The cloud machine utilization prediction using a multivariate time series model is explored in this study. The cloud machine usage data set samples are selected with varied patterns. The long sequence forecasting mode...
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Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detecti...
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Software defect prediction plays a critical role in software development and quality assurance processes. Effective defect prediction enables testers to accurately prioritize testing efforts and enhance defect detection efficiency. Additionally, this technology provides developers with a means to quickly identify errors, thereby improving software robustness and overall quality. However, current research in software defect prediction often faces challenges, such as relying on a single data source or failing to adequately account for the characteristics of multiple coexisting data sources. This approach may overlook the differences and potential value of various data sources, affecting the accuracy and generalization performance of prediction results. To address this issue, this study proposes a multivariate heterogeneous hybrid deep learning algorithm for defect prediction (DP-MHHDL). Initially, Abstract Syntax Tree (AST), Code Dependency Network (CDN), and code static quality metrics are extracted from source code files and used as inputs to ensure data diversity. Subsequently, for the three types of heterogeneous data, the study employs a graph convolutional network optimization model based on adjacency and spatial topologies, a Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) hybrid neural network model, and a TabNet model to extract data features. These features are then concatenated and processed through a fully connected neural network for defect prediction. Finally, the proposed framework is evaluated using ten promise defect repository projects, and performance is assessed with three metrics: F1, Area under the curve (AUC), and Matthews correlation coefficient (MCC). The experimental results demonstrate that the proposed algorithm outperforms existing methods, offering a novel solution for software defect prediction.
Software Maintenance is of utmost importance for any industry. So, to predict the value of software maintenance beforehand also becomes very important, hence many software maintenance prediction algorithms have been d...
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Human pose estimation (HPE) from images or video is not only a major issue of computer vision, but also it plays a vital role in many real-world applications. The most challenging problems of human pose estimation are...
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Nowadays, three-dimensional (3D) reconstruction techniques are becoming increasingly important in the fields of architecture, game development, movie production, and more. Due to common issues in the reconstruction pr...
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Software quality enhancement can be done by identifying the refactoring scope of the existing open-source projects in real time. The refactoring mechanism alters the internal structure of the code base without changin...
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In this paper use CIC-IDS2017 dataset to illustrate a comparative analysis of traditional and proposed models for intrusion detection in network security systems. The comparison includes DT, RF, ET and XGBoost classif...
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In the optimization of intelligent network architecture, limited resources at each node, including edge computing devices, have posed challenges for deploying large models in performance-demanding scenarios. Knowledge...
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Fruits are some of the most nutrient-dense cash crops that can be found on Earth. Because fruits can vary greatly in size, shape, color, and texture, manually classifying and disease-detecting a big amount of fruit is...
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Ensuring secure and accurate node localization in Underwater Wireless Sensor Networks (UWSN) is a significant challenge, as conventional methods tend to neglect the security risks associated with malicious node interf...
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