List of Articles

• Open Access Article
  • Abstract Page
  • Full-Text
  
  1 - Multivariate Time Series Prediction Considering Intra-Time-Series and Inter-Time-Series Dependencies
  Parinaz Eskandarian Jamshid Bagherzadeh Habibollah Pirnejad Zahra Niazkhani
  A few artificial neural networks have been proposed so far for multivariate time series prediction and they used simple general-purpose neural networks. Therefore, they cannot achieve high prediction accuracy. In this paper, we propose an artificial neural network calle More

  A few artificial neural networks have been proposed so far for multivariate time series prediction and they used simple general-purpose neural networks. Therefore, they cannot achieve high prediction accuracy. In this paper, we propose an artificial neural network called DBMTSP (Dependency Based Multivariate Time Series Prediction) to predict the next element of a time series in the multivariate case. Compared to the existing methods, DBMTSP considers both intra-time-series dependencies and inter-time-series dependencies efficiently to achieve more accurate predictions. We propose a hierarchical encoder in DBMTSP to discover inter-time-series dependencies. The proposed hierarchical encoder is able to encode secondary time series into a single parameter that represents dependencies that exist between the main time series and the secondary time series. The hierarchical encoder has a scalable design such that it can accept a large number of secondary time-series. We have trained DBMTSP using 32760 data matrices. We evaluated DBMTSP using 8190 test data matrices. Our evaluations show that DBMTSP surpasses the existing methods in term of prediction accuracy. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  2 - an Adaptive Routing Strategy to Reduce Energy Consumption in Network on Chip
  Mohammad Trik Saadat Pour Mozafari Amir Massoud Bidgoli
  Networks on chip (NoCs) are an idea for implementing multiprocessor systems that have been able to handle the communication between processing cores, inspired by computer networks. Efficient nonstop routing is one of the most significant applications of NOC. In this stu More

  Networks on chip (NoCs) are an idea for implementing multiprocessor systems that have been able to handle the communication between processing cores, inspired by computer networks. Efficient nonstop routing is one of the most significant applications of NOC. In this study, the performance improvement of Networks on Chip (NoC) is investigated by introducing an optimal selection strategy. One of the most important features of NoC is an efficient and continuous routing. The effect of any routing algorithm depends on the chosen strategy. Accordingly, in the proposed approach, packet traffic is first examined using an analyzer and then based on the number of steps, it is determined whether packets are local or not. Finally, packets are sent through the best output channel using the Regional Congestion Awareness (RCA) selection strategy for local traffic and Neighbors-on-Path (NoP) for non-local traffic. Using a simulation, it is shown that the proposed approach significantly increased the performance. Experiments show that in compared to BufferLevel and Random strategies, this method remarkably reduces the average delay and energy consumption. Manuscript profile
• Open Access Article
  • Abstract Page
  • Full-Text
  
  3 - NSE: An effective model for investigating the role of pre-processing using ensembles in sentiment classification
  Razieh Asgarnezhad Amirhassan Monadjemi
  With the extensive Internet applications, review sentiment classification has attracted increasing interest among text mining experts. Traditional bag of words approaches did not indicate multiple relationships connecting words while emphasizing the pre-processing phase More

  With the extensive Internet applications, review sentiment classification has attracted increasing interest among text mining experts. Traditional bag of words approaches did not indicate multiple relationships connecting words while emphasizing the pre-processing phase and data reduction techniques, making a huge performance difference in classification. This study suggests a model as a different efficient model for multi-class sentiment classification using sampling techniques, feature selection methods, and ensemble supervised classification to increase the performance of text classification. The feature selection phase of our model has applied n-grams, a computational method that optimizes feature selection procedure by extracting features based on the relationships of the words to improve a candidate selection of features. The proposed model classifies the sentiment of tweets and online reviews through ensemble methods, including boosting, bagging, stacking, and voting in conjunction with supervised methods. Besides, two sampling techniques were applied in the pre-processing phase. In the experimental study, a comprehensive range of comparative experiments was conducted to assess the effectiveness of our model using the best existing works in the literature on well-known movie reviews and Twitter datasets. The highest accuracy and f-measure for our model obtained 92.95 and 92.65% on the movie dataset, 90.61 and 87.73% on the Twitter dataset, respectively. Manuscript profile