Engineering Applications of Artificial Intelligence最新文献

Quantum-like evidence networks decision-making model 类量子证据网络决策模型

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-06-20 DOI: 10.1016/j.engappai.2025.111368

Xiaozhuan Gao , Huijun Yang , Lipeng Pan , Danilo Pelusi

{"title":"Quantum-like evidence networks decision-making model","authors":"Xiaozhuan Gao , Huijun Yang , Lipeng Pan , Danilo Pelusi","doi":"10.1016/j.engappai.2025.111368","DOIUrl":"10.1016/j.engappai.2025.111368","url":null,"abstract":"<div><div>For the purpose of addressing the deviations of the Sure Thing Principle observed in cognitive experiments, numerous artistic models have been developed and refined within the probabilistic framework. In spite of this, it is clear that these models still have room for improvement in terms of effectively expressing and processing global ignorance information and local ignorance information. As a result, a quantum-like evidence networks decision-making model (QLENDM) is proposed. The work we operates within the framework of evidence theory, rather than probability theory, which is different from those art models, such as quantum dynamics Markov model, quantum-like approach, quantum prospect decision theory, and quantum-like Bayesian networks. Quantum-like basic probability assignment can better model the global ignorance or local ignorance information presented by cognitive experiments than the probability distribution, which solves the issue of inadequate modeling of uncertain information within a probabilistic framework. Moreover, from a heuristic perspective, QLENDM automatically fits the parameters related to interference effect using the distance of quantum-like basic probability assignments based on focal element structure. Therefore, QLENDM with these two characteristics, in addition to its universality, has better predictive performance than the above art models. We then apply QLENDM to cognitive science and information retrieval. As a result of the experiment, it appears that compared with other models, QLENDM has the minimum average and standard deviation of fitting errors, indicating that it is better suited for addressing deviations of the Sure Thing Principle as well as predicting the behavior of participants more accurately in decision-making experiments.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"157 ","pages":"Article 111368"},"PeriodicalIF":7.5,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321836","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

Feature-refined adaptive modulation transformer for image deraining 特征细化自适应调制变压器图像脱轨

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-06-20 DOI: 10.1016/j.engappai.2025.111373

Yeting Huang, Lei Dai, Zhihua Chen, Wenlong Hu, Shouli Wang

{"title":"Feature-refined adaptive modulation transformer for image deraining","authors":"Yeting Huang, Lei Dai, Zhihua Chen, Wenlong Hu, Shouli Wang","doi":"10.1016/j.engappai.2025.111373","DOIUrl":"10.1016/j.engappai.2025.111373","url":null,"abstract":"<div><div>Recent image deraining methods demonstrate impressive reconstruction performance by leveraging the global modeling capability of Transformer architecture. However, unlike convolutional approach, Transformer inherently struggles to capture high-frequency detail effectively. Furthermore, existing methods primarily focus on spatial information while largely neglecting the frequency-domain characteristics of rain streaks, which are crucial for rain removal. To address these challenges, we propose a feature-refined adaptive modulation Transformer (FRAMT), which effectively integrates spatial-domain features with frequency-domain modulation to enhance deraining performance. To accurately identify rain streaks and efficiently separate them from the background, the detail-guided attention block enhances sensitivity to high-frequency components by integrating pooling operation with convolution. To mitigate image blurring and detail loss induced by rain streaks, the local feature refinement block employs a multi-scale content decomposition strategy, utilizing a parallel multi-branch architecture to extract diverse contextual features across varying spatial scales. Additionally, the adaptive fusion modulation block incorporates a frequency selection mechanism that dynamically modulates feature response, effectively suppressing redundant information and irrelevant features. Extensive experiments conducted on widely used benchmark datasets demonstrate that the proposed method is more competitive than advanced methods.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"157 ","pages":"Article 111373"},"PeriodicalIF":7.5,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321618","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

Shared mobility demand prediction via A fast spatiotemporal tensor autoregression 基于快速时空张量自回归的共享出行需求预测

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-06-20 DOI: 10.1016/j.engappai.2025.111467

Hongyu Yan , Zhiqiang Lv , Jianbo Li , Benjia Chu , Zhihao Xu

{"title":"Shared mobility demand prediction via A fast spatiotemporal tensor autoregression","authors":"Hongyu Yan , Zhiqiang Lv , Jianbo Li , Benjia Chu , Zhihao Xu","doi":"10.1016/j.engappai.2025.111467","DOIUrl":"10.1016/j.engappai.2025.111467","url":null,"abstract":"<div><div>Shared mobility is critical to urban transportation, yet its complex spatiotemporal dynamics challenge traditional prediction methods. We propose the Tucker Decomposition-based Spatiotemporal Tensor Autoregressive Model (T-STAR), which leverages tensor-structured data modeling and Tucker decomposition to efficiently capture multi-dimensional dependencies. Unlike conventional methods, T-STAR preserves high-dimensional structures by decomposing raw spatiotemporal data into a low-rank core tensor and mode-specific factor matrices, reducing complexity and enhancing interpretability by decoupling spatial, temporal, and modal interactions. Experimental results on three benchmark datasets demonstrate T-STAR's strong performance. On the Beijing Taxi Trajectory Dataset (TaxiBJ), T-STAR achieves Mean Absolute Error (MAE) of 23.53 and Root Mean Square Error (RMSE) of 37.71, improving performance by 18.5 % and 21.2 % over baseline averages. On the New York City Taxi Dataset (NYCtaxi), it records MAE of 18.18 and RMSE of 46.87, reducing errors by 22.7 % and 15.4 %. In the sparse-demand New York City Bike-Sharing Dataset (NYCbike), it maintains robust accuracy with MAE of 7.95 and RMSE of 14.32, outperforming baselines by 14.1 % and 17.9 %, respectively. Most notably, T-STAR achieves these results at high speed: on TaxiBJ, it completes a prediction in just 0.35 seconds–87 % faster than the Adaptive Graph Convolutional Recurrent Network (AGCRN) and 99.8 % faster than the Diffusion Convolutional Recurrent Neural Network (DCRNN). By retaining over 95 % of key spatiotemporal correlations through Tucker compression, T-STAR reduces prediction error by 20–30 % while delivering real-time performance, offering a scalable framework for urban traffic prediction and shared vehicle scheduling. Code and data are both available at yanhongyu0/TSTAR (github.com)</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111467"},"PeriodicalIF":7.5,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322350","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

Portable fair decision making through modular approach 通过模块化方法进行便携式公平决策

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-06-20 DOI: 10.1016/j.engappai.2025.111361

Fengyu Wu, Ayong Ye, Qiuling Chen, Huang Zhang, Jing Chen

{"title":"Portable fair decision making through modular approach","authors":"Fengyu Wu, Ayong Ye, Qiuling Chen, Huang Zhang, Jing Chen","doi":"10.1016/j.engappai.2025.111361","DOIUrl":"10.1016/j.engappai.2025.111361","url":null,"abstract":"<div><div>Influenced by real-world biases, machine learning based decision systems are prone to discriminatory outcomes, which has garnered considerable attention, and led to the proposal of numerous bias mitigation methods, such as adversarial debiasing and fair representation learning. However, relying on predefined fairness standards prevents these methods from adapting to evolving fairness requirements. To address this issue, we propose a modular fairness enhancement approach. In our approach, each fairness requirement is modeled as a distinct optimization task, with the corresponding model parameters encapsulated within an independent sub-module. In addition, a main module is designed to capture the shared classification features across all fairness tasks. This multi-task learning architecture enables the decision making system to meet multiple fairness requirements without retraining. Experimental evaluations on four real datasets, by comparing portability and four fairness metrics with state-of-the-art methods. The results demonstrate that our method achieves superior portability in addressing various fairness requirements compared to the existing methods.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"157 ","pages":"Article 111361"},"PeriodicalIF":7.5,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321837","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

GOM-MMOEA: Multimodal multi-objective evolutionary algorithm based on global orchestration mechanism GOM-MMOEA：基于全局编排机制的多模态多目标进化算法

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-06-20 DOI: 10.1016/j.engappai.2025.111412

Shaobo Deng , Hui Shi , Hangyu Liu , Jinyu Xu , Sujie Guan , Min Li , Zhuolei Duan

{"title":"GOM-MMOEA: Multimodal multi-objective evolutionary algorithm based on global orchestration mechanism","authors":"Shaobo Deng , Hui Shi , Hangyu Liu , Jinyu Xu , Sujie Guan , Min Li , Zhuolei Duan","doi":"10.1016/j.engappai.2025.111412","DOIUrl":"10.1016/j.engappai.2025.111412","url":null,"abstract":"<div><div>The core challenge of multimodal multi-objective optimization lies in identifying and discovering multiple equivalent sets of Pareto-optimal solutions, thereby offering diverse options for decision-makers. However, most existing algorithms suffer from premature convergence when tackling such problems. This issue often arises due to inadequate population diversity and ineffective global exploration mechanisms during the search process, which causes the algorithm to become trapped in local optima and hinders the exploration of other promising regions in the decision space. To address this challenge, this paper proposes a multimodal multi-objective evolutionary algorithm based on a global orchestration mechanism. First, the algorithm constructs and dynamically updates an orchestration vector to guide the search toward optimal solutions and accelerate population convergence. Second, an orchestration vector update strategy is designed to gradually diminish the influence of inferior solutions, thereby preventing convergence to local optima. During the early stages of evolution, larger increments are applied to high-quality solutions to speed up convergence, while these increments are gradually reduced over time to promote global exploration. Finally, a novel parent selection mechanism is introduced, which dynamically adjusts selection probabilities to optimize the search process while preserving population diversity. Moreover, the algorithm adopts a triple population synergistic orchestration method that simultaneously considers both the objective and decision spaces. Experimental results demonstrate that the proposed algorithm outperforms several state-of-the-art methods across a range of benchmark test problems.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111412"},"PeriodicalIF":7.5,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322512","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 modeled study of driver visual attention driven by driving tasks 驾驶任务驱动下驾驶员视觉注意的模型研究

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-06-20 DOI: 10.1016/j.engappai.2025.111382

Chuan Xu , Bo Jiang , Yukun Wang , Yan Su

{"title":"A modeled study of driver visual attention driven by driving tasks","authors":"Chuan Xu , Bo Jiang , Yukun Wang , Yan Su","doi":"10.1016/j.engappai.2025.111382","DOIUrl":"10.1016/j.engappai.2025.111382","url":null,"abstract":"<div><div>Visual attention is an indispensable component of driving, enabling drivers to swiftly identify critical objects within complex and dynamic traffic environments. Despite its significance, existing visual attention models predominantly focus on static or idealized driving scenarios, limiting their ability to capture attention distribution patterns in real-world, dynamic environments. Furthermore, most of these models rely heavily on data-driven approaches, extracting features exclusively from visual image data, while neglecting the profound influence of “the driver, the vehicle, and the road environment”. Consequently, these models frequently fail to effectively address the intricacies of practical driving scenarios. To bridge these gaps, this study introduces a driver visual attention prediction model that comprehensively incorporates the driving task, driver experience, and the impact of dynamic visual scenes. The proposed model leverages the advanced learning capabilities of Convolutional Neural Networks (CNN) and Vision Transformer (ViT), coupled with sequence modeling mechanisms, to effectively capture the nuanced attention allocation patterns of drivers in complex driving contexts. The model is meticulously designed to adapt to dynamically evolving driving task requirements. Experimental results demonstrate that the proposed model outperforms state-of-the-art (SOTA) visual attention prediction models across multiple benchmark evaluation metrics on the DR(eye)VE dataset, particularly excelling in dynamic driving conditions. Moreover, generalization experiments were conducted on the BDD-A and TDV datasets validate the model’s robustness and applicability across varied driving tasks and dynamic conditions.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"157 ","pages":"Article 111382"},"PeriodicalIF":7.5,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144321824","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

Generative artificial intelligence-based modified abstractive cross attention enabled sequence to sequence model for abstractive Hindi text summarization 基于生成式人工智能的改进抽象交叉注意序列到序列模型的抽象印地语文本摘要

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-06-20 DOI: 10.1016/j.engappai.2025.111478

Babita Verma , Ani Thomas , Rohit Kumar Verma

{"title":"Generative artificial intelligence-based modified abstractive cross attention enabled sequence to sequence model for abstractive Hindi text summarization","authors":"Babita Verma , Ani Thomas , Rohit Kumar Verma","doi":"10.1016/j.engappai.2025.111478","DOIUrl":"10.1016/j.engappai.2025.111478","url":null,"abstract":"<div><div>Abstractive Text summarization is the process of providing a concise as well as cohesive summary, which encapsulates the vital information from the original text. Although the supervised models now in use are competent, they frequently rely on annotated datasets and create challenges regarding uninterpretability and limited generalization ability. To overcome the limitations, this research proposes a Generative Artificial Intelligence Sequence to sequence the Bidirectional Encoder Representations from the Transformers model to generate concise summaries. During the generation of the output sequence, the model takes into account data from various segments of the input sequence by utilizing a modified abstractive cross-attention technique. Specifically, the Generative Artificial Intelligence assists in removing grammatical mistakes in summaries via the application of the Global Surrogate method, which ensures the clarity and fluency of the output summary. In addition, the encoder-decoder model enables the accurate summary generation process, vastly improving fluency and accuracy. Furthermore, the experimental outcomes show that the Generative Artificial Intelligence Sequence to sequence the Bidirectional Encoder Representations from the Transformers model surpasses the conventional text summarization techniques concerning Bilingual Evaluation Understudy of 0.71 and Metric for Evaluation of Translation with Explicit Ordering of 0.73, which shows that the proposed model generates a meaningful summary from the given text.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111478"},"PeriodicalIF":7.5,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322513","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 cascaded strategy-based hierarchical reinforcement learning algorithm for lane change decision-making 基于级联策略的分层强化学习变道决策算法

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-06-20 DOI: 10.1016/j.engappai.2025.111522

Heng Du , Dingfa Lin , Xiaolong Zhang , Lingtao Wei , Shizhao Zhou , Xuanhao Cheng , Luxin Zhang , Jin Jiang

{"title":"A cascaded strategy-based hierarchical reinforcement learning algorithm for lane change decision-making","authors":"Heng Du , Dingfa Lin , Xiaolong Zhang , Lingtao Wei , Shizhao Zhou , Xuanhao Cheng , Luxin Zhang , Jin Jiang","doi":"10.1016/j.engappai.2025.111522","DOIUrl":"10.1016/j.engappai.2025.111522","url":null,"abstract":"<div><div>Hierarchical reinforcement learning (HRL) has demonstrated considerable promise in addressing complex driving tasks. However, existing HRL-based autonomous driving decision systems face challenges such as inefficient convergence, lack of interdependence among driving maneuver strategies (including throttle/brake control and steering adjustments), and inadequate risk assessment mechanisms, all of which impede the safety and stability of lane-changing decisions. This study proposes a novel HRL framework for continuous lane-changing decision planning. This framework establishes cascaded relationships between driving maneuvers strategies and integrates a comprehensive risk assessment mechanism to address these challenges. Initially, a hierarchical decision model is developed, where the high-level determines the lane-changing intent, while the low-level manages continuous and precise maneuvers. Subsequently, by integrating a Bayesian network, the cascading between throttle/brake openings and steering angles is achieved, optimizing the system's joint strategy distribution. Furthermore, a comprehensive risk assessment mechanism that evaluates the cooperation level of drivers and the severity of potential collisions is designed to encourage agents to adopt strategies that minimize risk. The effectiveness of the proposed decision-making framework has been validated through comparative experiments in mixed traffic scenarios simulated within the Car Learning to Act (CARLA) environment and corroborated with human driving data from the Next Generation Simulation (NGSIM) database.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111522"},"PeriodicalIF":7.5,"publicationDate":"2025-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322821","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

Federated learning in network traffic classification: Taxonomy of implementation, application, and impact on sixth-generation wireless networks 网络流量分类中的联邦学习：对第六代无线网络的实现、应用和影响的分类

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-06-19 DOI: 10.1016/j.engappai.2025.111471

Azizi Ariffin , Firdaus Afifi , Faiz Zaki , Hazim Hanif , Nor Badrul Anuar

{"title":"Federated learning in network traffic classification: Taxonomy of implementation, application, and impact on sixth-generation wireless networks","authors":"Azizi Ariffin , Firdaus Afifi , Faiz Zaki , Hazim Hanif , Nor Badrul Anuar","doi":"10.1016/j.engappai.2025.111471","DOIUrl":"10.1016/j.engappai.2025.111471","url":null,"abstract":"<div><h3>Objectives</h3><div>Network traffic classification (NTC) is crucial for network management. However, the surge in Internet traffic poses scalability and privacy issues. Researchers are turning to federated learning (FL) to tackle these issues. Despite the importance of FL, there is lack of literature that comprehensively reviews its implementation and application within NTC and its impact on Sixth-Generation Wireless (6G) networks. Current surveys cover the technical aspects of NTC without fully addressing the integration of FL and its broader implications. This study aims to review the technical implementation of FL in NTC and its application to various network-related areas, including 6G.</div></div><div><h3>Methods</h3><div>This study presents a taxonomy for FL implementation in NTC, considering aspects such as learning and aggregation approaches, topology, and client operations. It examines the limitations of these elements and their effects on performance, efficiency, scalability, and their impact on 6G. This study outlines a taxonomy for FL applications, focusing on privacy preservation, scalable classification, and shared security intelligence.</div></div><div><h3>Novelty</h3><div>The proposed taxonomy provides insights into research landscape and highlights its limitations. The analysis of the impact of FL-based NTC on 6G provides insight into its integration and implementation challenges. This study discusses open issues and advocates for future research directions in FL for NTC, including 6G.</div></div><div><h3>Findings</h3><div>The study identifies areas needing improvement such as privacy, addressing security, single-point-of-failure, hardware limitations, delays and heterogeneity concerns. The findings of this paper show that an optimal implementation approach is essential to cater for heterogeneity and real-time requirements of the network environment.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111471"},"PeriodicalIF":7.5,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144322318","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 brain-inspired projection contrastive learning network for instantaneous learning 即时学习的脑启发投射对比学习网络

IF 7.5 2区计算机科学

Engineering Applications of Artificial Intelligence Pub Date : 2025-06-19 DOI: 10.1016/j.engappai.2025.111524

Yanli Yang

{"title":"A brain-inspired projection contrastive learning network for instantaneous learning","authors":"Yanli Yang","doi":"10.1016/j.engappai.2025.111524","DOIUrl":"10.1016/j.engappai.2025.111524","url":null,"abstract":"<div><div>The biological brain can learn quickly and efficiently, while the learning of artificial neural networks is astonishing time-consuming and energy-consuming. Biosensory information is quickly projected to the memory areas to be identified or to be signed with a label through biological neural networks. Inspired by the fast learning of biological brains, a projection contrastive learning model is designed for the instantaneous learning of samples. This model is composed of an information projection module for rapid information representation and a contrastive learning module for neural manifold disentanglement. An algorithm instance of projection contrastive learning is designed to process some machinery vibration signals and is tested on several public datasets. The test on a mixed dataset containing 1426 training samples and 14,260 testing samples shows that the running time of our algorithm is approximately 37 s and that the average processing time is approximately 2.31 ms per sample, which is comparable to the processing speed of a human vision system. A prominent feature of this algorithm is that it can track the decision-making process to provide an explanation of outputs in addition to its fast running speed.</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"158 ","pages":"Article 111524"},"PeriodicalIF":7.5,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144312541","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