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Subject: search.filters.subject.long short-term memory

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    Skin Video-based Blood Pressure Approximation Using CHROM with LSTM-NN
    (2023-01-01)
    Lumyong, Chayanin
    ;
    Yodrabum, Nutcha
    ;
    Winaikosol, Kengkart
    ;
    Titijaroonroj, Taravichet
    The measurement of blood pressure (BP) is an essential step in clinical practice. It is used to determine the patient's BP, which reflects the condition of the patient. Recently, there is a solution for extracting, non-invasively and with no contact, a blood pressure indicator from electrical signal like Photoplethysmography (PPG), called remote-Photoplethysmography (rPPG). This rPPG signal can be used to estimate from a video clip several vital physiological indicators for humans, especially, systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP). This paper proposed a computer method for blood pressure approximation from an input video. A chrominance method, or CHROM, was used to extract rPPG signal from a given video before forwarding it to estimate SBP and DBP values by LSTM-NN. Afterwards, MAP value was determined from SBP and DBP values by a weighting score technique. Experimental results showed that CHROM achieved the lowest mean absolute error (MAE) at 14.04, 8.37, and 9.78 for the SBP, DBP, and MAP, respectively, when compared among NN, RNN, and GRU.
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    Ionospheric TEC Forecasting over an Indian Low Latitude Location Using Long Short-Term Memory (LSTM) Deep Learning Network
    (2022-11-01)
    Reddybattula, Kanaka Durga
    ;
    Nelapudi, Likhita Sai
    ;
    Moses, Mefe
    ;
    Devanaboyina, Venkata Ratnam
    ;
    Ali, Masood Ashraf
    The forecasting of ionospheric electron density has been of great interest to the research scientists and engineers’ community as it significantly influences satellite-based navigation, positioning, and communication applications under the influence of space weather. Hence, the present paper adopts a long short-term memory (LSTM) deep learning network model to forecast the ionospheric total electron content (TEC) by exploiting global positioning system (GPS) observables, at a low latitude Indian location in Bangalore (IISC; Geographic 13.03° N and 77.57° E), during the 24th solar cycle. The proposed model uses about eight years of GPS-TEC data (from 2009 to 2017) for training and validation, whereas the data for 2018 was used for independent testing and forecasting of TEC. Apart from the input TEC parameters, the model considers sequential data of solar and geophysical indices to realize the effects. The performance of the model is evaluated by comparing the forecasted TEC values with the observed and global empirical ionosphere model (international reference ionosphere; IRI-2016) through a set of validation metrics. The analysis of the results during the test period showed that LSTM output closely followed the observed GPS-TEC data with a relatively minimal root mean square error (RMSE) of 1.6149 and the highest correlation coefficient (CC) of 0.992, as compared to IRI-2016. Furthermore, the day-to-day performance of LSTM was validated during the year 2018, inferring that the proposed model outcomes are significantly better than IRI-2016 at the considered location. Implementation of the model at other latitudinal locations of the region is suggested for an efficient regional forecast of TEC across the Indian region. The present work complements efforts towards establishing an efficient regional forecasting system for indices of ionospheric delays and irregularities, which are responsible for degrading static, as well as dynamic, space-based navigation system performances.
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    Thai Voice Recognition for Controlling Electrical appliances Using Long Short-Term Memory
    (2020-04-01)
    Saheaw, Wuttichai
    ;
    Jaiyen, Saichon
    ;
    Hanskunatai, Anantaporn
    Human speech possesses characteristics in each of the word that can be recognized and learned by computers. In this research, It is being proposed the use of the Deep Learning Model to predict speech turn-on and turn-off various electrical appliances, by using the sound conversion method that has been through the process to get the value of sound waves and applied toward training process in different ways. As the sound has more than 1 syllable and having characteristics of similar words that might difficult to predict. This research is based on Convolutional Neural Network (CNN) for comparison with the use of Long Short-Term Memory (LSTM), which is part of the Recurrent Neural Network (RNN) and Thai language Speech Dataset turn-on and turn-off by the 7 types of electrical appliances, the process of reducing noise and silence of the front and back of the audio files by 14 classes in total. The experimental results signify that the proposed Long Short-Term Memory can achieve the best accuracy.