Artificial Intelligence Review最新文献

{"title":"A systematic literature review on municipal solid waste management using machine learning and deep learning","authors":"Ishaan Dawar,&nbsp;Anisha Srivastava,&nbsp;Maanas Singal,&nbsp;Nirjara Dhyani,&nbsp;Suvi Rastogi","doi":"10.1007/s10462-025-11196-9","DOIUrl":"10.1007/s10462-025-11196-9","url":null,"abstract":"<div><p>Population growth and urbanization have led to a significant increase in solid waste. However, conventional methods of treating and recycling this waste have inherent problems, such as low efficiency, poor precision, high cost, and severe environmental hazards. To address these challenges, Artificial Intelligence (AI) has gained popularity in recent years as a potential solution for municipal solid-waste management (MSWM). A few applications of AI, based on Machine Learning (ML) and Deep Learning (DL) techniques, have been used for MSWM. This study reviews the current landscape in MSWM, highlighting the existing advantages and disadvantages of 69 studies published between 2018 and 2024 using the PRISMA methodology. The applications of ML and DL algorithms demonstrate their ability to enhance decision-making processes, improve resource recovery rates, and promote circular economy principles. Although these technologies offer promising solutions, challenges such as data availability, quality, and interdisciplinary collaboration hinder their effective implementation. The paper suggests future research directions focusing on developing robust datasets, fostering partnerships across sectors, and integrating advanced technologies with traditional waste management strategies. This research aligns with the United Nations’ Sustainable Development Goals (SDG), particularly Goal 11, which aims to make cities inclusive, safe, resilient, and sustainable. In the future, this research can contribute to making cities smarter, greener, and more resilient using ML and DL techniques.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11196-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143676512","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":"Emrnet: enhanced micro-expression recognition network with attention and distance correlation","authors":"Gaqiong Liu,&nbsp;Shucheng Huang,&nbsp;Gang Wang,&nbsp;Mingxing Li","doi":"10.1007/s10462-025-11159-0","DOIUrl":"10.1007/s10462-025-11159-0","url":null,"abstract":"<div><p>Micro-expression recognition (MER) is inherently challenging due to the difficulty of extracting subtle, localized changes in micro-expressions (MEs). Various optical flow-based methods have been proposed for MER, as optical flow can effectively suppress facial identity information while capturing the movement patterns of MEs. However, these methods, characterized by simple architectures, often fail to extract discriminative features, resulting in suboptimal performance. In this paper, we propose an Enhanced Micro-expression Recognition Network with attention and distance correlation (EMRNet) for MER. EMRNet consists of three key phases: First, we introduce a novel channel-wise region-aware attention mechanism within two identical Inception networks, designed to extract global and local expression features in parallel, based on the optical flow input of the same ME. Second, to enhance ME representations, we propose a regularized dilated loss function incorporating distance correlation, which improves the information entropy transferred between the two branches. Last, emotion categories are predicted by fusing the expression-dilated features in the classification branch. Extensive experiments conducted on the composite database from the MEGC 2019 challenge demonstrate the effectiveness of EMRNet under both leave-one-subject-out (LOSO) cross-validation and the composite database evaluation (CDE) protocol. The results show that our approach successfully generates discriminative features, achieving substantial performance gains. Furthermore, EMRNet outperforms existing single-stream and dual-stream models, delivering superior results in MER.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11159-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668026","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":"Bibliometric analysis of artificial intelligence cyberattack detection models","authors":"Blessing Guembe,&nbsp;Sanjay Misra,&nbsp;Ambrose Azeta,&nbsp;Ines Lopez-Baldominos","doi":"10.1007/s10462-025-11167-0","DOIUrl":"10.1007/s10462-025-11167-0","url":null,"abstract":"<div><p>Cybercriminals have increasingly adopted advanced and cutting-edge methods that expand the scale and speed of their attacks in recent years. This trend coincides with the rising demand for and scarcity of highly skilled cybersecurity specialists, making them both expensive and difficult to find. Recently, researchers have demonstrated the effectiveness of Artificial Intelligence (AI) approaches in combating sophisticated cyberattacks. However, comprehensive bibliometric data illustrating the study of AI approaches in cyberattack detection remain sparse. This study addresses this gap by investigating the current state of AI-based cyberattack detection research. The study analyzed the Scopus database using bibliometric analysis on a pool of over 2,338 articles published between 2014 and 2024, including 1217 journal articles, 828 conference papers, 121 conference reviews, 85 book chapters, 70 reviews, 5 editorials, and 2 books and short surveys. The study explores various AI-based cyberattack detection approaches globally, focusing on machine learning and deep learning algorithms. The bibliometric analysis was conducted using R, an open-source statistical tool, and Biblioshiny. The findings establish that AI, particularly machine learning and deep learning, enhances intrusion detection accuracy and is a growing research trend. Researchers have effectively employed these techniques for malware detection. The USA leads in AI cyberattack research, followed by India, China, Saudi Arabia, and Australia. Despite publishing fewer articles, Canada and Italy received significant citations. Additionally, strong research collaboration exists among the USA, China, Australia, Saudi Arabia, and India. Keyword analysis highlights AI’s effectiveness in identifying patterns and malicious behaviours, enhancing intrusion detection even in complex cyberattacks. Machine learning can detect intrusions based on anomalies caused by malicious or compromised devices, as well as unknown threats, with speed, accuracy, and a low false-positive rate.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11167-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668025","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 step gravitational search algorithm for function optimization and STTM’s synchronous feature selection-parameter optimization","authors":"Chaodong Fan,&nbsp;Laurence T. Yang,&nbsp;Leyi Xiao","doi":"10.1007/s10462-025-11193-y","DOIUrl":"10.1007/s10462-025-11193-y","url":null,"abstract":"<div><p>The support tensor train machine (STTM) can make full use of the correlation of tensor data structures, while the parameter training is inefficient and feature redundancy is large. For this, a step gravitational search algorithm (SGSA) is proposed and used for synchronous feature selection and parameter optimization of STTM in this paper. Since the single population structure of the gravitational search algorithm is difficult to balance exploration and exploitation effectively, a new dual population structure is defined by the step function. Subpopulation Pop1 focuses on exploration, and a <i>K</i>_<i>best</i><i>-Elite</i> hybrid learning strategy is designed to avoid the rapid decline of exploration ability due to the rapid reduction of the size of <i>K</i>_<i>best</i> set as well as the gravitational constant <i>G</i>. Subpopulation Pop2 focuses on exploitation, and a position update strategy that integrates Cauchy distribution and Gaussian distribution is designed to make Pop2 always have a certain exploration ability. Finally, use SGSA to solve the synchronous feature selection and parameter optimization problem of STTM (the resulting model is denoted as SGSA-STTM). The algorithm’s optimization performance test results show that SGSA can obtain relatively best results on most test functions compared with other state-of-the-art algorithms. The classification performance test on fMRI datasets shows that SGSA-STTM can remove more than 40% of redundant features on most datasets, which can effectively improve the efficiency of the algorithm, and the classification accuracy for the StarPlus fMRI dataset and the CMU Science 2008 fMRI dataset reached 60 and 70%, respectively.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11193-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143667953","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":"Dna coding theory and algorithms","authors":"Jin Xu,&nbsp;Wenbin Liu,&nbsp;Kai Zhang,&nbsp;Enqiang Zhu","doi":"10.1007/s10462-025-11132-x","DOIUrl":"10.1007/s10462-025-11132-x","url":null,"abstract":"<div><p>DNA computing is an emerging computational model that has garnered significant attention due to its distinctive advantages at the molecular biological level. Since it was introduced by Adelman in 1994, this field has made remarkable progress in solving <b>NP</b>-complete problems, enhancing information security, encrypting images, controlling diseases, and advancing nanotechnology. A key challenge in DNA computing is the design of DNA coding, which aims to minimize nonspecific hybridization and enhance computational reliability. The DNA coding design is a classical combinatorial optimization problem focused on generating high-quality DNA sequences that meet specific constraints, including distance, thermodynamics, secondary structure, and sequence requirements. This paper comprehensively examines the advances in DNA coding design, highlighting mathematical models, counting theory, and commonly used DNA coding methods. These methods include the template method, multi-objective evolutionary methods, and implicit enumeration techniques.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11132-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143668024","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":"Context in object detection: a systematic literature review","authors":"Mahtab Jamali,&nbsp;Paul Davidsson,&nbsp;Reza Khoshkangini,&nbsp;Martin Georg Ljungqvist,&nbsp;Radu-Casian Mihailescu","doi":"10.1007/s10462-025-11186-x","DOIUrl":"10.1007/s10462-025-11186-x","url":null,"abstract":"<div><p>Context is an important factor in computer vision as it offers valuable information to clarify and analyze visual data. Utilizing the contextual information inherent in an image or a video can improve the precision and effectiveness of object detectors. For example, where recognizing an isolated object might be challenging, context information can improve comprehension of the scene. This study explores the impact of various context-based approaches to object detection. Initially, we investigate the role of context in object detection and survey it from several perspectives. We then review and discuss the most recent context-based object detection approaches and compare them. Finally, we conclude by addressing research questions and identifying gaps for further studies. More than 265 publications are included in this survey, covering different aspects of context in different categories of object detection, including general object detection, video object detection, small object detection, camouflaged object detection, zero-shot, one-shot, and few-shot object detection. This literature review presents a comprehensive overview of the latest advancements in context-based object detection, providing valuable contributions such as a thorough understanding of contextual information and effective methods for integrating various context types into object detection, thus benefiting researchers.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11186-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645637","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":"LiDAR, IMU, and camera fusion for simultaneous localization and mapping: a systematic review","authors":"Zheng Fan,&nbsp;Lele Zhang,&nbsp;Xueyi Wang,&nbsp;Yilan Shen,&nbsp;Fang Deng","doi":"10.1007/s10462-025-11187-w","DOIUrl":"10.1007/s10462-025-11187-w","url":null,"abstract":"<div><p>Simultaneous Localization and Mapping (SLAM) is a crucial technology for intelligent unnamed systems to estimate their motion and reconstruct unknown environments. However, the SLAM systems with merely one sensor have poor robustness and stability due to the defects in the sensor itself. Recent studies have demonstrated that SLAM systems with multiple sensors, mainly consisting of LiDAR, camera, and IMU, achieve better performance due to the mutual compensation of different sensors. This paper investigates recent progress on multi-sensor fusion SLAM. The review includes a systematic analysis of the advantages and disadvantages of different sensors and the imperative of multi-sensor solutions. It categorizes multi-sensor fusion SLAM systems into four main types by the fused sensors: LiDAR-IMU SLAM, Visual-IMU SLAM, LiDAR-Visual SLAM, and LiDAR-IMU-Visual SLAM, with detailed analysis and discussions of their pipelines and principles. Meanwhile, the paper surveys commonly used datasets and introduces evaluation metrics. Finally, it concludes with a summary of the existing challenges and future opportunities for multi-sensor fusion SLAM.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11187-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645638","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 review of hyperspectral image classification based on graph neural networks","authors":"Xiaofeng Zhao,&nbsp;Junyi Ma,&nbsp;Lei Wang,&nbsp;Zhili Zhang,&nbsp;Yao Ding,&nbsp;Xiongwu Xiao","doi":"10.1007/s10462-025-11169-y","DOIUrl":"10.1007/s10462-025-11169-y","url":null,"abstract":"<div><p>Hyperspectral images provide rich spectral-spatial information but pose significant classification challenges due to high dimensionality, noise, mixed pixels, and limited labeled samples. Graph Neural Networks (GNNs) have emerged as a promising solution, offering a semi-supervised framework that can capture complex spatial-spectral relationships inherent in non-Euclidean hyperspectral image data. However, existing reviews often concentrate on specific aspects, thus limiting a comprehensive understanding of GNN-based hyperspectral image classification. This review systematically outlines the fundamental concepts of hyperspectral image classification and GNNs, and summarizes leading approaches from both traditional machine learning and deep learning. Then, it categorizes GNN-based methods into four paradigms: graph recurrent neural networks, graph convolutional networks, graph autoencoders, and hybrid graph neural networks, discussing their theoretical underpinnings, architectures, and representative applications. Finally, five key directions are further highlighted: adaptive graph construction, dynamic graph processing, deeper architectures, self-supervised strategies, and robustness enhancement. These insights aim to facilitate continued innovation in GNN-based hyperspectral imaging, guiding researchers toward more efficient and accurate classification frameworks.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11169-y.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632522","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 context and sequence-based recommendation framework using GRU networks","authors":"R. V. Karthik,&nbsp;V. Pandiyaraju,&nbsp;Sannasi Ganapathy","doi":"10.1007/s10462-025-11174-1","DOIUrl":"10.1007/s10462-025-11174-1","url":null,"abstract":"<div><p>Recommendation systems play a significant contribution in e-commerce for predicting the more relevant product to the customers based on their interests. The recommendation system refers to the user-item interaction and predicts the next item by considering the similar kind of user interest or item purchased. The context-aware and sequential recommendation is built to predict the interested product based on the current context and sequential behavior pattern interactions. To fulfill the customers’ requirements, this paper proposes a new hybrid personalized recommendation system framework called Target User Context Sequential Prediction Gated Recurrent Unit (TUCSP-GRU) using deep learning methods to recommend suitable products to the users based on their interests and context. The proposed system uses the newly calculated Target User Specific Product Rating (TUS-PR) score, the proposed TUS Gated Recurrent Unit (TUS-GRU) model, and the proposed Top-N item prediction method. Here, (i) the TUS-PR score is used to improve the product rating, (ii) the new TUS-GRU model is used to find the sequence purchase behavior pattern of customers by considering their long-term and short-term interests, and (iii) the proposed Top-N item dynamic prediction method is used to adjust the next interested item list based on the response using the back propagation continuous learning method. The experiment results of the TUCSP-GRU framework show better accuracy in predicting the interested and relevant products or items when compared to existing similar recommendation systems with respect to the standard evaluation metrics.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11174-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632517","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":"Polyp segmentation in medical imaging: challenges, approaches and future directions","authors":"Abdul Qayoom,&nbsp;Juanying Xie,&nbsp;Haider Ali","doi":"10.1007/s10462-025-11173-2","DOIUrl":"10.1007/s10462-025-11173-2","url":null,"abstract":"<div><p>Colorectal cancer has been considered as the third most dangerous disease among the most common cancer types. The early diagnosis of the polyps weakens the spread of colorectal cancer and is significant for more productive treatment. The segmentation of polyps from the colonoscopy images is very critical and significant to identify colorectal cancer. In this comprehensive study, we meticulously scrutinize research papers focused on the automated segmentation of polyps in clinical settings using colonoscopy images proposed in the past five years. Our analysis delves into various dimensions, including input data (datasets and preprocessing methods), model design (encompassing CNNs, transformers, and hybrid approaches), loss functions, and evaluation metrics. By adopting a systematic perspective, we examine how different methodological choices have shaped current trends and identify critical limitations that need to be addressed. To facilitate meaningful comparisons, we provide a detailed summary table of all examined works. Moreover, we offer in-depth future recommendations for polyp segmentation based on the insights gained from this survey study. We believe that our study will serve as a great resource for future researchers in the subject of polyp segmentation offering vital support in the development of novel methodologies.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 6","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11173-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143632518","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}