Artificial Intelligence Review最新文献_第9页

Deep learning techniques for atmospheric turbulence removal: a review

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-01-25 DOI: 10.1007/s10462-024-11086-6

Paul Hill, Nantheera Anantrasirichai, Alin Achim, David Bull

{"title":"Deep learning techniques for atmospheric turbulence removal: a review","authors":"Paul Hill, Nantheera Anantrasirichai, Alin Achim, David Bull","doi":"10.1007/s10462-024-11086-6","DOIUrl":"10.1007/s10462-024-11086-6","url":null,"abstract":"<div><p>Atmospheric turbulence significantly complicates the interpretation and analysis of images by distorting them, making it hard to classify and track objects within a scene using traditional methods. This distortion arises from unpredictable, spatially varying disturbances, challenging the effectiveness of standard model-based techniques. These methods often become impractical due to their complexity and high memory demands, further complicating the task of restoring scenes affected by atmospheric turbulence. Deep learning approaches offer faster operation and are capable of implementation on small devices. This paper reviews the characteristics of atmospheric turbulence and its impact on acquired imagery. It compares performances of a range of state-of-the-art deep neural networks, including Transformers, SWIN and MAMBA, when used to mitigate spatio-temporal image distortions. Furthermore, this review presents: a list of available datasets; applicable metrics for evaluation of mitigation methods; an exhaustive list of state-of-the-art and historical mitigation methods. Finally, a critical statistical analysis of a range of example models is included. This review provides a roadmap of how datasets and metrics together with currently used and newly developed deep learning methods could be used to develop the next generation of turbulence mitigation techniques.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 4","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11086-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109420","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

Artificial intelligence advances in anomaly detection for telecom networks

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-01-25 DOI: 10.1007/s10462-025-11108-x

Enerst Edozie, Aliyu Nuhu Shuaibu, Bashir Olaniyi Sadiq, Ukagwu Kelechi John

{"title":"Artificial intelligence advances in anomaly detection for telecom networks","authors":"Enerst Edozie, Aliyu Nuhu Shuaibu, Bashir Olaniyi Sadiq, Ukagwu Kelechi John","doi":"10.1007/s10462-025-11108-x","DOIUrl":"10.1007/s10462-025-11108-x","url":null,"abstract":"<div><p>Telecommunication networks are becoming increasingly dynamic and complex due to the massive amounts of data they process. As a result, detecting abnormal events within these networks is essential for maintaining security and ensuring seamless operation. Traditional methods of anomaly detection, which rely on rule-based systems, are no longer effective in today’s fast-evolving telecom landscape. Thus, making AI useful in addressing these shortcomings. This review critically examines the role of Artificial Intelligence (AI), particularly deep learning, in modern anomaly detection systems for telecom networks. It explores the evolution from early strategies to current AI-driven approaches, discussing the challenges, the implementation of machine learning algorithms, and practical case studies. Additionally, emerging AI technologies such as Generative Adversarial Networks (GANs) and Reinforcement Learning (RL) are highlighted for their potential to enhance anomaly detection. This review provides AI’s transformative impact on telecom anomaly detection, addressing challenges while leveraging 5G/6G, edge computing, and the Internet of Things (IoT). It recommends hybrid models, advanced data preprocessing, and self-adaptive systems to enhance robustness and reliability, enabling telecom operators to proactively manage anomalies and optimize performance in a data driven environment.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 4","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11108-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109471","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

Advancing cancer diagnosis and treatment: integrating image analysis and AI algorithms for enhanced clinical practice

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-01-25 DOI: 10.1007/s10462-025-11117-w

Hamid Reza Saeidnia, Faezeh Firuzpour, Marcin Kozak, Hooman Soleymani majd

{"title":"Advancing cancer diagnosis and treatment: integrating image analysis and AI algorithms for enhanced clinical practice","authors":"Hamid Reza Saeidnia, Faezeh Firuzpour, Marcin Kozak, Hooman Soleymani majd","doi":"10.1007/s10462-025-11117-w","DOIUrl":"10.1007/s10462-025-11117-w","url":null,"abstract":"<div><p>Cancer screening and diagnosis with the utilization of innovative Artificial Intelligence tools improved the treatment strategies and patients’ survival. With the rapid development of imaging technologies and the rise of artificial intelligence (AI), there is a significant opportunity to improve cancer diagnostics through the combination of image analysis and AI algorithms. This article provides a comprehensive review of studies that have investigated the application of AI-assisted image processing in cancer diagnosis. We searched the Web of Science and Scopus databases to identify relevant studies published between 2014 and January 2024. The search strategy utilized targeted keywords such as cancer diagnostics, image analysis, artificial intelligence, and advanced imaging techniques. We limited the review to articles written in English and using AI-assisted image processing in cancer diagnosis. The results show that by leveraging machine learning algorithms, including deep learning, computer-aided diagnosis systems have been developed that are efficient in detecting tumors, thereby facilitating early cancer detection. Additionally, various authors have explored the integration of personalized treatment approaches and precision medicine, allowing for the development of treatment plans tailored to individual patient characteristics and needs. The review emphasizes the potential of AI-assisted image processing in revolutionizing cancer diagnostics. The insights gained from this study contribute to the current understanding of the field and pave the way for future research and development aimed at advancing cancer diagnostics using image analysis and artificial intelligence.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 4","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11117-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109422","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 experimental survey of imbalanced learning algorithms for bankruptcy prediction

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-01-25 DOI: 10.1007/s10462-025-11107-y

Peter Gnip, Róbert Kanász, Martin Zoričak, Peter Drotár

{"title":"An experimental survey of imbalanced learning algorithms for bankruptcy prediction","authors":"Peter Gnip, Róbert Kanász, Martin Zoričak, Peter Drotár","doi":"10.1007/s10462-025-11107-y","DOIUrl":"10.1007/s10462-025-11107-y","url":null,"abstract":"<div><p>Information about imminent bankruptcy is crucial for financial institutions, decision-making managers, and state agencies. Since bankruptcy prediction is a prevalent research topic, many new methods have been continuously proposed. Bankruptcy prediction is frequently approached as a binary classification task. Since bankruptcy datasets are inherently imbalanced, bankruptcy classification is usually performed using class imbalance learning methods. The nature of these methods is very diverse, but they can usually be categorized as ensemble, cost-sensitive, sampling, and hybrid methods. In this paper, we provide a comprehensive experimental comparison of 45 methods. These methods were selected because they cover the approaches and algorithms frequently employed for bankruptcy prediction and imbalanced learning. Extensive experiments on 15 publicly available datasets with different imbalance ratios showed that the methods based on a combination of ensemble learning and undersampling are able to handle data imbalance and achieve the best results for bankruptcy classification.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 4","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11107-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109423","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

Intelligent strategy for severity scoring of skin diseases based on clinical decision-making thinking with lesion-aware transformer

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-01-25 DOI: 10.1007/s10462-024-11083-9

Kai Huang, Kai Sun, Jiayi Li, Zhe Wu, Xian Wu, Yuping Duan, Xiang Chen, Shuang Zhao

{"title":"Intelligent strategy for severity scoring of skin diseases based on clinical decision-making thinking with lesion-aware transformer","authors":"Kai Huang, Kai Sun, Jiayi Li, Zhe Wu, Xian Wu, Yuping Duan, Xiang Chen, Shuang Zhao","doi":"10.1007/s10462-024-11083-9","DOIUrl":"10.1007/s10462-024-11083-9","url":null,"abstract":"<div><p>Skin diseases are numerous in types and high in incidence, posing a serious threat to human health. Accurately assessing the severity of skin diseases helps dermatologists in making personalized treatment decisions. However, focusing solely on the skin lesion itself and ignoring the true state of the surrounding skin can lead to distorted results. Assessing the severity of the condition should be a holistic process. Specifically, dermatologists need to compare the abnormal skin with surrounding skin to conduct the diagnosis. To imitate such diagnosis practice of dermatologists, we propose LSATrans, a Transformer based framework customized for severity scoring of skin diseases. Different from the Standard Self-Attention module, we propose the Lesion-aware Self-Attention (LSA) module. LSA can capture the visual features of both lesion and normal surrounding skin areas and include their relationship in modeling. In addition to LSA, the proposed LSATrans also introduces a contrastive learning strategy for further optimization. We first evaluated the performance of LSATrans in scar, atopic dermatitis, and psoriasis scoring tasks, and it achieved mean absolute errors of 0.5895, 0.5614, and 0.5416 respectively in these three tasks. Furthermore, we conducted additional validation of LSATrans’s performance in two distinct skin disease diagnosis tasks, where it demonstrated remarkable outcomes with AUCs of 0.9774 and 0.9801, respectively, in the classification of common skin diseases and subtypes of skin diseases. These results are better than existing methods, indicating that LSATrans is expected to become a universal, accurate and objective intelligent tool for scoring the severity of skin diseases.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 4","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11083-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109344","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

Transfer learning in agriculture: a review

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-01-25 DOI: 10.1007/s10462-024-11081-x

Md Ismail Hossen, Mohammad Awrangjeb, Shirui Pan, Abdullah Al Mamun

{"title":"Transfer learning in agriculture: a review","authors":"Md Ismail Hossen, Mohammad Awrangjeb, Shirui Pan, Abdullah Al Mamun","doi":"10.1007/s10462-024-11081-x","DOIUrl":"10.1007/s10462-024-11081-x","url":null,"abstract":"<div><p>The rapid growth of the global population has placed immense pressure on agriculture to enhance food production while addressing environmental and socioeconomic challenges such as biodiversity loss, water scarcity, and climate variability. Addressing these challenges requires adopting modern techniques and advancing agricultural research. Although some techniques, such as machine learning and deep learning, are increasingly used in agriculture, progress is constrained by the lack of large labelled datasets. This constraint arises because collecting data is often time-consuming, labour-intensive, and requires expert knowledge for data annotation. To mitigate data limitations, transfer learning (TL) offers a viable solution by allowing pre-trained models to be adapted for agricultural applications. Many researchers have demonstrated TL’s potential to advance agriculture. Despite its importance, there is a lack of a comprehensive review, which could be essential to guide researchers in this field. Given the significance and the lack of a review paper, this paper provides a review dedicated to TL in agriculture, offering three main contributions. First, we provide an in-depth background study on TL and its applications in agriculture. Second, we offer a comprehensive examination of TL-based agricultural applications, covering pre-trained models, dataset sources, input image types, implementation platforms, and TL approaches. Third, based on an exploration of the existing studies, we identify the challenges faced when applying TL in agriculture. Finally, to address the identified challenges, we recommend suggestions for future research directions.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 4","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11081-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109468","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

HFA-Net: hybrid feature-aware network for large-scale point cloud semantic segmentation

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-01-25 DOI: 10.1007/s10462-025-11111-2

Changji Wen, Long Zhang, Junfeng Ren, Rundong Hong, Chenshuang Li, Ce Yang, Yanfeng Lv, Hongbing Chen, Ning yang

{"title":"HFA-Net: hybrid feature-aware network for large-scale point cloud semantic segmentation","authors":"Changji Wen, Long Zhang, Junfeng Ren, Rundong Hong, Chenshuang Li, Ce Yang, Yanfeng Lv, Hongbing Chen, Ning yang","doi":"10.1007/s10462-025-11111-2","DOIUrl":"10.1007/s10462-025-11111-2","url":null,"abstract":"<div><p>Semantic segmentation of large-scale point clouds in 3D computer vision is a challenging problem. Existing feature extraction modules often emphasize learning local geometry while not giving adequate consideration to the integration of color information. This limitation prevents the network from thoroughly learning local features, thereby impacting segmentation accuracy. In this study, we propose three modules for robust feature extraction and aggregation, forming a novel point cloud segmentation network (HFA-Net) for large-scale point cloud semantic segmentation. First, we introduce the Hybrid Feature Extraction Component (HFEC) and the Hybrid Bilateral Enhancement Component (HBAC) to comprehensively extract and enhance the geometric, color, and semantic information of point clouds. Second, we incorporate the Ternary-Distance Attention Pooling (TDAP) module, which leverages trilateral distances to further refine the network’s focus on various features, enabling it to emphasize both locally important features and broader local neighborhoods. These modules are stacked into dense residual components to expand the network’s receptive field. Our experiments on several large-scale benchmark datasets, including Semantic3D, Toronto3D, S3DIS and LASDU demonstrate the effectiveness of HFA-Net when compared to state-of-the-art networks.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 4","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11111-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109472","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

Adaptive graph diffusion networks: compact and expressive GNNs with large receptive fields

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-01-25 DOI: 10.1007/s10462-025-11114-z

Chuxiong Sun, Muhan Zhang, Jie Hu, Hongming Gu, Jinpeng Chen, Mingchuan Yang

引用次数: 0

Topological numbers in uniform intuitionistic fuzzy environment and their application in neural network

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-01-25 DOI: 10.1007/s10462-024-10965-2

Siti Norziahidayu Amzee Zamri, Haseeb Ahmad, Muhammad Azeem, Bandar Almohsen

{"title":"Topological numbers in uniform intuitionistic fuzzy environment and their application in neural network","authors":"Siti Norziahidayu Amzee Zamri, Haseeb Ahmad, Muhammad Azeem, Bandar Almohsen","doi":"10.1007/s10462-024-10965-2","DOIUrl":"10.1007/s10462-024-10965-2","url":null,"abstract":"<div><p>Intuitionistic Fuzzy sets combine the ideas of uniformity, membership, and non-membership grades of the elements. Similarly, Intuitionistic Fuzzy Graphs are the generalization of simple fuzzy graphs. Depending on the uniformity of the fuzzy graphs (USIF) they can be categorized in different ways via membership values. From the idea of uniform fuzzy topological indices, we have developed the concepts of uniform intuitionistic fuzzy topological indices for uniform intuitionistic fuzzy graphs. This idea provides a more adaptable and nuanced representation of structural properties in graphs or networks. According to the theory of fuzzy topological indices, the importance of topological indices changes depending on the circumstance and the specific problem at hand. When the interactions between nodes are uncertain but not always hesitant, fuzzy graph theory and its adjusted topological indices are sufficient to capture and assess the underlying structure. In such cases where uncertainty is more complicated and hesitation is a major problem then there are better ways to address by intuitionistic fuzzy graph theory and the topological indices that go along with it. This article, developed the concept of uniform intuitionistic fuzzy graphs afresh and proposed Intuitionistic Fuzzy Topological Indices. We determine these indices using the topological indices and labeling of crisp graphs, rather than relying on the degrees of intuitionistic fuzzy graphs and edge portions. This approach is then applied to find intuitionistic fuzzy topological indices. Also, we have provided the MATLAB algorithm to illustrate the concept of IF labeling of cellular neural networks of any order. An example is given to explain the idea and approach towards one kind of uniform intuitionistic fuzzy graph represented by Cellular Neural Networks and graphical plots of the indices involved are also made.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 4","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10965-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109469","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

Artificial intelligence in four-dimensional imaging for motion management in radiation therapy

IF 10.7 2区计算机科学

Artificial Intelligence Review Pub Date : 2025-01-25 DOI: 10.1007/s10462-025-11109-w

Wang Yinghui, Xiao Haonan, Wang Jing, Wang Lu, Li Wen, Jiang Zhuoran, Ren Ge, Zhi Shaohua, Qian Josh, Dai Jianrong, Men Kuo, Ren Lei, Yang Xiaofeng, Li Tian, Cai Jing

{"title":"Artificial intelligence in four-dimensional imaging for motion management in radiation therapy","authors":"Wang Yinghui, Xiao Haonan, Wang Jing, Wang Lu, Li Wen, Jiang Zhuoran, Ren Ge, Zhi Shaohua, Qian Josh, Dai Jianrong, Men Kuo, Ren Lei, Yang Xiaofeng, Li Tian, Cai Jing","doi":"10.1007/s10462-025-11109-w","DOIUrl":"10.1007/s10462-025-11109-w","url":null,"abstract":"<div><p>Four-dimensional imaging (4D-imaging) plays a critical role in achieving precise motion management in radiation therapy. However, challenges remain in 4D-imaging such as a long imaging time, suboptimal image quality, and inaccurate motion estimation. With the tremendous success of artificial intelligence (AI) in the image domain, particularly deep learning, there is great potential in overcoming these challenges and improving the accuracy and efficiency of 4D-imaging without the need for hardware modifications. In this review, we provide a comprehensive overview of how these AI-based methods could drive the evolution of 4D-imaging for motion management. We discuss the inherent issues associated with multiple 4D modalities and explore the current research progress of AI in 4D-imaging. Furthermore, we delve into the unresolved challenges and limitations in 4D-imaging and provide insights into the future direction of this field.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 4","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11109-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109393","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