A neural reordering model based on phrasal dependency tree for statistical machine translation (Journal: Intelligent Data Analysis). 2018. Saeed Farzi, Heshaam Faili, Sahar Kianian.

    Machine translation is an important field of research and development. Word reordering is one of the main problems in machine translation. It is an important factor of quality and efficiency of machine translations and becomes more difficult when it deals with structurally divergent language pairs. To overcome this problem, we introduce a neural reordering model, using phrasal dependency trees which depict dependency relations among contiguous non-syntactic phrases. The model makes the use of reordering rules, which are automatically learned by a probabilistic neural network classifier from a reordered phrasal dependency tree bank. The proposed model combines the power of the lexical reordering and syntactic pre-ordering models by performing long-distance reorderings. The proposed reordering model is integrated into a standard phrase-based statistical machine translation system to translate input sentences. Our method is evaluated on syntactically divergent language-pairs, English → Persian and English → German using WMT07 benchmark. The results illustrate the superiority of the proposed method in terms of BLEU, TER and LRscore on both translation tasks. On average the proposed method retrieves a significant impact on precision and recall values respect to the hierarchical, lexicalized and distortion reordering models.

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    A novel clustering algorithm for attributed graphs based on K-medoid algorithm (Journal of Experimental & Theoretical Artificial Intelligence). 2018. Saeed Farzi, Sahar Kianian.

    Articulateness and plasticity are two essential attributes that make a graph as an efficient model to real life problems. Nowadays, the attributed graph is received lots of attentions because of usability and effectiveness. In this study, a novel k-Medoid based clustering algorithm, which focuses simultaneously on both structural and contextual aspects using Signal and the weighted Jaccard similarities, are introduced. Two real life data-sets, Political Blogs and DBLP bibliography, are employed in order to evaluate and compare the proposed algorithm with state-of-the-art clustering algorithms.

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    A preordering model based on phrasal dependency tree (Journal: Digital Scholarship in the Humanities). 2018. Saeed Farzi, Heshaam Faili, Sahar Kianian.

    Intelligent machine translation (MT) is becoming an important field of research and development as the need for translations grows. Currently, the word reordering problem is one of the most important issues of MT systems. To tackle this problem, we present a source-side reordering method using phrasal dependency trees, which depict dependency relations between contiguous non-syntactic phrases. Reordering elements are automatically learned from a reordered phrasal dependency tree bank and are utilized to produce a source reordering lattice. The lattice finally is decoded by a monotone phrase-based SMT to translate a source sentence. The approach is evaluated on syntactically divergent language pairs, ie English to Persian and English to German, using the workshop of machine translation 2007 (WMT07) benchmark.

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    Katibeh: A Persian news summarizer using the novel semi-supervised approach (Journal: Digital Scholarship in the Humanities). 2018. Saeed Farzi, Sahar Kianian.

    Nowadays, text summarization is one of the most important active research fields in information retrieval. The most of the supervised extractive summarization systems utilize learning-to-rank methods to score sentences according to their importance. They need a high-quality comprehensive summarization corpus, which is labeled manually by human experts. Unfortunately, this sort of corpus is not available for most low-resource languages such as Persian. In this study, first of all, a comprehensive human-labeled summarization corpus (called Bistoon) collected by the crowdsourcing approach is introduced, and then a Persian summarizer based on a novel semi-supervised summarization approach, which is a combination of co-training and self-training, is presented to overcome the absence of sufficient data.

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    A comprehensive study of online event tracking algorithms in social networks (Journal of Information Science). 2018. Mahsa Seifikar, Saeed Farzi.

    Recently, social networks have provided an important platform to detect trends of real-world events. The trends of real-world events are detected by analysing flow of massive bulks of data in continuous time steps over various social media platforms. Today, many researchers have been interested in detecting social network trends, in order to analyse the gathered information for enabling users and organisations to satisfy their information need. This article is aimed at complete surveying the recent text-based trend detection approaches, which have been studied from three perspectives (algorithms, dimension and diversity of events). The advantages and disadvantages of the considered approaches have also been paraphrased separately to illustrate a comprehensive view of the previous works and open problems.

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      Community detection in social networks using a novel algorithm without parameter (Knowledge-Based Engineering and Innovation (KBEI), 2017 IEEE 4th International Conference on). 2017. Binazir Balegh, Saeed Farzi.

      With regard to the expansion of networks like computer network and virtual social networks, many researchers have been drawn to the analysis of social networks. Community detection in social networks is one of the most important issues in this domain. In recent years, numerous algorithms have been introduced in order to detect communities. In the present study, by determining the number of clusters and specifying initial centres of clusters, a novel algorithm has been introduced. By defining the neighbourhood of nodes, the proposed algorithm selects the initial centres of clusters in a way that the centres are at the maximum distance from each other.

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      TPC: An Automatically Generated Comprehensive English-Persian Parallel Corpus (Computational and Business Intelligence (ISCBI), 2017 5th International Symposium on). 2017. Saeed Farzi, Heshaam Faili.

      Nowadays, Parallel corpus is one of the most important resources which can be employed in different researches such as machine translation, bilingual lexicography, and linguistics. This paper describes the process of building a large-scale (about 400, 000 sentence pairs) English-Persian parallel corpus called Tehran Parallel Corpus (TPC). The aim of study is to introduce the structure and explain the materials utilized for constructing TPC. In addition, some useful tools developed within the project have been introduced and three sorts of the statistical machine translation systems trained by TPC have been considered. In order to develop a high quality parallel corpus, unsure alignments recognized via a MaxEnt classifier have been eliminated from the corpus.

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      Diagnosis of attention deficit hyperactivity disorder using deep belief network based on greedy approach (Computational and Business Intelligence (ISCBI), 2017 5th International Symposium on). 2017. Saeed Farzi, Sahar Kianian, Ilnaz Rastkhadive.

      Attention deficit hyperactivity disorder creates conditions for the child as s/he cannot sit calm and still, control his/her behavior and focus his/her attention on a particular issue. Five out of every hundred children are affected by the disease. Boys are three times more than girls at risk for this complication. The disorder often begins before age seven, and parents may not realize their children problem until they get older. Children with hyperactivity and attention deficit are at high risk of conduct disorder, antisocial personality, and drug abuse. Most children suffering from the disease will develop a feeling of depression, anxiety and lack of self-confidence. Given the importance of diagnosis the disease, Deep Belief Networks (DBNs) were used as a deep learning model to predict the disease.

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      Predicting serious diabetic complications using hidden pattern detection (Knowledge-Based Engineering and Innovation (KBEI), 2017 IEEE 4th International Conference on). 2017. Saeed Farzi, Sahar Kianian, Ilnaz Rastkhadive.

      Diabets is known as a metabolic disease. Type 1 diabetes is anti-immune disease whereby the body’s immune system kills off its own insulin producing beta cells in the pancreas. Type 2 diabetes is an advanced state of health in which the body becomes opposed to the usual impacts of insulin and/or progressively loses the capacity to produce adequate amount insulin in the pancreas, and it finally may not be able to produceany insulin. Type 2 diabetes is the most common form of the disease with complications including heart, vision, and foot conditions. This study aimed at predicting some important complications of Type 2 diabetes such as heart disease, retinopathy, diabetic foot, neuropathy, and nephropathy in order to provide useful information for patients. In this paper, inaddition to introduce verity of sophisticated features forpredicting Type 2 diabetics complications, a new dataset aiming at predicting Type 2 diabetics complications has been collected.

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