Huma Israr^1,*, Safdar Abbas Khan¹, Muhammad Ali Tahir¹, Muhammad Khuram Shahzad¹, Muneer Ahmad¹, Jasni Mohamad Zain^2,*

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 data. The trained models yielded… More >

Ebrahim Heidary¹, Hamïd Parvïn^2,3,4,*, Samad Nejatian^5,6, Karamollah Bagherifard^1,6, Vahideh Rezaie^6,7

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 input text, which improves the… More >

Morteza Okhovvat^1,∗, Mohsen Sharifi^2,†, Behrouz Minaei Bidgoli^2,‡

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 of achieving high accuracy. For… More >

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 (38 °C, 90-95 %… More >