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。
{"title":"Application of concept drift detection and adaptive framework for non linear time series data from cardiac surgery","authors":"Rajarajan Ganesan, Tarunpreet Kaur, Alisha Mittal, Mansi Sahi, Sushant Konar, Tanvir Samra, Goverdhan Dutt Puri, Shayam Kumar Singh Thingnum, Nitin Auluck","doi":"10.1111/coin.12658","DOIUrl":"https://doi.org/10.1111/coin.12658","url":null,"abstract":"<p>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.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"A novel feature integration method for named entity recognition model in product titles","authors":"Shiqi Sun, Kun Zhang, Jingyuan Li, Xinghang Sun, Jianhe Cen, Yuanzhuo Wang","doi":"10.1111/coin.12654","DOIUrl":"https://doi.org/10.1111/coin.12654","url":null,"abstract":"<p>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.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"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)","authors":"","doi":"10.1111/coin.12675","DOIUrl":"https://doi.org/10.1111/coin.12675","url":null,"abstract":"<p>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.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.12675","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430269","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
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.
{"title":"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)","authors":"","doi":"10.1111/coin.12677","DOIUrl":"https://doi.org/10.1111/coin.12677","url":null,"abstract":"<p>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.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.12677","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141430268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
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.
{"title":"Deep learning for personalized health monitoring and prediction: A review","authors":"Robertas Damaševičius, Senthil Kumar Jagatheesaperumal, Rajesh N. V. P. S. Kandala, Sadiq Hussain, Roohallah Alizadehsani, Juan M. Gorriz","doi":"10.1111/coin.12682","DOIUrl":"https://doi.org/10.1111/coin.12682","url":null,"abstract":"<p>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.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"Video text rediscovery: Predicting and tracking text across complex scenes","authors":"Veronica Naosekpam, Nilkanta Sahu","doi":"10.1111/coin.12686","DOIUrl":"https://doi.org/10.1111/coin.12686","url":null,"abstract":"<p>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.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141425110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
{"title":"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)","authors":"","doi":"10.1111/coin.12668","DOIUrl":"https://doi.org/10.1111/coin.12668","url":null,"abstract":"<p>The above article, published online on 21 October 2021 in Wiley Online Library (\u0000wileyonlinelibrary.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.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.12668","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424982","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
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.
{"title":"Breast tumor detection using multi-feature block based neural network by fusion of CT and MRI images","authors":"Bersha Kumari, Amita Nandal, Arvind Dhaka","doi":"10.1111/coin.12652","DOIUrl":"https://doi.org/10.1111/coin.12652","url":null,"abstract":"<p>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.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424942","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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。
{"title":"UReslham: Radar reflectivity inversion for smart agriculture with spatial federated learning over geostationary satellite observations","authors":"Zhengyong Jin, Xiaolong Xu, Muhammad Bilal, Songyu Wu, Huichao Lin","doi":"10.1111/coin.12684","DOIUrl":"https://doi.org/10.1111/coin.12684","url":null,"abstract":"<p>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.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.12684","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}
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.
{"title":"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)","authors":"","doi":"10.1111/coin.12667","DOIUrl":"https://doi.org/10.1111/coin.12667","url":null,"abstract":"<p>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.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":null,"pages":null},"PeriodicalIF":2.8,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.12667","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141424998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}