Lin Wang’s research while affiliated with Henan University of Science and Technology and other places

Flying ad hoc networks (FANETs) have highly dynamic and energy-limited characteristics. Compared with traditional mobile ad hoc networks, their nodes move faster and their topology changes more frequently. Therefore, the design of routing protocols faces greater challenges. The existing routing schemes rely on frequent and fixed-interval Hello transmissions, which exacerbates network load and leads to high communication energy consumption and outdated location information. MP-QGRD combined with the extended Kalman filter (EKF) is used for node position prediction, and the Hello packet transmission interval is dynamically adjusted to optimize neighbor discovery. At the same time, reinforcement learning methods are used to comprehensively consider link stability, energy consumption, and communication distance for routing decisions. The simulation results show that compared to QMR, QGeo, and GPSR, MP-QGRD has an increased packet delivery rate, end-to-end latency, and communication energy consumption by 10%, 30%, and 15%, respectively.

Multi-objective optimization (MOO) has become an important method in machine learning, which involves solving multiple competing objective problems simultaneously. Nowadays, many MOO algorithms assume that gradient information is easily available and use this information to optimize functions. However, when encountering situations where gradients are not available, such as black-box functions or non-differentiable functions, these algorithms become ineffective. In this paper, we propose a zeroth-order MOO algorithm named SZMG (stochastic zeroth-order multi-gradient algorithm), which approximates the gradient of functions by finite difference methods. Meanwhile, to avoid conflicting gradients between functions and reduce stochastic multi-gradient direction bias caused by stochastic gradients, an SGD-type method is adopted to acquire weight parameters. Under the non-convex setting and mild assumptions, the convergence rate is established for the SZMG algorithm. Simulation results demonstrate the effectiveness of the SZMG algorithm.

Cloud-to-Vehicle (C2V) integration serves as a fundamental infrastructure to provide robust computing and storage support for Vehicular Social Networks (VSNs). However, the proliferation of sensitive personal data within VSNs poses significant challenges in achieving secure and efficient data sharing while maintaining data usability and precise retrieval capabilities. Although existing searchable attribute-based encryption schemes offer the secure retrieval of encrypted data and fine-grained access control mechanisms, these schemes still exhibit limitations in terms of bilateral access control, dynamic index updates, and search result verification. This study presents a Dual-Policy Attribute-based Searchable Encryption (DP-ABSE) scheme with dynamic keyword update functionality for VSNs. The scheme implements a fine-grained decoupling mechanism that decomposes data attributes into two distinct components: immutable attribute names and mutable attribute values. This decomposition transfers the attribute verification process from data owners to the encrypted files themselves, enabling data attribute-level granularity in access control. Through the integration of an identity-based authentication mechanism derived from the data owner’s unique identifier and bilinear pairing verification, it achieves secure updates of the specified keywords index while preserving both the anonymity of the non-updated data and the confidentiality of the message content. The encryption process employs an offline/online two-phase design, allowing data owners to pre-compute ciphertext pools for efficient real-time encryption. Subsequently, the decryption process introduces an outsourcing local-phase mechanism, leveraging key encapsulation technology for secure attribute computation outsourcing, thereby reducing the terminal computational load. To enhance security at the terminal decryption stage, the scheme incorporates a security verification module based on retrieval keyword and ciphertext correlation validation, preventing replacement attacks and ensuring data integrity. Security analysis under standard assumptions confirms the theoretical soundness of the proposed solution, and extensive performance evaluations showcase its effectiveness.

The detection of pests and diseases in crops is currently a hot topic. The complexity of pest and disease object in the field, combined with inconsistent features across different levels, poses challenges for network detection. Additionally, the complex agricultural production environment tends to generate many interfering negative samples, which significantly complicates pest and disease differentiation. To address these two issues, the YOLOv7‐PSAFP network structure was first proposed. Based on YOLOV7, the progressive Spatial Adaptive Feature Pyramid (PSAFP) was introduced. Second, a combination of the Varifocal Loss and Loss Rank Mining loss functions was used for calculating the object loss, which reduces the interference of useless negative examples during training. On the filtered‐plant‐village‐dataset and rice‐corn pest dataset, the mAP results of YOLOv7‐PSAFP were 84.7%$\%$ and 93.3%$\%$, which are 2.9%$\%$ and 2.1%$\%$ higher than the baseline model (YOLOv7), respectively. The code for this paper is located at https://github.com/DuLJ72/PSAFP.

Weed recognition is an inevitable problem in smart agriculture, and to realise efficient weed recognition, complex background, insufficient feature information, varying target sizes and overlapping crops and weeds are the main problems to be solved. To address these problems, the authors propose a real‐time semantic segmentation network based on a multi‐branch structure for recognising crops and weeds. First, a new backbone network for capturing feature information between crops and weeds of different sizes is constructed. Second, the authors propose a weight refinement fusion (WRF) module to enhance the feature extraction ability of crops and weeds and reduce the interference caused by the complex background. Finally, a Semantic Guided Fusion is devised to enhance the interaction of information between crops and weeds and reduce the interference caused by overlapping goals. The experimental results demonstrate that the proposed network can balance speed and accuracy. Specifically, the 0.713 Mean IoU (MIoU), 0.802 MIoU, 0.746 MIoU and 0.906 MIoU can be achieved on the sugar beet (BoniRob) dataset, synthetic BoniRob dataset, CWFID dataset and self‐labelled wheat dataset, respectively.

Traditional Chinese medicine distinguishes tongue features such as tongue color, fur color, tongue shape and crack mainly through the visual observation and empirical analysis of traditional Chinese medicine doctors. Therefore, the judgment standard will be affected by the subjective factors of doctors and surrounding environment. These factors restrict the application and development of tongue diagnosis. Therefore, objectifying tongue diagnosis information and standardizing diagnosis is an important direction of tongue diagnosis automation research. This paper presents a classification method of TCM tongue image based on multi feature fusion. By constructing a multi feature fusion model, two sub networks are used to classify the different features of the tongue image, so as to realize the task of multi feature classification of the tongue image. The model classifies the tongue image into tongue color classification, fur color classification, tongue shape classification and crack classification, and outputs the color parameters of tongue color and fur color while outputting the classification results. The model adds the method of transfer learning, which can reduce the demand for the amount of tongue image data, At the same time, the accuracy of the model is improved.

In this manuscript, a TCM prescription recommendation model based on deep crossed neural network was proposed, which aims to solve the problem caused by sparse discrete features in TCM prescription information obtained after encoding and feature automatic cross-combination through embedding layer and residual network. Use residual neural network to reduce overfitting and make convergence faster. Using the multi-level residual network, the combined feature vector of medical records and prescriptions is fully residual operation, so that the model can obtain more nonlinear feature information between medical records and prescriptions, and realize the corresponding treatment according to the name of the disease. The function of prescription to improve the level of clinical treatment.

Tongue diagnosis is a non-invasive, painless diagnostic method by observing the tongue image of patients to diagnose and analyze their pathological conditions, which provides an opportunity for the future development of tongue diagnosis. However, the traditional tongue diagnosis method mainly relies on the experience and judgment of doctors, and is also easily affected by external factors. These factors hinder the development and application of tongue diagnosis. Currently, most studies use machine learning, which is time consuming and labor intensive. Other studies use deep learning based on convolutional neural network (CNN), but the affine transformation of CNN is less robust and easily loses the spatial relationship between features. In this work, we propose a traditional Chinese medicine (TCM) syndrome classification model of skin diseases based on tongue image hierarchical feature fusion. By adding a multi-scale residual module to the feature extraction part of the capsule network, we can extracted richer feature of tongue image. At the same time, the attention mechanism module is embedded in the multi-scale residual module, with the help of the attention mechanism module, the interference of tongue impurity information is suppressed, and accurate features are extracted for classification. Through experiments, it has been proven that our proposed method has achieved accuracy of 89.6\% in the classification of tongue for acne syndrome, and accuracy of 91.6\% in the dermatitis syndrome.