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Rotating Stomata Measurement Based on Anchor-Free Object Detection and Stomata Conductance Calculation

Plant Phenomics 2023年第4期5卷 767-778页

作者： Fan Zhang Bo Wang Fuhao Lu Xinhong Zhang Huaihe Hospital of Henan University Kaifeng 475004China Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng 475004China State Key Laboratory of Crop Stress Adaptation and Improvement Henan UniversityKaifeng 475004China School of Software Henan UniversityKaifeng 475004China

Stomata play an essential role in regulating water and carbon dioxide levels in plant leaves,which is important for *** deep learning-based plant stomata detection methods are based on horizontal *** detection anchor boxes of deep learning model are horizontal,while the angle of stomata is randomized,so it is not possible to calculate stomata traits directly from the detection anchor *** processing of image(e.g.,rotating image)is required before detecting stomata and calculating stomata *** paper proposes a novel approach,named DeepRSD(deep learning-based rotating stomata detection),for detecting rotating stomata and calculating stomata basic traits at the same ***,the stomata conductance loss function is introduced in the DeepRSD model training,which improves the efficiency of stomata detection and conductance *** experimental results demonstrate that the DeepRSD model reaches 94.3%recognition accuracy for stomata of maize *** proposed method can help researchers conduct large-scale studies on stomata morphology,structure,and stomata conductance models.

关键词： calculation. Deep rotating

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Unsupervised social network embedding via adaptive specific mappings

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Frontiers of Computer Science 2024年第3期18卷 61-71页

作者： Youming GE Cong HUANG Yubao LIU Sen ZHANG Weiyang KONG School of Computer Science and Engineering Sun Yat-Sen UniversityGuangzhou 510275China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou 510006China

In this paper,we address the problem of unsuperised social network embedding,which aims to embed network nodes,including node attributes,into a latent low dimensional *** recent methods,the fusion mechanism of node attributes and network structure has been proposed for the problem and achieved impressive prediction ***,the non-linear property of node attributes and network structure is not efficiently fused in existing methods,which is potentially helpful in learning a better network *** this end,in this paper,we propose a novel model called ASM(Adaptive Specific Mapping)based on encoder-decoder *** encoder,we use the kernel mapping to capture the non-linear property of both node attributes and network *** particular,we adopt two feature mapping functions,namely an untrainable function for node attributes and a trainable function for network *** the mapping functions,we obtain the low dimensional feature vectors for node attributes and network structure,***,we design an attention layer to combine the learning of both feature vectors and adaptively learn the node *** encoder,we adopt the component of reconstruction for the training process of learning node attributes and network *** conducted a set of experiments on seven real-world social network *** experimental results verify the effectiveness and efficiency of our method in comparison with state-of-the-art baselines.

关键词： network embedding specific kernel mapping attention mechanism

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Privacy-Preserving Fingerprint Recognition via Federated Adaptive Domain Generalization

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Computers, Materials & Continua 2025年第3期82卷 5035-5055页

作者： Yonghang Yan Xin Xie Hengyi Ren Ying Cao Hongwei Chang Henan Key Laboratory of Big Data Analysis and Processing Computer and Information EngineeringHenan UniversityKaifeng475004China College of Information Science and Technology Nanjing Forestry UniversityNanjing210037China Henan Branch China Life Insurance Co.Ltd.Zhengzhou450000China

Fingerprint features,as unique and stable biometric identifiers,are crucial for identity ***,traditional centralized methods of processing these sensitive data linked to personal identity pose significant privacy risks,potentially leading to user data *** Learning allows multiple clients to collaboratively train and optimize models without sharing raw data,effectively addressing privacy and security ***,variations in fingerprint data due to factors such as region,ethnicity,sensor quality,and environmental conditions result in significant heterogeneity across *** heterogeneity adversely impacts the generalization ability of the global model,limiting its performance across diverse *** address these challenges,we propose an Adaptive Federated Fingerprint Recognition algorithm(AFFR)based on Federated *** algorithm incorporates a generalization adjustment mechanism that evaluates the generalization gap between the local models and the global model,adaptively adjusting aggregation weights to mitigate the impact of heterogeneity caused by differences in data quality and feature ***,a noise mechanism is embedded in client-side training to reduce the risk of fingerprint data leakage arising from weight disclosures during model *** conducted on three public datasets demonstrate that AFFR significantly enhances model accuracy while ensuring robust privacy protection,showcasing its strong application potential and competitiveness in heterogeneous data environments.

关键词： Fingerprint recognition privacy protection federated learning adaptive weight adjustment

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Evolutionary genetics of wheat mitochondrial genomes

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The Crop Journal 2023年第6期11卷 1774-1781页

作者： Hui-Lin Hu Fan Zhang Pei Wang Fu-Hao Lu State Key Laboratory of Crop Stress Adaptation and Improvement School of Life SciencesHenan UniversityKaifeng 475004HenanChina Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information EngineeringHenan UniversityKaifeng 475004HenanChina School of Mathematics and Statistics Henan UniversityKaifeng 475004HenanChina

The Triticum-Aegilops complex provides ideal models for the study of polyploidization,and mitochondrial genomes(mtDNA)can be used to trace cytoplasmic inheritance and energy production following *** this study,gapless mitochondrial genomes for 19 accessions of five Triticum or Aegilops species were *** genomics confirmed that the BB-genome progenitor donated mtDNA to tetraploid ***(genome formula AABB),and that this mtDNA was then passed on to the hexaploid ***(AABBDD).T urartu(AA)was the paternal parent of ***(AAGG),and an earlier ***(DD)was the maternal parent of ***(CCDD).Genic sequences were highly conserved within species,but frequent rearrangements and nuclear or chloroplast DNA insertions occurred during *** highly variable mitochondrial genes(atp6,cob,nad6,and nad9)were established as marker genes for Triticum and Aegilops species *** BB/GG-specific atp6 and cob genes,which were imported from the nuclear genome,could facilitate identification of their diploid *** haplotypes and repeat-sequence patterns indicated that BB was much closer to GG than to ***(SS).These findings provide novel insights into the polyploid evolution of the Triticum/Aegilops complex from the perspective of mtDNA,advancing understanding of energy supply and adaptation in wheat species。

关键词： Wheat Mitochondrion mtDNA Comparative genomics Polyploidization

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Hyperbolic lorentz attention for synthetic lethality prediction 3

Hyperbolic lorentz attention for synthetic lethality predict...

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3rd International Conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Zhang, Ge Chen, Yitong Kou, Xuye Zhang, Zhou School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China

ISBN: (纸本)9781510681279

Synthetic lethal interactions are critical genetic interactions that have been discovered for identifying new drug targets and potential cancer drug combination strategies. As a targeted approach to selectively kill cancer cells, it has attracted great attention in the field of cancer treatment. However, with the rapid growth of high-throughput data, effective identification of synthetic lethal interactions remains challenging. Although many graph topology methods focus on predicting SL interactions, heterogeneous graphs that exist in the real world tend to be scale-free, where the number of high-order neighbor nodes of high-degree nodes grows exponentially. The graph embedding method based on Euclidean space may not be able to effectively capture the internal hierarchical structure of the scale-free network, and there is not enough space to accommodate the nodes, making the node embedding highly distorted, which greatly limits the modeling ability. In this paper, we propose a model named HLASL based on the hyperbolic Lorentz attention mechanism, it ensures that the learned node features follow a Lorentz manifold. Specifically, in hyperbolic space, we use Lorentzian manifolds to encode heterogeneous information from KGs and information about synthetic lethality. In addition, we introduce the Lorenz knowledge graph attention mechanism to distinguish the weights of different information from the hyperbolic space to optimize gene embedding. Experiments on two datasets show that HLASL outperforms several state-of-the-art baseline methods in synthetic lethality prediction. © 2024 SPIE.

关键词： Forecasting

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FF-LSTM: Phenotype prediction based on feature fusion 3

FF-LSTM: Phenotype prediction based on feature fusion

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3rd International Conference on Electronic Information Engineering and data processing, EIEDP 2024

作者： Zhang, Ge Zhang, Zhou Kou, Xuye Chen, Yitong School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China

ISBN: (纸本)9781510680531

Phenotypic prediction before crop planting and harvest facilitates plant phenotyping analysis and the implementation of precision agriculture, which is crucial for food security policy formulation, crop management, and food security. Environmental data collected in the field, as important factors affecting phenotypes, can be used together with genotypes to train phenotypic prediction models to improve their prediction capabilities. Although the rise of deep learning has provided a powerful tool for predicting phenotypes using genotype and environmental data, current research only performs simple splicing operations when processing genotype and environmental data and fails to effectively model the impact of genotype and environment on phenotype. Therefore, it is still challenging to accurately predict phenotype using genotype and environment. To solve the problems mentioned above, we design a phenotype prediction model named FF-LSTM. In this method, LSTM is used to extract features from SNP and environmental, and the feature fusion method weights the extracted features to simulate the influence of genotype and environment on phenotype. We conducted experiments on the dataset and verified the effectiveness of this method by analyzing and evaluating the experimental results. © 2024 SPIE.

关键词： Forecasting

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Single cell clustering using ZINB Model and variational graph attention autoencoder 3

Single cell clustering using ZINB Model and variational grap...

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3rd International Conference on Biomedical and Intelligent Systems, IC-BIS 2024

作者： Zhang, Ge Kou, Xuye Chen, Yitong Zhang, Zhou School of Computer and Information Engineering Henan University Kaifeng475004 China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China

ISBN: (纸本)9781510681279

Single-cell RNA sequencing allows to discovery of new cell subtypes based on transcriptomic information. Clustering analysis is an effective approach for exploring single-cell heterogeneity. Nevertheless, current single-cell clustering methodologies are challenged by the high-dimensional, sparse, and dropouts on the one hand, and on the other hand, they neglect the thorough exploration of potential relationships between cells and noise removal. To tackle these challenges, this study introduces a novel self-optimized single-cell clustering algorithm named scZVEA, which combines Zero- Inflated Negative Binomial (ZINB) model and variational graph attention autoencoder. The scZVEA framework comprises three key modules. Firstly, a Deep Count Autoencoder (DCA) is designed to model data distribution and eliminate noise. Then, a variational graph attention autoencoder to extract latent features from the data. Lastly, the self-optimized clustering module enables the two previously independent clustering and feature modules to mutually benefit each other by iteratively updating cluster centers to further enhance clustering performance. Experimental findings based on six authentic scRNAseq datasets illustrate that the proposed clustering algorithm significantly enhances clustering accuracy when compared to alternative methods. © 2024 SPIE.

关键词： Clustering algorithms

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Multi-Cluster Feature Selection Based on Isometric Mapping

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IEEE/CAA Journal of Automatica Sinica 2022年第3期9卷 570-572页

作者： Yadi Wang Zefeng Zhang Yinghao Lin Henan Key Laboratory of Big Data Analysis and Processing Henan UniversityKaifeng 475004 Institute of Data and Knowledge Engineering School of Computer and Information EngineeringHenan UniversityKaifeng 475004China

Dear editor,This letter presents an unsupervised feature selection method based on machine *** selection is an important component of artificial intelligence,machine learning,which can effectively solve the curse of d... 详细信息

关键词： problem letter dimensionality

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An Iterated Local Search Algorithm for the Heterogeneous Fixed Multi-compartment Vehicle Routing Problem

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IAENG International Journal of Applied Mathematics 2025年第5期55卷 1084-1091页

作者： Ma, Hengrui Hou, Yan-e Xu, Haizhe Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng475004 China

This paper addresses the multi-compartment vehicle routing problem with heterogeneous fleets with maximum number limitations. To solve this problem, we propose an iterated local search algorithm integrating with multiple strategies to find a solution with minimum total cost. The proposed algorithm starts by generating an initial solution using a cheapest insertion heuristic. Then, a load adjustment strategy is applied to transfer customers from larger vehicle to smaller one. Four neighborhood operators such as relocation, 2-opt, exchange and or-opt, are applied sequentially to refine the solution, allowing for vehicle type adjustments during the local search process. Additionally, a perturbation mechanism based on destroy-and-repair principle is introduced to explore a wider search space. Meanwhile, an acceptance strategy based on route load variance is designed to permit the acceptance of suboptimal solutions. Finally, the best global solution undergoes further optimization through a vehicle assignment strategy to improve vehicle allocation. Experimental results on benchmark instances demonstrate that the proposed algorithm outperforms existing methods. © (2025), (International Association of Engineers). All rights reserved.

关键词： Local search (optimization)

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A remote sensing work-flow recommendation algorithm based on logical structure

A remote sensing work-flow recommendation algorithm based on...

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2022 International Conference on Signal processing, Computer Networks, and Communications, SPCNC 2022

作者： Zuo, Xianyu Wu, Yaohua Liu, Yang Lin, Yinghao Zhang, Lei Henan Key Laboratory of Big Data Analysis and Processing School of Computer and Information Engineering Henan University Kaifeng475001 China

ISBN: (纸本)9781510664616

With the development of remote sensing products towards the direction of civilianization and popularization, work-flow customization plays an important role in the production of remote sensing products. The traditional customized work-flow model has large time cost, complex operation and high requirements for professional knowledge. The work-flow recommendation system can improve the construction efficiency of remote sensing work-flow to some extent and assist users to design high-quality remote sensing work-flow models. However, most of the existing remote sensing work-flow modeling methods ignore the logical structure characteristics of the work-flow, leading to large errors in the calculation results of similarity. Difficult to make work-flow recommendations effectively. Therefore, this paper proposes a customized recommendation algorithm for remote sensing work-flow based on logical structure. By focusing on logical structure, the reliability of similarity calculation between work-flow is improved, so as to find similar work-flow to help users recommend the next modeling node. Firstly, the work-flow model needs to be preprocessed: the user converts the constructed work-flow model into a process structure tree by using Petri net workflow, and uses the path table generation algorithm based on logical structure to convert the model information into data information and store it in the database for subsequent data processing;Then the flow data in the flow tree set was converted into a path table according to certain rules, and then the longest common subsequence similarity of each data in the path table was calculated to obtain the similarity calculation results based on the logical structure characteristics, the most similar work-flow in the work-flow library is found and the recommendation is made for the user. The method proposed in this paper is evaluated experimentally on the real data set, in terms of recall, precision and F1-score, which shows that the method

关键词： Remote sensing

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