Sarab AlMuhaideb^*, Yasmeen AlNegheimish, Taif AlOmar, Reem AlSabti, Maha AlKathery, Ghala AlOlyyan

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 195-220, 2023, DOI:10.32604/cmc.2023.038368

Abstract Healthcare organizations rely on patients’ feedback and experiences to evaluate their performance and services, thereby allowing such organizations to improve inadequate services and address any shortcomings. According to the literature, social networks and particularly Twitter are effective platforms for gathering public opinions. Moreover, recent studies have used natural language processing to measure sentiments in text segments collected from Twitter to capture public opinions about various sectors, including healthcare. The present study aimed to analyze Arabic Twitter-based patient experience sentiments and to introduce an Arabic patient experience corpus. The authors collected 12,400 tweets from Arabic patients discussing patient experiences related to… More >

Clément Chagnaud^1,3, Philippe Garat², Paule-Annick Davoine^1,3, Guylaine Brun-Trigaud⁴

Revue Internationale de Géomatique, Vol.30, No.1, pp. 67-83, 2020, DOI:10.3166/rig.2020.00107

Abstract We propose a multidimensional statistical analysis procedure using projection and classification methods, in order to identify coherent clusters into a set of surficial entities called dispersion areas. The methodology includes a geographical factor to build the representation space for the projection of the data. By applying this method on geolinguistic data, we are able to identify and explain new spatial patterns among a set of dispersion areas of linguistic features.

RÉSUMÉ
Nous proposons une procédure d’analyse statistique multidimensionnelle couplant des méthodes de projection et de classification pour identifier des ensembles cohérents au sein d’un corpus d’entités géographiques surfaciques que l’on… More >

V. Jayanthi^*, S. Thenmalar

Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3551-3564, 2023, DOI:10.32604/iasc.2023.036599

Abstract In this paper, Modified Multi-scale Segmentation Network (MMU-SNet) method is proposed for Tamil text recognition. Handwritten texts from digital writing pad notes are used for text recognition. Handwritten words recognition for texts written from digital writing pad through text file conversion are challenging due to stylus pressure, writing on glass frictionless surfaces, and being less skilled in short writing, alphabet size, style, carved symbols, and orientation angle variations. Stylus pressure on the pad changes the words in the Tamil language alphabet because the Tamil alphabets have a smaller number of lines, angles, curves, and bends. The small change in dots,… More >

Kuntharrgyal Khysru^1,*, Jianguo Wei^1,2, Jianwu Dang³

CMC-Computers, Materials & Continua, Vol.73, No.3, pp. 4897-4907, 2022, DOI:10.32604/cmc.2022.027591

Abstract In China, Tibetan is usually divided into three major dialects: the Am-do, Khams and Lhasa dialects. The Am-do dialect evolved from ancient Tibetan and is a local variant of modern Tibetan. Although this dialect has its own specific historical and social conditions and development, there have been different degrees of communication with other ethnic groups, but all the abovementioned dialects developed from the same language: Tibetan. This paper uses the particularity of Tibetan suffixes in pronunciation and proposes a lexicon for the Am-do language, which optimizes the problems existing in previous research. Audio data of the Am-do dialect are expanded… More >

Kamal Jambi^1,*, Hassanin Al-Barhamtoshy¹, Wajdi Al-Jedaibi¹, Mohsen Rashwan², Sherif Abdou³

Computer Systems Science and Engineering, Vol.43, No.3, pp. 1155-1173, 2022, DOI:10.32604/csse.2022.024967

Abstract Any natural language may have dozens of accents. Even though the equivalent phonemic formation of the word, if it is properly called in different accents, humans do have audio signals that are distinct from one another. Among the most common issues with speech, the processing is discrepancies in pronunciation, accent, and enunciation. This research study examines the issues of detecting, fixing, and summarising accent defects of average Arabic individuals in English-speaking speech. The article then discusses the key approaches and structure that will be utilized to address both accent flaws and pronunciation issues. The proposed SpeakCorrect computerized interface employs a… More >

Abeer A. AlFutamani, Heyam H. Al-Baity^*

Intelligent Automation & Soft Computing, Vol.29, No.1, pp. 89-109, 2021, DOI:10.32604/iasc.2021.016555

Abstract Social media sites produce a large amount of data and offer a highly competitive advantage for companies when they can benefit from and address data, as data provides a deeper understanding of clients and their needs. This understanding of clients helps in effectively making the correct decisions within the company, based on data obtained from social media websites. Thus, sentiment analysis has become a key tool for understanding that data. Sentiment analysis is a research area that focuses on analyzing people’s emotions and opinions to identify the polarity (e.g., positive or negative) of a given text. Since we need to… More >

Yue Zhao¹, Jianjian Yue¹, Wei Song^1,*, Xiaona Xu¹, Xiali Li¹, Licheng Wu¹, Qiang Ji²

Journal on Internet of Things, Vol.1, No.1, pp. 17-23, 2019, DOI:10.32604/jiot.2019.05866

Abstract We proposed a method using latent regression Bayesian network (LRBN) to extract the shared speech feature for the input of end-to-end speech recognition model. The structure of LRBN is compact and its parameter learning is fast. Compared with Convolutional Neural Network, it has a simpler and understood structure and less parameters to learn. Experimental results show that the advantage of hybrid LRBN/Bidirectional Long Short-Term Memory-Connectionist Temporal Classification architecture for Tibetan multi-dialect speech recognition, and demonstrate the LRBN is helpful to differentiate among multiple language speech sets. More >

Yue Zhao¹, Jianjian Yue¹, Wei Song^1,*, Xiaona Xu¹, Xiali Li¹, Licheng Wu¹, Qiang Ji²

CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1223-1235, 2019, DOI:10.32604/cmc.2019.05636

Abstract Tibetan language has very limited resource for conventional automatic speech recognition so far. It lacks of enough data, sub-word unit, lexicons and word inventories for some dialects. And speech content recognition and dialect classification have been treated as two independent tasks and modeled respectively in most prior works. But the two tasks are highly correlated. In this paper, we present a multi-task WaveNet model to perform simultaneous Tibetan multi-dialect speech recognition and dialect identification. It avoids processing the pronunciation dictionary and word segmentation for new dialects, while, in the meantime, allows training speech recognition and dialect identification in a single… More >