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  • Open Access

    ARTICLE

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

    Samar M. Alqhtani1, Toufique A. Soomro2,*, Faisal Bin Ubaid3, Ahmed Ali4, Muhammad Irfan5, Abdullah A. Asiri6

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1539-1562, 2024, DOI:10.32604/cmes.2024.051475

    Abstract Cancer-related to the nervous system and brain tumors is a leading cause of mortality in various countries. Magnetic resonance imaging (MRI) and computed tomography (CT) are utilized to capture brain images. MRI plays a crucial role in the diagnosis of brain tumors and the examination of other brain disorders. Typically, manual assessment of MRI images by radiologists or experts is performed to identify brain tumors and abnormalities in the early stages for timely intervention. However, early diagnosis of brain tumors is intricate, necessitating the use of computerized methods. This research introduces an innovative approach for… More > Graphic Abstract

    Contrast Normalization Strategies in Brain Tumor Imaging: From Preprocessing to Classification

  • Open Access

    ARTICLE

    Contrastive Consistency and Attentive Complementarity for Deep Multi-View Subspace Clustering

    Jiao Wang, Bin Wu*, Hongying Zhang

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 143-160, 2024, DOI:10.32604/cmc.2023.046011

    Abstract Deep multi-view subspace clustering (DMVSC) based on self-expression has attracted increasing attention due to its outstanding performance and nonlinear application. However, most existing methods neglect that view-private meaningless information or noise may interfere with the learning of self-expression, which may lead to the degeneration of clustering performance. In this paper, we propose a novel framework of Contrastive Consistency and Attentive Complementarity (CCAC) for DMVsSC. CCAC aligns all the self-expressions of multiple views and fuses them based on their discrimination, so that it can effectively explore consistent and complementary information for achieving precise clustering. Specifically, the More >

  • Open Access

    ARTICLE

    An Artificial Intelligence-Based Framework for Fruits Disease Recognition Using Deep Learning

    Irfan Haider1, Muhammad Attique Khan1,*, Muhammad Nazir1, Taerang Kim2, Jae-Hyuk Cha2

    Computer Systems Science and Engineering, Vol.48, No.2, pp. 529-554, 2024, DOI:10.32604/csse.2023.042080

    Abstract Fruit infections have an impact on both the yield and the quality of the crop. As a result, an automated recognition system for fruit leaf diseases is important. In artificial intelligence (AI) applications, especially in agriculture, deep learning shows promising disease detection and classification results. The recent AI-based techniques have a few challenges for fruit disease recognition, such as low-resolution images, small datasets for learning models, and irrelevant feature extraction. This work proposed a new fruit leaf leaf leaf disease recognition framework using deep learning features and improved pathfinder optimization. Three fruit types have been… More >

  • Open Access

    ARTICLE

    Differential Expression of Genes Related to Fruit Development and Capsaicinoids Synthesis in Habanero Pepper Plants Grown in Contrasting Soil Types

    Eduardo Burgos-Valencia1,#, Federico García-Laynes1,#, Ileana Echevarría-Machado1, Fatima Medina-Lara1, Miriam Monforte-González1, José Narváez-Zapata2,*, Manuel Martínez-Estévez1,*

    Phyton-International Journal of Experimental Botany, Vol.93, No.2, pp. 151-183, 2024, DOI:10.32604/phyton.2023.046943

    Abstract Habanero pepper (Capsicum chinense Jacq.) is a crop of economic relevance in the Peninsula of Yucatan. Its fruits have a high level of capsaicinoids compared to peppers grown in other regions of the world, which gives them industrial importance. Soil is an important factor that affects pepper development, nutritional quality, and capsaicinoid content. However, the effect of soil type on fruit development and capsaicinoid metabolism has been little understood. This work aimed to compare the effect of soils with contrasting characteristics, black soil (BS) and red soil (RS), on the expression of genes related to the… More >

  • Open Access

    ARTICLE

    Recommendation Method for Contrastive Enhancement of Neighborhood Information

    Hairong Wang, Beijing Zhou*, Lisi Zhang, He Ma

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 453-472, 2024, DOI:10.32604/cmc.2023.046560

    Abstract Knowledge graph can assist in improving recommendation performance and is widely applied in various personalized recommendation domains. However, existing knowledge-aware recommendation methods face challenges such as weak user-item interaction supervisory signals and noise in the knowledge graph. To tackle these issues, this paper proposes a neighbor information contrast-enhanced recommendation method by adding subtle noise to construct contrast views and employing contrastive learning to strengthen supervisory signals and reduce knowledge noise. Specifically, first, this paper adopts heterogeneous propagation and knowledge-aware attention networks to obtain multi-order neighbor embedding of users and items, mining the high-order neighbor information… More >

  • Open Access

    ARTICLE

    Person Re-Identification with Model-Contrastive Federated Learning in Edge-Cloud Environment

    Baixuan Tang1,2,#, Xiaolong Xu1,2,#, Fei Dai3, Song Wang4,*

    Intelligent Automation & Soft Computing, Vol.38, No.1, pp. 35-55, 2023, DOI:10.32604/iasc.2023.036715

    Abstract Person re-identification (ReID) aims to recognize the same person in multiple images from different camera views. Training person ReID models are time-consuming and resource-intensive; thus, cloud computing is an appropriate model training solution. However, the required massive personal data for training contain private information with a significant risk of data leakage in cloud environments, leading to significant communication overheads. This paper proposes a federated person ReID method with model-contrastive learning (MOON) in an edge-cloud environment, named FRM. Specifically, based on federated partial averaging, MOON warmup is added to correct the local training of individual edge… More >

  • Open Access

    ARTICLE

    A Fusion of Residual Blocks and Stack Auto Encoder Features for Stomach Cancer Classification

    Abdul Haseeb1, Muhammad Attique Khan2,*, Majed Alhaisoni3, Ghadah Aldehim4, Leila Jamel4, Usman Tariq5, Taerang Kim6, Jae-Hyuk Cha6

    CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3895-3920, 2023, DOI:10.32604/cmc.2023.045244

    Abstract Diagnosing gastrointestinal cancer by classical means is a hazardous procedure. Years have witnessed several computerized solutions for stomach disease detection and classification. However, the existing techniques faced challenges, such as irrelevant feature extraction, high similarity among different disease symptoms, and the least-important features from a single source. This paper designed a new deep learning-based architecture based on the fusion of two models, Residual blocks and Auto Encoder. First, the Hyper-Kvasir dataset was employed to evaluate the proposed work. The research selected a pre-trained convolutional neural network (CNN) model and improved it with several residual blocks.… More >

  • Open Access

    ARTICLE

    A Memory-Guided Anomaly Detection Model with Contrastive Learning for Multivariate Time Series

    Wei Zhang1, Ping He2,*, Ting Li2, Fan Yang1, Ying Liu3

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1893-1910, 2023, DOI:10.32604/cmc.2023.044253

    Abstract Some reconstruction-based anomaly detection models in multivariate time series have brought impressive performance advancements but suffer from weak generalization ability and a lack of anomaly identification. These limitations can result in the misjudgment of models, leading to a degradation in overall detection performance. This paper proposes a novel transformer-like anomaly detection model adopting a contrastive learning module and a memory block (CLME) to overcome the above limitations. The contrastive learning module tailored for time series data can learn the contextual relationships to generate temporal fine-grained representations. The memory block can record normal patterns of these… More >

  • Open Access

    ARTICLE

    Deep-Net: Fine-Tuned Deep Neural Network Multi-Features Fusion for Brain Tumor Recognition

    Muhammad Attique Khan1,2, Reham R. Mostafa3, Yu-Dong Zhang2, Jamel Baili4, Majed Alhaisoni5, Usman Tariq6, Junaid Ali Khan1, Ye Jin Kim7, Jaehyuk Cha7,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3029-3047, 2023, DOI:10.32604/cmc.2023.038838

    Abstract Manual diagnosis of brain tumors using magnetic resonance images (MRI) is a hectic process and time-consuming. Also, it always requires an expert person for the diagnosis. Therefore, many computer-controlled methods for diagnosing and classifying brain tumors have been introduced in the literature. This paper proposes a novel multimodal brain tumor classification framework based on two-way deep learning feature extraction and a hybrid feature optimization algorithm. NasNet-Mobile, a pre-trained deep learning model, has been fine-tuned and two-way trained on original and enhanced MRI images. The haze-convolutional neural network (haze-CNN) approach is developed and employed on the… More >

  • Open Access

    ARTICLE

    Reversible Data Hiding with Contrast Enhancement Using Bi-histogram Shifting and Image Adjustment for Color Images

    Goma Tshivetta Christian Fersein Jorvialom1,2, Lord Amoah1,2,*

    Journal of Quantum Computing, Vol.4, No.3, pp. 183-197, 2022, DOI:10.32604/jqc.2022.039913

    Abstract Prior versions of reversible data hiding with contrast enhancement (RDHCE) algorithms strongly focused on enhancing the contrast of grayscale images. However, RDHCE has recently witnessed a rise in contrast enhancement algorithms concentrating on color images. This paper implies a method for color images that uses the RGB (red, green, and blue) color model and is based on bi-histogram shifting and image adjustment. Bi-histogram shifting is used to embed data and image adjustment to achieve contrast enhancement by adjusting the images resulting from each channel of the color images before combining them to generate the final… More >

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