SVM regression based robust image watermarking technique in joint DWT-DCT domain

Modeling the behavior of groundwater levels is necessary to implement sustainable groundwater resource management. Groundwater is a non-linear and complex system, which can be modeled by data-driven models. This study evaluates the performances of data-driven models, support vector machine regression (SVR) and artificial neural network (ANN), for forecasting groundwater levels of confined and unconfined systems at 1-, 2-, and 3-month ahead. This is the first time that confined and unconfined aquifers have been compared using data-driven models. In addition, to identify the optimal input combination, a hybrid gamma test (GT) and genetic algorithm (GA) was used. The coefficient of correlation (R), Mean Absolute Error (MAE), root mean squared error (RMSE), Nash–Sutcliffe efficiency (NSE), and developed discrepancy ratio (DDR) were applied to evaluate the SVR and ANN models. Results showed that the SVR and ANN models were more accurate for the unconfined system than the confined system for forecasts up to 3-month ahead. In both hydrogeological systems for 1-month ahead, the models performed better than for 2- and 3-month ahead forecasts, and the accuracy of the models decreased as the months ahead increased. The SVR model performed better than the ANN model for 1-, 2-, and 3-month ahead groundwater-level forecasting. The SVR model could be successfully used in predicting monthly groundwater in confined and unconfined systems.

Application of robust principal component analysis–multivariate adaptive regression splines for the determination of °API gravity in crude oil samples using ATR-FTIR spectroscopy

Gas–solid fluidized beds are complex particle systems, and the electrostatic behavior of particles in fluidized beds is even more complex, which is influenced by numerous factors such as particle properties and operating conditions. Current studies focus on the effect of a certain factor on particle charging without a global picture. Furthermore, there is no mathematical model that can describe the interaction of multiple factors on particle charging because it is difficult to build a model for such a complex system. Therefore, a new approach is needed. In this study, a model capable of accurately predicting the surface charge density of particles in monodispersed gas–solid fluidized beds within a certain range was developed based on the literature and experimental data through several machine learning methods including kernel ridge regression (KRR), support vector machine regression (SVR), and multilayer perceptron (MLP). SVR and MLP models gave the best results with R2 equal to 0.980 and 0.979, respectively. However, the sensitivity analysis showed that the MLP model was more reliable than the SVR model. In conclusion, the feasibility of using machine learning to analyze the charging behavior of particles in fluidized beds is demonstrated, and the proposed MLP model can serve as an accurate correlative tool for fast and effective estimation of particle surface charge density in gas–solid fluidized beds.

Driving Decision-making Mechanism (DDM) is identified as the key technology to ensure the driving safety of autonomous vehicle, which is mainly influenced by vehicle states and road conditions. However, previous studies have seldom considered road conditions and their coupled effects on driving decisions. Therefore, road conditions are introduced into DDM in this paper, and are based on a Support Vector Machine Regression (SVR) model, which is optimized by a weighted hybrid kernel function and a Particle Swarm Optimization (PSO) algorithm, this study designs a DDM for autonomous vehicle. Then, the SVR model with RBF (Radial Basis Function) kernel function and BP (Back Propagation) neural network model are tested to validate the accuracy of the optimized SVR model. The results show that the optimized SVR model has the best performance than other two models. Finally, the effects of road conditions on driving decisions are analyzed quantitatively by comparing the reasoning results of DDM with different reference index combinations, and by the sensitivity analysis of DDM with added road conditions. The results demonstrate the significant improvement in the performance of DDM with added road conditions. It also shows that road conditions have the greatest influence on driving decisions at low traffic density, among those, the most influential is road visibility, then followed by adhesion coefficient, road curvature and road slope, while at high traffic density, they have almost no influence on driving decisions.

Empirical approach for strength prediction of geopolymer stabilized clayey soil using support vector machines

Support vector machine regression (SVR) technique was used for predicting the uniaxial compressive strength of FRP confined concrete. Two different SVR models, viz. SVR model for carbon fiber reinforced polymer confined concrete and SVR model for glass fiber reinforced polymer confined concrete, were developed. The prediction efficacy of developed SVR models was compared with that of ANN and existing empirical FRP strength prediction models. Furthermore, for assessing variable contributions in strength prediction process, a sensitivity analysis is also presented. The study showed that SVR could be a powerful alternative physical tool for strength prediction of FRP confined concrete.

In order to quantitatively predictive the content of the coalbed methane (CBM), we make use of the known parameters of the core tests data to establish the support vector machine regression model between the core data and coal-bed methane content. The model is based on the small sample size theory. Using the model, we can predict the volume of gas content. We choose the coal seam thickness, coal vitrinite reflectance value and coal ash 3 parameters as input feature vectors, and coal-bed methane content as the output vector of support vector machine regression prediction model. Application of the proposed model in Binchang mining shows that the prediction error between the measured results and prediction are small and meet the accuracy requirements.

This study aimed to compare stepwise multiple linear regression (SMLR), partial least squares regression (PLSR) and support vector machine regression (SVMR) for estimating soil total nitrogen (TN) contents with laboratory visible/near-infrared reflectance (Vis/NIR) of selected coarse and heterogeneous soils. Moreover, the effects of the first (1st) vs. second (2nd) derivative of spectral reflectance and the importance wavelengths were explored. The TN contents and the Vis/NIR were measured in the laboratory. Several methods were employed for Vis/NIR data pre-processing. The SMLR, PLSR and SVMR models were calibrated and validated using independent datasets. Results showed that the SVMR and the PLSR models had similar performances, and better performances than the SMLR. The spectral bands near 1450, 1850, 2250, 2330 and 2430 nm in the PLSR model were important wavelengths. In addition, the 1st derivative was more appropriate than the 2nd derivative for spectral data pre-processing. PLSR was the most suitable method for estimating TN contents in this study. SVMR may be a promising technique, and its potential needs to be further explored. Moreover, the future studies using outdoor and airborne/satellite hyperspectral data for estimating TN content are necessary for testing the findings.

This paper presents a new robust hybrid multiple watermarking technique using fusion of discrete wavelet transforms (DWT), discrete cosine transforms (DCT), and singular value decomposition (SVD) instead of applying DWT, DCT and SVD individually or combination of DWT-SVD / DCT-SVD. For identity authentication purposes, multiple watermarks are embedded into the same medical image / multimedia objects simultaneously, which provides extra level of security with acceptable performance in terms of robustness and imperceptibility. In the embedding process, the cover image is decomposed into first level discrete wavelet transforms where the A (approximation/lower frequency sub-band) is transformed by DCT and SVD. The watermark image is also transformed by DWT, DCT and SVD. The S vector of watermark information is embedded in the S component of the cover image. The watermarked image is generated by inverse SVD on modified S vector and original U, V vectors followed by inverse DCT and inverse DWT. The watermark is extracted using an extraction algorithm. Furthermore, the text watermark is embedding at the second level of the D (diagonal sub-band) of the cover image. The security of the text watermark considered as EPR (Electronic Patient Record) data is enhanced by using encryption method before embedding into the cover. The results are obtained by varying the gain factor, size of the text watermark, and cover medical images. The method has been extensively tested and analyzed against known attacks and is found to be giving superior performance for robustness, capacity and reduced storage and bandwidth requirements compared to reported techniques suggested by other authors.

Choosing appropriate multivariate calibration and preprocessing transformation techniques is important in the determination of soil organic carbon (SOC) content based on visible and near-infrared (Vis–NIR) spectroscopy. The performance levels of partial least-squares regression (PLSR), support vector machine regression (SVMR), and wavelet neural network (WNN) calibration methods coupled with different preprocessing approaches were compared using three kinds of criteria, including the coefficient of determination (R2), root mean square error (RMSE), and residual prediction deviation (RPD). A total of 328 soil samples collected from the south bank of Hangzhou Bay were used as the dataset for the calibration–validation procedure and SOC content inversion. The effects of spectra preprocessing transformation methods were evaluated for raw spectra, Savitzky–Golay smoothing with the first derivatives of reflectance (FDR) and Savitzky–Golay smoothing with logarithm of reciprocal of the reflectance (log R−1). The results indicate that the SVMR is superior to the PLSR, and WNN models for SOC content prediction. The combination of the SVMR model with FDR provided the best prediction results for the SOC content, with R2p = 0.92, RPDP = 2.82, RMSEP = 0.36%, and a kappa correlation coefficient of interpolation as high as 0.97. The FDR of Vis–NIR spectroscopy combined with the SVMR model is recommended over the PLSR and WNN modeling techniques for the high-accuracy determination of the SOC content.

Development of a screening method for adulteration detection in coconut milk via mid-infrared spectroscopy: A study of linear and nonlinear regression method

In this paper, a robust image watermarking technique has been proposed in lifting wavelet transform (LWT) domain. Neural network is incorporated in the watermark extraction process to achieve improved robustness against different attacks. The integration of neural network with LWT makes the system robust to various attacks maintaining an adequate level of imperceptibility. The 3-level LWT coefficients are randomized and arranged in 2×2 non-overlapping blocks. Each block is modified according to a binary watermark bit. Randomization of coefficients and blocks has been done to enhance the security of the system. The binary watermark bit is also encrypted using another key. The scheme provides an average imperceptibility of 43.88 dB for a watermark capacity of 512 bits. The robustness has been observed against all the intentional and non-intentional attacks. The technique provides satisfactory robustness against different attacks such as noising attacks, de-noising attacks, lossy compression attacks, image processing attacks and some geometric attacks. The algorithm has been tested on a large image database containing different class of images.

Towards an effective cross-task mental workload recognition model using electroencephalography based on feature selection and support vector machine regression

Compressive sensing (CS) is a new technique that give an approach to reconstruct the signal with few numbers of observations or measurements. The CS is based on L1 -norm minimizations to find the sparse solutions and it is known as basis pursuit. In this investigation, CS scheme with different transforms is proposed. The three utilized transform techniques are: Discrete Fourier Transform(DFT), Discrete Cosine Transform(DCT) and Discrete Wavelet Transform(DWT) with daubechies1(DB1) and coiflets1(coif1) basis. The proposed system is tested by employing the following signals: Blocks, Heavy Sine, ‘Bumps’ and ‘Doppler’ which cover wide range of applications. The four testing signals are represented in sparse domain using different transforms. In order to threshold the coefficients of the signals in sparse domain, the universal threshold is utilized in the case of CS with FFT and DWT whereas, the universal threshold is modified to prune the DCT coefficients. The main aim of this study is to investigate the differences among CS with DFT, CS with DCT and CS with DWT, and consequently a suitable transform domain used with CS to be selected. The comparative study is established by assessing the performance of the proposed system using Root Mean Square Error (RMSE), output SNR, and the time required to reconstruct approximated signals. Simulation results have shown that the CS with DWT outperforms the CS with FFT and DCT. CS with DWT has achieved good RMSE values about (0.0014 to 3.359e-8) even when half of the signal elements are removed. CS with FFT and DCT enhanced the noisy Blocks and Bumps signals by 3dB and 1dB respectively, while it is failed to enhance noisy Heavy Sine and Doppler signals. CS with DWT of two basis and for single decomposition level have improved the noisy Blocks, Bumps, Heavy Sine and Doppler signals by 5dB, 4dB, 3dB, and 3dB respectively.

In the fingerprint recognition application utilizing more information other than minutiae is much helpful. We present here a fingerprint matching scheme based on transform features and their comparison. The technique described here obviates the need for extracting minutiae points to match fingerprint images. The proposed scheme uses Discrete Cosine Transform (DCT), Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT) to create feature vector for fingerprints. After finding out the core point, fingerprint image of size 64times64 is cropped around the core point. The transform is applied on the cropped image without any pre-processing. The transform coefficients are arranged in specific manner and are used to obtain the feature vector in terms of standard deviation. The fingerprint matching is based on the minimum Euclidean distance between two feature vectors. Here database is formed by capturing 8 images per person using 500 dpi optical scanner. Training images used to form feature vector are 2, 4 or 6 per person. In the matching phase either all or remaining images are checked in identification mode to find out the percentage recognition rate. Comparison for all the three transform is presented here and it is observed that DCT and DFT gives better result as compared DWT.