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Madichetty Sreenivasulu
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2020 – today
- 2023
- [j9]Madichetty Sreenivasulu
, Sridevi M, Sreekanth Madisetty:
A RoBERTa based model for identifying the multi-modal informative tweets during disaster. Multim. Tools Appl. 82(24): 37615-37633 (2023) - 2021
- [j8]Madichetty Sreenivasulu
, M. Sridevi:
A novel method for identifying the damage assessment tweets during disaster. Future Gener. Comput. Syst. 116: 440-454 (2021) - [j7]Madichetty Sreenivasulu
, Sridevi Muthukumarasamy, P. Jayadev:
Multi-modal classification of Twitter data during disasters for humanitarian response. J. Ambient Intell. Humaniz. Comput. 12(11): 10223-10237 (2021) - [j6]Madichetty Sreenivasulu
, M. Sridevi:
A stacked convolutional neural network for detecting the resource tweets during a disaster. Multim. Tools Appl. 80(3): 3927-3949 (2021) - [j5]Madichetty Sreenivasulu
, M. Sridevi:
A Neural-Based Approach for Detecting the Situational Information From Twitter During Disaster. IEEE Trans. Comput. Soc. Syst. 8(4): 870-880 (2021) - 2020
- [j4]Madichetty Sreenivasulu, M. Sridevi:
Comparative study of statistical features to detect the target event during disaster. Big Data Min. Anal. 3(2): 121-130 (2020) - [j3]Madichetty Sreenivasulu
, M. Sridevi:
Classifying informative and non-informative tweets from the twitter by adapting image features during disaster. Multim. Tools Appl. 79(39-40): 28901-28923 (2020) - [j2]Madichetty Sreenivasulu
, M. Sridevi:
Identification of medical resource tweets using Majority Voting-based Ensemble during disaster. Soc. Netw. Anal. Min. 10(1): 66 (2020)
2010 – 2019
- 2019
- [j1]Madichetty Sreenivasulu
, M. Sridevi
:
Disaster damage assessment from the tweets using the combination of statistical features and informative words. Soc. Netw. Anal. Min. 9(1): 42:1-42:11 (2019) - [c2]Madichetty Sreenivasulu
, M. Sridevi
:
Detecting Informative Tweets during Disaster using Deep Neural Networks. COMSNETS 2019: 709-713 - 2017
- [c1]Madichetty Sreenivasulu, M. Sridevi
:
Mining Informative Words from the Tweets for Detecting the Resources During Disaster. MIKE 2017: 348-358

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