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

Yuting Xu^1,*, 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 >

Chun Mao¹, Ji He^1,*, 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 >

N. Gnanasekaran, C. Balaji^*

Frontiers in Heat and Mass Transfer, Vol.2, No.2, pp. 1-6, 2011, DOI:10.5098/hmt.v2.2.3008

Abstract This paper reports the results of an experimental investigation of transient, turbulent mixed convection in a vertical channel in which one of the walls is heated and the other is adiabatic. The goal is to simultaneously estimate the constants in a Nusselt number correlation whose form is assumed a priori by synergistically marrying the experimental results with repeated numerical calculations that assume guess values of the constants. The convective heat transfer coefficient “h”is replaced by the Nusselt number (Nu) and the constants in the Nusselt number are to be evaluated. From the experimentally obtained temperature time history and the simulated… More >

Xin Fan^1,2, Shuqing Zhang^1,2,*, Kaisheng Wu^1,2, Wei Zheng^1,2, Yu Ge^1,2

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1687-1711, 2024, DOI:10.32604/cmc.2023.046187

Abstract Cross-Project Defect Prediction (CPDP) is a method that utilizes historical data from other source projects to train predictive models for defect prediction in the target project. However, existing CPDP methods only consider linear correlations between features (indicators) of the source and target projects. These models are not capable of evaluating non-linear correlations between features when they exist, for example, when there are differences in data distributions between the source and target projects. As a result, the performance of such CPDP models is compromised. In this paper, this paper proposes a novel CPDP method based on Synthetic Minority Oversampling Technique (SMOTE)… More >

Ruchun Jia, Jianwei Zhang^*, Yi Lin

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2391-2418, 2024, DOI:10.32604/cmc.2024.044149

Abstract With the popularization of the Internet and the development of technology, cyber threats are increasing day by day. Threats such as malware, hacking, and data breaches have had a serious impact on cybersecurity. The network security environment in the era of big data presents the characteristics of large amounts of data, high diversity, and high real-time requirements. Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats. This paper proposes a machine-learning security defense algorithm based on metadata association features. Emphasize control over unauthorized users through privacy, integrity, and availability. The… More >

Ming Luo¹, Huili Dou², Ning Zheng^3,*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 265-282, 2024, DOI:10.32604/cmc.2023.040067

Abstract Traffic prediction already plays a significant role in applications like traffic planning and urban management, but it is still difficult to capture the highly non-linear and complicated spatiotemporal correlations of traffic data. As well as to fulfil both long-term and short-term prediction objectives, a better representation of the temporal dependency and global spatial correlation of traffic data is needed. In order to do this, the Spatiotemporal Graph Neural Network (S-GNN) is proposed in this research as a method for traffic prediction. The S-GNN simultaneously accepts various traffic data as inputs and investigates the non-linear correlations between the variables. In terms… More >

Tian-Jiao Wei¹, Guang Li¹, Yan-Ru Cui¹, Jiao Xie¹, Xing-Ai Gao¹, Xing Teng¹, Xin-Ying Zhao¹, Fa-Chun Guan^1,*, Zheng-Wei Liang^2,*

Phyton-International Journal of Experimental Botany, Vol.93, No.1, pp. 29-43, 2024, DOI:10.32604/phyton.2023.046078

Abstract Soil salinization is the main factor that threatens the growth and development of plants and limits the increase of yield. It is of great significance to study the key soil environmental factors affecting plant root traits to reveal the adaptation strategies of plants to saline-alkaline-stressed soil environments. In this study, the root biomass, root morphological parameters and root mineral nutrient content of two alfalfa cultivars with different sensitivities to alkaline stress were analyzed with black soil as the control group and the mixed saline-alkaline soil with a ratio of 7:3 between black soil and saline-alkaline soil as the saline-alkaline treatment… More >

Taiyu Yan¹, Yuxin Cao¹, Guoxia Xu¹, Xiaoran Zhao², Hu Zhu¹, Lizhen Deng^3,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3741-3764, 2023, DOI:10.32604/cmc.2023.039828

Abstract Unmanned Aerial Vehicle (UAV) tracking has been possible because of the growth of intelligent information technology in smart cities, making it simple to gather data at any time by dynamically monitoring events, people, the environment, and other aspects in the city. The traditional filter creates a model to address the boundary effect and time filter degradation issues in UAV tracking operations. But these methods ignore the loss of data integrity terms since they are overly dependent on numerous explicit previous regularization terms. In light of the aforementioned issues, this work suggests a dual-domain Jensen-Shannon divergence correlation filter (DJSCF) model address… More >

Faidance Mashauri^1,2,*, Mokili Mbuluyo^1,3, Nsalambi Nkongolo^2,4

Revue Internationale de Géomatique, Vol.32, pp. 79-98, 2023, DOI:10.32604/RIG.2023.044124

Abstract Les paramètres hydro-morphométriques les plus caractéristiques qui contrôlent l’écoulement des eaux du bassin versant de la Tshopo n’ont pas encore été déterminés. L’analyse de corrélation, la régression linéaire multiple et la classification ascendante hiérarchique ont été appliquées à l’ensemble des données afin d’identifier les variables les plus caractéristiques qui influencent considérablement la vitesse d’écoulement des eaux et regrouper les sous-bassins versants semblables physiquement. Les résultats obtenus mettent en évidence l’importance de la topographie sur l’écoulement des eaux. Trois variables topographiques, à savoir l’altitude médiane (H50), le dénivelé global (Dg) et le dénivelé spécifique (Ds), ont une influence significative (p-value ≤… More >