Artificial Intelligence Review最新文献_第9页

Artificial intelligence in chemical exchange saturation transfer magnetic resonance imaging 人工智能在化学交换饱和转移磁共振成像中的应用

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-17 DOI: 10.1007/s10462-025-11227-5

Swee Qi Pan, Yan Chai Hum, Khin Wee Lai, Wun-She Yap, Yi Zhang, Hye-Young Heo, Yee Kai Tee

{"title":"Artificial intelligence in chemical exchange saturation transfer magnetic resonance imaging","authors":"Swee Qi Pan, Yan Chai Hum, Khin Wee Lai, Wun-She Yap, Yi Zhang, Hye-Young Heo, Yee Kai Tee","doi":"10.1007/s10462-025-11227-5","DOIUrl":"10.1007/s10462-025-11227-5","url":null,"abstract":"<div><p>This review delves into the transformative role of Artificial Intelligence (AI) in advancing Chemical Exchange Saturation Transfer (CEST) Magnetic Resonance Imaging (MRI), a cutting-edge imaging method for non-invasive biochemical mapping. CEST MRI faces many technical challenges that hinder its clinical adoption. AI-driven approaches have emerged as one of the promising solutions to address some of these limitations. The evolution of AI in CEST MRI is traced from its inception, with pioneering studies in AI-driven image analysis, to current trends reflecting a marked increase in AI-related CEST publications. This review highlights AI’s impact on various stages of the CEST MRI pipeline, including accelerated imaging acquisition and reconstruction, improved pre-processing and denoising methods, and advanced quantification techniques. Furthermore, AI has demonstrated potential in clinical applications, such as disease diagnosis, molecular subtyping, and treatment monitoring, underscoring its growing relevance in the field. This review also examines the challenges in AI applications and future directions in CEST MRI, including the use of synthetic data, the explainability and interpretability of AI models, and their implications for clinical adoption. Overall, this review provides a comprehensive understanding of the current state of AI applications in CEST MRI and will inspire further research to unlock the full potential of this powerful molecular imaging technique.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11227-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143845637","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}

引用次数: 0

Enhancing decentralized energy storage investments with artificial intelligence-driven decision models 用人工智能驱动的决策模型加强分散式储能投资

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-16 DOI: 10.1007/s10462-025-11204-y

Gang Kou, Hasan Dinçer, Edanur Ergün, Serkan Eti, Serhat Yüksel, Ümit Hacıoğlu

{"title":"Enhancing decentralized energy storage investments with artificial intelligence-driven decision models","authors":"Gang Kou, Hasan Dinçer, Edanur Ergün, Serkan Eti, Serhat Yüksel, Ümit Hacıoğlu","doi":"10.1007/s10462-025-11204-y","DOIUrl":"10.1007/s10462-025-11204-y","url":null,"abstract":"<div><p>Decentralized energy storage investments play a crucial role in enhancing energy efficiency and promoting renewable energy integration. However, the complexity of these projects and the limited resources of the companies make it necessary to determine strategic priorities. This paper tries to define effective investment strategies for the improvements of the decentralized energy storage projects. In the first stage, the selection of mass experts is made via information gain-based mass expert selection. Next, the assessments of the experts are balanced based on the opinion of the best expert by using q-learning algorithm. Moreover, determinants of decentralized energy storage investments are examined with molecular fuzzy (MF) cognitive maps. Finally, strategy alternatives for decentralized energy storage investments are ranked with MF multi-objective particle swarm optimization (MOPSO). The main contribution of this study is the identification of the most effective decentralized energy storage investment alternatives by establishing a novel model. The main novelty of the proposed model is that considering information gain-based mass expert selection technique allows for higher consistency and decision efficiency. Owing to this issue, the decision-making process is accelerated, and the applicability of the results increases. The findings indicate that customer expectations (weight: 0.2577) and financial issues (weight: 0.2513) are the most essential criteria in improving the performance of decentralized energy storage investments. Furthermore, hydrogen-based energy storage (average value: 0.1878) and distributed battery swapping stations (average value: 0.1877) are the most important decentralized energy storage investment alternatives.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11204-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835561","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}

引用次数: 0

Modeling and effect analysis of machining parameters for surface roughness and specific energy consumption during TC18 machining using deep reinforcement learning and neural networks 基于深度强化学习和神经网络的TC18加工过程中表面粗糙度和比能耗的加工参数建模及影响分析

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-11 DOI: 10.1007/s10462-025-11178-x

Juan Lu, Huailong Mu, Haibin Ouyang, Zhenkun Zhang, Weiping Ding

{"title":"Modeling and effect analysis of machining parameters for surface roughness and specific energy consumption during TC18 machining using deep reinforcement learning and neural networks","authors":"Juan Lu, Huailong Mu, Haibin Ouyang, Zhenkun Zhang, Weiping Ding","doi":"10.1007/s10462-025-11178-x","DOIUrl":"10.1007/s10462-025-11178-x","url":null,"abstract":"<div><p>Under the impetus of green manufacturing and a low-carbon economy, the critical challenge lies in reducing energy consumption while maintaining machining quality. Against this background, this paper presents the method of modeling and effect analysis for surface roughness and specific energy consumption during TC18 machining using Deep Reinforcement Learning and Neural Networks. In this method, to reduce the experiment cost, multilayer-layer design (MLD) for computer simulation is applied to design a physical experiment, and to improve modeling accuracy, backpropagation neural network (BPNN) optimized by Double deep Q network algorithm (DDQN) is utilized to develop the prediction models of surface roughness (<i>Ra</i>) and specific energy consumption of cutting (<i>E</i><sub><i>sec</i></sub>). Finaly, the synergistic influence of cutting parameters on <i>Ra</i> and <i>E</i><sub><i>sec</i></sub> is analyzed based on the prediction models of <i>Ra</i> and <i>E</i><sub><i>sec</i></sub> built by MLD and DDQN-BPNN. The effectiveness and low cost of MLD and the excellent prediction performance of DDQN-BPNN are verified by comparisons of optimized BPNNs using common heuristic optimization algorithms through the milling experiment of TC18. These technologies provide effective solutions for modeling and factor impact of target features in machining field, and research results provides an effective guidance for the selection of milling parameters of TC18 to reduce the specific energy consumption of cutting under ensuring or improving machining quality.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11178-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821958","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}

引用次数: 0

AI-driven job scheduling in cloud computing: a comprehensive review 云计算中人工智能驱动的作业调度：综述

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-11 DOI: 10.1007/s10462-025-11208-8

Yousef Sanjalawe, Salam Al-E’mari, Salam Fraihat, Sharif Makhadmeh

{"title":"AI-driven job scheduling in cloud computing: a comprehensive review","authors":"Yousef Sanjalawe, Salam Al-E’mari, Salam Fraihat, Sharif Makhadmeh","doi":"10.1007/s10462-025-11208-8","DOIUrl":"10.1007/s10462-025-11208-8","url":null,"abstract":"<div><p>The demand for efficient job scheduling in cloud computing has grown significantly with the rise of dynamic and heterogeneous cloud environments. While effective in simpler systems, traditional scheduling algorithms fail to meet the complex requirements of modern cloud infrastructures. These limitations motivate the need for AI-driven solutions that offer adaptability, scalability, and energy efficiency. This paper comprehensively reviews AI-based job scheduling techniques, addressing several key research gaps in current approaches. The existing methods face challenges such as resource heterogeneity, energy consumption, and real-time adaptability in multi-cloud systems. Accordingly, the support of AI-based job scheduling in cloud computing is summarized here toward machine learning, optimization techniques, heuristic techniques, and hybrid AI models. This paper pointedly underlines the strengths and weaknesses of various approaches through deep comparative analysis and focuses on how AI will overcome traditional algorithm shortcomings. Is worth noticing that several important improvements this kind of AI-driven model provides, for example, in resource allocation, cost efficiency, energy consumption, and complex dependencies between jobs and system faults. In the end, AI-driven job scheduling seems to be a promising avenue toward effectively responding to the booming demands of cloud infrastructures. Future research should concentrate on three major outlooks: scalability, better integration of AI with traditional scheduling methods, and the use of other emerging technologies like edge computing and blockchain for better optimization of cloud-based job scheduling. The paper underscores the need for more adaptive, secure, and energy-efficient scheduling frameworks to meet the evolving challenges of cloud environments.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11208-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821957","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}

引用次数: 0

Multi-criteria decision support system for the evaluation of UAV intelligent agricultural sensors 无人机智能农业传感器多准则评价决策支持系统

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-11 DOI: 10.1007/s10462-025-11201-1

Bartłomiej Kizielewicz, Jarosław Wątróbski, Wojciech Sałabun

{"title":"Multi-criteria decision support system for the evaluation of UAV intelligent agricultural sensors","authors":"Bartłomiej Kizielewicz, Jarosław Wątróbski, Wojciech Sałabun","doi":"10.1007/s10462-025-11201-1","DOIUrl":"10.1007/s10462-025-11201-1","url":null,"abstract":"<div><p>Precision agriculture is an emerging approach aimed at enhancing agricultural productivity through advanced technological solutions. One of the key technologies integrated into modern agriculture is Unmanned Aerial Vehicles (UAVs), which rely on various sensors to provide critical information about crop fields. However, selecting the most suitable UAV sensors remains a significant challenge due to multiple evaluation criteria and compromises. This paper proposes a novel decision-support framework based on multi-criteria decision-making/analysis (MCDA/MCDM) methods to facilitate UAV sensor selection in precision agriculture. The framework incorporates objective weight selection techniques-Standard Deviation, Entropy, CRITIC, and MEREC-eliminating the need for subjective expert involvement. Furthermore, four MCDA/MCDM methods, including the newly proposed COmbined COmpromise solution with Characteristic Objects METhod (COCOCOMET), are applied to evaluate sensor alternatives. To validate the framework, a case study is conducted using a dataset of UAV sensors, where multiple evaluation criteria are analyzed to determine the most suitable sensor. The results confirm the framework’s effectiveness, demonstrating its robustness and stability in decision-making. Sensitivity analysis and comparative studies further highlight its reliability, particularly in addressing rank reversal issues commonly found in existing MCDA methods such as TOPSIS and AHP. The proposed framework not only provides a structured and adaptable evaluation process for UAV sensors but also offers broader applicability in agricultural decision-making.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11201-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822038","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}

引用次数: 0

A comprehensive review of machine learning applications for internet of nano things: challenges and future directions 机器学习在纳米物联网中的应用综述：挑战与未来方向

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-11 DOI: 10.1007/s10462-025-11211-z

Aryan Rana, Deepika Gautam, Pankaj Kumar, Kranti Kumar, Athanasios V. Vasilakos, Ashok Kumar Das, Vivekananda Bhat K

{"title":"A comprehensive review of machine learning applications for internet of nano things: challenges and future directions","authors":"Aryan Rana, Deepika Gautam, Pankaj Kumar, Kranti Kumar, Athanasios V. Vasilakos, Ashok Kumar Das, Vivekananda Bhat K","doi":"10.1007/s10462-025-11211-z","DOIUrl":"10.1007/s10462-025-11211-z","url":null,"abstract":"<div><p>In recent years, advances in nanotechnology and the Internet of Things (IoT) have led to the development of the revolutionary Internet of Nano Things (IoNT). IoNT, has found very similar real-life applications in agriculture, military, multimedia, and healthcare. However, despite the rapid advancements in both IoNT and machine learning (ML), there has been no comprehensive review explicitly focused on the integration of these two fields. Existing surveys and reviews on IoNT primarily address its architecture, communication methods, and domain-specific applications, yet overlook the critical role ML could play in enhancing IoNT’s capabilities–particularly in data processing, anomaly detection, and security. This survey addresses this gap by providing an in-depth analysis of IoNT-ML integration, reviewing state-of-the-art ML applications within IoNT, and systematically discussing the challenges that persist in this integration. Additionally, we propose future research directions, establishing a framework to guide advancements in IoNT through ML-driven solutions.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11211-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822040","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}

引用次数: 0

Power aggregation operators based on Aczel-Alsina T-norm and T-conorm for intuitionistic hesitant fuzzy information and their application to logistics service provider selection 基于Aczel-Alsina t -范数和t -符合性的直觉犹豫模糊信息权力聚合算子及其在物流服务商选择中的应用

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-11 DOI: 10.1007/s10462-025-11155-4

Peng Wang, Baoying Zhu, Keyan Yan, Ziyu Zhang, Zeeshan Ali, Dragan Pamucar

{"title":"Power aggregation operators based on Aczel-Alsina T-norm and T-conorm for intuitionistic hesitant fuzzy information and their application to logistics service provider selection","authors":"Peng Wang, Baoying Zhu, Keyan Yan, Ziyu Zhang, Zeeshan Ali, Dragan Pamucar","doi":"10.1007/s10462-025-11155-4","DOIUrl":"10.1007/s10462-025-11155-4","url":null,"abstract":"<div><p>Logistics service provider selection is a skilled and effective technique used to evaluate and identify third-party enterprises or organizations capable of managing and performing logistics tasks on behalf of a business. In this study, we aim to propose a logistics service provider selection method based on aggregation operators and fuzzy sets. First, we analyze the Aczel-Alsina operational laws building on the intuitionistic hesitant fuzzy sets technique, which combines hesitant fuzzy and intuitionistic fuzzy models. Subsequently, we derive power averaging/geometric aggregation operators for the intuitionistic hesitant fuzzy model based on Aczel-Alsina operational laws, called the intuitionistic hesitant fuzzy Aczel-Alsina power averaging operator, intuitionistic hesitant fuzzy Aczel-Alsina weighted power averaging operator, intuitionistic hesitant fuzzy Aczel-Alsina power geometric operator, and intuitionistic hesitant fuzzy Aczel-Alsina weighted power geometric operator, with their flexible and basic properties such as idempotency, monotonicity, and boundedness. The existing model of drastic aggregation operators, max–min aggregation operators, and algebraic aggregation operators are the special cases of the proposed theory. To address the selection of logistics service providers using the proposed operators, we explore the multi-attribute decision-making methods to evaluate the required problems. Finally, we compare the ranking results of the invented model with those of existing technologies to describe the effectiveness and stability of the proposed methods.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11155-4.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822039","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}

引用次数: 0

Speech emotion recognition in conversations using artificial intelligence: a systematic review and meta-analysis 人工智能对话中的语音情感识别：系统回顾与元分析

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-11 DOI: 10.1007/s10462-025-11197-8

Ghada Alhussein, Ioannis Ziogas, Shiza Saleem, Leontios J. Hadjileontiadis

{"title":"Speech emotion recognition in conversations using artificial intelligence: a systematic review and meta-analysis","authors":"Ghada Alhussein, Ioannis Ziogas, Shiza Saleem, Leontios J. Hadjileontiadis","doi":"10.1007/s10462-025-11197-8","DOIUrl":"10.1007/s10462-025-11197-8","url":null,"abstract":"<div><p> Manifestations of emotion in social conversational interactions stand at a focal point in the rapidly growing affective computing area, with applications in healthcare, education and human-computer interaction. Artificial intelligence (AI) holds great potential in modeling the challenging dynamic nature of affect in speech conversation. In this paper, we analyze and criticize latest trends and open problems through a systematic review and multi-subgroup meta-analysis of AI approaches for emotion recognition in conversation (ERC). We adopt the PRISMA-DTA guidelines toward analysis of AI-driven speech ERC. A comprehensive database search through predefined query strings and selection criteria allowed for data extraction of essential diagnostic performance parameters. We analyze salient patterns related to methodological quality and risk of bias. Univariate random-effects models are then designed with a multi-subgroup perspective, centered around affective annotations models, while encompassing the ERC parameters of modalities, feature extraction and conversation style. 51 studies were systematically reviewed for qualitative analysis, whereas 27 articles were included in the meta-analysis. Diagnostic test performance manifested with high heterogeneity, with intriguing insights regarding affective state annotation, input modality, feature extraction methods, and dataset conversation style. Our analysis raised concerns regarding bias, reporting quality and inter-rater reliability in annotations. Our research contributes fine-grained insights as recommendations that tackle open-problems in ERC. While providing valuable information on diagnostic performance of AI in speech ERC, we underscore the imperative need for further advancements in annotations and models capable of handling diverse emotional expressions. </p><p>Trial Registration: PROSPERO identifier - CRD42023416879.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11197-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143822042","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}

引用次数: 0

Dual representation learning for one-step clustering of multi-view data 多视图数据一步聚类的双表示学习

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-11 DOI: 10.1007/s10462-025-11183-0

Wei Zhang, Zhaohong Deng, Kup-Sze Choi, Jun Wang, Shitong Wang

{"title":"Dual representation learning for one-step clustering of multi-view data","authors":"Wei Zhang, Zhaohong Deng, Kup-Sze Choi, Jun Wang, Shitong Wang","doi":"10.1007/s10462-025-11183-0","DOIUrl":"10.1007/s10462-025-11183-0","url":null,"abstract":"<div><p>In real-world applications, multi-view data is widely available and multi-view learning is an effective method for mining multi-view data. In recent years, multi-view clustering, as an important part of multi-view learning, has been receiving more and more attention, while how to design an effective multi-view data mining method and make it more pertinent for clustering is still a challenging mission. For this purpose, a new one-step multi-view clustering method with dual representation learning is proposed in this paper. First, based on the fact that multi-view data contain both consistent knowledge between views and unique knowledge of each view, we propose a new dual representation learning method by improving the matrix factorization to explore them and to form common and specific representations. Then, we design a novel one-step multi-view clustering framework, which unifies the dual representation learning and multi-view clustering partition into one process. In this way, a mutual self-taught mechanism is developed in this framework and leads to more promising clustering performance. Finally, we also introduce the maximum entropy and orthogonal constraint to achieve optimal clustering results. Extensive experiments on seven real world multi-view datasets demonstrate the effectiveness of the proposed method.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11183-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821956","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}

引用次数: 0

An enhanced framework for real-time dense crowd abnormal behavior detection using YOLOv8 基于YOLOv8的密集人群异常行为实时检测的增强框架

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-04-11 DOI: 10.1007/s10462-025-11206-w

Rabia Nasir, Zakia Jalil, Muhammad Nasir, Tahani Alsubait, Maria Ashraf, Sadia Saleem

{"title":"An enhanced framework for real-time dense crowd abnormal behavior detection using YOLOv8","authors":"Rabia Nasir, Zakia Jalil, Muhammad Nasir, Tahani Alsubait, Maria Ashraf, Sadia Saleem","doi":"10.1007/s10462-025-11206-w","DOIUrl":"10.1007/s10462-025-11206-w","url":null,"abstract":"<div><p>Abnormal behavior detection in dense crowd, during the Hajj pilgrimage is vital to public security. Existing approaches face challenges due to factors like occlusions, illumination variations, and uniform attire. This research introduces the Crowd Anomaly Detection Framework (CADF), an improved YOLOv8-based model, integrating Soft-NMS to improve detection accuracy under complex conditions. CADF extensively evaluated on the Hajjv2 dataset, delivering an AUC of 88.27%, a 13.09% improvement over YOLOv2 and 12.19% over YOLOv5, with an Accuracy of 91.6%. To validate its generalizability, the framework is also tested on UCSD and ShanghaiTech datasets. Comparisons with state-of-the-art models, including VGG19 and EfficientDet, demonstrated CADF’s superiority in accuracy, AUC, precision, recall, and mAP metrics. By addressing the unique challenges of Hajj crowd and achieving strong performance across diverse datasets, CADF highlights its potential for real-time crowd anomaly detection, contributing to enhanced safety in large-scale public gatherings and aligning with Sustainable Development Goals 3 and 11.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 7","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11206-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821955","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}

引用次数: 0