Support vector machines combined with wavelet-based feature extraction for identification of drugs hidden in anthropomorphic phantom

Qu, Dingjun; Li, Wei; Zhang, Yu; Sun, Bai; Zhong, Yu; Liu, Jinhuai; Yu, Daoyang; Li, Minqiang

doi:10.1016/j.measurement.2012.06.016

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…This step repeats at every node until each node contains only samples from one class (shown in Figure B). OAO and BT approaches have been used widely in previous research. − …”

Section: Methodsmentioning

confidence: 99%

In Silico Prediction of Chemical Acute Oral Toxicity Using Multi-Classification Methods

Chen

Cheng

et al. 2014

J. Chem. Inf. Model.

160

116

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Chemical acute oral toxicity is an important end point in drug design and environmental risk assessment. However, it is difficult to determine by experiments, and in silico methods are hence developed as an alternative. In this study, a comprehensive data set containing 12, 204 diverse compounds with median lethal dose (LD₅₀) was compiled. These chemicals were classified into four categories, namely categories I, II, III and IV, based on the criterion of the U.S. Environmental Protection Agency (EPA). Then several multiclassification models were developed using five machine learning methods, including support vector machine (SVM), C4.5 decision tree (C4.5), random forest (RF), κ-nearest neighbor (kNN), and naïve Bayes (NB) algorithms, along with MACCS and FP4 fingerprints. One-against-one (OAO) and binary tree (BT) strategies were employed for SVM multiclassification. Performances were measured by two external validation sets containing 1678 and 375 chemicals, separately. The overall accuracy of the MACCS-SVM(OAO) model was 83.0% and 89.9% for external validation sets I and II, respectively, which showed reliable predictive accuracy for each class. In addition, some representative substructures responsible for acute oral toxicity were identified using information gain and substructure frequency analysis methods, which might be very helpful for further study to avoid the toxicity.

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“…This step repeats at every node until each node contains only samples from one class (shown in Figure B). OAO and BT approaches have been used widely in previous research. − …”

Section: Methodsmentioning

confidence: 99%

In Silico Prediction of Chemical Acute Oral Toxicity Using Multi-Classification Methods

Chen

Cheng

et al. 2014

J. Chem. Inf. Model.

160

116

View full text Add to dashboard Cite

show abstract

“…Theoretical background SVM is a machine learning algorithm, which is based on the principle of structural risk minimization and kernel-based method (Qu et al 2013;Ao et al 2014). SVM utilizes afinite number of samples to train the model toexplore the optimal compromise between generalization performance and classification accuracy, and reveals the distinct benefits in dealing with problems of nonlinearity, small samples and high dimension.…”

Section: Feature Level Fusion Based On Svmmentioning

confidence: 99%

Condition Assessment of Timber Utility Poles Based on a Hierarchical Data Fusion Model

Dackermann

Liu

et al. 2016

J. Comput. Civ. Eng.

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This paper proposes a novel hierarchical data fusion technique for the non-destructive testing (NDT) and condition assessment of timber utility poles. The new method analyses stress wave data from multi-sensor multi-excitation guided wave testing using a hierarchical data fusion model consisting of feature extraction, data compression, pattern recognition and decision fusion algorithms. The proposed technique is validated on guided waved at a of in-situ timber poles. The actual health states of these poles are known from autopsies conducted after the testing, forming a ground-truth for supervised classification. In the proposed method, a data fusion level extracts the main features from the sampled stress wave signals using power spectrum density (PSD) estimation, wavelet packet transform (WPT) and empirical mode decomposition (EMD). These features are then compiled to a feature vector via real-number encoding and sent to the next level for further processing. Principal component analysis (PCA) is also adopted for feature compression and to minimise information redundancy and noise interference. In the feature fusion level, two classifiers based on support vector machine (SVM) are applied to sensor separated data of the two excitation types and the pole condition is identified. In the decision making fusion level, the D-S evidence theory is employed to integrate the results from the individual sensors obtaining a final decision. The results of the in-situ timber pole testing show that the proposed hierarchical data fusion model was able to distinguish between healthy and faulty poles demonstrating the effectiveness of the new method.

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“…SVM-based classification methods have been widely used in various applications, such as face detection, handwriting recognition, chemical pattern classification, and fault diagnosis. SVM performs satisfactorily in situations involving a small sampling size and high dimension, and it exhibits high accuracy and favorable generalization capabilities [18]. Therefore, this paper proposes a dynamic feature-based strategy that is based on SVM assessments and ARG/LARG segmentations for inspecting hybrid blurred/multiple objects.…”

Section: Related Workmentioning

confidence: 99%

A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

Lin

2016

Mathematical Problems in Engineering

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Vision-based inspection has been applied for quality control and product sorting in manufacturing processes. Blurred or multiple objects are common causes of poor performance in conventional vision-based inspection systems. Detecting hybrid blurred/multiple objects has long been a challenge in manufacturing. For example, single-feature-based algorithms might fail to exactly extract features when concurrently detecting hybrid blurred/multiple objects. Therefore, to resolve this problem, this study proposes a novel vision-based inspection algorithm that entails selecting a dynamic feature-based method on the basis of a multiclassifier of support vector machines (SVMs) for inspecting hybrid blurred/multiple object images. The proposed algorithm dynamically selects suitable inspection schemes for classifying the hybrid images. The inspection schemes include discrete wavelet transform, spherical wavelet transform, moment invariants, and edge-feature-descriptor-based classification methods. The classification methods for single and multiple objects are adaptive region growing- (ARG-) based and local adaptive region growing- (LARG-) based learning approaches, respectively. The experimental results demonstrate that the proposed algorithm can dynamically select suitable inspection schemes by applying a selection algorithm, which uses SVMs for classifying hybrid blurred/multiple object samples. Moreover, the method applies suitable feature-based schemes on the basis of the classification results for employing the ARG/LARG-based method to inspect the hybrid objects. The method improves conventional methods for inspecting hybrid blurred/multiple objects and achieves high recognition rates for that in manufacturing processes.

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Support vector machines combined with wavelet-based feature extraction for identification of drugs hidden in anthropomorphic phantom

Cited by 10 publications

References 22 publications

In Silico Prediction of Chemical Acute Oral Toxicity Using Multi-Classification Methods

In Silico Prediction of Chemical Acute Oral Toxicity Using Multi-Classification Methods

Condition Assessment of Timber Utility Poles Based on a Hierarchical Data Fusion Model

A Dynamic Feature-Based Method for Hybrid Blurred/Multiple Object Detection in Manufacturing Processes

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