Browsing by Author "Shekhar, A."

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    A Bag-of-Phonetic-Codes Modelfor Cyber-Bullying Detection in Twitter
    (Institute of Electrical and Electronics Engineers Inc., 2018) Shekhar, A.; Venkatesan, M.
    Social networking sites such as Twitter, Facebook, MySpace, Instagram are emerging as a strong medium of communication these days. These have become a part and parcel of daily life. People can express their thoughts and activities among their social circle with brings them closer to their community. However this freedom of expression has its drawbacks. Sometimes people show their aggression on Social Media which in turn hurts the sentiments of the targeted victims. Certain forms of cyber-bullying are sexual, racial and physical disability based. Hence a proper surveillance is necessary to tackle such situations. Twitter as a micro-blogging site sees cyber abuse on a daily basis. However, tweets are raw texts; containing a lot of misspelled words and censored words. This paper proposes a novel method to detect cyber-bullying, a Bag-of-Phonetic-Codes model. Using pronunciation of words as features can rectify misspelled words and can identify censored words. Correctly identifying duplicate words can lead to smaller vocabulary of words, thereby reducing the feature space. The inspiration for this proposed work is drawn from the famous Bag-of-Words model for extracting textual features. Phonetic code generation has been done using the Soundex Algorithm. Besides the proposed model, experiments were carried out with both supervised and unsupervised machine learning approaches on multiple datasets to understand the approaches and challenges in the domain of cyber-bullying detection. © 2018 IEEE.
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    A Bag-of-Phonetic-Codes Modelfor Cyber-Bullying Detection in Twitter
    (2018) Shekhar, A.; Venkatesan, M.
    Social networking sites such as Twitter, Facebook, MySpace, Instagram are emerging as a strong medium of communication these days. These have become a part and parcel of daily life. People can express their thoughts and activities among their social circle with brings them closer to their community. However this freedom of expression has its drawbacks. Sometimes people show their aggression on Social Media which in turn hurts the sentiments of the targeted victims. Certain forms of cyber-bullying are sexual, racial and physical disability based. Hence a proper surveillance is necessary to tackle such situations. Twitter as a micro-blogging site sees cyber abuse on a daily basis. However, tweets are raw texts; containing a lot of misspelled words and censored words. This paper proposes a novel method to detect cyber-bullying, a Bag-of-Phonetic-Codes model. Using pronunciation of words as features can rectify misspelled words and can identify censored words. Correctly identifying duplicate words can lead to smaller vocabulary of words, thereby reducing the feature space. The inspiration for this proposed work is drawn from the famous Bag-of-Words model for extracting textual features. Phonetic code generation has been done using the Soundex Algorithm. Besides the proposed model, experiments were carried out with both supervised and unsupervised machine learning approaches on multiple datasets to understand the approaches and challenges in the domain of cyber-bullying detection. � 2018 IEEE.
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    Tropospheric NO2 and O3 Response to COVID-19 Lockdown Restrictions at the National and Urban Scales in Germany
    (John Wiley and Sons Inc, 2021) Balamurugan, V.; Chen, J.; Qu, Z.; Bi, X.; Gensheimer, J.; Shekhar, A.; Bhattacharjee, S.; Keutsch, F.N.
    This study estimates the influence of anthropogenic emission reductions on nitrogen dioxide ((Formula presented.)) and ozone ((Formula presented.)) concentration changes in Germany during the COVID-19 pandemic period using in-situ surface and Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI) satellite column measurements and GEOS-Chem model simulations. We show that reductions in anthropogenic emissions in eight German metropolitan areas reduced mean in-situ (& column) (Formula presented.) concentrations by 23 (Formula presented.) (& 16 (Formula presented.)) between March 21 and June 30, 2020 after accounting for meteorology, whereas the corresponding mean in-situ (Formula presented.) concentration increased by 4 (Formula presented.) between March 21 and May 31, 2020, and decreased by 3 (Formula presented.) in June 2020, compared to 2019. In the winter and spring, the degree of (Formula presented.) saturation of ozone production is stronger than in the summer. This implies that future reductions in (Formula presented.) emissions in these metropolitan areas are likely to increase ozone pollution during winter and spring if appropriate mitigation measures are not implemented. TROPOMI (Formula presented.) concentrations decreased nationwide during the stricter lockdown period after accounting for meteorology with the exception of North-West Germany which can be attributed to enhanced (Formula presented.) emissions from agricultural soils. © 2021. The Authors.