Artificial Intelligence Review最新文献

{"title":"A systematic review of multilingual numeral recognition systems","authors":"Meenal Jabde,&nbsp;Chandrashekhar H. Patil,&nbsp;Amol D. Vibhute,&nbsp;Jatinderkumar R. Saini","doi":"10.1007/s10462-025-11105-0","DOIUrl":"10.1007/s10462-025-11105-0","url":null,"abstract":"<div><p>Multilingual numeral recognition systems in online-offline environments play an essential role in several applications like banking or financial transactions, educational sectors, hospitals, etc. Several approaches have been proposed and executed for multilingual numeral recognition for various languages. This study systematically reviews eighty-four articles on the current research on multilingual numeral recognition in offline and online environments. According to the screening criteria, 489 relevant studies were retrieved from standard databases, and only 84 studies were used for further analysis based on the insertion and elimination measures. Our study investigates and analyzes the earlier approaches, datasets developed and utilized, and machine and deep learning methods applied in multilingual numeral recognition across different languages and handwritings. It also provides possible applications and challenges for future studies. Our analysis shows that some datasets are available for scientific research, but comprehensive multilingual datasets and cross-lingual models for multilingual recognition systems are urgently needed. In addition, this review finds that convolutional neural networks (CNN) and support vector machines (SVM) are mainly applied methods in multilingual numeral recognition due to their high recognition accuracy. The findings of this review will provide valuable insights for researchers directing the development of multilingual datasets and robust and effective systems for offline and online multilingual numeral recognition for several multilingual applications.</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/s10462-025-11105-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109421","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":"Comprehensive exploration of diffusion models in image generation: a survey","authors":"Hang Chen,&nbsp;Qian Xiang,&nbsp;Jiaxin Hu,&nbsp;Meilin Ye,&nbsp;Chao Yu,&nbsp;Hao Cheng,&nbsp;Lei Zhang","doi":"10.1007/s10462-025-11110-3","DOIUrl":"10.1007/s10462-025-11110-3","url":null,"abstract":"<div><p>The rapid development of deep learning technology has led to the emergence of diffusion models as a promising generative model with diverse applications. These include image generation, audio and video synthesis, molecular design, and text generation. The distinctive generation mechanism and exceptional generation quality of diffusion models have made them a valuable tool in these diverse fields. However, with the extensive deployment of diffusion models in the domain of image generation, concerns pertaining to data privacy, data security, and artistic ethics have emerged with increasing prominence. Given the accelerated pace of development in the field of diffusion models, the majority of extant surveys are deficient in two respects: firstly, they fail to encompass the latest advances in diffusion-based image synthesis; and secondly, they seldom consider the potential social implications of diffusion models. In order to address these issues, this paper presents a comprehensive survey of the most recent applications of diffusion models in the field of image generation. Furthermore, it provides an in-depth analysis of the potential social impacts that may result from their use. Firstly, this paper presents a systematic survey of the background principles and theoretical foundations of diffusion models. Subsequently, this paper provides a detailed examination of the most recent applications of diffusion models across a range of image generation subfields, including style transfer, image completion, image editing, super-resolution, and beyond. Finally, we present a comprehensive examination of these social issues, addressing data privacy concerns, such as the potential for data leakage and the implementation of protective measures during model training. We also analyse the risk of malicious exploitation of the model and the defensive strategies employed to mitigate such risks. Additionally, we examine the implications of the authenticity and originality of generated images on artistic creativity and copyright protection.</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/s10462-025-11110-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109473","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 comprehensive review of deep learning-based hyperspectral image reconstruction for agri-food quality appraisal","authors":"Md. Toukir Ahmed,&nbsp;Ocean Monjur,&nbsp;Alin Khaliduzzaman,&nbsp;Mohammed Kamruzzaman","doi":"10.1007/s10462-024-11090-w","DOIUrl":"10.1007/s10462-024-11090-w","url":null,"abstract":"<div><p>Hyperspectral imaging (HSI) has recently emerged as a promising tool for various agricultural applications. However, high equipment cost, instrumentation complexity, and data-intensive nature have limited its widespread adoption. To overcome these challenges, reconstructing hyperspectral data from simple, cost-effective color or RGB (red-green-blue) images using advanced deep learning algorithms offers a practically attractive solution for a wide range of applications in food quality control and assurance. Through advanced deep learning algorithms, it is possible to capture and reconstruct spectral information from simple, cost-effective RGB imaging to create a reliable, efficient, and scalable system with accuracy comparable to dedicated, expensive HSI systems. This review provides a comprehensive overview of recent advances in deep learning techniques for HSI reconstruction and highlights the transformative impact of deep learning-based hyperspectral image reconstruction on agricultural and food products and anticipates a future where these innovations will lead to more advanced and widespread applications in the agri-food industry.</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/s10462-024-11090-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109504","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":"Artificial intelligence in choroid through optical coherence tomography: a comprehensive review","authors":"Amrish Selvam,&nbsp;Matthew Driban,&nbsp;Joshua Ong,&nbsp;Sandeep Chandra Bollepalli,&nbsp;José-Alain Sahel,&nbsp;Jay Chhablani,&nbsp;Kiran Kumar Vupparaboina","doi":"10.1007/s10462-024-11067-9","DOIUrl":"10.1007/s10462-024-11067-9","url":null,"abstract":"<div><p>Vision-threatening conditions, such as age-related macular degeneration (AMD) and central serous chorioretinopathy (CSCR), arise from dysfunctions in the highly vascular choroid layer in the eye’s posterior segment. Optical coherence tomography (OCT) images play a crucial role in diagnosing choroidal structural changes in clinical practice. This review emphasizes the significant efforts in developing precise detection, quantification, and automated disease classification of choroidal biomarkers. The rapid progress of artificial intelligence (AI) has triggered transformative breakthroughs across sectors including medical image analysis. Recently, the integration of AI within the diagnosis and treatment of choroidal diseases has captured significant attention. Multiple studies highlight AI’s potential to enhance diagnostic precision and optimize clinical outcomes in this context. The review provides an extensive overview of AI’s current applications in choroidal analysis using OCT imaging. It encompasses a diverse array of algorithms and techniques employed for biomarker detection, such as thickness and vascularity index, and for identifying diseases like AMD and CSCR. The overarching goal of this review is to provide an updated and comprehensive exploration of AI’s impact on the choroid, highlighting its potential, challenges, and role in driving innovation in the 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/s10462-024-11067-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109467","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":"Metaheuristic optimization algorithms for multi-area economic dispatch of power systems: Part I—a comprehensive survey","authors":"Yang Wang,&nbsp;Guojiang Xiong","doi":"10.1007/s10462-024-11070-0","DOIUrl":"10.1007/s10462-024-11070-0","url":null,"abstract":"<div><p>Multi-area economic dispatch (MAED) provides an indispensable component for the security and economic operation of contemporary power systems. Over recent years, numerous metaheuristic optimization algorithms (MOAs) have surfaced for addressing the MAED problem. However, none of the literature to date conducted a comprehensive statistical research work on the MAED problem. In part I of this series, we present a comprehensive survey on this problem. (1) We collect all eleven reported MAED cases studied over the years. These cases have different structures, scales, and constraints. We illustrate the structures of all cases and provide their corresponding system parameters. (2) We collect all the MOA solution algorithms. These algorithms are inspired by different ways, and we categorize them in detail and review them comprehensively. (3) We list the detailed applications of MOAs on different cases and count the percentage of studies on each case. (4) Finally, we summarize the current research progress and point out the future research directions in terms of MAED models and solution methods, respectively. This survey provides an extensive overview of the MAED cases and its solution methods. It can provide applicable and reference suggestions for future research on the MAED problem.</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/s10462-024-11070-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109470","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":"Intelligent Cinematography: a review of AI research for cinematographic production","authors":"Adrian Azzarelli,&nbsp;Nantheera Anantrasirichai,&nbsp;David R. Bull","doi":"10.1007/s10462-024-11089-3","DOIUrl":"10.1007/s10462-024-11089-3","url":null,"abstract":"<div><p>This paper offers the first comprehensive review of artificial intelligence (AI) research in the context of real camera content acquisition for entertainment purposes and is aimed at both researchers and cinematographers. Addressing the lack of review papers in the field of <i>intelligent cinematography</i> (IC) and the breadth of related computer vision research, we present a holistic view of the IC landscape while providing technical insight, important for experts across disciplines. We provide technical background on generative AI, object detection, automated camera calibration and 3-D content acquisition, with references to assist non-technical readers. The application sections categorize work in terms of four production types: General Production, Virtual Production, Live Production and Aerial Production. Within each application section, we (1) sub-classify work according to research topic and (2) describe the trends and challenges relevant to each type of production. In the final chapter, we address the greater scope of IC research and summarize the significant potential of this area to influence the creative industries sector. We suggest that work relating to virtual production has the greatest potential to impact other mediums of production, driven by the growing interest in LED volumes/stages for in-camera virtual effects (ICVFX) and automated 3-D capture for virtual modeling of real world scenes and actors. We also address ethical and legal concerns regarding the use of creative AI that impact on artists, actors, technologists and the general public.</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/s10462-024-11089-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143109542","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":"Edge deep learning in computer vision and medical diagnostics: a comprehensive survey","authors":"Yiwen Xu,&nbsp;Tariq M. Khan,&nbsp;Yang Song,&nbsp;Erik Meijering","doi":"10.1007/s10462-024-11033-5","DOIUrl":"10.1007/s10462-024-11033-5","url":null,"abstract":"<div><p>Edge deep learning, a paradigm change reconciling edge computing and deep learning, facilitates real-time decision making attuned to environmental factors through the close integration of computational resources and data sources. Here we provide a comprehensive review of the current state of the art in edge deep learning, focusing on computer vision applications, in particular medical diagnostics. An overview of the foundational principles and technical advantages of edge deep learning is presented, emphasising the capacity of this technology to revolutionise a wide range of domains. Furthermore, we present a novel categorisation of edge hardware platforms based on performance and usage scenarios, facilitating platform selection and operational effectiveness. Following this, we dive into approaches to effectively implement deep neural networks on edge devices, encompassing methods such as lightweight design and model compression. Reviewing practical applications in the fields of computer vision in general and medical diagnostics in particular, we demonstrate the profound impact edge-deployed deep learning models can have in real-life situations. Finally, we provide an analysis of potential future directions and obstacles to the adoption of edge deep learning, with the intention to stimulate further investigations and advancements of intelligent edge deep learning solutions. This survey provides researchers and practitioners with a comprehensive reference shedding light on the critical role deep learning plays in the advancement of edge computing applications.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 3","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11033-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142994861","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 taxonomy of literature reviews and experimental study of deepreinforcement learning in portfolio management","authors":"Mohadese Rezaei,&nbsp;Hossein Nezamabadi-Pour","doi":"10.1007/s10462-024-11066-w","DOIUrl":"10.1007/s10462-024-11066-w","url":null,"abstract":"<div><p>Portfolio management involves choosing and actively overseeing various investment assets to meet an investor’s long-term financial goals, considering their risk tolerance and desired return potential. Traditional methods, like mean–variance analysis, often lack the flexibility needed to navigate the complexities of today’s financial markets. Recently, Deep Reinforcement Learning (DRL) has emerged as a promising approach, enabling continuous adjustments to investment strategies based on market feedback without explicit price predictions. This paper presents a comprehensive literature review of DRL applications in portfolio management, aimed at finance researchers, data scientists, AI experts, FinTech engineers, and students seeking advanced portfolio optimization methodologies. We also conducted an experimental study to evaluate five DRL algorithms—Advantage Actor-Critic (A2C), Deep Deterministic Policy Gradient (DDPG), Proximal Policy Optimization (PPO), Soft Actor-Critic (SAC), and Twin Delayed DDPG (TD3)—in managing a portfolio of 30 Dow Jones Industrial Average (DJIA) stocks. Their performance is compared with the DJIA index and traditional strategies, demonstrating DRL’s potential to improve portfolio outcomes while effectively managing risk.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 3","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11066-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142994962","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":"Deep learning and computer vision in plant disease detection: a comprehensive review of techniques, models, and trends in precision agriculture","authors":"Abhishek Upadhyay,&nbsp;Narendra Singh Chandel,&nbsp;Krishna Pratap Singh,&nbsp;Subir Kumar Chakraborty,&nbsp;Balaji M. Nandede,&nbsp;Mohit Kumar,&nbsp;A. Subeesh,&nbsp;Konga Upendar,&nbsp;Ali Salem,&nbsp;Ahmed Elbeltagi","doi":"10.1007/s10462-024-11100-x","DOIUrl":"10.1007/s10462-024-11100-x","url":null,"abstract":"<div><p>Plant diseases cause significant damage to agriculture, leading to substantial yield losses and posing a major threat to food security. Detection, identification, quantification, and diagnosis of plant diseases are crucial parts of precision agriculture and crop protection. Modernizing agriculture and improving production efficiency are significantly affected by using computer vision technology for crop disease diagnosis. This technology is notable for its non-destructive nature, speed, real-time responsiveness, and precision. Deep learning (DL), a recent breakthrough in computer vision, has become a focal point in agricultural plant protection that can minimize the biases of manually selecting disease spot features. This study reviews the techniques and tools used for automatic disease identification, state-of-the-art DL models, and recent trends in DL-based image analysis. The techniques, performance, benefits, drawbacks, underlying frameworks, and reference datasets of more than 278 research articles were analyzed and subsequently highlighted in accordance with the architecture of computer vision and deep learning models. Key findings include the effectiveness of imaging techniques and sensors like RGB, multispectral, and hyperspectral cameras for early disease detection. Researchers also evaluated various DL architectures, such as convolutional neural networks, vision transformers, generative adversarial networks, vision language models, and foundation models. Moreover, the study connects academic research with practical agricultural applications, providing guidance on the suitability of these models for production environments. This comprehensive review offers valuable insights into the current state and future directions of deep learning in plant disease detection, making it a significant resource for researchers, academicians, and practitioners in precision agriculture.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 3","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11100-x.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142994860","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":"Advancements in securing federated learning with IDS: a comprehensive review of neural networks and feature engineering techniques for malicious client detection","authors":"Naila Latif,&nbsp;Wenping Ma,&nbsp;Hafiz Bilal Ahmad","doi":"10.1007/s10462-024-11082-w","DOIUrl":"10.1007/s10462-024-11082-w","url":null,"abstract":"<div><p>Federated Learning (FL) is a technique that can learn a global machine-learning model at a central server by aggregating locally trained models. This distributed machine-learning approach preserves the privacy of local models. However, FL systems are inherently vulnerable to significant security challenges such as cyber-attacks, handling non-independent and identically distributed (non-IID) data, and data privacy concerns. This systematic literature review addresses these issues by examining advanced neural network models, feature engineering methods, and privacy-preserving techniques within intrusion detection systems (IDS) for FL environments. These are key elements for improving the security of FL systems. To the best of our knowledge, this review is among the first to comprehensively explore the combined impacts of these technologies. We analyzed 88 studies published between 2021 and October 2024. This study offers valuable insights for future research directions, including scaling FL in a real-world environment.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 3","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11082-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142963029","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}