}, pages = {37-48}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-75-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-75-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2013} } @article{ author = {KHALILZADEH, MOHAMAD ALI and SARAFAN, RASOOL and AZARNOOSH, MAHDI}, title = {Lie detector system based on PhotoPlethysmoGraph (PPG) and Galvanic Skin Response (GSR) signals by means of neural network}, abstract ={The aim of this article is to design a lie detector system using GSR and PPG.The data set was including of photoplethysmograph signals and galvanic skin response record through an inductive test and using classic polygraph device. Thenceforth, features of time and frequency were extracted. Consequently data were classified and accuracy coefficient was calculated by applying these features to linear Discriminant analysis LDA and MLP and Elman neural networks. 20 people participated in the study. The mean age of participants was 36 years. Finally determination of lie was done with accuracy coefficient of 87% by applying Elman neural network. According to the findings, the new method introduced in this study has while offering a more comfortable recording and less diagnostic costs. This new method can be suggested for use as a lie screening system}, Keywords = {Lie Detection, PhotoPlethysmoGraph, Galvanic Skin Response, Neural Network }, volume = {9}, Number = {2}, pages = {49-60}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-77-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-77-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2013} } @article{ author = {}, title = {Compression time variable information using Huffman code}, abstract ={Abstract: In this paper, we fit a function on probability density curve representing an information stream using artificial neural network . This methodology result is a specific function which represent a memorize able probability density curve . we then use the resulting function for information compression by Huffman algorithm . the difference between the proposed me then with the general methods is , using the Huffman algorithm in several times . In every time , the probability density function is fitted , estimated and then the information representing the function is added to end of the information stream . we next propose two different algorithms for information encoding and decoding using time variable estimation of probability density function . In order to evaluate the proposed algorithm , the percentage of the compression resulting our method has been compared with two popular methods named FDR code [4] and Golomb1 at the end}, Keywords = {variable time probability density function , neural network , compression information , memorize able information stream, Huffman code}, volume = {9}, Number = {2}, pages = {61-74}, publisher = {Research Center on Developing Advanced Technologies}, url = {http://jsdp.rcisp.ac.ir/article-1-144-en.html}, eprint = {http://jsdp.rcisp.ac.ir/article-1-144-en.pdf}, journal = {Signal and Data Processing}, issn = {2538-4201}, eissn = {2538-421X}, year = {2013} }