, which measure of your curvature, five. The plot of your slope around
, which measure with the curvature, five. The plot on the slope about the peaks across data, which is a is actually a measure of the curvature,as well as the standard deviation of error (SDE) across signal to decide the the curvature, Figure five. The plot ofdeviationaround the peaksacross thethe signal to determine the doable prediction along with the common the slope of error (SDE) across thedata, which is a measure of probable prediction trends regular deviation number of wavelet the signal the S-range. green, red, and white and also the with respect towards the number of wavelet bases as well as the S-range. TheThe green, red, and white lines trends with respect for the of error (SDE) across bases and to identify the feasible prediction lines trends withthe observed trends 1,of waveletrespectively. S-range. The green, red, and white lines represent respect to the quantity two, and three, bases and the represent the observed trends 1, 2, and 3, respectively. represent the observed trends 1, 2, and three, respectively.1 1 0.five 0.50 0S7 – N10S7 – Nresults predictions predictionsresultsAmplitude Amplitude1 1 0.five 0.5 00011 0.5 0.5 00 00 11 0.5 0.5 00Trend30 Trend(a)(a)10Trend Trend 23040(b) 50(b)(c) (c)10Trend Trend 3304050(d) (d)ten 10Sample Sample304050Figure six.six. The plot in the representative trends observed from selected selection of numbers of wave-waveThe plot of of representative trends observed from in the chosen range numbers of Figure six. The plot thethe representative trends observed the the chosen array of of numbers of wavelet let bases and S-range. The blue line represents the the anticipated result, whilered lines lines are the prelet bases and S-range. The blue line represents anticipated result, while the the red will be the prebases and S-range. The blue line represents the expected result, while the red lines are the prediction diction benefits. (a) represents the signal with all the the lowest slope and SDE,represents the signalsignal diction (a) represents the signal signal with lowest slope and SDE, (b) results. final results. (a) represents the together with the the signal with High SDE and (b) represents the with Low SDE and 20(S)-Hydroxycholesterol Metabolic Enzyme/Protease Higher slope, (c) representslowest slope and SDE, (b) represents theand (d) with Low Low slope, signal with Low SDE and High slope, (c) represents the signal with Higher SDE and Low slope, and (d) represents Higher slope, (c) represents the signal withslope. SDE and Low slope, and (d) represents the SDE and also the signal with Low SDE and Relatively low Highrepresents the signal with Low SDE and Relatively low slope. signal with Low SDE and Somewhat low slope. 4. ExamplesIn this section, we demonstrate the overall performance with the FWNN on synthetic EMT data In 3 noise we demonstrate the efficiency on the FWNN on synthetic EMT set with this section,variations in addition to a pseudo-synthetic field information set utilizing the full information set with 3 noise variationsadaptive PHA-543613 supplier processing [12]. The FWNNset using the comprehensive data processing workflow with and also a pseudo-synthetic field data program is applied information processing prediction model with the signal code employing The FWNN system on a for constructing a workflow with adaptive processing [12]. the Matlab script run is applied4. ExamplesAppl. Sci. 2021, 11,13 ofAppl. Sci. 2021, 11, x FOR PEER REVIEW4. Examples14 ofIn this section, we demonstrate the overall performance in the FWNN on synthetic EMT data set with three noise variations and a pseudo-synthetic field data set using the full information processing workflow with adaptive processing [12]. The FWNN technique is appli.