Prediction of Human Microbe-Drug Association based on Layer Attention Graph Convolutional Network.

Unlabelled: Human microbes are closely associated with a variety of complex diseases and have emerged as drug targets. Identification of microbe-related drugs is becoming a key issue in drug development and precision medicine. It can also provide guidance for solving the increasingly serious problem of drug resistance enhancement in viruses.

Identifying SM-miRNA associations based on layer attention graph convolutional network and matrix decomposition.

The accurate prediction of potential associations between microRNAs (miRNAs) and small molecule (SM) drugs can enhance our knowledge of how SM cures endogenous miRNA-related diseases. Given that traditional methods for predicting SM-miRNA associations are time-consuming and arduous, a number of computational models have been proposed to anticipate the potential SM-miRNA associations. However, several of these strategies failed to eliminate noise from the known SM-miRNA association information or failed to prioritize the most significant known SM-miRNA associations.

A Novel Segmentation Scheme with Multi-Probability Threshold for Human Activity Recognition Using Wearable Sensors.

In recent years, much research has been conducted on time series based human activity recognition (HAR) using wearable sensors. Most existing work for HAR is based on the manual labeling. However, the complete time serial signals not only contain different types of activities, but also include many transition and atypical ones.

Human Behavior Recognition Model Based on Feature and Classifier Selection.

With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data.

Integrally Cooperative Spatio-Temporal Feature Representation of Motion Joints for Action Recognition.

In contemporary research on human action recognition, most methods separately consider the movement features of each joint. However, they ignore that human action is a result of integrally cooperative movement of each joint. Regarding the problem, this paper proposes an action feature representation, called Motion Collaborative Spatio-Temporal Vector (MCSTV) and Motion Spatio-Temporal Map (MSTM).

Fabric Defect Detection Based on Illumination Correction and Visual Salient Features.

Aiming at the influence of uneven illumination on fabric feature extraction and the limitations of traditional frequency-based visual saliency algorithms, we propose a fabric defect detection method based on the combination of illumination correction and visual salient features-(1) Construct a multi-scale side window box (MS-BOX) filter to extract the illumination component of the image, then use the constructed two-dimensional gamma correction function to perform illumination correction on the image in the global angle, and finally enhance the local contrast of the image in the local angle; (2) Use the L0 gradient minimization method to remove the background texture of fabric images and highlight the defects; (3) Represent the fabric image as a quaternion image, where each pixel in the image is represented by a quaternion consisting of color, intensity and edge characteristics. The two-dimensional fractional Fourier transform (2D-FRFT) is used to obtain the saliency map of the quaternion image. Experiments show that our method has a higher overall recall rate for defect detection of star-patterned, box-patterned, and dot-patterned fabrics, and the overall recall-precision effect is better than other existing methods.

Implementation of Steiner point of fuzzy set.

This paper deals with the implementation of Steiner point of fuzzy set. Some definitions and properties of Steiner point are investigated and extended to fuzzy set. This paper focuses on establishing efficient methods to compute Steiner point of fuzzy set.

[Identification of semen celosiae and semen celosiae cristatae using continuous wavelet transform with FTIR].

Infrared spectra of semen celosiae and semen celosiae cristatae were obtained directly, quickly and accurately by Fourier transform infrared spectroscopy (FTIR) with OMNI sampler. Continuous wavelet transform was used to extrude local region of infrared spectra of semen celosiae and its confusable varieties. The difference of infrared spectra between semen celosiae and semen celosiae cristatae was extruded greatly.