Research on Classification Model of Panax notoginseng Taproots Based on Machine Vision Feature Fusion

The existing classification methods for Panax notoginseng taproots suffer from low accuracy, low efficiency, and poor stability. In this study, a classification model based on image feature fusion is established for Panax notoginseng taproots. The images of Panax notoginseng taproots collected in the experiment are preprocessed by Gaussian filtering, binarization, and morphological methods. Then, a total of 40 features are extracted, including size and shape features, HSV and RGB color features, and texture features. Through BP neural network, extreme learning machine (ELM), and support vector machine (SVM) models, the importance of color, texture, and fusion features for the classification of the main roots of Panax notoginseng is verified. Among the three models, the SVM model performs the best, achieving an accuracy of 92.037% on the prediction set. Next, iterative retaining information variables (IRIVs), variable iterative space shrinkage approach (VISSA), and stepwise regression analysis (SRA) are used to reduce the dimension of all the features. Finally, a traditional machine learning SVM model based on feature selection and a deep learning model based on semantic segmentation are established. With the model size of only 125 kb and the training time of 3.4 s, the IRIV-SVM model achieves an accuracy of 95.370% on the test set, so IRIV-SVM is selected as the main root classification model for Panax notoginseng. After being optimized by the gray wolf optimizer, the IRIV-GWO-SVM model achieves the highest classification accuracy of 98.704% on the test set. The study results of this paper provide a basis for developing online classification methods of Panax notoginseng with different grades in actual production.

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An Efficient Coding Network Based Feature Extraction with Support Vector Machine Based Classification Model for CT Lung Images

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3263 ◽

2020 ◽

Vol 10 (11) ◽

pp. 2628-2633 ◽

Cited By ~ 4

Author(s):

A. Sheryl Oliver ◽

M. Anuradha ◽

J. Jean Justus ◽

Kiranmai Bellam ◽

T. Jayasankar

Keyword(s):

Lung Cancer ◽

Support Vector Machine ◽

Feature Extraction ◽

Feature Fusion ◽

Classification Model ◽

Support Vector ◽

Efficient Coding ◽

Svm Model ◽

High Level ◽

Lung Ct

Lung cancer is a serious illness affects people all over the globe. To increase the survival rate of patients affected by lung cancer, in advance recognition of lung cancer with effective treatments is important. This study introduces a new deep learning (DL) based feature extraction and classification technique for CT lung images. A DL model using Coding Network (CN) is presented for the extraction of high-level features and classical features. Initially, the convolution neural network is trained as a coding network and the actual pixels are coded into feature vectors for representing the high-level concepts for classification. Next, an extraction of chosen classical features takes place depending upon background knowledge of lung CT images. In addition, an automatic feature fusion takes place to avoid annoying parameter choice. Besides, support vector machine (SVM) model is employed for classify CT lung images in an effective way. For experimentation, a benchmark dataset is utilized to appraise the outcome of the presented CN-SVM model and is validated under several dimensions.

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Correlation between the structure and skin permeability of compounds

Scientific Reports ◽

10.1038/s41598-021-89587-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ruolan Zeng ◽

Jiyong Deng ◽

Limin Dang ◽

Xinliang Yu

Keyword(s):

Large Data ◽

Qsar Model ◽

Coefficient Of Determination ◽

Support Vector ◽

Skin Permeability ◽

Data Set ◽

Test Set ◽

Svm Algorithm ◽

Svm Model ◽

Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

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New Multi-View Classification Method with Uncertain Data

ACM Transactions on Knowledge Discovery from Data ◽

10.1145/3458282 ◽

2021 ◽

Vol 16 (1) ◽

pp. 1-23

Author(s):

Bo Liu ◽

Haowen Zhong ◽

Yanshan Xiao

Keyword(s):

Learning Strategy ◽

State Of The Art ◽

Uncertain Data ◽

Real Life ◽

Support Vector ◽

Classification Methods ◽

Complementary Information ◽

Novel Approach ◽

Svm Model ◽

Iterative Framework

Multi-view classification aims at designing a multi-view learning strategy to train a classifier from multi-view data, which are easily collected in practice. Most of the existing works focus on multi-view classification by assuming the multi-view data are collected with precise information. However, we always collect the uncertain multi-view data due to the collection process is corrupted with noise in real-life application. In this case, this article proposes a novel approach, called uncertain multi-view learning with support vector machine (UMV-SVM) to cope with the problem of multi-view learning with uncertain data. The method first enforces the agreement among all the views to seek complementary information of multi-view data and takes the uncertainty of the multi-view data into consideration by modeling reachability area of the noise. Then it proposes an iterative framework to solve the proposed UMV-SVM model such that we can obtain the multi-view classifier for prediction. Extensive experiments on real-life datasets have shown that the proposed UMV-SVM can achieve a better performance for uncertain multi-view classification in comparison to the state-of-the-art multi-view classification methods.

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Cancer Discrimination Using Fourier Transform Near-Infrared Spectroscopy with Chemometric Models

Journal of Chemistry ◽

10.1155/2015/619685 ◽

2015 ◽

Vol 2015 ◽

pp. 1-8 ◽

Cited By ~ 1

Author(s):

Hui Chen ◽

Zan Lin ◽

Chao Tan

Keyword(s):

Cancer Diagnosis ◽

Near Infrared ◽

Nir Spectroscopy ◽

Principal Component ◽

Classification Model ◽

Support Vector ◽

Biomedical Analysis ◽

First Derivative ◽

Svm Model ◽

High Prediction

Near-infrared (NIR) spectroscopy technique offers many potential advantages as tool for biomedical analysis since it enables the subtle biochemical signatures related to pathology to be detected and extracted. In conjunction with advanced chemometrics, NIR spectroscopy opens the possibility of their use in cancer diagnosis. The study focuses on the application of near-infrared (NIR) spectroscopy and classification models for discriminating colorectal cancer. A total of 107 surgical specimens and a corresponding NIR diffuse reflection spectral dataset were prepared. Three preprocessing methods were attempted and least-squares support vector machine (LS-SVM) was used to build a classification model. The hybrid preprocessing of first derivative and principal component analysis (PCA) resulted in the best LS-SVM model with the sensitivity and specificity of 0.96 and 0.96 for the training and 0.94 and 0.96 for test sets, respectively. The similarity performance on both subsets indicated that overfitting did not occur, assuring the robustness and reliability of the developed LS-SVM model. The area of receiver operating characteristic (ROC) curve was 0.99, demonstrating once again the high prediction power of the model. The result confirms the applicability of the combination of NIR spectroscopy, LS-SVM, PCA, and first derivative preprocessing for cancer diagnosis.

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A New Multitasking Malware Classification Model Based on Feature Fusion

2018 2nd IEEE Advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC) ◽

10.1109/imcec.2018.8469663 ◽

2018 ◽

Author(s):

Wei Shi ◽

Xin Zhou ◽

Jianmin Pang ◽

Guanghui Liang ◽

Haoran Gu

Keyword(s):

Feature Fusion ◽

Classification Model ◽

Malware Classification ◽

Model Based

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EEG-Based Emotion Classification for Alzheimer’s Disease Patients Using Conventional Machine Learning and Recurrent Neural Network Models

Sensors ◽

10.3390/s20247212 ◽

2020 ◽

Vol 20 (24) ◽

pp. 7212

Author(s):

Jungryul Seo ◽

Teemu H. Laine ◽

Gyuhwan Oh ◽

Kyung-Ah Sohn

Keyword(s):

Neural Network ◽

Machine Learning ◽

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Recurrent Neural Network ◽

Classification Model ◽

Support Vector ◽

Number Of Patients ◽

Study Results ◽

Conventional Machine

As the number of patients with Alzheimer’s disease (AD) increases, the effort needed to care for these patients increases as well. At the same time, advances in information and sensor technologies have reduced caring costs, providing a potential pathway for developing healthcare services for AD patients. For instance, if a virtual reality (VR) system can provide emotion-adaptive content, the time that AD patients spend interacting with VR content is expected to be extended, allowing caregivers to focus on other tasks. As the first step towards this goal, in this study, we develop a classification model that detects AD patients’ emotions (e.g., happy, peaceful, or bored). We first collected electroencephalography (EEG) data from 30 Korean female AD patients who watched emotion-evoking videos at a medical rehabilitation center. We applied conventional machine learning algorithms, such as a multilayer perceptron (MLP) and support vector machine, along with deep learning models of recurrent neural network (RNN) architectures. The best performance was obtained from MLP, which achieved an average accuracy of 70.97%; the RNN model’s accuracy reached only 48.18%. Our study results open a new stream of research in the field of EEG-based emotion detection for patients with neurological disorders.

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Discrimination of Gentiana and Its Related Species Using IR Spectroscopy Combined with Feature Selection and Stacked Generalization

Molecules ◽

10.3390/molecules25061442 ◽

2020 ◽

Vol 25 (6) ◽

pp. 1442 ◽

Cited By ~ 2

Author(s):

Tao Shen ◽

Hong Yu ◽

Yuan-Zhong Wang

Keyword(s):

Genetic Algorithm ◽

Support Vector Machine ◽

Feature Selection ◽

Related Species ◽

Predictive Accuracy ◽

Classification Model ◽

Venn Diagram ◽

Support Vector ◽

Stacked Generalization ◽

Svm Model

Gentiana, which is one of the largest genera of Gentianoideae, most of which had potential pharmaceutical value, and applied to local traditional medical treatment. Because of the phytochemical diversity and difference of bioactive compounds among species, which makes it crucial to accurately identify authentic Gentiana species. In this paper, the feasibility of using the infrared spectroscopy technique combined with chemometrics analysis to identify Gentiana and its related species was studied. A total of 180 batches of raw spectral fingerprints were obtained from 18 species of Gentiana and Tripterospermum by near-infrared (NIR: 10,000–4000 cm−1) and Fourier transform mid-infrared (MIR: 4000–600 cm−1) spectrum. Firstly, principal component analysis (PCA) was utilized to explore the natural grouping of the 180 samples. Secondly, random forests (RF), support vector machine (SVM), and K-nearest neighbors (KNN) models were built while using full spectra (including 1487 NIR variables and 1214 FT-MIR variables, respectively). The MIR-SVM model had a higher classification accuracy rate than the other models that were based on the results of the calibration sets and prediction sets. The five feature selection strategies, VIP (variable importance in the projection), Boruta, GARF (genetic algorithm combined with random forest), GASVM (genetic algorithm combined with support vector machine), and Venn diagram calculation, were used to reduce the dimensions of the data variable in order to further reduce numbers of variables for modeling. Finally, 101 NIR and 73 FT-MIR bands were selected as the feature variables, respectively. Thirdly, stacking models were built based on the optimal spectral dataset. Most of the stacking models performed better than the full spectra-based models. RF and SVM (as base learners), combined with the SVM meta-classifier, was the optimal stacked generalization strategy. For the SG-Ven-MIR-SVM model, the accuracy (ACC) of the calibration set and validation set were both 100%. Sensitivity (SE), specificity (SP), efficiency (EFF), Matthews correlation coefficient (MCC), and Cohen’s kappa coefficient (K) were all 1, which showed that the model had the optimal authenticity identification performance. Those parameters indicated that stacked generalization combined with feature selection is probably an important technique for improving the classification model predictive accuracy and avoid overfitting. The study result can provide a valuable reference for the safety and effectiveness of the clinical application of medicinal Gentiana.

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Annular Spatial Pyramid Mapping and Feature Fusion-Based Image Coding Representation and Classification

Wireless Communications and Mobile Computing ◽

10.1155/2020/8838454 ◽

2020 ◽

Vol 2020 ◽

pp. 1-9

Author(s):

Mengxi Xu ◽

Yingshu Lu ◽

Xiaobin Wu

Keyword(s):

Image Classification ◽

Feature Fusion ◽

Image Feature ◽

Classification Model ◽

Scale Invariant Feature Transform ◽

Scale Invariant ◽

Invariant Feature ◽

Feature Transform ◽

Scale Invariant Feature ◽

Spatial Pyramid

Conventional image classification models commonly adopt a single feature vector to represent informative contents. However, a single image feature system can hardly extract the entirety of the information contained in images, and traditional encoding methods have a large loss of feature information. Aiming to solve this problem, this paper proposes a feature fusion-based image classification model. This model combines the principal component analysis (PCA) algorithm, processed scale invariant feature transform (P-SIFT) and color naming (CN) features to generate mutually independent image representation factors. At the encoding stage of the scale-invariant feature transform (SIFT) feature, the bag-of-visual-word model (BOVW) is used for feature reconstruction. Simultaneously, in order to introduce the spatial information to our extracted features, the rotation invariant spatial pyramid mapping method is introduced for the P-SIFT and CN feature division and representation. At the stage of feature fusion, we adopt a support vector machine with two kernels (SVM-2K) algorithm, which divides the training process into two stages and finally learns the knowledge from the corresponding kernel matrix for the classification performance improvement. The experiments show that the proposed method can effectively improve the accuracy of image description and the precision of image classification.

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Non-Destructive Trace Detection of Explosives Using Pushbroom Scanning Hyperspectral Imaging System

Sensors ◽

10.3390/s19010097 ◽

2018 ◽

Vol 19 (1) ◽

pp. 97 ◽

Cited By ~ 4

Author(s):

Siddharth Chaudhary ◽

Sarawut Ninsawat ◽

Tai Nakamura

Keyword(s):

Hyperspectral Imaging ◽

Hyperspectral Image ◽

Imaging System ◽

Classification Model ◽

Support Vector ◽

Trace Detection ◽

Svm Model ◽

Detection Of Explosives ◽

Non Destructive ◽

Hyperspectral Sensor

The aim of this study was to investigate the potential of the non-destructive hyperspectral imaging system (HSI) and accuracy of the model developed using Support Vector Machine (SVM) for determining trace detection of explosives. Raman spectroscopy has been used in similar studies, but no study has been published which is based on measurement of reflectance from hyperspectral sensor for trace detection of explosives. HSI used in this study has an advantage over existing techniques due to its combination of imaging system and spectroscopy, along with being contactless and non-destructive in nature. Hyperspectral images of the chemical were collected using the BaySpec hyperspectral sensor which operated in the spectral range of 400–1000 nm (144 bands). Image processing was applied on the acquired hyperspectral image to select the region of interest (ROI) and to extract the spectral reflectance of the chemicals which were stored as spectral library. Principal Component Analysis (PCA) and first derivative was applied to reduce the high dimensionality of the image and to determine the optimal wavelengths between 400 and 1000 nm. In total, 22 out of 144 wavelengths were selected by analysing the loadings of principal components (PC). SVM was used to develop the classification model. SVM model established on the whole spectrum from 400 to 1000 nm achieved an accuracy of 81.11%, whereas an accuracy of 77.17% with less computational load was achieved when SVM model was established on the optimal wavelengths selected. The results of the study demonstrate that the hyperspectral imaging system along with SVM is a promising tool for trace detection of explosives.

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