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  • 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 development of fruits, and… 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 of users and items. Next,… 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 servers and improve the model’s… 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. This process aims to improve… 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 representations through the utilization of… 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 original images for contrast enhancement.… 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 enhanced image. Images are first… More >

  • Open Access

    ARTICLE

    Leveraging Vision-Language Pre-Trained Model and Contrastive Learning for Enhanced Multimodal Sentiment Analysis

    Jieyu An1,*, Wan Mohd Nazmee Wan Zainon1, Binfen Ding2

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1673-1689, 2023, DOI:10.32604/iasc.2023.039763

    Abstract Multimodal sentiment analysis is an essential area of research in artificial intelligence that combines multiple modes, such as text and image, to accurately assess sentiment. However, conventional approaches that rely on unimodal pre-trained models for feature extraction from each modality often overlook the intrinsic connections of semantic information between modalities. This limitation is attributed to their training on unimodal data, and necessitates the use of complex fusion mechanisms for sentiment analysis. In this study, we present a novel approach that combines a vision-language pre-trained model with a proposed multimodal contrastive learning method. Our approach harnesses the power of transfer learning… More >

  • Open Access

    ARTICLE

    Alzheimer’s Disease Stage Classification Using a Deep Transfer Learning and Sparse Auto Encoder Method

    Deepthi K. Oommen*, J. Arunnehru

    CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 793-811, 2023, DOI:10.32604/cmc.2023.038640

    Abstract Alzheimer’s Disease (AD) is a progressive neurological disease. Early diagnosis of this illness using conventional methods is very challenging. Deep Learning (DL) is one of the finest solutions for improving diagnostic procedures’ performance and forecast accuracy. The disease’s widespread distribution and elevated mortality rate demonstrate its significance in the older-onset and younger-onset age groups. In light of research investigations, it is vital to consider age as one of the key criteria when choosing the subjects. The younger subjects are more susceptible to the perishable side than the older onset. The proposed investigation concentrated on the younger onset. The research used… More >

  • Open Access

    ARTICLE

    Response of Contrasting Rice Genotypes to Zinc Sources under Saline Conditions

    Muhammad Jan1,*, Muhammad Anwar-Ul-Haq2, Talha Javed3, Sadam Hussain4,*, Ilyas Ahmad5, Muhammad Ashraf Sumrah6, Javed Iqbal7, Babar Hussain Babar8, Aqsa Hafeez9, Muhammad Aslam5, Muhammad Tahir Akbar10, Marjan Aziz6, Khadiga Alharbi11, Izhar Ullah12

    Phyton-International Journal of Experimental Botany, Vol.92, No.5, pp. 1361-1375, 2023, DOI:10.32604/phyton.2023.026620

    Abstract Abiotic stresses are among the major limiting factors for plant growth and crop productivity. Among these, salinity is one of the major risk factors for plant growth and development in arid to semi-arid regions. Cultivation of salt tolerant crop genotypes is one of the imperative approaches to meet the food demand for increasing population. The current experiment was carried out to access the performance of different rice genotypes under salinity stress and Zinc (Zn) sources. Four rice genotypes were grown in a pot experiment and were exposed to salinity stress (7 dS m−1), and Zn (15 mg kg−1 soil) was… More >

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