Computational Intelligence最新文献

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

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

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

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

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

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

Reinforcement Learning Driven Cross-Trained Worker Assignment Approach Based on Big Models: A Study for A Hybrid Seru Production System Considering Learning Effect

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-03-24 DOI: 10.1111/coin.70048

Taixin Li, Chenxi Ye, Lang Wu, Feng Liu, Chengxiao Yu

{"title":"Reinforcement Learning Driven Cross-Trained Worker Assignment Approach Based on Big Models: A Study for A Hybrid Seru Production System Considering Learning Effect","authors":"Taixin Li, Chenxi Ye, Lang Wu, Feng Liu, Chengxiao Yu","doi":"10.1111/coin.70048","DOIUrl":"https://doi.org/10.1111/coin.70048","url":null,"abstract":"<div>\u0000 \u0000 <p>As manufacturing faces evolving customer demands, the integration of Industrial Internet of Things (IIoT) networks is crucial for enhancing production flexibility. In this context, the Seru Production System (SPS) has emerged as a highly adaptable production mode and emphasizes the strategic assignment of cross-trained workers, particularly in hybrid configurations combining divisional and rotating serus. This paper proposes a novel bi-objective mathematical model incorporating learning effects to minimize makespan and balance workloads among workers. With the development of Artificial Intelligence Generated Content (AIGC) empowered big models, new breakthroughs have emerged in industrial manufacturing decision-making. These models utilize deep learning for foundational content processing and leverage reinforcement learning to optimize strategies. This process provides robust support for achieving efficient decision optimization. Building on the concepts of AIGC big models training, this study employs reinforcement learning to refine the results of multi-objective genetic algorithms, thereby improving the solution capability of the bi-objective model. Experimental results demonstrate that the proposed algorithm effectively provides optimal strategies for tuning crossover and mutation operations. Additionally, numerical experiments offer insights into the formation of hybrid SPS configurations.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689282","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

Motor Imagery Classification Using fNIRS Brain Signals: A Method Based on Synthetic Data Augmentation and Cosine-Modulated Attention

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-03-21 DOI: 10.1111/coin.70044

Cheng Peng, Baojiang Li, Haiyan Wang, Xinbing Shi, Yuxing Qin

{"title":"Motor Imagery Classification Using fNIRS Brain Signals: A Method Based on Synthetic Data Augmentation and Cosine-Modulated Attention","authors":"Cheng Peng, Baojiang Li, Haiyan Wang, Xinbing Shi, Yuxing Qin","doi":"10.1111/coin.70044","DOIUrl":"https://doi.org/10.1111/coin.70044","url":null,"abstract":"<div>\u0000 \u0000 <p>Functional near-infrared spectroscopy (fNIRS), renowned for its high spatial resolution, shows substantial promise in brain-computer interface (BCI) applications. However, challenges such as lengthy data acquisition processes and susceptibility to noise can limit data availability and reduce classification accuracy. To overcome these limitations, we introduce the CosineGAN-transformer network (CGTNet), which integrates a dual discriminator GAN for generating high-quality synthetic data with a Transformer-based classification network. Equipped with a multi-head self-attention mechanism, this network excels at capturing the intricate spatiotemporal relationships inherent in high-resolution fNIRS signals. The dual discriminator framework ensures that both the temporal and spatial aspects of the synthetic data closely resemble the original signals, thereby enhancing data diversity and fidelity. Experimental results on a publicly available fNIRS dataset, comprising 30 participants performing motor imagery tasks (right-hand tapping, left-hand tapping, and foot tapping), demonstrate that CGTNet achieves an accuracy of 82.67%, outperforming existing methods. Key contributions of this work include the use of multi-head self-attention for refined feature extraction and a dual discriminator Generative Adversarial Networks (GAN) framework that maintains data quality and consistency. These advancements significantly improve the robustness and accuracy of BCI systems, offering promising applications in neurorehabilitation and assistive technologies.</p>\u0000 </div>","PeriodicalId":55228,"journal":{"name":"Computational Intelligence","volume":"41 2","pages":""},"PeriodicalIF":1.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689240","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

RETRACTION

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-03-20 DOI: 10.1111/coin.70040

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RETRACTION 返回

IF 1.8 4区计算机科学

Computational Intelligence Pub Date : 2025-03-20 DOI: 10.1111/coin.70039

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