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Syed Tanzeel Rabani
Researcher at Baba Ghulam Shah Badshah University
Publications - 15
Citations - 315
Syed Tanzeel Rabani is an academic researcher from Baba Ghulam Shah Badshah University. The author has contributed to research in topics: Computer science & Social media. The author has an hindex of 3, co-authored 10 publications receiving 104 citations. Previous affiliations of Syed Tanzeel Rabani include St. Joseph's College, Bangalore.
Papers
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Journal ArticleDOI
Machine learning based approaches for detecting COVID-19 using clinical text data.
Akib Mohi Ud Din Khanday,Syed Tanzeel Rabani,Qamar Rayees Khan,Nusrat Rouf,Masarat Mohi Ud Din +4 more
TL;DR: This paper classified textual clinical reports into four classes by using classical and ensemble machine learning algorithms, and Logistic regression and Multinomial Naïve Bayes showed better results than other ML algorithms by having 96.2% testing accuracy.
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Identifying propaganda from online social networks during COVID-19 using machine learning techniques.
TL;DR: The binary classification of tweets is being performed with the help of machine learning algorithms and deep learning algorithms for better results, decision tree gives better results among all other algorithms.
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Detection of Suicidal Ideation on Twitter using Machine Learning & Ensemble Approaches
TL;DR: This paper presents a methodology and experimentation using social media as a tool to analyse the suicidal ideation in a better way, thus helping in preventing the chances of being the victim of this unfortunate mental disorder.
SVMBPI: Support Vector Machine-Based Propaganda Identification
TL;DR: This paper will act as a base for researchers to use various other machine and deep learning techniques in differentiating the propagandist text from non-propagandistText using supervised machine learning algorithm.
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Detecting twitter hate speech in COVID-19 era using machine learning and ensemble learning techniques
TL;DR: In this article , a study was conducted to detect hate speech using machine learning and ensemble learning techniques during the COVID-19 pandemic, where tweets were manually annotated into two categories based on different factors.