Polynomial and Wavelet-Type Transfer Function Models to Improve Fisheries’ Landing Forecasting with Exogenous Variables

Vivas, Eliana; Allende‐Cid, Héctor; Salas, Rodrigo; Bravo, Lelys

doi:10.3390/e21111082

Cited by 10 publications

(3 citation statements)

References 23 publications

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“…Furthermore, it would be interesting to explore other methods such as neuro-fuzzy models [40,44], machine learning models [45], and deep learning techniques [46]. Moreover, analysing the wavelet domain of the signals could be relevant for healthcare applications [47,48]. On the other hand, information from experts could be included in the models [49] and potential biomarkers could be found using machine learning techniques [50].…”

Section: Discussionmentioning

confidence: 99%

An Extreme Learning Machine for Blood Pressure Waveform Estimation using the Photoplethysmography Signal

Tapia¹,

Salas²,

Salinas³

et al. 2022

J. Engg. Res. & Sci.

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Blood Pressure (BP) waveform is a result of the response of the arteries to the blood ejection produced by the heart and, therefore, it is an important indicator of the state of the cardiovascular system. Currently, its measurement is performed invasively in critically ill patients who need a continuous and real time monitoring of their treatment response, however, it is possible to measure the BP, continuously and non-invasively, in non-critical patients to detect, monitor and control possible hypertensive events. Nevertheless, current non-invasive techniques can cause discomfort in patients and they are not used in critically ill patients. Consequently, non-Invasive and minimally-Intrusive methodologies (nImI) are required to estimate BP and its waveform. In the current study, the performance of machine learning algorithms, specifically the Extreme Learning Machine (ELM) algorithm, is evaluated to estimate both Blood Pressure and its waveform from the Photoplethysmography (PPG) signal and its first derivative's (VPG) waveforms. A total of 15 healthy volunteers participated in this study. They performed two handgrips, which is isometric maneuver to induce controlled BP rises. The first handgrip is used to train ELM and the second handgrip is used to test the ELM. Our results show that there are high correlation performances (0.98) between the estimated and measured BP waveforms, and a relative error of 3.3 ± 1.4%. An arterial volume-clamp at the middle finger is used as the gold-standard measurement. Meanwhile, BP extreme values estimations, Systolic BP (SBP) and Diastolic BP (DBP), are also performed. ELMs have a performance with an average RMSE of 5.9 ± 2.7 mmHG for SBP and 4.8 ± 2.0 mmHg for DBP and, an average relative error of 5.0 ± 2.7% for SBP and 7.0 ± 4.0% for DBP.

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Section: Discussionmentioning

confidence: 99%

An Extreme Learning Machine for Blood Pressure Waveform Estimation using the Photoplethysmography Signal

Tapia¹,

Salas²,

Salinas³

et al. 2022

J. Engg. Res. & Sci.

Self Cite

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“…Machine Learning and deep learning methods have been successfully applied for time series forecasting [34][35][36][37][38][39] . For instance, RNNs are dynamic models frequently used for processing sequences of real data step by step, predicting what comes next, and they are applied in many domains, such as the prediction of pollutants 40 .…”

Section: Modelingmentioning

confidence: 99%

Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

Cordova

Portocarrero

Salas

et al. 2021

Preprint

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The prediction of air pollution is of great importance in highly populated areas because it has a direct impact on both the management of the city's economic activity and the health of its inhabitants. In this work, the spatio-temporal behavior of air quality in Metropolitan Lima was evaluated and predicted using the recurrent artificial neural network known as Long-Short Term Memory networks (LSTM). The LSTM was implemented for the hourly prediction of PM10 based on the past values of this pollutant and three meteorological variables obtained from five monitoring stations. The model was evaluated under two validation schemes: the hold-out (HO) and the blocked-nested cross-validation (BNCV). The simulation results show that periods of low PM10 concentration are predicted with high precision. Whereas, for periods of high contamination, the LSTM network with BNCV has better predictability performance. In conclusion, recurrent artificial neural networks with BNCV adapt more precisely to critical pollution episodes and have better performance to forecast this type of environmental data, and can also be extrapolated to other pollutants.

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“…do not allow us to describe the time series of complex structure adequately [23]. At present, hybrid approaches [16,17,19,[23][24][25][26][27][28] are widely applied. They make it possible to improve the efficiency of the procedure of data analysis in case of its complicated structure.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Model for Time Series of Complex Structure with ARIMA Components

2021

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A hybrid model for the time series of complex structure (HMTS) was proposed. It is based on the combination of function expansions in a wavelet series with ARIMA models. HMTS has regular and anomalous components. The time series components, obtained after expansion, have a simpler structure that makes it possible to identify the ARIMA model if the components are stationary. This allows us to obtain a more accurate ARIMA model for a time series of complicated structure and to extend the area for application. To identify the HMTS anomalous component, threshold functions are applied. This paper describes a technique to identify HMTS and proposes operations to detect anomalies. With the example of an ionospheric parameter time series, we show the HMTS efficiency, describe the results and their application in detecting ionospheric anomalies. The HMTS was compared with the nonlinear autoregression neural network NARX, which confirmed HMTS efficiency.

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Polynomial and Wavelet-Type Transfer Function Models to Improve Fisheries’ Landing Forecasting with Exogenous Variables

Cited by 10 publications

References 23 publications

An Extreme Learning Machine for Blood Pressure Waveform Estimation using the Photoplethysmography Signal

An Extreme Learning Machine for Blood Pressure Waveform Estimation using the Photoplethysmography Signal

Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

Hybrid Model for Time Series of Complex Structure with ARIMA Components

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