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  • Open Access

    ARTICLE

    Multimodality Medical Image Fusion Based on Pixel Significance with Edge-Preserving Processing for Clinical Applications

    Bhawna Goyal1, Ayush Dogra2, Dawa Chyophel Lepcha1, Rajesh Singh3, Hemant Sharma4, Ahmed Alkhayyat5, Manob Jyoti Saikia6,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4317-4342, 2024, DOI:10.32604/cmc.2024.047256

    Abstract Multimodal medical image fusion has attained immense popularity in recent years due to its robust technology for clinical diagnosis. It fuses multiple images into a single image to improve the quality of images by retaining significant information and aiding diagnostic practitioners in diagnosing and treating many diseases. However, recent image fusion techniques have encountered several challenges, including fusion artifacts, algorithm complexity, and high computing costs. To solve these problems, this study presents a novel medical image fusion strategy by combining the benefits of pixel significance with edge-preserving processing to achieve the best fusion performance. First, the method employs a cross-bilateral… More >

  • Open Access

    ARTICLE

    Deep Neural Network Architecture Search via Decomposition-Based Multi-Objective Stochastic Fractal Search

    Hongshang Xu1, Bei Dong1,2,*, Xiaochang Liu1, Xiaojun Wu1,2

    Intelligent Automation & Soft Computing, Vol.38, No.2, pp. 185-202, 2023, DOI:10.32604/iasc.2023.041177

    Abstract Deep neural networks often outperform classical machine learning algorithms in solving real-world problems. However, designing better networks usually requires domain expertise and consumes significant time and computing resources. Moreover, when the task changes, the original network architecture becomes outdated and requires redesigning. Thus, Neural Architecture Search (NAS) has gained attention as an effective approach to automatically generate optimal network architectures. Most NAS methods mainly focus on achieving high performance while ignoring architectural complexity. A myriad of research has revealed that network performance and structural complexity are often positively correlated. Nevertheless, complex network structures will bring enormous computing resources. To cope… More >

  • Open Access

    ARTICLE

    Study on the Fractal Characteristics of Cavitation Shedding over a Twisted Hydrofoil

    Zilong Hu1, Weilong Guang1, Ran Tao1,2,*, Ruofu Xiao1,2, Di Zhu3

    Frontiers in Heat and Mass Transfer, Vol.21, pp. 161-178, 2023, DOI:10.32604/fhmt.2023.041402

    Abstract Cavitation and cavitation erosion often occur and seriously threaten the safe and stable operation of hydraulic machinery. However, during the operation of hydraulic machinery, the cavitation flow field is often difficult to contact and measure, and the shedding and development characteristics of cavitation flow are unknown. This paper uses the Detached Eddy Simulation (DES) turbulence model and Zwart-Gerber-Belamri (ZGB) cavitation model to conduct numerical research on the cavitation flow of a twisted hydrofoil and verifies the effectiveness of numerical simulation by comparing it with experimental results. Then, based on the fractal dimension method, the number and fractal dimension of the… More >

  • Open Access

    ARTICLE

    Fractal Fractional Order Operators in Computational Techniques for Mathematical Models in Epidemiology

    Muhammad Farman1,2,4, Ali Akgül3,9,*, Mir Sajjad Hashemi5, Liliana Guran6,7, Amelia Bucur8,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1385-1403, 2024, DOI:10.32604/cmes.2023.028803

    Abstract New fractional operators, the COVID-19 model has been studied in this paper. By using different numerical techniques and the time fractional parameters, the mechanical characteristics of the fractional order model are identified. The uniqueness and existence have been established. The model’s Ulam-Hyers stability analysis has been found. In order to justify the theoretical results, numerical simulations are carried out for the presented method in the range of fractional order to show the implications of fractional and fractal orders. We applied very effective numerical techniques to obtain the solutions of the model and simulations. Also, we present conditions of existence for… More >

  • Open Access

    PROCEEDINGS

    Spontaneous Imbibition Considering Fractal Theory and Dynamic Contact Angle in Tight Sandstone

    Jingjing Ping1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.25, No.4, pp. 1-1, 2023, DOI:10.32604/icces.2023.08787

    Abstract In the process of tight oil reservoir development, there are a lot of spontaneous imbibition phenomena which are beneficial to achieving the purpose of enhancing oil recovery. It is of great significance to study the law of spontaneous imbibition of oil and water at the pore scale of tight sandstone. In this paper, we study the law of spontaneous imbibition at the pore scale of tight sandstone by combining theoretical research and numerical simulation. Based on the fractal theory and the capillary bundle model, we establish a mathematical model of spontaneous imbibition in porous media considering the dynamic contact angle.… More >

  • Open Access

    ARTICLE

    Sparsity-Enhanced Model-Based Method for Intelligent Fault Detection of Mechanical Transmission Chain in Electrical Vehicle

    Wangpeng He1,*, Yue Zhou1, Xiaoya Guo2, Deshun Hu1, Junjie Ye3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2495-2511, 2023, DOI:10.32604/cmes.2023.027896

    Abstract In today’s world, smart electric vehicles are deeply integrated with smart energy, smart transportation and smart cities. In electric vehicles (EVs), owing to the harsh working conditions, mechanical parts are prone to fatigue damages, which endanger the driving safety of EVs. The practice has proved that the identification of periodic impact characteristics (PICs) can effectively indicate mechanical faults. This paper proposes a novel model-based approach for intelligent fault diagnosis of mechanical transmission train in EVs. The essential idea of this approach lies in the fusion of statistical information and model information from a dynamic process. In the algorithm, a novel… More >

  • Open Access

    ARTICLE

    Real-Time Multi Fractal Trust Evaluation Model for Efficient Intrusion Detection in Cloud

    S. Priya1, R. S. Ponmagal2,*

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1895-1907, 2023, DOI:10.32604/iasc.2023.039814

    Abstract Handling service access in a cloud environment has been identified as a critical challenge in the modern internet world due to the increased rate of intrusion attacks. To address such threats towards cloud services, numerous techniques exist that mitigate the service threats according to different metrics. The rule-based approaches are unsuitable for new threats, whereas trust-based systems estimate trust value based on behavior, flow, and other features. However, the methods suffer from mitigating intrusion attacks at a higher rate. This article presents a novel Multi Fractal Trust Evaluation Model (MFTEM) to overcome these deficiencies. The method involves analyzing service growth,… More >

  • Open Access

    ARTICLE

    Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

    Doaa Sami Khafaga1, El-Sayed M. El-kenawy2, Amel Ali Alhussan1,*, Marwa M. Eid3

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2117-2132, 2023, DOI:10.32604/iasc.2023.038811

    Abstract The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments. Meanwhile, the accurate prediction can be realized using the recent advances in machine learning and predictive models. This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory (LSTM) units. The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy. This optimization algorithm is based on the recently emerged dipper-throated optimization (DTO) and stochastic… More >

  • Open Access

    ARTICLE

    Evaluation of Water Transfer Capacity of Poplar with Pectinase Treated under the Solar Interface Evaporation

    Wei Xiong1,2, Dagang Li1,*, Peixing Wei2, Lin Wang2, Qian Feng1

    Journal of Renewable Materials, Vol.11, No.5, pp. 2265-2278, 2023, DOI:10.32604/jrm.2023.025483

    Abstract Poplar wood, which was used as the absorption material for the solar-driven interfacial evaporation, was treated for 3 days, 6 days and 9 days with the pectinase, and then was simulated for photothermal evaporation test at one standard solar radiation intensity (1 kW⋅m−2). The effects of pectinase treatment on cell passage and water migration capacity of poplars were analyzed by the mercury intrusion porosimetry, the scanning electron microscope and fractal theory. It was found that the pit membrane and the ray parenchyma cells of poplar wood were degraded and destroyed after pectinase treatment. Compared with the untreated poplar wood, the evaporation… More >

  • Open Access

    ARTICLE

    Xception-Fractalnet: Hybrid Deep Learning Based Multi-Class Classification of Alzheimer’s Disease

    Mudiyala Aparna, Battula Srinivasa Rao*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6909-6932, 2023, DOI:10.32604/cmc.2023.034796

    Abstract Neurological disorders such as Alzheimer’s disease (AD) are very challenging to treat due to their sensitivity, technical challenges during surgery, and high expenses. The complexity of the brain structures makes it difficult to distinguish between the various brain tissues and categorize AD using conventional classification methods. Furthermore, conventional approaches take a lot of time and might not always be precise. Hence, a suitable classification framework with brain imaging may produce more accurate findings for early diagnosis of AD. Therefore in this paper, an effective hybrid Xception and Fractalnet-based deep learning framework are implemented to classify the stages of AD into… More >

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