انجمن کامپیوتر ایران

In this paper we proposed a new approach for improving distribution and coverage of mobile sensors in a target filed. The goal is to deploy sensors such that a target area is optimally covered by them. Proposed method works based on number of neighbors. The nodes which have the minimum overlaps with its neighbors start to move away according to summation of forces from their neighbors, boundaries and obstacles. In order to achieve optimum coverage, the effect of field corners is also calculated. For evaluating the effectiveness of our proposed algorithm, we compared the performance of our algorithm with TRI and VEC which are well known algorithms in field of mobile sensors coverage. Simulation results show that our algorithm surely surpasses TRI and VEC in coverage.

In this paper we propose an artificial immune system in which learning automata are used to adaptively determine the values of its parameters. Learning automata are used for altering the shape of receptor portion of antibodies to better complementarily match the confronted antigen. In order to show the effectiveness of the proposed artificial immune computer experiments have been conducted. The result of experimentations confirms the effectiveness of the proposed model.

Text retrieval engines, such as search engines, always return a list of documents in response to a given query. Existing evaluations of text retrieval algorithms mostly use Precision and Recall of the returned list of documents as main quality measures of a search engine. In this paper, we propose a novel approach for comparing different algorithms adopted by different search engines and evaluate their performance. In our approach, the results of each algorithm is treated as an inter-related set of documents and the effectiveness of the algorithm is evaluated based on the degree of relation in the set of documents. After verifying the correctness of the evaluation measure by examining the results of the two retrieval algorithms, BM25 and pivoted normalization, and comparing these results with an ideal ranking, we compare the results of these algorithms and investigate the impact of certain major factors like stemming on the results of the suggested algorithm. The effectiveness of our proposed method is justified through obtained experimental results.

One of the most effective ways to extract knowledge from large information resources is applying data mining methods. Since the amount of information on the Internet is exploding, using XML documents is common as they have many advantages. Knowledge extraction from XML documents is a way to provide more utilizable results. XCLS is one of the most efficient algorithms for XML documents clustering. In this paper we represent a new algorithm for clustering XML documents. This algorithm is an improvement over XCLS algorithm which tries to obviate its problems. We implemented both algorithms and evaluated their clustering quality and running time on the same data sets. In both cases, it is shown that the performance of the new algorithm is better.

In a quest for modeling human brain, we are going to introduce a brain model based on a general framework for brain called Memory-Prediction Framework. The model is a hierarchical Bayesian structure that uses Reservoir Computing methods as the state-of-the-art and the most biological plausible Temporal Sequence Processing method for online and unsupervised learning. So, the model is called Hierarchical Bayesian Reservoir Memory (HBRM). HBRM uses a simple stochastic gradient descent learning algorithm to learn and organize common multi-scale spatio-temporal patterns/features of the input signals in a hierarchical structure in an unsupervised manner to provide robust and real-time prediction of future inputs. We suggest HBRM as a real-time high-dimensional stream processing model for the basic brain computations. In this paper we will describe the model and assess its prediction accuracy in a simulated real-world environment.

Gender identification based on speech signal has become gradually a matter of concern in recent years. In this context 6 feature types including MFCC, LPC, RC, LAR, pitch values and formants are compared for automatic gender identification and three best feature types are selected using four feature selection techniques. These techniques are GMM, Decision Tree, Fisher’s Discriminant Ratio, and Volume of Overlap Region. A dimension reduction is done on the best three feature types and the best coefficients are then selected from each feature vector. Selected coefficients are evaluated for gender classification using three types of classifiers including GMM, SVM and MLP neural network. 96.09% gender identification performance was obtained as the best performance using the selected coefficients and MLP classifier.