Badriyya B. Al-onazi¹, Mohamed K. Nour², Hassan Alshamrani³, Mesfer Al Duhayyim^4,*, Heba Mohsen⁵, Amgad Atta Abdelmageed⁶, Gouse Pasha Mohammed⁶, Abu Sarwar Zamani⁶

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2529-2544, 2023, DOI:10.32604/iasc.2023.034623

Abstract Gender analysis of Twitter could reveal significant socio-cultural differences between female and male users. Efforts had been made to analyze and automatically infer gender formerly for more commonly spoken languages’ content, but, as we now know that limited work is being undertaken for Arabic. Most of the research works are done mainly for English and least amount of effort for non-English language. The study for Arabic demographic inference like gender is relatively uncommon for social networking users, especially for Twitter. Therefore, this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification on Arabic Twitter (OMSDAE-GIAT) model.… More >

Manar Ahmed Hamza^1,*, Hala J. Alshahrani², Jaber S. Alzahrani³, Heba Mohsen⁴, Mohamed I. Eldesouki⁵, Mohammed Rizwanullah¹

Computer Systems Science and Engineering, Vol.46, No.2, pp. 2619-2635, 2023, DOI:10.32604/csse.2023.034519

Abstract Aspect-Based Sentiment Analysis (ABSA) on Arabic corpus has become an active research topic in recent days. ABSA refers to a fine-grained Sentiment Analysis (SA) task that focuses on the extraction of the conferred aspects and the identification of respective sentiment polarity from the provided text. Most of the prevailing Arabic ABSA techniques heavily depend upon dreary feature-engineering and pre-processing tasks and utilize external sources such as lexicons. In literature, concerning the Arabic language text analysis, the authors made use of regular Machine Learning (ML) techniques that rely on a group of rare sources and tools. These sources were used for… More >

Abdelwahed Motwakel^1,*, Badriyya B. Al-onazi², Jaber S. Alzahrani³, Radwa Marzouk⁴, Amira Sayed A. Aziz⁵, Abu Sarwar Zamani¹, Ishfaq Yaseen¹, Amgad Atta Abdelmageed¹

Computer Systems Science and Engineering, Vol.45, No.3, pp. 3097-3113, 2023, DOI:10.32604/csse.2023.033945

Abstract With a population of 440 million, Arabic language users form the rapidly growing language group on the web in terms of the number of Internet users. 11 million monthly Twitter users were active and posted nearly 27.4 million tweets every day. In order to develop a classification system for the Arabic language there comes a need of understanding the syntactic framework of the words thereby manipulating and representing the words for making their classification effective. In this view, this article introduces a Dolphin Swarm Optimization with Convolutional Deep Belief Network for Short Text Classification (DSOCDBN-STC) model on Arabic Corpus. The… More >

Muhammad Islam Satti¹, Jawad Ahmed², Hafiz Syed Muhammad Muslim¹, Akber Abid Gardezi³, Shafiq Ahmad⁴, Abdelaty Edrees Sayed⁴, Salman Naseer⁵, Muhammad Shafiq^6,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3913-3929, 2023, DOI:10.32604/cmc.2023.033001

Abstract Daily newspapers publish a tremendous amount of information disseminated through the Internet. Freely available and easily accessible large online repositories are not indexed and are in an un-processable format. The major hindrance in developing and evaluating existing/new monolingual text in an image is that it is not linked and indexed. There is no method to reuse the online news images because of the unavailability of standardized benchmark corpora, especially for South Asian languages. The corpus is a vital resource for developing and evaluating text in an image to reuse local news systems in general and specifically for the Urdu language.… More >

Zhichen Hu¹, Huali Ren², Jielin Jiang¹, Yan Cui⁴, Xiumian Hu³, Xiaolong Xu^1,*

CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 91-108, 2023, DOI:10.32604/cmes.2022.022268

Abstract An obviously challenging problem in named entity recognition is the construction of the kind data set of entities. Although some research has been conducted on entity database construction, the majority of them are directed at Wikipedia or the minority at structured entities such as people, locations and organizational nouns in the news. This paper focuses on the identification of scientific entities in carbonate platforms in English literature, using the example of carbonate platforms in sedimentology. Firstly, based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts, this paper designs… More >

Mohamed Hashim Changrampadi^1,*, A. Shahina², M. Badri Narayanan², A. Nayeemulla Khan³

Intelligent Automation & Soft Computing, Vol.32, No.2, pp. 1309-1323, 2022, DOI:10.32604/iasc.2022.022021

Abstract Research in speech recognition is progressing with numerous state-of-the-art results in recent times. However, relatively fewer research is being carried out in Automatic Speech Recognition (ASR) for languages with low resources. We present a method to develop speech recognition model with minimal resources using Mozilla DeepSpeech architecture. We have utilized freely available online computational resources for training, enabling similar approaches to be carried out for research in a low-resourced languages in a financially constrained environments. We also present novel ways to build an efficient language model from publicly available web resources to improve accuracy in ASR. The proposed ASR model… More >

Muhammad Aasim Qureshi^1,*, Muhammad Asif¹, Mohd Fadzil Hassan², Ghulam Mustafa¹, Muhammad Khurram Ehsan¹, Aasim Ali¹, Unaza Sajid¹

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4987-5004, 2022, DOI:10.32604/cmc.2022.020544

Abstract In machine learning, sentiment analysis is a technique to find and analyze the sentiments hidden in the text. For sentiment analysis, annotated data is a basic requirement. Generally, this data is manually annotated. Manual annotation is time consuming, costly and laborious process. To overcome these resource constraints this research has proposed a fully automated annotation technique for aspect level sentiment analysis. Dataset is created from the reviews of ten most popular songs on YouTube. Reviews of five aspects—voice, video, music, lyrics and song, are extracted. An N-Gram based technique is proposed. Complete dataset consists of 369436 reviews that took 173.53… More >

Wei Qin, Xiangfeng Luo

Intelligent Automation & Soft Computing, Vol.24, No.3, pp. 653-661, 2018, DOI:10.31209/2018.100000032

Abstract Concept learning has attracted considerable attention as a means to tackle problems of representation and learning corpus knowledge. In this paper, we investigate a challenging problem to automatically construct a patent concept learning model. Our model consists of two main processes; which is the acquisition of the initial concept graph and refined process for the initial concept graph. The learning algorithm of a patent concept graph is designed based on the Association Link Network (ALN). A concept is usually described by multiple documents utilizing ALN here in concept learning. We propose a mixture-ALN, which add links between documents and the… More >

Zijian Li¹, Chengying Chi^{1, *}, Yunyun Zhan^{2, *}

CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 637-650, 2020, DOI:10.32604/cmc.2020.010265

Abstract The translation quality of neural machine translation (NMT) systems depends largely on the quality of large-scale bilingual parallel corpora available. Research shows that under the condition of limited resources, the performance of NMT is greatly reduced, and a large amount of high-quality bilingual parallel data is needed to train a competitive translation model. However, not all languages have large-scale and high-quality bilingual corpus resources available. In these cases, improving the quality of the corpora has become the main focus to increase the accuracy of the NMT results. This paper proposes a new method to improve the quality of data by… More >

JUAN CARLOS CAVICCHIA*, GUSTAVO GUEMBE, MABEL FÓSCOLO

BIOCELL, Vol.34, No.2, pp. 81-90, 2010, DOI:10.32604/biocell.2010.34.081

Abstract In a previous paper we described a pronounced increase of apoptotic nuclei in rat corpus luteum of pregnancy whose programmed chromatin degeneration was induced by the progesterone antagonist mifepristone. Those observations encouraged us to study the apoptotic nuclear membrane during pregnancy and after parturition and pup removal, by using a freeze-fracture technique which allows us to observe ‘en face’ the nuclear envelop and also permits nuclear pore counting. This study was complemented with the TUNEL assay (TdT-mediated dUTP nick-end labelling). Changes in nuclear pores during pregnancy begin with an intense reduction in number but still showing an even distribution on… More >