IEEE Transactions on Emerging Topics in Computational Intelligence最新文献

{"title":"Data-Driven Container Marking Detection and Recognition System With an Open Large-Scale Scene Text Dataset","authors":"Ying Xu;Zhangzhao Liang;Yanyang Liang;Xinru Li;Wenfeng Pan;Jie You;Zhihao Long;Yikui Zhai;Angelo Genovese;Vincenzo Piuri;Fabio Scotti","doi":"10.1109/TETCI.2024.3377680","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3377680","url":null,"abstract":"With the widespread use of containers, the demand for Container Marking Detection and Recognition (CMDR) is gradually increasing. The use of deep learning algorithms can greatly improve the efficiency of marking detection and recognition. However, there is still a lack of research on CMDR in both academia and industry, resulting in the current task being completed manually and inefficiently. In this paper, we probe into the importance of data-driven and task paradigms for CMDR tasks. Firstly, we constructed an open large scale container surface marking text dataset called ContainerText. This dataset consists of 12 k high-resolution images and provides two types of annotation information: bounding box used for detection and text for recognition tasks. In addition, we also propose an efficient semi-automatic annotation method based on deep learning, which reduces the cost of manual annotation. Subsequently, we have innovatively proposed a CMDR method combining Scene Text Recognition (STR) with CMDR tasks. The method based on STR can locate and recognize container marking from a fine-grained level. We conducted a comprehensive series of experiments on the ContainerText dataset using state-of-the-art (SOTA) scene text detection and scene text recognition models. The experimental results demonstrate that the CMDR method, based on STR, exhibits exceptional adaptability and feasibility. All experimental results obtained from the ContainerText dataset will act as performance benchmarks for future researchers. Finally, an automated Container Marking Image Acquisition Mechanism (CMIAM) are construucted, which can effectively avoid complex lighting in the workshop environment and achieve high-quality and automated image acquisition. We have conducted extensive experiments to measure the distance, resolution, and field of view required for clearly capturing container markings. Our research providing reference for future CMDR research from task solution and hardware selection.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3368-3381"},"PeriodicalIF":5.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information","authors":"","doi":"10.1109/TETCI.2024.3377151","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3377151","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 2","pages":"C2-C2"},"PeriodicalIF":5.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10480103","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140310111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"From Concept to Instance: Hierarchical Reinforced Knowledge Graph Reasoning","authors":"Cheng Yan;Feng Zhao;Yudong Zhang","doi":"10.1109/TETCI.2024.3372350","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3372350","url":null,"abstract":"The knowledge graph, a networked structure designed to organize the vast and heterogeneous knowledge existing in the real world, has gained widespread adoption as a background knowledge base for intelligent systems. Nevertheless, the incompleteness of knowledge graphs has been widely recognized as a significant challenge in their development and application. Recently, multi-hop knowledge graph reasoning has been an attractive method for completing a knowledge graph. The multi-hop knowledge graph reasoning based on the reinforcement learning (RL) framework has achieved promising performance in terms of interpretability and scalability. An RL agent automatically reasons over a KG under the guidance of a policy network. When faced with a query, obtaining the approximate range of the answer first and then delving into individual options is a more efficient approach compared to traversing all candidate answers. However, existing RL-based methods have two limitations. First, they lack the ability to filter candidate decisions, making it challenging to handle entities with a large number of neighbors. Second, they fail to consider the intrinsic correlations between entities. To address these limitations, we propose a novel hierarchical knowledge graph reasoning approach \u0000<italic>HiKGR</i>\u0000, which leverages the concept information of entities. Specifically, \u0000<italic>HiKGR</i>\u0000 reconstructs the previous action space in RL into a concept space and an instance space, enabling two policies to alternate reasoning at the concept level and the instance level. Furthermore, we propose hierarchical reward functions for the two-level policies to achieve joint optimization. The hierarchical reasoning approach we propose is capable of selecting more reasonable candidate decisions and optimizing the decision space. Experimental results reveal that \u0000<italic>HiKGR</i>\u0000 significantly outperforms existing RL-based methods and drastically reduces the action space size.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3665-3677"},"PeriodicalIF":5.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Centralized and Federated Learning for COVID-19 Detection With Chest X-Ray Images: Implementations and Analysis","authors":"Sadaf Naz;Khoa Phan;Yi-Ping Phoebe Chen","doi":"10.1109/TETCI.2024.3371222","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3371222","url":null,"abstract":"In the health domain, due to privacy issues, many important datasets are isolated, which nonetheless need to be analyzed collaboratively for conclusions to be drawn efficiently. To maintain data privacy, federated learning (FL) trains a communal model from scattered datasets without centralized data integration. In this paper, we compare and analyze the performance of traditional deep learning (DL) and FL techniques using the chest X-Ray (CXR) image dataset for COVID-19 detection. We first implemented DL techniques VGG-16, ResNet50, and Inceptionv3, where ResNet50 is found to be best on the classification task with 98% accuracy. We then proposed FL implementations - federated averaging and federated learning using ResNet50 for training local and global models. The proposed FL converges faster and outperforms the base FL for both independent and identically distributed (IID) and non-IID datasets. While the FL handles bigger data efficiently, compared to DL, it compromised 3.56% in accuracy to preserve privacy. Our results provide a platform for the further investigation of FL in COVID-19 detection.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 4","pages":"2987-3000"},"PeriodicalIF":5.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141964859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Computational Intelligence Society Information","authors":"","doi":"10.1109/TETCI.2024.3377153","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3377153","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 2","pages":"C3-C3"},"PeriodicalIF":5.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10480104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140321746","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors","authors":"","doi":"10.1109/TETCI.2024.3377155","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3377155","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 2","pages":"C4-C4"},"PeriodicalIF":5.3,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10479968","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140321714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"Surrogate-Assisted Evolutionary Multi-Objective Optimization of Medium-Scale Problems by Random Grouping and Sparse Gaussian Modeling","authors":"Haofeng Wu;Yaochu Jin;Kailai Gao;Jinliang Ding;Ran Cheng","doi":"10.1109/TETCI.2024.3372378","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3372378","url":null,"abstract":"Gaussian processes (GPs) are widely employed in surrogate-assisted evolutionary algorithms (SAEAs) because they can estimate the level of uncertainty in their predictions. However, the computational complexity of GPs grows cubically with the number of training samples, the time required for constructing a GP becomes excessively long. Additionally, in SAEAs, the GP is updated using the new data sampled in each round, which significantly impairs its efficiency in addressing medium-scale optimization problems. This issue is exacerbated in multi-objective scenarios where multiple GP models are needed. To address this challenge, we propose a fast SAEA using sparse GPs for medium-scale expensive multi-objective optimization problems. We construct a sparse GP for each objective on randomly selected sub-decision spaces and optimize a multi-objective acquisition function using a multi-objective evolutionary algorithm. The resulting population is combined with the previously evaluated solutions, and k-means is used for clustering to obtain candidate solutions. Before real function evaluations, the candidate solutions in the subspace are completed with the values of the knee point in the archive. Experimental results on three benchmark test suites up to 80 decision variables demonstrate the algorithm's computational efficiency and competitive performance compared to state-of-the-art methods. Additionally, we verify its performance on a real-world optimization problem.","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 5","pages":"3263-3278"},"PeriodicalIF":5.3,"publicationDate":"2024-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142368452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"IEEE Transactions on Emerging Topics in Computational Intelligence Information for Authors","authors":"","doi":"10.1109/TETCI.2024.3398387","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3398387","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 3","pages":"C4-C4"},"PeriodicalIF":5.3,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10538465","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096294","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"IEEE Computational Intelligence Society Information","authors":"","doi":"10.1109/TETCI.2024.3398385","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3398385","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 3","pages":"C3-C3"},"PeriodicalIF":5.3,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10538449","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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":"IEEE Transactions on Emerging Topics in Computational Intelligence Publication Information","authors":"","doi":"10.1109/TETCI.2024.3398383","DOIUrl":"https://doi.org/10.1109/TETCI.2024.3398383","url":null,"abstract":"","PeriodicalId":13135,"journal":{"name":"IEEE Transactions on Emerging Topics in Computational Intelligence","volume":"8 3","pages":"C2-C2"},"PeriodicalIF":5.3,"publicationDate":"2024-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10538452","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141096367","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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}