Artificial Intelligence Review最新文献

{"title":"Effectiveness of data resampling and ensemble learning in multiclass imbalance learning","authors":"Muhammad Fachrie,&nbsp;Aina Musdholifah,&nbsp;Reza Pulungan","doi":"10.1007/s10462-025-11357-w","DOIUrl":"10.1007/s10462-025-11357-w","url":null,"abstract":"<div><p>Classification tasks in many real-world problems often involve multiclass datasets with imbalanced class distributions, which have more difficulty factors than binary classification. Previous studies have proposed various methods to address this multiclass imbalanced learning issue. Data resampling and ensemble learning are the most popular among the proposed methods. However, no comprehensive review or survey has provided an in-depth comparison of ad hoc methods in multiclass imbalance learning, particularly with a focus on data resampling and ensemble learning. Moreover, there is a lack of studies that analyze the effectiveness of each method in terms of the difficulty factors in multiclass imbalance learning. This paper provides a comprehensive review and comparative analysis to identify the strengths and weaknesses of each method and assess their effectiveness in improving classification performance. The analysis shows that not all methods effectively enhance classification performance on multiclass imbalanced datasets. Some methods even perform worse than the baseline performance. The review also reveals that datasets with certain difficulty factors are more challenging for most existing methods to handle. Ultimately, this paper summarizes several important lessons and identifies research gaps to guide future work in the field.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 12","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11357-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145230283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revisiting natural selection: evolving dynamic neural networks using genetic algorithms for complex control tasks","authors":"Mohamed A. Taha,&nbsp;Mahmoud M. Saafan,&nbsp;Sarah M. Ayyad","doi":"10.1007/s10462-025-11382-9","DOIUrl":"10.1007/s10462-025-11382-9","url":null,"abstract":"<div><p>Reinforcement learning (RL) and Genetic Algorithms (GAs) are widely used in decision-making and control tasks, but they often suffer from prolonged training times and inefficiencies. This paper addresses the need for a faster and more precise method to train neural networks in RL tasks, without sacrificing performance. The proposed approach enhances GAs by introducing mechanisms that optimize network architectures dynamically, minimizing unnecessary complexity while maintaining accuracy. The methodology includes a dynamic architecture adaptation technique that trims the neural network to its most compact and effective configuration. A Blending mechanism is introduced to improve the propagation of essential features across network layers, reducing the usage of non-linearity until necessary. An experience replay buffer is integrated to avoid redundant fitness evaluations, significantly reducing computational overhead. Additionally, a novel approach combines back-propagation with GAs for further refinement in supervised or RL tasks, using it as a mutation method to fine-tune the model. Experimental results demonstrate convergence speeds of around several seconds for simple tasks with well-defined rewards, and several minutes for more complex tasks. Training time is reduced by nearly 70%, and the approach provides faster inference speeds due to minimal architecture, making it applicable for mobile and edge devices. The method reduces computation, especially during inference, by over 90% due to the extremely low number of parameters. The performance metrics show comparable results to conventional approaches at the end of training. The proposed method is scalable and resource-efficient, outperforming existing neural network optimization techniques in both simulated environments and real-world applications. The developed framework is publicly available under the MIT license at https://github.com/AhmedBoin/atgen offering an open-source solution for the broader research community.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11382-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145062264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A dynamic operation room scheduling DORS strategy based on explainable AI and fuzzy interface engine","authors":"Rana Mohamed El-Balka,&nbsp;Noha Sakr,&nbsp;Asmaa H. Rabie,&nbsp;Ahmed I. Saleh","doi":"10.1007/s10462-025-11366-9","DOIUrl":"10.1007/s10462-025-11366-9","url":null,"abstract":"<div><p>Poor surgical scheduling causes major problems in hospital operating rooms, such as long patient wait times, underutilized operating rooms, and high costs. Existing scheduling approaches, which are static or less adaptable, fail to handle real-time unpredictability. To overcome these constraints, this study presents Dynamic Operation Room Scheduling (DORS), a new intraday surgical scheduling system. DORS uses a two-layered architecture: (1) Explainable AI for feature selection that is based on critical scheduling criteria such as Round Robin, and (2) a dynamic scheduling system that includes a Receiving Module, a Checking Module for patient prioritization, and a Scheduling Module provided by a Fuzzy Interface Engine. This system allows for proactive schedule preparation and reactive modifications, making it possible to smoothly include unscheduled surgical operations. In comparison to traditional (FCFS, Round Robin) and optimization-based (genetic algorithm) methods. DORS dynamically modifies schedules to reduce average wait times (AWT), consistently outperforming other approaches by 120–560 min. DORS completes surgical operations more quickly (half of surgical operations in 255–725 min). In addition, DORS retains a modest runtime (45 ms) while increasing scheduling efficiency (98.6%). DORS also demonstrates strong stability, with low Relative Percentage Deviation (RPD) on high-demand days. Finally, DORS achieves the optimal blend of speed, efficiency, and responsiveness, making it the greatest choice for hospitals aiming to eliminate delays, optimize operating room usage, and effectively manage changing surgical needs.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11366-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144929336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Swarm intelligence techniques and their applications in fog/edge computing: an in-depth review","authors":"Reyhane Ghafari,&nbsp;Najme Mansouri","doi":"10.1007/s10462-025-11351-2","DOIUrl":"10.1007/s10462-025-11351-2","url":null,"abstract":"<div><p>Recent advances in the Internet of Things (IoT) have connected diverse devices that often have limited resources and processing power. Artificial intelligence (AI) applications in fog and edge computing are greatly enhanced by Swarm Intelligence (SI) techniques. These SI methods improve resource allocation, task scheduling, and load balancing, making distributed systems more efficient and responsive to changing conditions. This paper systematically reviews 91 studies (2019–2023) on SI applications in fog/edge environments. We compare fog, edge, and cloud computing paradigms and analyze SI-based approaches using case studies, performance metrics, and evaluation tools. This review identifies key advantages and limitations of current SI-based approaches and highlights open issues and future research directions to enhance distributed computing systems. These insights aim to guide the development of more efficient and responsive AI-driven resource management strategies in fog/edge environments.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11351-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV target tracking: a survey","authors":"Pengnian Wu,&nbsp;Yixuan Li,&nbsp;Dong Xue","doi":"10.1007/s10462-025-11348-x","DOIUrl":"10.1007/s10462-025-11348-x","url":null,"abstract":"<div><p>Unmanned Aerial Vehicles (UAVs) have become critical enablers of integrated air-space-ground Internet of Things (IoT) ecosystems, with target tracking serving as a foundational technology. This paper classifies UAV target tracking into two distinct paradigms: active tracking and passive tracking, differentiated by their operational scopes and technical objectives. Active tracking is defined as a closed-loop spatial pursuit system, whereby UAVs dynamically track targets through iterative cycles centered on three primary stages: online passive tracking, state fusion estimation, and tracking strategy generation, with subsequent execution phases implied in the loop. This workflow bridges perception and action, enabling spatial engagement through continuous sensor-to-control feedback. In contrast, passive tracking acts as a vision-centric analytical module that exclusively extracts target image-domain attributes from visual sensors—devoid of physical state inference or control mechanisms. As a preprocessing stage for active systems, it is constrained to the visual perception layer, lacking the spatial engagement capabilities inherent in closed-loop tracking systems. This paper conducts an in-depth analysis of the application, key challenges, and future trends in both active and passive UAV target tracking. By systematically discussing the relationships among relevant technologies, this work aims to establish a foundational reference framework and offer citation material for guiding the future development of UAV target tracking technologies.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11348-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ameliorated elk herd optimizer for global optimization and engineering problems","authors":"Mohammed Azmi Al-Betar,&nbsp;Malik Sh. Braik,&nbsp;Qusai Yousef Shambour,&nbsp;Ghazi Al-Naymat,&nbsp;Thantrira Porntaveetus","doi":"10.1007/s10462-025-11360-1","DOIUrl":"10.1007/s10462-025-11360-1","url":null,"abstract":"<div><p>Optimization techniques have received significant attention for reliably addressing practical problems. A potential meta-heuristic called elk herd optimizer (EHO) was created, inspired by the social behavior and reproduction of elks. EHO has drawbacks, including poor convergence competency and a tendency to fall into local extrema in various optimization problems. Furthermore, this algorithm does not account for the memory of its search agents and has difficulty effectively balancing exploration and exploitation, which can lead to early convergence toward a local optimum. This study addresses the above issues by proposing an ameliorated EHO (AEHO) by incorporating several modifications into the basic EHO algorithm, which can be described as follows: A new hybrid memory-based EHO is developed that uses the particle swarm optimization (PSO) algorithm to guide EHO to search for reasonable candidate solutions. This hybrid approach was proposed to enhance EHO’s diversity and balance search capabilities to achieve strong search performance. Initially, a memory component was added to EHO using the idea of <i>pbest</i> from PSO to tap into promising search regions, which focuses on improving the best solutions and preventing the algorithm from getting stuck in a local optimum. In addition, the PSO concepts of (<i>gbest</i>) and (<i>pbest</i>) are used to enhance the best placements of the search agents in EHO. Finally, a greedy selection method was used to improve the efficiency of exhaustive exploration in AEHO, using the fitness values before and after updates as an indicator for efficacy of the best solutions. To evaluate the performance of the AEHO algorithm against a group of well-known competitors, we use ten complex test functions from the global CEC2022 test suite and thirty complex test functions from the global CEC2014 test suite. Based on the analysis of the experimental findings, AEHO performed optimally on 84% of the CEC2014 functions and 74% of the CEC2022 functions, ranking first in both suites with an average ranking of 3.11 and 1.62, respectively. The mean computation time of AEHO is about one-third of the average computation time for the first-ranked method, indicating that AEHO not only performs very well in global searches but also exhibits greater search efficiency when compared to newer optimization algorithms. The applicability and reliability of AEHO were thoroughly studied on four constrained engineering design problems and a real-world industrial process. The results demonstrate the superiority and promising potential of AEHO in addressing a wide range of challenging real-world problems.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11360-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fault-tolerant control strategies for industrial robots: state of the art and future perspective on AI-based fault management","authors":"Zeashan Khan,&nbsp;Ali Nasir,&nbsp;Samir Mekid","doi":"10.1007/s10462-025-11327-2","DOIUrl":"10.1007/s10462-025-11327-2","url":null,"abstract":"<div><p>Fault-tolerant control schemes are essential for affirming the safe and dependable operation of industrial robots. In this detailed review, we discuss the current developments in fault-tolerant control strategies for industrial robots. The main focus is given to highlight some major contributions in fault-tolerant control systems used in robotic manipulators with single or multiple joints, incorporating any linear or non-linear robust approach for industrial robots, design, and implementation. The paper also discusses adaptive fault-tolerant control of robots with sensor and/or actuator faults and unknown parameters, and fault-tolerant cooperative control of multiple robot teams for collaborative tasks. The present work provides a comprehensive overview of the recent advancements in fault-tolerant control strategies for industrial robots using both classical nonlinear methods as well as intelligent approaches using AI and machine learning, which will be useful for researchers and engineers working in this field. </p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11327-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918582","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improved decisions for unknown behaviours in interactive dynamic influence diagrams","authors":"Yinghui Pan,&nbsp;Mengen Zhou,&nbsp;Biyang Ma,&nbsp;Yifeng Zeng,&nbsp;Yew-soon Ong,&nbsp;Guoquan Liu","doi":"10.1007/s10462-025-11355-y","DOIUrl":"10.1007/s10462-025-11355-y","url":null,"abstract":"<div><p>Interactive dynamic influence diagrams (I-DIDs) are a general decision framework for a subject agent who interacts with other agents (of either collaborative or competitive) in a common environment with partial observability. The subject agent aims to optimize its decision-making (response strategy) while other agents concurrently adapt their behaviors over time. The I-DID model has faced a long-term challenge when other agents exhibit unknown behaviors that go beyond what the subject agent has planned for prior to their interactions. This is because the subject agent does not hold the capability of modeling unknown behaviours of other agents in traditional I-DID techniques. In this article, we adapt two different swarm intelligence (SI) techniques to develop new behaviours for other agents in I-DIDs. The SI-based algorithms have the strength of generating a collective set of behaviours that could potentially contain various types of agents’ behaviours. We theoretically analyze how the two algorithms impact the subject agent’s decision quality, and empirically demonstrate the algorithm performance in two commonly used problem domains.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11355-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"DAoG: decayed adaptation over gradients for parameter-free step size control","authors":"Yifan Zhang,&nbsp;Di Zhao,&nbsp;Hongyi Li,&nbsp;Chengwei Pan","doi":"10.1007/s10462-025-11362-z","DOIUrl":"10.1007/s10462-025-11362-z","url":null,"abstract":"<div><p>As the scale of parameters in deep learning models continues to grow, the cost of training such models increases accordingly, posing increasingly significant challenges for stochastic optimization methods. A central issue in gradient-based optimization lies in the selection of the step size, whose appropriateness directly affects training efficiency and model performance. To address this issue, a series of parameter-free optimization methods that do not require manual tuning of the step size have been proposed in recent years. Among them, DoG and its improved variant DoWG are the most representative. Despite demonstrating strong performance across various tasks, DoG and DoWG still suffer from performance instability or slow convergence under certain model architectures or training conditions. This paper introduces Decayed Adaptation over Gradients (DAoG), a novel parameter-free optimization method that systematically addresses these limitations. Our key innovation lies in incorporating a principled step size decay mechanism for the first time within the parameter-free optimization framework, which substantially enhances both optimization stability and model generalization. Additionally, a parameter compression strategy is employed to reduce sensitivity to the initial step size. Theoretical analysis demonstrates that DAoG exhibits favorable convergence properties under L-smooth and G-Lipschitz conditions. Empirical studies across representative tasks in natural language processing and computer vision demonstrate that DAoG outperforms both DoG and DoWG in terms of convergence speed and generalization performance. Notably, it even rivals or surpasses Adam with cosine annealing in several challenging scenarios. These theoretical and experimental results suggest that DAoG effectively mitigates the overly conservative step size issue in DoG and the instability problem in DoWG, thereby advancing the development of parameter-free optimization methods in deep learning.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11362-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Place recognition meet multiple modalities: a comprehensive review, current challenges and future development","authors":"Zhenyu Li,&nbsp;Tianyi Shang,&nbsp;Pengjie Xu,&nbsp;Zhaojun Deng","doi":"10.1007/s10462-025-11367-8","DOIUrl":"10.1007/s10462-025-11367-8","url":null,"abstract":"<div><p>Place recognition is a cornerstone of vehicle navigation and mapping, which is pivotal in enabling systems to determine whether a location has been previously visited. This capability is critical for tasks such as loop closure in Simultaneous Localization and Mapping (SLAM) and long-term navigation under varying environmental conditions. This survey comprehensively reviews recent advancements in place recognition, emphasizing three representative methodological paradigms: Convolutional Neural Network (CNN)-based approaches, Transformer-based frameworks, and cross-modal strategies. We begin by elucidating the significance of place recognition within the broader context of autonomous systems. Subsequently, we trace the evolution of CNN-based methods, highlighting their contributions to robust visual descriptor learning and scalability in large-scale environments. We then examine the emerging class of Transformer-based models, which leverage self-attention mechanisms to capture global dependencies and offer improved generalization across diverse scenes. Furthermore, we discuss cross-modal approaches that integrate heterogeneous data sources such as Lidar, vision, and text description, thereby enhancing resilience to viewpoint, illumination, and seasonal variations. We also summarize standard datasets and evaluation metrics widely adopted in the literature. To the best of our knowledge, no prior survey has systematically reviewed visual, LiDAR, and cross-modal place recognition concurrently. This work thus resolves a critical gap in existing literature dominated by single-modality studies. Finally, we identify current research challenges and outline prospective directions, including domain adaptation, real-time performance, and lifelong learning, to inspire future advancements in this domain. The unified framework of leading-edge place recognition methods, i.e., code library, and the results of their experimental evaluations are available at https://github.com/CV4RA/SOTA-Place-Recognitioner.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 11","pages":""},"PeriodicalIF":13.9,"publicationDate":"2025-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11367-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}