Applied Intelligence最新文献_第6页

TADSRNet: A triple-attention dual-scale residual network for super-resolution image quality assessment 一种用于超分辨率图像质量评估的三注意双尺度残差网络

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-08-28 DOI: 10.1007/s10489-023-04932-7

Xing Quan, Kaibing Zhang, Hui Li, Dandan Fan, Yanting Hu, Jinguang Chen

引用次数: 0

iMVAN: integrative multimodal variational autoencoder and network fusion for biomarker identification and cancer subtype classification iMVAN:用于生物标志物识别和癌症亚型分类的综合多模式变分自动编码器和网络融合

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-08-28 DOI: 10.1007/s10489-023-04936-3

Arwinder Dhillon, Ashima Singh, Vinod Kumar Bhalla

{"title":"iMVAN: integrative multimodal variational autoencoder and network fusion for biomarker identification and cancer subtype classification","authors":"Arwinder Dhillon, Ashima Singh, Vinod Kumar Bhalla","doi":"10.1007/s10489-023-04936-3","DOIUrl":"10.1007/s10489-023-04936-3","url":null,"abstract":"<div><p>Numerous research has been conducted to define the molecular and clinical aspects of various tumors from a multi-omics point of view. However, there are significant obstacles in integrating multi-omics via Machine Learning (ML) for biomarker identification and cancer subtype classification. In this research, iMVAN, an integrated Multimodal Variational Autoencoder and Network fusion, is presented for biomarker discovery and classification of cancer subtypes. First, MVAE is used on multi-omics data consisting of Copy Number Variation (CNV), mRNA, and Reverse Protein Phase Array (rppa) to discover the biomarkers associated with distinct cancer subtypes. Then, multi-omics integration is accomplished by fusing similarity networks. Ultimately, the MVAE latent data and network fusion are given to a Simplified Graph Convolutional Network (SGC) for categorizing cancer subtypes. The suggested study extracts the top 100 features, which are then submitted to the KEGG analysis and survival analysis test. The survival study identifies nine biomarkers, including AGT, CDH1, CALML5, ERBB2, CCND1, FZD6, BRAF, AR, and MSH6, as poor prognostic markers. In addition, the cancer subtypes are classified, and the performance is assessed. The experimental findings demonstrate that the iMVAN performed well, with an accuracy of 87%.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"53 22","pages":"26672 - 26689"},"PeriodicalIF":5.3,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71910943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Improving image classification of one-dimensional convolutional neural networks using Hilbert space-filling curves 利用Hilbert空间填充曲线改进一维卷积神经网络的图像分类

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-08-28 DOI: 10.1007/s10489-023-04945-2

Bert Verbruggen, Vincent Ginis

{"title":"Improving image classification of one-dimensional convolutional neural networks using Hilbert space-filling curves","authors":"Bert Verbruggen, Vincent Ginis","doi":"10.1007/s10489-023-04945-2","DOIUrl":"10.1007/s10489-023-04945-2","url":null,"abstract":"<div><p>Convolutional neural networks (CNNs) have significantly contributed to recent advances in machine learning and computer vision. Although initially designed for image classification, the application of CNNs has stretched far beyond the context of images alone. Some exciting applications, e.g., in natural language processing and image segmentation, implement one-dimensional CNNs, often after a pre-processing step that transforms higher-dimensional input into a suitable data format for the networks. However, local correlations within data can diminish or vanish when one converts higher-dimensional data into a one-dimensional string. The Hilbert space-filling curve can minimize this loss of locality. Here, we study this claim rigorously by comparing an analytical model that quantifies locality preservation with the performance of several neural networks trained with and without Hilbert mappings. We find that Hilbert mappings offer a consistent advantage over the traditional flatten transformation in test accuracy and training speed. The results also depend on the chosen kernel size, agreeing with our analytical model. Our findings quantify the importance of locality preservation when transforming data before training a one-dimensional CNN and show that the Hilbert space-filling curve is a preferential transformation to achieve this goal.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"53 22","pages":"26655 - 26671"},"PeriodicalIF":5.3,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71910945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

TADSRNet: A triple-attention dual-scale residual network for super-resolution image quality assessment TADSRNet：一种用于超分辨率图像质量评估的三注意双尺度残差网络

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-08-28 DOI: 10.1007/s10489-023-04932-7

Xing Quan, Kaibing Zhang, Hui Li, Dandan Fan, Yanting Hu, Jinguang Chen

{"title":"TADSRNet: A triple-attention dual-scale residual network for super-resolution image quality assessment","authors":"Xing Quan, Kaibing Zhang, Hui Li, Dandan Fan, Yanting Hu, Jinguang Chen","doi":"10.1007/s10489-023-04932-7","DOIUrl":"10.1007/s10489-023-04932-7","url":null,"abstract":"<div><p>Image super-resolution (SR) has been extensively investigated in recent years. However, due to the absence of trustworthy and precise perceptual quality standards, it is challenging to objectively measure the performance of different SR approaches. In this paper, we propose a novel triple attention dual-scale residual network called TADSRNet for no-reference super-resolution image quality assessment (NR-SRIQA). Firstly, we simulate the human visual system (HVS) and construct a triple attention mechanism to acquire more significant portions of SR images through cross-dimensionality, making it simpler to identify visually sensitive regions. Then a dual-scale convolution module (DSCM) is constructed to capture quality-perceived features at different scales. Furthermore, in order to collect more informative feature representation, a residual connection is added to the network to compensate for perceptual features. Extensive experimental results demonstrate that the proposed TADSRNet can predict visual quality with greater accuracy and better consistency with human perception compared with existing IQA methods. The code will be available at https://github.com/kbzhang0505/TADSRNet.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"53 22","pages":"26708 - 26724"},"PeriodicalIF":5.3,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71910944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Multi-view stereo network with point attention 具有点关注的多视点立体网络

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-08-26 DOI: 10.1007/s10489-023-04806-y

Rong Zhao, Zhuoer Gu, Xie Han, Ligang He, Fusheng Sun, Shichao Jiao

引用次数: 0

Morphing aircraft acceleration and deceleration task morphing strategy using a reinforcement learning method 基于强化学习方法的飞机加减速任务变形策略

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-08-26 DOI: 10.1007/s10489-023-04876-y

Ruichen Ming, Xiaoxiong Liu, Yu Li, Yi Yin, WeiGuo Zhang

{"title":"Morphing aircraft acceleration and deceleration task morphing strategy using a reinforcement learning method","authors":"Ruichen Ming, Xiaoxiong Liu, Yu Li, Yi Yin, WeiGuo Zhang","doi":"10.1007/s10489-023-04876-y","DOIUrl":"10.1007/s10489-023-04876-y","url":null,"abstract":"<div><p>This paper proposes a design scheme for a whole morphing strategy based on the reinforcement learning (RL) method. A novel morphing aircraft is designed, and its nonlinear dynamic equations are established based on the calculated aerodynamic data. Further, a soft actor critic (SAC) approach is utilized to design the scheme, whose structure consists of the environment, the agent, and the reward function. In the environment design part, the incremental backstepping approach is employed to design the morphing aircraft controller. The safety and feasibility of deployment are verified. In the agent design part, in addition to using the entropy regularization RL algorithm, the generalization ability of the agent is enhanced in three ways: adding environmental noise, adding control command randomness, and adding output momentum terms. For the reward function, a structure with dynamic and steady-state performance is designed to accurately describe the aircraft dynamics. Finally, the designed SAC strategy is verified under the acceleration and deceleration tasks and compared with a GA and PPO strategy. Simulation results validate the effectiveness and superiority of the designed SAC scheme.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"53 22","pages":"26637 - 26654"},"PeriodicalIF":5.3,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71910685","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Simple yet effective joint guidance learning for few-shot semantic segmentation 一种简单而有效的联合引导学习方法

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-08-26 DOI: 10.1007/s10489-023-04937-2

Zhaobin Chang, Yonggang Lu, Xingcheng Ran, Xiong Gao, Hong Zhao

{"title":"Simple yet effective joint guidance learning for few-shot semantic segmentation","authors":"Zhaobin Chang, Yonggang Lu, Xingcheng Ran, Xiong Gao, Hong Zhao","doi":"10.1007/s10489-023-04937-2","DOIUrl":"10.1007/s10489-023-04937-2","url":null,"abstract":"<div><p>Fully-supervised semantic segmentation methods are difficult to generalize to novel objects, and their fine-tuning often requires a sufficient number of fully-labeled images. Few-shot semantic segmentation (FSS) has recently attracted lots of attention due to its excellent capability for segmenting the novel object with only a few labeled images. Most of recent approaches follow the prototype learning paradigm and have made a significant improvement in segmentation performance. However, there exist two critical bottleneck problems to be solved. (1) Previous methods mainly focus on mining the foreground information of the target object, and class-specific prototypes are generated by solely leveraging average operation on the whole support image, which may lead to information loss, underutilization, or semantic confusion of the object. (2) Most existing methods unilaterally guide the object segmentation in the query image with support images, which may result in semantic misalignment due to the diversity of objects in the support and query sets. To alleviate the above challenging problems, we propose a simple yet effective joint guidance learning architecture to generate and align more compact and robust prototypes from two aspects. (1) We propose a coarse-to-fine prototype generation module to generate coarse-grained foreground prototypes and fine-grained background prototypes. (2) We design a joint guidance learning module for the prototype evaluation and optimization on both support and query images. Extensive experiments show that the proposed method can achieve superior segmentation results on PASCAL-5<span>(^{i})</span> and COCO-20<span>(^{i})</span> datasets.</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"53 22","pages":"26603 - 26621"},"PeriodicalIF":5.3,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71910686","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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

Morphing aircraft acceleration and deceleration task morphing strategy using a reinforcement learning method 基于强化学习方法的飞机加减速任务变形策略

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-08-26 DOI: 10.1007/s10489-023-04876-y

Ruichen Ming, Xiaoxiong Liu, Yu Li, Yi Yin, Weiguo Zhang

引用次数: 0

Simple yet effective joint guidance learning for few-shot semantic segmentation 一种简单而有效的联合引导学习方法

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-08-26 DOI: 10.1007/s10489-023-04937-2

Zhaobin Chang, Y. Lu, Xingcheng Ran, Xiong Gao, Hong Zhao

引用次数: 0

Multi-view stereo network with point attention 具有点关注的多视点立体网络

IF 5.3 2区计算机科学

Applied Intelligence Pub Date : 2023-08-26 DOI: 10.1007/s10489-023-04806-y

Rong Zhao, Zhuoer Gu, Xie Han, Ligang He, Fusheng Sun, Shichao Jiao

{"title":"Multi-view stereo network with point attention","authors":"Rong Zhao, Zhuoer Gu, Xie Han, Ligang He, Fusheng Sun, Shichao Jiao","doi":"10.1007/s10489-023-04806-y","DOIUrl":"10.1007/s10489-023-04806-y","url":null,"abstract":"<div><p>In recent years, learning-based multi-view stereo (MVS) reconstruction has gained superiority when compared with traditional methods. In this paper, we introduce a novel point-attention network, with an attention mechanism, based on the point cloud structure. During the reconstruction process, our method with an attention mechanism can guide the network to pay more attention to complex areas such as thin structures and low-texture surfaces. We first infer a coarse depth map using a modified classical MVS deep framework and convert it into the corresponding point cloud. Then, we add the high-frequency features and different-resolution features of the raw images to the point cloud. Finally, our network guides the weight distribution of points in different dimensions through the attention mechanism and computes the depth displacement of each point iteratively as the depth residual, which is added to the coarse depth prediction to obtain the final high-resolution depth map. Experimental results show that our proposed point-attention architecture can achieve a significant improvement in some scenes without reasonable geometrical assumptions on the <i>DTU</i> dataset and the <i>Tanks and Temples</i> dataset, suggesting that our method has a strong generalization ability.\u0000</p></div>","PeriodicalId":8041,"journal":{"name":"Applied Intelligence","volume":"53 22","pages":"26622 - 26636"},"PeriodicalIF":5.3,"publicationDate":"2023-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71910687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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