Computational Intelligence最新文献_第5页

RETRACTION 收缩

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-05-02 DOI: 10.1111/coin.70058

引用次数: 0

Retraction 收缩

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-05-02 DOI: 10.1111/coin.70057

引用次数: 0

Rethinking Correlation Filter Trackers for Small Unmanned Aircraft Systems 对小型无人机系统相关滤波跟踪器的再思考

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-04-21 DOI: 10.1111/coin.70053

Wei Liu, Shuang Wu, Xin Yun, Youfa Liu

引用次数: 0

Safety Monitoring of Machine Learning Perception Functions: A Survey 机器学习感知功能的安全监控：调查

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-04-20 DOI: 10.1111/coin.70032

Raul Sena Ferreira, Joris Guérin, Kevin Delmas, Jérémie Guiochet, Hélène Waeselynck

引用次数: 0

Vitamin D Analysis for Sustainable Healthcare in Inner London Borough 内伦敦区可持续医疗的维生素 D 分析

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-04-16 DOI: 10.1111/coin.70050

Sandra Fernando, Viktor Sowinski-Mydlarz, Subeksha Shrestha, Sunila Maharjan, Duncan Stewart, Dee Bhakta, Gary McLean, Sarah Illingworth

{"title":"Vitamin D Analysis for Sustainable Healthcare in Inner London Borough","authors":"Sandra Fernando, Viktor Sowinski-Mydlarz, Subeksha Shrestha, Sunila Maharjan, Duncan Stewart, Dee Bhakta, Gary McLean, Sarah Illingworth","doi":"10.1111/coin.70050","DOIUrl":"https://doi.org/10.1111/coin.70050","url":null,"abstract":"<p>Vitamin D is vital for bone health, immune system support, and muscle function. Deficiency in Vitamin D is widespread, with up to 65% of individuals in certain populations, including Black students at London Metropolitan University, UK, being affected. This study focuses on the need for a deeper understanding of Vitamin D prescription patterns, specifically within an inner London borough, using advanced data analytics. Previous analysis, such as ones conducted by \u0000OpenPrescribing.net, has investigated NHS prescription data but lacked a focused examination on Vitamin D. Our study introduces a novel computational approach, integrating NHS datasets from 2013 to 2023. We developed a web-hosted dashboard using Python, Flask, Cesium, PowerBI, and libraries such as Pandas, Scikit-learn to provide real-time data visualization and predictive analytics. Our methodology involved API-driven ingestion of large-scale data, focusing on Vitamin D prescriptions in a borough, and mapping this against patient numbers. We used feature manipulation and model training to gain insights into prescription counts, dosages, medication types, and formulations. This interactive platform supports dynamic reporting through PowerBI and Cesium. Our findings reveal significant variations in prescription patterns among GP surgeries influenced by socioeconomic factors. This interdisciplinary project, in future collaboration with local GP federations, United Kingdom, enhances computational health data analysis and aims to inform better prescription practices and healthcare policies, ultimately improving policy practice and public health outcomes.</p>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/coin.70050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143836340","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

Convolutional Attention-Based Bidirectional Recurrent Neural Network for Human Action Recognition 基于卷积注意的人类动作识别双向递归神经网络

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-04-15 DOI: 10.1111/coin.70049

Aditya Mahamkali, Manvitha Gali, Soumya Ranjan Jena, Velagapudi Sreenivas

{"title":"Convolutional Attention-Based Bidirectional Recurrent Neural Network for Human Action Recognition","authors":"Aditya Mahamkali, Manvitha Gali, Soumya Ranjan Jena, Velagapudi Sreenivas","doi":"10.1111/coin.70049","DOIUrl":"https://doi.org/10.1111/coin.70049","url":null,"abstract":"<div>\u0000 \u0000 <p>Human activity recognition (HAR) technology plays a major role in today's world and is used in detecting human actions and poses in real-time. In the past, researchers employed statistical machine learning methods to build and extract attributes of various movements manually. However, typical techniques are becoming increasingly ineffective in the face of exponentially increasing waveform data that lacks unambiguous principles. With the advancement of deep learning technology, manual feature extraction is no longer required, and performance on challenging human activity recognition problems can be improved. However, various deep learning models have problems such as time consumption, inaccuracy, and the vanishing gradient problem. Therefore, to solve these problems, the proposed study used a deep convolutional attention-based bidirectional recurrent neural network to detect human activities in the provided samples. The input images are first pre-processed using an adaptive bilateral filtering approach to improve their quality and remove image noise. Then, the crucial features are recovered using the convolutional neural network (CNN) based encoder-decoder model. Finally, a deep convolutional attention-based bidirectional recurrent neural network is used to identify human activities. The model recognizes human actions with higher effectiveness and lower latency. The human behaviors are identified using the HMDB51 dataset. The proposed model acquired the highest accuracy of 95.46%, which is 10.51% superior to multi-layer perceptron (MLP), 6.99% superior to CNN, 12.76% superior to long short-term memory (LSTM), 5.59% superior to Bidirectional LSTM (BiLSTM), and 4.82% superior to CNN-LSTM, respectively.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835766","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

Speech2Dementia: A Novel Deep Learning Framework Integrating Enhanced CNN and Large Language Models for Automatic Detection of Alzheimer's Dementia 一种集成增强CNN和大型语言模型的新型深度学习框架用于阿尔茨海默氏痴呆症的自动检测

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-04-09 DOI: 10.1111/coin.70051

Bandaru A. Chakravarthi, Gandla Shivakanth

{"title":"Speech2Dementia: A Novel Deep Learning Framework Integrating Enhanced CNN and Large Language Models for Automatic Detection of Alzheimer's Dementia","authors":"Bandaru A. Chakravarthi, Gandla Shivakanth","doi":"10.1111/coin.70051","DOIUrl":"https://doi.org/10.1111/coin.70051","url":null,"abstract":"<div>\u0000 \u0000 <p>Early diagnosis of Alzheimer's disease (AD) is important for early intervention, but current diagnostic tools tend to use unimodal methods, processing either speech or text separately. Although models such as the ComParE Baseline for audio and BERT-based text classifiers have been successful, they do not take advantage of the complementary strengths of both modalities, which restricts their diagnostic power. To overcome this, we suggest SPID-AD (Speech-Based Intelligent Detection of Alzheimer's Dementia), a multimodal deep-learning approach that combines linguistic and acoustic features for the automated detection of Alzheimer's. Our approach uses a BERT-based architecture to mine semantic patterns from transcripts and an augmented Convolutional Neural Network (CNN) to process Mel-spectrogram representations of speech. By combining these features in dense layers, the model retains language-related as well as auditory biomarkers of cognitive impairment. Assessed on the DementiaBank Pitt Corpus, SPID-AD has 95.6% classification accuracy, surpassing state-of-the-art models in precision, recall, and F1-score. The findings demonstrate the strength of multimodal analysis in detecting dementia speech patterns, providing a non-invasive, AI-based diagnostic tool that may assist clinicians in the early detection of Alzheimer's.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809489","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

Semantic Communication System for Standard Knowledge in Power Iot Networks 电力物联网中标准知识的语义通信系统

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-04-04 DOI: 10.1111/coin.70045

Zhengping Lin, Yanrong Yang, Xin Wang, Yuan La, Jie Lin

{"title":"Semantic Communication System for Standard Knowledge in Power Iot Networks","authors":"Zhengping Lin, Yanrong Yang, Xin Wang, Yuan La, Jie Lin","doi":"10.1111/coin.70045","DOIUrl":"https://doi.org/10.1111/coin.70045","url":null,"abstract":"<div>\u0000 \u0000 <p>The growing complexity of power Internet of Things (IoT) networks necessitates efficient and reliable communication capable of handling the continuous stream of data generated by distributed sensors, smart meters, and control systems. To handle this system, this paper proposes a semantic communication system for transmitting standard knowledge in power IoT networks, leveraging deep joint source-channel coding (Deep JSCC) to enhance communication efficiency and resilience. Unlike traditional communication approaches that prioritize bit-level accuracy, semantic communication focuses on conveying the meaning and relevance of information, ensuring that critical control signals and operational data are transmitted accurately, even under noisy channel conditions. The integration of Deep JSCC unifies data compression and error correction into a single neural network, enabling the system to dynamically balance the trade-off between compression efficiency and robustness to interference. The proposed semantic communication system also incorporates reinforcement learning (RL) to optimize network resource allocation on the bandwidth and transmission power, based on the semantic relevance of the transmitted knowledge. Experimental results demonstrate the effectiveness of the system in maintaining high reliability and low latency, even in resource-constrained environments, ensuring seamless grid operation and real-time decision-making. This research offers a novel framework for intelligent communication in power IoT networks, paving the way for sustainable energy management through efficient data handling, adaptive resource optimization, and improved communication reliability.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143770099","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

Facilitating Air Transportation of Agricultural Systems via Intelligent Runway Detection 通过智能跑道探测促进农业系统的航空运输

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-03-30 DOI: 10.1111/coin.70046

Zhaozi Zu, Hongjie Lei, Zhongjun Qu, Zhiyi Huang, Wenbo Suo

{"title":"Facilitating Air Transportation of Agricultural Systems via Intelligent Runway Detection","authors":"Zhaozi Zu, Hongjie Lei, Zhongjun Qu, Zhiyi Huang, Wenbo Suo","doi":"10.1111/coin.70046","DOIUrl":"https://doi.org/10.1111/coin.70046","url":null,"abstract":"<div>\u0000 \u0000 <p>Intelligent runway detection technology is crucial for the development of low-carbon, smart agricultural systems pertaining to the air transportation of agricultural products. Accurate detection of the location and orientation of the runway can effectively assist in safe aircraft landings and avoid potential risks. However, existing runway detection methods struggle in foggy conditions due to light scattering, causing blurry images and obscuring runway details, resulting in poor detection performance. Towards this issue, this paper proposes an adaptive image-based runway boundary detection method by combining image processing and filter prediction to enhance images automatically. It leverages runway symmetry to enhance feature maps and global-local information fusion. A shape loss function based on the runway's parallel boundaries is also introduced. These developments finally endow the proposed method with robustness towards foggy conditions. Experimental results demonstrate the method's effectiveness, achieving an average IoU of 73.58<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$$ % $$</annotation>\u0000 </semantics></math> on internal datasets, surpassing other advanced methods.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143741033","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

Superpixel-Based Hyperspectral Image Denoising via Local-Global Low-Rank Approximation 基于局部-全局低秩逼近的超像素高光谱图像去噪

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-03-25 DOI: 10.1111/coin.70047

Ya-Ru Fan, Daihui Li

引用次数: 0