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Search Results (118)
  • Open Access

    REVIEW

    Ensemble Deep Learning Approaches in Health Care: A Review

    Aziz Alotaibi*

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 3741-3771, 2025, DOI:10.32604/cmc.2025.061998 - 06 March 2025

    Abstract Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data. Recently, both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions. With the growth in popularity of deep learning and ensemble learning algorithms, they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data. Ensemble deep learning has exhibited significant performance in enhancing learning generalization through… More >

  • Open Access

    ARTICLE

    Harnessing Trend Theory to Enhance Distributed Proximal Point Algorithm Approaches for Multi-Area Economic Dispatch Optimization

    Yaming Ren1,*, Xing Deng2

    CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4503-4533, 2025, DOI:10.32604/cmc.2024.059864 - 06 March 2025

    Abstract The exponential growth in the scale of power systems has led to a significant increase in the complexity of dispatch problem resolution, particularly within multi-area interconnected power grids. This complexity necessitates the employment of distributed solution methodologies, which are not only essential but also highly desirable. In the realm of computational modelling, the multi-area economic dispatch problem (MAED) can be formulated as a linearly constrained separable convex optimization problem. The proximal point algorithm (PPA) is particularly adept at addressing such mathematical constructs effectively. This study introduces parallel (PPPA) and serial (SPPA) variants of the PPA… More >

  • Open Access

    ARTICLE

    Feature Engineering Methods for Analyzing Blood Samples for Early Diagnosis of Hepatitis Using Machine Learning Approaches

    Mohamed A.G. Hazber1,*, Ebrahim Mohammed Senan2,3, Hezam Saud Alrashidi1

    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3229-3254, 2025, DOI:10.32604/cmes.2025.062302 - 03 March 2025

    Abstract Hepatitis is an infection that affects the liver through contaminated foods or blood transfusions, and it has many types, from normal to serious. Hepatitis is diagnosed through many blood tests and factors; Artificial Intelligence (AI) techniques have played an important role in early diagnosis and help physicians make decisions. This study evaluated the performance of Machine Learning (ML) algorithms on the hepatitis data set. The dataset contains missing values that have been processed and outliers removed. The dataset was counterbalanced by the Synthetic Minority Over-sampling Technique (SMOTE). The features of the data set were processed… More >

  • Open Access

    REVIEW

    Targeting myeloid-derived suppressor cells in the tumor microenvironment: potential therapeutic approaches for osteosarcoma

    HYE IN KA#, SE HWAN MUN#, SORA HAN#, YOUNG YANG*

    Oncology Research, Vol.33, No.3, pp. 519-531, 2025, DOI:10.32604/or.2024.056860 - 28 February 2025

    Abstract Osteosarcoma is a bone malignancy characterized by strong invasiveness and rapid disease progression. The tumor microenvironment of osteosarcoma contains various types of immune cells, including myeloid-derived suppressor cells, macrophages, T cells, and B cells. Imbalances of these immune cells can promote the proliferation and metastasis of osteosarcoma. Recent studies have indicated a substantial increase in the levels of myeloid-derived suppressor cells, an immune cell associated with immunosuppressive and pro-cancer effects, in the peripheral blood of patients with osteosarcoma. Moreover, the levels of the pro-inflammatory cytokine interleukin 18 are positively correlated with those of myeloid-derived suppressor More >

  • Open Access

    REVIEW

    A Review on Vision-Language-Based Approaches: Challenges and Applications

    Huu-Tuong Ho1,#, Luong Vuong Nguyen1,#, Minh-Tien Pham1, Quang-Huy Pham1, Quang-Duong Tran1, Duong Nguyen Minh Huy2, Tri-Hai Nguyen3,*

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1733-1756, 2025, DOI:10.32604/cmc.2025.060363 - 17 February 2025

    Abstract In multimodal learning, Vision-Language Models (VLMs) have become a critical research focus, enabling the integration of textual and visual data. These models have shown significant promise across various natural language processing tasks, such as visual question answering and computer vision applications, including image captioning and image-text retrieval, highlighting their adaptability for complex, multimodal datasets. In this work, we review the landscape of Bootstrapping Language-Image Pre-training (BLIP) and other VLM techniques. A comparative analysis is conducted to assess VLMs’ strengths, limitations, and applicability across tasks while examining challenges such as scalability, data quality, and fine-tuning complexities. More >

  • Open Access

    ARTICLE

    Innovative Approaches to Task Scheduling in Cloud Computing Environments Using an Advanced Willow Catkin Optimization Algorithm

    Jeng-Shyang Pan1,2, Na Yu1, Shu-Chuan Chu1,*, An-Ning Zhang1, Bin Yan3, Junzo Watada4

    CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2495-2520, 2025, DOI:10.32604/cmc.2024.058450 - 17 February 2025

    Abstract The widespread adoption of cloud computing has underscored the critical importance of efficient resource allocation and management, particularly in task scheduling, which involves assigning tasks to computing resources for optimized resource utilization. Several meta-heuristic algorithms have shown effectiveness in task scheduling, among which the relatively recent Willow Catkin Optimization (WCO) algorithm has demonstrated potential, albeit with apparent needs for enhanced global search capability and convergence speed. To address these limitations of WCO in cloud computing task scheduling, this paper introduces an improved version termed the Advanced Willow Catkin Optimization (AWCO) algorithm. AWCO enhances the algorithm’s… More >

  • Open Access

    REVIEW

    Macrophage polarization in cardiac transplantation: Insights into immune modulation and therapeutic approaches

    JINGWEI JIANG1,2, BO JIA3, CHUAN WANG3, CHEN FANG1, YUGUI LI1, GUOXING LING1, BAOSHI ZHENG1,*, CHENG LUO1,*

    BIOCELL, Vol.49, No.1, pp. 61-78, 2025, DOI:10.32604/biocell.2024.056981 - 24 January 2025

    Abstract The role and regulatory mechanisms of macrophage polarization in cardiac transplantation have gained significant attention. Macrophages can polarize into either the M1 (pro-inflammatory) or M2 (anti-inflammatory) phenotype in response to environmental cues. M1 macrophages facilitate transplant rejection by releasing inflammatory mediators and activating T cells, whereas M2 macrophages support graft survival by secreting anti-inflammatory factors and promoting tissue repair. Mitochondrial quality control regulation plays a crucial role in macrophage polarization, which may influence graft survival and immune responses. This review provides an overview of the current understanding of mitochondrial quality control-regulated macrophage polarization in cardiac More >

  • Open Access

    REVIEW

    A Comprehensive Review of Next-Gen UAV Swarm Robotics: Optimisation Techniques and Control Strategies for Dynamic Environments

    Ghulam E Mustafa Abro1,*, Ayman M Abdallah1,2, Faizan Zahid3, Saleem Ahmed4

    Intelligent Automation & Soft Computing, Vol.40, pp. 99-123, 2025, DOI:10.32604/iasc.2025.060364 - 23 January 2025

    Abstract This review synthesises and assesses the most recent developments in Unmanned Aerial Vehicles (UAVs) and swarm robotics, with a specific emphasis on optimisation strategies, path planning, and formation control. The study identifies key methodologies that are driving progress in the field by conducting a comprehensive analysis of seven critical publications. The following are included: sensor-based platforms that facilitate effective obstacle avoidance, cluster-based hierarchical path planning for efficient navigation, and adaptive hybrid controllers for dynamic environments. The review emphasises the substantial contribution of optimisation techniques, including Max-Min Ant Colony Optimisation (MMACO), to the improvement of convergence… More >

  • Open Access

    ARTICLE

    Unveiling the predictive power of bacterial response-related genes signature in hepatocellular carcinoma: with bioinformatics analyses and experimental approaches

    ATIEH POURBAGHERI-SIGAROODI1, MAJID MOMENY2, NIMA REZAEI3,4,5, FATEMEH FALLAH1,*, DAVOOD BASHASH6,*

    BIOCELL, Vol.48, No.12, pp. 1781-1804, 2024, DOI:10.32604/biocell.2024.055848 - 30 December 2024

    Abstract Background: Despite progress in therapeutic strategies, treatment failure in hepatocellular carcinoma (HCC) remains a major challenge, resulting in low survival rates. The presence of bacteria and the host’s immune response to bacteria can influence the pathogenesis and progression of HCC. We developed a risk model based on bacterial response-related genes (BRGs) using gene sets from molecular signature databases to identify new markers for predicting HCC outcomes and categorizing patients into different risk groups. Methods: The data from the Cancer Genome Atlas (TCGA) portal was retrieved, and differentially expressed BRGs were identified. Uni- and multivariate Cox… More >

  • Open Access

    REVIEW

    Blockchain-Assisted Electronic Medical Data-Sharing: Developments, Approaches and Perspectives

    Chenquan Gan1,*, Xinghai Xiao2, Qingyi Zhu1, Deepak Kumar Jain3,4, Akanksha Saini5

    CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 3421-3450, 2024, DOI:10.32604/cmc.2024.059359 - 19 December 2024

    Abstract Medical blockchain data-sharing is a technique that employs blockchain technology to facilitate the sharing of electronic medical data. The blockchain is a decentralized digital ledger that ensures data-sharing security, transparency, and traceability through cryptographic technology and consensus algorithms. Consequently, medical blockchain data-sharing methods have garnered significant attention and research efforts. Nevertheless, current methods have different storage and transmission measures for original data in the medical blockchain, resulting in large differences in performance and privacy. Therefore, we divide the medical blockchain data-sharing method into on-chain sharing and off-chain sharing according to the original data storage location. More >

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