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  • Open Access


    Medical Image Fusion Based on Anisotropic Diffusion and Non-Subsampled Contourlet Transform

    Bhawna Goyal1,*, Ayush Dogra2, Rahul Khoond1, Dawa Chyophel Lepcha1, Vishal Goyal3, Steven L. Fernandes4

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 311-327, 2023, DOI:10.32604/cmc.2023.038398

    Abstract The synthesis of visual information from multiple medical imaging inputs to a single fused image without any loss of detail and distortion is known as multimodal medical image fusion. It improves the quality of biomedical images by preserving detailed features to advance the clinical utility of medical imaging meant for the analysis and treatment of medical disorders. This study develops a novel approach to fuse multimodal medical images utilizing anisotropic diffusion (AD) and non-subsampled contourlet transform (NSCT). First, the method employs anisotropic diffusion for decomposing input images to their base and detail layers to coarsely split two features of input… More >

  • Open Access


    Intelligent Networked Control of Vasoactive Drug Infusion for Patients with Uncertain Sensitivity

    Mohamed Esmail Karar1,2,*, Amged Sayed A. Mahmoud2,3

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 721-739, 2023, DOI:10.32604/csse.2023.039235

    Abstract Abnormal high blood pressure or hypertension is still the leading risk factor for death and disability worldwide. This paper presents a new intelligent networked control of medical drug infusion system to regulate the mean arterial blood pressure for hypertensive patients with different health status conditions. The infusion of vasoactive drugs to patients endures various issues, such as variation of sensitivity and noise, which require effective and powerful systems to ensure robustness and good performance. The developed intelligent networked system is composed of a hybrid control scheme of interval type-2 fuzzy (IT2F) logic and teaching-learning-based optimization (TLBO) algorithm. This networked IT2F… More >

  • Open Access


    Predictive Multimodal Deep Learning-Based Sustainable Renewable and Non-Renewable Energy Utilization

    Abdelwahed Motwakel1,*, Marwa Obayya2, Nadhem Nemri3, Khaled Tarmissi4, Heba Mohsen5, Mohammed Rizwanulla6, Ishfaq Yaseen6, Abu Sarwar Zamani6

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 1267-1281, 2023, DOI:10.32604/csse.2023.037735

    Abstract Recently, renewable energy (RE) has become popular due to its benefits, such as being inexpensive, low-carbon, ecologically friendly, steady, and reliable. The RE sources are gradually combined with non-renewable energy (NRE) sources into electric grids to satisfy energy demands. Since energy utilization is highly related to national energy policy, energy prediction using artificial intelligence (AI) and deep learning (DL) based models can be employed for energy prediction on RE and NRE power resources. Predicting energy consumption of RE and NRE sources using effective models becomes necessary. With this motivation, this study presents a new multimodal fusion-based predictive tool for energy… More >

  • Open Access


    Multi-Feature Fusion Book Recommendation Model Based on Deep Neural Network

    Zhaomin Liang, Tingting Liang*

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 205-219, 2023, DOI:10.32604/csse.2023.037124

    Abstract The traditional recommendation algorithm represented by the collaborative filtering algorithm is the most classical and widely recommended algorithm in the practical industry. Most book recommendation systems also use this algorithm. However, the traditional recommendation algorithm represented by the collaborative filtering algorithm cannot deal with the data sparsity well. This algorithm only uses the shallow feature design of the interaction between readers and books, so it fails to achieve the high-level abstract learning of the relevant attribute features of readers and books, leading to a decline in recommendation performance. Given the above problems, this study uses deep learning technology to model… More >

  • Open Access


    Leveraging Multimodal Ensemble Fusion-Based Deep Learning for COVID-19 on Chest Radiographs

    Mohamed Yacin Sikkandar1,*, K. Hemalatha2, M. Subashree3, S. Srinivasan4, Seifedine Kadry5,6,7, Jungeun Kim8, Keejun Han9

    Computer Systems Science and Engineering, Vol.47, No.1, pp. 873-889, 2023, DOI:10.32604/csse.2023.035730

    Abstract Recently, COVID-19 has posed a challenging threat to researchers, scientists, healthcare professionals, and administrations over the globe, from its diagnosis to its treatment. The researchers are making persistent efforts to derive probable solutions for managing the pandemic in their areas. One of the widespread and effective ways to detect COVID-19 is to utilize radiological images comprising X-rays and computed tomography (CT) scans. At the same time, the recent advances in machine learning (ML) and deep learning (DL) models show promising results in medical imaging. Particularly, the convolutional neural network (CNN) model can be applied to identifying abnormalities on chest radiographs.… More >

  • Open Access


    Experimental Study on the Influence of Fracturing Fluid Retention on Shale Gas Diffusion Law

    Zhiyuan Yao1,2,3, Jing Sun1,2,3,*, Dehua Liu1,2,3

    Energy Engineering, Vol.120, No.8, pp. 1853-1866, 2023, DOI:10.32604/ee.2023.025846

    Abstract Shale gas reservoirs have poor physical properties and a large number of micro-nano pores have been developed. Shale gas wells have no natural productivity and need fracturing reconstruction measures to put into production. However, the fracturing fluid will enter the reservoir space of shale matrix after fracturing and affect the production of shale gas. At present, there is no consensus on the influence of fracturing fluid retention on gas well production. Based on this, the paper adopts gas molecular transport analyzer to carry out experimental research on the influence of fracturing fluid on shale gas diffusion law after entering matrix… More >

  • Open Access



    Zhiyun Wang , Zixuan Zhou, Mo Yang

    Frontiers in Heat and Mass Transfer, Vol.14, No.1, pp. 1-7, 2020, DOI:10.5098/hmt.14.13

    Abstract Double diffusive natural convection in an open cavity under the Soret and Dufour effect is simulated numerically. The influences of different Rayleigh numbers (range from 103 to 107), Lewis numbers (range from 0.5 to 8), buoyancy ratios (range from -5 to 5) and Soret and Dufour (range from 0 to 0.5) on the flow field, temperature and concentration distributions, as well as on the variation of the average Nusselt number and the average Sherwood number are investigated. The result shows that, when buoyancy ratios is -1, the average Nusselt number and the average Sherwood number reaches the minimum, namely the… More >

  • Open Access


    PF-YOLOv4-Tiny: Towards Infrared Target Detection on Embedded Platform

    Wenbo Li, Qi Wang*, Shang Gao

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 921-938, 2023, DOI:10.32604/iasc.2023.038257

    Abstract Infrared target detection models are more required than ever before to be deployed on embedded platforms, which requires models with less memory consumption and better real-time performance while considering accuracy. To address the above challenges, we propose a modified You Only Look Once (YOLO) algorithm PF-YOLOv4-Tiny. The algorithm incorporates spatial pyramidal pooling (SPP) and squeeze-and-excitation (SE) visual attention modules to enhance the target localization capability. The PANet-based-feature pyramid networks (P-FPN) are proposed to transfer semantic information and location information simultaneously to ameliorate detection accuracy. To lighten the network, the standard convolutions other than the backbone network are replaced with depthwise… More >

  • Open Access


    Optimizing Spatial Relationships in GCN to Improve the Classification Accuracy of Remote Sensing Images

    Zimeng Yang, Qiulan Wu, Feng Zhang*, Xuefei Chen, Weiqiang Wang, Xueshen Zhang

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 491-506, 2023, DOI:10.32604/iasc.2023.037558

    Abstract Semantic segmentation of remote sensing images is one of the core tasks of remote sensing image interpretation. With the continuous development of artificial intelligence technology, the use of deep learning methods for interpreting remote-sensing images has matured. Existing neural networks disregard the spatial relationship between two targets in remote sensing images. Semantic segmentation models that combine convolutional neural networks (CNNs) and graph convolutional neural networks (GCNs) cause a lack of feature boundaries, which leads to the unsatisfactory segmentation of various target feature boundaries. In this paper, we propose a new semantic segmentation model for remote sensing images (called DGCN hereinafter),… More >

  • Open Access


    Deep Learning Based Energy Consumption Prediction on Internet of Things Environment

    S. Balaji*, S. Karthik

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 727-743, 2023, DOI:10.32604/iasc.2023.037409

    Abstract The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption; this is because EC is intimately tied to other forms of energy, such as oil and natural gas. For the purpose of determining and bettering overall energy consumption, there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things (IoT). Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable, and it has proven to be an effective tool for… More >

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