Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Das, M.Subject: search.filters.subject.Hidden Markov Models (HMMs)

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    Reliability Analysis and predictive maintenance of Elevator System Using Hidden Markov Model
    (Institute of Electrical and Electronics Engineers Inc., 2024) Jayasimha, S.; Premkumar; Harikiran; Fahad, M.; Mohan, B.R.; Das, M.
    Safety and operational efficiency in elevator systems are critical, our method leverages Hidden Markov Models (HMMs) to provide a comprehensive solution. We introduce an innovative approach to enhance the reliability analysis and predictive maintenance of elevator systems. It can predict the most probable sequence of hidden states based on new sensor data, offering real-time insights into the elevator system's health and enabling early issue detection. This approach not only enhances safety and reliability but also improves operational efficiency by allowing data-driven decision-making in elevator system maintenance. © 2024 IEEE.
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    Forecasting COVID-19 Transmission Patterns with Hidden Markov Model
    (Institute of Electrical and Electronics Engineers Inc., 2024) Prakash, A.; Tomar, K.; Poptani, P.; Kumar, P.; Das, M.; Mohan, B.R.
    Global health and healthcare system have faced major hurdles as a result of coronavirus. Pandemic still had impacts on community in every part of the world even with the efforts made to curb the disease transmission. We have sought to address some of these issues by utilising data sourced from JHU's CSSE [1]. This article concentrated on U.S. COVID-19 statistics concerning the number of infections and deaths in major towns. Only the relevant infection rates, death rates, and time columns were left in the pre-processing dataset. The above finding proves that the pandemic is evolving and began as a low rate of infections and deaths which increase with every passing moment. Secondly, we look at how death rates correlates with the highest infection rate. In an attempt to improve the forecast of COVID-19 spread for health care, manufacturers, economies and academic institutions this research is developed. © 2024 IEEE.