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  • Open Access

    ARTICLE

    Stigma-Specific Comparative Proteomic Analysis Reveals the Distyly Response to Self-Incompatibility in Plumbago auriculata Lam

    Di Hu1, Shouli Yi1,*, Di Lin2, Suping Gao3, Ting Lei3, Wenji Li4, Tingdan Xu1, Songlin Jiang1

    Phyton-International Journal of Experimental Botany, Vol.93, No.4, pp. 681-697, 2024, DOI:10.32604/phyton.2024.049166 - 29 April 2024

    Abstract In plants, heteromorphic self-incompatibility (HetSI) is a strategy for avoiding self-pollination and promoting outcrossing, and during this process, numerous protein-protein interaction events occur between the pistil and pollen. Previous studies in Primula and Fagopyrum that focused on HetSI systems have provided interesting insights; however, the molecular mechanism underlying HetSI remains largely unknown. In this study, we profiled the proteome of Plumbago auriculata stigmas before and after self-incompatible (SI) and self-compatible (SC) pollination. Comparative analyses were conducted by 4D-DIA (Four-dimensional data independent acquisition), a promising technology that increases the sensitivity and reduces the spectral complexity of proteomic analysis… More >

  • Open Access

    ARTICLE

    Strengthening Network Security: Deep Learning Models for Intrusion Detection with Optimized Feature Subset and Effective Imbalance Handling

    Bayi Xu1, Lei Sun2,*, Xiuqing Mao2, Chengwei Liu3, Zhiyi Ding2

    CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1995-2022, 2024, DOI:10.32604/cmc.2023.046478 - 27 February 2024

    Abstract In recent years, frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security. This paper presents a novel intrusion detection system consisting of a data preprocessing stage and a deep learning model for accurately identifying network attacks. We have proposed four deep neural network models, which are constructed using architectures such as Convolutional Neural Networks (CNN), Bi-directional Long Short-Term Memory (BiLSTM), Bidirectional Gate Recurrent Unit (BiGRU), and Attention mechanism. These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the More >

  • Open Access

    ARTICLE

    Predicting the International Roughness Index of JPCP and CRCP Rigid Pavement: A Random Forest (RF) Model Hybridized with Modified Beetle Antennae Search (MBAS) for Higher Accuracy

    Zhou Ji1, Mengmeng Zhou2, Qiang Wang2, Jiandong Huang3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1557-1582, 2024, DOI:10.32604/cmes.2023.046025 - 29 January 2024

    Abstract To improve the prediction accuracy of the International Roughness Index (IRI) of Jointed Plain Concrete Pavements (JPCP) and Continuously Reinforced Concrete Pavements (CRCP), a machine learning approach is developed in this study for the modelling, combining an improved Beetle Antennae Search (MBAS) algorithm and Random Forest (RF) model. The 10-fold cross-validation was applied to verify the reliability and accuracy of the model proposed in this study. The importance scores of all input variables on the IRI of JPCP and CRCP were analysed as well. The results by the comparative analysis showed the prediction accuracy of… More > Graphic Abstract

    Predicting the International Roughness Index of JPCP and CRCP Rigid Pavement: A Random Forest (RF) Model Hybridized with Modified Beetle Antennae Search (MBAS) for Higher Accuracy

  • Open Access

    REVIEW

    AI Fairness–From Machine Learning to Federated Learning

    Lalit Mohan Patnaik1,5, Wenfeng Wang2,3,4,5,6,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1203-1215, 2024, DOI:10.32604/cmes.2023.029451 - 29 January 2024

    Abstract This article reviews the theory of fairness in AI–from machine learning to federated learning, where the constraints on precision AI fairness and perspective solutions are also discussed. For a reliable and quantitative evaluation of AI fairness, many associated concepts have been proposed, formulated and classified. However, the inexplicability of machine learning systems makes it almost impossible to include all necessary details in the modelling stage to ensure fairness. The privacy worries induce the data unfairness and hence, the biases in the datasets for evaluating AI fairness are unavoidable. The imbalance between algorithms’ utility and humanization More >

  • Open Access

    REVIEW

    Pharmacological effects of denervated muscle atrophy due to metabolic imbalance in different periods

    JIAYING QIU1, YAN CHANG5, WENPENG LIANG1, MENGSI LIN1, HUI XU2, WANQING XU4, QINGWEN ZHU1, HAIBO ZHANG3,*, ZHENYU ZHANG1,*

    BIOCELL, Vol.47, No.11, pp. 2351-2359, 2023, DOI:10.32604/biocell.2023.031043 - 27 November 2023

    Abstract Denervation-induced skeletal muscle atrophy can potentially cause the decline in the quality of life of patients and an increased risk of mortality. Complex pathophysiological mechanisms with dynamic alterations have been documented in skeletal muscle atrophy resulting from innervation loss. Hence, an in-depth comprehension of the key mechanisms and molecules governing skeletal muscle atrophy at varying stages, along with targeted treatment and protection, becomes essential for effective atrophy management. Our preliminary research categorizes the skeletal muscle atrophy process into four stages using microarray analysis. This review extensively discusses the pathways and molecules potentially implicated in regulating… More >

  • Open Access

    ARTICLE

    A Credit Card Fraud Detection Model Based on Multi-Feature Fusion and Generative Adversarial Network

    Yalong Xie1, Aiping Li1,*, Biyin Hu2, Liqun Gao1, Hongkui Tu1

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2707-2726, 2023, DOI:10.32604/cmc.2023.037039 - 08 October 2023

    Abstract Credit Card Fraud Detection (CCFD) is an essential technology for banking institutions to control fraud risks and safeguard their reputation. Class imbalance and insufficient representation of feature data relating to credit card transactions are two prevalent issues in the current study field of CCFD, which significantly impact classification models’ performance. To address these issues, this research proposes a novel CCFD model based on Multifeature Fusion and Generative Adversarial Networks (MFGAN). The MFGAN model consists of two modules: a multi-feature fusion module for integrating static and dynamic behavior data of cardholders into a unified highdimensional feature… More >

  • Open Access

    ARTICLE

    A Stacked Ensemble Deep Learning Approach for Imbalanced Multi-Class Water Quality Index Prediction

    Wen Yee Wong1, Khairunnisa Hasikin1,*, Anis Salwa Mohd Khairuddin2, Sarah Abdul Razak3, Hanee Farzana Hizaddin4, Mohd Istajib Mokhtar5, Muhammad Mokhzaini Azizan6

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1361-1384, 2023, DOI:10.32604/cmc.2023.038045 - 30 August 2023

    Abstract A common difficulty in building prediction models with realworld environmental datasets is the skewed distribution of classes. There are significantly more samples for day-to-day classes, while rare events such as polluted classes are uncommon. Consequently, the limited availability of minority outcomes lowers the classifier’s overall reliability. This study assesses the capability of machine learning (ML) algorithms in tackling imbalanced water quality data based on the metrics of precision, recall, and F1 score. It intends to balance the misled accuracy towards the majority of data. Hence, 10 ML algorithms of its performance are compared. The classifiers… More >

  • Open Access

    ARTICLE

    Fusion of Feature Ranking Methods for an Effective Intrusion Detection System

    Seshu Bhavani Mallampati1, Seetha Hari2,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1721-1744, 2023, DOI:10.32604/cmc.2023.040567 - 30 August 2023

    Abstract Expanding internet-connected services has increased cyberattacks, many of which have grave and disastrous repercussions. An Intrusion Detection System (IDS) plays an essential role in network security since it helps to protect the network from vulnerabilities and attacks. Although extensive research was reported in IDS, detecting novel intrusions with optimal features and reducing false alarm rates are still challenging. Therefore, we developed a novel fusion-based feature importance method to reduce the high dimensional feature space, which helps to identify attacks accurately with less false alarm rate. Initially, to improve training data quality, various preprocessing techniques are… More >

  • Open Access

    ARTICLE

    A PSO Improved with Imbalanced Mutation and Task Rescheduling for Task Offloading in End-Edge-Cloud Computing

    Kaili Shao1, Hui Fu1, Ying Song2, Bo Wang3,*

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2259-2274, 2023, DOI:10.32604/csse.2023.041454 - 28 July 2023

    Abstract To serve various tasks requested by various end devices with different requirements, end-edge-cloud (E2C) has attracted more and more attention from specialists in both academia and industry, by combining both benefits of edge and cloud computing. But nowadays, E2C still suffers from low service quality and resource efficiency, due to the geographical distribution of edge resources and the high dynamic of network topology and user mobility. To address these issues, this paper focuses on task offloading, which makes decisions that which resources are allocated to tasks for their processing. This paper first formulates the problem… More >

  • Open Access

    ARTICLE

    Predicting Lumbar Spondylolisthesis: A Hybrid Deep Learning Approach

    Deepika Saravagi1, Shweta Agrawal2,*, Manisha Saravagi3, Sanjiv K. Jain4, Bhisham Sharma5, Abolfazl Mehbodniya6,*, Subrata Chowdhury7, Julian L. Webber6

    Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2133-2151, 2023, DOI:10.32604/iasc.2023.039836 - 21 June 2023

    Abstract Spondylolisthesis is a chronic disease, and a timely diagnosis of it may help in avoiding surgery. Disease identification in x-ray radiographs is very challenging. Strengthening the feature extraction tool in VGG16 has improved the classification rate. But the fully connected layers of VGG16 are not efficient at capturing the positional structure of an object in images. Capsule network (CapsNet) works with capsules (neuron clusters) rather than a single neuron to grasp the properties of the provided image to match the pattern. In this study, an integrated model that is a combination of VGG16 and CapsNet… More >

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