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Chatbot to Map Medical Prognosis and Symptoms Using Machine Learning

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Cyber Security and Digital Forensics

Abstract

Computer-aided system is a subject of great importance and extensive requirement. Nowadays, deep learning and machine learning have gained quite a knack in people’s eye and widely used among them. Gone are the occasions when the products were utilized for complex count issues or graphical portrayal alone. And Chatbots are proven revolutionary in our day-to-day lives where they are present in health, career, insurance and customer care support. In this paper, we have built up a Health-Bot using RNN network and Keras classifier. During such a pandemic period when there is an enormous crowd present in hospitals, people can get themselves checked at their homes with this interactive language system. Neural network adds more exactness to our work and reactions. And we further implemented our model on StreamLit which is an open-source framework for machine learning and deep learning.

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References

  1. Szymczak, A.: Introduction to chatbots in healthcare. https://blog.infermedica.com/introduction-to-chatbots-in-healthcare/. Accessed July 2017

  2. World Health Organization: Management of substance abuse advice for the public. Available https://www.who.int/substance_abuse/facts/en/. Accessed 23 June 2020

  3. Alqudah, A.M., Qazan, S., Alqudah, A.: Automated systems for detection of COVID-19 using chest X-ray images and lightweight convolutional neural networks (2020). https://doi.org/10.21203/rs.3.rs-24305/v1

  4. Kumar, A., Shanmugvadivu, P.: Space of RGB-H-CMYK. 1(Feb). Spring Singapore (2019). https://doi.org/10.1007/978-981-13-1708-8

  5. Mckie, I.A.S., Narayan, B.: Enhancing the academic library experience with Chatbots: an exploration of research and implications for practice. J. Aust. Lib. Inf. Asso. (2019). https://doi.org/10.1080/24750158.2019.1611694

  6. Melián-González, S., Gutiérrez-Taño, D., Bulchand-Gidumal, J.: Predicting the intentions to use chatbots for travel and tourism. Curr. Issue Tour. (2019). https://doi.org/10.1080/13683500.2019.1706457

    Article  Google Scholar 

  7. Emima, Y., Rajesh, M., Rao, K.S.: Experimental investigation on performance and exhaust emission characteristics of diesel engine using eesame blends with diesel and additive. Int. J, Rec. Technol. Eng. 8(1), 6–11 (2019)

    Google Scholar 

  8. Pati, B., et al. (eds.): Progress in advanced computing and intelligent engineering. In: Advances in Intelligent Systems and Computing, vol. 713. https://doi.org/10.1007/978-981-13-1708-8_10

  9. Kucherbaev, P., Bozzon, A., Houben, G.: Human-aided bots. IEEE Internet Comput. 22(6), 36–43. https://doi.org/10.1109/MIC.2018.252095348

  10. Singh, R., Paste, M., Shinde, N., Patel, H., Mishra, N.: Chatbot using TensorFlow for small Businesses. In: 2018 Second International Conference on Inventive Communication and Computational Technologies (ICICCT), Coimbatore, India, pp. 1614–1619. https://doi.org/10.1109/ICICCT.2018.8472998

  11. López, G., Quesada, L., Guerrero. L.A.: Alexa vs. Siri vs. Cortana vs. Google assistant: a comparison of speech-based natural user interfaces. In: Nunes, I. (eds.) Advances in Human Factors and Systems Interaction. AHFE 2017. Advances in Intelligent Systems and Computing, vol. 592. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-60366-7_23

  12. Agarap, A.F.: Deep learning using rectified linear units (relu). arXiv preprint arXiv:1803.08375 (2018)

  13. Marques, N.C., Lopes, G.P.: A neural network approach to part-of-speech tagging. In: Proceedings of the 2nd Meeting for Computational Processing of Spoken and Written Portuguese, pp. 21–22 (1996)

    Google Scholar 

  14. Yousif, J.: Neural computing based part of speech tagger for Arabic language: a review study. Int. J. Comput. Appl. Sci. IJOCAAS 5(1) (2018)

    Google Scholar 

  15. Ramadevi, R., Sheela Rani, B., Prakash, V.: Role of hidden neurons in an Elman recurrent neural network in classification of cavitation signals. Int. J. Comput. Appl. 37(7), 9–13 (2012)

    Google Scholar 

  16. Chen, J., Jing, H., Chang, Y., Liu, Q.: Gated recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process. Reliab. Eng. Syst. Saf. 185, 372–382 (2019)

    Article  Google Scholar 

  17. Shim, K., Lee, M., Choi, I., Boo, Y., Sung, W.: Svd-Softmax: Fast Softmax approximation on large vocabulary neural networks. In: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 5469–5479 (2017)

    Google Scholar 

  18. Abdul-Kader, S.a., Woods, J.: Survey on Chatbot design techniques in speech conversation systems. School of Computer Science and Electronic Engineering/University of Essex Colchester/UK

    Google Scholar 

  19. Vidnerova, P., Neruda, R.: Evolving Keras architectures for sensor data analysis. In: 2017 Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 109–112. IEEE (2017)

    Google Scholar 

  20. Choi, K., Joo, D., Kim, J.: Kapre: On-gpu audio preprocessing layers for a quick implementation of deep neural network models with Keras (2017). arXiv preprint arXiv:1706.05781

  21. Atanasov, A.: Dynamic Working Memory in Recurrent Neural Networks

    Google Scholar 

  22. Awwalu, J., Garba, A., Ghazvini, A., Atuah, R.: Artificial intelligence in personalized medicine application of AI algorithms in solving personalized medicine problems

    Google Scholar 

  23. Chantarotwong, B.: The learning Chatbot. Fall (2006)

    Google Scholar 

  24. Kelly, J.E.: Computing, cognition and the future of knowing how humans and machines are forging a new age of understanding. IBM Research and Solutions Portfolio

    Google Scholar 

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Aggarwal, H., Kapur, S., Bahuguna, V., Nagrath, P., Jain, R. (2022). Chatbot to Map Medical Prognosis and Symptoms Using Machine Learning. In: Khanna, K., Estrela, V.V., Rodrigues, J.J.P.C. (eds) Cyber Security and Digital Forensics . Lecture Notes on Data Engineering and Communications Technologies, vol 73. Springer, Singapore. https://doi.org/10.1007/978-981-16-3961-6_8

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  • DOI: https://doi.org/10.1007/978-981-16-3961-6_8

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  • Online ISBN: 978-981-16-3961-6

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