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Application of concept drift detection and adaptive framework for non linear time series data from cardiac surgery 概念漂移检测和自适应框架在心脏手术非线性时间序列数据中的应用
IF 2.8 4区 计算机科学 Q1 Mathematics Pub Date : 2024-06-18 DOI: 10.1111/coin.12658
Rajarajan Ganesan, Tarunpreet Kaur, Alisha Mittal, Mansi Sahi, Sushant Konar, Tanvir Samra, Goverdhan Dutt Puri, Shayam Kumar Singh Thingnum, Nitin Auluck

The quality of machine learning (ML) models deployed in dynamic environments tends to decline over time due to disparities between the data used for training and the upcoming data available for prediction, which is commonly known as drift. Therefore, it is important for ML models to be capable of detecting any changes or drift in the data distribution and updating the ML model accordingly. This study presents various drift detection techniques to identify drift in the survival outcomes of patients who underwent cardiac surgery. Additionally, this study proposes several drift adaptation strategies, such as adaptive learning, incremental learning, and ensemble learning. Through a detailed analysis of the results, the study confirms the superior performance of ensemble model, achieving a minimum mean absolute error (MAE) of 10.684 and 2.827 for predicting hospital stay and ICU stay, respectively. Furthermore, the models that incorporate a drift adaptive framework exhibit superior performance compared to the models that do not include such a framework.

由于用于训练的数据与即将用于预测的数据之间存在差异,在动态环境中部署的机器学习(ML)模型的质量往往会随着时间的推移而下降,这就是通常所说的漂移。因此,ML 模型必须能够检测数据分布中的任何变化或漂移,并相应地更新 ML 模型。本研究介绍了各种漂移检测技术,以识别心脏手术患者生存结果中的漂移。此外,本研究还提出了几种漂移适应策略,如自适应学习、增量学习和集合学习。通过对结果的详细分析,研究证实了集合模型的卓越性能,在预测住院时间和重症监护室住院时间方面,集合模型的最小平均绝对误差(MAE)分别为 10.684 和 2.827。此外,与不包含漂移自适应框架的模型相比,包含漂移自适应框架的模型表现出更优越的性能。
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引用次数: 0
A novel feature integration method for named entity recognition model in product titles 产品标题中命名实体识别模型的新型特征整合方法
IF 2.8 4区 计算机科学 Q1 Mathematics Pub Date : 2024-06-18 DOI: 10.1111/coin.12654
Shiqi Sun, Kun Zhang, Jingyuan Li, Xinghang Sun, Jianhe Cen, Yuanzhuo Wang

Entity recognition of product titles is essential for retrieving and recommending product information. Due to the irregularity of product title text, such as informal sentence structure, a large number of professional attribute words, a large number of unrelated independent entities of various combinations, the existing general named entity recognition model is limited in the e-commerce field of product title entity recognition. Most of the current studies focus on only one of the two challenges instead of considering the two challenges together. Our approach proposes NEZHA-CNN-GlobalPointer architecture with the addition of label semantic network, and uses multigranularity contextual and label semantic information to fully capture the internal structure and category information of words and texts to improve the entity recognition accuracy. Through a series of experiments, we proved the efficiency of our approach over a dataset of Chinese product titles from JD.com, improving the F1-value by 5.98%, when compared to the BERT-LSTM-CRF model on the product title corpus.

产品标题的实体识别对于检索和推荐产品信息至关重要。由于产品标题文本的不规则性,如不正规的句子结构、大量的专业属性词、大量不相关的独立实体的各种组合等,现有的通用命名实体识别模型在电子商务领域的产品标题实体识别中受到了限制。目前的研究大多只关注这两个挑战中的一个,而没有将这两个挑战放在一起考虑。我们的方法提出了NEZHA-CNN-GlobalPointer架构,并加入了标签语义网络,利用多粒度上下文和标签语义信息,充分捕捉词和文本的内部结构和类别信息,提高实体识别准确率。通过一系列实验,我们证明了我们的方法在 JD.com 中文产品标题数据集上的效率,与产品标题语料库上的 BERT-LSTM-CRF 模型相比,F1 值提高了 5.98%。
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引用次数: 0
Retraction: Ala Saleh Alluhaidan. Artificial intelligence for public perception of drones as a tool for telecommunication technologies. Comput Intell 40: e12507, 2024 (10.1111/coin.12507) 撤回: Ala Saleh Alluhaidan. 人工智能促进公众对作为电信技术工具的无人机的认知。 Comput Intell 40: e12507, 2024 (10.1111/coin.12507)
IF 2.8 4区 计算机科学 Q1 Mathematics Pub Date : 2024-06-18 DOI: 10.1111/coin.12675

The above article, published online on 17 February 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Diana Inkpen, and Wiley Periodicals LLC. The article was published as part of a guest-edited special issue. Following publication, it came to our attention that two of those named as Guest Editors of this issue were being impersonated and/or misrepresented by a fraudulent entity. An investigation by the publisher found that all of the articles, including this one, experienced compromised editorial handling and peer review which was not in line with the journal's ethical standards. Therefore, a decision has been made to retract this article. We did not find any evidence of misconduct by the authors. The authors have been informed of the decision to retract but do not agree with this decision.

上述文章于 2022 年 2 月 17 日在线发表于 Wiley Online Library (wileyonlinelibrary.com),现经主编 Diana Inkpen 和 Wiley Periodicals LLC 协议撤回。这篇文章是作为客座编辑特刊的一部分发表的。文章发表后,我们注意到有两个被指定为本期特邀编辑的人被一个欺诈实体冒充和/或歪曲。出版商调查后发现,包括本期在内的所有文章在编辑处理和同行评审过程中都受到了损害,这不符合期刊的道德标准。因此,决定撤回这篇文章。我们没有发现作者有任何不当行为的证据。我们已将撤稿决定告知作者,但他们并不同意这一决定。
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引用次数: 0
Retraction: K Logeswaran, P Suresh. High utility itemset mining using genetic algorithm assimilated with off policy reinforcement learning to adaptively calibrate crossover operation. Comput Intell 38: 1596–1615, 2022 (10.1111/coin.12490) 撤回: K Logeswaran, P Suresh. 利用遗传算法与非策略强化学习同化以适应性校准交叉操作的高效用项集挖掘。 Comput Intell 38: 1596-1615, 2022 (10.1111/coin.12490)
IF 2.8 4区 计算机科学 Q1 Mathematics Pub Date : 2024-06-18 DOI: 10.1111/coin.12677

The above article, published online on 14 November 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Diana Inkpen, and Wiley Periodicals LLC. The article was published as part of a guest-edited special issue. Following publication, it came to our attention that two of those named as Guest Editors of this issue were being impersonated and/or misrepresented by a fraudulent entity. An investigation by the publisher found that all of the articles, including this one, experienced compromised editorial handling and peer review which was not in line with the journal's ethical standards. Therefore, a decision has been made to retract this article. We did not find any evidence of misconduct by the authors. The authors have been informed of the decision to retract.

上述文章于 2021 年 11 月 14 日在线发表于 Wiley Online Library (wileyonlinelibrary.com),现经主编 Diana Inkpen 和 Wiley Periodicals LLC 协议撤回。这篇文章是作为客座编辑特刊的一部分发表的。文章发表后,我们注意到有两个被指定为本期特邀编辑的人被一个欺诈实体冒充和/或歪曲。出版商调查后发现,包括本期在内的所有文章在编辑处理和同行评审过程中都受到了损害,这不符合期刊的道德标准。因此,决定撤回这篇文章。我们没有发现作者有任何不当行为的证据。撤稿决定已通知作者。
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引用次数: 0
Deep learning for personalized health monitoring and prediction: A review 用于个性化健康监测和预测的深度学习:综述
IF 2.8 4区 计算机科学 Q1 Mathematics Pub Date : 2024-06-18 DOI: 10.1111/coin.12682
Robertas Damaševičius, Senthil Kumar Jagatheesaperumal, Rajesh N. V. P. S. Kandala, Sadiq Hussain, Roohallah Alizadehsani, Juan M. Gorriz

Personalized health monitoring and prediction are indispensable in advancing healthcare delivery, particularly amidst the escalating prevalence of chronic illnesses and the aging population. Deep learning (DL) stands out as a promising avenue for crafting personalized health monitoring systems adept at forecasting health outcomes with precision and efficiency. As personal health data becomes increasingly accessible, DL-based methodologies offer a compelling strategy for enhancing healthcare provision through accurate and timely prognostications of health conditions. This article offers a comprehensive examination of recent advancements in employing DL for personalized health monitoring and prediction. It summarizes a diverse range of DL architectures and their practical implementations across various realms, such as wearable technologies, electronic health records (EHRs), and data accumulated from social media platforms. Moreover, it elucidates the obstacles encountered and outlines future directions in leveraging DL for personalized health monitoring, thereby furnishing invaluable insights into the immense potential of DL in this domain.

个性化健康监测和预测是推进医疗保健服务不可或缺的因素,尤其是在慢性病发病率不断攀升和人口老龄化的背景下。深度学习(DL)是打造个性化健康监测系统的一条大有可为的途径,该系统善于精准、高效地预测健康结果。随着个人健康数据变得越来越容易获取,基于深度学习的方法为通过准确、及时地预报健康状况来提高医疗保健服务水平提供了令人信服的策略。本文全面探讨了在利用 DL 进行个性化健康监测和预测方面的最新进展。文章总结了可穿戴技术、电子健康记录(EHR)和社交媒体平台积累的数据等不同领域的各种数字语言架构及其实际应用。此外,它还阐明了在利用数字语言进行个性化健康监测方面遇到的障碍,并概述了未来的发展方向,从而为数字语言在这一领域的巨大潜力提供了宝贵的见解。
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引用次数: 0
Video text rediscovery: Predicting and tracking text across complex scenes 视频文本再发现:预测和跟踪复杂场景中的文字
IF 2.8 4区 计算机科学 Q1 Mathematics Pub Date : 2024-06-18 DOI: 10.1111/coin.12686
Veronica Naosekpam, Nilkanta Sahu

Dynamic texts in scene videos provide valuable insights and semantic cues crucial for video applications. However, the movement of this text presents unique challenges, such as blur, shifts, and blockages. While efficient in tracking text, state-of-the-art systems often need help when text becomes obscured or complicated scenes. This study introduces a novel method for detecting and tracking video text, specifically designed to predict the location of obscured or occluded text in subsequent frames using a tracking-by-detection paradigm. Our approach begins with a primary detector to identify text within individual frames, thus enhancing tracking accuracy. Using the Kalman filter, Munkres algorithm, and deep visual features, we establish connections between text instances across frames. Our technique works on the concept that when text goes missing in a frame due to obstructions, we use its previous speed and location to predict its next position. Experiments conducted on the ICDAR2013 Video and ICDAR2015 Video datasets confirm our method's efficacy, matching or surpassing established methods in performance.

场景视频中的动态文本提供了对视频应用至关重要的宝贵见解和语义线索。然而,文字的移动带来了独特的挑战,如模糊、移动和阻塞。虽然跟踪文本的效率很高,但当文本变得模糊或场景变得复杂时,最先进的系统往往需要帮助。本研究介绍了一种用于检测和跟踪视频文本的新方法,该方法专门设计用于通过检测跟踪模式预测后续帧中模糊或遮挡文本的位置。我们的方法首先使用主检测器来识别单个帧内的文本,从而提高跟踪精度。利用卡尔曼滤波器、Munkres 算法和深度视觉特征,我们建立了跨帧文本实例之间的联系。我们的技术基于这样一个概念:当文字在某一帧中因障碍物而丢失时,我们会利用它之前的速度和位置来预测它的下一个位置。在 ICDAR2013 视频和 ICDAR2015 视频数据集上进行的实验证实了我们方法的有效性,在性能上与现有方法不相上下甚至有过之而无不及。
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引用次数: 0
Retraction: Sunita Satish Patil, Thangamuthu Senthil Kumaran. Fuzzy based rendezvous points selection for mobile data gathering in wireless sensor network. Comput Intell 40: e12486, 2024 (10.1111/coin.12486) 撤回: Sunita Satish Patil、Thangamuthu Senthil Kumaran. 基于模糊的交会点选择,用于无线传感器网络中的移动数据采集。 Comput Intell 40: e12486, 2024 (10.1111/coin.12486)
IF 2.8 4区 计算机科学 Q1 Mathematics Pub Date : 2024-06-17 DOI: 10.1111/coin.12668

The above article, published online on 21 October 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Diana Inkpen, and Wiley Periodicals LLC. The article was published as part of a guest-edited special issue. Following publication, it came to our attention that two of those named as Guest Editors of this issue were being impersonated and/or misrepresented by a fraudulent entity. An investigation by the publisher found that all of the articles, including this one, experienced compromised editorial handling and peer review which was not in line with the journal's ethical standards. Therefore, a decision has been made to retract this article. We did not find any evidence of misconduct by the authors. The authors have been informed of the decision to retract.

上述文章于 2021 年 10 月 21 日在线发表于 Wiley Online Library (wileyonlinelibrary.com),现经主编 Diana Inkpen 和 Wiley Periodicals LLC 协议撤回。这篇文章是作为客座编辑特刊的一部分发表的。文章发表后,我们注意到有两个被指定为本期特邀编辑的人被一个欺诈实体冒充和/或歪曲。出版商调查后发现,包括本期在内的所有文章在编辑处理和同行评审过程中都受到了损害,这不符合期刊的道德标准。因此,决定撤回这篇文章。我们没有发现作者有任何不当行为的证据。撤稿决定已通知作者。
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引用次数: 0
Breast tumor detection using multi-feature block based neural network by fusion of CT and MRI images 通过融合 CT 和 MRI 图像,使用基于多特征块的神经网络检测乳腺肿瘤
IF 2.8 4区 计算机科学 Q1 Mathematics Pub Date : 2024-06-17 DOI: 10.1111/coin.12652
Bersha Kumari, Amita Nandal, Arvind Dhaka

Radiologists and clinicians must automatically examine breast and tumor locations and sizes accurately. In recent years, several neural network-based feature fusion versions have been created to improve medical image segmentation. Multi-modal image fusion photos may efficiently identify tumors. This work uses image fusion to identify computed tomography and magnetic resonance imaging alterations. A Gauss-log ratio operator is recommended for difference image production. The Gauss-log ratio and log ratio difference image complement the objective of improving the difference map through image fusion. The feature change matrix extracts edge, texture, and intensity from each picture pixel. The final change detection map classifies feature vectors as “changed” or “unchanged” which has been mapped for high-resolution or low-resolution pixels. This paper proposes a multi-feature blocks (MFB) based neural network for multi-feature fusion. This neural network modeling approach globalizes pixel spatial relationships. MFB-based feature fusion also aims to capture channel interactions between feature maps. The proposed technique outperforms state-of-the-art approaches which have been discussed in detail in experimental results section.

放射科医生和临床医生必须自动准确地检查乳腺和肿瘤的位置和大小。近年来,一些基于神经网络的特征融合版本已经问世,以改进医学图像分割。多模态图像融合照片可有效识别肿瘤。这项研究利用图像融合来识别计算机断层扫描和磁共振成像的改变。建议使用高斯-对数比算子生成差异图像。高斯对数比和对数比差分图像通过图像融合实现了改善差分图的目标。特征变化矩阵从每个图像像素中提取边缘、纹理和强度。最终的变化检测图将高分辨率或低分辨率像素映射的特征向量分为 "变化 "或 "不变"。本文提出了一种基于多特征块(MFB)的多特征融合神经网络。这种神经网络建模方法将像素空间关系全球化。基于 MFB 的特征融合还旨在捕捉特征图之间的信道交互。所提出的技术优于最先进的方法,实验结果部分对此进行了详细讨论。
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引用次数: 0
UReslham: Radar reflectivity inversion for smart agriculture with spatial federated learning over geostationary satellite observations UReslham:利用对地静止卫星观测数据的空间联合学习,雷达反射率反演用于智能农业
IF 2.8 4区 计算机科学 Q1 Mathematics Pub Date : 2024-06-17 DOI: 10.1111/coin.12684
Zhengyong Jin, Xiaolong Xu, Muhammad Bilal, Songyu Wu, Huichao Lin

The frequent occurrence of severe convective weather has certain adverse effects on the smart agriculture industry. To enhance the prediction of severe convective weather, the inversion model effectively fills radar reflectivity data gaps by leveraging geostationary satellite data, offering more comprehensive and accurate support for meteorological information in smart agriculture systems. Nevertheless, collaborative cross-regional inversion driven by dispersed radar data faces challenges in efficiency, privacy, and model accuracy. To this end, we employ an U-shaped residual network with an embedded light hybrid attention mechanism and utilize a federated averaging algorithm for efficient distributed training across multiple devices which could preserve the privacy of data from different locations, thereby improving inversion performance. In addition, to address the unbalanced nature of radar data, a weighted loss function is designed to enhance the model's sensitivity to high radar reflectivity. Experimental results demonstrate that the proposed model exhibits a certain level of improvement in evaluating radar reflectivity inversion performance across different thresholds compared to other models, thus substantiating the superiority of the proposed approach.

强对流天气的频繁发生对智慧农业产业产生了一定的不利影响。为加强对强对流天气的预报,反演模型借助静止卫星数据,有效填补了雷达反射率数据空白,为智慧农业系统提供更全面、更准确的气象信息支持。然而,由分散的雷达数据驱动的跨区域协同反演在效率、隐私和模型精度方面都面临挑战。为此,我们采用了具有嵌入式轻混合注意力机制的 U 型残差网络,并利用联合平均算法在多个设备上进行高效的分布式训练,从而保护了不同地点数据的隐私,提高了反演性能。此外,针对雷达数据的不平衡性,还设计了加权损失函数,以提高模型对高雷达反射率的灵敏度。实验结果表明,与其他模型相比,所提出的模型在评估不同阈值的雷达反射率反演性能方面有一定程度的提高,从而证实了所提出方法的优越性。
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引用次数: 0
Retraction: Vinod Kumar, R, Kavithaa, G, Jayanthi, D. Lifetime maximization energy-aware routing protocol for route optimization to improve quality of service in wireless sensor networks. Comput Intell 40: e12485, 2024 (10.1111/coin.12485) 撤回: Vinod Kumar, R, Kavithaa, G, Jayanthi, D. 用于路由优化以提高无线传感器网络服务质量的寿命最大化能量感知路由协议。 Comput Intell 40: e12485, 2024 (10.1111/coin.12485)
IF 2.8 4区 计算机科学 Q1 Mathematics Pub Date : 2024-06-17 DOI: 10.1111/coin.12667

The above article, published online on 06 January 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor-in-Chief, Diana Inkpen, and Wiley Periodicals LLC. The article was published as part of a guest-edited special issue. Following publication, it came to our attention that two of those named as Guest Editors of this issue were being impersonated and/or misrepresented by a fraudulent entity. An investigation by the publisher found that all of the articles, including this one, experienced compromised editorial handling and peer review which was not in line with the journal's ethical standards. Therefore, a decision has been made to retract this article. We did not find any evidence of misconduct by the authors. The authors have been informed of the decision to retract.

上述文章于 2022 年 1 月 6 日在线发表于 Wiley Online Library (wileyonlinelibrary.com),经主编 Diana Inkpen 和 Wiley Periodicals LLC 协议,该文章已被撤回。这篇文章是作为客座编辑特刊的一部分发表的。文章发表后,我们注意到有两个被指定为本期特邀编辑的人被一个欺诈实体冒充和/或歪曲。出版商调查后发现,包括本期在内的所有文章在编辑处理和同行评审过程中都受到了损害,这不符合期刊的道德标准。因此,决定撤回这篇文章。我们没有发现作者有任何不当行为的证据。撤稿决定已通知作者。
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引用次数: 0
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Computational Intelligence
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