Zobrazeno 1 - 10
of 318
pro vyhledávání: '"M. Anand Kumar"'
Publikováno v:
Engineering Science and Technology, an International Journal, Vol 22, Iss 2, Pp 637-645 (2019)
The objective of this paper is to build a handwritten character image database for Malayalam language script. Standard handwritten document image databases are an essential requirement for the development and objective evaluation of different handwri
Externí odkaz:
https://doaj.org/article/ae77de9777ce45bebc3178d561e7eece
Publikováno v:
In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval 2024), pages 193 to 199, Mexico City, Mexico. Association for Computational Linguistics
This paper summarizes Team SCaLAR's work on SemEval-2024 Task 5: Legal Argument Reasoning in Civil Procedure. To address this Binary Classification task, which was daunting due to the complexity of the Legal Texts involved, we propose a simple yet no
Externí odkaz:
http://arxiv.org/abs/2403.13107
Signed networks allow us to model conflicting relationships and interactions, such as friend/enemy and support/oppose. These signed interactions happen in real-time. Modeling such dynamics of signed networks is crucial to understanding the evolution
Externí odkaz:
http://arxiv.org/abs/2207.03408
Autor:
Dorairaj Prabhakaran, Kavita Singh, Shuchi Anand, Dimple Kondal, Nikhil Tandon, K M Venkat Narayan, Rajesh Khadgawat, Nikhil M Bhagwat, Kanika Aggarwal, Mohammed K Ali, Adeel Khan, Vivek Mathew, Ankush Desai, Prem Pais, Prashant Singh, Ram Jagannathan, Mala Dharmalingam, Abdul Jabbar, Sabahat Naz, Imran Naeem, Premlata K Varthakavi, Prerna Gupta, Rakesh Kumar Sahay, Nandini Menon, Manoj D Chadha, Roshan D’Britto, Vaibhavi Mungekar, Rohini Gajare, Abhishek Matkar, Charul Arora, Isha Verma, Yogesh Varge, K Neelaveni, A Prashanthi, Priyanka Parvatini, Ramachandra Reddy, Kedareshwar Narvencar, Vivek Naik, Prashant Ramesh Navelkar, Praciya Gaonkar, Rupali Naik, Santoshi Malkarnekar, Aparna Pai, Mansi Chopra, Samita Ambekar, Manish Sachdeva, Bhanvi Arora, Ganapati Bantwal, Vaggesh Aiyyar, Anantharaman Ramakrishnan, Sudha Suresh, AG Unnikrishnan, V Usha Menon, VP Praveen, Nisha Bhavani, Nithya Abraham, Akhila Ghosh, PV Nimmi, K Kamaljith, Vijay Vishwanathan, M Jai Ganesh, M Anand Kumar, K Anitha, Kavya, Muhammad Qamar Masood, Hassan Daudzai, Nida Zaidi
Publikováno v:
BMJ Open Diabetes Research & Care, Vol 12, Iss 4 (2024)
Introduction People with diabetes are at risk of developing chronic kidney disease. However, limited data are available to quantify their risk of kidney function decline in South Asia. This study evaluates the rate and predictors of kidney function d
Externí odkaz:
https://doaj.org/article/35454b63874c4b36a200fa2ddaa96799
Sarcasm is a form of communication in whichthe person states opposite of what he actually means. It is ambiguous in nature. In this paper, we propose using machine learning techniques with BERT and GloVe embeddings to detect sarcasm in tweets. The da
Externí odkaz:
http://arxiv.org/abs/2006.11512
Behavioral cues play a significant part in human communication and cognitive perception. In most professional domains, employee recruitment policies are framed such that both professional skills and personality traits are adequately assessed. Hiring
Externí odkaz:
http://arxiv.org/abs/2006.07909
Deep neural networks (DNNs) have witnessed as a powerful approach in this year by solving long-standing Artificial intelligence (AI) supervised and unsupervised tasks exists in natural language processing, speech processing, computer vision and other
Externí odkaz:
http://arxiv.org/abs/1812.03519
HOTTEST: Hate and Offensive content identification in Tamil using Transformers and Enhanced STemming
Autor:
Rajalakshmi, Ratnavel, Selvaraj, Srivarshan, R., Faerie Mattins, Vasudevan, Pavitra, M., Anand Kumar
Publikováno v:
In Computer Speech & Language March 2023 78
Health related social media mining is a valuable apparatus for the early recognition of the diverse antagonistic medicinal conditions. Mostly, the existing methods are based on machine learning with knowledge-based learning. This working note present
Externí odkaz:
http://arxiv.org/abs/1710.08396