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p-Index From 2021 - 2026
0.444
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This Author published in this journals
All Journal IAES International Journal of Robotics and Automation (IJRA) Indonesian Journal of Electrical Engineering and Computer Science
Monisha Awasthi
Uttaranchal University
Automotive Engineering Electrical & Electronics Engineering

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Sentiment analysis of Twitter data regarding the agnipath scheme of the defense forces Vijaylakshmi Sajwan; Monisha Awasthi; Ankur Goel; Priyank Sharma
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i3.pp1643-1650

Abstract

Due to the popularity of social media today, people frequently share such criticism on Facebook, Twitter, Instagram, and other platforms. Therefore needs to know how your input from users of social media is generated in order to ascertain the public reaction to the policy that has been enacted. However, because of the comments, it is challenging to tell how many people have responded positive or negative. The objective of sentiment analysis of tweets is to provide insight into people’s attitudes and perceptions regarding an event. This study illustrates the role of Twitter in the announcement of a new army vacancy through the “agnipath scheme” dubbed “agniveer”. The result of this study can be used by the defense forces and government for decision making or policies related to the agnipath scheme. The study studied 4,000 English-language Twitter posts from July 3, 2022 to July 9, 2022. Manual text analysis revealed seven basic groups of tweet sentiments. The tweets’ positive, negative, and neutral emotions were shown using orange data mining software, a powerful machine learning, data mining, and data visualization toolset. Result shows that agnipath scheme is mostly accepted by the people.
A comparative look at how emerging technologies evolve to managing otitis media Divya Pandey; Monisha Awasthi; Dharmendra Kumar; Deepak Kumar Pant; Ankur Goel
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 1: March 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i1.pp162-170

Abstract

Otitis media (OM) is an epidemic of middle ear infection in tens of millions of patients across the globe, most vulnerable of whom are children, with hearing loss and other negative consequences unless treated. Conventional diagnosis and treatment are marred by failure to diagnose, service shortage, and delayed diagnosis. This present paper is directed towards a comparative outlook of the newly emerging technologies, such as artificial intelligence (AI), machine learning, telemedicine, and wearable biosensors, that are revolutionizing the management of OM. We emphasize the way such devices enhance diagnostic accuracy, facilitate remote and real-time monitoring, and provide tailored treatment schemes. Our approach is more sophisticated compared to the currently available state-of-the-art methods reported in the literature based on real-time telemedicine systems, multimodal data fusion, and interpretable AI. Privacy issues of information, model generalizability issues, and technological adoption barriers are also discussed. The results also substantiate that adoption of these advanced devices can effectively reduce OM's burden globally and improve patient outcomes.
Co-Authors Ankur Goel Ankur Goel Deepak Kumar Pant Dharmendra Kumar Divya Pandey Priyank Sharma Vijaylakshmi Sajwan