Neural Computing and Applications最新文献_第7页

Attention-optimized vision-enhanced prompt learning for few-shot multi-modal sentiment analysis 针对少镜头多模态情感分析的注意力优化视觉增强提示学习

Neural Computing and Applications Pub Date : 2024-08-22 DOI: 10.1007/s00521-024-10297-w

Zikai Zhou, Baiyou Qiao, Haisong Feng, Donghong Han, Gang Wu

{"title":"Attention-optimized vision-enhanced prompt learning for few-shot multi-modal sentiment analysis","authors":"Zikai Zhou, Baiyou Qiao, Haisong Feng, Donghong Han, Gang Wu","doi":"10.1007/s00521-024-10297-w","DOIUrl":"https://doi.org/10.1007/s00521-024-10297-w","url":null,"abstract":"To fulfill the explosion of multi-modal data, multi-modal sentiment analysis (MSA) emerged and attracted widespread attention. Unfortunately, conventional multi-modal research relies on large-scale datasets. On the one hand, collecting and annotating large-scale datasets is challenging and resource-intensive. On the other hand, the training on large-scale datasets also increases the research cost. However, the few-shot MSA (FMSA), which is proposed recently, requires only few samples for training. Therefore, in comparison, it is more practical and realistic. There have been approaches to investigating the prompt-based method in the field of FMSA, but they have not sufficiently considered or leveraged the information specificity of visual modality. Thus, we propose a vision-enhanced prompt-based model based on graph structure to better utilize vision information for fusion and collaboration in encoding and optimizing prompt representations. Specifically, we first design an aggregation-based multi-modal attention module. Then, based on this module and the biaffine attention, we construct a syntax–semantic dual-channel graph convolutional network to optimize the encoding of learnable prompts by understanding the vision-enhanced information in semantic and syntactic knowledge. Finally, we propose a collaboration-based optimization module based on the collaborative attention mechanism, which employs visual information to collaboratively optimize prompt representations. Extensive experiments conducted on both coarse-grained and fine-grained MSA datasets have demonstrated that our model significantly outperforms the baseline models.","PeriodicalId":18925,"journal":{"name":"Neural Computing and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Polar fox optimization algorithm: a novel meta-heuristic algorithm 极狐优化算法：一种新型元启发式算法

Neural Computing and Applications Pub Date : 2024-08-21 DOI: 10.1007/s00521-024-10346-4

Ahmad Ghiaskar, Amir Amiri, Seyedali Mirjalili

{"title":"Polar fox optimization algorithm: a novel meta-heuristic algorithm","authors":"Ahmad Ghiaskar, Amir Amiri, Seyedali Mirjalili","doi":"10.1007/s00521-024-10346-4","DOIUrl":"https://doi.org/10.1007/s00521-024-10346-4","url":null,"abstract":"The proposed paper introduces a new optimization algorithm inspired by nature called the polar fox optimization algorithm (PFA). This algorithm addresses the herd life of polar foxes and especially their hunting method. The polar fox jumping strategy for hunting, which is performed through high hearing power, is mathematically formulated and implemented to perform optimization processes in a wide range of search spaces. The performance of the polar fox algorithm is tested with 14 classic benchmark functions. To provide a comprehensive comparison, all 14 test functions are expanded, shifted, rotated and combined for this test. For further testing, the recent CEC 2021 test’s complex functions are studied in the unimodal, basic, hybrid and composition modes. Finally, the rate of convergence and computational time of PFA are also evaluated by several changes with other algorithms. Comparisons show that PFA has numerous benefits over other well-known meta-heuristic algorithms and determines the solutions with fewer control parameters. So it offers competitive and promising results. In addition, this research tests PFA performance with 6 different challenging engineering problems. Compared to the well-known meta-artist methods, the superiority of the PFA is observed from the experimental results of the proposed algorithm in real-world problem-solving. The source codes of the PFA are publicly available at https://github.com/ATR616/PFA.","PeriodicalId":18925,"journal":{"name":"Neural Computing and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sampled-data synchronization for heterogeneous delays inertial neural networks with generally uncertain semi-Markovian jumping and its application 具有一般不确定半马尔可夫跳跃的异质延迟惯性神经网络的采样数据同步及其应用

Neural Computing and Applications Pub Date : 2024-08-21 DOI: 10.1007/s00521-024-10192-4

Junyi Wang, Wenyuan He, Hongli Xu, Haibin Cai, Xiangyong Chen

引用次数: 0

Evolutionary variational inference for Bayesian generalized nonlinear models 贝叶斯广义非线性模型的进化变异推理

Neural Computing and Applications Pub Date : 2024-08-21 DOI: 10.1007/s00521-024-10349-1

Philip Sebastian Hauglie Sommerfelt, Aliaksandr Hubin

引用次数: 0

Multimodal fusion: advancing medical visual question-answering 多模态融合：推进医学视觉问题解答

Neural Computing and Applications Pub Date : 2024-08-20 DOI: 10.1007/s00521-024-10318-8

Anjali Mudgal, Udbhav Kush, Aditya Kumar, Amir Jafari

引用次数: 0

The innovation dynamic mechanism of platform enterprise business model based on deep learning 基于深度学习的平台型企业商业模式创新动力机制

Neural Computing and Applications Pub Date : 2024-08-20 DOI: 10.1007/s00521-024-10242-x

Yanjun Kang, Guoquan Liu

引用次数: 0

SEAformer: frequency domain decomposition transformer with signal enhanced for long-term wind power forecasting SEAformer：用于长期风电预测的信号增强型频域分解变压器

Neural Computing and Applications Pub Date : 2024-08-19 DOI: 10.1007/s00521-024-10295-y

Leiming Yan, Siqi Wu, Shaopeng Li, Xianyi Chen

{"title":"SEAformer: frequency domain decomposition transformer with signal enhanced for long-term wind power forecasting","authors":"Leiming Yan, Siqi Wu, Shaopeng Li, Xianyi Chen","doi":"10.1007/s00521-024-10295-y","DOIUrl":"https://doi.org/10.1007/s00521-024-10295-y","url":null,"abstract":"Accurate and reliable wind power forecasting is of great importance for stable grid operation and advanced dispatch planning. Due to the complex, non-stationary, and highly volatile nature of wind power data, Transformer-based methods find it difficult to capture long-term trend features and incur high computational costs. To address these challenging problems, we propose a frequency domain decomposition Transformer architecture with signal enhanced attention mechanism (SEAformer). Firstly, we devise a frequency domain-based trend decomposition structure that enables the Transformer to extract more effective long-term trend features, thereby further improving the long-term prediction accuracy of the model. Secondly, in response to the large fluctuations and instability of wind power data, we design an internal signal enhanced substructure combined with an attention mechanism in the Transformer, which filters out high-frequency noise signals and reduces the computational cost of the Transformer. We conduct extensive experiments on the benchmark dataset, the experimental analysis demonstrates that SEAformer outperforms the baseline methods (Transformer-based, MLP-based, and Traditional methods) in both multivariate and univariate prediction tasks, exhibiting the best prediction performance.","PeriodicalId":18925,"journal":{"name":"Neural Computing and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predictions of steel price indices through machine learning for the regional northeast Chinese market 通过机器学习预测中国东北地区市场的钢材价格指数

Neural Computing and Applications Pub Date : 2024-08-19 DOI: 10.1007/s00521-024-10270-7

Bingzi Jin, Xiaojie Xu

引用次数: 0

Computational and artificial neural network study on ternary nanofluid flow with heat and mass transfer with magnetohydrodynamics and mass transpiration 三元纳米流体流动的计算与人工神经网络研究--磁流体力学与质量蒸腾的传热传质关系

Neural Computing and Applications Pub Date : 2024-08-19 DOI: 10.1007/s00521-024-10325-9

U. S. Mahabaleshwar, K. M. Nihaal, Dia Zeidan, T. Dbouk, D. Laroze

{"title":"Computational and artificial neural network study on ternary nanofluid flow with heat and mass transfer with magnetohydrodynamics and mass transpiration","authors":"U. S. Mahabaleshwar, K. M. Nihaal, Dia Zeidan, T. Dbouk, D. Laroze","doi":"10.1007/s00521-024-10325-9","DOIUrl":"https://doi.org/10.1007/s00521-024-10325-9","url":null,"abstract":"Ternary nanofluids have been an interesting field for academics and researchers in the modern technological era because of their advanced thermophysical properties and the desire to increase heat transfer rates. Furthermore, the innovative, sophisticated artificial neural network strategy with the Levenberg–Marquardt backpropagation technique (LMBPT) is proposed for research on heat and mass transport over non-Newtonian ternary Casson fluid on a radially extending surface with magnetic field and convective boundary conditions. The main objective of the current research is to conduct a comparative study of numerical solutions of the ternary nanofluid model of heat/mass transport utilizing the artificial neural network (ANN) together with the (LMBPT). To accurately represent complex patterns, neural networks modify their parameters flexibly, resulting in more accurate predictions and greater generalization with numerical outcomes. The model equations were reduced from partial to ODEs through applying appropriate similarity variables. The shooting technique and the byp-4c algorithm were then used to analyze the numerical data. The current study reveals that a rise in the Casson parameter diminishes the fluid velocity but an opposite nature is seen in thermal distribution for rising behavior of heat source/sink and Biot number, and the concentration profile tends to deteriorate when the mass transfer is elevated. Furthermore, the resulting values of the significant engineering coefficients are numerically analyzed and tabulated.","PeriodicalId":18925,"journal":{"name":"Neural Computing and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A reduced-form multigrid approach for ANN equivalent to classic multigrid expansion 等同于经典多网格扩展的简化形式多网格 ANN 方法

Neural Computing and Applications Pub Date : 2024-08-19 DOI: 10.1007/s00521-024-10311-1

Jeong-Kweon Seo

{"title":"A reduced-form multigrid approach for ANN equivalent to classic multigrid expansion","authors":"Jeong-Kweon Seo","doi":"10.1007/s00521-024-10311-1","DOIUrl":"https://doi.org/10.1007/s00521-024-10311-1","url":null,"abstract":"In this paper, we investigate the method of solving partial differential equations (PDEs) using artificial neural network (ANN) structures, which have been actively applied in artificial intelligence models. The ANN model for solving PDEs offers the advantage of providing explicit and continuous solutions. However, the ANN model for solving PDEs cannot construct a conventionally solvable linear system with known matrix solvers; thus, computational speed could be a significant concern. We study the implementation of the multigrid method, developing a general concept for a coarse-grid correction method to be integrated into the ANN-PDE architecture, with the goal of enhancing computational efficiency. By developing a reduced form of the multigrid method for ANN, we demonstrate that it can be interpreted as an equivalent representation of the classic multigrid expansion. We validated the applicability of the proposed method through rigorous experiments, which included analyzing loss decay and the number of iterations along with improvements in terms of accuracy, speed, and complexity. We accomplished this by employing the gradient descent method and the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method to update the gradients while solving the given ANN systems of PDEs.","PeriodicalId":18925,"journal":{"name":"Neural Computing and Applications","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142188360","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0