Browsing by Author "Rakesh, R."

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    A comprehensive analysis of water quality of open wells at Alevuru and Badagabettu-76 village of Udupi Taluk, Karnataka, India
    (Institute of Physics, 2024) Sharma, H.S.; Rakesh, R.; Shrihari, S.
    The availability of pure water is one of the most essential requirements for all living organisms. In rural areas of Udupi, Karnataka, India, well water serves as the primary source of water for residents. Hence the objectives of the study were to find the physical and chemical characteristics of the well water sources in Udupi taluk; and to assess the suitability of the well water sources in Udupi taluk for drinking purposes by determining water quality index (WQI). Water samples (n=24) were collected from open wells from Alevuru and Badagabettu-76 villages of Udupi taluk during October 2023. Water quality parameters analyzed were pH, total dissolved solids, electrical conductivity, turbidity, total alkalinity, total hardness, dissolved oxygen, nitrate, chloride, sulphate, chemical oxygen demand and iron. The WQI revealed that the well water in majority of the sites was fit for drinking. All parameters were within the permissible limits in majority of the wells except for iron and pH. © Published under licence by IOP Publishing Ltd.
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    Bot and gender identification from twitter notebook for PAN at CLEF 2019
    (2019) Rakesh, R.; Vishwakarma, Y.; Sai, Gopal, A.S.; Anand, Kumar, M.
    The popularity of social media raises a concern about the quality of content over its platforms. The quality of data is important, especially for fair and considerable predictive analysis. If the quality of data is less, it may result in the prediction of wrong circumstances of an event. This causes misleading trending problems and more importantly, the sensitive stock price may fluctuate. The contents available on social media can be corrupted and overflowed by bots. There are a variety of bots available such as Spam Bots, Influence Bots, etc. Our target is to identify such bots on Twitter. Twitter data is mostly used by data analysts for applications related to scientific predictions or opinion analysis. This working note is capitalized on earlier approaches and Machine Learning (ML) approaches used to classify between a bot and human and find the gender further for interesting studies in crime detection etc. By sharing many attributes for user profiles, we have identified the pattern to find out that the given user is a bot or human based on the tweets posted. � 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). CLEF 2019, 9-12 September 2019, Lugano, Switzerland.