School of Management Studies

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Browsing School of Management Studies by Subject "Artificial intelligence"
  • Item
    Impact of AI and robotics in the tourism sector: a critical insight
    ( 2020-01-01) Samala, Nagaraj ; Katkam, Bharath Shashanka ; Bellamkonda, Raja Shekhar ; Rodriguez, Raul Villamarin
    Purpose: The purpose of the present article is to highlight the role of Artificial Intelligence (AI) and Robotics in the tourism industry. The various technologies being integrated to improve the service and customer experience in tourism. The expected changes and challenges in tourism in the future are focused in this paper. Design/methodology/approach: A systematic study on the emerging technologies of AI and Robotics applied in the tourism sector is presented in the form of a viewpoint. Findings: AI certainly enhances tourism experiential services however cannot surpass the human touch which is an essential determinant of experiential tourism. AI acts as an effective complementary dimension to the future of tourism. With the emergence of artificial travel intelligence, it is simpler to make travel arrangements. AI offers travel services that are automated, customized and insightful. AI allows travelers to learn about their behaviors, interests to inclinations and provide a personalized experience. Gone are the days to consult a travel agent, meet him physically and indulge in an endless chain of troubling phone calls to inquire about travel arrangements. Practical implications: Tourism marketing to see a positive and improved change that will enhance the tourists’ overall experience due to the application of AI and Robotics. New emerging technologies like chatbots, virtual reality, language translators, etc. can be effectively applied in Travel, Tourism & Hospitality industry. Originality/value: The present viewpoint discusses the application and role of AI and Robotics with the help of relevant industry examples and theory. The present paper highlights the different technologies being used and will be used in the future.
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    Using artificial intelligence-based models to predict the risk of mucormycosis among COVID-19 survivors: An experience from a public hospital in India
    ( 2022-03-01) Syed-Abdul, Shabbir ; Babu, A. Shoban ; Bellamkonda, Raja Shekhar ; Itumalla, Ramaiah ; Acharyulu, G. V.R.K. ; Krishnamurthy, Surya ; Ramana, Y. Venkat Santosh ; Mogilicharla, Naresh ; Malwade, Shwetambara ; Li, Yu Chuan Jack
    Introduction: India reported a severe public health challenge not only due to the COVID-19 outbreak but also the increasing number of associated mucormycosis cases since 2021.This study aimed at developing artificial intelligence based models to predict the risk of mucormycosis among the patients at the time of discharge from hospital. Methods: The dataset included of 1229 COVID-19 positive patients, and additional 214 inpatients, COVID-19 positive as well as infected with mucormycosis. We used logistic regression, decision tree and random forest and the extreme gradient boosting algorithm. All our models were evaluated with 5-fold validation to derive a reliable estimate of the model error. Results: The logistic regression, XGBoost and random forest performed equally well with AUROC 95.0, 94.0, and 94.0 respectively. The best accuracy and precision (PPV) were 0.91 ± 0.026 and 0.67 ± 0.0526, respectively achieved by XGBoost, followed by logistic regression. This study also determined top five variables namely obesity, anosmia, de novo diabetes, myalgia, and nasal discharge, which showed positive impact towards the risk of mucormycosis. Conclusion: The developed model has the potential to predict the patients at high risk and thus, consequently initiating preventive care or aiding in early detection of mucormycosis infection. Thus, this study, holds potential for early treatment and better management of patients suffering from COVID-19 associated mucormycosis.
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    Using artificial intelligence-based models to predict the risk of mucormycosis among COVID-19 survivors: An experience from a public hospital in India
    ( 2022-03-01) Syed-Abdul, Shabbir ; Babu, A. Shoban ; Bellamkonda, Raja Shekhar ; Itumalla, Ramaiah ; Acharyulu, G. V.R.K. ; Krishnamurthy, Surya ; Ramana, Y. Venkat Santosh ; Mogilicharla, Naresh ; Malwade, Shwetambara ; Li, Yu Chuan Jack
    Introduction: India reported a severe public health challenge not only due to the COVID-19 outbreak but also the increasing number of associated mucormycosis cases since 2021.This study aimed at developing artificial intelligence based models to predict the risk of mucormycosis among the patients at the time of discharge from hospital. Methods: The dataset included of 1229 COVID-19 positive patients, and additional 214 inpatients, COVID-19 positive as well as infected with mucormycosis. We used logistic regression, decision tree and random forest and the extreme gradient boosting algorithm. All our models were evaluated with 5-fold validation to derive a reliable estimate of the model error. Results: The logistic regression, XGBoost and random forest performed equally well with AUROC 95.0, 94.0, and 94.0 respectively. The best accuracy and precision (PPV) were 0.91 ± 0.026 and 0.67 ± 0.0526, respectively achieved by XGBoost, followed by logistic regression. This study also determined top five variables namely obesity, anosmia, de novo diabetes, myalgia, and nasal discharge, which showed positive impact towards the risk of mucormycosis. Conclusion: The developed model has the potential to predict the patients at high risk and thus, consequently initiating preventive care or aiding in early detection of mucormycosis infection. Thus, this study, holds potential for early treatment and better management of patients suffering from COVID-19 associated mucormycosis.