8-dimensional Feature Research Articles

Variational Quantum Linear Solver Enhanced Quantum Support Vector Machine

Quantum Support Vector Machines (QSVM) play a vital role in using quantum resources for supervised machine learning tasks, such as classification. However, current methods are strongly limited in terms of scalability on Noisy Intermediate Scale Quantum (NISQ) devices. In this work, we propose a novel approach called the Variational Quantum Linear Solver (VQLS) enhanced QSVM. This is built upon our idea of utilizing the variational quantum linear solver to solve system of linear equations of a Least Squares-SVM on a NISQ device. The implementation of our approach is evaluated by an extensive series of numerical experiments with the Iris dataset, which consists of three distinct iris plant species. Based on this, we explore the effectiveness of our algorithm by constructing a classifier capable of classification in a feature space ranging from one to seven dimensions. Furthermore, we exploit both classical and quantum computing for various subroutines of our algorithm, and effectively mitigate challenges associated with the implementation. These include significant improvement in the trainability of the variational ansatz and notable reductions in run-time for cost calculations. Based on the numerical experiments, our approach exhibits the capability of identifying a separating hyperplane in an 8-dimensional feature space. Moreover, it consistently demonstrated strong performance across various instances with the same dataset.

Prediction and customized design of Curie temperature of Fe-based amorphous alloys based on interpretable machine learning

Predicting the Curie temperature (Tc) is a crucial problem in the field of amorphous alloys. In this study, Fe-based amorphous alloys are taken as an example, and the composition and corresponding Tc are collected through a literature review. Three feature construction strategies are employed to establish the relationship between the composition and Tc using machine learning. The research findings demonstrate that the combination of the Meredig rule and the GBT algorithm yields the highest prediction accuracy. The features are constructed using recursive elimination and enumeration methods, ultimately resulting in an optimal 8-dimensional feature subset. Furthermore, the Shapley Additive exPlanations (SHAP) values are introduced to analyze the interpretability of the prediction model, providing a feature importance ranking and their critical values. Finally, by replacing the Fe and P atoms with Mn and Si atoms, respectively, in the Fe80P13C7 alloy, Fe62Mn18P(13-x)C7Six (x = 4,5,6) alloys are successfully designed to exhibit a Tc close to room temperature (335 K), enabling customized Tc design for Fe-based amorphous alloys.

Identification method of cashmere and wool based on texture features of GLCM and Gabor

The common texture feature extraction method is only in spatial or frequency domain, leading to insufficient texture information and low accuracy. The main aim of this paper is to present a novel texture feature analysis method based on gray level co-occurrence matrix and Gabor wavelet transform to sufficiently extract texture feature of cashmere and wool fibers. Firstly, the gray level co-occurrence matrix is constructed to calculate the four texture feature vectors including of contrast, angular second moment, dissimilarity and energy in spatial domain, and four texture feature vectors, which are contrast, angular second moment, mean and entropy, in frequency domain is obtained through Gabor wavelet transform and Gray-Scale difference statistics method. Then, because the contrast and angle second moment are used as descriptors of fiber image in both spatial and frequency domain, they are fused respectively by introducing a weight to make linear addition, making eight feature values compose a 6-dimensional feature vector. Finally, these feature vectors are fed into the Fisher classifier. The experimental results show that the identification accuracy of the proposed algorithm is improved by 0.682% compared to use 8-dimensional feature vectors describing the sample image. It verifies that the fused method based on texture feature in spatial and frequency domain is an effective approach to identify fibers of cashmere and wool.

A maximum relevancy and minimum redundancy feature selection approach for median filtering forensics

The forensics of the median filtering is a challenging task due to its content preserving nature. Several methods have been proposed for median filtering forensics in digital images. However the performance of these methods deteriorates for compressed images, small resolutions of images and for anti-forensic operations. Moreover large feature set dimensions of these methods also pose a computational challenge. This paper proposes, a 8-dimensional feature set, derived from two state-of-the-art techniques by employing maximum relevancy and minimum redundancy (mRMR) feature selection approach. Features are selected by mRMR on the basis of distance correlation as an association measure. Extensive experiments are performed to evaluate the efficacy of proposed method through six different databases. The proposed method outperforms state-of-the-art techniques for uncompressed images, compressed images at low quality factors, low resolutions images and for an anti-forensic operation. The performance of the proposed method is also compared with convolutional neural network (CNN) based features for the detection of median filtering at low resolutions and for compressed images. Also, experimental results support the performance of proposed method over other manipulations (average and Gaussian filtering).

Investigation of a novel automatic micro image-based method for the recognition of animal fibers based on Wavelet and Markov Random Field

In the analysis of fiber recognition, the challenge lies in the texture feature extraction. The main aim of this paper is to present a novel texture feature analysis method based on wavelet multi-scale analysis to fully extract texture features of microscopic images resulting in better recognition of similar animal fibers. Thousands of three kinds of similar fiber images including cashmere, sheep wool and goat hair were captured by the optical microscope and the digital camera. They were pre-processed to obtain the enhanced images with background removed. Then the pretreated fiber images were decomposed by 3-layer wavelet transform, four sub-images under the third-layer wavelet decomposition scale were analyzed by Gauss Markov Random Field (GMRF) model and their model parameters were obtained. Through the difference analysis of different kinds of fibers, two model parameters were selected from each sub-image to generate an 8-dimensional feature vectors, which was used to describe the fiber images. The parameters, which were extracted from 1000 images of each kind of fiber, were copied three times and randomly arranged to generate the final data sets. Finally, the data sets were processed by 10-times cross validation method as the training set and testing set of support vector machine (SVM). Ten different recognition rates could be obtained through the experiment, and the mean value was used as the final recognition accuracy of wool and cashmere fibers. The experimental results indicated that the method had a great recognition rate with 90.07% and the performance was robust. It verifies that the method based on wavelet multi-scale analysis is effective for the recognition of similar fibers.

Autonomous road detection and modeling for UGVs using vision-laser data fusion

Autonomous road detection and modeling play a key role for UGVs navigating in complex outdoor environments. This paper investigates road detection and description for UGVs in various outdoor scenes under different weather conditions. A novel environment perception system that includes two cameras and multiple laser range finders is introduced firstly. Taking classification accuracy and time-cost into account, 8-dimensional features are selected from a 91-dimensional candidate feature set using Adaboost algorithm. To adapt to the diversity of road scenes under different weather conditions in different seasons, an online classifier based on SVM is proposed to replace the fixed one. Moreover, a road region adjusting algorithm is present to eliminate misclassified regions especially when the roads have fuzzy boundaries or obstacles. Finally, a RANSAC spline fitting algorithm is adopted to provide an accurate road border model for UGVs’ autonomous path planning and navigation. A series of experiments are conducted by using a self-built UGV platform and experimental results show the validity and practicality of the proposed approaches.

Exploring the relationship between hub proteins and drug targets based on GO and intrinsic disorder

Protein–protein interactions (PPIs) play essential roles in many biological processes. In protein–protein interaction networks, hubs involve in numbers of PPIs and may constitute an important source of drug targets. The intrinsic disorder proteins (IDPs) with unstable structures can promote the promiscuity of hubs and also involve in many disease pathways, so they also could serve as potential drug targets. Moreover, proteins with similar functions measured by semantic similarity of gene ontology (GO) terms tend to interact with each other. Here, the relationship between hub proteins and drug targets based on GO terms and intrinsic disorder was explored. The semantic similarities of GO terms and genes between two proteins, and the rate of intrinsic disorder residues of each protein were extracted as features to characterize the functional similarity between two interacting proteins. Only using 8 feature variables, prediction models by support vector machine (SVM) were constructed to predict PPIs. The accuracy of the model on the PPI data from human hub proteins is as high as 83.72%, which is very promising compared with other PPI prediction models with hundreds or even thousands of features. Then, 118 of 142 PPIs between hubs are correctly predicted that the two interacting proteins are targets of the same drugs. The results indicate that only 8 functional features are fully efficient for representing PPIs. In order to identify new targets from IDP dataset, the PPIs between hubs and IDPs are predicted by the SVM model and the model yields a prediction accuracy of 75.84%. Further research proves that 3 of 5 PPIs between hubs and IDPs are correctly predicted that the two interacting proteins are targets of the same drugs. All results demonstrate that the model with only 8-dimensional features from GO terms and intrinsic disorder still gives a good performance in predicting PPIs and further identifying drug targets.

Polarimetric SAR data classification using competitive neural networks

A classification method for polarimetric SAR data analysis using a competitive neural network is considered. The network is trained by two LVQ algorithms. In addition, a specific feature vector as the input for the network employing the JM distance is determined. As a result of experiments using SIR-C data, average accuracy for classification results was 86.40%, where (i) the competitive neural network with 8-input and 40-output neurons was trained by LVQ1 and LVQ2.1, and (ii) the 8-dimensional feature vector with backscattering coefficients (dB) and pseudo-relative phases between HH and VV from L and C bands was used. It is shown that the proposed method outperforms other methods in average accuracy.