Complex & Intelligent Systems最新文献_第9页

Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-29 DOI: 10.1007/s40747-024-01623-9

Shougi S. Abosuliman, Saleem Abdullah, Nawab Ali

{"title":"Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks","authors":"Shougi S. Abosuliman, Saleem Abdullah, Nawab Ali","doi":"10.1007/s40747-024-01623-9","DOIUrl":"https://doi.org/10.1007/s40747-024-01623-9","url":null,"abstract":"Artificial neural networks, a major element of machine learning, focus additional attention on the decision-making process. We extended the idea of artificial neural networks to continuous linear Diophantine fuzzy neural networks. A few operational concepts for continuous linear Diophantine fuzzy sets are further developed, and they are subsequently made simpler to apply to more than two such sets. Also, a real multi-criteria decision-making problem has been formulated. The environment plays a very important role in our daily lives. We cause different types of pollution in our environment, and it has a bad impact on our lives. Air pollution is one of the various forms of pollution that is thought to affect the entire globe. Millions of people die due to air pollution, and industries are the main contributors to air pollution. To overcome air pollution, green supply chain management plays a vital role, but green supply chain management faces some barriers as well. According to the proposed model, ({mathfrak{R}}_{1}) is the best alternative and green supply chain management faces financial problems more than other barriers and also provides strategies to overcome financial barriers. In addition, a comparative analysis develops to illustrate the reliability and feasibility of the suggested technique in relation to current techniques.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"52 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055103","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

XTNSR: Xception-based transformer network for single image super resolution

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-25 DOI: 10.1007/s40747-024-01760-1

Jagrati Talreja, Supavadee Aramvith, Takao Onoye

{"title":"XTNSR: Xception-based transformer network for single image super resolution","authors":"Jagrati Talreja, Supavadee Aramvith, Takao Onoye","doi":"10.1007/s40747-024-01760-1","DOIUrl":"https://doi.org/10.1007/s40747-024-01760-1","url":null,"abstract":"Single image super resolution has significantly advanced by utilizing transformers-based deep learning algorithms. However, challenges still need to be addressed in handling grid-like image patches with higher computational demands and addressing issues like over-smoothing in visual patches. This paper presents a Deep Learning model for single-image super-resolution. In this paper, we present the XTNSR model, a novel multi-path network architecture that combines Local feature window transformers (LWFT) with Xception blocks for single-image super-resolution. The model processes grid-like image patches effectively and reduces computational complexity by integrating a Patch Embedding layer. Whereas the Xception blocks use depth-wise separable convolutions for hierarchical feature extraction, the LWFT blocks capture long-range dependencies and fine-grained qualities. A multi-layer feature fusion block with skip connections, part of this hybrid architecture, guarantees efficient local and global feature fusion. The experimental results show better performance in Peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and visual quality than the state-of-the-art techniques. By optimizing parameters, the suggested architecture also lowers computational complexity. Overall, the architecture presents a promising approach for advancing image super-resolution capabilities.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"35 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143031202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient guided inpainting of larger hole missing images based on hierarchical decoding network 基于分层解码网络的大孔缺失图像的高效引导补图

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-23 DOI: 10.1007/s40747-024-01686-8

Xiucheng Dong, Yaling Ju, Dangcheng Zhang, Bing Hou, Jinqing He

{"title":"Efficient guided inpainting of larger hole missing images based on hierarchical decoding network","authors":"Xiucheng Dong, Yaling Ju, Dangcheng Zhang, Bing Hou, Jinqing He","doi":"10.1007/s40747-024-01686-8","DOIUrl":"https://doi.org/10.1007/s40747-024-01686-8","url":null,"abstract":"When dealing with images containing large hole-missing regions, deep learning-based image inpainting algorithms often face challenges such as local structural distortions and blurriness. In this paper, a novel hierarchical decoding network for image inpainting is proposed. Firstly, the structural priors extracted from the encoding layer are utilized to guide the first decoding layer, while residual blocks are employed to extract deep-level image features. Secondly, multiple hierarchical decoding layers progressively fill in the missing regions from top to bottom, then interlayer features and gradient priors are used to guide information transfer between layers. Furthermore, a proposed Multi-dimensional Efficient Attention is introduced for feature fusion, enabling more effective extraction of image features across different dimensions compared to conventional methods. Finally, Efficient Context Fusion combines the reconstructed feature maps from different decoding layers into the image space, preserving the semantic integrity of the output image. Experiments have been conducted to validate the effectiveness of the proposed method, demonstrating superior performance in both subjective and objective evaluations. When inpainting images with missing regions ranging from 50% to 60%, the proposed method achieves improvements of 0.02 dB (0.22 dB) and 0.001 (0.003) in PSNR and SSIM, on the CelebA-HQ (Places2) dataset, respectively.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"50 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143020697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Short-term urban traffic forecasting in smart cities: a dynamic diffusion spatial-temporal graph convolutional network 智慧城市短期交通预测：动态扩散时空图卷积网络

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-22 DOI: 10.1007/s40747-024-01769-6

Xiang Yin, Junyang Yu, Xiaoyu Duan, Lei Chen, Xiaoli Liang

{"title":"Short-term urban traffic forecasting in smart cities: a dynamic diffusion spatial-temporal graph convolutional network","authors":"Xiang Yin, Junyang Yu, Xiaoyu Duan, Lei Chen, Xiaoli Liang","doi":"10.1007/s40747-024-01769-6","DOIUrl":"https://doi.org/10.1007/s40747-024-01769-6","url":null,"abstract":"Short-term traffic forecasting is an important part of intelligent transportation systems. Accurately predicting short-term traffic trends can avoid traffic congestion and plan travel routes, which is of great significance to urban management and traffic scheduling. The difficulty of short-term urban traffic forecasting is that the traffic flow is random and will be dynamically changed by the traffic conditions of nearby nodes. In order to solve this problem, this paper proposes a model based on Dynamic Diffusion Spatial-Temporal Graph Convolutional Network. It first combines the dynamic generation matrix and the static distance matrix to grasp real-time traffic conditions, and then introduces the diffusion random walk strategy to capture the correlation of spatial nodes. Finally, the convolutional LSTM module is used to mine the spatiotemporal dependence of traffic data to improve the accuracy of traffic prediction. Compared to several baseline models, the experimental results show that the model is 7% better than other models on several metrics and demonstrates the necessity of the module through ablation experiments.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"11 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A multitasking ant system for multi-depot pick-up and delivery location routing problem with time window 一种多工场带时间窗口的多任务蚂蚁系统

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-22 DOI: 10.1007/s40747-024-01750-3

Haoyuan Lv, Ruochen Liu, Jianxia Li

{"title":"A multitasking ant system for multi-depot pick-up and delivery location routing problem with time window","authors":"Haoyuan Lv, Ruochen Liu, Jianxia Li","doi":"10.1007/s40747-024-01750-3","DOIUrl":"https://doi.org/10.1007/s40747-024-01750-3","url":null,"abstract":"Instant delivery service has brought great convenience to our modern life. In order to improve its efficiency, multi-depot pick-up-and-delivery location routing problem with time windows (MDPDLRPTW) is proposed in this paper. Existing works related to MDPDLRPTW focus on obtaining a depot location scheme by clustering and perform route planning on it through single-task optimization. They are powerless to simultaneously explore the solution spaces of multiple routing tasks under different location schemes. Furthermore, ignoring the potential general knowledge among different schemes leads to redundant optimization. In this work, MDPDLRPTW is modeled as a multi-transformation optimization (MTFO) problem and a novel two-stage algorithm based on multitasking ant system (MTAS) is designed to solve it. In the first stage, a clustering algorithm based on spatio-temporal feature is used to group similar customer pairs, and the clustering centers are set as warehouses. Afterward, multiple localization schemes are selected through non-dominated sorting based on spatio-temporal density. In the second stage, MTAS concurrently optimizes multiple routing tasks based on these location schemes, each task is assigned to an ant system solver. Furthermore, MTAS achieves knowledge sharing among all routing tasks through adaptive similarity measurement and cross-task pheromone fusion strategy. The former can dynamically capture the relationship between tasks to adjust the transfer strength of task pairs, and the latter realizes adaptive knowledge transfer by pheromone-matrix mixing. Experimental results show that MTAS can efficiently utilize the common knowledge to achieve competitive performance.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"46 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A crossover operator for objective functions defined over graph neighborhoods with interdependent and related variables 具有相互依赖和相关变量的图邻域上目标函数的交叉算子

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-22 DOI: 10.1007/s40747-024-01721-8

Jaume Jordan, Javier Palanca, Victor Sanchez-Anguix, Vicente Julian

{"title":"A crossover operator for objective functions defined over graph neighborhoods with interdependent and related variables","authors":"Jaume Jordan, Javier Palanca, Victor Sanchez-Anguix, Vicente Julian","doi":"10.1007/s40747-024-01721-8","DOIUrl":"https://doi.org/10.1007/s40747-024-01721-8","url":null,"abstract":"This article presents a new crossover operator for problems with an underlying graph structure where edges point to prospective interdependence relationships between decision variables and neighborhoods shape the definition of the global objective function via a sum of different expressions, one for each neighborhood. The main goal of this work is to propose a crossover operator that is broadly applicable, adaptable, and effective across a wide range of problem settings characterized by objective functions that are expressed in terms of graph neighbourhoods with interdependent and related variables. Extensive experimentation has been conducted to compare and evaluate the proposed crossover operator with both classic and specialized crossover operators. More specifically, the crossover operators have been tested under a variety of graph types, which model how variables are involved in interdependencies, different types of expressions in which interdependent variables are combined, and different numbers of decision variables. The results suggest that the new crossover operator is statistically better or at least as good as the best-performing crossover in 75% of the families of problems tested.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"74 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142992204","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Vehicle-routing problem for low-carbon cold chain logistics based on the idea of cost–benefit 基于成本效益思想的低碳冷链物流车辆选线问题

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-20 DOI: 10.1007/s40747-024-01756-x

Yan Liu, Fengming Tao, Rui Zhu

{"title":"Vehicle-routing problem for low-carbon cold chain logistics based on the idea of cost–benefit","authors":"Yan Liu, Fengming Tao, Rui Zhu","doi":"10.1007/s40747-024-01756-x","DOIUrl":"https://doi.org/10.1007/s40747-024-01756-x","url":null,"abstract":"In the low-carbon economy, the fresh industry constitutes an “impossible triangle” in products, prices and services. Therefore, based on the idea of cost–benefit, a comprehensive vehicle routing problem optimization model with the objective function of minimizing the cost of unit satisfied customer is presented. Then, a hybrid algorithm called local search genetic algorithm (LSGA) is proposed, which amalgamates the destroy-repair operator with GA algorithm. Extensive numerical experiments verify the feasibility and effectiveness of the proposed model and algorithm. Furthermore, the sensitivity analysis of freshness-keeping cost, carbon price and customer satisfaction weights were conducted. The experimental results show that appropriate freshness-keeping effort can reduce total costs and improve customer satisfaction. Increasing carbon price within a certain range can effectively reduce carbon emissions, and there is a trade-off relationship between carbon emissions and customer satisfaction. The results of considering both time satisfaction and freshness satisfaction are better than considering time satisfaction alone.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"56 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142990043","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Robust underwater object tracking with image enhancement and two-step feature compression 基于图像增强和两步特征压缩的鲁棒水下目标跟踪

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-17 DOI: 10.1007/s40747-024-01755-y

Jiaqing Li, Chaocan Xue, Xuan Luo, Yubin Fu, Bin Lin

引用次数: 0

Mape: defending against transferable adversarial attacks using multi-source adversarial perturbations elimination Mape：利用多源对抗扰动消除防御可转移的对抗攻击

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-17 DOI: 10.1007/s40747-024-01770-z

Xinlei Liu, Jichao Xie, Tao Hu, Peng Yi, Yuxiang Hu, Shumin Huo, Zhen Zhang

{"title":"Mape: defending against transferable adversarial attacks using multi-source adversarial perturbations elimination","authors":"Xinlei Liu, Jichao Xie, Tao Hu, Peng Yi, Yuxiang Hu, Shumin Huo, Zhen Zhang","doi":"10.1007/s40747-024-01770-z","DOIUrl":"https://doi.org/10.1007/s40747-024-01770-z","url":null,"abstract":"Neural networks are vulnerable to meticulously crafted adversarial examples, leading to high-confidence misclassifications in image classification tasks. Due to their consistency with regular input patterns and the absence of reliance on the target model and its output information, transferable adversarial attacks exhibit a notably high stealthiness and detection difficulty, making them a significant focus of defense. In this work, we propose a deep learning defense known as multi-source adversarial perturbations elimination (MAPE) to counter diverse transferable attacks. MAPE comprises the single-source adversarial perturbation elimination (SAPE) mechanism and the pre-trained models probabilistic scheduling algorithm (PPSA). SAPE utilizes a thoughtfully designed channel-attention U-Net as the defense model and employs adversarial examples generated by a pre-trained model (e.g., ResNet) for its training, thereby enabling the elimination of known adversarial perturbations. PPSA introduces model difference quantification and negative momentum to strategically schedule multiple pre-trained models, thereby maximizing the differences among adversarial examples during the defense model’s training and enhancing its robustness in eliminating adversarial perturbations. MAPE effectively eliminates adversarial perturbations in various adversarial examples, providing a robust defense against attacks from different substitute models. In a black-box attack scenario utilizing ResNet-34 as the target model, our approach achieves average defense rates of over 95.1% on CIFAR-10 and over 71.5% on Mini-ImageNet, demonstrating state-of-the-art performance.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"86 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing navigation performance in unknown environments using spiking neural networks and reinforcement learning with asymptotic gradient method 利用尖峰神经网络和渐近梯度强化学习增强未知环境下的导航性能

IF 5.8 2区计算机科学

Complex & Intelligent Systems Pub Date : 2025-01-17 DOI: 10.1007/s40747-024-01777-6

Xiaode Liu, Yufei Guo, Yuanpei Chen, Jie Zhou, Yuhan Zhang, Weihang Peng, Xuhui Huang, Zhe Ma

{"title":"Enhancing navigation performance in unknown environments using spiking neural networks and reinforcement learning with asymptotic gradient method","authors":"Xiaode Liu, Yufei Guo, Yuanpei Chen, Jie Zhou, Yuhan Zhang, Weihang Peng, Xuhui Huang, Zhe Ma","doi":"10.1007/s40747-024-01777-6","DOIUrl":"https://doi.org/10.1007/s40747-024-01777-6","url":null,"abstract":"Achieving accurate and generalized autonomous navigation in unknown environments poses a significant challenge in robotics and artificial intelligence. Animals exhibits superlative navigation capabilities by combining the representation of internal neurals and sensory cues of self-motion and external information. This paper proposes a brain-inspired navigation method based upon the spiking neural networks (SNN) and reinforcement learning, integrated with a lidar system that serves as the local environment explorer, by which realizes high performance of obstacle avoidance and target arrival in mapless circumstances. An asymptotic gradient method is introduced to optimize the backpropagation during training, which facilitates the improvement of model robustness. The results of our experiments conducted on the Gazebo platform showcase how our approach effectively improves navigation performance in various intricate environments. Our approach yielded a higher success navigation rate ranging from 2% to 5%, depending on the SNN timesteps. Considering the inherent lower computational cost of SNN, this work contributes to advancing the fusion of SNN and reinforcement learning techniques for energy-efficient autonomous navigation tasks in real-world mapless scenarios.","PeriodicalId":10524,"journal":{"name":"Complex & Intelligent Systems","volume":"30 1","pages":""},"PeriodicalIF":5.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0