Support Vector Classification Model Research Articles

A maximum entropy-driven support vector classification model for seismic collapse fragility curves estimation of reinforced concrete frame structures

A novel method called maximum entropy-driven support vector classification (MED-SVC) for estimating the seismic collapse fragility curves of reinforced concrete (RC) frames is proposed in this paper. In comparison to kernel-based learning (KBL) methods, a subset selection process based on the maximum Renyi entropy criterion is embedded into the proposed MED-SVC, which can actively select an informative small-scale subset from a large-scale training dataset. The selected subset can accurately reflect the data distribution of the large-scale training dataset, which can be further used to explicitly establish a high-dimensional feature variable. Due to this, the computational complexity of training the proposed MED-SVC model only depends on the dimension of the feature variable, which can effectively solve the problem related to the low efficacy of training the KBL model in the context of a large-scale training dataset. Meanwhile, the proposed MED-SVC model can also reduce the negative effect of randomness in the subset selection process on predictive performance. The robustness, accuracy, and efficiency of the proposed MED-SVC model are thoroughly validated by comparison with random sampling driven SVC (RSD-SVC), KBL methods, random forest, neural network, and naive bayes based on 33,000 damage data of RC frames under earthquakes. Then, the proposed MED-SVC model is applied to estimate the seismic collapse fragility curve of an RC frame structure outside the aforementioned dataset. The results show that the proposed MED-SVC model can generate seismic collapse fragility curves that agree well with those produced by the finite element method (FEM) and performs more robustly than RSD-SVC.

Data acquisition in a simplified turbine model for prediction of unsteady vortex phenomena

Abstract The utilization of machine learning in finding decisions of engineering problems is the optimal way. This study presents a new tool that applies machine learning algorithms, to predict the frequency response of an unsteady vortex phenomenon known as the precessing vortex core (PVC) that appears in a conical draft tube behind a runner. The basic values involved in Linear Support Vector Classification model training are the two components of the time-averaged velocity profile at the cone diffuser inlet and cone angle which should be accurately measured. The paper introduces the approach to accumulating an experimental database and conducting primary analysis of the implemented regimes of swirling flow in a simplified hydraulic turbine model. It was obtained that it is necessary to clearly identify the zone of recirculation flow. The presence of this zone is a necessary, but not sufficient condition for the formation of the PVC in the flow. Injection of an axial jet in a situation with moderate swirl flow allows to shift the PVC frequency about by 10% relative to the PVC frequency without an additional jet.

Intelligent model for the detection and classification of encrypted network traffic in cloud infrastructure

This article explores detecting and categorizing network traffic data using machine-learning (ML) methods, specifically focusing on the Domain Name Server (DNS) protocol. DNS has long been susceptible to various security flaws, frequently exploited over time, making DNS abuse a major concern in cybersecurity. Despite advanced attack, tactics employed by attackers to steal data in real-time, ensuring security and privacy for DNS queries and answers remains challenging. The evolving landscape of internet services has allowed attackers to launch cyber-attacks on computer networks. However, implementing Secure Socket Layer (SSL)-encrypted Hyper Text Transfer Protocol (HTTP) transmission, known as HTTPS, has significantly reduced DNS-based assaults. To further enhance security and mitigate threats like man-in-the-middle attacks, the security community has developed the concept of DNS over HTTPS (DoH). DoH aims to combat the eavesdropping and tampering of DNS data during communication. This study employs a ML-based classification approach on a dataset for traffic analysis. The AdaBoost model effectively classified Malicious and Non-DoH traffic, with accuracies of 75% and 73% for DoH traffic. The support vector classification model with a Radial Basis Function (SVC-RBF) achieved a 76% accuracy in classifying between malicious and non-DoH traffic. The quadratic discriminant analysis (QDA) model achieved 99% accuracy in classifying malicious traffic and 98% in classifying non-DoH traffic.

SAFEPARK: Vehicle Detection and Traffic Violation Parking Management System

Abstract: The escalating motorcycle accident rates highlight the pressing need for improved safety measures. Helmets, a crucial safety gear, are often neglected, contributing significantly to fatalities. This paper addresses the pervasive issue of noncompliance with motorcycle safety rules, focusing on helmet usage and triple riding. Existing systems for monitoring lack precision, prompting our proposed Bike Traffic Violation System. Leveraging Haar Cascade and YOLOv3 models, it identifies motorcycles, detects riders without helmets, instances of triple riding, and even empty parking spots with unprecedented accuracy. The Machine Learning component employs a Support Vector Classification model, bolstered by 4-fold cross-validation, ensuring robustness. This innovative system provides real-time insights into traffic violations, enabling prompt interventions by law enforcement agencies. It overcomes manual identification shortcomings, offering a comprehensive solution for enforcement and awareness campaigns. In summary, our Bike Traffic Violation System not only advances automated traffic rule monitoring but introduces a novel methodology for precise detection, significantly contributing to enhanced road safety and accident prevention.

A machine learning constitutive model for plasticity and strain hardening of polycrystalline metals based on data from micromechanical simulations

Machine learning (ML) methods have emerged as promising tools for generating constitutive models directly from mechanical data. Constitutive models are fundamental in describing and predicting the mechanical behavior of materials under arbitrary loading conditions. In recent approaches, the yield function, central to constitutive models, has been formulated in a data-oriented manner using ML. Many ML approaches have primarily focused on initial yielding, and the effect of strain hardening has not been widely considered. However, taking strain hardening into account is crucial for accurately describing the deformation behavior of polycrystalline metals. To address this problem, the present study introduces an ML-based yield function formulated as a support vector classification model, which encompasses strain hardening. This function was trained using a 12-dimensional feature vector that includes stress and plastic strain components resulting from crystal plasticity finite element method (CPFEM) simulations on a 3-dimensional RVE with 343 grains with a random crystallographic texture. These simulations were carried out to mimic multi-axial mechanical testing of the polycrystal under proportional loading in 300 different directions, which were selected to ensure proper coverage of the full stress space. The training data were directly taken from the stress–strain results obtained for the 300 multi-axial load cases. It is shown that the ML yield function trained on these data describes not only the initial yield behavior but also the flow stresses in the plastic regime with a very high accuracy and robustness. The workflow introduced in this work to generate synthetic mechanical data based on realistic CPFEM simulations and to train an ML yield function, including strain hardening, will open new possibilities in microstructure-sensitive materials modeling and thus pave the way for obtaining digital material twins.

A complexity evaluation system for mitral valve repair based on preoperative echocardiographic and machine learning

BackgroundTo develop a novel complexity evaluation system for mitral valve repair based on preoperative echocardiographic data and multiple machine learning algorithms. MethodsFrom March 2021 to March 2023, 231 consecutive patients underwent mitral valve repair. Clinical and echocardiographic data were included in the analysis. The end points included immediate mitral valve repair failure (mitral replacement secondary to mitral repair failure) and recurrence regurgitation (moderate or greater mitral regurgitation [MR] before discharge). Various machine learning algorithms were used to establish the complexity evaluation system. ResultsA total of 231 patients were included in this study; the median ejection fraction was 66% (63–70%), and 159 (68.8%) patients were men. Mitral repair was successful in 90.9% (210 of 231) of patients. The linear support vector classification model has the best prediction results in training and test cohorts and the variables of age, A2 lesions, leaflet height, MR grades, and so on were risk factors for failure of mitral valve repair. ConclusionThe linear support vector classification prediction model may allow the evaluation of the complexity of mitral valve repair. Age, A2 lesions, leaflet height, MR grades, and so on may be associated with mitral repair failure.

US recession prediction using statistical and natural language processing methods

This study mainly predicts the recession in the United States. We build our model based on the data of more than ten recessions experienced by the United States since the mid-20th century. Our research can be divided into two parts, one part is a machine learning model constructed using econometrics theory, and the other part is a text analysis model based on natural language processing (NLP) techniques. We collected quarterly data from January 1, 1950, to September 1, 2020, to examine each historical recessionary period. We select key macroeconomic variables such as real GDP growth rate, unemployment rate, and interest rates as variables to build the machine learning model. Depending on the data type and model accuracy, we adopted three models, Support Vector Classification (SVC), Naive Bayes, and Logistic Regression, where the SVC model has the highest accuracy, above 80%. Regarding NLP models, we choose the reports based on Bank of International Settlements central bank speeches (BIS) to complete the relevant analysis. We evaluate bag-of-words and convolutional neural networks in conjunction with Epoch loss to determine how well the model's predictions match the actual data. Although we have debugged the NLP model many times, its accuracy still needs to be higher than that of the econometric model. How to effectively improve the prediction accuracy of the NLP model will be the main problem we hope to solve in the future.

A novel method for petroleum and natural gas resource potential evaluation and prediction by support vector machines (SVM)

Petroleum and natural gas resources (PNGR) are some of the major forms of fossil energy that are important for the development of industry and energy security. Along with the growing demand of petroleum consumption and the requirement for enhancing drilling success rate, reducing the exploration risk and saving exploration cost, prediction method for PNGR potential with high accuracy and wide practicability is needed. However, the existing PNGR evaluation and prediction methods based on traditional statistical principles are far from meeting the requirements of the present petroleum exploration and exploitation. Therefore, this study introduces a novel method for PNGR potential prediction by applying support vector machines (SVM) in the context of the rapid development of artificial intelligence and machine learning. This novel machine learning methodology first proposed a combination of support vector classification (SVC) for hydrocarbon accumulation probability prediction and then support vector regression (SVR) for reserve abundance prediction. The combining use of classification and regression model can fully utilize the professional knowledge of petroleum geology and the powerful data processing capabilities of machine learning algorithms and hence significantly improve the performance of the method. Furthermore, the dataset is set based on petroleum geology knowledge with the feature variables of source rock, sandstone reservoirs, sealing capacity and hydrocarbon migration, whose distribution are predictable and thus ensures the predictive effect in practical petroleum exploration. The results show that the testing accuracy of the hydrocarbon accumulation probability evaluation model by SVC ranges from 80% to 100% with an average of 88.92%. The performance of the SVR model for evaluating reserve abundance also performs well with the highest correlation coefficient of 0.767. In addition, several validation ways are applied for testing the reliability and stability of the model. For a hold-out test for a new zone, the model provides precise prediction of hydrocarbon accumulation probability and reserve abundance with an accuracy of 72.5% and a correlation coefficient of 0.744. The evaluation metric of the F1-score shows an average of 0.91 for the SVC models, the 4-fold cross-validation shows an average correlation coefficient of 0.663 for SVR model, which indicates the good performance of the SVC and SVR model. To conclude, this study not only provides an intelligent ML method system for PNGR potential precisely evaluation and prediction with the combination of SVC and SVR which is firstly used by application of ML in petroleum industry field, but is also significant for the application of ML in petroleum and natural gas exploration and exploitation.

AraMAMS: Arabic Multi-Aspect, Multi-Sentiment Restaurants Reviews Corpus for Aspect-Based Sentiment Analysis

The abundance of data on the internet makes analysis a must. Aspect-based sentiment analysis helps extract valuable information from textual data. Because of limited Arabic resources, this paper enriches the Arabic dataset landscape by creating AraMA, the first and largest Arabic multi-aspect corpus. AraMA comprises 10,750 Google Maps reviews for restaurants in Riyadh, Saudi Arabia. It covers four aspect categories—food, environment, service, and price—along with four sentiment polarities: positive, negative, neutral, and conflict. All AraMA reviews are labeled with at least two aspect categories. A second version, named AraMAMS, includes reviews labeled with at least two different sentiments, making it the first Arabic multi-aspect, multi-sentiment dataset. AraMAMS has 5312 reviews covering the same four aspect categories and sentiment polarities. Both corpora were evaluated using naïve biased (NB), support vector classification (SVC), linear SVC, and stochastic gradient descent (SGD) models. In the AraMA corpus, the aspect categories task achieved a 91.41% F1 measure result using the SVC model, while in the AraMAMS corpus, the best F1 measure result for aspect categories task reached 91.70% using the linear SVC model.

Inspection of Enamel Removal Using Infrared Thermal Imaging and Machine Learning Techniques.

Within aerospace and automotive manufacturing, the majority of quality assurance is through inspection or tests at various steps during manufacturing and assembly. Such tests do not tend to capture or make use of process data for in-process inspection and certification at the point of manufacture. Inspection of the product during manufacturing can potentially detect defects, thus allowing consistent product quality and reducing scrappage. However, a review of the literature has revealed a lack of any significant research in the area of inspection during the manufacturing of terminations. This work utilises infrared thermal imaging and machine learning techniques for inspection of the enamel removal process on Litz wire, typically used for aerospace and automotive applications. Infrared thermal imaging was utilised to inspect bundles of Litz wire containing those with and without enamel. The temperature profiles of the wires with or without enamel were recorded and then machine learning techniques were utilised for automated inspection of enamel removal. The feasibility of various classifier models for identifying the remaining enamel on a set of enamelled copper wires was evaluated. A comparison of the performance of classifier models in terms of classification accuracy is presented. The best model for enamel classification accuracy was the Gaussian Mixture Model with expectation maximisation; it achieved a training accuracy of 85% and enamel classification accuracy of 100% with the fastest evaluation time of 1.05 s. The support vector classification model achieved both the training and enamel classification accuracy of more than 82%; however, it suffered the drawback of a higher evaluation time of 134 s.

A rapid method of identifying mastitis degrees of bovines based on dielectric spectra of raw milk

AbstractBovine mastitis is the most complex and costly disease in the dairy industry worldwide. Somatic cell count (SCC) is accepted as an international standard for diagnosing mastitis in cows, but most instruments used to detect SCC are expensive, or the detection speed is very low. To develop a rapid method for identifying mastitis degree, the dielectric spectra of 301 raw milk samples at three mastitis grades, i.e., negative, weakly positive, and positive grades based on SCC, were obtained in the frequency range of 20–4500 MHz using coaxial probe technology. Variable importance in the projection method was used to select characteristic variables, and principal component analysis (PCA) and partial least squares (PLS) were used to reduce data dimension. Linear discriminant analysis, support vector classification (SVC), and feed-forward neural network models were established to predict the mastitis degrees of cows based on 22 principal components and 24 latent variables obtained by PCA and PLS, respectively. The results showed that the SVC model with PCA had the best classification performance with an accuracy rate of 95.8% for the prediction set. The research indicates that dielectric spectroscopy technology has great potential in developing a rapid detector to diagnose mastitis in cows in situ or online.

Constructing a high-performance self-training model based on support vector classifiers to detect gold mineralization-related geochemical anomalies for gold exploration targeting

In an area where mineral deposits have been discovered, both anomaly detection algorithms and supervised classification algorithms can be adopted to detect mineralization-related geochemical anomalies for mineral exploration targeting. However, anomaly detection algorithms themselves cannot use known mineral deposits as the supervisors to improve the performance of geochemical anomaly detection models. Supervised classification algorithms can not properly deal with the extreme class-imbalance of geochemical exploration data in the establishment of classification models. Therefore, neither anomaly detection models nor supervised classification models perform well in the detection of mineralization-related geochemical anomalies. In order to obtain a high-performance mineral exploration targeting model, the self-training algorithm was adopted to construct a self-training model based on support vector classifiers to detect gold mineralization-related geochemical anomalies in the Chengde area in Hebei Province (China). The self-training model was compared with the support vector classification model, logistic regression model and five dictionary learning models in terms of area under the curve (AUC) and lift index. The AUC value of the self-training model (0.89) is much higher than those of the two supervised classification models (0.81–0.82) and five anomaly detection models (0.79–0.81). The lift index of the self-training model (11.87) is also much higher than those of the two supervised classification models (2.77–4.04) and five anomaly detection models (2.64–3.48). Therefore, the self-training model performs much better than the two supervised classification models and five anomaly detection models in the detection of gold mineralization-related geochemical anomalies. The gold mineralization-related geochemical anomalies detected by the self-training model highly conform to the metallogenic and geological features in the study area. Therefore, it is a prospective method to establish a self-training model to detect mineralization-related geochemical anomalies in an area where mineral deposits have been discovered.

Deqi Sensation to Predict Acupuncture Effect on Functional Dyspepsia: A Machine Learning Study.

Objectives The aim of the study was to predict the effect of acupuncture for treating functional dyspepsia (FD) using the support vector machine (SVM) techniques based on initial deqi sensations of patients. Methods This retrospective study involved 90 FD patients who had received four weeks of acupuncture treatment. The support vector classification model was used to distinguish higher responders (patients with Symptom Index of Dyspepsia improvement score ≥ 2) from lower responders (patients with Symptom Index of Dyspepsia improvement score < 2). A support vector regression model was used to predict the change in the Symptom Index of Dyspepsia at the end of acupuncture treatment. Deqi sensations of patients in the first acupuncture treatment of a 20-session acupuncture intervention were defined as features and used to train models. Models were validated by 10-fold cross-validation and evaluated by accuracy, specificity, sensitivity, the area under the receive-operating curve, the coefficient of determination (R2), and the mean squared error. Results The two models could predict the efficacy of acupuncture successfully. These models had an accuracy of 0.84 in predicting acupuncture response, and an R2 of 0.16 in the prediction of symptom improvements, respectively. The presence or absence of deqi sensation, the duration of deqi sensation, distention, and pain were finally selected as significant predicting features. Conclusion Based on the SVM algorithms and deqi sensation, the current study successfully predicted the acupuncture response as well as clinical symptom improvement in FD patients at the end of treatment. Our prediction models are expected to promote the clinical efficacy of acupuncture treatment for FD, reduce medical expenditures, and optimize the allocation of medical resources.

Landslide Susceptibility Mapping Based on the Germinal Center Optimization Algorithm and Support Vector Classification

A landslide susceptibility model based on a metaheuristic optimization algorithm (germinal center optimization (GCO)) and support vector classification (SVC) is proposed and applied to landslide susceptibility mapping in the Three Gorges Reservoir area in this paper. The proposed GCO-SVC model was constructed via the following steps: First, data on 11 influencing factors and 292 landslide polygons were collected to establish the spatial database. Then, after the influencing factors were subjected to multicollinearity analysis, the data were randomly divided into training and testing sets at a ratio of 7:3. Next, the SVC model with 5-fold cross-validation was optimized by hyperparameter space search using GCO to obtain the optimal hyperparameters, and then the best model was constructed based on the optimal hyperparameters and training set. Finally, the best model acquired by GCO-SVC was applied for landslide susceptibility mapping (LSM), and its performance was compared with that of 6 popular models. The proposed GCO-SVC model achieved better performance (0.9425) than the genetic algorithm support vector classification (GA-SVC; 0.9371), grid search optimized support vector classification (GRID-SVC; 0.9198), random forest (RF; 0.9085), artificial neural network (ANN; 0.9075), K-nearest neighbor (KNN; 0.8976), and decision tree (DT; 0.8914) models in terms of the area under the receiver operating characteristic curve (AUC), and the trends of the other metrics were consistent with that of the AUC. Therefore, the proposed GCO-SVC model has some advantages in LSM and may be worth promoting for wide use.

Population and Age-Based Cardiorespiratory Fitness Level Investigation and Automatic Prediction.

Maximal oxygen consumption (VO2max) reflects aerobic capacity and is crucial for assessing cardiorespiratory fitness and physical activity level. The purpose of this study was to classify and predict the population-based cardiorespiratory fitness based on anthropometric parameters, workload, and steady-state heart rate (HR) of the submaximal exercise test. Five hundred and seventeen participants were recruited into this study. This study initially classified aerobic capacity followed by VO2max predicted using an ordinary least squares regression model with measured VO2max from a submaximal cycle test as ground truth. Furthermore, we predicted VO2max in the age ranges 21–40 and above 40. For the support vector classification model, the test accuracy was 75%. The ordinary least squares regression model showed the coefficient of determination (R2) between measured and predicted VO2max was 0.83, mean absolute error (MAE) and root mean square error (RMSE) were 3.12 and 4.24 ml/kg/min, respectively. R2 in the age 21–40 and above 40 groups were 0.85 and 0.75, respectively. In conclusion, this study provides a practical protocol for estimating cardiorespiratory fitness of an individual in large populations. An applicable submaximal test for population-based cohorts could evaluate physical activity levels and provide exercise recommendations.

DMSO Solubility Assessment for Fragment-Based Screening.

In this paper, we report comprehensive experimental and chemoinformatics analyses of the solubility of small organic molecules (“fragments”) in dimethyl sulfoxide (DMSO) in the context of their ability to be tested in screening experiments. Here, DMSO solubility of 939 fragments has been measured experimentally using an NMR technique. A Support Vector Classification model was built on the obtained data using the ISIDA fragment descriptors. The analysis revealed 34 outliers: experimental issues were retrospectively identified for 28 of them. The updated model performs well in 5-fold cross-validation (balanced accuracy = 0.78). The datasets are available on the Zenodo platform (DOI:10.5281/zenodo.4767511) and the model is available on the website of the Laboratory of Chemoinformatics.

Identifying Internet Addiction and Evaluating the Efficacy of Treatment Based on Functional Connectivity Density: A Machine Learning Study.

Although mounting neuroimaging studies have greatly improved our understanding of the neurobiological mechanism underlying internet addiction (IA), the results based on traditional group-level comparisons are insufficient in guiding individual clinical practice directly. Specific neuroimaging biomarkers are urgently needed for IA diagnosis and the evaluation of therapy efficacy. Therefore, this study aimed to develop support vector machine (SVM) models to identify IA and assess the efficacy of cognitive behavior therapy (CBT) based on unbiased functional connectivity density (FCD). Resting-state fMRI data were acquired from 27 individuals with IA before and after 8-week CBT sessions and 30 demographically matched healthy controls (HCs). The discriminative FCDs were computed as the features of the support vector classification (SVC) model to identify individuals with IA from HCs, and the changes in these discriminative FCDs after treatment were further used as features of the support vector regression (SVR) model to evaluate the efficacy of CBT. Based on the informative FCDs, our SVC model successfully differentiated individuals with IA from HCs with an accuracy of 82.5% and an area under the curve (AUC) of 0.91. Our SVR model successfully evaluated the efficacy of CBT using the FCD change ratio with a correlation efficient of 0.59. The brain regions contributing to IA classification and CBT efficacy assessment were the left inferior frontal cortex (IFC), middle frontal cortex (MFC) and angular gyrus (AG), the right premotor cortex (PMC) and middle cingulate cortex (MCC), and the bilateral cerebellum, orbitofrontal cortex (OFC) and superior frontal cortex (SFC). These findings confirmed the FCDs of hyperactive impulsive habit system, hypoactive reflecting system and sensitive interoceptive reward awareness system as potential neuroimaging biomarkers for IA, which might provide objective indexes for the diagnosis and efficacy evaluation of IA.

Machine Learning Model to Map Tribocorrosion Regimes in Feature Space

Degradation by wear and corrosion are frequently encountered in a variety of tribosystems, including materials and tools in forming operations. The combined effect of wear and corrosion, known as tribocorrosion, can result in accelerated material degradation. Interfacial conditions can affect this degradation. Tribocorrosion maps serve the purpose of identifying operating conditions at the interface for an acceptable rate of degradation. This paper proposes a machine learning-based approach to generate tribocorrosion maps, which can be used to predict tribosystem performance. Two tribocorrosion datasets from the published literature are used. The materials have been chosen based on the wide availability of their tribocorrosion data in the literature. First, unsupervised machine learning is used to identify and label clusters from tribocorrosion data. The identified clusters are then used to train a support vector classification model. The trained support vector machine is used to generate tribocorrosion maps. The generated maps are compared with those from the literature. The general approach can be applied to create tribocorrosion maps of materials widely used in material forming.

Fecal Multidimensional Assay for Non-Invasive Detection of Colorectal Cancer: Fecal Immunochemical Test, Stool DNA Mutation, Methylation, and Intestinal Bacteria Analysis.

BackgroundFecal immunochemical test (FIT), DNA mutation, DNA methylation, and microbial dysbiosis all showed promising in colorectal cancer (CRC) non-invasive detection. We assessed CRC detection with an assay combining all these strategies and investigated the effect of clinical features on the performance of this comprehensive test.MethodsWe performed a multidimensional analysis study using stool samples collected from 108 patients with CRC, 18 patients with colorectal adenoma, and 36 individuals with no evidence of colorectal disease. The multidimensional analysis of stool samples including FIT, stool DNA (sDNA) tests for three methylated genes (Septin9, NDRG4, BMP3) and three mutated genes (KRAS, BRAF, PI3KCA) using next generation sequencing as well as detection of stool bacteria level of Fusobacterium nucleatum and Parvimonas micra using qPCR method. We used a linear support vector classification model to analyze the data.ResultsThe sensitivity of FIT alone was 69.4% for CRC and 11.1% for adenoma. Separately, the sensitivity of the detection of intestinal bacteria, DNA mutation, and DNA methylation for CRC was 58.3, 50.0, and 51.9%, respectively. The combination of FIT and sDNA tests had a sensitivity of 81.5% for CRC (AUC: 0.93, better than FIT alone, P = 0.017) and 27.8% for adenoma with 94.4% specificity. Sensitivity of the multidimensional test to detect CRC with stage II (84.6%) and III (91.9%) CRC was relatively higher (88.2%) than that of patients with stage I (60.0%) and stage IV (75.0%) (P = 0.024). The rate of CRC detection increased with tumor size (P = 0.008) and age (P = 0.04). Interestingly, the rate of CRC detection was higher in smoking persons than non-smokers with marginal significance (P = 0.08).ConclusionsThe multidimensional assay of stool samples combining FIT and stool DNA tests further improved the diagnostic sensitivity for CRC. This could provide new approach for improvement of CRC screening and further demonstrations are warranted.

Prediction of landslide sharp increase displacement by SVM with considering hysteresis of groundwater change

The displacement-time curves of reservoir landslide mostly show step-by-step growth characteristics. The sharp increase of the displacement plays a significant role in the evolution process of the step-like landslide, so it is extremely important to accurately predict the suddenly change displacement of landslide. In order to overcome the shortcomings existed in the current displacement monitoring methods and prediction models for the mutation displacement of landslide, this paper proposed a hybrid machine learning displacement prediction model based on Support Vector Machine, including Support Vector Classification(SVC) and Support Vector Regression(SVR), optimized by Particle Swarm Optimization (SVC-PSO-SVR) and considering the hysteresis of groundwater level change. The Majiagou No. 1 Landslide in the Three Gorges Reservoir (TGR) Area, whose deformation shows typical step-by-step tendency, was illustrated. Base on the deep displacement data of Majiagou landslide from January 2016 to December 2017, the landslide deformation pattern can be divided into two states: stability and acceleration. Firstly, the SVC model was adopted to predict the time range of acceleration state. Then, considering the lag fluctuation of groundwater level, the concept of “equivalent reservoir level” was proposed. Finally, based on SVC model, the sharp increase displacement of landslide was predicted using PSO-SVR model. The proposed SVC-PSO-SVR model yielded the root mean square error (RMSE) of 0.08827 mm and the mean absolute percentage error (MAPE) of 0.02105 mm. Besides, the prediction accuracy of SVC can attain 96.49% (55 / 57). The results show that the model can accurately predict the time range and displacement of acceleration deformation section of Majiagou landslide. The proposed model is of great significance for landslide prediction and early warning.