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  • Open Access

    ARTICLE

    Optical Based Gradient-Weighted Class Activation Mapping and Transfer Learning Integrated Pneumonia Prediction Model

    Chia-Wei Jan1, Yu-Jhih Chiu1, Kuan-Lin Chen2, Ting-Chun Yao3, Ping-Huan Kuo1,4,*

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 2989-3010, 2023, DOI:10.32604/csse.2023.042078 - 09 November 2023

    Abstract Pneumonia is a common lung disease that is more prone to affect the elderly and those with weaker respiratory systems. However, hospital medical resources are limited, and sometimes the workload of physicians is too high, which can affect their judgment. Therefore, a good medical assistance system is of great significance for improving the quality of medical care. This study proposed an integrated system by combining transfer learning and gradient-weighted class activation mapping (Grad-CAM). Pneumonia is a common lung disease that is generally diagnosed using X-rays. However, in areas with limited medical resources, a shortage of… More >

  • Open Access

    REVIEW

    An Overview of Modern Cartographic Trends Aligned with the ICA’s Perspective

    Maan Habib1,*, Maan Okayli2

    Revue Internationale de Géomatique, Vol.32, pp. 1-16, 2023, DOI:10.32604/rig.2023.043399 - 30 September 2023

    Abstract This study provides a comprehensive overview of modern cartography innovations and emerging trends, highlighting the importance of geospatial representation in various fields. It discusses recent advancements in geospatial data collection techniques, including satellite and aerial imagery, Light Detection and Ranging (LiDAR) technology, and crowdsourcing. The research also investigates the integration of big data, machine learning, and real-time processing in Geographic Information Systems (GIS), as well as advances in geospatial visualization. In addition, it examines the role of cartography in addressing global challenges such as climate change, disaster management, and urban planning in line with the More >

  • Open Access

    ARTICLE

    Conception et production d’un guide patient pour accompagner la reprise du travail après un cancer du sein : une application de l’Intervention Mapping

    Guillaume Broc1,*, Julien Carretier2,3, Sabrina Rouat4, Laure Guittard5,6, Julien Péron7,8, Béatrice Fervers3,9, Laurent Letrilliart6,10, Philippe Sarnin4, Jean-Baptiste Fassier11,12, Marion Lamort-Bouché6,10

    Psycho-Oncologie, Vol.17, No.3, pp. 167-179, 2023, DOI:10.32604/po.2023.044730 - 30 September 2023

    Abstract Objectif : Le retour au travail (RAT) après un cancer du sein est un processus complexe qui interroge les trajectoires individuelles des patients et celle des acteurs dans leur environnement (ou écosystème). La planification d’une intervention dans ce contexte nécessite une méthodologie appropriée qui intègre cette complexité, à l’image de l’Intervention Mapping (IM). L’objectif de l’article est de décrire une application de l’IM pour la conception et la production d’un guide patient de RAT après un cancer du sein. Matériel et méthodes : Suivant le protocole d’IM, le guide a été coconstruit avec un comité stratégique (COS)… More >

  • Open Access

    ARTICLE

    An Improved Honey Badger Algorithm through Fusing Multi-Strategies

    Zhiwei Ye1, Tao Zhao1, Chun Liu1,*, Daode Zhang1, Wanfang Bai2

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1479-1495, 2023, DOI:10.32604/cmc.2023.038787 - 30 August 2023

    Abstract The Honey Badger Algorithm (HBA) is a novel meta-heuristic algorithm proposed recently inspired by the foraging behavior of honey badgers. The dynamic search behavior of honey badgers with sniffing and wandering is divided into exploration and exploitation in HBA, which has been applied in photovoltaic systems and optimization problems effectively. However, HBA tends to suffer from the local optimum and low convergence. To alleviate these challenges, an improved HBA (IHBA) through fusing multi-strategies is presented in the paper. It introduces Tent chaotic mapping and composite mutation factors to HBA, meanwhile, the random control parameter is More >

  • Open Access

    ARTICLE

    An Improved High Precision 3D Semantic Mapping of Indoor Scenes from RGB-D Images

    Jing Xin1,*, Kenan Du1, Jiale Feng1, Mao Shan2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2621-2640, 2023, DOI:10.32604/cmes.2023.027467 - 03 August 2023

    Abstract This paper proposes an improved high-precision 3D semantic mapping method for indoor scenes using RGB-D images. The current semantic mapping algorithms suffer from low semantic annotation accuracy and insufficient real-time performance. To address these issues, we first adopt the Elastic Fusion algorithm to select key frames from indoor environment image sequences captured by the Kinect sensor and construct the indoor environment space model. Then, an indoor RGB-D image semantic segmentation network is proposed, which uses multi-scale feature fusion to quickly and accurately obtain object labeling information at the pixel level of the spatial point cloud More >

  • Open Access

    ARTICLE

    Quantitative Trait Loci (QTL) Mapping and Marker Analysis of Fatty Acids in Peanut

    Xiao Han, Songnan Yang, Xueying Li, Qiulin Wu, Yongyi Xing, Jun Zhang*, Fenglou Ling*

    Phyton-International Journal of Experimental Botany, Vol.92, No.9, pp. 2577-2589, 2023, DOI:10.32604/phyton.2023.029440 - 28 July 2023

    Abstract Peanut, with high oil content, has been a major oil and food crop globally. The compositions of the fatty acids are the common factors in determining the oil quality. In the present study, an F2 segregated population with 140 individuals derived from the cross of Weihua8 (a cultivar) and 12L49 (a line with high oleic acid concentration) was used to construct a genetic map and conduct QTL mapping analysis. A total of 103 polymorphic SSR primers were utilized for genotyping the RILs and finally generating the SSR loci. Within the 103 SSR loci, a genetic… More >

  • Open Access

    ARTICLE

    Parameterization Transfer for a Planar Computational Domain in Isogeometric Analysis

    Jinlan Xu*, Shuxin Xiao, Gang Xu, Renshu Gu

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1957-1973, 2023, DOI:10.32604/cmes.2023.028665 - 26 June 2023

    Abstract In this paper, we propose a parameterization transfer algorithm for planar domains bounded by B-spline curves, where the shapes of the planar domains are similar. The domain geometries are considered to be similar if their simplified skeletons have the same structures. One domain we call source domain, and it is parameterized using multi-patch B-spline surfaces. The resulting parameterization is C1 continuous in the regular region and G1 continuous around singular points regardless of whether the parameterization of the source domain is C1/G1 continuous or not. In this algorithm, boundary control points of the source domain… More >

  • Open Access

    ARTICLE

    Two-Layer Information Granulation: Mapping-Equivalence Neighborhood Rough Set and Its Attribute Reduction

    Changshun Liu1, Yan Liu1, Jingjing Song1,*, Taihua Xu1,2

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2059-2075, 2023, DOI:10.32604/iasc.2023.039592 - 21 June 2023

    Abstract Attribute reduction, as one of the essential applications of the rough set, has attracted extensive attention from scholars. Information granulation is a key step of attribute reduction, and its efficiency has a significant impact on the overall efficiency of attribute reduction. The information granulation of the existing neighborhood rough set models is usually a single layer, and the construction of each information granule needs to search all the samples in the universe, which is inefficient. To fill such gap, a new neighborhood rough set model is proposed, which aims to improve the efficiency of attribute… More >

  • Open Access

    ARTICLE

    Semi-Supervised Clustering Algorithm Based on Deep Feature Mapping

    Xiong Xu1, Chun Zhou2,*, Chenggang Wang1, Xiaoyan Zhang2, Hua Meng2

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 815-831, 2023, DOI:10.32604/iasc.2023.034656 - 29 April 2023

    Abstract Clustering analysis is one of the main concerns in data mining. A common approach to the clustering process is to bring together points that are close to each other and separate points that are away from each other. Therefore, measuring the distance between sample points is crucial to the effectiveness of clustering. Filtering features by label information and measuring the distance between samples by these features is a common supervised learning method to reconstruct distance metric. However, in many application scenarios, it is very expensive to obtain a large number of labeled samples. In this… More >

  • Open Access

    ARTICLE

    Robust Watermarking Algorithm for Medical Images Based on Non-Subsampled Shearlet Transform and Schur Decomposition

    Meng Yang1, Jingbing Li1,2,*, Uzair Aslam Bhatti1,2, Chunyan Shao1, Yen-Wei Chen3

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5539-5554, 2023, DOI:10.32604/cmc.2023.036904 - 29 April 2023

    Abstract With the development of digitalization in healthcare, more and more information is delivered and stored in digital form, facilitating people’s lives significantly. In the meanwhile, privacy leakage and security issues come along with it. Zero watermarking can solve this problem well. To protect the security of medical information and improve the algorithm’s robustness, this paper proposes a robust watermarking algorithm for medical images based on Non-Subsampled Shearlet Transform (NSST) and Schur decomposition. Firstly, the low-frequency subband image of the original medical image is obtained by NSST and chunked. Secondly, the Schur decomposition of low-frequency blocks… More >

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