Temporal Knowledge Graph (TKG) representation learning embeds entities and relations into a low-dimensional space while preserving relational structures across time steps. Existing methods often neglect the critical role of timestamps in capturing evolving relational patterns. To bridge this gap, we propose MTT-TKG, a novel framework integrating three synergistic modules: (1) a Multi-Time Gate module modeling Knowledge Graph (KG) evolution across historical timestamps via multilayer gating; (2) a Time-Aware module capturing timestampspecific relational characteristics; (3) a Time-Guided module handling cross-graph temporal dependencies. An embeddingtime decoder completes the representation learning. Experiments on three real-world datasets demonstrate MTT-TKG’s superior performance in capturing temporal dynamics and relational structures.

The Vehicle Routing Problem (VRP) is a fundamental combinatorial optimization task that involves determining cost-effective routes subject to operational constraints. This study proposes a deep reinforcement learning framework that integrates Off-policy Proximal Policy Optimization (PPO) with a transductive LSTM (TLSTM), further enhanced by a differential evolution-based hyperparameter optimization (HO) procedure. The TLSTM module captures spatiotemporal dependencies more effectively than conventional LSTMs, while the off-policy PPO component enables robust policy updates under diverse VRP variants. The HO module ensures stable convergence by adaptively balancing exploration and exploitation. The proposed approach is evaluated on five VRP variants—including the Traveling Salesman Problem (TSP), Capacitated VRP (CVRP), Split Delivery VRP (SDVRP), Orienteering Problem (OP), and Prize Collecting TSP (PCTSP)—demonstrating consistent improvements in both solution quality and computational efficiency. On average, the framework achieves a 4.12% reduction in route costs and a 94.34% improvement in computational speed, underscoring its potential for advancing VRP research and supporting practical applications in logistics and transportation.

This paper presents a control framework integrated with a predefined-time observer and a neural network for unmanned surface vehicle (USV) trajectory-tracking. To achieve rapid estimation of the system state, a predefined-time observer (PTO) has been designed to efficiently estimate and compensate for trajectory errors. Subsequent, a neural network estimator is designed to estimate the uncertainty and disturbance set of the USV. Then, a control law is constructed using the non-singular fast terminal sliding mode control (NFTSMC) method by integrating the predefined-time observer and the neural network (NN). This approach overcomes the singularity issue and accelerates the state convergence speed of the system at different stages. Finally, the stability of the system is proved using Lyapunov stability theory, and the effectiveness of the method is verified by a simulation example. The results show that the control scheme ensures that the tracking error converges to zero in a predefined-time and to have improved tracking accuracy and control performance.

The performance of image-based visual servoing (IBVS) in dynamic, high-speed, and high-precision applications is a major issue in sustainable and smart manufacturing systems. The proposed solution addresses the need for systematic optimization of control laws and constraint treatments in IBVS processes. Limited exploration of this topic is evident in the literature. Central to our approach is a smart fuzzy control-based scheme optimized for the sustainable and intelligent operation of robotic arms in manufacturing environments. The scheme incorporates a Mamdani fuzzy inference method for the adaptive adjustment of servoing gain, improving convergence and aligning with smart manufacturing principles. This method ensures precision and responsiveness, which are essential for high-speed and high-precision tasks. We address field-of-view constraints through an innovative online generation method of virtual features with a variable radius in the image space. The effectiveness of this approach, which synergizes sustainable manufacturing with smart, technology-driven solutions, is demonstrated through various comparative experiments. The experimental results show that the number of convergence iterations and the average initial velocity of the proposed method are reduced to 59% and 12%, respectively, of those of the existing methods on average; the optimization of the convergence efficiency and the continuity of the initial speed are obvious. Additionally, the maximum value of the vertical coordinate of the image is 1011 pixel, and has the best security performance.

The primary objective of interactive medical image segmentation systems is to achieve more precise segmentation outcomes with reduced human intervention. This endeavor holds significant clinical importance for both pre-diagnostic pathological assessments and prognostic recovery. Among the various interaction methods available, click-based interactions stand out as an intuitive and straightforward approach compared to alternatives such as graffiti, bounding boxes, and extreme points. To improve the model's ability to interpret click-based interactions, we propose a comprehensive interactive segmentation framework that leverages an iterative weighted loss function based on user clicks. To enhance the segmentation capabilities of the Plain-ViT backbone, we introduce a Residual Multi-Headed Self-Attention encoder with hierarchical inputs and residual connections, offering multiple perspectives on the data. This innovative architecture leads to a remarkable improvement in segmentation model performance. In this research paper, we assess the robustness of our proposed framework using a self-compiled T2-MRI image dataset of the prostate and three publicly available datasets containing images of other organs. Our experimental results convincingly demonstrate that our segmentation model surpasses existing state-of-the-art methods. Furthermore, the incorporation of an iterative loss function training strategy significantly accelerates the model's convergence rate during interactions. In the prostate dataset, we achieved an impressive Intersection over Union (IoU) score of 88.11% and Number of Clicks(NoC) at 80% are 7.03 clicks.

Convolutional neural networks (CNNs) have received widespread attention due to their powerful modeling capabilities and have been successfully applied in natural language processing, image recognition, and other fields. On the other hand, traditional CNN can only deal with Euclidean spatial data. In contrast, many real‐life scenarios, such as transportation networks, social networks, reference networks, and so on, exist in graph data. The creation of graph convolution operators and graph pooling is at the heart of migrating CNN to graph data analysis and processing. With the advancement of the Internet and technology, graph convolution network (GCN), as an innovative technology in artificial intelligence (AI), has received more and more attention. GCN has been widely used in different fields such as image processing, intelligent recommender system, knowledge‐based graph, and other areas due to their excellent characteristics in processing non‐European spatial data. At the same time, communication networks have also embraced AI technology in recent years, and AI serves as the brain of the future network and realizes the comprehensive intelligence of the future grid. Many complex communication network problems can be abstracted as graph‐based optimization problems and solved by GCN, thus overcoming the limitations of traditional methods. This survey briefly describes the definition of graph‐based machine learning, introduces different types of graph networks, summarizes the application of GCN in various research fields, analyzes the research status, and gives the future research direction.

The technology of visual servoing, with the digital twin as its driving force, holds great promise and advantages for enhancing the flexibility and efficiency of smart manufacturing assembly and dispensing applications. The effective deployment of visual servoing is contingent upon the robust and accurate estimation of the vision‐motion correlation. Network‐based methodologies are frequently employed in visual servoing to approximate the mapping between 2D image feature errors and 3D velocities, offering promising avenues for improving the accuracy and reliability of visual servoing systems. These developments have the potential to fully leverage the capabilities of digital twin technology in the realm of smart manufacturing. However, obtaining sufficient training data for these methods is challenging, and thus improving model generalization to reduce data requirements is imperative. To address this issue, we offer a learning‐based approach for estimating Jacobian matrices of visual servoing that organically combines an extreme learning machine (ELM) and a differential evolutionary algorithm (DE). In the first stage, the pseudoinverse of the image Jacobian matrix is approximated using the ELM, which solves the problems associated with traditional visual servoing and is resistant to outside influences such as image noise and mistakes in camera calibration. In the second stage, differential evolution is utilized to select input weights and hidden layer bias and to determine ELM’s output weights. Experimental results conducted on a digital twin operating platform for 4‐DOF robot with an eye‐in‐hand configuration demonstrate better performance than classical visual servoing and traditional ELM‐based visual servoing in various cases.

In recent years, ozone (O3) has gradually become the primary pollutant plaguing urban air quality. Accurate and efficient ozone prediction is of great significance to the prevention and control of ozone pollution. The air quality monitoring network provides multisource pollutant concentration monitoring data for ozone prediction, but ozone prediction based on multisource monitoring data still faces the challenges of each station’s series of data. Aiming at the problems of low prediction accuracy and low computational efficiency in traditional atmospheric ozone concentration prediction, ozone concentration prediction using dual series decomposition was proposed by variational mode decomposition (VMD), ensemble empirical mode decomposition (EEMD), and long short‐term memory (LSTM). First, the historical data series of Nanjing air quality monitoring stations is decomposed by VMD, and then the EEMD algorithm is applied to the residual of VMD to obtain several characteristic intrinsic mode function (IMF) components; each characteristic IMF component is trained by LSTM to obtain the prediction result of each component, and then the final result can be obtained by linear superposition. The proposed method achieved the best results with R2 = 99%, MSE = 5.38, MAE = 4.54, and MAPE = 3.12. Because LSTM has strong adaptive learning ability and good memory function, it has the learning advantage of long‐term memory for long‐term data, and the prediction results are more accurate. According to the data, the proposed method is superior to the baseline models in terms of statistical metrics. As a result, the proposed hybrid method can serve as a reliable model for ozone forecasting.

Currently, the different deep neural network (DNN) learning approaches have done much for the classification of hyperspectral images (HSIs), especially most of them use the convolutional neural network (CNN). HSI data have the characteristics of multidimensionality, correlation, nonlinearity, and a large amount of data. Therefore, it is particularly important to extract deeper features in HSIs by reducing dimensionalities which help improve the classification in both spectral and spatial domains. In this article, we present a spatial–spectral HSI classification algorithm, local similarity projection Gabor filtering (LSPGF), which uses local similarity projection (LSP)-based reduced dimensional CNN with a 2-D Gabor filtering algorithm. First, use the local similarity analysis to reduce the dimensionality of the hyperspectral data, and then we use the 2-D Gabor filter to filter the reduced hyperspectral data to generate spatial tunnel information. Second, use the CNN to extract features from the original hyperspectral data to generate spectral tunnel information. Third, the spatial tunnel information and the spectral tunnel information are fused to form the spatial–spectral feature information, which is input into the deep CNN to extract more effective features; and finally, a dual optimization classifier is used to classify the final extracted features. This article compares the performance of the proposed method with other algorithms in three public HSI databases and shows that the overall accuracy of the classification of LSPGF outperforms all datasets.

With the increase of online businesses, recommendation algorithms are being researched a lot to facilitate the process of using the existing information. Such multi-criteria recommendation (MCRS) helps a lot the end-users to attain the required results of interest having different selective criteria – such as combinations of implicit and explicit interest indicators in the form of ranking or rankings on different matched dimensions. Current approaches typically use label correlation, by assuming that the label correlations are shared by all objects. In real-world tasks, however, different sources of information have different features. Recommendation systems are more effective if being used for making a recommendation using multiple criteria of decisions by using the correlation between the features and items content (content-based approach) or finding a similar user rating to get targeted results (Collaborative filtering). To combine these two filterings in the multicriteria model, we proposed a features-based fb-knn multi-criteria hybrid recommendation algorithm approach for getting the recommendation of the items by using multicriteria features of items and integrating those with the correlated items found in similar datasets. Ranks were assigned to each decision and then weights were computed for each decision by using the standard deviation of items to get the nearest result. For evaluation, we tested the proposed algorithm on different datasets having multiple features of information. The results demonstrate that proposed fb-knn is efficient in different types of datasets.

Pre‐segmentation is known as a crucial step in medical image analysis. Many approaches have been proposed to make improvement to both the quality and efficiency of segmentation. However, existing methods are lacking in robustness to the variation in the edges and textures of the target. In order to address these drawbacks, a novel attention Gabor network (AGnet) based on deep learning for medical image segmentation that is capable of automatically paying more attention to the edge and consistently for improvement to the segmentation performance is proposed. The proposed model consists of two components. The first one is to determine the approximate location of the organs of interest in the image using convolution filters, and the other one is to highlight salient edge features intended for a specific segmentation task using Gabor filters. In order to facilitate collaboration in between the two parts, a region attention mechanism based on Gabor maps is suggested. The mechanism improved performance by learning to focus on the salient regions of the image that are useful for the authors' tasks. As indicated by the experimental results, the AGnet is capable of enhancing the prediction performance while maintaining the computational efficiency, which makes it comparable with other state‐of‐the‐art approaches.

To keep pace with the developments in medical informatics, health medical data is being collected continually. But, owing to the diversity of its categories and sources, medical data has become so complicated in many hospitals that it now needs a clinical decision support (CDS) system for its management. To effectively utilize the accumulating health data, we propose a CDS framework that can integrate heterogeneous health data from different sources such as laboratory test results, basic information of patients, and health records into a consolidated representation of features of all patients. Using the electronic health medical data so created, multilabel classification was employed to recommend a list of diseases and thus assist physicians in diagnosing or treating their patients' health issues more efficiently. Once the physician diagnoses the disease of a patient, the next step is to consider the likely complications of that disease, which can lead to more diseases. Previous studies reveal that correlations do exist among some diseases. Considering these correlations, a k-nearest neighbors algorithm is improved for multilabel learning by using correlations among labels (CML-kNN). The CML- kNN algorithm first exploits the dependence between every two labels to update the origin label matrix and then performs multilabel learning to estimate the probabilities of labels by using the integrated features. Finally, it recommends the top N diseases to the physicians. Experimental results on real health medical data establish the effectiveness and practicability of the proposed CDS framework.