Artificial Intelligence Review最新文献

{"title":"A review of Artificial Intelligence methods in bladder cancer: segmentation, classification, and detection","authors":"Ayah Bashkami,&nbsp;Ahmad Nasayreh,&nbsp;Sharif Naser Makhadmeh,&nbsp;Hasan Gharaibeh,&nbsp;Ahmed Ibrahim Alzahrani,&nbsp;Ayed Alwadain,&nbsp;Jia Heming,&nbsp;Absalom E. Ezugwu,&nbsp;Laith Abualigah","doi":"10.1007/s10462-024-10953-6","DOIUrl":"10.1007/s10462-024-10953-6","url":null,"abstract":"<div><p>Artificial intelligence (AI) and other disruptive technologies can potentially improve healthcare across various disciplines. Its subclasses, artificial neural networks, deep learning, and machine learning, excel in extracting insights from large datasets and improving predictive models to boost their utility and accuracy. Though research in this area is still in its early phases, it holds enormous potential for the diagnosis, prognosis, and treatment of urological diseases, such as bladder cancer. The long-used nomograms and other classic forecasting approaches are being reconsidered considering AI’s capabilities. This review emphasizes the coming integration of artificial intelligence into healthcare settings while critically examining the most recent and significant literature on the subject. This study seeks to define the status of AI and its potential for the future, with a special emphasis on how AI can transform bladder cancer diagnosis and treatment.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10953-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452994","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 survey of recent approaches to form understanding in scanned documents","authors":"Abdelrahman Abdallah,&nbsp;Daniel Eberharter,&nbsp;Zoe Pfister,&nbsp;Adam Jatowt","doi":"10.1007/s10462-024-11000-0","DOIUrl":"10.1007/s10462-024-11000-0","url":null,"abstract":"<div><p>This paper presents a comprehensive survey of over 100 research works on the topic of form understanding in the context of scanned documents. We delve into recent advancements and breakthroughs in the field, with particular focus on transformer-based models, which have been shown to improve performance in form understanding tasks by up to 25% in accuracy compared to traditional methods. Our research methodology involves an in-depth analysis of popular documents and trends over the last decade, including 15 state-of-the-art models and 10 benchmark datasets. By examining these works, we offer novel insights into the evolution of this domain. Specifically, we highlight how transformers have revolutionized form-understanding techniques by enhancing the ability to process noisy scanned documents with significant improvements in OCR accuracy. Furthermore, we present an overview of the most relevant datasets, such as FUNSD, CORD, and SROIE, which serve as benchmarks for evaluating the performance of the models. By comparing the capabilities of these models and reporting an average improvement of 10–15% in key form extraction tasks, we aim to provide researchers and practitioners with useful guidance in selecting the most suitable solutions for their form understanding applications.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11000-0.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142452996","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":"Tire wear monitoring using feature fusion and CatBoost classifier","authors":"C. V. Prasshanth,&nbsp;V. Sugumaran","doi":"10.1007/s10462-024-10999-6","DOIUrl":"10.1007/s10462-024-10999-6","url":null,"abstract":"<div><p>Addressing the critical issue of tire wear is essential for enhancing vehicle safety, performance, and maintenance. Worn-out tires often lead to accidents, underscoring the need for effective monitoring systems. This study is vital for several reasons: safety, as worn tires increase the risk of accidents due to reduced traction and longer braking distances; performance, as uneven tire wear affects vehicle handling and fuel efficiency; maintenance costs, as early detection can prevent more severe damage to suspension and alignment systems; and regulatory compliance, as ensuring tire integrity helps meet safety regulations imposed by transportation authorities. In response, this study systematically evaluates tire conditions at 25%, 50%, 75%, and 100% wear, with an intact tire as a reference, using vibration signals as the primary data source. The analysis employs statistical, histogram, and autoregressive–moving-average (ARMA) feature extraction techniques, followed by feature selection to identify key parameters influencing tire wear. CatBoost is used for feature classification, leveraging its adaptability and efficiency in distinguishing varying wear patterns. Additionally, the study incorporates feature fusion to combine different types of features for a more comprehensive analysis. The proposed methodology not only offers a robust framework for accurately classifying tire wear levels but also holds significant potential for real-time implementation, contributing to proactive maintenance practices, prolonged tire lifespan, and overall vehicular safety.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10999-6.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451092","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":"Clarity in complexity: how aggregating explanations resolves the disagreement problem","authors":"Oana Mitruț,&nbsp;Gabriela Moise,&nbsp;Alin Moldoveanu,&nbsp;Florica Moldoveanu,&nbsp;Marius Leordeanu,&nbsp;Livia Petrescu","doi":"10.1007/s10462-024-10952-7","DOIUrl":"10.1007/s10462-024-10952-7","url":null,"abstract":"<div><p>The Rashômon Effect, applied in Explainable Machine Learning, refers to the disagreement between the explanations provided by various attribution explainers and to the dissimilarity across multiple explanations generated by a particular explainer for a single instance from the dataset (differences between feature importances and their associated signs and ranks), an undesirable outcome especially in sensitive domains such as healthcare or finance. We propose a method inspired from textual-case based reasoning for aligning explanations from various explainers in order to resolve the disagreement and dissimilarity problems. We iteratively generated a number of 100 explanations for each instance from six popular datasets, using three prevalent feature attribution explainers: LIME, Anchors and SHAP (with the variations Tree SHAP and Kernel SHAP) and consequently applied a global cluster-based aggregation strategy that quantifies alignment and reveals similarities and associations between explanations. We evaluated our method by weighting the <span>(:k)</span>-NN algorithm with agreed feature overlap explanation weights and compared it to a non-weighted <span>(:k)</span>-NN predictor, having as task binary classification. Also, we compared the results of the weighted <span>(:k)</span>-NN algorithm using aggregated feature overlap explanation weights to the weighted <span>(:k)</span>-NN algorithm using weights produced by a single explanation method (either LIME, SHAP or Anchors). Our global alignment method benefited the most from a hybridization with feature importance scores (information gain), that was essential for acquiring a more accurate estimate of disagreement, for enabling explainers to reach a consensus across multiple explanations and for supporting effective model learning through improved classification performance.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10952-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142451093","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":"Controllable image synthesis methods, applications and challenges: a comprehensive survey","authors":"Shanshan Huang,&nbsp;Qingsong Li,&nbsp;Jun Liao,&nbsp;Shu Wang,&nbsp;Li Liu,&nbsp;Lian Li","doi":"10.1007/s10462-024-10987-w","DOIUrl":"10.1007/s10462-024-10987-w","url":null,"abstract":"<div><p>Controllable Image Synthesis (CIS) is a methodology that allows users to generate desired images or manipulate specific attributes of images by providing precise input conditions or modifying latent representations. In recent years, CIS has attracted considerable attention in the field of image processing, with significant advances in consistency, controllability and harmony. However, several challenges still remain, particularly regarding the fine-grained controllability and interpretability of synthesized images. In this paper, we comprehensively and systematically review the CIS from problem definition, taxonomy and evaluation systems to existing challenges and future research directions. First, the definition of CIS is given, and several representative deep generative models are introduced in detail. Second, the existing CIS methods are divided into three categories according to the different control manners used and discuss the typical work in each category critically. Furthermore, we introduce the public datasets and evaluation metrics commonly used in image synthesis and analyze the representative CIS methods. Finally, we present several open issues and discuss the future research direction of CIS.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10987-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142447318","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":"The atmospheric boundary layer: a review of current challenges and a new generation of machine learning techniques","authors":"Linda Canché-Cab,&nbsp;Liliana San-Pedro,&nbsp;Bassam Ali,&nbsp;Michel Rivero,&nbsp;Mauricio Escalante","doi":"10.1007/s10462-024-10962-5","DOIUrl":"10.1007/s10462-024-10962-5","url":null,"abstract":"<div><p>Atmospheric boundary layer (ABL) structure and dynamics are important aspects to consider in human health. The ABL is characterized by a high degree of spatial and temporal variability that hinders their understanding. This paper aims to provide a comprehensive overview of machine learning (ML) methodologies, encompassing deep learning and ensemble approaches, within the scope of ABL research. The goal is to highlight the challenges and opportunities of using ML in turbulence modeling and parameterization in areas such as atmospheric pollution, meteorology, and renewable energy. The review emphasizes the validation of results to ensure their reliability and applicability. ML has proven to be a valuable tool for understanding and predicting how ABL spatial and seasonal variability affects pollutant dispersion and public health. In addition, it has been demonstrated that ML can be used to estimate several variables and parameters, such as ABL height, making it a promising approach to enhance air quality management and urban planning.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10962-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443223","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":"Surface defect inspection of industrial products with object detection deep networks: a systematic review","authors":"Yuxin Ma,&nbsp;Jiaxing Yin,&nbsp;Feng Huang,&nbsp;Qipeng Li","doi":"10.1007/s10462-024-10956-3","DOIUrl":"10.1007/s10462-024-10956-3","url":null,"abstract":"<div><p>One of the focal points in industrial product defect detection lies in the utilization of deep learning-based object detection algorithms. With the continuous introduction of these algorithms and their refined models, notable achievements have been attained. However, challenges persist in industrial settings, such as substantial variations in defect scales, the delicate balance between accuracy and speed, and the detection of small objects. Various methods have been proposed to address these challenges and propel the advancement of defect detection. To comprehensively review the latest developments in deep learning-based industrial product defect detection algorithms and foster further progress, this paper encompasses typical datasets and evaluation metrics used in industrial product defect detection, traces the development history of supervised one-stage and two-stage object detection algorithm-based and unsupervised algorithm-based industrial defect detection methods, discusses major challenges, and outlines future directions. It highlights the potential for further improving the accuracy, speed, and reliability of defect detection systems in industrial applications.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10956-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443225","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":"Recent applications and advances of African Vultures Optimization Algorithm","authors":"Abdelazim G. Hussien,&nbsp;Farhad Soleimanian Gharehchopogh,&nbsp;Anas Bouaouda,&nbsp;Sumit Kumar,&nbsp;Gang Hu","doi":"10.1007/s10462-024-10981-2","DOIUrl":"10.1007/s10462-024-10981-2","url":null,"abstract":"<div><p>The African Vultures Optimization Algorithm (AVOA) is a recently developed meta-heuristic algorithm inspired by the foraging behavior of African vultures in nature. This algorithm has gained attention due to its simplicity, flexibility, and effectiveness in tackling many optimization problems. The significance of this review lies in its comprehensive examination of the AVOA’s development, core principles, and applications. By analyzing 112 studies, this review highlights the algorithm’s versatility and the growing interest in enhancing its performance for real-world optimization challenges. This review methodically explores the evolution of AVOA, investigating proposed improvements that enhance the algorithm’s ability to adapt to various search geometries in optimization problems. Additionally, it introduces the AVOA solver, detailing its functionality and application in different optimization scenarios. The review demonstrates the AVOA’s effectiveness, particularly its unique weighting mechanism, which mimics vulture behavior during the search process. The findings underscore the algorithm’s robustness, ease of use, and lack of dependence on derivative information. The review also critically evaluates the AVOA’s convergence behavior, identifying its strengths and limitations. In conclusion, the study not only consolidates the existing knowledge on AVOA but also proposes directions for future research, including potential adaptations and enhancements to address its limitations. The insights gained from this review offer valuable guidance for researchers and practitioners seeking to apply or improve the AVOA in various optimization tasks.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10981-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443224","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":"An efficient propositional system for Abductive Logic Programming","authors":"Marco Gavanelli,&nbsp;Pascual Julián-Iranzo,&nbsp;Fernando Sáenz-Pérez","doi":"10.1007/s10462-024-10928-7","DOIUrl":"10.1007/s10462-024-10928-7","url":null,"abstract":"<div><p>Abductive logic programming (ALP) extends logic programming with hypothetical reasoning by means of abducibles, an extension able to handle interesting problems, such as diagnosis, planning, and verification with formal methods. Implementations of this extension have been using Prolog meta-interpreters and Prolog programs with Constraint Handling Rules (<span>CHR</span>). While the latter adds a clean and efficient interface to the host system, it still suffers in performance for large programs. Here, the concern is to obtain a more performant implementation of the <span>SCIFF</span> system following a compiled approach. This paper, as a first step in this long term goal, sets out a propositional ALP system following <span>SCIFF</span>, eliminating the need for <span>CHR</span> and achieving better performance.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10928-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443351","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":"An adaptive snow ablation-inspired particle swarm optimization with its application in geometric optimization","authors":"Gang Hu,&nbsp;Yuxuan Guo,&nbsp;Weiguo Zhao,&nbsp;Essam H. Houssein","doi":"10.1007/s10462-024-10946-5","DOIUrl":"10.1007/s10462-024-10946-5","url":null,"abstract":"<div><p>In response to the shortcomings of particle swarm optimization (PSO), such as low execution efficiency and difficulty in overcoming local optima, this paper proposes a multi-strategy PSO method incorporating snow ablation operation (SAO), known as SAO-MPSO. Firstly, Cubic initialization is performed on particles to obtain a good initial environment. Subsequently, SAO and PSO are combined in parallel, and a balanced search mechanism led by multiple sub-populations is devised, significantly improving the search efficiency of overall population. Finally, the degree day method of SAO is introduced, and particles are endowed with memory of environmental changes to prevent premature convergence of PSO, while balancing the exploration and exploitation (ENE) capabilities in later phases. All adaptive parameters are used throughout this method in place of fixed parameters to improve the robustness and adaptability. For a comprehensive analysis of SAO-MPSO, its good ENE ability is verified on CEC 2020 and CEC 2022 and this method is compared with existing improved PSO versions on both test sets. The results show that SAO-MPSO has certain advantages in the comparison of similar improved algorithms. In order to further validate the strength of SAO-MPSO in dealing with nonlinear optimization problems (OPs) with strong constraints, firstly, based on the ball Wang-Ball (BWB) curve, a combined BWB (CBWB) curve is constructed, and a construction method for CBWB curves that satisfy <i>G</i>¹ and <i>G</i>² continuity is derived. Then, with the energy minimization and scale parameters of the CBWB curve as the optimization objective and variables respectively, a shape optimization model that satisfies <i>G</i>² continuity is established. Finally, three numerical optimization examples based on this model are solved using SAO-MPSO and compared with 10 other methods. The results show that the energy obtained by SAO-MPSO is the smallest, which verifies the effectiveness of this method applied to shape OPs of CBWB curve.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"57 12","pages":""},"PeriodicalIF":10.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-10946-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443317","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}