Time series generation is essential for advancing data-driven modeling and decision-making across a wide range of domains. However, existing approaches primarily focus on global patterns, often failing to capture local key patterns such as abrupt changes or anomalies. These key patterns are crucial for interpretability and operational decision making, as they frequently represent intervention points with significant real-world impact. To bridge this gap, we propose Key Prototypes-Guided Diffusion (K-ProtoDiff) for time series generation , a new model that learns the global data distribution while preserving localized key patterns critical for temporal dynamics. In K-ProtoDiff, we first derive time series prototype representations through adaptive self-supervised learning. Then, a key prototype assignment module is used to extract prototype weights, forming key prototype-aware representations that serve as conditional guidance for generation. During sampling, to further enhance the fidelity of key patterns during the denoising process, we propose Reflection Sampling (R-Sampling), a step-wise refinement strategy that encourages the reverse trajectory to better align with key prototype constraints. Experiments on nine real-world datasets demonstrate that K-ProtoDiff significantly outperforms state-of-the-art baselines in key pattern retention, achieving an average 77.6% improvement in key pattern preservation.

Brucellar spondylitis (BS), a prevalent zoonotic disease caused by Brucella, poses a significant global health threat. Accurate and timely diagnosis of BS is crucial for effective treatment; however, no specialized deep learning model has been developed for detecting BS in MR images. In this study, we proposed Brucella Spondylitis MRI Diagnosis Network (BSMRINet), a fully automated diagnostic framework designed for the detection of BS from T2-weighted (T2W) MR images. The model was developed and validated using 582 cohorts collected from four hospitals between January 2018 and August 2023. The BSMRINet architecture comprised two key modules. The vertebral body lesion detection module was designed to detect BS in intact vertebral bodies by integrating a corner detection algorithm with a ResNet-based deep learning model. This module provided accurate identification and localization of potential lesions of Brucella and calculated intervertebral disc height (DH) values. The spine lesion detection module was specifically designed to detect BS in damaged vertebral bodies by utilizing a DenseNet architecture with modified squeeze-and-excitation (scSE) networks. This module further evaluated paravertebral injuries, including abscess formation, soft tissue swelling, and joint involvement. BSMRINet demonstrated strong robustness and generalization across both internal and external validation phases. Additionally, it outperformed two radiologists with 10 to 15 years of experience in diagnosing spinal MR images. The results suggested that BSMRINet can assist in the diagnostic process of BS and enhance the diagnostic capabilities of radiologists.

Underwater Wireless Optical Networks (UWONs) play a critical role in tasks such as ocean monitoring and resource exploration, which require high connectivity and reliability. However, water turbidity, ocean currents, and ambient light noise significantly affect communication stability, creating serious deployment challenges. To address this, we propose a network topology optimization method based on resilience evaluation. First, a resilience evaluation method is designed to quantify the network's ability to adapt to disturbances. Then, an improved predecessor-based evolutionary algorithm (Pred-EA) is used for resilience-guided topology optimization. To improve algorithmic efficiency and search quality, the prim algorithm is introduced to ensure chromosome feasibility, enhancing both the diversity of the initial population and computational efficiency. Experimental results show that our method achieves better recovery performance than three comparison methods in all scenarios. The average number of recovered edges improves by up to 18.30% over the second-best method. Under non-recoverable conditions, resilience improves by up to 20.52%. These results confirm the strong topological robustness and practical value of the proposed method in dynamic underwater environments.

Functional brain network analysis based on fMRI is a vital tool for understanding neural mechanisms and diagnosing neurological disorders. However, existing approaches often overlook the joint modeling of topological structures and attribute information in brain connectivity graphs, limiting their performance in disease classification. To address this issue, we propose a novel Graph Neural Network framework combining multi-view modeling and supervised contrastive learning for fMRI-based Alzheimer's disease classification. Specifically, our method employs a Graph Attention Network (GAT) to encode structural relationships and a Multi-Layer Perceptron (MLP) to capture node attribute features. Furthermore, unsupervised clustering is utilized to extract community-level representations, capturing mesoscale brain network organization. To enhance feature robustness and discriminability, we introduce a dual-view supervised contrastive learning strategy. Extensive experiments on a cohort of 480 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate that our proposed model consistently outperforms state-of-the-art methods across key metrics, including accuracy, recall, F1-score, and AUC, highlighting its robustness and effectiveness for Alzheimer's disease diagnosis.

Bronchoalveolar lavage fluid (BALF) is a liquid obtained from the alveoli and bronchi, often used to study pulmonary diseases. So far, proteomic analyses have identified over three thousand proteins in BALF. However, the comprehensive characterization of these proteins remains challenging due to their complexity and technological limitations. This paper presented a novel deep learning framework called SecProGNN, designed to predict secretory proteins in BALF. Firstly, SecProGNN represented proteins as graph-structured data, with amino acids connected based on their interactions. Then, these graphs were processed through graph neural networks (GNNs) model to extract graph features. Finally, the extracted feature vectors were fed into a multi-layer perceptron (MLP) module to predict BALF secreted proteins. Additionally, by utilizing SecProGNN, we investigated potential biomarkers for lung adenocarcinoma and identified 16 promising candidates that may be secreted into BALF.

In the field of diagnosing lung diseases, the application of neural networks (NNs) in image classification exhibits significant potential. However, NNs are considered "black boxes," making it difficult to discern their decision-making processes, thereby leading to skepticism and concern regarding NNs. This compromises model reliability and hampers intelligent medicine's development. To tackle this issue, we introduce the Evolutionary Neural Architecture Search (EvoNAS). In image classification tasks, EvoNAS initially utilizes an Evolutionary Algorithm to explore various Convolutional Neural Networks, ultimately yielding an optimized network that excels at separating between redundant texture features and the most discriminative ones. Retaining the most discriminative features improves classification accuracy, particularly in distinguishing similar features. This approach illuminates the intrinsic mechanics of classification, thereby enhancing the accuracy of the results. Subsequently, we incorporate a Differential Evolution algorithm based on distribution estimation, significantly enhancing search efficiency. Employing visualization techniques, we demonstrate the effectiveness of EvoNAS, endowing the model with interpretability. Finally, we conduct experiments on the diffuse lung disease texture dataset using EvoNAS. Compared to the original network, the classification accuracy increases by 0.56%. Moreover, our EvoNAS approach demonstrates significant advantages over existing methods in the same dataset.

Unmanned aerial vehicles (UAVs) can be deployed in the areas where traditional network infrastructure is insufficient or absent because of flexibility and collaboration. The deployment of edge intelligence on UAVs in UAV-assisted mobile-edge networks significantly enhance data processing efficiency, which is a critical factor for applications requiring delay-sensitive data processing in the areas mentioned above. However, the limited energy capacity poses a challenge when running complex deployment algorithms. Therefore, lightweight network model is essential for deployment algorithms, as it significantly reduces energy and time consumption. In this article, we propose a cooperative framework-based knowledge distillation to compressed deep neural network (DNN). The subnetworks collaborate to train interactive node parameters, resulting in the optimal evolution of the student network. Then, we introduce a novel result-driven model training approach for simulation data sets. To further enhance efficiency and significantly reduce overall latency, we meticulously refine the internal architecture of the knowledge distillation algorithm. We incorporate a collaborative evolution mechanism into the core of the algorithm, utilizing multinetwork and subnetwork learning to facilitate knowledge transfer, and incorporate some optimization mechanisms into the framework. Finally, we perform a series of experiments to acquiredata sets and conduct algorithm simulation analysis to evaluate the proposed method. The results demonstrate that our work achieves good research results.

Accidents are inevitable in the transportation systems; however, harnessing the big data generated from traffic accidents can significantly enhance the intelligence of the transportation system. Multiple unmanned aerial vehicles (multi-UAVs), owing to its flexibility and collaboration, can rapidly track the accidents and gather the acquire relevant data with reasonable deployment. Therefore, we propose an architecture utilizing multiple UAVs to track traffic accidents (targets) and collect relevant data. However, dynamic factors such as no-fly zones and reappearing targets in real-world traffic environments pose challenges to the rapid deployment of UAVs with current algorithms. In this paper, we design a model for deploying multiple UAVs, considering no-fly zones and dynamically reappearing targets. This model aims to optimize UAV deployment by minimizing both the flight distance of each UAV and the associated risk. Risk is defined as the urgency of processing accident scenes, which escalates with the elapsed time from the initial appearance of the targets to their processing. To address the joint UAV deployment problem, we first introduce an algorithm termed preprocessing and group-crossover nondominated sort genetic algorithm II (PGC-NSGAII). In PGC-NSGAII, we employ a group-based selection crossover (GBSC) method to enhance the algorithm’s search capability. This method segregates the initial population into two groups, selecting two individuals for crossover within each group. Secondly, building upon the framework of NSGAII, we incorporate a novel preprocessing component to enrich solution diversity. Thirdly, we develop a prediction method for dynamic environment parameters and a no-fly zone avoidance strategy for multi-UAV deployment. Finally, our experimental results demonstrate that PGC-NSGAII surpasses other existing methods in scenarios with varying numbers of UAVs or targets. Compared with state-of-the-art optimization methods, PGC-NSGAII proves to be more efficient in UAV deployment in dynamic environment.

The sudden outbreak of COVID-19 brings many unpredictable situations to human travel, such as temporarily closed highways, parking lots, etc. The scenarios mentioned above will lead to a large backlog of vehicles, and the requirements of Internet of vehicle (IoV) applications increase sharply in a period of short time correspondingly. Mobile edge computing (MEC) is a key enabling technology that can guarantee the diverse requirements of IoV applications through the optimization of resource scheduling. However, the sharp increasing in requirements of IoV applications caused by the congestion of highways or parking lots still bring great challenges to the deployment of traditional MEC. Therefore, in this paper, we construct an unmanned aerial vehicle (UAV) enabled MEC system, in which the data generated from IoV applications is processed by offloading to UAVs with MEC servers to ensure the efficiency of data processing and the response time of IoV applications. In order to approximate real-world UAV enabled MEC system, we consider the stochastic offloading and downloading processing time. Moreover, the priority constraints of sensors from the same vehicle are taken into consideration since they have different importance degrees. Then, we propose an Markov network-based cooperative evolutionary algorithm (MNCEA) to search out the optimal UAV scheduling solution to guarantee the shortest response time, in which the solution space is divided into multiple sub-solution spaces with the help of MN structure and parameters. Finally, we construct multiple simulation experiments with different probability distributions to simulate uncertainty factors. The simulation results verify the validity of MNCEA compared with the state-of-the-art methods, which is reflected by the shortest response time of requirements of IoV applications.

Retinal vessel segmentation and centerline extraction are crucial steps in building a computer-aided diagnosis system on retinal images. Previous works treat them as two isolated tasks, while ignoring their tight association. In this paper, we propose a deep semantics and multi-scaled cross-task aggregation network that takes advantage of the association to jointly improve their performances. Our network is featured by two sub-networks. The forepart is a deep semantics aggregation sub-network that aggregates strong semantic information to produce more powerful features for both tasks, and the tail is a multi-scaled cross-task aggregation sub-network that explores complementary information to refine the results. We evaluate the proposed method on three public databases, which are DRIVE, STARE and CHASE_DB1. Experimental results show that our method can not only simultaneously extract retinal vessels and their centerlines but also achieve the state-of-the-art performances on both tasks.

Neural network pruning is widely applied to various mobile applications. Previous pruning methods mainly leverage ad-hoc criteria to evaluate channel importance. In this paper, we propose an effective metaheuristic sub-structure selection (MetaSelection) method for neural network pruning. MetaSelection exploits evolutionary algorithm (EA) to search the proper sub-structure satisfying the resource constraints. In comparison with previous AutoML based methods, MetaSelection can automatically achieve the pruning rate and channel selection at the same time instead of hand-crafted criteria in a cascaded way. Regarding the tremendous search space of channel selection as a combinatorial optimization problem, we further utilize a coarse-to-fine strategy and the novel probability distribution crossover (PDC) to speed up the search procedure. Besides, MetaSelection prunes the network globally rather than in a layer-by-layer way. We evaluate MetaSelection on several appealing deep neural networks, achieving superior results with adaptive depth and width. Concretely, on ImageNet, MetaSelection achieves a top-1 accuracy of 71.5% on MobileNetV2 under 70% FLOPs constraint and a FLOPs reduction of 30% with 76.4% top-1 accuracy for ResNet50.

COVID-19 emerged towards the end of 2019 which was identified as a global pandemic by the world heath organization (WHO). With the rapid spread of COVID-19, the number of infected and suspected patients has increased dramatically. Chest computed tomography (CT) has been recognized as an efficient tool for the diagnosis of COVID-19. However, the huge CT data make it difficult for radiologist to fully exploit them on the diagnosis. In this paper, we propose a computer-aided diagnosis system that can automatically analyze CT images to distinguish the COVID-19 against to community-acquired pneumonia (CAP). The proposed system is based on an unsupervised pulmonary opacity detection method that locates opacity regions by a detector unsupervisedly trained from CT images with normal lung tissues. Radiomics based features are extracted insides the opacity regions, and fed into classifiers for classification. We evaluate the proposed CAD system by using 200 CT images collected from different patients in several hospitals. The accuracy, precision, recall, f1-score and AUC achieved are 95.5%, 100%, 91%, 95.1% and 95.9% respectively, exhibiting the promising capacity on the differential diagnosis of COVID-19 from CT images.

Lung segmentation on CT images is a crucial step for a computer-aided diagnosis system of lung diseases. The existing deep learning based lung segmentation methods are less efficient to segment lungs on clinical CT images, especially that the segmentation on lung boundaries is not accurate enough due to complex pulmonary opacities in practical clinics. In this paper, we propose a boundary-guided network (BG-Net) to address this problem. It contains two auxiliary branches that seperately segment lungs and extract the lung boundaries, and an aggregation branch that efficiently exploits lung boundary cues to guide the network for more accurate lung segmentation on clinical CT images. We evaluate the proposed method on a private dataset collected from the Osaka university hospital and four public datasets including StructSeg [1], HUG [2], VESSEL12 [3], and a Novel Coronavirus 2019 (COVID-19) dataset [4]. Experimental results show that the proposed method can segment lungs more accurately and outperform several other deep learning based methods.

Accurate classification and comprehensive explanation is crucial to build a computer aided diagnosis (CAD) system of diffuse lung disease (DLD). Although deep neural networks (DNNs) have been applied to this task, the classification performance and reliability are not satisfied for medical clinical requirements. Specifically, DNNs are regarded as unexplainable “black-box” in general, and, thus, are not deemed reliable by expects. In this paper, we propose a neural network structure search approach based on evolutionary algorithm to improve the DNN's effectiveness and interpretability, and applied to the pulmonary textures classification problem. Through this network structure search approach, we find out how a DNN's subnet recognize the pulmonary textures features, then filter out the redundant subnets, and retain the most distinctive feature subnets. Besides, we utilize the method of feature visualization and the fine-grained heat map of the activation to interpret network's decision-making process. Finally, through quantitatively and qualitatively evaluate on a real dataset of diffuse lung disease, we verify the effectiveness of this neural network structure search approach on VggNet and ResNet, and achieve the state-of-the-art performance. We can classify the pulmonary textures on high-resolution computed tomography (HRCT) images.

Flexible scheduling is one of the most significant core techniques for intelligent manufacturing systems. Realization of an optimized schedule through flexible resources assignment is critical to the application and popularization of flexible scheduling, especially in uncertain manufacturing environments. In this paper, we consider flexible job shop scheduling with uncertain processing time represented by fuzzy numbers, which is named fuzzy flexible job shop scheduling. We propose an effective hybrid cooperative coevolution algorithm (hCEA) for the minimization of fuzzy makespan. The hCEA combines particle swarm optimization with the genetic algorithm to improve the convergence ability. A parameter self-adaptive strategy is applied to the problems with different scale effectively as well. Five benchmarks and three large-scale problems with fuzzy processing time are adopted to test the hCEA. Computational results show that the hCEA performs better than the existing methods from the literature.

Considering Particle Swarm Optimization (PSO) could enhance solutions generated during the evolution process by exploiting their social knowledge and individual memory, we used PSO as a local search strategy in Genetic Algorithm (GA) framework for fine tuning the search space. GA is to make sure that every region of the search space is covered so that we have a reliable estimate of the global optimal solution and PSO is for further pruning the good solutions by searching around the neighborhood. In this paper, proposed approach is used for subspace clustering, which is an extension of traditional clustering that seeks to find clustering in different subspaces within a dataset. Subspace clustering is to find a subset of dimensions on which to improve cluster quality by removing irrelevant and redundant dimensions in high dimensions problems. The experimental results demonstrate the positive effects of PSO as a local optimizer.

The Assembly Line Balancing (ALB) problem is a well-known manufacturing optimization problem, which determines the assignment of various tasks to an ordered sequence of stations, while optimizing one or more objectives without violating restrictions imposed on the line. As Genetic Algorithms (GAs) have established themselves as a useful optimization technique in the manufacturing field, the application of GAs to ALB problem has expanded a lot. This paper describes a generalized Pareto-based scale-independent fitness function (gp-siffGA) for solving ALB problem with worker allocation (ALB-wa) to minimize the cycle time, the variation of workload and the total cost under the constraint of precedence relationships at the same time. For this approach, first a random key-based representation method adapting the GA was proposed. Following, advanced genetic operators adapted to the specific chromosome structure and the characteristics of the ALB-wa problem were used. Moreover, Pareto dominance relationship was used to solve the ALB-wa problem without using relative preferences of multiple objectives. Finally, the performance of proposed method was validated through numerical experiments. The results indicated that the proposed approach improved the quality of solutions more than the other existing GA approaches.

Routing problem is one of the important research issues in communication network fields. In this paper, we consider a bicriteria shortest path routing (bSPR) model dedicated to calculating nondominated paths for (1) the minimum total cost and (2) the minimum transmission delay. To solve this bSPR problem, we propose a new multiobj ective genetic algorithm (moGA): (1) an efficient chromosome representation using the priority-based encoding method; (2) a new operator of GA parameters auto-tuning, is adaptively regulation of exploration and exploitation based on the change of the average fitness of parents and offspring which is occurred at each generation; and (3) an interactive adaptive-weight fitness assignment mechanism is implemented that assigns weights to each objective and combines the weighted objectives into a single objective function. Numerical experiments with various scales of network design problems show the effectiveness and the efficiency of our approach by comparing with the recent researches.

This paper considers an optimization of a communication network expansion with a reliability constraint. It is one of NP-hard problems. We propose a self-controlled genetic algorithm (scGA) to all-terminal network reliability problem. This scGA adopts fuzzy logic control (FLC) to tune the probabilities of the genetic operators depending on the change of the average fitness. The numerical analysis for various scales of problems shows that the proposed approach has a higher search capability that improve quality of solution and enhanced rate of convergence.

In this paper, we propose a Genetic Algorithm (GA) approach using a new paths growth procedure by the random key-based encoding for solving Shortest Path Routing (SPR) problem. And we also develop a combined algorithm by arithmetical crossover, swap mutation, and immigration operator as genetic operators. Numerical analysis for various scales of SPR problems shows the proposed random key-based genetic algorithm (rkGA) approach has a higher search capability that enhanced rate of reaching optimal solutions and improve computation time than other GA approaches using different genetic representation methods.