Computational Intelligence最新文献_第3页

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

Computational Intelligence 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

{"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":"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.","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}

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

Computational Intelligence Pub Date : 2024-06-18 DOI: 10.1111/coin.12654

Shiqi Sun, Kun Zhang, Jingyuan Li, Xinghang Sun, Jianhe Cen, Yuanzhuo Wang

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

Computational Intelligence Pub Date : 2024-06-18 DOI: 10.1111/coin.12675

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

Computational Intelligence Pub Date : 2024-06-18 DOI: 10.1111/coin.12677

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

Computational Intelligence 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

{"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":"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.","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}

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

Computational Intelligence Pub Date : 2024-06-18 DOI: 10.1111/coin.12686

Veronica Naosekpam, Nilkanta Sahu

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

Computational Intelligence Pub Date : 2024-06-17 DOI: 10.1111/coin.12668

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

Computational Intelligence Pub Date : 2024-06-17 DOI: 10.1111/coin.12652

Bersha Kumari, Amita Nandal, Arvind Dhaka

{"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":"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.","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}

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

Computational Intelligence Pub Date : 2024-06-17 DOI: 10.1111/coin.12684

Zhengyong Jin, Xiaolong Xu, Muhammad Bilal, Songyu Wu, Huichao Lin

{"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":"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.","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}

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

Computational Intelligence Pub Date : 2024-06-17 DOI: 10.1111/coin.12667

引用次数: 0