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  • Open Access


    Neural Machine Translation Models with Attention-Based Dropout Layer

    Huma Israr1,*, Safdar Abbas Khan1, Muhammad Ali Tahir1, Muhammad Khuram Shahzad1, Muneer Ahmad1, Jasni Mohamad Zain2,*

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2981-3009, 2023, DOI:10.32604/cmc.2023.035814

    Abstract In bilingual translation, attention-based Neural Machine Translation (NMT) models are used to achieve synchrony between input and output sequences and the notion of alignment. NMT model has obtained state-of-the-art performance for several language pairs. However, there has been little work exploring useful architectures for Urdu-to-English machine translation. We conducted extensive Urdu-to-English translation experiments using Long short-term memory (LSTM)/Bidirectional recurrent neural networks (Bi-RNN)/Statistical recurrent unit (SRU)/Gated recurrent unit (GRU)/Convolutional neural network (CNN) and Transformer. Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively, with a scalable data set, make precise predictions on unseen… More >

  • Open Access


    Automatic Persian Text Summarization Using Linguistic Features from Text Structure Analysis

    Ebrahim Heidary1, Hamïd Parvïn2,3,4,*, Samad Nejatian5,6, Karamollah Bagherifard1,6, Vahideh Rezaie6,7

    CMC-Computers, Materials & Continua, Vol.69, No.3, pp. 2845-2861, 2021, DOI:10.32604/cmc.2021.014361

    Abstract With the remarkable growth of textual data sources in recent years, easy, fast, and accurate text processing has become a challenge with significant payoffs. Automatic text summarization is the process of compressing text documents into shorter summaries for easier review of its core contents, which must be done without losing important features and information. This paper introduces a new hybrid method for extractive text summarization with feature selection based on text structure. The major advantage of the proposed summarization method over previous systems is the modeling of text structure and relationship between entities in the More >

  • Open Access


    An Accurate Persian Part-of-Speech Tagger

    Morteza Okhovvat1,∗, Mohsen Sharifi2,†, Behrouz Minaei Bidgoli2,‡

    Computer Systems Science and Engineering, Vol.35, No.6, pp. 423-430, 2020, DOI:10.32604/csse.2020.35.423

    Abstract The processing of any natural language requires that the grammatical properties of every word in that language are tagged by a part of speech (POS) tagger. To present a more accurate POS tagger for the Persian language, we propose an improved and accurate tagger called IAoM that supports properties of text to speech systems such as Lexical Stress Search, Homograph words Disambiguation, Break Phrase Detection, and main aspects of Persian morphology. IAoM uses Maximum Likelihood Estimation (MLE) to determine the tags of unknown words. In addition, it uses a few defined rules for the sake More >

  • Open Access


    Effect of heat conditioning on chilling injury susceptibility of Persian lime (Citrus latifolia Tanaka)

    Bosquez Molina1 E, J Domínguez Soberanes1, L Perez Flores2, S Bautista Baños3, F Díaz de León Sánchez2, F Rivera-Cabrera2

    Phyton-International Journal of Experimental Botany, Vol.77, pp. 161-174, 2008, DOI:10.32604/phyton.2008.77.161

    Abstract Cold storage is one of the most important technologies used to maintain the postharvest quality of many horticultural commodities. However, cold storage is restricted for Persian lime because of its susceptibility to chilling injury. The objective of this study was to evaluate the effects of high temperature conditioning on the susceptibility to chilling injury in Persian lime (Citrus latifolia Tanaka). It was harvested in Martínez de la Torre, Veracruz, Mexico, and stored at different refrigeration temperatures. After harvesting, fruits received the following treatments: (1) hot water conditioning (53 °C during 3 min); (2) hot air conditioning More >

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