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Mapping characteristics based on four kinds of kernel functions (a) RBF kernel function, (b) polynomial kernel function, (c) linear kernel function, and (d) sigmoid kernel function.
Source publication
The types of kernel function and relevant parameters’ selection in support vector machine (SVM) have a major impact on the performance of the classifier. In order to improve the accuracy and generalization ability of the model, we used mixed kernel function SVM classification algorithm based on the information entropy particle swarm optimization (P...
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In this work we investigate the use of deep learning for image quality classification problem. We use a pre-trained Convolutional Neural Network (CNN) for image description, and the Support Vector Machine (SVM) model is trained as an image quality classifier whose inputs are normalized features extracted by the CNN model. We report on different des...
Citations
... Support Vector Machines (SVMs) are a well-established technique, based on statistical learning that analyzes complex bioprocess data with high nonlinearity and time-varying in biological fermentation, they have been widely used to construct soft-sensor models in the biological development process [74]. For instance, Li et al. used SVM to predict the penicillin titer in real-time in the industrial production [75]. ...
The Valley of Death confronts industrial biotechnology with a significant challenge to the commercialization of products. Fortunately, with the integration of computation, automation and artificial intelligence (AI) technology, the industrial biotechnology accelerates to cross the Valley of Death. The Fourth Industrial Revolution (Industry 4.0) has spurred advanced development of intelligent
biomanufacturing, which has evolved the industrial structures in line with the worldwide trend. To achieve this, intelligent biomanufacturing can be structured into three main parts that comprise digitalization, modeling and intellectualization, with modeling forming a crucial link between the other two components. This paper provides an overview of mechanistic models, data-driven models and their applications in bioprocess development. We provide a detailed elaboration
of the hybrid model and its applications in bioprocess engineering, including strain design, process control and optimization, as well as bioreactor scale-up. Finally, the challenges and opportunities of biomanufacturing towards Industry 4.0 are also discussed.
... These operations directly affected the kernel matrix and the operation result was the positive semi-definite matrix at all times. The polynomial kernel function was a global kernel function that provided a better dissemination capability and a weaker learning ability [33], while the sigmoid kernel function provided a better global performance [46]. ...
Structural health monitoring for bridges is a crucial concern in engineering due to the degradation risks caused by defects, which can become worse over time. In this respect, enhancement of various models that can discriminate between healthy and non-healthy states of structures have received extensive attention. These models are concerned with implementation algorithms, which operate on the feature sets to quantify the bridge’s structural health. The functional correlation between the feature set and the health state of the bridge structure is usually difficult to define. Therefore, the models are derived from machine learning techniques. The use of machine learning approaches provides the possibility of automating the SHM procedure and intelligent damage detection. In this study, we propose four classification algorithms to SHM, which uses the concepts of support vector machine (SVM) algorithm. The laboratory experiment, which intended to validate the results, was performed at Western Sydney University (WSU). The results were compared with the basic SVM to evaluate the performance of proposed algorithms.
... The SVM model in this study was introduced to discriminate isolates of MRSA from MSSA and validated by using the "e1071" and "caret" packages in R. 31,32 The m/z features screened by Lasso regression were randomly split into subsets with 90% as training data and 10% as test data. The kernel parameter (γ) and cost parameter (C) in the SVM were determined by performing ten-fold cross-validation and grid search on the training data, and the ten-fold cross-validation was repeated ten times. ...
Rationale
Methicillin‐resistant Staphylococcus aureus (MRSA) is one of major clinical pathogens responsible for both hospital‐ and community‐acquired infections worldwide. A delay in targeted antibiotic treatment contributes to longer hospitalization stay, higher costs, and increasing in‐hospital mortality. Matrix‐assisted laser desorption/ionization time‐of‐flight mass spectrometry (MALDI‐TOF MS) has been integrated into the routine workflow for microbial identification over the past decade, and it has also shown promising functions in the detection of bacterial resistance. Therefore, we describe a rapid MALDI‐TOF MS‐based methodology for MRSA screening with machine‐learning algorithms.
Methods
A total of 452 clinical S. aureus isolates were included in this study, of which 194 were MRSA and 258 were methicillin‐sensitive S. aureus (MSSA). The mass‐to‐charge ratio (m/z) features from MRSA and MSSA strains were binned and selected through Lasso regression. These features were then used to train a non‐linear support vector machine (SVM) with radial basis function (RBF) kernels to evaluate the discrimination performance. The classifiers' accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were evaluated and compared with those from the random forest (RF) model.
Results
A total of 2601 unique spectral peaks of all isolates were identified and 38 m/z features were selected for the classifying model. The AUCs of the non‐linear RBF‐SVM model and the RF model were 0.89 and 0.87, respectively, and the accuracy ranged between 0.86 (RBF‐SVM) and 0.82 (RF).
Conclusions
Our study demonstrates that MALDI‐TOF MS coupled with machine‐learning algorithms could be used to develop a rapid and easy‐to‐use method to discriminate MRSA from MSSA. Considering that this method is easy to implement in routine microbiology laboratories, it suggests a cost‐effective and time‐efficient alternative to conventional resistance detection in the future to improve clinical treatment.
... The types of kernel function and relevant parameters' selection in support vector machine (SVM) have a major impact on the performance of the classifier [28]. And the criterion of discrimination is set for whether the distribution characteristics of the data belong to the Gaussian distribution. ...
With the rapid development of cloud computing and big data technology and the increasing amount of data derived from intellectual property, data mining analysis methods to a certain extent have been already unable to meet the demand for data processing, thus affecting the development of cloud computing and big data. Therefore, this paper introduces two kinds of mathematical algorithms, GRNN and SVM, to analyze the intellectual property data. By comparing the two kinds of algorithms of data mining for intellectual property, we initially established analysis method of data mining technology based on intellectual property rights. By comparing the accuracy of the two algorithms, it is found that the classification accuracy of the target algorithm is higher than that of the comparison algorithm. Therefore, in the early stage, the sampling probability based on the GRNN network algorithm is better than SVM, but it is not high in the later stage. The SVM algorithm at the early stages of the data mining reduces the individual classifier’s classification accuracy. However, due to the gradual increase of the number of classifiers including the individual, the classification accuracy of the integrated classifier is continuously improved, and it makes the calculation of the sampling probability of the data more accurate. Therefore, it is concluded that the hybrid GRNN and SVM algorithms for intellectual property data mining is much better than the ordinary single algorithm, and it will be widely used in the future data mining data processing. It has laid the foundation for further research on the depth of intellectual property data processing in the future.
... Further understanding of the nature of kernel functions will help to build a more powerful SVM classifier. In 2001, Scholkopf et al. first divided the kernel function into local kernel function and global kernel function [32]. Surveys conducted by Simts and Jordaan [33] showed that local kernels have a good interpolation ability, while global kernels have a good extrapolation ability. ...
As is well known, the correct diagnosis for cancer is critical to save patients’ lives. Support vector machine (SVM) has already made an important contribution to the field of cancer classification. However, different kernel function configurations and their parameters will significantly affect the performance of SVM classifier. To improve the classification accuracy of SVM classifier for cancer diagnosis, this paper proposed a novel cancer classification algorithm based on the dragonfly algorithm and SVM with a combined kernel function (DA-CKSVM) which was constructed from a radial basis function (RBF) kernel and a polynomial kernel. Experiments were performed on six cancer data sets from University of California, Irvine (UCI) machine learning repository and two cancer data sets from Cancer Program Legacy Publication Resources to evaluate the validity of the proposed algorithm. Compared with four well-known algorithms: dragonfly algorithm-SVM (DA-SVM), particle swarm optimization-SVM (PSO-SVM), bat algorithm-SVM (BA-SVM), and genetic algorithm-SVM (GA-SVM), the proposed algorithm was able to find the optimal parameters of SVM classifier and achieved better classification accuracy on cancer datasets.
... Li and Liu proposed a stochastic mutation method (RPSO) [62]. The particle mutated by the following (13) where popmax and popmin denote the maximum and the minimum of the particle search range respectively. ...
With the rapid development of information technology and biomedical engineering, people can get more and more information. At the same time, they begin to study how to apply the advanced technology in biomedical information. The main research of this paper is to optimize the machine learning method by particle swarm optimization (PSO) and apply it in the classification of biomedical data. In order to improve the performance of the classification model, we compared the different inertia weight strategies and mutation strategies and their combinations with PSO, and obtained the best inertia weight strategy without mutation, the best mutation strategy without inertia weight and the best combination of the two. Then, we used the three PSO algorithms to optimize the parameters of support vector machine in the classification of biomedical data. We found that the PSO algorithm with the combination of inertia weight and mutation strategy and the inertia weight strategy that we proposed could improve the classification accuracy. This study has an important reference value for the prediction of clinical diseases.
... The particles move within the search area to find the optimal solution by updating their velocity and position. The PSO algorithm has been successfully applied in various fields, such as biomedical engineering [2], [3], [4], voltage management [5], power allocation [6], and robotics [7], [8]. Due to the success of PSO, several variations of PSO have proposed in recent years. ...
... The Multiple Kernel Learning Support Vector Machine (MKL-SVM) algorithm has achieved good recognition accuracy not just in recognition of lung nodules in [1] but also in other application fields (see [19,20]). In [21], the Multiple Kernel Learning (MKL) method was elaborated and the latest research progresses were presented in this field. ...
... s, respectively. The lower adjacent optimization time of the inertia weight obtained by (19) is 443.456 s, which is close to the upper adjacent value of the box obtained by (18). ...
... Thus, the three indexes, the maximum, the median value, and the Euclidean norm of the normalized error vector after reaching convergence, are able to reflect better convergence performance after convergence. The indexes obtained by (19) and (20) are basically superior to that of (17); that is, the nonlinear inertial weight algorithm is superior to the linear inertial weight algorithm generally. In summary, the Particle Swarm Optimization algorithm with dynamic inertia weight is better than the one with constant inertia weight, and the algorithm using nonlinear inertia weight is better than that one using linear inertia weight. ...
Pulmonary nodule recognition is the core module of lung CAD. The Support Vector Machine (SVM) algorithm has been widely used in pulmonary nodule recognition, and the algorithm of Multiple Kernel Learning Support Vector Machine (MKL-SVM) has achieved good results therein. Based on grid search, however, the MKL-SVM algorithm needs long optimization time in course of parameter optimization; also its identification accuracy depends on the fineness of grid. In the paper, swarm intelligence is introduced and the Particle Swarm Optimization (PSO) is combined with MKL-SVM algorithm to be MKL-SVM-PSO algorithm so as to realize global optimization of parameters rapidly. In order to obtain the global optimal solution, different inertia weights such as constant inertia weight, linear inertia weight, and nonlinear inertia weight are applied to pulmonary nodules recognition. The experimental results show that the model training time of the proposed MKL-SVM-PSO algorithm is only 1/7 of the training time of the MKL-SVM grid search algorithm, achieving better recognition effect. Moreover, Euclidean norm of normalized error vector is proposed to measure the proximity between the average fitness curve and the optimal fitness curve after convergence. Through statistical analysis of the average of 20 times operation results with different inertial weights, it can be seen that the dynamic inertial weight is superior to the constant inertia weight in the MKL-SVM-PSO algorithm. In the dynamic inertial weight algorithm, the parameter optimization time of nonlinear inertia weight is shorter; the average fitness value after convergence is much closer to the optimal fitness value, which is better than the linear inertial weight. Besides, a better nonlinear inertial weight is verified.
Lost circulation events during drilling operations are known for their abruptness and are difficult to control. Traditional diagnostic methods rely on qualitative indicators, such as mud pit volume changes or anomalous logging curve patterns. However, these methods are subjective and rely heavily on empirical knowledge, resulting in delayed or inaccurate predictions. To address this problem, there is an urgent need to develop efficient methods for a timely and accurate lost circulation prediction. In this study, a novel approach is proposed by combining principal component analysis (PCA) and empirical analysis to reduce the dimensionality of the model data. This dimensionality reduction helps to streamline the analysis process and improve prediction accuracy. The predictive model also incorporates an improved fruit fly optimization algorithm (IFOA) in conjunction with support vector machine (SVM) techniques. The actual instances of lost circulation serve as the evaluation criteria for this integrated method. To overcome the challenges associated with irregular population distribution within randomly generated individuals, a tent map strategy is introduced to ensure a more balanced and representative sample. In addition, the model addresses issues such as premature convergence and slow optimization rates by employing a sine–cosine search strategy. This strategy helps to achieve optimal results and speeds up the prediction process. The improved prediction model demonstrates exceptional performance, achieving accuracy, precision, recall, and F1 scores of 96.8, 97, 96, and 96%, respectively. These results indicate that the IFOA-SVM approach achieves the highest accuracy with a reduced number of iterations, proving to be an efficient and fast method for predicting the lost circulation events. Implementation of this methodology in drilling operations can lead to improved efficiency, reliability, and overall performance.
The Corona virus disease 2019 (COVID-19) pandemic has caused substantial increase in distress among people all over the world. This work aims to study depression during the COVID-19 among the educational sector and to develop a novel stroop test based depression detection system by analyzing the response time (RT) for normal stroop test (ST) and emotional stroop test (EST). The data for this work is collected from 44 participants. It is found that 66% of the participants have depression. The analysis of RT for ST and EST before and after showing video stimulus, indicates that there is a significant difference in change of response time (dRT) for both normal and depressive cases. Further this feature along with the physiological parameters (PhyP) of the participants are given to support vector machine (SVM) and extreme gradient boost (XGBoost) classifier to develop depression detection systems. The XGBoost provides highest accuracy of 85.71% with PhyP + ST_dRT data and an accuracy of 71.43% with PhyP + EST_dRT data. Therefore, the proposed systems may serve as a screening tool for depression during this pandemic situation.
