Dendritic Cell Algorithm with Grouping Genetic Algorithm for Input Signal Generation

The artificial immune system,an excellent prototype for developingMachine Learning,is inspired by the function of the powerful natural immune system.As one of the prevalent classifiers,the Dendritic Cell Algorithm(DCA)has been widely used to solve binary problems in the real world.The classification of DCA depends on a data preprocessing procedure to generate input signals,where feature selection and signal categorization are themain work.However,the results of these studies also show that the signal generation of DCA is relatively weak,and all of them utilized a filter strategy to remove unimportant attributes.Ignoring filtered features and applying expertise may not produce an optimal classification result.To overcome these limitations,this study models feature selection and signal categorization into feature grouping problems.This study hybridizes Grouping Genetic Algorithm(GGA)with DCA to propose a novel DCA version,GGA-DCA,for accomplishing feature selection and signal categorization in a search process.The GGA-DCA aims to search for the optimal feature grouping scheme without expertise automatically.In this study,the data coding and operators of GGA are redefined for grouping tasks.The experimental results show that the proposed algorithm has significant advantages over the compared DCA expansion algorithms in terms of signal generation.

An alfalfa MYB-like transcriptional factor MsMYBH positively regulates alfalfa seedling drought resistance and undergoes MsWAV3-mediated degradation

Drought is a major threat to alfalfa(Medicago sativa L.)production.The discovery of important alfalfa genes regulating drought response will facilitate breeding for drought-resistant alfalfa cultivars.Here,we report a genome-wide association study of drought resistance in alfalfa.We identified and functionally characterized an MYB-like transcription factor gene(MsMYBH),which increases the drought resistance in alfalfa.Compared with the wild-types,the biomass and forage quality were enhanced in MsMYBH overexpressed plants.Combined RNA-seq,proteomics and chromatin immunoprecipitation analysis showed that MsMYBH can directly bind to the promoters of MsMCP1,MsMCP2,MsPRX1A and MsCARCAB to improve their expression.The outcomes of such interactions include better water balance,high photosynthetic efficiency and scavenge excess H_(2)O_(2)in response to drought.Furthermore,an E3 ubiquitin ligase(MsWAV3)was found to induce MsMYBH degradation under long-term drought,via the 26S proteasome pathway.Furthermore,variable-number tandem repeats in MsMYBH promoter were characterized among a collection of germplasms,and the variation is associated with promoter activity.Collectively,our findings shed light on the functions of MsMYBH and provide a pivotal gene that could be leveraged for breeding drought-resistant alfalfa.This discovery also offers new insights into the mechanisms of drought resistance in alfalfa.

Classification of Remote Sensing Images Based on Band Selection and Multi-mode Feature Fusion

As feature data in multimodal remote sensing images belong to multiple modes and are complementary to each other,the traditional method of single-mode data analysis and processing cannot effectively fuse the data of different modes and express the correlation between different modes.In order to solve this problem,make better fusion of different modal data and the relationship between the said features,this paper proposes a fusion method of multiple modal spectral characteristics and radar remote sensing imageaccording to the spatial dimension in the form of a vector or matrix for effective integration,by training the SVM model.Experimental results show that the method based on band selection and multi-mode feature fusion can effectively improve the robustness of remote sensing image features.Compared with other methods,the fusion method can achieve higher classification accuracy and better classification effect.

Facial Expression Recognition Based on PCD-CNN with Pose and Expression

In order to achieve high recognition rate,most facial expression recognition(FER)methods generate sufficient labeled facial images based on generative adversarial networks(GAN)to train model.However,these methods do not estimate the facial pose before passing the images to the generator,which affects the quality of generated images.And mode collapse is prone to occur during the training process,leading to generate a single-style facial images.To solve these problems,a FER model is proposed based on pose conditioned dendritic convolution neural network(PCD-CNN)with pose and expression.Before passing the facial images to the generator,PCD-CNN was used to process facial images,effectively estimating the facial landmarks to detect face and disentangle the pose.In order to accelerate the training speed of the model,PCD-CNN was based on the ShuffleNet-v2 framework.Every landmark of facial image was modeled by a separate ShuffleNet-DeconvNet,maintaining better performance with fewer parameters.To solve the mode collapse during image generation,we theoretically analyzed the causes,and implemented mini-batch processing on the discriminator in the model and directly calculated the statistical characteristics of the mini-batch samples.Experiments were carried out on the Multi-PIE and BU-3DFE facial expression datasets.Compared with current advanced methods,our method achieves higher accuracy 93.08%,and the training process is more stable.