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577 results on '"Dash, Satya"'

18. Universal Dependency Treebank for Odia Language

Author

Parida, Shantipriya, Sahoo, Kalyanamalini, Ojha, Atul Kr., Sahoo, Saraswati, Dash, Satya Ranjan, and Dash, Bijayalaxmi

Subjects
Computer Science - Computation and Language
Abstract

This paper presents the first publicly available treebank of Odia, a morphologically rich low resource Indian language. The treebank contains approx. 1082 tokens (100 sentences) in Odia selected from "Samantar", the largest available parallel corpora collection for Indic languages. All the selected sentences are manually annotated following the ``Universal Dependency (UD)" guidelines. The morphological analysis of the Odia treebank was performed using machine learning techniques. The Odia annotated treebank will enrich the Odia language resource and will help in building language technology tools for cross-lingual learning and typological research. We also build a preliminary Odia parser using a machine learning approach. The accuracy of the parser is 86.6% Tokenization, 64.1% UPOS, 63.78% XPOS, 42.04% UAS and 21.34% LAS. Finally, the paper briefly discusses the linguistic analysis of the Odia UD treebank., Comment: To be appear in 6th Workshop on Indian Language Data: Resources and Evaluation (WILDRE-6) @ LREC 2022

Published
2022

19. Hausa Visual Genome: A Dataset for Multi-Modal English to Hausa Machine Translation

Author

Abdulmumin, Idris, Dash, Satya Ranjan, Dawud, Musa Abdullahi, Parida, Shantipriya, Muhammad, Shamsuddeen Hassan, Ahmad, Ibrahim Sa'id, Panda, Subhadarshi, Bojar, Ondřej, Galadanci, Bashir Shehu, and Bello, Bello Shehu

Subjects
Computer Science - Computation and Language, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Multi-modal Machine Translation (MMT) enables the use of visual information to enhance the quality of translations. The visual information can serve as a valuable piece of context information to decrease the ambiguity of input sentences. Despite the increasing popularity of such a technique, good and sizeable datasets are scarce, limiting the full extent of their potential. Hausa, a Chadic language, is a member of the Afro-Asiatic language family. It is estimated that about 100 to 150 million people speak the language, with more than 80 million indigenous speakers. This is more than any of the other Chadic languages. Despite a large number of speakers, the Hausa language is considered low-resource in natural language processing (NLP). This is due to the absence of sufficient resources to implement most NLP tasks. While some datasets exist, they are either scarce, machine-generated, or in the religious domain. Therefore, there is a need to create training and evaluation data for implementing machine learning tasks and bridging the research gap in the language. This work presents the Hausa Visual Genome (HaVG), a dataset that contains the description of an image or a section within the image in Hausa and its equivalent in English. To prepare the dataset, we started by translating the English description of the images in the Hindi Visual Genome (HVG) into Hausa automatically. Afterward, the synthetic Hausa data was carefully post-edited considering the respective images. The dataset comprises 32,923 images and their descriptions that are divided into training, development, test, and challenge test set. The Hausa Visual Genome is the first dataset of its kind and can be used for Hausa-English machine translation, multi-modal research, and image description, among various other natural language processing and generation tasks., Comment: Accepted at Language Resources and Evaluation Conference 2022 (LREC2022)

Published
2022

24. Machine Learning Approach to Analyze Breast Cancer

Author

Dash, Satya Ranjan, Roy, Saurav, Mohanty, Jnyana Ranjan, Meedeniya, Dulani, Mishra, Manoj Ranjan, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Bhateja, Vikrant, editor, Yang, Xin-She, editor, Chun-Wei Lin, Jerry, editor, and Das, Ranjita, editor

Published
2023
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25. Machine Learning Models to Predict Girl-Child Dropout from Schools in India

Author

Samantaray, Annapurna, Dash, Satya Ranjan, Sharma, Aditi, Parida, Shantipriya, Kacprzyk, Janusz, Series Editor, Pal, Nikhil R., Advisory Editor, Bello Perez, Rafael, Advisory Editor, Corchado, Emilio S., Advisory Editor, Hagras, Hani, Advisory Editor, Kóczy, László T., Advisory Editor, Kreinovich, Vladik, Advisory Editor, Lin, Chin-Teng, Advisory Editor, Lu, Jie, Advisory Editor, Melin, Patricia, Advisory Editor, Nedjah, Nadia, Advisory Editor, Nguyen, Ngoc Thanh, Advisory Editor, Wang, Jun, Advisory Editor, Dutta, Paramartha, editor, Bhattacharya, Abhishek, editor, Dutta, Soumi, editor, and Lai, Wen-Cheng, editor

Published
2023
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28. Hindi Visual Genome: A Dataset for Multimodal English-to-Hindi Machine Translation

Author

Parida, Shantipriya, Bojar, Ondřej, and Dash, Satya Ranjan

Subjects
Computer Science - Computation and Language
Abstract

Visual Genome is a dataset connecting structured image information with English language. We present ``Hindi Visual Genome'', a multimodal dataset consisting of text and images suitable for English-Hindi multimodal machine translation task and multimodal research. We have selected short English segments (captions) from Visual Genome along with associated images and automatically translated them to Hindi with manual post-editing which took the associated images into account. We prepared a set of 31525 segments, accompanied by a challenge test set of 1400 segments. This challenge test set was created by searching for (particularly) ambiguous English words based on the embedding similarity and manually selecting those where the image helps to resolve the ambiguity. Our dataset is the first for multimodal English-Hindi machine translation, freely available for non-commercial research purposes. Our Hindi version of Visual Genome also allows to create Hindi image labelers or other practical tools. Hindi Visual Genome also serves in Workshop on Asian Translation (WAT) 2019 Multi-Modal Translation Task., Comment: 6 pages, 3 figures

Published
2019

34. BertOdia: BERT Pre-training for Low Resource Odia Language

Author

Parida, Shantipriya, Biswal, Satya Prakash, Nayak, Biranchi Narayan, Fabien, Maël, Villatoro-Tello, Esaú, Motlicek, Petr, Dash, Satya Ranjan, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Dehuri, Satchidananda, editor, Prasad Mishra, Bhabani Shankar, editor, Mallick, Pradeep Kumar, editor, and Cho, Sung-Bae, editor

Published
2022
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38. Summarizing Bengali Text: An Extractive Approach

Author

Dash, Satya Ranjan, Guha, Pubali, Mallick, Debasish Kumar, Parida, Shantipriya, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Satapathy, Suresh Chandra, editor, Peer, Peter, editor, Tang, Jinshan, editor, Bhateja, Vikrant, editor, and Ghosh, Anumoy, editor

Published
2022
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46. Glucagon‐like peptide‐1 receptor agonists and sodium‐glucose cotransporter‐2 inhibitors for the treatment of diabetes mellitus in liver transplant recipients.

Author

Zheng, Katina, Azhie, Amirhossein, You, Xiaoting, Naghibzadeh, Maryam, Tan, Eunice, Naimimohasses, Sara, Sridhar, Vikas S., Gupta, Sarang, Chen, Shiyi, Dash, Satya, Tsien, Cynthia, Selzner, Nazia, Lilly, Leslie, Jaeckel, Elmar, Woo, Minna, Singh, Sunita, Cherney, David, and Bhat, Mamatha

Subjects
LINEAR statistical models, SODIUM-glucose cotransporter 2 inhibitors, SODIUM-glucose cotransporters, ALANINE aminotransferase, BODY mass index, BODY weight
Abstract

Aim: To investigate the efficacy and safety of glucagon‐like peptide‐1 receptor agonists (GLP‐1RAs) and sodium‐glucose cotransporter‐2 (SGLT2) inhibitors in liver transplant (LT) recipients with diabetes. Methods: A single‐centre, retrospective analysis of prospectively collected data from an LT recipient database (1990–2023) was conducted. We included adults with pre‐existing diabetes and post‐transplant diabetes, newly started on GLP‐1RAs and/or SGLT2 inhibitors after LT. Metabolic and biochemical parameters and outcomes were collected for up to 12 months after starting medications and were compared to those in patients receiving dipeptidyl peptidase‐4 (DPP‐4) inhibitors. Statistical analysis included descriptive statistics and linear mixed models. Results: We included participants on GLP‐1RAs (n = 46), SGLT2 inhibitors (n = 87), combination therapy (n = 12), and a DPP‐4 inhibitor comparator (n = 217). Both GLP‐1RAs and combination therapy decreased mean glycated haemoglobin (HbA1c) levels, and combination therapy remained significant when adjusted for DPP‐4 inhibitor treatment (−3.5%, 95% CI [−6.1, −0.95]; p = 0.0089) at 12 months. All three groups had significant decreases in mean weight and body mass index, but these remained significant in the GLP‐1RA (−5.2 kg, 95% CI [−8.7, −1.7], p = 0.0039 and 1.99 kg/m2, 95% CI [−3.4, −0.6], p = 0.0048) and combination therapy groups (−5.4 kg, 95% CI [−10.5, −0.36], p = 0.04 and −3.4 kg/m2, 95% CI [−5.5, −1.3], p = 0.0015) when adjusted for DPP‐4 inhibitor treatment at 12 months. Alanine aminotransferase levels decreased with GLP‐1RA and combination therapy. There were two (1.4%) cases of graft rejection. Conclusion: We found that GLP‐1RAs, SGLT2 inhibitors, and their combination, led to significant weight loss in LT recipients with diabetes. Combination therapy, in particular, lowered HbA1c and alanine aminotransferase levels compared to DPP‐4 inhibitors. Further studies are needed to assess long‐term safety and efficacy. [ABSTRACT FROM AUTHOR]

Published
2024
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47. German News Article Classification: A Multichannel CNN Approach

Author

Parida, Shantipriya, Motlicek, Petr, Dash, Satya Ranjan, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zhang, Junjie James, Series Editor, Bhoi, Akash Kumar, editor, Mallick, Pradeep Kumar, editor, Balas, Valentina Emilia, editor, and Mishra, Bhabani Shankar Prasad, editor

Published
2021
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50. Second-Generation Rearview Mirror

Author

Kabi, Kunal, Panigrahi, Jyoti Prakash, Mishra, Bhabani Shankar Prasad, Rath, Manas Kumar, Dash, Satya Ranjan, Howlett, Robert J., Series Editor, Jain, Lakhmi C., Series Editor, Satapathy, Suresh Chandra, editor, Bhateja, Vikrant, editor, Mohanty, J. R., editor, and Udgata, Siba K., editor

Published
2020
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