Seismic Facies Analysis by ANFIS and Fuzzy Clustering Methods to Extract Channel Patterns

Hadiloo, S.; Radad, Mohammad; Mirzaei, Saeid; Foomezhi, M.

doi:10.3997/2214-4609.201700917

Cited by 6 publications

(2 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, it is urgent to find a more efficient and intelligent method for MSW classification. Data about semismic images can be used to identify hydrocarbon structure to help classify wet waste and residual waste (Radad et al, 2016;Hadiloo et al, 2017;Mousavi et al, 2022). The application methods in indentifying hydrocarbon resevoirs and structure related to hydrocarbon also have been discussed (Soleimani and Balarostaghi, 2016;Farrokhnia et al, 2018;Khayer et al, 2022a;Khayer et al, 2022b;Hosseini-Fard et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste

Lin²

2022

Front. Environ. Sci.

View full text Add to dashboard Cite

It is crucial to realize the municipal solid waste (MSW) classification in terms of its treatments and disposals. Deep learning used for the classification of residual waste and wet waste from MSW was considered as a promising method. While few studies reported using the method of deep learning with transfer learning to classify organic waste and residual waste. Thus, this study aims to discuss the effect of the transfer learning on the performance of different deep learning structures, VGGNet-16 and ResNet-50, for the classification of organic waste and residual waste, which were compared in terms of the training time, confusion matric, accuracy, precision, and recall. In addition, the algorithms of PCA and t-SNE were also adopted to compare the representation extracted from the last layer of various deep learning models. Results indicated that transfer learning could shorten the training time and the training time of various deep learning follows this order: VGGNet-16 (402 s) > VGGNet-16 with TL (272 s) > ResNet-50 (238 s) > ResNet-50 with TL (223 s). Compared with the method of PAC, waste representations were better separated from high dimension to low dimension by t-SNE. The values of organic waste in terms of F1 score follows this order: ResNet-50 with transfer learning (97.8%) > VGGNet-16 with transfer learning (97.1%) > VGGNet-16 (95.0%) > ResNet-50 (92.5%).Therefore, the best performance for the classification of organic and residual waste was ResNet-50 with transfer learning, followed by VGGNet-16 with transfer learning and VGGNet-16, and ResNet-50 in terms of accuracy, precision, recall, and F1 score.

show abstract

Section: Introductionmentioning

confidence: 99%

Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste

Lin²

2022

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Specifically, (i) FCSs show on average t IDW = 0.5 ms and t 2CD = 3.3 ms whereas CCSs are represented on average by t IDW = 1.0 ms and t 2CD = 5.1 ms according to Hoevers et al [11]; (ii) FCSs show on average t IDW = 0.9 ms and t 2CD = 6.0 ms whereas CCSs are represented on average by t IDW = 1.25 ms and t 2CD = 9.50 ms according to Cohen et al [12]; (iii) FCSs show on average t IDW = 0.7 ms and t 2CD = 5 ms whereas CCSs are represented on average by t IDW = 1.5 ms and t 2CD = 10 ms according to the American Thoracic Society (ATS) [13]. For many years, signal processing and machine learning approaches have been combined for event detection and classification tasks using spectro-temporal features [14][15][16]. Specifically for the task of crackle sound detection, several approaches have been proposed based on spectrogram analysis [17,18], autoregressive (AR) models [19,20], wavelet transform [21][22][23][24], fractal dimension filtering [25][26][27][28], entropy [29,30], empirical mode decomposition (EMD) [31], fuzzy systems [32], Gaussian mixture models (GMM) [33], logistic regression [34], support vector machines (SVM) [35][36][37], independent component analysis (ICA) [38], multi-perceptron networks (MPNs) [39], non-negative matrix factorization (NMF) [40], convolutional neural networks (CNNs) [41,42], recurrent neural networks (RNNs) [43,44] and hybrid neural networks [45,46].…”

Section: Introductionmentioning

confidence: 99%

Automatic Robust Crackle Detection and Localization Approach Using AR-Based Spectral Estimation and Support Vector Machine

Mang,

Carabias-Orti,

Canadas-Quesada

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Auscultation primarily relies upon the acoustic expertise of individual doctors in identifying, through the use of a stethoscope, the presence of abnormal sounds such as crackles because the recognition of these sound patterns has critical importance in the context of early detection and diagnosis of respiratory pathologies. In this paper, we propose a novel method combining autoregressive (AR)-based spectral features and a support vector machine (SVM) classifier to detect the presence of crackle events and their temporal location within the input signal. A preprocessing stage is performed to discard information out of the band of interest and define the segments for short-time signal analysis. The AR parameters are estimated for each segment to be classified by means of support vector machine (SVM) classifier into crackles and normal lung sounds using a set of synthetic crackle waveforms that have been modeled to train the classifier. A dataset composed of simulated and real coarse and fine crackles sound signals was created with several signal-to-noise (SNR) ratios to evaluate the robustness of the proposed method. Each simulated and real signal was mixed with noise that shows the same spectral energy distribution as typically found in breath noise from a healthy subject. This study makes a significant contribution by achieving competitive results. The proposed method yields values ranging from 80% in the lowest signal-to-noise ratio scenario to a perfect 100% in the highest signal-to-noise ratio scenario. Notably, these results surpass those of other methods presented by a margin of at least 15%. The combination of an autoregressive (AR) model with a support vector machine (SVM) classifier offers an effective solution for detecting the presented events. This approach exhibits enhanced robustness against variations in the signal-to-noise ratio that the input signals may encounter.

show abstract

Fault Enhancement in Seismic Images by Introducing a Novel Strategy Integrating Attributes and Image Analysis Techniques

Mousavi

Radad

Monfared

et al. 2022

Pure Appl. Geophys.

View full text Add to dashboard Cite

Seismic Facies Analysis by ANFIS and Fuzzy Clustering Methods to Extract Channel Patterns

Cited by 6 publications

References 0 publications

Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste

Effect of transfer learning on the performance of VGGNet-16 and ResNet-50 for the classification of organic and residual waste

Automatic Robust Crackle Detection and Localization Approach Using AR-Based Spectral Estimation and Support Vector Machine

Fault Enhancement in Seismic Images by Introducing a Novel Strategy Integrating Attributes and Image Analysis Techniques

Contact Info

Product

Resources

About