In modern supply chain management, efficient communication and timely decision-making are essential to ensuring smooth operations and avoiding descriptions. This is ensured by efficient communication and timely decisi...
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The integrated automated safety monitoring system for construction sites utilizes RFID, Wi-Fi, and vision-based recognition systems to enhance worker safety and ensure adherence to safety regulations. This system comb...
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The Editor-in-Chief has retracted this article because of concerns regarding the substance and overall soundness of this work. An investigation conducted after its publication discovered instances of nonsensical state...
Detecting the behavior of the crowd that is regarded as anomalous is the need for ***, several machine learning (ML) systems were built for the automatic detection of an anomaly in the crowd video. Still, certain limi...
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Networks plays a vital role in modern society and cyber security has become a critical area that needs to be acknowledged. A crucial part of cybersecurity frameworks intended to track network traffic and spot suspicio...
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Breast cancer is often curable if discovered in its early stages. Machine learning algorithms are used to automate disease identification. To increase the likelihood of recognizing the condition at an early stage, an ...
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Emotions are a vital semantic part of human correspondence. Emotions are significant for human correspondence as well as basic for human–computer cooperation. Viable correspondence between people is possibly achieved...
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Artificial intelligence (AI) applications in colonoscopy have shown promise in terms of improving disease detection and classification, such as polyp detection. However, specular reflections from the camera flash...
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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.
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