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  • Open Access


    A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

    Wei Wu*, Yuan Zhang, Yunpeng Li, Chuanyang Li, Yan Hao

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 537-555, 2024, DOI:10.32604/cmes.2024.049174

    Abstract Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities. Additionally, it leverages inter-modal correlation to enhance recognition performance. Concurrently, the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features. Nevertheless, two issues persist in multi-modal feature fusion recognition: Firstly, the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities. Secondly, during modal fusion, improper weight selection diminishes the salience of crucial modal features, thereby diminishing the overall recognition performance. To address these two issues, we introduce an enhanced DenseNet multimodal recognition network… More > Graphic Abstract

    A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

  • Open Access


    Application of the CatBoost Model for Stirred Reactor State Monitoring Based on Vibration Signals

    Xukai Ren1,2,*, Huanwei Yu2, Xianfeng Chen2, Yantong Tang2, Guobiao Wang1,*, Xiyong Du2

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 647-663, 2024, DOI:10.32604/cmes.2024.048782

    Abstract Stirred reactors are key equipment in production, and unpredictable failures will result in significant economic losses and safety issues. Therefore, it is necessary to monitor its health state. To achieve this goal, in this study, five states of the stirred reactor were firstly preset: normal, shaft bending, blade eccentricity, bearing wear, and bolt looseness. Vibration signals along x, y and z axes were collected and analyzed in both the time domain and frequency domain. Secondly, 93 statistical features were extracted and evaluated by ReliefF, Maximal Information Coefficient (MIC) and XGBoost. The above evaluation results were then fused by D-S evidence… More >

  • Open Access


    Spatio-Temporal Dynamics of Land Use in Cotton and Cereal Production Zones, Mali

    Dynamiques Spatio-Temporelles de l’Occupation des Terres dans les Zones de Production Cotonnière et Céréalière au Mali

    Moumouni Sidibé1,2,*, Augustin K. N. Aoudji1, Yaya Issifou Moumouni3,*, Issa Sacko4, Idelphonse Saliou1, Bourema Koné2, Achille Ephrem Assogbadjo5, Afio Zannou1

    Revue Internationale de Géomatique, Vol.33, pp. 51-76, 2024, DOI:10.32604/rig.2024.045505

    Abstract Land use dynamics is a prerequisite for identifying natural resource management constraints, the evolution of agrarian practices and population growth. The objective of this research is to improve knowledge of the dynamics of agricultural land use in the dryland (Cinzana) and cotton (Kléla) areas of Mali. The methodology used consisted of planimetric data collection and diachronic analysis using Landsat TM (Thematic Mapper) satellite images from 2000 and OLI (Operational Land Image) from 2020. Degradation and deforestation rates of natural formations were calculated on the one hand, and on the other hand, the speed and intensity of changes were evaluated using… More > Graphic Abstract

    Spatio-Temporal Dynamics of Land Use in Cotton and Cereal Production Zones, Mali<br/><br/>Dynamiques Spatio-Temporelles de l’Occupation des Terres dans les Zones de Production Cotonnière et Céréalière au Mali

  • Open Access


    Causality-Driven Common and Label-Specific Features Learning

    Yuting Xu1,*, Deqing Zhang1, Huaibei Guo2, Mengyue Wang1

    Journal on Artificial Intelligence, Vol.6, pp. 53-69, 2024, DOI:10.32604/jai.2024.049083

    Abstract In multi-label learning, the label-specific features learning framework can effectively solve the dimensional catastrophe problem brought by high-dimensional data. The classification performance and robustness of the model are effectively improved. Most existing label-specific features learning utilizes the cosine similarity method to measure label correlation. It is well known that the correlation between labels is asymmetric. However, existing label-specific features learning only considers the private features of labels in classification and does not take into account the common features of labels. Based on this, this paper proposes a Causality-driven Common and Label-specific Features Learning, named CCSF algorithm. Firstly, the causal learning… More >

  • Open Access


    Mathematical Modelling and Simulations of Active Direct Methanol Fuel Cell


    Journal of Polymer Materials, Vol.40, No.3-4, pp. 125-139, 2023, DOI:10.32381/JPM.2023.40.3-4.1

    Abstract A one dimensional isothermal model is proposed by modelling the kinetics of methanol transport at anode flow channel (AFC), membrane and cathode catalyst layer of direct methanol fuel cell (DMFC). Analytical model is proposed to predict methanol cross-over rate through the electrolyte membrane and cell performance. The model presented in this paper considered methanol diffusion and electrochemical oxidation at the anode and cathode channels. The analytical solution of the proposed model was simulated in a MATLAB environment to obtain the polarization curve and leakage current. The effect of methanol concentration on cell voltage and leakage current is studied. The methanol… More >

  • Open Access


    Correlation and Pathway Analysis of the Carbon, Nitrogen, and Phosphorus in Soil-Microorganism-Plant with Main Quality Components of Tea (Camellia sinensis)

    Chun Mao1, Ji He1,*, Xuefeng Wen1, Yangzhou Xiang2, Jihong Feng1, Yingge Shu1

    Phyton-International Journal of Experimental Botany, Vol.93, No.3, pp. 487-502, 2024, DOI:10.32604/phyton.2024.048246

    Abstract The contents of carbon (C), nitrogen (N), and phosphorus (P) in soil-microorganisms-plant significantly affect tea quality by altering the main quality components of tea, such as tea polyphenols, amino acids, and caffeine. However, few studies have quantified the effects of these factors on the main quality components of tea. The study aimed to explore the interactions of C, N, and P in soil-microorganisms-plants and the effects of these factors on the main quality components of tea by using the path analysis method. The results indicated that (1) The contents of C, N, and P in soil, microorganisms, and tea plants… More >

  • Open Access


    BSTFNet: An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features

    Hong Huang1, Xingxing Zhang1,*, Ye Lu1, Ze Li1, Shaohua Zhou2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918

    Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT) model. At the byte-level granularity,… More >

  • Open Access


    Secrecy Outage Probability Minimization in Wireless-Powered Communications Using an Improved Biogeography-Based Optimization-Inspired Recurrent Neural Network

    Mohammad Mehdi Sharifi Nevisi1, Elnaz Bashir2, Diego Martín3,*, Seyedkian Rezvanjou4, Farzaneh Shoushtari5, Ehsan Ghafourian2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3971-3991, 2024, DOI:10.32604/cmc.2024.047875

    Abstract This paper focuses on wireless-powered communication systems, which are increasingly relevant in the Internet of Things (IoT) due to their ability to extend the operational lifetime of devices with limited energy. The main contribution of the paper is a novel approach to minimize the secrecy outage probability (SOP) in these systems. Minimizing SOP is crucial for maintaining the confidentiality and integrity of data, especially in situations where the transmission of sensitive data is critical. Our proposed method harnesses the power of an improved biogeography-based optimization (IBBO) to effectively train a recurrent neural network (RNN). The proposed IBBO introduces an innovative… More >

  • Open Access


    Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

    Ying Su1, Morgan C. Wang1, Shuai Liu2,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3529-3549, 2024, DOI:10.32604/cmc.2024.047189

    Abstract Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning (AutoML). At present, forecasting, whether rooted in machine learning or statistical learning, typically relies on expert input and necessitates substantial manual involvement. This manual effort spans model development, feature engineering, hyper-parameter tuning, and the intricate construction of time series models. The complexity of these tasks renders complete automation unfeasible, as they inherently demand human intervention at multiple junctures. To surmount these challenges, this article proposes leveraging Long Short-Term Memory, which is the variant of Recurrent Neural Networks, harnessing memory cells and gating mechanisms… More >

  • Open Access


    A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural Networks with Leveraging LSTM

    Navaneetha Krishnan Muthunambu1, Senthil Prabakaran2, Balasubramanian Prabhu Kavin3, Kishore Senthil Siruvangur4, Kavitha Chinnadurai1, Jehad Ali5,*

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3089-3127, 2024, DOI:10.32604/cmc.2023.043172

    Abstract The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet. Regrettably, this development has expanded the potential targets that hackers might exploit. Without adequate safeguards, data transmitted on the internet is significantly more susceptible to unauthorized access, theft, or alteration. The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks. This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks (RNN) integrated with… More >

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