SVM Prediction Research Articles

A Novel Approach for Asparagus Comprehensive Classification Based on TOPSIS Evaluation and SVM Prediction

A Novel Approach for Asparagus Comprehensive Classification Based on TOPSIS Evaluation and SVM Prediction

Prediction models for deep vein thrombosis after knee/hip arthroplasty: A systematic review and network meta-analysis.

Deep vein thrombosis (DVT) is one of the common complications after joint replacement, which seriously affects the quality of life of patients. We systematically searched nine databases, a total of eleven studies on prediction models to predict DVT after knee/hip arthroplasty were included, eight prediction models for DVT after knee/hip arthroplasty were chosen and compared. The results of network meta-analysis showed the XGBoost model (SUCRA 100.0%), LASSO (SUCRA 84.8%), ANN (SUCRA 72.1%), SVM (SUCRA 53.0%), ensemble model (SUCRA 40.8%), RF (SUCRA 25.6%), LR (SUCRA 21.8%), GBT (SUCRA 1.1%), and best prediction performance is XGB (SUCRA 100%). Results show that the XGBoost model has the best predictive performance. Our study provides suggestions and directions for future research on the DVT prediction model. In the future, well-designed studies are still needed to validate this model.

Construction and Optimization of Macroeconomic Data Forecasting Model Based on Machine Learning

Human capital was first viewed as a production component in macroeconomic development, but endogenous growth theories eventually replaced this perspective. The majority of earlier research used econometric models to investigate the GDP forecasting. Since machine learning models can efficiently resolve nonlinear interactions, this study offers a new perspective by examining the linkages using machine learning approaches. The prediction model for economic development was created for this reason using the best machine learning techniques, specifically the Support Vector Machine. In order to improve SVM prediction, the hyper parameters were optimised using the Bayes approach and several kernel functions. Three statistical metrics—the coefficient of determination, mean absolute error, and root mean square error are used to assess the models' effectiveness.

Multi-influencing Factor Weighted WPSO–SVM Prediction of Subway Tunnel Settlement under GRA Supports

Multi-influencing Factor Weighted WPSO–SVM Prediction of Subway Tunnel Settlement under GRA Supports

The measurement error prediction method of voltage transformer based on TCN-RBF

Accurate prediction of voltage transformer error is an important guarantee for the stable and economic operation of the power grid. This paper presents a measurement error prediction method for voltage transformers based on time convolution and radial basis neural networks (TCN-RBF). Firstly, the ensemble empirical mode decomposition is used to decompose the original error sequence into multiple components. Secondly, a TCN prediction model was established based on each component and historical error sequence to achieve error prediction. To further improve the prediction progress, the RBF model was used to further fit the predicted results with the environmental parameter data and confirm the final predicted value. The operation error of the transformer in a substation is tested. The test results show that compared with LSTM, SVM, BP, and other prediction models, this method has better performance in error prediction.

Exploring the machine learning approach to the cultural and ecological sustainable development of Fuzhou tea picking opera

Abstract The article introduces machine learning based on a support vector machine to illustrate the scenario of Fuzhou Tea Casting Opera, a folkloric non-heritage research and ecologically sustainable development, from the perspective of economic return prediction. Fine-grained information recognition and extraction of massive, unstructured UGC data are used for data collection preprocessing in this paper. Mostly, the relationship is established by analyzing the training samples to establish the connection between input and output data. The limited sample information is used to find the best compromise between the model’s complexity and learning ability. Then, search for the parameters that make the geometric interval the largest so as to construct the function and obtain the optimal hyperplane. In this paper, the kernel function is introduced using a ɛ -insensitive loss function. To obtain the decision function in different cases, two cases are modeled using support vector machine regression. Finally, the support vector machine model is used to predict the economic benefits of the Fuzhou tea-picking operation. The SVM prediction model’s prediction error of the economic benefits of tea-picking opera for the nine years from 2010 to 2018 is always maintained at a safe value below 0.035. The accuracy and stability of the prediction effect of the decision tree model and the gray prediction model are not as good as this paper’s model. It shows that the model presented in this paper is more suitable for predicting the benefits of the cultural and ecological sustainable development of Fuzhou tea-picking opera.

Prediction model of goaf coal temperature based on PSO-GRU deep neural network

The accurate determination of coal temperature in hidden space such as goaf has always been a worldwide problem that needs to be solved. The research of coal temperature prediction model has important practical significance for the accurate detection of loose coal temperature. Based on the coal natural ignition experiment and coal spontaneous combustion gas characterization index as the initial data set GRU neurons are used to mine the nonlinear relationship between the index gas and temperature, and the GRU model parameters are optimized by PSO to obtain the predicted value of coal body temperature. The results show that the predicted MAE value of the PSO-GRU model is 1.37 °C, 6.51 °C, 11.40 °C, 15.90 °C, 20.20 °C lower than that of the PSO-SVM, PSO-BP, BP, RF and SVM prediction models respectively. The RMSE value decreased by 0.45 °C, 4.44 °C, 10.33 °C, 15.71 °C and 24.24 °C respectively. The judgment coefficient R2 on the test set and the training set of the PSO-GRU model is 0.99, and the generalization, prediction accuracy and robustness are all good. The experimental results show that the inversion temperature error of the model is within the range of 3.87 %, the maximum temperature difference is 5.69 °C, and the average temperature difference is 2.83 °C, which can meet the accuracy requirements of the field temperature measurement.

Machine learning-based predictive model for the differential diagnosis of ≤ 5 cm gastric stromal tumor and gastric schwannoma based on CT images

The clinical symptoms of ≤ 5 cm gastric stromal tumor (GST) and gastric schwannoma (GS) are similar, but the treatment regimens are different. This study explored the value of computed tomography (CT) combined with machine learning (ML) algorithms to find the best model to discriminate them. A total of 126 patients with GST ≤ 5 cm and 35 patients with GS ≤ 5 during 2013–2022 were included. CT imaging features included qualitative data (tumor location, growth pattern, lobulation, surface ulcer status, necrosis, calcification, and surrounding lymph nodes) and quantitative data [long diameter (LD); short diameter (SD); LD/SD ratio; degree of enhancement (DE); heterogeneous degree (HD)]. Patients were randomly divided into a training set (n = 112) and test set (n = 49) using 7:3 stratified sampling. The univariate and multivariate logistic regression analysis were used to identify independent risk factors. Five ML algorithms were used to build prediction models: Support Vector Machine, k-Nearest Neighbor, Random Forest, Extra Trees, and Extreme Gradient Boosting Machine. The analysis identified that HDv, lobulation, and tumor growth site were independent risk factors (P < 0.05). We should focus on these three imaging features of tumors, which are relatively easy to obtain. The area under the curve for the SVM, KNN, RF, ET, and XGBoost prediction models were, respectively, 0.790, 0.895, 0.978, 0.988, and 0.946 for the training set, and were, respectively, 0.848, 0.892, 0.887, 0.912, and 0.867 for the test set. The CT combined with ML algorithms generated predictive models to improve the differential diagnosis of ≤ 5 cm GST and GS which has important clinical practical value. The Extra Trees algorithm resulted in the optimal model.

Research on the prediction of short time series based on EMD-LSTM

An algorithm based on EMD-LSTM (Empirical Mode Decision – Long Short Term Memory) is proposed for predicting short time series with uncertainty, rapid changes, and no following cycle. First, the algorithm eliminates the abnormal data; second, the processed time series are decomposed into basic modal components for different characteristic scales, which can be used for further prediction; finally, an LSTM neural network is used to predict each modal component, and the prediction results for each modal component are summed to determine a final prediction. Experiments are performed on the public datasets available at UCR and compared with a machine learning algorithm based on LSTMs and SVMs. Several experiments have shown that the proposed EMD-LSTM-based short-time series prediction algorithm performs better than LSTM and SVM prediction methods and provides a feasible method for predicting short-time series.

Automated breast volume scanner based Radiomics for non-invasively prediction of lymphovascular invasion status in breast cancer

PurposeLymphovascular invasion (LVI) indicates resistance to preoperative adjuvant chemotherapy and a poor prognosis and can only be diagnosed by postoperative pathological examinations in breast cancer. Thus, a technique for preoperative diagnosis of LVI is urgently needed. We aim to explore the ability of an automated breast volume scanner (ABVS)-based radiomics model to noninvasively predict the LVI status in breast cancer.MethodsWe conducted a retrospective analysis of data from 335 patients diagnosed with T1-3 breast cancer between October 2019 and September 2022. The patients were divided into training cohort and validation cohort with a ratio of 7:3. For each patient, 5901 radiomics features were extracted from ABVS images. Feature selection was performed using LASSO method. We created machine learning models for different feature sets with support vector machine algorithm to predict LVI. And significant clinicopathologic factors were identified by univariate and multivariate logistic regression to combine with three radiomics signatures as to develop a fusion model.ResultsThe three SVM-based prediction models, demonstrated relatively high efficacy in identifying LVI of breast cancer, with AUCs of 79.00%, 80.00% and 79.40% and an accuracy of 71.00%, 80.00% and 75.00% in the validation cohort for AP, SP and CP plane image. The fusion model achieved the highest AUC of 87.90% and an accuracy of 85.00% in the validation cohort.ConclusionsThe combination of radiomics features from ABVS images and an SVM prediction model showed promising performance for preoperative noninvasive prediction of LVI in breast cancer.

Analysis and prediction of rockburst intensity using improved D-S evidence theory based on multiple machine learning algorithms

Accurate prediction of rockburst hazards is an effective means to improve the development and utilization of underground space. When using a single machine learning algorithm for rockburst prediction, the reliability of the classification results can hardly be guaranteed. In this paper, a fusion model for rockburst prediction based on multiple machine learning algorithms is proposed in combination with D-S evidence theory. Six characteristic parameters (σθ, σc, σt, SR, BR and Wet) were statistically analyzed for 304 sets of rockburst cases, and the data had outliers and class imbalances. The box plot method was used to detect replacement of a small number of outliers, and the Adasyn oversampling method eliminated the effect of data class imbalance. The visualization of the data by using the t-SNE method showed improved classifiability. SVM, BP, RBF, RF and ELM rockburst prediction models were developed with the six characteristic parameters as model inputs. In addition, the global optimization algorithms were used to find the optimal hyperparameters of the five machine learning algorithms. The accuracy of the optimized models was improved by 2.63%, 5.27%, 2.63%, 6.58% and 7.5% compared to the unoptimized models. Based on the prediction results, the precision was proposed as the fusion index. Additionally, based on improved D-S evidence theory, a fusion model for rockburst prediction was developed with five optimized machine learning algorithm models as base classifiers. Finally, the fusion model was applied to the rockburst prediction of Jiangbian Hydropower Station and Sanshan Island Gold Mine in China with the accuracy of 92.86%, and the prediction was outperformed by a single base classifier. The fusion model compensated for the uncertainty and poor robustness of the prediction results of the single-base classifier, and improved the accuracy and reliability of rockburst prediction.

Predicting efficacy of sub-anesthetic ketamine/esketamine i.v. dose during course of cesarean section for PPD prevention, utilizing traditional logistic regression and machine learning models

ObjectiveIncreasing researches supported that intravenous ketamine/esketamine during the perioperative period of cesarean section could prevent postpartum depression(PPD). With the effective rate ranging from 87.2 % to 95.5 % in PPD, ketamine/esketamine's responsiveness was individualized. To optimize ketamine dose/form based on puerpera prenatal characteristics, reducing adverse events and improving the total efficacy rate, prediction models were developed to predict ketamine/esketamine's efficacy. MethodBased on two randomized controlled trials, 12 prenatal features of 507 women administered the ketamine/esketamine intervention were collected. Traditional logistics regression, SVM, random forest, KNN and XGBoost prediction models were established with prenatal features and dosage regimen as predictors. ResultsAccording to the logistic regression model (ain = 0.10, aout = 0.15, area under the receiver operating characteristic curve, AUC = 0.728), prenatal Edinburgh Postnatal Depression Scale (EPDS) score ≥ 10, thoughts of self-injury and bad mood during pregnancy were associated with poorer ketamine efficacy in PPD prevention, whilst a high dose of esketamine (0.25 mg/kg loading dose+2 mg/kg PCIA) was the most effective dosage regimen and esketamine was more recommended rather than ketamine in PPD. The AUCvalidation set of KNN and XGBoost model were 0.815 and 0.651, respectively. ConclusionLogistic regression and machine learning algorithm, especially the KNN model, could predict the effectiveness of ketamine/esketamine iv. during the course of cesarean section for PPD prevention. An individualized preventative strategy could be developed after entering puerpera clinical features into the model, possessing great clinical practice value in reducing PPD incidence.

SmAMPsTK: a toolkit to unravel the smORFome encoding AMPs of plant species

The pervasive repertoire of plant molecules with the potential to serve as a substitute for conventional antibiotics has led to obtaining better insights into plant-derived antimicrobial peptides (AMPs). The massive distribution of Small Open Reading Frames (smORFs) throughout eukaryotic genomes with proven extensive biological functions reflects their practicality as antimicrobials. Here, we have developed a pipeline named smAMPsTK to unveil the underlying hidden smORFs encoding AMPs for plant species. By applying this pipeline, we have elicited AMPs of various functional activity of lengths ranging from 5 to 100 aa by employing publicly available transcriptome data of five different angiosperms. Later, we studied the coding potential of AMPs-smORFs, the inclusion of diverse translation initiation start codons, and amino acid frequency. Codon usage study signifies no such codon usage biases for smORFs encoding AMPs. Majorly three start codons are prominent in generating AMPs. The evolutionary and conservational study proclaimed the widespread distribution of AMPs encoding genes throughout the plant kingdom. Domain analysis revealed that nearly all AMPs have chitin-binding ability, establishing their role as antifungal agents. The current study includes a developed methodology to characterize smORFs encoding AMPs, and their implications as antimicrobial, antibacterial, antifungal, or antiviral provided by SVM score and prediction status calculated by machine learning-based prediction models. The pipeline, complete package, and the results derived for five angiosperms are freely available at https://github.com/skbinfo/smAMPsTK. Communicated by Ramaswamy H. Sarma

Early detection of squamous cell carcinoma of the oral tongue using multidimensional plasma protein analysis and interpretable machine learning

AbstractBackgroundInterpretable machine learning (ML) for early detection of cancer has the potential to improve risk assessment and early intervention.MethodsData from 261 proteins related to inflammation and/or tumor processes in 123 blood samples collected from healthy persons, but of whom a sub‐group later developed squamous cell carcinoma of the oral tongue (SCCOT), were analyzed. Samples from people who developed SCCOT within less than 5 years were classified as tumor‐to‐be and all other samples as tumor‐free. The optimal ML algorithm for feature selection was identified and feature importance computed by the SHapley Additive exPlanations (SHAP) method. Five popular ML algorithms (AdaBoost, Artificial neural networks [ANNs], Decision Tree [DT], eXtreme Gradient Boosting [XGBoost], and Support Vector Machine [SVM]) were applied to establish prediction models, and decisions of the optimal models were interpreted by SHAP.ResultsUsing the 22 selected features, the SVM prediction model showed the best performance (sensitivity = 0.867, specificity = 0.859, balanced accuracy = 0.863, area under the receiver operating characteristic curve [ROC‐AUC] = 0.924). SHAP analysis revealed that the 22 features rendered varying person‐specific impacts on model decision and the top three contributors to prediction were Interleukin 10 (IL10), TNF Receptor Associated Factor 2 (TRAF2), and Kallikrein Related Peptidase 12 (KLK12).ConclusionUsing multidimensional plasma protein analysis and interpretable ML, we outline a systematic approach for early detection of SCCOT before the appearance of clinical signs.

Action recognition for sports combined training based on wearable sensor technology and SVM prediction

In the field of sports, coaches have mainly relied on observing the performance of athletes on the spot to formulate suitable training plans for athletes, which has extremely high requirements for the professionalism of coaches. Based on the above requirements, this paper designs a sports action recognition system for sports enthusiasts based on the SVM algorithm optimization model, and for the purpose of verifying the applicability of the system to different sports fields, experiments are carried out on basketball actions and race walking actions. The system uses wearable sensors to capture the motion data of the user, and then analyzes and identifies the user's actions through the SVM algorithm optimization model. By standardizing the user's sports combination training under the system algorithm, the user can improve their training efficiency and reduce the risk of injury. To establish the human body motion model, this paper divides the human skeleton model into five motion branches. The rotation freedom constraints and joint rotation angle range limits are added to the model to ensure the accuracy of the motion analysis. Combining the forward kinematics of the robot and the homogeneous coordinate transformation, the human body joint rotation motion model and the human bone position and posture model are established. In the end, the user can standardize the sports combination training under the system algorithm. In this paper, through the research of wearable sensor technology and sports combined training action recognition, and apply it to practical life, it aims to promote its development and application.

Improving brain tumor classification performance with an effective approach based on new deep learning model named 3ACL from 3D MRI data

Many machine learning-based studies have been carried out in the literature for the detection of brain tumors using MRI data and most of what has been done in the last 6 years is based on deep learning. Most of them have been designed to work with 2D data. Since many tumor-free slice images are in the models designed in 3D, the classification performance is less than in the 2D models. However, 2D models are unsuitable for practical applications as they use the slice image representing the best tumor image. Therefore, in this study, for brain tumor classification, a new 3 (Attention-Convolutional-LSTM) 3ACL deep learning model that will work with MRI data is presented. Attention, convolutional, and LSTM structures were designed in the same learning architecture in the 3 ACL models, which had an end-to-end learning strategy. Thus, the representation power of the features was increased. In addition, since the proposed model was designed in 3 dimensions, 3D MR images were used directly in the 3ACL model without transforming the 3D MR images into 2D data. Highly representative deep features are extracted from the fully connected layer of the 3ACL model. The feature set is passed to the SVM. Besides, the weighted majority vote technique, which used SVM prediction results conveyed from all slices, improved classification achievement. BRATS 2015 and 2018 datasets were used in this study. For the BRATS 2015 and 2018 datasets, the proposed approach gave 98.90% and 99.29% accuracies, respectively.

Tensile properties and prediction of DP780 treated with rare earth Ce based on DE-SVM-Ⅱ

In order to solve the weak tensile properties of DP780 welded joints, rare earth Ce was added to improve the tensile properties of DP780 welded joints. In this paper, the microstructure of DP780 was characterized by metallographic microscopy, scanning electron microscopy and energy spectrum analyzer. An improved differential evolution algorithm optimized support vector machine prediction model (DE-SVM-II) was established to predict the tensile properties of DP780. The results show that when the content of Ce was 0.05%, the tensile strength increased by 378 MPa, the microstructure of molten metal was uniform and the grain boundary of austenite was tight. In the heat affected zone, the size of ferrite decreases, the fracture morphology shows equiaxial depression, and the fiber zone accounts for a large proportion. In addition, the fitness of the mean square error (MSE) of the tensile strength curve in the DE-SVM-Ⅱ prediction was lower than that of the SVM prediction error, and begin to converge in the 10th generation. The average relative error of the DE-SVM model prediction of elongation was within 3%. Compared with the predicted value of BP neural network, the error of predicting tensile strength and elongation of DE-SVM-II was reduced by 1.36% and 15.10%, respectively, which had high stability and accuracy.

Machine Learning Approach to Predict the Performance of a Stratified Thermal Energy Storage Tank at a District Cooling Plant Using Sensor Data.

In the energy management of district cooling plants, the thermal energy storage tank is critical. As a result, it is essential to keep track of TES results. The performance of the TES has been measured using a variety of methodologies, both numerical and analytical. In this study, the performance of the TES tank in terms of thermocline thickness is predicted using an artificial neural network, support vector machine, and k-nearest neighbor, which has remained unexplored. One year of data was collected from a district cooling plant. Fourteen sensors were used to measure the temperature at different points. With engineering judgement, 263 rows of data were selected and used to develop the prediction models. A total of 70% of the data were used for training, whereas 30% were used for testing. K-fold cross-validation were used. Sensor temperature data was used as the model input, whereas thermocline thickness was used as the model output. The data were normalized, and in addition to this, moving average filter and median filter data smoothing techniques were applied while developing KNN and SVM prediction models to carry out a comparison. The hyperparameters for the three machine learning models were chosen at optimal condition, and the trial-and-error method was used to select the best hyperparameter value: based on this, the optimum architecture of ANN was 14-10-1, which gives the maximum R-Squared value, i.e., 0.9, and minimum mean square error. Finally, the prediction accuracy of three different techniques and results were compared, and the accuracy of ANN is 0.92%, SVM is 89%, and KNN is 96.3%, concluding that KNN has better performance than others.

Research on soft sensing method of photosynthetic bacteria fermentation process based on ant colony algorithm and least squares support vector machine

Photosynthetic bacteria wastewater treatment is an efficient water pollution treatment method, but photosynthetic bacteria fermentation is a multivariable, non-linear, and time-varying process. So it is difficult to establish an accurate model. Aiming at the difficulty of online measurement of key parameters, such as bacterial concentration and matrix concentration in photosynthetic bacteria fermentation process, an improved ant colony algorithm least squares support vector machine (AC-LSSVM) soft sensing model method is proposed in this paper. Firstly, the virtual sensing subsystem of the photosynthetic bacteria fermentation process is proposed, with measurable parameters as input and unmeasurable key parameters as output, and the left inverse soft sensing model of virtual sensing is constructed. Then, the ant colony algorithm can quickly find the shortest path to optimize the parameters of the traditional PI regulation, to improve the dynamic performance and accuracy of parameter measurement in the fermentation process. After that, the ant colony algorithm is used to optimize penalty parameters C and kernel parameters σ of LSSVM, which effectively avoids the local optimization and improves the computing power and global optimization ability. Finally, the soft sensing prediction model of the photosynthetic bacteria fermentation process based on AC-LSSVM is established. Compared with SVM and LSSVM prediction models, the root mean square error of bacterial concentration and matrix concentration based on the AC-LSSVM model are 0.468 and 0.126, respectively. The simulation analysis shows that this model has less error and better prediction ability, and it can meet the needs of online prediction of key parameters of photosynthetic bacteria fermentation.

Chatter detection in milling process based on the combination of wavelet packet transform and PSO-SVM

Chatter is one of the biggest unfavorable factors during the high speed machining process of a machine tool. It severely affects the surface finish and geometric accuracy of the workpiece. To address this obstacle and improve the quality and efficiency of products, it is significantly essential to detect chatter during machining. Therefore, a multi-feature recognition system for chatter detection on the basis of the fusion technology of wavelet packet transform (WPT) and particle swarm optimization support vector machine (PSO-SVM) was proposed in this paper. Firstly, the original vibration signals collected from the acceleration sensor were processed through wavelet packet transform (WPT). The noise and the irrelevant information were remarkably decreased. In addition, the wavelet packets containing chatter-emerging information were chosen and reconstructed. The fourteen time–frequency domain characteristics of the reconstructed vibration signal were calculated and chosen as the multi-feature vectors of chatter detection. Finally, to obtain the optimal radial basis function parameter g and penalty parameter C of the SVM prediction model, the optimization algorithms of k-fold cross-validation (k-CV), genetic algorithm (GA), and particle swarm optimization (PSO) were employed in optimizing the model parameters of SVM. It was indicated that the PSO-SVM improved obviously the accuracy of chatter recognition than the others. In addition, we applied the optimized SVM prediction model by PSO for detecting chatter state in end milling machining. Chatter recognition results indicated that the model accurately predicted the slight chatter state in advance.