Browsing by Author "Cowlessur, S.K."

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    Alphabetic cryptography: Securing communication over cloud platform
    (2019) Cowlessur, S.K.; Annappa, B.; Manoj, Kumar, M.V.; Thomas, L.; Sneha, M.M.; Puneetha, B.H.
    This paper introduces alphabetic cryptography inspired by bidirectional DNA encryption algorithm. Alphabetic cryptography first offers higher randomization and secure communication over the cloud computing platform, and second supports the exchange of complete UNICODE character set. Alphabetic cryptography has been implemented on mobile and desktop platforms. Through experimental studies, it has been observed that randomness of encryption increases exponentially with the increase in the number of alphabets of the alphabetic encryption scheme. � Springer Nature Singapore Pte Ltd. 2019.
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    Alphabetic cryptography: Securing communication over cloud platform
    (Springer Verlag service@springer.de, 2019) Cowlessur, S.K.; Annappa, B.; Manoj Kumar, M.V.; Thomas, L.; Sneha, M.M.; Puneetha, B.H.
    This paper introduces alphabetic cryptography inspired by bidirectional DNA encryption algorithm. Alphabetic cryptography first offers higher randomization and secure communication over the cloud computing platform, and second supports the exchange of complete UNICODE character set. Alphabetic cryptography has been implemented on mobile and desktop platforms. Through experimental studies, it has been observed that randomness of encryption increases exponentially with the increase in the number of alphabets of the alphabetic encryption scheme. © Springer Nature Singapore Pte Ltd. 2019.
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    Measuring the influence of moods on stock market using Twitter analysis
    (2019) Cowlessur, S.K.; Annappa, B.; Sree, B.K.; Gupta, S.; Velaga, C.
    It is a well-known fact that sentiments play a vital role and is an incredibly influential tool in several aspects of human life. Sentiments also drive proactive business solutions. Studies have shown that the more appropriate data is gathered and analyzed at the right time, the higher the success of sentiment analysis. This paper analyses the correlation between the public mood and the variation in stock prices towards companies in different domains. For each tweet, scores are assigned to eight predefined moods namely �Joy�, �Sadness�, �Fear�, �Anger�, �Trust�, �Disgust�, �Surprise� and �Anticipation�. A regression model is applied to the mood scores and the stock prices dataset to obtain the R-squared score, which is a metric used to evaluate the model. The paper aims to find the moods that best reflect the stock values of the respective companies. From the results, it is observed that there is a definite correlation between public mood and stock market. � Springer Nature Singapore Pte Ltd. 2019.
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    Measuring the influence of moods on stock market using Twitter analysis
    (Springer Verlag service@springer.de, 2019) Cowlessur, S.K.; Annappa, B.; Sree, B.K.; Gupta, S.; Velaga, C.
    It is a well-known fact that sentiments play a vital role and is an incredibly influential tool in several aspects of human life. Sentiments also drive proactive business solutions. Studies have shown that the more appropriate data is gathered and analyzed at the right time, the higher the success of sentiment analysis. This paper analyses the correlation between the public mood and the variation in stock prices towards companies in different domains. For each tweet, scores are assigned to eight predefined moods namely “Joy†, “Sadness†, “Fear†, “Anger†, “Trust†, “Disgust†, “Surprise†and “Anticipation†. A regression model is applied to the mood scores and the stock prices dataset to obtain the R-squared score, which is a metric used to evaluate the model. The paper aims to find the moods that best reflect the stock values of the respective companies. From the results, it is observed that there is a definite correlation between public mood and stock market. © Springer Nature Singapore Pte Ltd. 2019.
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    PIONEER: An Interest-Aware POI Recommendation Engine
    (Instituto Politecnico Nacional, 2024) Cowlessur, S.K.; Annappa, B.; Pati, B.
    Over the past decades, tourism has become a key economic industry for many countries. In today’s global economy, it is an essential source of employment and revenue. Tourism as a leisure activity is a very popular form of recreation which involves the movement of people to foreign cities to visit new and unfamiliar places of interest (POIs). The task of recommending personalised tours for tourists is very demanding and time-consuming. The recommended tours must satisfy the tourist’s interests and must at the same time be completed within a limited time span and within some budget. In existing itinerary recommender systems, if there is no past visit history about a particular POI, then that POI is not included in the recommended itinerary. To address this challenge, we have devised an algorithm called PIONEER which is based on a genetic algorithm for suggesting an itinerary based on tourist interests, POI popularity, and travel costs. Our algorithm recommends itineraries for tourists who want to visit locations which are unfamiliar to them. We have used the publicly available Flickr dataset in our work. The results demonstrate the superiority of our PIONEER algorithm compared to the baseline algorithms with regards to metrics like precision, recall and F1-Score. © 2024 Instituto Politecnico Nacional. All rights reserved.