Squares-support Vector Machine Research Articles

Hybrid Ensemble Model for Predicting the Strength of FRP Laminates Bonded to the Concrete.

The goal of this work was to use a hybrid ensemble machine learning approach to estimate the interfacial bond strength (IFB) of fibre-reinforced polymer laminates (FRPL) bonded to the concrete using the results of a single shear-lap test. A database comprising 136 data was used to train and validate six standalone machine learning models, namely, artificial neural network (ANN), extreme machine learning (ELM), the group method of data handling (GMDH), multivariate adaptive regression splines (MARS), least square-support vector machine (LSSVM), and Gaussian process regression (GPR). The hybrid ensemble (HENS) model was subsequently built, employing the combined and trained predicted outputs of the ANN, ELM, GMDH, MARS, LSSVM, and GPR models. In comparison with the standalone models employed in the current investigation, it was observed that the suggested HENS model generated superior predicted accuracy with R2 (training = 0.9783, testing = 0.9287), VAF (training = 97.83, testing = 92.87), RMSE (training = 0.0300, testing = 0.0613), and MAE (training = 0.0212, testing = 0.0443). Using the training and testing dataset to assess the predictive performance of all models for IFB prediction, it was discovered that the HENS model had the greatest predictive accuracy throughout both stages with an R2 of 0.9663. According to the findings of the experiments, the newly developed HENS model has a great deal of promise to be a fresh approach to deal with the overfitting problems of CML models and thus may be utilised to forecast the IFB of FRPL.

Financial Time Series Model Based on Least Squares Support Vector Machine Predictive Control Algorithm in Financial Market

China’s financial market also faces some outstanding problems, namely, obvious structural imbalance in the size of the financial market, on the one hand, the absolute dominance of the size of the indirect financing market, mainly the bank lending market and on the other hand, the imbalance in the size of the direct financing market and the indirect financing market. At the same time, there is also a structural imbalance of the financial market among regions in China. How to verify the objective existence of such structural imbalance in different regions and measure the difference in the level of financial market development among regions is the focus of this paper. This paper constructs a financial market development level indicator system from three dimensions, equity market, bond market, and lending market, and measures the financial market development level by using inter-provincial panel data from 2001 to 2020 through neural network algorithm, time series model, and support vector machine algorithm, and analyzes the regional heterogeneity of financial market development on this basis. The results show that the overall market level of the financial industry in the eastern coastal region of China is the highest, but the intra-regional differences are also the most obvious; the overall market level of the financial industry in the northeastern region of China is the lowest, and the differences with the eastern region rise significantly; the market level of the financial industry at the provincial level in the central and eastern regions shows an all-time decreasing trend, while the western and northeastern regions of China show a convergence, then divergence, and then convergence trend. Therefore, differentiated financial policies should be implemented according to the stage of regional development in order to enhance the financial development level of each economic region and gradually narrow the regional financial development gap.

Detection of early decayed oranges by structured-illumination reflectance imaging coupling with texture feature classification models.

Citrus fruits are susceptible to fungal infection after harvest. To reduce the economic loss, it is necessary to reject the infected citrus fruit before storage and transportation. However, the infected area in the early stage of decay is almost invisible on the fruit surface, so the detection of early decayed citrus is very challenging. In this study, a structured-illumination reflectance imaging (SIRI) system combined with a visible light-emitting diode (LED) lamp and a monochrome camera was developed to detect early fungal infection in oranges. Under sinusoidal modulation illumination with spatial frequencies of 0.05, 0.15, and 0.25 cycles mm–1, three-phase-shifted images with phase offsets of − 2π/3, 0, and 2π/3 were acquired for each spatial frequency. The direct component (DC) and alternating component (AC) images were then recovered by image demodulation using a three-phase-shifting approach. Compared with the DC image, the decayed area can be clearly identified in the AC image and RT image (AC/DC). The optimal spatial frequency was determined by analyzing the AC image and pixel intensity distribution. Based on the texture features extracted from DC, AC, and RT images, four kinds of classification models including partial least square discriminant analysis (PLS-DA), support vector machine (SVM), least squares-support vector machine (LS-SVM), and k-nearest neighbor (KNN) were established to detect the infected oranges, respectively. Model optimization was also performed by extracting important texture features. Compared to all models, the PLS-DA model developed based on eight texture features of RT images achieved the optimal classification accuracy of 96.4%. This study showed for the first time that the proposed SIRI system combined with appropriate texture features and classification model can realize the early detection of decayed oranges.

A Multi-Joint Continuous Motion Estimation Method of Lower Limb Using Least Squares Support Vector Machine and Zeroing Neural Network based on sEMG signals

A Multi-Joint Continuous Motion Estimation Method of Lower Limb Using Least Squares Support Vector Machine and Zeroing Neural Network based on sEMG signals

Application of artificial intelligence models for prediction of groundwater level fluctuations: case study (Tehran-Karaj alluvial aquifer).

The nonlinear groundwater level fluctuations depend on the interaction of many factors such as evapotranspiration, precipitation, groundwater abstraction, and hydrogeological characteristics, making groundwater level prediction a complex task. Groundwater level changes are among the most critical issues in water resource management, which can be predicted to effectively provide management solutions to conserve renewable water resources. Understanding the aquifer status using numerical models is time-consuming and also is associated with inherent uncertainty; therefore, in recent decades, the application of artificial intelligence methods to predict water table fluctuations has significantly gained momentum. In this study, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS), and least square support vector machine (SVM) methods were utilized to predict groundwater level (GWL) with 1-, 2-, and 3-month lead time in Tehran-Karaj plain. Several input scenarios were developed considering groundwater levels, average temperature, total precipitation, total evapotranspiration, and average river flow on a monthly interval. The four error criteria, the correlation coefficient (R), root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), and mean absolute error (MAE), were the basis to evaluate the models. Results showed that all the applied methods could provide acceptable GWL prediction, but the ANFIS was the most accurate. However, the ANFIS model showed slightly better performance by yielding R = 0.98 for the training stage and R = 0.98 for the testing stage in the P84 observation well and the second combination of inputs and 1-month lead time. The outcomes also revealed that all the approaches mentioned above could appropriately predict GWL for the leading time of 1 and 2months, but the models provided unsatisfactory results for a 3-month leading time.

A novel method for prediction of refractive index of ionic liquids and alcohol binary systems

Recently, ionic liquids as a new group of solvent have successfully showed high capacity for applications in chemical engineering industries because of their desirable properties so knowledge of their properties such refractive indices of the binary system of alcohols and ionic liquids have high importance. This paper presents predictive tool based Least Squares Support Vector Machine optimized by Particle swarm optimization to determined refractive index of this binary system. In order to prepare the mentioned algorithm, a comprehensive databank contains 688 experimental refractive indices of the binary system of different alcohols and ionic liquids is used. The developed method shows satisfying results based on statistical and graphical comparisons. Furthermore the effects of different inputs on refractive index term has been investigated by a novel sensitivity analysis.

Detection of pears with moldy core using online full-transmittance spectroscopy combined with supervised classifier comparison and variable optimization

Moldy core is a serious disease that influences the quality of pears. There is no apparent difference between the diseased and sound fruit because this kind of disease mainly occurs in core of pears. In automatic detection and grading of pear quality, it is very desirable to detect pears with moldy core, especially at the early stage. This study used the visible and near infrared (Vis/NIR) full-transmittance spectroscopy, combined with wavelength selection algorithms and supervised classifiers, to discriminate 'Ya' pears with moldy core. The spectra of pears were collected using an online measurement system. Savitzky-Golay smoothing and standard normal variables (SGS-SNV) were used to preprocess the spectra. Four variable selection algorithms, including Monte Carlo-uninformative variable elimination (MC-UVE), bootstrapping soft shrinkage (BOSS), combination algorithm MC-UVE-SPA (successive projections algorithm), and combination algorithm BOSS-SPA, were used to extract the effective wavelengths. Four supervised classifiers, including support vector machine (SVM), least squares-support vector machine (LS-SVM), random forest (RF), and partial least square discriminant analysis (PLS-DA), were used for modeling. The two-class classification (i.e., sound and diseased) and the three-class classification (i.e., sound, slightly moldy, and severely moldy) models were established based on full wavelengths and selected wavelengths, respectively. The performance of all models was evaluated by considering some indicators such as 'Tolerability', ‘Stability’, ‘Accuracy’ and ‘Complexity’. It indicated that BOSS-SPA-PLS-DA and BOSS-SPA-LS-SVM were the optimal models for two-class and three-class classification with the overall accuracy of 99.76 % and 94.71 %, respectively. This study indicated that the online detection of 'Ya' pears with moldy core using the Vis/NIR full-transmittance spectra technology was feasible. Moreover, the proposed method has the potential to be used for online detection of early moldy core.

Study on Model Evolution Method Based on the Hybrid Modeling Technology With Support Vector Machine for an SOFC-GT System

Abstract The mechanism models of solid oxide fuel cell–gas turbine (SOFC-GT) systems are very useful to analyze the thermodynamic performance details, including the internal complex transfers of mass, heat, and electrochemical processes. However, several physical-property parameters in the mechanism model are unmeasurable and difficult to accurately quantify from the operation data when the inevitable degradation occurs. As a result, it is difficult for the mechanism model to accurately capture the SOFC electrochemical characteristic during the full operating cycle. In this paper, a model evolution method based on hybrid modeling technology is proposed to address this problem. A hybrid modeling framework of a SOFC-GT system is designed by combining a least squares-support vector machine algorithm (LS-SVM) electrochemical model with our previous mechanism model. The electrochemical characteristic of SOFC is easily identified and evolved by re-training the LS-SVM model from operating data, no longer needing a mechanism electrochemical model. The validated full-mechanism model from our previous work is taken to simulate a physical SOFC-GT system to generate the operating data. Various LS-SVM models are trained by different data sets. The comparison results demonstrate that the LS-SVM model trained by large-size data set 3 performs the highest accuracy in predicting the local current density. The maximum absolute error of prediction is only about 1.379 A/m2, and the prediction mean square error of the normalized test data reaches 4.36 × 10−9. Then, the LS-SVM hybrid model is applied to evaluate the thermodynamic performance of a SOFC-GT system. The comparison results between the hybrid model and our previous full-mechanism model show that the hybrid model can accurately predict the SOFC-GT system performance. The maximum error is 1.97% at the design condition and 0.60% at off-design conditions. Therefore, the LS-SVM hybrid model is significant for accurately identifying the real electrochemical characteristic from operation data for a physical SOFC-GT system during the full operation cycle.

Quantitative and direct serum albumin detection by label-free SERS using tunable hydroxyapatite nanostructure for prostate cancer detection

With the aid of good biocompatibility and stability with hydroxyapatite (HAp) in protein separation and adsorption fields, we developed a novel extraction-isolation albumin analysis method by relying on the specific adsorption capacity of HAp, combining with surface-enhanced Raman spectroscopy (SERS) for prostate cancer screening. Two different nanostructures of HAp particles, including the HAp flower and HAp sphere, were synthesized with a hydrothermal method, and the targeted binding and extraction abilities of serum albumin of these two HAp particles were compared. By changing the morphology of the nanostructure, the albumin-adsorption capacity of HAp varied significantly. Compared with spherical HAp particles, HAp flower particles have more albumin binding sites per unit area. Thus, the HAp flower displayed the superior capacity for adsorption-release of albumin, which was further employed for clinical prostate cancer screening. Based on the superior adsorption-extraction ability of albumin of HAp flower, serum albumin was adsorbed and extracted by HAp flower from serum samples of prostate cancer patients (n = 30) and healthy volunteers (n = 30), and mixed with silver colloids to perform SERS spectral analysis. The partial least square-support vector machines (PLS-SVM) model is used to analyze the obtained serum albumin SERS spectra and establish the diagnostic model, the diagnostic accuracy was up to 95.00% for differentiating the normal volunteer from prostate patient groups. The results demonstrate that the PLS-SVM model provides superior performance in the classification of a prostate cancer diagnosis. Due to the advantages of simplicity and rapidness, the HAp flower-adsorbed-released albumin combined with SERS was expected to become a promising tool for prostate cancer detection.

Research on Hyper-Parameter Optimization of Activity Recognition Algorithm Based on Improved Cuckoo Search

Activity recognition methods often include some hyper-parameters based on experience, which greatly affects their effectiveness in activity recognition. However, the existing hyper-parameter optimization algorithms are mostly for continuous hyper-parameters, and rarely for the optimization of integer hyper-parameters and mixed hyper-parameters. To solve the problem, this paper improved the traditional cuckoo algorithm. The improved algorithm can optimize not only continuous hyper-parameters, but also integer hyper-parameters and mixed hyper-parameters. This paper validated the proposed method with the hyper-parameters in Least Squares Support Vector Machine (LS-SVM) and Long-Short-Term Memory (LSTM), and compared the activity recognition effects before and after optimization on the smart home activity recognition data set. The results show that the improved cuckoo algorithm can effectively improve the performance of the model in activity recognition.

Knowledge-based rigorous machine learning techniques to predict the deliverability of underground natural gas storage sites for contributing to sustainable development goals

This study presents a method to develop a series of unique deliverability smart models for underground natural gas storage (UNGS) in different types of target formations. The natural gas supply loop is defined by periodic mismatches between demand and supply. Efficient and faster approaches for forecasting UNGS deliverability may not only assist stakeholders but also the competitive natural gas industry. Due to this fact, this article suggests a series of robust deliverability estimation models for 387 UNGS sites in depleted fields, aquifers, and salt domes based on rigorous machine learning (ML) techniques. To this end, the potential of three ML algorithms, including Gaussian Process Regression (GPR), Least Squares Support Vector Machine (LSSVM), and Extra Tree (ET), is employed. To assess and compare the proposed models, statistical parameters including coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and standard deviation (SD) were employed. Accordingly, in the case of depleted fields, the GPR, LSSVM, and ET paradigms show overall R2, RMSE, and MAE of 0.999999998, 4.75E−06, 0.00021, and 0.00021. For salt domes, the GPR, LSSVM, and ET models indicate overall R2, RMSE, and MAE of 0.987, 0.0046, and 0.11. Finally, for aquifers, the GPR, LSSVM, and ET algorithms represent overall R2, RMSE, and MAE of 0.999999997, 7.1094E−06, and 0.0002102. The prediction performance reveals that the GPR model is superior to the LSSVM and ET models. This study found that the proposed intelligent models could be utilized as a template for fast estimating the deliverability of UNGS in depleted fields, aquifers, and salt domes with high accuracy. In the end, the outcomes of this study contribute to a deeper understanding of the critical role of machine learning in resolving the difficulty of forecasting the UNSG on cleaner production and sustainable development strategies.

Privacy-Preserving Outsourcing Scheme for SVM on Vertically Partitioned Data

Support vector machine (SVM) is an important technique for data classification. Traditional SVM assumes free access to data. If the data are split and held by different users, for privacy reasons, users are likely unwilling to submit their data to a third party for classification. In this paper, by using additive homomorphic encryption and random transformations (matrix transformation and vector decomposition), we design a privacy-preserving outsourcing scheme for conducting Least Squares SVM (LS-SVM) classification on vertically partitioned data. In our system, multiple data owners (users) submit their encrypted data to two non-colluding service providers, which conduct SVM algorithm on it. During the execution of our algorithm, neither service provider learns anything about the input data, the intermediate results, or the predicted result. In other words, our algorithm is encrypted in the whole process. Extensive theoretical analysis and experimental evaluation demonstrate the correctness, security, and efficiency of the method.

A machine learning-based weather prediction model and its application on smart irrigation

Weather prediction is paramount for many applications and scenarios, among them is agriculture. In order to efficiently irrigate the crops with the exact needed water amount, weather forecasting can be used to optimize the quantity of required irrigation water such that the crops are neither dried up nor over-irrigated. This paper proposes a Machine Learning (ML)-based weather forecasting model, which utilizes the Social Spider Algorithm-Least Square-Support Vector Machine (SSA-LS-SVM) algorithm. The simulation results are used to predict the prime weather and soil parameters such as the atmospheric temperature, pressure, and soil humidity for 24, 48, and 72 hours based on previous 39 days’ hourly data for Amman city. The predicted values showed low relative mean square errors compared with the actual values and the LS-SVM predictor.

1035-P: Early Pregnancy Alterations in the Metabolome of Obese Women

Obesity in human pregnancy associates with changes in the glucose-insulin axis. We hypothesized that these changes affect the maternal metabolome already in the first trimester, and thus aimed to identify these metabolites. We performed untargeted metabolomics (HPLC-MS/MS) on maternal serum (n=111, gestational age week 4+0-11+6) . In addition to BMI and leptin as measures of obesity and adiposity, we metabolically phenotyped women by their fasting glucose, C-peptide and insulin sensitivity index (ISHOMA) . We identified 2449 serum metabolites of which 277 were annotated. After stringent filtering employing a combination of univariate and multivariate analyses with multiple confounders and machine learning methods (Partial Least Squares Discriminant Analysis, Random Forest and Support Vector Machine) we identified 14 metabolites robustly and significantly (FDR<0.1) associated with at least one exposure (BMI, leptin, glucose, C-peptide, ISHOMA) . Among these, palmitoleoyl ethanolamine (POEA) , an endocannabinoid-like lipid endogenously synthesized from palmitoleic acid, was consistently associated with C-peptide in all analyses explaining 21% of variance in POEA concentration. Decreased POEA levels in T2D and its affinity for the novel endocannabinoid GRP1receptor made it a strong candidate for further exploration. In network analysis most metabolites correlating with POEA and regulated by C-peptide, were amino acids or dipeptides (n=9, 35%) , followed by lipids (n=7, 27%) . We conclude that the metabolome of overweight/obese pregnant women is already altered early in pregnancy. Changes of POEA concentration in pregnant women with obesity-associated hyperinsulinemia may reflect dysfunctional endocannabinoid signalling and, thus, compromise pregnancy maintenance. Disclosure J.Bandres-meriz: None. C.Kunz: None. J.Havelund: None. N.J.Færgeman: None. I.Hurtado de mendoza: None. A.Majali-martinez: None. R.Ensenauer: None. G.Desoye: None. Funding Austrian Science Fund 339 FWF (DOC 31-B26) and the Medical University Graz through the PhD Program Inflammatory Disorders in Pregnancy (DP-iDP)

Machine learning-based bridge cable damage detection under stochastic effects of corrosion and fire

This paper proposes a novel machine learning-based cable damage detection model to investigate the upper and lower bounds of bridges’ cable damage degrees under the effects of corrosion and fire. In the proposed approach, the surrogate model for bridge cable damage detection under stochastic effects of corrosion and fire was established by combining machine learning and finite-element analysis to estimate the remaining life of cables. Then the accuracy and generalization performance of three typical machine learning methods for cable damage prediction are compared, such as Back Propagation neural network(BPNN), Radial Basis Function neural network(RBFNN) and Least Square-Support Vector Machine (LS-SVM). It is conducted that LS-SVM owns better prediction accuracy for cable damage under the coupling effects of corrosion and fire than the others. Additionally, the LS-SVM surrogate model combined with stochastic analysis and time-dependent deterioration model of steel wires under corrosion and fire is used to obtain the upper and lower bounds of cable damage under coupling effect of corrosion and fire. The proposed surrogate model can assist management in diagnosing and evaluating cable damage more quickly, efficiently, and flexibly once the real-time monitoring data is obtained. In addition, the surrogate model can guide bridge maintenance in advance.

Estimating the distribution of chlorophyll content in CYVCV infected lemon leaf using hyperspectral imaging

Yellow vein clearing disease of lemon caused by the citrus yellow vein clearing virus (CYVCV) is a new devastating disease in lemon. Chlorophyll contents and distributions are critical biochemical parameters to assess this new disease. The objective of this study was to develop an efficient method for detecting chlorophyll contents and distributions in lemon leaves infected with CYVCV. Spectral reflectance and chlorophyll content data of healthy, nitrogen deficient, pesticide-damaged and the corresponding CYVCV-infected leaves were obtained. Successive projections algorithm, random frog, competitive adaptive reweighted sampling (CARS), synergy interval partial least squares and synergy interval partial least squares combined with successive projections algorithm were applied for reducing data dimension, respectively. Prediction models were built by applying least squares-support vector machine algorithm (LS-SVM). The results showed that the characteristic wavelengths were basically located in “green peak” (near 550 nm), “red valley” (near 680 nm), and “red edge” (680–750 nm). Prediction model based on the wavelengths extracted by CARS achieved the optimal prediction results with determination coefficient of 0.94, relative prediction deviation of 3.91 and root mean square error of 0.10 in testing set. Thus, the CARS algorithm combined with LS-SVM was applied for distributions of chlorophyll contents. The chlorophyll content of each pixel of the six types of leaves were displayed, and significantly reduced in the mesophyll tissue near the veins of CYVCV-infected leaves. These results suggest that the hyperspectral imaging coupled with CARS and LS-SVM can be used to efficiently measure the distribution of chlorophyll content in CYVCV infected lemon leaf, which provides a reference for a better understanding of the symptoms of disease.

Lane-Level Regional Risk Prediction of Mainline at Freeway Diverge Area.

Real-time regional risk prediction can play a crucial role in preventing traffic accidents. Thus, this study established a lane-level real-time regional risk prediction model. Based on observed data, the least squares-support vector machines (LS-SVM) algorithm was used to identify each lane region of the mainline, and the initial traffic parameters and surrogate safety measures (SSMs) were extracted and aggregated. The negative samples that characterized normal traffic and the positive samples that characterized regional risk were identified. Mutual information (MI) was used to determine the information gain of various feature variables in the samples, and the key feature variables affecting the regional conditions were tested and screened by means of binary logit regression analysis. Upon screening the variables and corresponding labels, the construction and verification of a lane-level regional risk prediction model was completed using the catastrophe theory. The results showed that lane difference is an important parameter to reduce the uncertainty of regional risk, and its odds ratio (OR) was 16.30 at the 95% confidence level. The 10%-quantile modified time to collision (MTTC) inverse, the speed difference between lanes, and 10%-quantile headway (DHW) had an obvious influence on regional status. The model achieved an overall accuracy of 86.50%, predicting 84.78% of regional risks with a false positive rate of 13.37% and 86.63% of normal traffic with a false positive rate of 15.22%. The proposed model can provide a basis for formulating individualized active traffic control strategies for different lanes.

Optimization of Ultrasonic-Assisted Extraction of Active Components and Antioxidant Activity from Polygala tenuifolia: A Comparative Study of the Response Surface Methodology and Least Squares Support Vector Machine

Dried roots of Polygala tenuifolia (YuanZhi in Chinese) are widely used in Chinese herbal medicine. These components in YuanZhi have significant anti-oxidation properties owing to high levels of 3,6’-disinapoylsucrose (DISS) and Polygalaxanthone III (PolyIII). In order to efficiently extract natural medicines, response surface methodology (RSM) and least squares support vector machine (LSSVM) were used for the modeling and optimization of ultrasound-assisted extraction of DISS and PolyIII together to determine the antioxidant activity of the extracts obtained from YuanZhi. For the optimal combination of the comprehensive yield of DISS and PolyIII (Y), the Box-Behnken design (BBD) was used to improve extraction time (X1), extraction temperature (X2), liquid–solid ratio (X3), and ethanol concentration (X4). The optimal process parameters were determined to be as follows: extraction time, 93 min; liquid–solid ratio, 40 mL/g; extraction temperature, 48 °C; and ethanol concentration, 67%. With these conditions, the predictive optimal combination comprehensive evaluation value is 13.0217. It was clear that the LS-SVM model had higher accuracy in predictive and optimization capabilities, with higher antioxidant activity and lower relative deviations values, than did RSM. Hence, the LS-SVM model proved to be more effective for the analysis and improvement of the extraction process.

Fault Diagnosis of Bearing Based on Improved Refined Composite Hierarchical Fuzzy Entropy and Least Squares Support Vector Machine

Fault Diagnosis of Bearing Based on Improved Refined Composite Hierarchical Fuzzy Entropy and Least Squares Support Vector Machine

Label-free detection of bladder cancer and kidney cancer plasma based on SERS and multivariate statistical algorithm

In this study, we mainly aimed to investigate the diagnostic potential of surface-enhanced Raman spectroscopy for bladder cancer and kidney cancer which are the most common cancers of the urinary system, and evaluate the classification ability of three statistical algorithms: principal component analysis-linear discriminate analysis (PCA-LDA), partial least square-random forest (PLS-RF), and partial least square-support vector machine (PLS-SVM). The plasma of 26 bladder cancer patients, 38 kidney cancer patients and 39 normal subjects was mixed with the same volume of silver nanoparticles, respectively, and then high-quality SERS signal was obtained. The SERS spectra in the range of 400–1800 cm−1 were compared and analyzed. There were some significant differences in SERS peak intensity, which may reflect the changes in the content of some biomacromolecules in the plasma of cancer patients. Based on the three algorithms of PCA-LDA, PLS-RF and PLS-SVM, the classification accuracy of SERS spectra of plasma from cancer patients and normal subjects was 98.1%, 100% and 100%, respectively.In addition, the classification accuracy of the three diagnostic algorithms to classify the SERS spectra of bladder cancer and kidney cancer was 81.3%, 91.7%, and 98.4%, respectively. This exploratory work demonstrates that SERS combined with PLS-SVM algorithm has superior performance for clinical screening of bladder cancer and kidney cancer through peripheral plasma.