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1

K. V. V. Satyanarayana, M. S. V. S. Bhadri Raju, B. N. V. Narasimha Raju,. "BiLSTMs and BPE for English to Telugu CLIR". Journal of Electrical Systems 20, n. 3s (4 aprile 2024): 2022–29. http://dx.doi.org/10.52783/jes.1798.

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A crucial component of Cross Lingual Information Retrieval (CLIR) is Neural Machine Translation (NMT). NMT performs a good job of transforming queries in the English language into Indian languages. This study focuses on the translation of English queries into Telugu. For translations, the NMT will make use of a parallel corpus. Due to a lack of resources in the Telugu language, it is exceedingly challenging to provide NMT with sizable parallel corpora. Thus, the NMT will encounter an issue known as Out of Vocabulary (OOV). Long Short-Term Memory (LSTM) with Byte Pair Encoding (BPE), which breaks up rare words into subwords and attempts to translate them to solve the OOV issue. Issues such as Named Entity Recognition (NER) continue to plague it. In sequence-to-sequence models, bidirectional LSTMs can solve certain NER challenges. Systems that need to be trained in both directions to recognize named entities can benefit from the use of Bidirectional LSTMs (BiLSTMs). The translation efficiency of NMT with BiLSTMs is significantly higher than normal LSTMs, as indicated by the accuracy metrics and Bilingual Evaluation Understudy (BLEU) score.
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Irvine, Ann, e Chris Callison-Burch. "A Comprehensive Analysis of Bilingual Lexicon Induction". Computational Linguistics 43, n. 2 (giugno 2017): 273–310. http://dx.doi.org/10.1162/coli_a_00284.

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Bilingual lexicon induction is the task of inducing word translations from monolingual corpora in two languages. In this article we present the most comprehensive analysis of bilingual lexicon induction to date. We present experiments on a wide range of languages and data sizes. We examine translation into English from 25 foreign languages: Albanian, Azeri, Bengali, Bosnian, Bulgarian, Cebuano, Gujarati, Hindi, Hungarian, Indonesian, Latvian, Nepali, Romanian, Serbian, Slovak, Somali, Spanish, Swedish, Tamil, Telugu, Turkish, Ukrainian, Uzbek, Vietnamese, and Welsh. We analyze the behavior of bilingual lexicon induction on low-frequency words, rather than testing solely on high-frequency words, as previous research has done. Low-frequency words are more relevant to statistical machine translation, where systems typically lack translations of rare words that fall outside of their training data. We systematically explore a wide range of features and phenomena that affect the quality of the translations discovered by bilingual lexicon induction. We provide illustrative examples of the highest ranking translations for orthogonal signals of translation equivalence like contextual similarity and temporal similarity. We analyze the effects of frequency and burstiness, and the sizes of the seed bilingual dictionaries and the monolingual training corpora. Additionally, we introduce a novel discriminative approach to bilingual lexicon induction. Our discriminative model is capable of combining a wide variety of features that individually provide only weak indications of translation equivalence. When feature weights are discriminatively set, these signals produce dramatically higher translation quality than previous approaches that combined signals in an unsupervised fashion (e.g., using minimum reciprocal rank). We also directly compare our model's performance against a sophisticated generative approach, the matching canonical correlation analysis (MCCA) algorithm used by Haghighi et al. ( 2008 ). Our algorithm achieves an accuracy of 42% versus MCCA's 15%.
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Arnold, B. J., H. Du, S. Eremenco e D. Cella. "Using the FACT-Neurotoxicity Subscale to evaluate quality of life in patients from across the globe". Journal of Clinical Oncology 25, n. 18_suppl (20 giugno 2007): 17032. http://dx.doi.org/10.1200/jco.2007.25.18_suppl.17032.

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17032 Background: Translation of Patient Reported Outcomes measures is an essential component of research methodology in preparation for multinational clinical trials. One such measure is the FACT-Neurotoxicity Subscale (FACT-Ntx) which is aimed at the evaluation of quality of life of cancer patients suffering from neurotoxicity, a side effect of certain treatments. Methods: This study set out to linguistically validate the FACT-Ntx for use in Denmark, India, Lithuania and S. Africa. The sample consisted of 176 patients (96 males & 80 females), with varying cancer diagnoses and a mean age of 51 years, speaking 11 languages: Afrikaans (15), Danish (25), Gujarati (15), Hindi (15), Kannada (15), Lithuanian (15), Malayalam (15), Marathi (15), Punjabi (15), Tamil (15) and Telugu (16). The FACT-Ntx was translated using standard FACIT methodology. Patients diagnosed with cancer, at any stage, receiving any treatment experiencing neurotoxicity completed the respective translated version and participated in cognitive debriefing interviews to give their opinion on any problems with the translations or the content of the FACT-Ntx. Statistical analyses (descriptive statistics, one-way ANOVA and reliability analyses) were performed on the quantitative data. Participant comments were analyzed qualitatively. Results: The FACT-Ntx translations showed good reliability and linguistic validity. The internal consistency of all languages combined was .86. All items correlated at an acceptable level. The Ntx score differed across self-reported Performance Status Rating (PSR) groups (nonparametric Kruskal-Wallis test p<.0001). A nonparametric Generalized Linear Model (GLM) approach (with multiple comparison adjusted significance level 0.017) showed a difference between ‘PSR=0’ and ‘PSR=1’ (p=0.0002) and a difference between ‘PSR=0’ and ‘PSR=2’ (p<.0001), both with ‘PSR=0’ patients reporting less neurotoxicity. Conclusions: The FACT-Ntx has shown acceptable reliability and linguistic validity in 11 languages. The instrument has also shown adequate sensitivity in differentiating patients with no symptoms and normal activity from patients reporting some symptoms. We consider these translations acceptable for use in international research and clinical trials. No significant financial relationships to disclose.
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Raju, B. N. V. Narasimha, M. S. V. S. Bhadri Raju e K. V. V. Satyanarayana. "Effective preprocessing based neural machine translation for English to Telugu cross-language information retrieval". IAES International Journal of Artificial Intelligence (IJ-AI) 10, n. 2 (1 giugno 2021): 306. http://dx.doi.org/10.11591/ijai.v10.i2.pp306-315.

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<span id="docs-internal-guid-5b69f940-7fff-f443-1f09-a00e5e983714"><span>In cross-language information retrieval (CLIR), the neural machine translation (NMT) plays a vital role. CLIR retrieves the information written in a language which is different from the user's query language. In CLIR, the main concern is to translate the user query from the source language to the target language. NMT is useful for translating the data from one language to another. NMT has better accuracy for different languages like English to German and so-on. In this paper, NMT has applied for translating English to Indian languages, especially for Telugu. Besides NMT, an effort is also made to improve accuracy by applying effective preprocessing mechanism. The role of effective preprocessing in improving accuracy will be less but countable. Machine translation (MT) is a data-driven approach where parallel corpus will act as input in MT. NMT requires a massive amount of parallel corpus for performing the translation. Building an English - Telugu parallel corpus is costly because they are resource-poor languages. Different mechanisms are available for preparing the parallel corpus. The major issue in preparing parallel corpus is data replication that is handled during preprocessing. The other issue in machine translation is the out-of-vocabulary (OOV) problem. Earlier dictionaries are used to handle OOV problems. To overcome this problem the rare words are segmented into sequences of subwords during preprocessing. The parameters like accuracy, perplexity, cross-entropy and BLEU scores shows better translation quality for NMT with effective preprocessing.</span></span>
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Ashari, Erwin, Marida Hannum Harahap e Shalehoddin Shalehoddin. "TRANSLATION TECHNIQUES USED BY ENGLISH THIRD SEMESTER STUDENTS OF UNIVERSITY OF RIAU KEPULAUAN". ANGLO-SAXON: Jurnal Ilmiah Program Studi Pendidikan Bahasa Inggris 13, n. 2 (30 dicembre 2022): 285–97. http://dx.doi.org/10.33373/as.v13i2.5002.

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Translation is the transferring meaning of the text from source language in which to equivalent text in target language that communicates similar messages by changing structure without changing meaning. The qualitative descriptive method as the research design of this study which aimed to find out translation techniques used by English third semester students in translating a narrative text entitled “Telaga Warna” from English into Indonesian and to describe the dominant technique used by the students in translating the text. The collecting of the data collected taken from the result translation of narrative text from English into Indonesian by 14 participants of English third semester students. In this study, the data analyzed based on translation techniques of Molina and Albir (2002) theory. Based on data analysis, it was found that the translation techniques of narrative text entitled “Telaga Warna” from English into Indonesian done by English third semester students of University of Riau Kepulauan uses fourteen (14) translation techniques found in 1302 data while translating the text. The translation techniques were namely literal translation, calque, reduction, amplification, discursive creation, linguistic amplification, established equivalent, particularization, transposition, linguistic compression, modulation, borrowing, variation, and generalization. The most dominant technique of translation used by students was literal translation techniques
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Sindhu, D. V., e B. M. Sagar. "Dictionary Based Machine Translation from Kannada to Telugu". IOP Conference Series: Materials Science and Engineering 225 (agosto 2017): 012182. http://dx.doi.org/10.1088/1757-899x/225/1/012182.

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Gaur, Albertine. "Śiva's warriors: the Basava Purāna of Pālkuriki Somanātha. Translated from the Telugu by Velcheru Narayana Rao assisted by Gene H. Roghair. (Princeton Library of Asian Translations.) pp. xviii, 321, i illus. Princeton, NJ, Princeton University Press, 1990. US $49.50." Journal of the Royal Asiatic Society 1, n. 3 (novembre 1991): 416–18. http://dx.doi.org/10.1017/s135618630000136x.

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BV Subba Rao, Et al. "Unicode-driven Deep Learning Handwritten Telugu-to-English Character Recognition and Translation System". International Journal on Recent and Innovation Trends in Computing and Communication 11, n. 10 (2 novembre 2023): 344–59. http://dx.doi.org/10.17762/ijritcc.v11i10.8497.

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Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.
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Dasari, Philemon Benison, Himanshu Verma e G. V. M. Hariprasad. "Translation and validation of communicative quality of life in the dysarthric speaker questionnaire in telugu". Clinical Archives of Communication Disorders 6, n. 3 (31 dicembre 2021): 187–91. http://dx.doi.org/10.21849/cacd.2021.00591.

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Purpose: Several tools are available to assess the quality of life for speech, language, voice, and swallowing disorders in Indian languages. However, limited tools are present to assess the quality of life of dysarthria. So, the present study aims to translate and validate the Quality of Life in the Dysarthric Speaker (QoL-DyS) questionnaire into the Telugu language.Methods: A total of 30 different types of dysarthria and 30 age-matched controls participated in the present study. The QoL-DyS tool was trans-adapted in the Telugu language using a standardized procedure and administered to the participants. The internal consistency, test-retest reliability, construct validity, scale statistics, and item statistics were computed.Results: The present study revealed that the developed tool has good (i.e., 0.81) overall internal consistency and excellent (i.e., 0.92) test-retest reliability. The significant difference (i.e., <i>p</i><0.05) was found between the experimental and control group.Conclusions: From the present study, we can conclude that the Telugu QOL-DyS is a reliable tool. It is applicable in clinical practice for the self-assessment of the quality of life in dysarthric speakers. Hence, the Telugu QOL-DyS is recommended in clinical practice, and there is a need to develop QoL-DyS in other Indian languages.
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Prasad, Bathaloori Reddy. "Classification of Analyzed Text in Speech Recognition Using RNN-LSTM in Comparison with Convolutional Neural Network to Improve Precision for Identification of Keywords". Revista Gestão Inovação e Tecnologias 11, n. 2 (5 giugno 2021): 1097–108. http://dx.doi.org/10.47059/revistageintec.v11i2.1739.

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Aim: Text classification is a method to classify the features from language translation in speech recognition from English to Telugu using a recurrent neural network- long short term memory (RNN-LSTM) comparison with convolutional neural network (CNN). Materials and Methods: Accuracy and precision are performed with dataset alexa and english-telugu of size 8166 sentences. Classification of language translation is performed by the recurrent neural network where a number of the samples (N=62) and convolutional neural network were a number of samples (N=62) techniques, the algorithm RNN implies speech recognition that can be compared with convolutional is the second technique. Results and Discussion: RNN-LSTM from the dataset speech recognition, feature Telugu_id produce accuracy 93% and precision 68.04% which can be comparatively higher than CNN accuracy 66.11%, precision 61.90%. It shows a statistical significance as 0.007 from Independent Sample T-test. Conclusion: The RNN-LSTM performs better in finding accuracy and precision when compared to CNN.
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Tikka, Sai Krishna, Govindrao N. Kusneniwar, Neeraj Agarwal, Giovanni D’Avossa e Mohammad Zia Ul Haq Katshu. "Linguistic equivalence, internal consistency, and factor structure of the Telugu version of the PRIME Screen-Revised, a tool to screen individuals at clinical high risk for psychosis". Telangana Journal of Psychiatry 10, n. 1 (gennaio 2024): 6–12. http://dx.doi.org/10.4103/tjp.tjp_70_23.

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Background: Research from India studying individuals at high risk of psychosis is deemed necessary. The Prevention through Risk Identification, Management, and Education (PRIME) Screen-Revised (PS-R) is a commonly used tool to screen individuals at high risk of psychosis. We aimed to translate PS-R into Telugu and assess the linguistic equivalence, reliability (internal consistency), and factor structure of the PS-R, administered in a community youth sample. Methodology: PS-R was translated to Telugu by the standard “forward-translation-back-translation” method, and linguistic equivalence was assessed in 20 bilingual youth by Haccoun’s technique. Data for assessing reliability and factor structure were collected using a community-based household study conducted in the Yadadri Bhuvanagiri district of Telangana. Two villages from a rural area, Bommalaramaram, and two wards from an urban area, Bhongir, were chosen. Data from 613 (387 rural and 226 urban) youth aged 15–24 years were included in the analysis. Spearman–Brown coefficient was calculated as a measure of split-half reliability. An exploratory factor analysis was conducted to measure its factor structure. Results: Linguistic equivalence was statistically confirmed using inter-version correlation coefficients. Spearman–Brown reliability coefficient was 0.774. Principal component analysis showed that 12 scale items were significantly loaded by 3 latent factors with eigenvalues of 3.105, 1.223, and 1.08, respectively. Factor solution showed that 6, 3, and 2 items correlated with the three factors, respectively. Conclusions: We conclude that the Telugu version of the PS-R is fairly reliable and valid for screening individuals at high risk for psychosis among community youth. The three factors represent “positive symptoms of schizophrenia and distress,” “positive schizotypy,” and “apophenia and magical foretelling.”
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Kumar K., Vimal, e Divakar Yadav. "Word Sense Based Hindi-Tamil Statistical Machine Translation". International Journal of Intelligent Information Technologies 14, n. 1 (gennaio 2018): 17–27. http://dx.doi.org/10.4018/ijiit.2018010102.

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Corpus based natural language processing has emerged with great success in recent years. It is not only used for languages like English, French, Spanish, and Hindi but also is widely used for languages like Tamil, Telugu etc. This paper focuses to increase the accuracy of machine translation from Hindi to Tamil by considering the word's sense as well as its part-of-speech. This system works on word by word translation from Hindi to Tamil language which makes use of additional information such as the preceding words, the current word's part of speech and the word's sense itself. For such a translation system, the frequency of words occurring in the corpus, the tagging of the input words and the probability of the preceding word of the tagged words are required. Wordnet is used to identify various synonym for the words specified in the source language. Among these words, the one which is more relevant to the word specified in source language is considered for the translation to target language. The introduction of the additional information such as part-of-speech tag, preceding word information and semantic analysis has greatly improved the accuracy of the system.
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Ben-Herut, Gil. "From marginal to canonical: The afterlife of a late medieval Telugu hagiography in a Kannada translation". Translation Studies 14, n. 2 (4 marzo 2021): 133–49. http://dx.doi.org/10.1080/14781700.2021.1888785.

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Ganganwar, Vaishali, e Ratnavel Rajalakshmi. "MTDOT: A Multilingual Translation-Based Data Augmentation Technique for Offensive Content Identification in Tamil Text Data". Electronics 11, n. 21 (1 novembre 2022): 3574. http://dx.doi.org/10.3390/electronics11213574.

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The posting of offensive content in regional languages has increased as a result of the accessibility of low-cost internet and the widespread use of online social media. Despite the large number of comments available online, only a small percentage of them are offensive, resulting in an unequal distribution of offensive and non-offensive comments. Due to this class imbalance, classifiers may be biased toward the class with the most samples, i.e., the non-offensive class. To address class imbalance, a Multilingual Translation-based Data augmentation technique for Offensive content identification in Tamil text data (MTDOT) is proposed in this work. The proposed MTDOT method is applied to HASOC’21, which is the Tamil offensive content dataset. To obtain a balanced dataset, each offensive comment is augmented using multi-level back translation with English and Malayalam as intermediate languages. Another balanced dataset is generated by employing single-level back translation with Malayalam, Kannada, and Telugu as intermediate languages. While both approaches are equally effective, the proposed multi-level back-translation data augmentation approach produces more diverse data, which is evident from the BLEU score. The MTDOT technique proposed in this work achieved a promising improvement in F1-score over the widely used SMOTE class balancing method by 65%.
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Bhardwaj,, Deepanshu. "Translation of English Videos to Indian Regional Languages". INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, n. 04 (9 aprile 2024): 1–5. http://dx.doi.org/10.55041/ijsrem30431.

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In spite of many languages being spoken in India, it is difficult for the people to understand Indian regional languages like English, Gujrati, Kannada, Tamil, Telugu, Punjabi, Malayalam, etc. The recognition and synthesis of speech are prominent emerging technologies in natural language processing and communication domains. This paper aims to leverage the open-source applications of these technologies, machine translation, text-to-speech system (TTS), and speech-to-text system (STT) to convert available online resources to Indian languages. This application takes an English language video as an input and separates the audio from video. It then divides the audio file into several smaller chunks based on the timestamps. These audio chunks are then individually converted into text using WhisperAI speech recognition model.And facebooks M2M100 model for translation. After this translation, a TTS system is required to convert the text into the desired audio output. Not many open source TTS systems are available for Indian regional languages. This application is beneficial to visually impaired people as well as individuals who are not capable of reading text to acquire knowledge in their native language. In future, this application aims to achieve ubiquitous communication enabling people of different regions to communicate with each other breaking the language barriers. Index Terms- Translation of English to Regional indian languages, ,translation and transcription using WhisperAI and FacebookM2M100, Jupiter Notebook
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H M, Prof Kotresh. "AI Based Multilingual College Enquiry Voice Bot Using Python". International Journal for Research in Applied Science and Engineering Technology 12, n. 4 (30 aprile 2024): 4300–4305. http://dx.doi.org/10.22214/ijraset.2024.60961.

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Abstract: This paper addresses the growing need for seamless cross-language communication in our global society. Leveraging AI, it offers a versatile conversational agent capable of understanding and responding to inquiries in multiple languages. Through speech recognition and advanced NLP, the system interprets user intent and retrieves relevant information from a college database. Integrated language translation services enable communication in various languages namely-Kannada, English, Tamil and Telugu enhancing accessibility. Responses are dynamically translated into the user's preferred language using text-to-speech conversion. This project demonstrates AI's potential in bridging linguistic barriers and improving access to educational resources. Future enhancements could include personalized responses and expanded language support in five South Indian languages, further enriching the user experience and solidifying the bot's role as a valuable educational tool.
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Shukla, Sudhir Krishna, Rashi Srivastava e Kum Kum Ray. "Translating Land into Stage: Observations on the Patterns and Presentations in Girish Karnad’s ‘Hayavadana’ and ‘Nagamandala’". International Journal of English Literature and Social Sciences 9, n. 2 (2024): 218–26. http://dx.doi.org/10.22161/ijels.92.32.

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This research paper examines the patterns and presentations in Girish Karnad’s major plays. Girish Raghunath Karnad (19 May 1938-10 June 2019) was the foremost Kannada playwright of India. In addition to writing plays, he was also an actor, film director, and a Jnanpith awardee, who dominated in the fields of Hindi, Kannada, Tamil, Malayalam, Telugu, and Marathi films. His ascent to prominence as a playwright in the 1960s signaled the advent of contemporary Indian playwriting in Kannada as Badal Sarkar, Vijay Tendulkar, and Mohan Rakesh did in Bengali, Marathi, and Hindi respectively. He used his intellectual power to use distilled themes from history, folktales, and myths. He is able to give identity to Indian art and culture in other countries. Tughlaq(1964), Yayati(1961), Hayavadana(1971), Hittina Hunja(1980), Nagamandala(1988), Tale-Danda(1990), Fire and the Rain(1995)
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Rajesh, Mudivedu Shroff, e Nandikotkur Padmaja. "Now I know Dorothy!" Acta Crystallographica Section A Foundations and Advances 70, a1 (5 agosto 2014): C1314. http://dx.doi.org/10.1107/s2053273314086859.

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"Our message is – dare I say – crystal clear," observed UNESCO Director-General Irina Bokova in her opening remarks at UNESCO headquarters in Paris on 20 January 2014. At exactly the same time some 6480.2 miles away in a school at Hyderabad, India echoed a message "Now I know Dorothy" this was an excited exclamation from hundreds of high school children. The occasion was an IYCr2014 outreach programme motivated and supported by the President of the International Union of Crystallography (IUCr) Professor Gautam R. Desiraju. The occasion was an IYCr2014 outreach programme that matched IYCr2014 goals and objectives. The project next moved to smaller places. To make IYCr2014 relevant specifically to young students in villages and small towns, it was thought that the student audience must be comprised from non-English medium schools. This prompted translating "Crystallography Matters!" from English to a widely spoken (60 million) South Indian language called Telugu. the next step was to prepare power point presentations in Telugu, prepare crystallography related simple multiple choice questions, quiz papers, buy chocolates to represent crystallization process in making chocolates, sugar candy (Kalkand) to show them real crystals so that students connect to the subject with ease. Then travel to schools and start with an introduction to what and why is IYCr, demonstrate uses of crystals with examples, tell them why we cannot use microscope to "see" the inside of crystals, lecture, demo interactive sessions and so on .The presentation involved introducing science behind crystallography, explaining how to grow crystals, relevance to everyday life with references to NaCl and other medical uses. Sessions end with taking questions, ask mass questions like who is Dorothy, poster readings, who is Bragg, valuations of quiz papers and distribution of prizes, chocolates and sugar candy. Finally Crystallography Matters! books are given to the students and copies to school libraries.
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Siahaan, Basar Lolo, Novita Gresiana Manurung, Oktavianti Sianturi e Miranda Sinaga. "Accuracy of the Translation of the Indonesian Short Story "Angsa dengan Telur Emas" Into English Using U-Dictionary". Journal on Education 5, n. 4 (3 aprile 2023): 14432–44. http://dx.doi.org/10.31004/joe.v5i4.2497.

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This research was focused on the extent of the accuracy of the translation of the Indonesian Short Story “ Angsa dengan Telur Emas" into English Using U-Dictionary. The short story “Angsa dengan Telur Emas" of translation were analyzed from Using U-Dictionary.Theories used in this research was Nababan (2012) Scale for scoring accuracy divided into 3 namely accurate, less accurate, inaccurate. This research was conducted using a descriptive qualitative method and the objective of the research is to find out the level of accuracy of the translation of the Indonesian short story “Angsa dengan Telur Emas" into English using U-Dictionary. After conducting the analysis, this research showed the accurate, less accurate and inaccurate. And the kinds of inaccurate divided 4 namely, mistranslation, omission, addition and un-translated According Specia and Shah (2014). They were 182 data as samples. The accuracy percentage was 48, 35%. The less accurate percentage was 17, 58%.The inaccurate percentage was 34, 06 %. After calculating the average accuracy level, the result showed that Short Story “Angsa dengan Telur Emas” Using U-Dictionary was considered as accurate translation.
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Agarwal, Pushkal, Kiran Garimella, Sagar Joglekar, Nishanth Sastry e Gareth Tyson. "Characterising User Content on a Multi-Lingual Social Network". Proceedings of the International AAAI Conference on Web and Social Media 14 (26 maggio 2020): 2–11. http://dx.doi.org/10.1609/icwsm.v14i1.7274.

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Social media has been on the vanguard of political information diffusion in the 21st century. Most studies that look into disinformation, political influence and fake-news focus on mainstream social media platforms. This has inevitably made English an important factor in our current understanding of political activity on social media. As a result, there has only been a limited number of studies into a large portion of the world, including the largest, multilingual and multicultural democracy: India. In this paper we present our characterisation of a multilingual social network in India called ShareChat. We collect an exhaustive dataset across 72 weeks before and during the Indian general elections of 2019, across 14 languages. We investigate the cross lingual dynamics by clustering visually similar images together, and exploring how they move across language barriers. We find that Telugu, Malayalam, Tamil and Kannada languages tend to be dominant in soliciting political images (often referred to as memes), and posts from Hindi have the largest cross-lingual diffusion across ShareChat (as well as images containing text in English). In the case of images containing text that cross language barriers, we see that language translation is used to widen the accessibility. That said, we find cases where the same image is associated with very different text (and therefore meanings). This initial characterisation paves the way for more advanced pipelines to understand the dynamics of fake and political content in a multi-lingual and non-textual setting.
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R S, Anjana, Resmi B e Anoop A K. "A critical study of the Ayurveda Medical manuscript ‘Chikitsasara’". International Journal of Ayurvedic Medicine 13, n. 1 (5 aprile 2022): 147–52. http://dx.doi.org/10.47552/ijam.v13i1.2435.

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Abstract (sommario):
Ayurveda being a practical science is codified through centuries in written documents called Manuscripts. A manuscript is any document written by hand or typewritten as opposed to being mechanically printed or reproduced in some automated way. A very few of these manuscripts have been published during the past decades. As such, several treatment methods contained in these texts are being lost by decaying. As part of a humble step towards this, Chikitsasara authored by Gopaladasa, a paper manuscript in the Sanskrit language documented in Devanagari script preserved at Unmesha Research Institute of Indology, Mysore was taken. The objectives of the study are critical edition of the manuscript and its English translation which is not available. It is a unique book belonging to the kayachikitsa parampara. The time period of the text by considering the internal and external evidence, influence of the text on other medieval texts can be placed as the late seventeenth century.The text was translated to Telugu and Marathi languages in 1877 and 1881 respectively, which are not widely available today. Chikitsasara is a treatise arranged in three sections. Some rare diseases like sparsavata, seethavata, takraprameha, and ghrtaprameha have found a place in this text. Prognosis of disease based on astrology is a unique feature of this text. After the critical edition, a maximum number of accepted readings was obtained from the manuscript from Oriental research institute & manuscript library, Kariavattom. The content of the text is also very much similar to the seventeenth century work Yogaratnakara.
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Nikolskaia, Kseniia D. "At the Origins of European Oriental Studies: an Unknown Letter by Benjamin Schultze to Georg Jacob Kehr". Vostok. Afro-aziatskie obshchestva: istoriia i sovremennost, n. 5 (2023): 254. http://dx.doi.org/10.31857/s086919080026912-0.

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The Russian Archive of Ancient Acts funds hold an archive belonging to G. J. Kehr (1692–1740), who stood at the very origins of European Oriental studies. There is very little information about this person. His archive is very large and extremely poorly parsed. Among the letters preserved in it are messages from eminent personalities of those days. Some papers of G.J. Kehr are connected with the South of India (Tranquebar), where Lutheran priests who were part of the so-called Danish Royal Mission were working at that time. Among these papers there is a small letter from B. Schultze (1689–-1760). Schultze became the head of the mission in Tranquebar after the death of B. Ziegenbalg (1682–1719), its first organizer. Like Ziegenbalg, Schulze did a lot for the Christianization of the region and for the formation of Oriental studies as a science. He was the first among Europeans to study the Telugu language, published the grammar of this language, translated the texts of the Bible into it. He studied dakkhinī, a dialect of Hindustani. Schulze published a grammar of this language, outlining its basic rules in Latin. His letter below, addressed to Kehr, is obviously a continuation of the previous correspondence. Among other things, the message contains some rules for reading Tamil texts. In addition, valuable information is given about the work of missionaries on the translation of Christian literature into Tamil and about the activity of the Printing house established in Tranquebar. Finally, the letter mentions the names of people significant for the era (language teachers and translators), who probably formed a circle of acquaintances for both G.J.Kehr and B. Schultze.
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Ani, Sari. "ANALISIS FUNGSI LEGENDA TELAGA SARANGAN DI KELURAHAN SARANGAN KEC. PLAOSAN KAB. MAGETAN JAWA TIMUR". HUMANIS: Jurnal Ilmu-Ilmu Sosial dan Humaniora 11, n. 1 (31 gennaio 2019): 39–44. http://dx.doi.org/10.52166/humanis.v11i1.1420.

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This research was conducted with the aim of describing the functions of legend of lake sarangan stories in Sarangan Village, Kec. Plaosan Kab. East Java Magetan. This research includes qualitative research based on naturalistic approaches. The research data is based on the results of interviews with informants. Data is collected by observation, recording, interviewing and recording methods. Data analysis was carried out in five stages, namely (1) transcribing oral data in written form, (2) summarizing and translating, (3) interpreting. Analysis of the data used in this study is content analysis techniques (conten analysis). The content analysis technique is used in analyzing the function of folklore in the Sarangan lake. The results of the study show that there are functions that include (1) entertainment, (2) institutions of cultural institutions, (3) education, (4) social order, (5) group solidarity, (6) social criticism, (7) pleasant divorce from reality, and (8) potential weapons in society.
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Azmi, Bayu, Indra Milyardi, Megy Stefanus, Fajar Adi Prasetyo, Rasi Prasetio, Mikhail S. Kuznetsov e Oleg Yu Dolmatov. "On-Stream Pipe Scale Inspection Using Gamma-Ray Tomography Technique: Field Experiment in Karaha Geothermal Power Plant, Indonesia". IOP Conference Series: Earth and Environmental Science 1344, n. 1 (1 maggio 2024): 012018. http://dx.doi.org/10.1088/1755-1315/1344/1/012018.

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Abstract Pipe scaling is a common problem in geothermal power plants. Currently, the pipeline withinside the Karaha Telaga Bodas geothermal subject is indicated to incorporate scale because of pressure anomalies. On-stream pipe scale inspection techniques are needed to maintain the production process. This research aims to apply the gamma-ray transmission tomography technique to pipelines in geothermal fields. The gamma-ray tomography system consists of a 137Cs (2.96 GBq) gamma radiation source, a scintillation detector, mechanical parts, control parts, and data acquisition. Scanning was performed at three predetermined points: brine pipe 1, two-phase pipe, and brine pipe 2. The system scans half of the pipe circumference (180°) and divides it into 32 projections (5.625°). Each projection performs a translational scan with a resolution of 4 mm. Three cross-sectional images of the pipe were obtained, showing the inside condition. Pipe scale was observed at three scanning points with a thickness of 12 mm-48 mm. Gamma tomography can potentially be a tool for examining pipe scale thickness in geothermal fields. The use of a single detector is a limitation that needs to be improved. Apart from that, the stability of the control and mechanical parts must also be improved.
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Ruiz Álvarez, J. I., J. M. Teijeiro, A. R. Charmandarian, J. P. Haumüller e P. E. Marini. "142 DESIGN OF ANTIBODIES SPECIFIC FOR PEPTIDES PRESENT EXCLUSIVELY IN DIFFERENT MEMBERS OF THE PREGNANCY ASSOCIATED GLYCOPROTEINS FAMILY". Reproduction, Fertility and Development 22, n. 1 (2010): 230. http://dx.doi.org/10.1071/rdv22n1ab142.

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Accurate diagnosis of non-pregnancy and prompt re-enlistment of cattle into an appropriate breeding protocol are essential components of successful reproductive programs. The search for biochemical markers of early pregnancy has led to the characterization of the pregnancy-associated glycoproteins (PAG), a large family expressed exclusively in the placenta. In cattle, the PAG family is composed of at least 22 translated genes, with different spatiotemporal expression (Telugu B. et al. 2009 BMC Genomics 10, 185). The PAG may be detected in placenta and some members have been detected in serum by immunological methods (Garmo B et al. 2008 J. Dairy Sci. 91, 3025-3033). However, the lack of antibodies specific for different PAG has prevented the identification of any member of the family as being present in serum at early pregnancy and undetectable after parturition. The objective of this study was to develop a method that allows the preparation of antibodies specific for different PAG, as candidates of early pregnancy markers. The method employed was in silico analysis of the bovine PAG family members that have been reported as of early expression in placenta and in binucleated cells (which are fusion cells between maternal and embryonic tissues), searching for peptide regions that differ between them. Selected peptides were analyzed for localization in reported exposed regions, antigenicity in the protein context, lack of consensus sequences for post-translational modifications and of putative homologous peptides using the reported bovine genome, and feasibility of chemical synthesis with low rates of contamination. Chosen peptides were synthesized by a facility and coupled to rabbit albumin. Rabbits were immunized and the serum was analyzed for the presence of specific antibodies. After in silico analysis, 43 candidate peptides contained in 13 different PAG were considered suitable. Four of these were chosen for synthesis and coupled successfully to rabbit albumin. Rabbits were immunized with peptide-rabbit albumin, according to procedures used by Bioterio of Facultad de Ciencias Bioquímicas y Farmacéuticas, UNR. Analysis of the obtained serum by dot-blot showed the presence of anti-peptide antibodies only for one of the peptides. This result is in accordance with the efficiency of anti-peptide antibodies development in rabbits. In conclusion, we designed a method for selection of peptides specific for different members of the PAG family and with high chances of being exposed in the surface of the molecule, which would allow immunological detection of the native glycoprotein in cow serum. These antibodies are being used for analysis of serum from pregnant and non-pregnant cows. Fundación Nuevo Banco de Santa Fe Programa de fortalecimiento de las capacidades de investigación y desarrollo de la provincia de Santa Fe, Secretaria de Estado de Ciencia, Tecnología e Innovacón, Provincia de Santa Fe.
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R, Bhuvaneswari, Cynthiya Rose J S e Maria Baptist S. "Editorial: Indian Literature: Past, Present and Future". Studies in Media and Communication 11, n. 2 (22 febbraio 2023): 1. http://dx.doi.org/10.11114/smc.v11i2.5932.

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Abstract (sommario):
IntroductionIndian Literature with its multiplicity of languages and the plurality of cultures dates back to 3000 years ago, comprising Vedas, Upanishads, Puranas and Epics like Ramayana and Mahabharata. India has a strong literary tradition in various Indian regional languages like Sanskrit, Prakrit, Pali, Hindi, Marathi, Bengali, Oriya, Tamil, Kannada, Telugu, Malayalam and so on. Indian writers share oral tradition, indigenous experiences and reflect on the history, culture and society in regional languages as well as in English. The first Indian novel in English is Bankim Chandra Chatterjee’s Rajmohan’s Wife (1864). Indian Writing in English can be viewed in three phases - Imitative, First and Second poets’ phases. The 20th century marks the matrix of indigenous novels. The novels such as Mulk Raj Anand’s Untouchable (1935), Anita Nair’s Ladies Coupé (2001), and Khuswant Singh’s Memories of Madness: Stories of 1947 (2002) depict social issues, vices and crises (discrimination, injustice, violence against women) in India. Indian writers, and their contribution to world literature, are popular in India and abroad.Researchers are keen on analysing the works of Indian writers from historical, cultural, social perspectives and on literary theories (Post-Colonialism, Postmodernity, Cultural Studies). The enormity of the cultural diversity in India is reflected in Indian novels, plays, dramas, short stories and poems. This collection of articles attempts to capture the diversity of the Indian land/culture/landscape. It focuses on the history of India, partition, women’s voices, culture and society, and science and technology in Indian narratives, documentaries and movies.Special Issue: An Overview“Whatever has happened, has happened for goodWhatever is happening, is also for goodWhatever will happen, shall also be good.”- The Bhagavad-Gita.In the Mahabharata’s Kurukshetra battlefield, Lord Krishna counsels Arjuna on how everything that happens, regardless of whether it is good or bad, happens for a reason.Indian Literature: Past, Present and Future portrays the glorious/not-so-glorious times in history, the ever-changing crisis/peace of contemporary and hope for an unpredictable future through India’s literary and visual narratives. It focuses on comparison across cultures, technological advancements and diverse perspectives or approaches through the work of art produced in/on India. It projects India’s flora, fauna, historical monuments and rich cultural heritage. It illustrates how certain beliefs and practices come into existence – origin, evolution and present structure from a historical perspective. Indian Literature: Past, Present and Future gives a moment to recall, rectify and raise to make a promising future. This collection attempts to interpret various literary and visual narratives which are relevant at present.The Epics Reinterpreted: Highlighting Feminist Issues While Sustaining Deep Motif, examines the Women characters in the Epics – Ramayana and Mahabharata. It links the present setting to the violence against women described in the Epics Carl Jung’s archetypes are highlighted in a few chosen characters (Sita, Amba, Draupati). On one note, it emphasises the need for women to rise and fight for their rights.Fictive Testimony and Genre Tension: A Study of ‘Functionality’ of Genre in Manto’s Toba Tek Singh, analyses the story as a testimony and Manto as a witness. It discusses the ‘Testimony and Fictive Testimony’ in Literature. It explains how the works are segregated into a particular genre. The authors conclude that the testimony is to be used to understand or identify with the terror.Tangible Heritage and Intangible Memory: (Coping) Precarity in the select Partition writings by Muslim Women, explores the predicament of women during the Partition of India through Mumtaz Shah Nawaz’s The Heart Divided (1990) and Attia Hosain’s Sunlight on a Broken Column (2009). It addresses ‘Feminist Geography’ to escape precarity. It depicts a woman who is cut off from her own ethnic or religious group and tries to conjure up her memories as a means of coping with loneliness and insecurity.Nation Building Media Narratives and its Anti-Ecological Roots: An Eco-Aesthetic Analysis of Khushwant Singh’s Train to Pakistan, analyses the post-Partition trauma in the fictional village, Mano Majra. It illustrates the cultural and spiritual bond between Mano Majrans — the inhabitants of Mano Majra — and nature (the land and river). It demonstrates how the media constructs broad myths about culture, religion, and nation. According to the authors, Mano Majrans place a high value on the environment, whilst the other boundaries are more concerned with nationalism and religion.Pain and Hopelessness among Indian Farmers: An Analysis of Deepa Bhatia’s Nero’s Guests documents the farmers’ suicides in India as a result of debt and decreased crop yield. The travels of Sainath and his encounters with the relatives of missing farmers have been chronicled in the documentary Nero’s Guests. It uses the Three Step Theory developed by David Klonsky and Alexis May and discusses suicide as a significant social issue. The authors conclude that farmers are the foundation of the Indian economy and that without them, India’s economy would collapse. It is therefore everyone’s responsibility—the people and the government—to give farmers hope so that they can overcome suicidal thoughts.The link between animals and children in various cultures is discussed in The New Sociology of Childhood: Animal Representations in Leslie Marmon Silko’s Garden in the Dunes, Amazon’s Oh My Dog, and Netflix’s Mughizh: A Cross-Cultural Analysis. It examines the chosen works from the perspectives of cross-cultural psychology and the New Sociology of Childhood. It emphasises kids as self-sufficient, engaged, and future members of society. It emphasises universal traits that apply to all people, regardless of culture. It acknowledges anthropomorphized cartoons create a bond between kids and animals.Life in Hiding: Censorship Challenges faced by Salman Rushdie and Perumal Murugan, explores the issues sparked by their writings. It draws attention to the aggression and concerns that were forced on them by the particular sect of society. It explains the writers’ experiences with the fatwa, court case, exile, and trauma.Female Body as the ‘Other’: Rituals and Biotechnical Approach using Perumal Murugan’s One Part Woman and Matrubhoomi: A Nation Without Women, questions the society that limits female bodies for procreation and objectification. It talks about how men and women are regarded differently, as well as the cultural ideals that apply to women. It explains infertility, which is attributed to women, as well as people’s ignorance and refusal to seek medical help in favour of adhering to traditional customs and engaging in numerous rituals for procreation.Life and (non) Living: Technological and Human Conglomeration in Android Kunjappan Version 5.25, explores how cyborgs and people will inevitably interact in the Malayalam film Android Kunjappan Version 5.25. It demonstrates the advantages, adaptability, and drawbacks of cyborgs in daily life. It emphasises how the cyborg absorbs cultural and religious notions. The authors argue that cyborgs are an inevitable development in the world and that until the flaws are fixed, humans must approach cyborgs with caution. The Challenges of Using Machine Translation While Translating Polysemous Words, discusses the difficulty of using machine translation to translate polysemous words from French to English (Google Translate). It serves as an example of how the machine chooses the formal or often-used meaning rather than the pragmatic meaning and applies it in every situation. It demonstrates how Machine Translation is unable to understand the pragmatic meaning of Polysemous terms because it is ignorant of the cultures of the source and target languages. It implies that Machine Translation will become extremely beneficial and user-friendly if the flaws are fixed.This collection of articles progresses through the literary and visual narratives of India that range from historical events to contemporary situations. It aims to record the stories that are silenced and untold through writing, film, and other forms of art. India’s artistic output was influenced by factors such as independence, partition, the Kashmir crisis, the Northeast Insurgency, marginalisation, religious disputes, environmental awareness, technical breakthroughs, Bollywood, and the Indian film industry. India now reflects a multitude of cultures and customs as a result of these occurrences. As we examine the Indian narratives produced to date, we can draw the conclusion that India has a vast array of tales to share with the rest of the world.Guest Editorial BoardGuest Editor-in-ChiefDr. Bhuvaneswari R, Associate Professor, School of Social Sciences and Languages, Vellore Institute of Technology, Chennai. She has pursued her master’s at the University of Madras, Chennai and doctoral research at HNB Central University, Srinagar. Her research areas of interest are ELT, Children/Young Adult Literature, Canadian writings, Indian literature, and Contemporary Fiction. She is passionate about environmental humanities. She has authored and co-authored articles in National and International Journals.Guest EditorsCynthiya Rose J S, Assistant Professor (Jr.), School of Social Sciences and Languages, Vellore Institute of Technology, Chennai. Her research interests are Children’s Literature, Indian Literature and Graphic Novels.Maria Baptist S, Assistant Professor (Jr.), School of Social Sciences and Languages, Vellore Institute of Technology, Chennai. His research interests include Crime/Detective fiction and Indian Literature.MembersDr. Sufina K, School of Science and Humanities, Sathyabama Institute of Science and Technology, Chennai, IndiaDr. Narendiran S, Department of Science and Humanities, St. Joseph’s Institute of Technology, Chennai, India
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Dias Caldeira, Francois Isnaldo, Leandro Araujo Fernandes e Daniela Coelho De Lima. "A utilização do P-CPQ na percepção da qualidade de vida em saúde bucal na visão de pais e cuidadores: uma revisão". ARCHIVES OF HEALTH INVESTIGATION 9, n. 6 (13 novembre 2020): 576–81. http://dx.doi.org/10.21270/archi.v9i6.4946.

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Abstract (sommario):
O questionário de percepção de pais e cuidadores (P-CPQ) está se tornando uma ferramenta crescente e positiva na detecção das doenças bucais pediátricas na visão de pais e cuidadores. O objetivo desta revisão foi avaliar a utilização do instrumento P-CPQ na detecção das doenças bucais infanto-juvenis que interfere significativamente na qualidade de vida. Foram realizadas pesquisas bibliográficas no banco de dados PubMed Medline correlacionando as estratégias de buscas por palavras-chaves em artigos que utilizaram o P-CPQ como instrumento da avaliação da qualidade de vida em saúde bucal. Dos 107 artigos iniciais, foram excluídos 68, totalizando a busca final de 39 artigos que foram incluídos para a leitura completa do texto. As condições investigadas na qualidade de vida na visão de pais e cuidadores são: uso de aparelhos ortodônticos; maloclusões; cárie dentária; defeitos no esmalte dental; condições periodontais; pacientes especiais e tratamentos dentário sobre anestesia geral.O instrumento P-CPQ parecer ser um indicador sensível para mensurar a qualidade de vida em saúde bucal na visão de pais e cuidadores em diversas condições de saúde bucal. Descritores: Qualidade de Vida; Criança; Cuidadores. Referências The World Health Organization Quality of Life assessment (WHOQOL): position paper from the World Health Soc Sci Med. 1995; 41(10):1403-9. Ferreira MC, Goursand D, Bendo CB, Ramos-Jorge ML, Pordeus IA, Paiva SM. Agreement between adolescents' and their mothers' reports of oral health-related quality of life. Braz Oral Res. 2012;26(2):112-8. Jokovic A, Locker D, Stephens M, Kenny D, Tompson B, Guyatt G. Measuring parental perceptions of child oral health-related quality of life. J Public Health Dent. 2003;63(2):67-72. Al-Riyami IA, Thomson WM, Al-Harthi LS. Testing the Arabic short form versions of the Parental-Caregivers Perceptions Questionnaire and the Family Impact Scale in Oman. Saudi Dent J. 2016;28(1):31-5. Antunes LA, Luiz RR, Leao AT, Maia LC. Initial assessment of responsiveness of the P-CPQ (Brazilian Version) to describe the changes in quality of life after treatment for traumatic dental injury. Dent Traumatol. 2012;28(4):256-62. Kumar S, Kroon J, Lalloo R, Johnson NW. Validity and reliability of short forms of parental-caregiver perception and family impact scale in a Telugu speaking population of India. Health Qual Life Outcomes. 2016;14:34. Thomson WM, Foster Page LA, Gaynor WN, Malden PE. Short-form versions of the Parental-Caregivers Perceptions Questionnaire and the Family Impact Scale. Community Dent Oral Epidemiol. 2013;41(5):441-50. Dimberg L, Arvidsson C, Lennartsson B, Bondemark L, Arnrup K. Agreement between children and parents in rating oral health-related quality of life using the Swedish versions of the short-form Child Perceptions Questionnaire 11-14 and Parental Perceptions Questionnaire. Acta Odontol Scand. 2019;77(7):534-40. Albites U, Abanto J, Bonecker M, Paiva SM, Aguilar-Galvez D, Castillo JL. Parental-caregiver perceptions of child oral health-related quality of life (P-CPQ): Psychometric properties for the peruvian spanish language. Med Oral Patol Oral Cir Bucal. 2014;19(3):e220-40. Razanamihaja N, Boy-Lefevre ML, Jordan L, Tapiro L, Berdal A, de la Dure-Molla M et al. Parental-Caregivers Perceptions Questionnaire (P-CPQ): translation and evaluation of psychometric properties of the French version of the questionnaire. BMC Oral Health. 2018;18(1):211. Thomson WM, Foster Page LA, Malden PE, Gaynor WN, Nordin N. Comparison of the ECOHIS and short-form P-CPQ and FIS scales. Health Qual Life Outcomes. 2014;12:36. Barbosa Tde S, Gaviao MB. Validation of the Parental-Caregiver Perceptions Questionnaire: agreement between parental and child reports. J Public Health Dent. 2015;75(4):255-64. Goursand D, Paiva SM, Zarzar PM, Pordeus IA, Grochowski R, Allison PJ. Measuring parental-caregiver perceptions of child oral health-related quality of life: psychometric properties of the Brazilian version of the P-CPQ. Braz Dent J. 2009;20(2):169-74. Goursand D, Ferreira MC, Pordeus IA, Mingoti SA, Veiga RT, Paiva SM. Development of a short form of the Brazilian Parental-Caregiver Perceptions Questionnaire using exploratory and confirmatory factor analysis. Qual Life Res. 2013;22(2):393-402. Bendo CB, Paiva SM, Viegas CM, Vale MP, Varni JW. The PedsQL Oral Health Scale: feasibility, reliability and validity of the Brazilian Portuguese version. Health Qual Life Outcomes. 2012;10:42. Baghdadi ZD, Muhajarine N. Effects of dental rehabilitation under general anesthesia on children's oral-health-related quality of life: saudi arabian parents' perspectives. Dent J (Basel). 2014;3(1):1-13. Sonbol HN, Al-Bitar ZB, Shraideh AZ, Al-Omiri MK. Parental-caregiver perception of child oral-health related quality of life following zirconia crown placement and non-restoration of carious primary anterior teeth. Eur J Paediatr Dent. 2018;19(1):21-8. Abanto J, Carvalho TS, Bonecker M, Ortega AO, Ciamponi AL, Raggio DP. Parental reports of the oral health-related quality of life of children with cerebral palsy. BMC Oral Health. 2012;12:15. Abanto J, Ortega AO, Raggio DP, Bonecker M, Mendes FM, Ciamponi AL. Impact of oral diseases and disorders on oral-health-related quality of life of children with cerebral palsy. Spec Care Dentist. 2014;34(2):56-63. Khoun T, Malden PE, Turton BJ. Oral health-related quality of life in young Cambodian children: a validation study with a focus on children with cleft lip and/or palate. Int J Paediatr Dent. 2018;28(3):326-34. de Souza MC, Harrison M, Marshman Z. Oral health-related quality of life following dental treatment under general anaesthesia for early childhood caries - a UK-based study. Int J Paediatr Dent. 2017;27(1):30-6. Gaynor WN, Thomson WM. Changes in young children's OHRQoL after dental treatment under general anaesthesia. Int J Paediatr Dent. 2012;22(4):258-64. Ridell K, Borgstrom M, Lager E, Magnusson G, Brogardh-Roth S, Matsson L. Oral health-related quality-of-life in Swedish children before and after dental treatment under general anesthesia. Acta odontologica Scandinavica. 2015;73(1):1-7. Ridell K, Borgström M, Lager E, Magnusson G, Brogårdh-Roth S, Matsson L. Oral health-related quality-of-life in Swedish children before and after dental treatment under general anesthesia. Acta Odontol Scand. 2015;73(1):1-7. Abreu LG, Melgaço CA, Abreu MH, Lages EM, Paiva SM. Perception of parents and caregivers regarding the impact of malocclusion on adolescents' quality of life: a cross-sectional study. Dental Press J Orthod. 2016;21(6):74-81. Abreu LG, Melgaço CA, Abreu MH, Lages EM, Paiva SM. Agreement between adolescents and parents/caregivers in rating the impact of malocclusion on adolescents' quality of life. Angle Orthod. 2015;85(5):806-11. Benson P, O'Brien C, Marshman Z. Agreement between mothers and children with malocclusion in rating children's oral health-related quality of life. Am J Orthod Dentofacial Orthop. 2010;137(5):631-38. Abreu LG, Melgaço CA, Lages EM, Abreu MH, Paiva SM. Parents' and caregivers' perceptions of the quality of life of adolescents in the first 4 months of orthodontic treatment with a fixed appliance. J Orthod. 2014;41(3):181-87. Abreu LG, Melgaço CA, Abreu MH, Lages EM, Paiva SM. Parent-assessed quality of life among adolescents undergoing orthodontic treatment: a 12-month follow-up. Dental Press J Orthod. 2015;20(5):94-100. Jaeken K, Cadenas de Llano-Pérula M, Lemiere J, Verdonck A, Fieuws S, Willems G. Difference and relation between adolescents' and their parents or caregivers' reported oral health-related quality of life related to orthodontic treatment: a prospective cohort study. Health Qual Life Outcomes. 2019;17(1):40. Dantas-Neta NB, Moura LF, Cruz PF, Moura MS, Paiva SM, Martins CC, et al. Impact of molar-incisor hypomineralization on oral health-related quality of life in schoolchildren. Braz Oral Res. 2016;30(1):e11-7. Kotecha S, Turner PJ, Dietrich T, Dhopatkar A. The impact of tooth agenesis on oral health-related quality of life in children. J Orthod. 2013;40(2):122-29. Richa YR, Puranik MP. Oral health status and parental perception of child oral health related quality-of-life of children with autism in Bangalore, India. J Indian Soc Pedod Prev Dent. 2014;32(2):135-39. Santos M, Nascimento KS, Carazzato S, Barros AO, Mendes FM, Diniz MB. Efficacy of photobiomodulation therapy on masseter thickness and oral health-related quality of life in children with spastic cerebral palsy. Lasers Med Sci. 2017;32(6):1279-88. Pani SC, Mubaraki SA, Ahmed YT, Alturki RY, Almahfouz SF. Parental perceptions of the oral health-related quality of life of autistic children in Saudi Arabia. Spec Care Dentist. 2013;33(1):8-12. Jabarifar SE, Eshghi AR, Shabanian M, Ahmad S. Changes in Children's Oral Health Related Quality of Life Following Dental Treatment under General Anesthesia. Dent Res J (Isfahan). 2009;6(1):13-6. Chao Z, Gui Jin H, Cong Y. The effect of general anesthesia for ambulatory dental treatment on children in Chongqing, Southwest China. Paediatr Anaesth. 2017;27(1):98-105. Yawary R, Anthonappa RP, Ekambaram M, McGrath C, King NM. Changes in the oral health-related quality of life in children following comprehensive oral rehabilitation under general Int J Paediatr Dent. 2016;26(5):322-29.
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Rohayati, Yeti, e Diani Indah. "The Performance Of Employees Of The Bandung Civil Service Police Unit (Satpol PP) In The Implementation Of Illegal Advertising Control Insidentil And Permanent In 2020 Base On Administrative Law". Pena Justisia: Media Komunikasi dan Kajian Hukum 22, n. 3 (30 dicembre 2023): 374. http://dx.doi.org/10.31941/pj.v22i3.3403.

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Abstract (sommario):
<em>Along with the progress and development of the business world in Indonesia, especially the city of Bandung, the more advanced the establishment of billboards both isidentil and permanent. The installation of billboards is currently increasing in number and piling up without paying attention to the predetermined procedures for organizing billboards. So it is necessary to carry out supervision or control, this regulation is an obligation of the Bandung City Civil Service Police Unit (Satpol PP) as stated in the Bandung City Regional Regulation Number 02 of 2017 in Article 19 paragraph (1) Challenge the implementation of the regulation of the implementation of advertising. However, in reality, in the implementation of billboards, there are still many people who do not follow the installation procedure and not all violations of billboard organizers can be put in order by satpol PP Bandung City. The purpose of this study is to determine the performance of Satpol PP Bandung City employees in the Regulation of Billboards 2020 and to find out the supporting and inhibiting factors in the control of violations of billboard organizers in Satpol PP Bandung City. The method used in this study is a descriptive method with a qualitative type of research. This is done in the context of collecting primary data by means of observation, interviews and documentation. In addition, data collection was carried out using several book references with research themes to support previous data. The results showed that the performance of Satpol PP Bandung City Employees in the Implementation of Billboard Control has not been optimal, this is evidenced by the many violations of billboard organizers in the city of Bandung. This is due to the lack of personnel and facilities and infrastructure. The way to overcome this is that there must be additional personnel or employees of billboard control and the provision of adequate equipment to support the implementation of advertising control in the field</em><textarea id="BFI_DATA" style="width: 1px; height: 1px; display: none;"></textarea><textarea id="BFI_DATA" style="width: 1px; height: 1px; display: none;"></textarea><div class="TnITTtw-fp-collapsed-button" style="display: block;"> </div><textarea id="BFI_DATA" style="width: 1px; height: 1px; display: none;"></textarea><div class="TnITTtw-fp-collapsed-button" style="display: block;"> </div><textarea id="BFI_DATA" style="width: 1px; height: 1px; display: none;"></textarea><div id="WidgetFloaterPanels" class="LTRStyle" style="display: none; text-align: left; direction: ltr; visibility: hidden;"><div id="WidgetFloater" style="display: none;" onmouseover="Microsoft.Translator.OnMouseOverFloater()" onmouseout="Microsoft.Translator.OnMouseOutFloater()"><div id="WidgetLogoPanel"><span id="WidgetTranslateWithSpan"><span>TRANSLATE with </span><img id="FloaterLogo" alt="" /></span> <span id="WidgetCloseButton" title="Exit Translation" onclick="Microsoft.Translator.FloaterOnClose()">x</span></div><div id="LanguageMenuPanel"><div class="DDStyle_outer"><input id="LanguageMenu_svid" style="display: none;" onclick="this.select()" type="text" name="LanguageMenu_svid" value="en" /> <input id="LanguageMenu_textid" style="display: none;" onclick="this.select()" type="text" name="LanguageMenu_textid" /> <span id="__LanguageMenu_header" class="DDStyle" onclick="return LanguageMenu &amp;&amp; !LanguageMenu.Show('__LanguageMenu_popup', event);" onkeydown="return LanguageMenu &amp;&amp; !LanguageMenu.Show('__LanguageMenu_popup', event);">English</span><div style="position: relative; text-align: left; left: 0;"><div style="position: absolute; ;left: 0px;"><div id="__LanguageMenu_popup" class="DDStyle" style="display: none;"><table id="LanguageMenu" border="0"><tbody><tr><td><a onclick="return LanguageMenu.onclick('ar');" tabindex="-1" href="#ar">Arabic</a></td><td><a onclick="return LanguageMenu.onclick('he');" tabindex="-1" href="#he">Hebrew</a></td><td><a onclick="return LanguageMenu.onclick('pl');" tabindex="-1" href="#pl">Polish</a></td></tr><tr><td><a onclick="return LanguageMenu.onclick('bg');" tabindex="-1" href="#bg">Bulgarian</a></td><td><a onclick="return LanguageMenu.onclick('hi');" tabindex="-1" href="#hi">Hindi</a></td><td><a onclick="return LanguageMenu.onclick('pt');" tabindex="-1" href="#pt">Portuguese</a></td></tr><tr><td><a onclick="return LanguageMenu.onclick('ca');" tabindex="-1" href="#ca">Catalan</a></td><td><a onclick="return LanguageMenu.onclick('mww');" tabindex="-1" href="#mww">Hmong Daw</a></td><td><a onclick="return LanguageMenu.onclick('ro');" tabindex="-1" href="#ro">Romanian</a></td></tr><tr><td><a onclick="return LanguageMenu.onclick('zh-CHS');" tabindex="-1" href="#zh-CHS">Chinese Simplified</a></td><td><a onclick="return 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LanguageMenu.onclick('es');" tabindex="-1" href="#es">Spanish</a></td></tr><tr><td><a onclick="return LanguageMenu.onclick('nl');" tabindex="-1" href="#nl">Dutch</a></td><td><a onclick="return LanguageMenu.onclick('tlh');" tabindex="-1" href="#tlh">Klingon</a></td><td><a onclick="return LanguageMenu.onclick('sv');" tabindex="-1" href="#sv">Swedish</a></td></tr><tr><td><a onclick="return LanguageMenu.onclick('en');" tabindex="-1" href="#en">English</a></td><td><a onclick="return LanguageMenu.onclick('ko');" tabindex="-1" href="#ko">Korean</a></td><td><a onclick="return LanguageMenu.onclick('th');" tabindex="-1" href="#th">Thai</a></td></tr><tr><td><a onclick="return LanguageMenu.onclick('et');" tabindex="-1" href="#et">Estonian</a></td><td><a onclick="return LanguageMenu.onclick('lv');" tabindex="-1" href="#lv">Latvian</a></td><td><a onclick="return LanguageMenu.onclick('tr');" tabindex="-1" href="#tr">Turkish</a></td></tr><tr><td><a onclick="return LanguageMenu.onclick('fi');" tabindex="-1" href="#fi">Finnish</a></td><td><a onclick="return LanguageMenu.onclick('lt');" tabindex="-1" href="#lt">Lithuanian</a></td><td><a onclick="return LanguageMenu.onclick('uk');" tabindex="-1" href="#uk">Ukrainian</a></td></tr><tr><td><a onclick="return LanguageMenu.onclick('fr');" tabindex="-1" href="#fr">French</a></td><td><a onclick="return LanguageMenu.onclick('ms');" tabindex="-1" href="#ms">Malay</a></td><td><a onclick="return LanguageMenu.onclick('ur');" tabindex="-1" href="#ur">Urdu</a></td></tr><tr><td><a onclick="return LanguageMenu.onclick('de');" tabindex="-1" href="#de">German</a></td><td><a onclick="return LanguageMenu.onclick('mt');" tabindex="-1" href="#mt">Maltese</a></td><td><a onclick="return LanguageMenu.onclick('vi');" tabindex="-1" href="#vi">Vietnamese</a></td></tr><tr><td><a onclick="return LanguageMenu.onclick('el');" tabindex="-1" href="#el">Greek</a></td><td><a onclick="return LanguageMenu.onclick('no');" tabindex="-1" 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Daw","Hungarian","Indonesian","Italian","Japanese","Klingon","Korean","Latvian","Lithuanian","Malay","Maltese","Norwegian","Persian","Polish","Portuguese","Romanian","Russian","Slovak","Slovenian","Spanish","Swedish","Thai","Turkish","Ukrainian","Urdu","Vietnamese","Welsh"]; var LanguageMenu_callback=function(){ }; var LanguageMenu_popupid='__LanguageMenu_popup'; // ]]></script></div><div id="CTFLinksPanel"><span id="ExternalLinksPanel"><a id="HelpLink" title="Help" href="https://go.microsoft.com/?linkid=9722454" target="_blank"> <img id="HelpImg" alt="" /></a> <a id="EmbedLink" title="Get this widget for your own site" href="javascript:Microsoft.Translator.FloaterShowEmbed()"> <img id="EmbedImg" alt="" /></a> <a id="ShareLink" title="Share translated page with friends" href="javascript:Microsoft.Translator.FloaterShowSharePanel()"> <img id="ShareImg" alt="" /></a> </span></div><div id="FloaterProgressBar"> </div></div><div id="WidgetFloaterCollapsed" style="display: none;" 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translated from {1}" /> <input id="EmailBody" type="hidden" name="EmailBody" value="Translated: {0}%0d%0aOriginal: {1}%0d%0a%0d%0aAutomatic translation powered by Microsoft® Translator%0d%0ahttp://www.bing.com/translator?ref=MSTWidget" /> <input id="ShareHelpText" type="hidden" value="This link allows visitors to launch this page and automatically translate it to {0}." /></div><div id="FloaterEmbed" style="display: none;"><div id="EmbedTextDiv"><span id="EmbedTextSpan">EMBED THE SNIPPET BELOW IN YOUR SITE</span> <a id="EmbedHelpLink" title="Copy this code and place it into your HTML."> <img id="EmbedHelpImg" alt="" /></a></div><div id="EmbedTextboxDiv"><input id="EmbedSnippetTextBox" onclick="this.select()" type="text" name="EmbedSnippetTextBox" value="&lt;div id='MicrosoftTranslatorWidget' class='Dark' style='color:white;background-color:#555555'&gt;&lt;/div&gt;&lt;script type='text/javascript'&gt;setTimeout(function(){var s=document.createElement('script');s.type='text/javascript';s.charset='UTF-8';s.src=((location &amp;&amp; location.href &amp;&amp; location.href.indexOf('https') == 0)?'https://ssl.microsofttranslator.com':'http://www.microsofttranslator.com')+'/ajax/v3/WidgetV3.ashx?siteData=ueOIGRSKkd965FeEGM5JtQ**&amp;ctf=true&amp;ui=true&amp;settings=manual&amp;from=en';var p=document.getElementsByTagName('head')[0]||document.documentElement;p.insertBefore(s,p.firstChild); },0);&lt;/script&gt;" readonly="readonly" /></div><div id="EmbedNoticeDiv"><span id="EmbedNoticeSpan">Enable collaborative features and customize widget: <a href="http://www.bing.com/widget/translator" target="_blank">Bing Webmaster Portal</a></span></div><div id="EmbedFooterDiv"><span id="EmbedBackSpan"><a title="Back To Translation" href="javascript:Microsoft.Translator.FloaterOnEmbedBackClick()">Back</a></span></div></div><script type="text/javascript">// <![CDATA[ var intervalId = setInterval(function () { if (MtPopUpList) { LanguageMenu = new MtPopUpList(); var langMenu = document.getElementById(LanguageMenu_popupid); var origLangDiv = document.createElement("div"); origLangDiv.id = "OriginalLanguageDiv"; origLangDiv.innerHTML = "<span id='OriginalTextSpan'>ORIGINAL: </span><span id='OriginalLanguageSpan'></span>"; langMenu.appendChild(origLangDiv); LanguageMenu.Init('LanguageMenu', LanguageMenu_keys, LanguageMenu_values, LanguageMenu_callback, LanguageMenu_popupid); window["LanguageMenu"] = LanguageMenu; clearInterval(intervalId); } }, 1); // ]]></script></div><div class="TnITTtw-fp-collapsed-button" style="display: block;"> </div><div class="TnITTtw-mate-fp-bar" style="z-index: 2; width: 0px; height: 0px; opacity: 0; display: none;"><div class="TnITTtw-hide-fp-bar" style="display: none;"> </div><div class="TnITTtw-current-page-lang" style="display: none;">This page is in English</div><div class="TnITTtw-cta-button-layout" style="display: none;"><div class="TnITTtw-spinner"> </div><div class="TnITTtw-mw-button TnITTtw-fp-translate TnITTtw-high-cta" style="display: none;">Translate to Indonesian</div></div><div class="TnITTtw-change-language TnITTtw-select" style="display: none;" data-for-serial="3"> </div><div class="TnITTtw-stop-fp"> </div><div class="TnITTtw-toggle-iphone-settings" style="display: none;"> </div><div class="TnITTtw-ui_selector" style="display: none;"><div class="TnITTtw-options-arrow"> </div><div class="TnITTtw-options TnITTtw-opt-3 TnITTtw-standalone" style="display: none; z-index: 998;" data-serial="3"><div class="TnITTtw-dd-search"><input class="TnITTtw-dd-input" type="text" data-dir="to" data-width="NaN" /></div><div id="selVisibleScroll-3"><div id="selEntireScroll-3"><div class="TnITTtw-inner-options-layout"><ul class="TnITTtw-list"><li class="lang-af TnITTtw-option"><span id="lang-af" class="lang-af">Afrikaans</span></li><li class="lang-sq TnITTtw-option"><span id="lang-sq" class="lang-sq">Albanian</span></li><li class="lang-am TnITTtw-option"><span id="lang-am" class="lang-am">Amharic</span></li><li class="lang-ar TnITTtw-option"><span id="lang-ar" class="lang-ar">Arabic</span></li><li class="lang-hy TnITTtw-option"><span id="lang-hy" class="lang-hy">Armenian</span></li><li class="lang-az TnITTtw-option"><span id="lang-az" class="lang-az">Azerbaijani</span></li><li class="lang-bn TnITTtw-option"><span id="lang-bn" class="lang-bn">Bengali</span></li><li class="lang-bg TnITTtw-option"><span id="lang-bg" class="lang-bg">Bulgarian</span></li><li class="lang-ca TnITTtw-option"><span id="lang-ca" class="lang-ca">Catalan</span></li><li class="lang-hr TnITTtw-option"><span id="lang-hr" class="lang-hr">Croatian</span></li><li class="lang-cs TnITTtw-option"><span id="lang-cs" class="lang-cs">Czech</span></li><li class="lang-da TnITTtw-option"><span id="lang-da" class="lang-da">Danish</span></li><li class="lang-nl TnITTtw-option"><span id="lang-nl" class="lang-nl">Dutch</span></li><li class="lang-en TnITTtw-option"><span id="lang-en" class="lang-en">English</span></li><li class="lang-et TnITTtw-option"><span id="lang-et" class="lang-et">Estonian</span></li><li class="lang-fi TnITTtw-option"><span id="lang-fi" class="lang-fi">Finnish</span></li><li class="lang-fr TnITTtw-option"><span id="lang-fr" class="lang-fr">French</span></li><li class="lang-de TnITTtw-option"><span id="lang-de" class="lang-de">German</span></li><li class="lang-el TnITTtw-option"><span id="lang-el" class="lang-el">Greek</span></li><li class="lang-gu TnITTtw-option"><span id="lang-gu" class="lang-gu">Gujarati</span></li><li class="lang-ht TnITTtw-option"><span id="lang-ht" class="lang-ht">Haitian Creole</span></li><li class="lang-iw TnITTtw-option"><span id="lang-iw" class="lang-iw">Hebrew</span></li><li class="lang-hi TnITTtw-option"><span id="lang-hi" class="lang-hi">Hindi</span></li><li class="lang-hu TnITTtw-option"><span id="lang-hu" class="lang-hu">Hungarian</span></li><li class="lang-is TnITTtw-option"><span id="lang-is" class="lang-is">Icelandic</span></li><li class="lang-id TnITTtw-option_selected"><span id="lang-id" class="lang-id">Indonesian</span></li><li class="lang-it TnITTtw-option"><span id="lang-it" class="lang-it">Italian</span></li><li class="lang-ja TnITTtw-option"><span id="lang-ja" class="lang-ja">Japanese</span></li><li class="lang-kn TnITTtw-option"><span id="lang-kn" class="lang-kn">Kannada</span></li><li class="lang-kk TnITTtw-option"><span id="lang-kk" class="lang-kk">Kazakh</span></li><li class="lang-km TnITTtw-option"><span id="lang-km" class="lang-km">Khmer</span></li><li class="lang-ko TnITTtw-option"><span id="lang-ko" class="lang-ko">Korean</span></li><li class="lang-ku TnITTtw-option"><span id="lang-ku" class="lang-ku">Kurdish (Kurmanji)</span></li><li class="lang-lo TnITTtw-option"><span id="lang-lo" class="lang-lo">Lao</span></li><li class="lang-lv TnITTtw-option"><span id="lang-lv" class="lang-lv">Latvian</span></li><li class="lang-lt TnITTtw-option"><span id="lang-lt" class="lang-lt">Lithuanian</span></li><li class="lang-mg TnITTtw-option"><span id="lang-mg" class="lang-mg">Malagasy</span></li><li class="lang-ms TnITTtw-option"><span id="lang-ms" class="lang-ms">Malay</span></li><li class="lang-ml TnITTtw-option"><span id="lang-ml" class="lang-ml">Malayalam</span></li><li class="lang-mt TnITTtw-option"><span id="lang-mt" class="lang-mt">Maltese</span></li><li class="lang-mi TnITTtw-option"><span id="lang-mi" class="lang-mi">Maori</span></li><li class="lang-mr TnITTtw-option"><span id="lang-mr" class="lang-mr">Marathi</span></li><li class="lang-my TnITTtw-option"><span id="lang-my" class="lang-my">Myanmar (Burmese)</span></li><li class="lang-ne TnITTtw-option"><span id="lang-ne" class="lang-ne">Nepali</span></li><li class="lang-no TnITTtw-option"><span id="lang-no" class="lang-no">Norwegian</span></li><li class="lang-ps TnITTtw-option"><span id="lang-ps" class="lang-ps">Pashto</span></li><li class="lang-fa 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class="lang-sv TnITTtw-option"><span id="lang-sv" class="lang-sv">Swedish</span></li><li class="lang-ta TnITTtw-option"><span id="lang-ta" class="lang-ta">Tamil</span></li><li class="lang-te TnITTtw-option"><span id="lang-te" class="lang-te">Telugu</span></li><li class="lang-th TnITTtw-option"><span id="lang-th" class="lang-th">Thai</span></li><li class="lang-zh-TW TnITTtw-option"><span id="lang-zh-TW" class="lang-zh-TW">Traditional Chinese</span></li><li class="lang-tr TnITTtw-option"><span id="lang-tr" class="lang-tr">Turkish</span></li><li class="lang-uk TnITTtw-option"><span id="lang-uk" class="lang-uk">Ukrainian</span></li><li class="lang-ur TnITTtw-option"><span id="lang-ur" class="lang-ur">Urdu</span></li><li class="lang-vi TnITTtw-option"><span id="lang-vi" class="lang-vi">Vietnamese</span></li><li class="lang-cy TnITTtw-option"><span id="lang-cy" class="lang-cy">Welsh</span></li></ul></div></div><div id="sel-scrollbar-3"> </div></div></div></div><div class="TnITTtw-fp-options" style="display: none;"><input id="TnITTtw-always-translate" style="display: none;" type="checkbox" readonly="readonly" /><label class="TnITTtw-always-translate-label TnITTtw-not-pro" style="display: none;" for="TnITTtw-always-translate"><span class="TnITTtw-always-translate-inner-label" style="display: none;">Always translate English to Indonesian</span><span class="TnITTtw-pro-label" style="display: none;">PRO</span></label> <br style="display: none;" /> <input id="TnITTtw-never-translate-lang" style="display: none;" type="checkbox" /><label class="TnITTtw-never-translate-lang-label" style="display: none;" for="TnITTtw-never-translate-lang">Never translate English</label> <br style="display: none;" /> <input id="TnITTtw-never-translate-site" style="display: none;" type="checkbox" /><label class="TnITTtw-never-translate-site-label" style="display: none;" for="TnITTtw-never-translate-site">Never translate jurnal.unikal.ac.id</label></div></div>
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29

"A PRE-TRAINED MODEL BERT FOR MACHINE TRANSLATION FROM ENGLISH TO TELUGU". International Journal For Innovative Engineering and Management Research, 22 maggio 2022, 121–26. http://dx.doi.org/10.48047/ijiemr/v10/i08/19-1.

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Abstract (sommario):
At present, Neural Machine Translation (NMT) is an innovative and latest approach for machine translation, which is way better than statistical machine translation. It has drawn so much attention to it, that most of researchers are exploring new states of art approaches to get better translations. As the area of deep learning and transfer learning are also being implemented a lot, we are trying to fit a pre-trained model's unique way of tokenization into a NMT architecture so that the pre-trained weights gives better translation. In this work we study how BERT pre-trained models might be exploited for supervised NMT. We compare various ways to integrate pre-trained BERT model with NMT model and study the impact of the monolingual data that is used to train BERT which we are proposing to use in the translation of parallel corpus.
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30

Haribandi, Lakshmi. "The Ecology of Translation: A Case Study of Two Different Translations of Kanyasulkam in English". Translation Today 15, n. 1 (2021). http://dx.doi.org/10.46623/tt/2021.15.1.ar1.

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Abstract (sommario):
The interface between the translators and their ecological environment becomes vital in understanding the nature of the translation carried outand the final shape the target texts take. The translators’ subjectivity can only be understood in relation to their context of production, circulation,and reception. It is therefore important in any product-oriented research to study the ecological environment of the translators and its influence on their decision-making process and the translation strategy that they adopt. The present paper is an attempt in that direction. It presents a case study of two different translations of a Telugu classical text, Kanyasulkam, in English. The study reveals how the overall context of translation becomes a major agency in conditioning the work of the translators and how it accounts for the divergence between the two translations of the text selected. It also brings to the fore a very interesting technique of translating a classical text from India by a transnational translatorin an alien environment for the consumption of the distant other.
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31

Mr. Satheesh Chandra Reddy, Ms. Medhini TR, Ms. Shirisha L, Ms. Tanushree P e Ms. Supritha KS. "Educational Resource Material Translation System". International Journal of Advanced Research in Science, Communication and Technology, 4 maggio 2024, 339–44. http://dx.doi.org/10.48175/ijarsct-18057.

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Abstract (sommario):
The proposed "Educational Resource Material Translation System" represents a significant leap in bridging language barriers, particularly for individuals who lack proficiency in English. By leveraging advanced technologies such as Python, Flask, Recurrent Neural Network (RNN) and Large Language Models (LLM), the system offers a robust solution for translating educational content from English to prominent Indian regional languages like Kannada, Telugu and Hindi. Its user- friendly web interface, developed with Flask, ensures a seamless and intuitive translation experience for users of varying technical backgrounds. Beyond mere language translation, this project facilitates cross- cultural communication and enhances accessibility to educational resources, thereby empowering individuals and businesses alike. The utilization of Machine Learning further underscores the system's adaptability and potential for delivering precise and efficient translations. In essence, the "Language Translator" not only serves as a valuable tool for linguistic inclusivity but also fosters knowledge dissemination and cultural exchange on a broader scale.
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32

REDDY, MALLAMMA V., e DR M. HANUMANTHAPPA. "NLP CHALLENGES FOR MACHINE TRANSLATION FROM ENGLISH TO INDIAN LANGUAGES". International Journal of Computer Science and Informatics, luglio 2014, 19–24. http://dx.doi.org/10.47893/ijcsi.2014.1169.

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This Natural Langauge processing is carried particularly on English-Kannada/Telugu. Kannada is a language of India. The Kannada language has a classification of Dravidian, Southern, Tamil-Kannada, and Kannada. Regions Spoken: Kannada is also spoken in Karnataka, Andhra Pradesh, Tamil Nadu, and Maharashtra. Population: The total population of people who speak Kannada is 35,346,000, as of 1997. Alternate Name: Other names for Kannada are Kanarese, Canarese, Banglori, and Madrassi. Dialects: Some dialects of Kannada are Bijapur, Jeinu Kuruba, and Aine Kuruba. There are about 20 dialects and Badaga may be one. Kannada is the state language of Karnataka. About 9,000,000 people speak Kannada as a second language. The literacy rate for people who speak Kannada as a first language is about 60%, which is the same for those who speak Kannada as a second language (in India). Kannada was used in the Bible from 1831-2000. Statistical machine translation (SMT) is a machine translation paradigm where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual text corpora. The statistical approach contrasts with the rule-based approaches to machine translation as well as with example-based machine translation.
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33

-, Dr K. Uday kiran. "Kendra Sahitya Akademi Puraskarala Graheeta Acharya Yarlagadda Lakshmiprasad". International Journal For Multidisciplinary Research 5, n. 6 (9 novembre 2023). http://dx.doi.org/10.36948/ijfmr.2023.v05i06.8498.

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Prof. Yarlagadda Lakshmi Prasad who got two Kendra Sahitya Akademi awards for his great literature. he got his this two awards for the novels "Yayati" and "Draupadi" in Telugu. The first novel is translated from Marathi to Telugu and he made lot of changes during the translation. the second novel is from direct Telugu but he made changes of the character Draupadi which is different from kavitraya Mahabharat's Draupadi character. in this article I tried to found the changes made in the both novels which awarded by Kendra Sahitya Akademi.
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Khullar, Payal. "Are Ellipses important for Machine Translation?" Computational Linguistics, 5 agosto 2021, 1–10. http://dx.doi.org/10.1162/coli_a_00414.

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Abstract This article describes an experiment to evaluate the impact of different types of ellipses discussed in theoretical linguistics on Neural Machine Translation (NMT), using English to Hindi/Telugu as source and target languages. Evaluation with manual methods shows that most of the errors made by Google NMT are located in the clause containing the ellipsis, the frequency of such errors is slightly more in Telugu than Hindi, and the translation adequacy shows improvement when ellipses are reconstructed with their antecedents. These findings not only confirm the importance of ellipses and their resolution for MT, but also hint towards a possible correlation between the translation of discourse devices like ellipses with the morphological incongruity of the source and target. We also observe that not all ellipses are translated poorly and benefit from reconstruction, advocating for a disparate treatment of different ellipses in MT research.
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Suryakanthi, T., Dr S.V.A.V. e Dr T. "Translation of Pronominal Anaphora from English to Telugu Language". International Journal of Advanced Computer Science and Applications 4, n. 4 (2013). http://dx.doi.org/10.14569/ijacsa.2013.040413.

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36

Mahanty, Mohan, Bandi Vamsi e Dasari Madhavi. "A Corpus-Based Auto-encoder-and-Decoder Machine Translation Using Deep Neural Network for Translation from English to Telugu Language". SN Computer Science 4, n. 4 (26 aprile 2023). http://dx.doi.org/10.1007/s42979-023-01678-4.

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37

Rao, Rayala Upendar, Karthick Seshadri e Nagesh Bhattu Sristy. "Sentiment analysis of code-mixed Telugu-English data leveraging syllable and word embeddings". ACM Transactions on Asian and Low-Resource Language Information Processing, 4 settembre 2023. http://dx.doi.org/10.1145/3620670.

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Learning the inherent meaning of a word in Natural Language Processing (NLP) had motivated researchers to represent a word at various levels of abstraction namely, character-level, morpheme-level and, sub-word-level vector representations. Syllable-aware word embeddings could effectively handle agglutinative and fusion-based NLP tasks. However, research attempts on assessing the syllable-aware word embeddings on such extrinsic NLP tasks were scanty especially for low-resource languages in the context of code-mixing with English. A model to learn Syllable-aware word embeddings to extract semantics at fine-grained sub-units of a word was proposed in this paper and the representative ability of the embeddings was assessed through sentiment analysis of code-mixed Telugu-English review corpora. Multilingual societies and advancements in communication technologies had accounted for the prolific usage of mixed data, which rendered the state-of-art sentiment analysis models developed based on monolingual data ineffective. Social media users in the Indian sub-continent exhibited a tendency to mix English and their respective native language (using the phonetic form of English) in expressing their opinions or sentiments. A code-mixing scenario provided flexibility to borrow words from a foreign language, usage of shorthand notations, elongation of vowels, and usage of words without following syntactic/grammatical rules, which rendered the sentiment analysis of code-mixed data challenging to perform. Deep neural architectures like Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks were shown to be effective in solving several NLP tasks like sequence labeling, named entity recognition and, machine translation. In this paper, a framework to perform sentiment analysis on Code-Mixed Telugu-English (CMTE) review corpus was implemented. Both word embeddings (WE) and syllable-aware word embeddings (SAWE) were input to an unified deep neural network which contained a two-level Bidirectional LSTM/GRU Network and Softmax as the output layer. The proposed model leveraged the advantages of both WE and SAWE which enabled the proposed model to outperform existing state-of-the-art (SOTA) code-mixed sentiment analysis models on Telugu-English code-mixed dataset of International Institute of Information Technology-Hyderabad (IIIT-H) and a dataset curated by the authors. The improvement realized by the proposed model respectively on these datasets were [ 3% increase in F1-score, 2% increase in accuracy] and [7% increase in F1-score and 5% in accuracy], in comparison with the best performing SOTA model.
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M, Sangeetha, e Nimala K. "Unravelling Emotional Tones: A Hybrid Optimized Model for Sentiment Analysis in Tamil Regional Languages". Journal of Machine and Computing, 5 gennaio 2024, 114–26. http://dx.doi.org/10.53759/7669/jmc202404012.

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Abstract (sommario):
Review comments from digital platform such as Facebook, Twitter and YouTube used for identification of emotional tones from text. Nowadays, reviews are posted in different languages such as English, French, Chinese, and Indian regional languages such as Tamil, Telegu, and Hindi. Identification of emotional tones from text written in Indian regional language is challenging. During the translation of the regional language to the English language for sentiment analysis, lexical and pragmatic ambiguity are the major problem. The above problem arises due to dialects in language such as regional, standard, and social dialects. In this paper, dialect-based ambiguity problems solve through proposed Hybrid optimized deep learning transformer Models like M-BERT, M-Roberta, and M-XLM-Roberta for Tamil language dialects recognise and classified. The proposed algorithms provide better sentimental analysis after Hybrid optimization due to adaptation mechanisms, dynamic changes in the parameters and strategies in fine-tuning the search. The proposed Hybrid optimized algorithms perform better than existing algorithms such as SVM, Naïve Bayes, and LSTM with an accuracy of 95%.
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